H.-P. Koepchen and T. Huopaniemi (Eds.)

Cardiorespiratory and Motor Coordination

With 161 Figures and 9 Tables

Springer-Verlag Berlin Heidelberg New York London Paris Tokyo Hong Kong Barcelona Budapest

Prof. Dr. Hans-Peter Koepchen Institut fiir Physiologie FU Berlin Arnimallee 22 1000 Berlin 33

Dr. Timo Huopaniemi Department of Physiology University of Helsinki

. Siltavuorenpenger 20 J SF-00170 Helsinki

ISBN-13 :978-3-540-52279-9 DOl: 10.1007/978-3-642-75507-1

e-ISBN-13 :978-3-642-75507-1

Library of Congress Cataloging-in-Publication Data Cardiorespiratory and motor coordination / H.-P. Koepchen and T. Huopaniemi (eds.).

p. em. Proceedings of a satellite symposium of the XXXI International Congress of Physiological Sciences in Espoo, Helsinki, Finland, July 15-17, 1989.

Includes index. ISBN-13 :978-3-540-52279-9 (alk. paper) : DM 168.00.

1. Autonomic nervous system-Congresses. 2. Cardiopulmonary system-Congresses. 3. Efferent pathways-Congresses. I. Koepchen, Hans Peter. II. Huopaniemi, T. (Timo) III. International Union of Physiological Sciences. Congress (31st: 1989 : Helsinki, Finland)

[DNLM: 1. Autonomic Nervous System-physiology-congresses. 2. Cardiovascular System-physiology-congresses. 3. Respiratory System-physiology-congresses. WL 600 C267 1989] QP368.C37 1991 612.8'9-dc20 DNLM/DLC for Library of Congress 91-5146 CIP

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XXX I I nternational Congress of I UPS Helsinki 1989

Satellite Symposium

CARDIORESPIRATORY AND MOTOR COORDINATION

Espoo 15 -17 July 1989

Dedicated to the memory of C. M cc. Brooks

Dr. Brooks served as Professor and Chair of Physiology (1948 to 1972) and Pharmacology (1948 to 1956), Director of the Graduate Educational Program (1956 to 1966) and founding Dean of the School of Graduate Studies (1966 to 1972), Acting President and Dean of the College of Medicine (1970 to 1971), and Distinguished Professor of the University since 1971. He continued an active program of research in neurophysiology and, at the age of 75, became the founder and editor-in-chief of the Journal of the Autonomic Nervous System. In 1986, the State University of New York awarded him the honorary degree of Doctor of Science.

Chandler Brooks was elected to the National Academy of Sciences of the United States in 1975, based on his fundamental work on integrative neurophysiology and electrophysiology of the heart, especially the mechanism of cardiac excitation. Dr. Brooks was keenly interested in the development of young scientists, and many of his students and associates have gone on to distinguished careers in physiology and medicine. Dr. Brooks also had a worldwide outlook and encouraged post-doc­toral fellows and visiting scientists to come to this institution from many countries throughout the Far East, Western Europe and South America. For his extraordi­nary contributions to rebuilding science in Japan after World War II, he was awarded the Order of the Rising Sun, Third Class, conferred by the Emperor of Japan in 1979, one of the few foreigners to be so honored.

Most recently, Dr. Brooks was a fellow at the Center for Theological Study in Princeton. For almost two decades he was Chairman of the Grants Committee of the International Foundation, which assists developing peoples in meeting their needs for nutrition, medicine, education and the means to preserve their cultures. Dr. Brooks considered this to be his last major effort, a fitting end to his great work for the Health Science Center at Brooklyn, the University, Physiology and humankind.

Preface

This volume contains the contributions to a Satellite Symposium of the XXXI In­ternational Congress of Physiological Sciences in Espoo, Helsinki, Finland, July 15-17,1989.

The general purpose of this Symposium was to bring together specialists from different fields of physiology who work on systems that are closely linked function­ally with regard to behavioral adaptation. In a certain sense it represents a contin­uation of two former books on the Central Interaction Between Respiratory and Cardiovascular Control Systems 1 and on Neurovegetative Control Systems: Basic Function, Integration and Disorders 2 , but explicitly includes the relationship with motor control.

Since the first book appeared, much has been achieved in the field of physiology of respiratory, cardiovascular, and somatomotor control. It is not intended that this book compete with other publications from more specialized meetings which deal with the most recent findings in a particular field of research, and rightly so. This specialization in research leads laboratories in these as in other fields of physiology to work independently of each other. Consequently, there are border­line areas between the specialities which are neglected, leaving gaps in our under­standing of the central integration of various physiological systems. Frequently, the same central structure is studied in different laboratories and is only considered in terms of the particular system under investigation. Therefore, the main emphasis of this symposium was on the principles and mechanisms of central interaction and coordination and on key findings in some unique areas of research representing the extremely successful modern reductionistic approach, in order to promote the necessary mutual cooperation between analytical and integrative research.

Concrete findings on the interrelationships among cardiovascular, respiratory, and motor control provided the basis for interpreting the general principles of central interaction. These principles confront us with difficulty in defining the term "central control system" as soon as we enter the study of complex central net­works. The recognition that "specificity" and "nonspecificity" are not qualitative but quantitative and variable properties of the different central nervous substrates may help us to overcome useless semantic controversies. A major question arising

1 Koepchen HP, Hilton SM, Trzebski A (eds) (1980) Central interaction between respiratory and cardiovascular control systems. Springer, Berlin Heidelberg New York 2 Koepchen HP, Brooks CMcC, Koizumi K (eds) (1986) Neurovegetative control systems: basic functions, integration and disorders. J Auton Nerv Syst Suppl.

Preface IX

from the studies on central interactions presented in this book is how nature brings about specific control functions using common networks.

The different contributions made to this issue are representative of general trends in research, proceeding from specific findings to more overall aspects of the control systems. Work on integrative functions is in its infancy, and it must be remembered that some of the more integrative concepts are still in an early state of development; these will become modified and refined as research progresses. Therefore, contributions of the various authors present quite different conceptual approaches. The editors did not attempt to unify the material in any way. The contents of the respective chapters remain each individual author's own responsi­bility. Our intention was to enable the publication of ideas, some of which might seem too unusual to appear in conventional journals. Thus, this book is directed toward the future, and we hope that it will encourage further studies on the interactions among different control systems in order to corroborate, or even to contest, the concepts presented.

The topics presented here have practical implications for somatovegetative be­havioral control in humans. The ultimate goal of analytical work using animal experiments is to understand human physiology and to provide a scientific basis for the treatment of dysfunction. Finally, research on control systems must include studies in humans. We are restricted by the lack of reducibility in the investigation of the human organism. Over the past decade, indirect methods have been devel­oped for the computer-assisted evaluation of noninvasive monitoring of motor and autonomic parameters. Examples of this rapidly developing field of clinical­physiological research are included in the last part of this book.

Recent developments in systems analysis provide effective new tools for the mathematical treatment of time series. We are pleased that leading experts from this new interdisciplinary approach have joined forces with us in our endeavor to understand and to describe in a quantitative manner the phenomena stemming from the interaction of the control systems. We are learning that rhythmicity is not only an important indicator of central coordination but can also act as an "order parameter" in the integration of flexibly coupled systems. The various kinds of rhythm coordination have certain common features that are independent of any specific system and apply generally to vegetative and somatomotor control sys­tems. This book represents a bridge between conventional physiological ap­proaches to the interaction of control systems and the application of concepts in the new science of "synergetics" predominantly to analyze and interpret physio­logical rhythms. 3

The general intention of the symposium was to promote integrative thinking and research, taking the interaction of cardiorespiratory and motor control as a model. Of course, such an undertaking can only be exemplary and fragmentary. The aim is most aptly summed up by Professor Chandler Brooks in the final contribution, in which he observes that "We have considered only a small portion of what occurs in life." Much remains to do in the field of integrative research and concepts.

3 Haken H, Koepchen HP (eds) (1991)Synergetics of physiological rhythms. Springer, Berlin Heidelberg New York (in press)

X Preface

Everyone working in the field of the autonomic nervous system has received encouragement and stimulation over the years from the example set by Chandler Brooks. He always stressed the necessity to see the single phenomenon within the framework of the function of the whole organism. Even in his later years, he remained active and attended many meetings on different aspects of autonomic functions; his concluding remarks were often the highlight of the meeting. The example he set in his scientific work is an important one: integrative thinking is not an abstract idea but must be based on conscientious studies of single elements, which must be carried out methodically and with the utmost care. Therefore, we were delighted that Professor Brooks was able to participate in this meeting and to take up our invitation to give the concluding talk. His article, "Thoughts Concerning the Essence of Life", was to be the last in a remarkable list of scientific publications. A few months later he died in a car accident.

This last article goes far beyond the limits of his particular field of research. It is a precious gift for us, his pupils, colleagues, and friends, and is a legacy for the next generation of scientists. Thus, we gratefully dedicate this book to the memory of an impressive personality in science and humanity, Chandler McCuskey Brooks.

We are indebted to Dr. Gertrude Lange-Brooks and the Health Science Center, Brooklyn, N. Y., for permission to print the scientific biography of Professor Brooks presented at the memorial ceremony at the State University of New York in February 1990.

Hans-Peter Koepchen Timo Huopaniemi

Contents

Common and Differentiated Rhythmicities in Cardiorespiratory Efferents

Mechanical and Neural Interactions Between Positive Pressure, Artificial Ventilation, and Cardiovascular Function in Anesthetized Dogs Ao Federici, Mo Dambrosio, L. Nocera, Ao Chiddo, T. Fiore, and Po Rizzon 0 3

Interrelations Between Slow and Fast Rhythms in Sympathetic Discharge Mol. Cohen, Ro Barnhardt, and c.-F. Shaw 0 0 0 0 0 0 0 0 0 0 0 9

Common and Specific Sources of Regional Sympathetic Outflows in Cerebral Ischemia, Cushing Reaction, and Asphyxia Bo Kocsis, Go L. Gebber, and L. Fedina 0 0 0 0 0 0 0 0 0 0 0 0 0 16

Interrelationships Between the Respiratory and Sympathetic Rhythm Generating Systems in Neonates as Revealed by Alterations in Afferent Inputs Po Mo Gootman, Ao L. Sica, Ao Mo Steele, Ho L. Cohen, Bo W Hundley, Go Condemi, Mo Ro Gandhi, L. Eberle, and N. Gootman 0 0 0 0 26

Identification of Postganglionic Thoracic Sympathetic Neurons: Cardiac and Respiratory Discharge Patterns Po Szulczyk and Bo Kamosinska 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33

Species-Dependent Respiratory and Autonomic Nerve Activities: Respiratory-Sympathetic Synchronization and Autonomic Nerve Responses to Hypoxia and Hypercapnia in the Rat Ao Trzebski 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 39

Discussion on Respiratory Related and Non-related Rhythms in Sympathetic Efferents Moderator: M.I. Cohen 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49

Neurochemical Characterization of Cardiovascular and Respiratory Control Systems

CO2-Induced Depolarization of Neurons in Nucleus Tractus Solitarii: A Potential Substrate for Central Chemoreceptors J Bo Dean and Do Eo Millhorn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53

XII Contents

Organization of Respiratory Reflexes in the Caudal Region of the Nucleus of the Tractus Solitarius G. D. Housley, S. Brew, D. de Castro, and J D. Sinclair . .

The Role of the Nucleus Raphe Magnus in the Control of Cold Shivering and Respiratory Evaporative Heat Loss P. Hinckel ..................... .

Gene Expression for Neuropeptides in the Ganglia of the Vagus (Nodose) and Glossopharyngeal (petrosal) Nerves

60

71

D. E. Mil/horn, M. F. Czyzyk-Krzeska, D. A.Bayliss, andK. B. Seroogy.. 77

Specificity and/or Non-Specificity in Brainstem Cardiorespiratory Networks

The Rostral Ventrolateral Medulla: Anatomical Substrates of Cardiopulmonary Integration D. A. Ruggiero, R. E. Gomez, S. L. Cravo, E. Mtui, M. Anwar, and D.J Reis ...................... .

Descending Projections of Hypothalamic Sympathoexcitatory Neurons in the Cat

89

S. M. Barman . . . . . . . . . . . . . . . . . . . . . . . . 103

Mechanism of the Modulatory Effect of Somatic Nerves Input on Abnormal Cardiovascular Function P. Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

On the Existence of a Common Cardiorespiratory Network D. W Richter, K. M. Spyer, M. P. Gilbey, E. E. Lawson, C. R. Bainton, and Z. Wilhelm . . . . . . . . . . . . . . . . . . . . . . . .. . 118

Cooperativity in Distributed Respiratory and Cardiovascular-Related Brainstem Neural Assemblies: Insights from Many-Neuron Recordings B. G. Lindsey, Y. M. Hernandez, and R. Shannon . . . . . . . .. . 131

Polymorphic Nature of Central Networks Controlling Sympathetic Nerve Discharge G. L. Gebber, B. Kocsis, S. M. Barman, and M. J Kenney . . . . . . . . 138

Interrelation and Superposition of Respiratory and Cardiovascular Rhythms in EEG and Brainstem Reticular Unit Activity as Studied by Quantitative Spectral Analyses T. Hukuhara Jr., K. Takano, N. Kimura, and F. Kato . . . . . . . 147

Functional Organization of the Common Brainstem System to Different States at Different Times F. Ebinger, M. Lambertz, and P. Langhorst ....... . . . 158

Discussion on Coordinated Activity in Brainstem Reticular Networks Moderator: G. L. Gebber . . . . . . . . . . . . . . . . . . . . . . . 175

Contents XIII

Peripheral and Central Interrelations Between Cardiorespiratory and Motor Control

Somato-Vegetative Interaction at the Peripheral Level: Possible Effects on Motor Performance M. Passatore and C. Grassi. . . . . . . . . . .

Muscular Activity and Cardiovascular Regulation D.l. McCloskey and S. F. Hobbs . . . . . . . .

Brainstem Mechanisms Involved in Reflex Cardiovascular Responses to Muscular Contraction

181

188

G. A. Iwamoto, T. G. Waldrop, and R. M. Bauer. . . . . . . . . . . 193

Simultaneous Suppression of Postural Tone and Respiration and its Functional Significance in the Respiratory-Motor Coordination K. Kawahara, Y. Nakazono, Y. Yamauchi, Y. Miyamoto, and S. Kumagai . 200

Hypothalamic Modulation of Cardiovascular, Respiratory and Locomotor Activity During Exercise T. G. Waldrop, R. M. Bauer, G. A. Iwamoto, and R. W. Stremel .

Approaches of Systems Theory to Cardiorespiratory and Motor Coordination

The Approach of Synergetics to the Study of Coordination of Rhythms

208

H. Haken . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

Behavioral and Neural Pattern Generation: The Concept of Neurobehavioral Dynamical Systems J A. S. Kelso . . . . . . . . . . . . . . . . . . .

The Applicability of Chaos Theory to Rhythmic Breathing Patterns

. .. 224

C. L. Webber Jr. and J P. Zbilut . . . . . . . . . . . . . . . . . . . 239

Discussion on the Theoretical and Neuronal Basis of Rhythm Coordination C. L. Webber Jr. . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

Analysis of Cardiorespiratory Variability and Rhythmicity in Humans: Physiological Basis and Clinical Application

Human Respiratory-Cardiovascular Interactions in Health and Disease D. L. Eckberg . . . . . . . . . . . . . . . . . . . . . . . . . 253

Respiratory Heart Rate Variability in Fetal and Neonatal Lambs T. Metsiilii, J Gronlund, A. Siimes, and I. Viilimiiki . . . . . . . . . . . 259

Disturbed Brainstem Interaction and Forebrain Influences in Cardiorespiratory Coordination: Experimental and Clinical Results U. Zwiener, R. Bauer, M. Rother, G. Schwarz, H. Witte, G. Litscher, and M. Wohlfarth . . . . . . . . . . . . . . . . . . . . . . . . . . 265

XIV Contents

Low-Frequency Rhythms in the Respiratory and Cardiovascular Systems (With a Reference to Obstructive Sleep Apnea Syndrome) J M. Karemaker and J G. van den Aardweg . . . . . . . . . . . . . . 277

Thermal and Postural Influences on Cutaneous Microvascular Blood Cell Flux in Young Men A. Lindqvist and 1. Viilimiiki . . . . . . . . . . . . . . . .

Power Spectral Analysis of Heart Rate and Arterial Pressure Variabilities as an Experimental and Clinical Tool

283

A. Malliani, M. Pagani, F. Lombardi, G. Baselli, and S. Cerutti . . .. 291

Heart Rate Control and Metabolic Parameters After Fatiguing Exercise E. Schubert, W Dinter, and W Rielke . . . . . . . . . . . . . . .. 300

Cardiorespiratory Relations in Human Heart Rate Pattern H.-H. Abel, D. KlUfJendorf, R. Droh, and H. P. Koepchen ....... 307

Concluding Lecture

Thoughts Concerning the Essence of Life: Integrative Power and Governance of Function C. McC. Brooks

Subject Index . . . . . . .

321

327

Contributors

Aardweg, 1. G. van den, Department of Internal Medicine, University of Amsterdam, Academic Medical Center, Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands

Abel, H.-H., Institut fiir Physiologie, Freie Universitiit Berlin, Arnimallee 22, W-1000 Berlin 33, Federal Republic of Germany

Anwar, M., Division of Neurobiology, Cornell University Medical College, 411 East 69th Street, New York, NY 10021, USA

Bainton, C. R., Institut fiir Physiologie, Universitiit G6ttingen, Humboldtallee 23, W-3400 G6ttingen, Federal Republic of Germany

Barman, S. M., Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA

Barnhardt, R., Department of Physiology, Albert Einstein College of Medicine, Bronx, NY 10461, USA

Baselli, G., Dipartimento Automazione Industriale, Universita di Brescia, 25100 Brescia, Italy

Bauer, R. M., Department of Physiology and Biophysics, University of Illinois, Urbana-Champaign, Urbana, Illinois 61801, USA

Bayer, R., Institut fiir Pathologische Physiologie, Friedrich-Schiller-Universitiit, L6bderstraBe 3, 0-6900 Jena, Federal Republic of Germany

Bayliss, D. A., Department of Physiology and Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC 27599, USA

Brew, S., Department of Physiology, University of Auckland, Private Bag, Auckland, New Zealand

Brooks, C. McC. t, State University of New York, Health Science Center at Brooklyn and The Center of Theological Inquiry, 50 Stockton Street, Princeton, New Jersey, 08540, USA

Castro, D. de, Department of Physiology, University of Auckland, Private Bag, Auckland, New Zealand

XVI Contributors

Cerutti, S., Centro Teoria Sistemi CNR, Departimento Elletronica, Politecnico Milano, 20100 Milano, Italy

Chiddo, A., Istituto di Cardiologia, Universita di Bari, Policlinico, Piazza Giulio Cesare, 70124 Bari, Italy

Cohen, H. L., Department of Physiology/Box 31, SUNY-Health Science Center at Brooklyn, 450 Clarkson Avenue, Brooklyn, NY 11203, USA

Cohen, M.1., Department of Physiology, Albert Einstein College of Medicine, Bronx, NY 10461, USA

Condemi, G., Department of Physiology/Box 31, SUNY-Health Science Center at Brooklyn, 450 Clarkson Avenue, Brooklyn, NY 11203, USA

Cravo, S. L., Division of Neurobiology, Cornell University Medical College, 411 East 69th Street, New York, NY 10021, USA

Czyzyk-Krzeska, M. F., Department of Physiology and Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC 27599, USA

Dambrosio, M., Istituto di Anestesiologia e Rianimazione, Universita di Bari, Polic1inico, Piazza Giulio Cesare, 70124 Bari, Italy

Dean, J. B., Department of Physiology and Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC 27599, USA

Droh, R., Krankenhaus fUr Sportverletzte, Paulmannsh6her StraBe 17, W-5880 Liidenscheid-Hellersen, Federal Republic of Germany

Eberle, L., Department of Physiology/Box 31, SUNY-Health Science Center at Brooklyn, 450 Clarkson Avenue, Brooklyn, NY 11203, USA

Ebinger, F., Institut fUr Physiologie, Freie Universitiit Berlin, Arnimallee 22, W-1000 Berlin 33, Federal Republic of Germany

Eckberg, D. L., Cardiovascular Physiology, Hunter Holmes McGuire Veterans Administration Medical Center, 1201 Broad Rock BId., Richmond, Virginia 23249, USA

Federici, A., Istituto di Fisiologia Umana, Polic1inico, Piazza Giulio Cesare, 70124 Bari, Italy

Fedina, L., Department of Physiology, National Institute of Neurosurgery, Budapest, Hungary

Fiore, T., Istituto di Anestesiologia e Rianimazione, Universita di Bari, Polic1inico, Piazza Giulio Cesare, 70124 Bari, Italy

Gandhi, M. R., Department of Pediatrics, Schneider Children's Hospital, Long Island Jewish Medical Center, Lakeville Road, New Hyde Park, NY 11 042, USA

Contributors XVII

Gebber, G. L., Department of Physiology and Toxicology, Michigan State University, East Lansing, MI 48824, USA

Gilbey, M. P., Institut fUr Physiologie, Universitat Gottingen, Humboldtallee 23, W-3400 Gottingen, Federal Republic of Germany

Gomez, R. E., Division of Neurobiology, Cornell University Medical College, 411 East 69th Street, New York, NY 10021, USA

Gootman, N., Department of Pediatrics, Schneider Children's Hospital, Long Island Jewish Medical Center, Lakeville Road, New Hyde Park, NY 11042, USA

Gootman, P. M., Department of Physiology/Box 31, SUNY-Health Science Center at Brooklyn, 450 Clarkson Avenue, Brooklyn, NY 11203, USA

Grassi, C., Institute of Human Physiology, Catholic University "S. Cuore", Largo E Vito 1, 00168 Rome, Italy

Gronlund, 1., Cardiorespiratory Unit, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland

Haken, H., Institut fur Theoretische Physik und Synergetik, Pfaffenwaldring 57/4, W-7000 Stuttgart 80, Federal Republic of Germany

Hernandez, Y M., Department of Physiology and Biophysics, University of South Florida, Health Sciences Center, Tampa, FL 33612-4799, USA

Hinckel, P., Physiologisches Institut, Universitat GieBen, Aulweg 129, W-6300 GieBen 1, Federal Republic of Germany

Hobbs, S. E, School of Physiology and Pharmacology, University of New South Wales, Kensington, Sydney, NSW, Australia 2033

Housley, G. D., Department of Physiology, University of Auckland, Private Bag, Auckland, New Zealand

Hukuhara, T., Department of Pharmacology II, The Jikei University School of Medicine, 3-25-8 Nishishinbashi, Minato-ku, Tokyo 105, Japan

Hundley, B. W, Department of Physiology/Box 31, SUNY-Health Science Center at Brooklyn, 450 Clarkson Avenue, Brooklyn, NY 11203, USA

Huopaniemi, T., Department of Physiology, University of Helsinki, Siltavuorenpenger 20 J, SF-00170 Helsinki

Iwamoto, G. A., Department of Veterinary Biosciences, College of Veterinary Medicine, University of Illinois, Urbana-Champaign, Urbana, Illinois 61801, USA

Kamosinska, B., Department of Physiology, Warsaw Medical School, Krakowskie Przedmiescie 26/28, 00-927 Warsaw, Poland

XVIII Contributors

Karemaker, J. M., Department of Internal Medicine, University of Amsterdam, Academic Medical Center, Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands

Kato, F., Department of Pharmacology II, The Jikei University School of Medicine, 3-25-8 Nishishinbashi, Minato-ku, Tokyo 105, Japan

Kawahara, K., Department of Electrical & Information Engineering, Faculty of Engineering, Yamagata University, Yonezawa 992, Japan

Kelso, J. A. S., Program in Complex Systems and Brain Sciences, Center for Complex Systems, Florida Atlantic University, Boca Raton, FL 33431, USA

Kenney, M. J., Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA

Kimura, N., Department of Pharmacology II, The Jikei University School of Medicine, 3-25-8 Nishishinbashi, Minato-ku, Tokyo 105, Japan

KliiBendorf, D., Institut fiir Physiologie, Freie Universitat Berlin, Arnimallee 22, W-1000 Berlin 33, Federal Republic of Germany

Kocsis, B., Florida Atlantic University, Center for Complex Systems, Boca Raton, FL, 33431, USA

Koepchen, H. P., Institut fiir Physiologie, Freie Universitat Berlin, Arnimallee 22, W-1000 Berlin 33, Federal Republic of Germany

Kumagai, S., Department of Computer Fabrication, Mitsubishi Electric Company, Kamakura 247, Japan

Lambertz, M., Institut fUr Physiologie, Freie Universitat Berlin, Arnimallee 22, W-1000 Berlin 33, Federal Republic of Germany

Langhorst, P., Institut fUr Physiologie, Freie Universitat Berlin, Arnimallee 22, W-l000 Berlin 33, Federal Republic of Germany

Lawson, E. E., Institut fiir Physiologie, Universitat G6ttingen, Humboldtallee 23, W-3400 G6ttingen, Federal Republic of Germany

Li, P., Department of Physiology, Shanghai Medical University, Shanghai 200032, China

Lindqvist, A., University Hospital Lund, Department of Clinical Physiology, 22185 Lund, Sweden

Lindsey, B. G., Department of Physiology and Biophysics, University of South Florida, College of Medicine, MDC Box 8, 12901 Bruce B. Downs Boulevard, Tampa, Florida 33612, USA

Litscher, G., Institute of Electro- and Biomedical Engineering, Technical University of Graz, Inffeldgasse 18, 8030 Graz, Austria

Contributors XIX

Lombardi, F., Istituto Ricerche Cardiovascolari, Centro Ricerche Cardio­vascolari CNR, Patologia Medica, Centro "Fidia", Ospedale "L. Sacco", Universita Milano, 20138 Milano, Italy

Malliani, A., Istituto Ricerche Cardiovascolari, Via Bonfandi 214, 20138 Milano, Italy

McCloskey, D. I., School of Physiology and Pharmacology, University of New South Wales, Kensington, Sydney, NSW, Australia 2033

Metsiilii, T., Cardiorespiratory Research Unit, University of Turku, Kiinamyllynkatu 10, 20520 Turku 52, Finland

Millhorn, D. E., Department of Physiology and Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC 27599, USA

Miyamoto, Y, Department of Electrical & Information Engineering, Faculty of Engineering, Yamagata University, Yonezawa 992, Japan

Mtui, E., Division of Neurobiology, Cornell University Medical College, 411 East 69th Street, New York, NY 10021, USA

Nakazono, Y, Department of Physiology, Sapporo Medical College, Sapporo 060, Japan

Nocera, L., Istituto di Fisiologia Umana, Policlinico, Piazza Giulio Cesare, 70124 Bari, Italy

Pagani, M., Istituto Ricerche Cardiovascolari, Centro Ricerche Cardiovascolari CNR, Patologia Medica, Centro "Fidia", Ospedale "L. Sacco", Universita Milano, 20138 Milano, Italy

Passatore, M., Department of Anatomy and Human Physiology, University of Turin, Corso Raffaello 30, 10125 Turin, Italy

Reis, D. 1., Division of Neurobiology, Cornell University Medical College, 411 East 69th Street, New York, NY 10021, USA

Richter, D. W, Institut fUr Physiologie, Universitiit Gottingen, Humboldtallee 23, W-3400 Gottingen, Federal Republic of Germany

Rielke, W, Institut fiir Physiologie, Bereich Medizin (Charite) der Humboldt­Universitiit zu Berlin, Hessische StraBe 3-4, 0-1040 Berlin, Federal Republic of Germany

Rizzon, P., Istituto di Cardiologia, Universita di Bari, Policlinico, Piazza Giulio Cesare, 70124 Bari, Italy

Rother, M., Institut fiir Pathologische Physiologie, Friedrich-Schiller­Universitiit, LobderstraBe 3, 0-6900 Jena, Federal Republic of Germany

Ruggiero, D. A., Division of Neurobiology, Cornell University Medical College, 411 East 69th Street, New York, NY 10021, USA

XX Contributors

Schubert, E., Institut fUr Physiologie, Bereich Medizin (Charite) der Humboldt-UniversiHit zu Berlin, Hessische StraJ3e 3-4, 0-1040 Berlin, Federal Republic of Germany

Schwarz, G., Department of Anaesthesiology, University of Graz, Auenbrugger Platz, 8030 Graz, Austria

Seroogy, K. B., Department of Physiology and Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC 27599, USA

Shannon, R., Department of Physiology and Biophysics, University of South Florida, Health Sciences Center, Tampa, FL 33612-4799, USA

Shaw, C.-F., Department of Physiology, Albert Einstein College of Medicine, Bronx, NY 10461, USA

Sica, A. L., Department of Pediatrics, Schneider Children's Hospital, Long Island Jewish Medical Center, Lakeville Road, New Hyde Park, NY 11042, USA

Siimes, A., Cardiorespiratory Unit, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland

Sinclair, 1. D., Department of Physiology, University of Auckland, Private Bag, Auckland, New Zealand

Spyer, K. M., Department of Physiology, Royal Free Hospital, School of Medicine, Rowland Hill Street, London NW3 2PF, Great Britain

Steele, A. M., Department of Pediatrics, Schneider Children's Hospital, Long Island Jewish Medical Center, Lakeville Road, New Hyde Park, NY 11042, USA

Stremel, R. W, Department of Physiology and Biophysics, University of Illinois, Urbana-Champaign, 524 Burrill Hall, Urbana, Illinois 61801, USA

Szulczyk, P., Department of Physiology, Warsaw Medical Academy, Krakowskie Przedmiescie 26/28, 00-027 Warsaw, Poland

Takano, K., Department of Pharmacology II, The Jikei University School of Medicine, 3-25-8 Nishishinbashi, Minato-ku, Tokyo 105, Japan

Trzebski, A., Department of Physiology, Warsaw Medical Academy, Krakowskie Przedmiescie 26/28, 00-027 Warsaw, Poland

Valimaki, I., Cardiorespiratory Research Unit, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland

Waldrop, T. G., Department of Physiology and Biophysics, University of Illinois, Urbana-Champaign, 524 Burrill Hall, Urbana, Illinois 61801, USA

Webber, C. L. Jr., Department of Physiology, Loyola University of Chicago, Stritch School of Medicine, 2160 South First Avenue, Maywood, IL 60153, USA

Contributors XXI

Wilhelm, Z., Institut fiir Physiologie, Universitat G6ttingen, Humboldtallee 23, W-3400 G6ttingen, Federal Republic of Germany

Witte, H., Institut fUr Pathologische Physiologie, Friedrich-Schiller-Universitat, L6bderstraBe 3, 0-6900 Jena, Federal Republic of Germany

Wohlfarth, M., Institut fUr Pathologische Physiologie, Friedrich-Schiller­Universitat, L6bderstraBe 3, 0-6900 Jena, Federal Republic of Germany

Yamauchi, Y, Department of Electrical & Information Engineering, Faculty of Engineering, Yamagata University, Yonezawa 992, Japan

Zbilut, J. P., Department of OR/Surgical Nursing, College of Nursing and Department of Physiology, Rush Medical College, Rush-Presbyterian Saint Luke's Medical Center, 1753 West Harrison St., Chicago, IL 60612, USA

Zwiener, U., Institut fiir Pathologische Physiologie, Friedrich-Schiller­Universitat, L6bderstraBe 3, 0-6900 Jena, Federal Republic of Germany

Common and Differentiated Rhythmicities in Cardiorespiratory Efferents

Mechanical and Neural Interactions Between Positive Pressure, Artificial Ventilation,and Cardiovascular Function in Anesthetized Dogs A. FEDERICI, M. DAMBROSIO, L. NOCERA, A. CHIDDO, T. FIORE, and P. RIZZON

Introduction

The mechanics of respiration affects the left ventricular preload in acting on the pulmonary circulation. It also affects the left afterload, heart rate, and contractility by eliciting reflexes from cardiopulmonary afferents. The consequent changes in the circulation are further modified by reflexes from aortic and carotid barorecep­tors. Mechanical ventilation with positive end-expiratory pressure (PEEP) is a technique that strongly affects the mechanics of respiration. It is applied to pa­tients with acute respiratory failure to recruit collapsed alveoli. Despite improve­ment in arterial oxygenation, the use of PEEP causes a decrease in the cardiac output and arterial pressure. This is due to a reduction in the left ventricular preload, which depends on the decrease in the venous return, increase in the right ventricle afterload, and the leftward displacement of the interventricular septum [1-3]. The integrated effect of mechanical and neural interactions on aortic and ventricular hemodynamics has been studied in this paper during the application of PEEP on anesthetized dogs.

Method

Fifteen closed-chest mongrel dogs (15-27 kg), anesthetized with pentobarbital sodium (25 mg/kg i.v.), were ventilated with room air by a volumetri~pump set to obtain normal Pa02 and PaC02 values as checked by hemogas analysis; respira­tory cycles lasted 5 s, and the ratio between inspiration and expiration was 1: 2; anesthesia was maintained by continuously infusing thiopental sodium and fen­tanyl refracta dosi intravenously as required.

The cardiac output was measured by a Swan-Ganz catheter, inserted in the pulmonary artery via an external jugular vein, and using the thermodilution tech­nique, while the aortic and left ventricular pressures were recorded by transducer­tip catheters. For each animal the mean pressure in the aorta (AoP) and in the left ventricle (LVP), the duration of the cardiac cycle (C), the cardiac output (CO) and derived stroke volume (SV), were measured at the end of expiration for end"expira­tory pressures (EEP) of 0, 5, and 10 mmHg, recorded in the tracheal tube; varia­tions in EEP were obtained by inserting a water valve on the expiratory branch of

4 A. FEDERICI et al.

the ventilator, and measurements at PEEP were performed at a steady state, about 10 min after having applied positive pressures.

Measurements were taken on eight dogs in normal conditions (N), and on seven of them measurements were repeated after a block of the vagi, obtained by cutting the nerves in the neck (VB). On seven dogs, experiments were performed following a sympathetic block (SB), obtained by guanethidine (5 mg/kg per day for 10 days), and were repeated after combined sympathetic and vagal block (SVB). The sta­tionary powers consumed in the ventricle and the aorta could be obtained by performing the operations LVP/C'SV and AoP/C· SV (kPa's- 1 'ml=mW), while the stationary powers consumed to displace a volume unit (specific powers, WSp) were calculated with the ratios of LVP/C and AoP/C (WSpLV and WSpAo = kPa' s -1 = m W' ml- 1). A statistical analysis was performed on SV in relation to other hemodynamic parameters using the binomial regression model.

Results

The values of C, LVP, and AoP obtained at different EEP are plotted against SV in Fig. 1. Curves of stationary work per beat and lines whose points correspond to the same CO are drawn. The systemic arterial resistance (R=AoP· C/SV) is

N

" 50 Hlltl;. 20 VB

'" I / ,/ ,/ /

I I / ,/ " I y/ ", // '~ '~/~

/ I //~/~ , 1,/ ,/,/

I/~: ,/ IJ~ ;/

, ~, -"".----~,;;;_, -00"-=":"",, '0, -"".-----"", -00"-=":'7:0,, i>, -"".----~,;;;_, -""'='7,,, ,

~ 213 _ 15

~ "

°0 10 20 30 413 0 HI 20 30 400 10 20 30 40"

SV [mIl sv [mIl SV [mil

,? \\~>.... 240mJ

\\~:e"~""'- 160.J .... ~ ----__ B0 mJ

---========= ~g:j 10 20 30 40

SV Imll

Fig. 1. Duration of cardiac cycle (C), mean left ventricular pressure (P LV), and mean aortic pressure (PAo) plotted against stroke volume (SV), at three levels of end-expiratory pressure (zero mmHg, ZEEP, circles; 5 mmHg, PEEP 5, squares; 10 mmHg, PEEP 10, triangles) of anesthetized dogs in normal condition (N), after vagal block (VB), after sympathetic block (SB), and after combined block (SVB). Symbols connected by lines, values obtained from a single dog at the three levels of end-expiratory pressure. Each symbol is the average of at least three consecutive measures. Lines whose points correspond to the same value of cardiac output and curves, whose points correspond to the same work per beat, have been drawn

VB

• • " · · " ~

3. " · ~

• • I. " " " . SV {mIl SV [11111

Mechanical and Neural Interactions 5

58

10 20 38 400

sv C.,ll

SVB

,

o IEEP o PEEP 5 t:. PEEP 18

"

',\ '~"''''' \ \"h~"__ ----__ ,,,.w \' -- -

_ ~----- '" .w ____ ::::::-_ l~:::

I • 28 38 48

Fig. 2. Systemic arterial resistance (R), specific power consumed in the left ventricle (WSpLV), and in the aorta (WSpAo) plotted against stroke volume (SV) at the three levels of end-expiratory pressure (zero mmHg, ZEEP, circles; 5 mmHg, PEEP 5, squares; 10 mmHg, PEEP 10, triangles) of anesthetized dogs, in normal condition (N) after vagal block (VB), after sympathetic block (SB), and after combined block (SVB). Symbols connect­ed by lines, values obtained from a single dog at the three levels of end-expiratory pressure. Each symbol is the average of at least three consecutive measures. Curves whose points correspond to the same power, have been drawn

25 g lEEP 60 a.8 PEEP 5

u 0

20 A PEEP 10 · ~ · 0.6 ~ ~ , · 40

IS 0 ~ . Q. ADO

D ~ 0.4 ~ 10

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""'24 "' ",-24 n =24

p ,( 0.881 p < B.BIB p <B.B1

00 10 20 30 40 00 10 20 30 40 0.00 10 20 30 40

12 30 y == 2.65+1.49)1-9.848.' y '" e.94-B.B4b+0.00B71b: 2

u ,. '" a.7BB 1.. I'" = IiLB44 · n = 28 · ~ 0.8 n .. 24

~ ~, · p < B.a01

~ ~ 2. . ~ "'0. ~ 0.6 0 ...

> > ~ .0. 0.

--' --' 10 ~ 0.4 y '" 2.B95+B.S4x-9.0Ibc 0- 0

,. = 0.B17 "' ~ 0.2 n = 20 p < B.IBH • '.0

• 0 10 20 30 40 ~ 0 10 20 30 40 • 10 20 30 40

sv {m11 SV {ml J SV {ml J

Fig. 3. Mean aortic and left ventricular pressures (PAo, PLV), specific power in the aorta and the ventricle (WSpAo, WSpLV), duration of cardiac cycle (C), and systemic arterial resis­tance (R), plotted against stroke volume (SV) at three levels of end-expiratory pressure (zero mmHg, ZEEP, circles; 5 mmHg, PEEP 5, squares; 10 mmHg, PEEP 10, triangles) of anes­thetized dogs with intact neurovegetative system. Each symbol is the average of at least three consecutive measures obtained from the same dog. Data have been fitted by binomial regression curves

6 A. FEDERICI et al.

plotted in Fig. 2 in the same way. WSpLV and WSpAo are plotted against SV in Fig. 2, and theoretical curves of stationary power are drawn.

In each autonomic condition, as EEP increased, SV progressively decreased. In all of the above conditions R was found to increase with EEP as SV decreased (Figs. 2, 3). However this R increase was only slight in SB and somewhat irregular in SVB (Fig. 2). In the VB, SB, and SVB conditions, AoP, LVP (Fig. 1), WSpAo, and WSpLV (Fig. 2) were found to decrease simultaneously with SV, without variations in C. On the basis of the theoretical curves of work per beat (Fig. 1) and power (Fig. 2), it is worthy to note that in VB, SB, and SVB, the aortic and ventricular work and power declined with Sv. In N, on the other hand, the LVP, WSpLV, and WSpAo increased, the ventricular power and AoP remained almost constant, the decrease in aortic power was only slight, and C was shortened as long as the decrease in SV remained above 15 -16 ml. Work per beat decreased in both the ventricle and the aorta. All of these parameters decreased abruptly, without further variations and, in some cases with a slight lengthening in C, when SV fell below 15 ml. This biphasic pattern of data in N could be significantly fitted by binomial regression curves, as shown in Fig. 3.

Discussion

As previously demonstrated by other investigators [1-5], our data show that CO, SV, AoP, and LVP decrease with the application of PEEP, but that the relation between the decline in SV and the variations in other cardiovascular parameters depends on the activity of the neurovegetative system. When both neurovegetative branches are active (N), some integration between the mechanics of respiration and cardiocirculatory function does exist even if the mechanics of respiration are strongly modified. This integration minimizes the interferences in the circulation. In fact, the cardiopulmonary system in N seems to counterbalance the reduction in SV caused by the increase in EEP by maintaining almost constant values of the left ventricular power and AoP.

For the perfusion of peripheral organs to be successful, both the CO and the pressure must be adequate together. The ventricular power depends on both of these: it is transferred into the aorta against a resistance and a compliance in such a way that AoP and CO (Fig. 1) are prevented from falling as long as the SV remains above 15 -16 ml, and the aortic power decreases slightly (Fig. 1, Fig. 2). This goal is achieved through an increase in heart rate, LVP, and WSpLV as SV decreases. If we take into account the decrease in the left ventricular filling caused by increasing EEP, the increase in LVP and WSpLV may be explained on the basis of a reflex increase in the ventricular performance. The match between the left ventricle and its arterial load [6] is thus maintained by a reciprocal adjustment of resistance, compliance, heart rate and contractility, and SV, so that the left ventric­ular power, cardiac output, and AoP are prevented from falling, despite the decrease in the left ventricular preload, which is due to restriction in the venous return, to increased right ventricular afterload, and to an alteration in the left

Mechanical and Neural Interactions 7

ventricular geometry with septal shifting from right to left [1-3]. This compensa­tory mechanism does not operate in SB, VB, or SVB, or for SV values below 15 -16 ml, which are likely to correspond to a critical value of ventricular teledias­tolic volume. Under these conditions, the heart rate does not increase, the powers decrease as SVdecreases, and the arterial and ventricular pressures decrease in parallel because the ratio between ventricular power output and arterial afterload does not prevent the aortic pressure from falling.

Our experimental model does not allow one to discover either the nature or the location of the cardiopulmonary and/or vascular afferents from which the com­pensating reflex mechanism starts. Depressive cardiovascular reflexes, elicited by lung distension, have been described during PEEP in open-chest dogs [4, 5], but there are some doubts concerning their effectiveness in closed-chest animals [7 -9]. Furthermore, it is difficult to explain their absence in N for SV above 15 -16 ml. Aortic and carotid baroreceptors may account for the compensatory mechanism but not for the abrnpt suppression occurring below 15-16 ml. Moreover, a reflex compensatory effect was demonstrated to persist in closed chest animals when PEEP was applied following sino aortic denervation [7]. On the other hand, only receptors located in the heart and mediastinic vessels are likely to be mechanically stimulated by lung inflation in closed-chest animals more than in open-chest ones.

Hypothetically, the ventricular nerve endings act with vagal afferents, eliciting depressive cardiovascular reflexes that may be activated either by cardiac disten­sion or by forceful and rapid systolic contraction [10]. The stimulus on these endings is likely to be progressively reduced when the diastolic filling decreases with PEEP, so that an increase in sympathetic activity and a decrease in the parasympathetic one may be produced, until the reduced diastolic filling and SV are above a certain critical value. Below this value the contraction of the ventricle against a reduced volume of blood is likely to squeeze the nerve endings rapidly in such a way that they may be restimulated to elicit their depressive effects.

The biphasic trend of data reported in Fig. 3 might be explained in the following manner. PEEP is not a physiological condition, but its effect on the left ventricular filling mimics the decrease in the venous return due to hemorrhage or to dilatation of capacitance vessels. The compensatory mechanism described in our hypothesis might operate under these conditions as well as with PEEP to prevent CO and AoP from falling [10]. Its conversion into an apparently depressive mechanism, below 15 ml, might be useful to reduce the excessive oxygen demand of the overstimulat­ed heart and to prevent excessive shortening of the diastolic time during which most coronary perfusions take place [10].

Summary

The left ventricular and aortic hemodynamics have been studied in fifteen anes­thetized dogs in normal conditions at different levels of airway end-expiratory pressure, following vagus nerve cuttings, sympathetic block, and associated block of the two branches. Subsequent to the neurovegetative blocks, an increase in the

8 A. FEDERICI et al.: Mechanical and Neural Interactions

airway pressure causes a simultaneous decrease in stroke volume and blood. pres­sure, while a reflex mechanism in normal conditions prevents the blood pressure from falling until the stroke volume is above a critical value of about 15 m!.

References

1. Rankin JS, Olsen CO, Arentzen CE, Tyson GS, Maier G, Smith PK, Hammon JW, Davis JW, McHale PA, Andersen RW, Sabiston DC Jr (1982) The effects of airway pressure on cardiac function in intact dogs and man. Circulation 66: 108-120

2. Smith PK, Tyson GS Jr, Hammon JW Jr, Olsen CD, Hopkins RA, Maier GW, Sabiston DC Jr, Rankin JS (1982) Cardiovascular effects of ventilation with positive expiratory airway pressure. Ann Surg 195: 121-130

3. Jardin F, Farcot JC, Boisante L, Curien N, Margairaz A, Bourdarias JP (1981) Influence of positive end-expiratory pressure on left ventricular performance. N Engl J Med 304:387-392

4. Cassidy SS, Robertson CH Jr, Pierre AK, Johnson RL Jr (1978) Cardiovascular effects of positive end-expiratory pressure in dogs. J Appl Physiol 44: 743-750

5. Schreuder JJ, Jansen JRC, Versprille A (1984) Contribution of lung stretch depressor reflex to nonlinear fall in cardiac output during PEEP. J Appl Physiol 56: 1578 -1582

6. Myhre ESP, Johansen A, Piene H (1988) Optimal matching between canine left ventricle and afterload. Am J PhysioI254:H1051-H1058

7. Kaufman MP, Ordway GA, Waldrop TJ (1985) Effect of PEEP on discharge of pul­monary C-fibers in dogs. J Appl Physiol 59: 1085-1089

8. Sellden H, Sj6vall H, Ricksten SE (1987) Reflex changes in sympathetic nerve activity during mechanical ventilation with PEEP in sino-aortic denervated rats. Acta Physiol Scand 130:15-24

9. Federici A, Sorrentino G, Dambrosio M, Fiore T, Chiddo A (1987) The relation between stroke volume and aortic pressure at different levels of positive end expiratory pressure. Boll Soc Ital BioI Sper 63: 787 - 793

10. Abboud FM (1989) Is the heart a sensory organ? N Engl J Med 320:390-392

Interrelations Between Slow and Fast Rhythms in Sympathetic Discharge * M.l. COHEN, R. BARNHARDT, and c.-F. SHAW

Various investigators have reported the presence of slow or fast rhythms in efferent sympathetic discharges. The slow rhythms are usually locked to the central respira­tory rhythm, as demonstrated by analysis of rectified and integrated versions of phrenic and sympathetic discharges (Barman and Gebber 1976; Cohen and Gootman 1970). As seen in cycle-triggered histograms (CTHs), the most common pattern of sympathetic discharge consists of augmented firing during the central inspiratory (I) phase and reduction of activity below the tonic level during the early expiratory (E) phase. Moreover, the respiratory-related component of sympathetic discharge is strongly inhibited by lung afferent input (Gootman et al. 1980).

Sympathetic discharges also have faster rhythms (range 2-11 Hz) that are often entrained to the cardiac cycle by means of baroreceptor afferents (Barman and Gebber 1980; Cohen and Gootman 1970; Gebber 1980). Individual cats differ in the composition of their fast rhythms: some have only a rhythm at a frequency similar to the cardiac rhythm (4-5 Hz), whereas others have a mixture of the former rhythm and a faster rhythm (8 -11 Hz). The fast rhythms are often modu­lated by the respiratory cycle.

We have conducted a systematic study of the interrelation between the fast rhythms and the respiratory rhythm in decerebrate paralyzed cats. Some of these were vagotomized; others, with intact vagi, were ventilated by a cycle-triggered pump, where lung inflation is applied during the time of central I activity as indicated by phrenic for consistency discharge. We have used spectral analysis (fast Fourier transform, FFT) to study how these rhythms vary in relation to: (a) time in the respiratory cycle, (b) lung afferent input.

A typical result of these analyses is seen in Fig. 1, which shows the spectra of cervical sympathetic for consistency (CS) discharge at different times in the I phase and under different inflation conditions. The overall patterns of CS activity, in relation to phrenic activity, are shown in the CTHs (Fig. 1, top panels), derived from cycles where inflation was delivered (light lines) or withheld (heavy lines) during the I phase. It can be seen that during inflation phases there was only a weak respiratory modulation of CS activity, whereas during no-inflation phases a strong respiratory modulation, consisting of a ramp activity pattern, appeared. This change indicates strong inhibition of CS activity by lung afferents. The inhibition is associated with changes in the fast rhythms, as reported earlier (Goot­man and Cohen 1983).

* This research was supported by NIH grant HL-27 300.

10 M.1. COHEN et al.

A B -INFL. - NO INFL.

PHR

cs

_ 200 msec

4.5 9.0 Hz

.01 Al - INFLATION

(/) U

L Q) 3: 0 A2 - NO INFLATION

0... .01 Q)

> .-+-ro Q)

0::

B - NO INFLATION

.01

o 6 12 18 24 30 Hz

Fig. 1. Relationship of fast rhythms in cervical sympathetic (CS) discharge to afferent input (lung inflation) and to time in the inspiratory (I) phase. Top traces, cycle-triggered histograms (CTHs) of phrenic (PHR) and CS activities derived from 23 inflation (light lines) and no-inflation (heavy lines) cycles; 40-ms bins. Note strong inhibition of CS discharge by lung inflation. Vertical lines indicate windows used for FFT computations: A1 and A2, 80-650 ms from 1 onset (inflation I phase duration); B, 650-962 ms from 1 onset (later portion of no-inflation 1 phase). Bottom traces, autospectra of CS activity during different windows: Ai, inflation cycles; A2, no-inflation cycles; B,no-inflation cycles. Vertical lines, 4.5 and 9 Hz spectral bins

As shown by the peaks in the auto spectra (Fig. i, bottom panels), in this cat CS activity had a strong rhythm in the range of 9-11 Hz, accompanied by a weaker rhythm in the range of 4-5 Hz. When overall activity increased, there was an increase in both relative amplitude and frequency of the predominant spectral component. Thus as total power increased, the relative power in the 9- to ii-Hz

Interrelations Between Slow and Fast Rhythms in Sympathetic Discharge 11

band increased, and that in the 4- to 5-Hz band decreased; the frequency of the main spectral peak increased: from 9.0 Hz during inflation phases (Al), to 9.5 Hz during the corresponding (early) portion of no-inflation phases (A2), to 11 Hz in the later portion of no-inflation phases (B).

In cats with various patterns of fast rhythms, the general effects of timing and of lung inflation were similar in direction to those shown in Fig. 1. Thus, in cases where spectra had two prominent peaks (e.g., 4-5 Hz versus 8-10 Hz) the lower frequency peak was relatively larger in the early part of the I phase, and the higher frequency peak was relatively larger in the later part of the I phase. For corre­sponding times in the I phase, the higher frequency component was larger during no-inflation than during inflation.

Thus, when there is an increase of overall sympathetic activity, there is also an increased synchronization of discharge as well as increased frequency and relative power of the main spectral components. These effects may be caused by increased strength of synaptic interactions between populations of brainstem sympathetic­generating neurons.

There have been several studies with recordings from brainstem neurons that were presumed to be related to generation of sympathetic activity on the basis of correlations of their rhythms with rhythms in efferent sympathetic activity (e.g., Barman and Gebber 1981). However, studies which attempted to ascertain whether a brainstem neuron has both a respiratory rhythm and a faster sympa­thetic-related rhythm are rare (Gootman et al. 1975; Haselton and Guyenet 1989; McAllen 1987). In recent experiments in our laboratory, we have searched for such coexistent rhythms in discharges of rostral pontine I-modulated neurons.

It has been known since 1976 (Feldman et al. 1976), and confirmed in a more recent study (Shaw et al. 1989), that I-modulated neurons of the pontine respira­tory group (region of K6lliker-Fuse nucleus and nucleus parabrachialis medialis) are strongly inhibited by lung inflation. The similarity of this response to sympa­thetic responses (Gootman et al. 1980), as well as the sympathetic-excitatory ef­fects of stimulation in the region (Gootman and Cohen 1971; Hade et al. 1988; Mraovitch et al. 1982), suggested that these neurons may playa role in patterning of sympathetic discharge. Therefore, we recorded CS activity simultaneously with activities of these neurons and observed the responses in both types of activity to withdrawal of lung afferent input (no-inflation test).

For many of these pontine I-modulated neurons, the change of discharge pat­tern as a result of no-inflation was very similar to the simultaneously recorded change of sympathetic activity. In the example of Fig. 2, both activities were strongly inhibited by lung inflation, whereas phrenic activity was excited.

In order to ascertain whether pontine unit activity had rhythms correlated (coherent) to sympathetic rhythms, the auto spectra of unit and CS activities, as well as coherences between the two activities, were computed. Such computations were also done for phrenic versus unit activities. However, of over 100 neurons whose activity was recorded together with CS activity, only one had a rhythm coherent to the CS rhythm. This neuron also had high-frequency oscillation (HFO) coherent to phrenic HFO.

12 M.1. COHEN et al.

PHR

__ 200 msec

--INFLATION -- NO INFLATION

Fig. 2. CTHs showing responses in activities of phrenic (PHR), cervical sympathetic (CS), and a pontine I-modulated neuron to withdrawal of lung inflation, derived from 25 inflation (light lines) and no-infla­tion (heavy lines) cycles; 40-ms bins. Note the facilitation of PHR activ­ity by lung inflation, a phenomenon observed in some preparations (Cohen et al. 1987b)

The results of spectral analysis of this neuron's activity and of CS and phrenic activities are shown in Fig. 3. The recordings were taken in admittedly extreme conditions: this cat was apneustic and hypercapnic (end-tidal CO2 9.0%), the latter condition being due to the difficulty of controlling CO2 with use of the cycle-trig­gered pump when the respiratory rhythm is slow. The neuron had an EI discharge pattern, with low-frequency firing in late E and a higher frequency burst in 1. The CTHs (left panels) show that no-inflation converted the discharge in I from a decrementing to an augmenting pattern, indicating strong inhibition by lung affer­ents; CS activity was also strongly inhibited by inflation. Spectral analysis showed (middle panels) that during the later part of no-inflation I phases (window B) there was a high unit-CS peak coherence (0.65) at a frequency of 9.5 Hz, the intrinsic frequency of the CS rhythm indicated by the CS autospectrum. During the early part of no-inflation I phases, there was a smaller peak coherence (0.18) at a lower frequency (8.5 Hz; trace not shown). This difference between the two halves of the I phase is consistent with the general features of timing dependence of CS spectra (e.g., Fig. 1).

In addition, the spectral analysis of unit-phrenic relations (right panels) showed that during the early part of no-inflation I phases (window A) there was a high unit-phrenic peak coherence (0.52) at the HFO frequency of 61.0 Hz. However, during the later part of no-inflation I phases there was only a small unit-HFO peak coherence (value =0.14; trace not shown). This phenomenon of weakness or ab-

A

Interrelations Between Slow and Fast Rhythms in Sympathetic Discharge 13

_INFLATION

_ NO INFLATION

B

_ 400msec

L

" ~ o

B 0.2

9.5 Hz

~ ~~~~~~~~~~ > +­~

" '"

Hz

A 61 Hz

0.06

PHR Autospectrum

100 125

Fig. 3. Analysis of frequency components in discharge during I of a pontine EI neuron located in the Kiilliker-Fuse nucleus. Left panels, CTHs of phrenic (PHR), cervical sympa­thetic (CS), and unit activity, derived from 30 inflation (light lines) and no-inflation (heavy lines) cycles; 40-ms bins. Note strong inhibition of both CS and unit activity by inflation. Vertical lines, windows used for FFT computations: A, 80-460 ms from I onset (inflation I phase duration); B, 460-2098 ms from I onset (extended portion of no-inflation I phase). Middle panels, CS and unit auto spectra and CS-unit coherence, derived from activities during window B in no-inflation cycles. Vertical line, 9.S-Hz spectral peak. Right panels, PHR and unit autospectra and PHR-unit coherence, derived from activity during window A in no-in­flation cycles. Vertical line, 61-Hz HFO spectral peak. Note that middle and right panels have different frequency scales

sence of HFO during late I has previously been reported for medullary I neurons (Christakos et al. 1989).

The high unit-CS coherence, as well as the high unit-phrenic coherence, did not arise from a predominance of interspike intervals corresponding to the dominant spectral frequency peak (9.5 and 61.0 Hz, respectively). The unit autospectrum in late I had a peak at 28.5 Hz (not appearing in the middle panel), which was about three times the frequency of the CS spectral peak. The unit auto spectrum during early I had a peak at 21.0 Hz (right panel), which was about one-third the frequen­cy of the HFO peak. Seemingly, every third interspike interval was locked to the CS rhythm, and conversely every third (phrenic) HFO cycle was locked to an interspike interval. Thus the coherences of unit with nerve activities arose from modulation of the neuron's firing by inputs having the respective sympathetic and HFO rhythms (see detailed treatment of this phenomenon in Christakos et al. 1988).

The great rarity of short-term correlation (coherence) between pontine I-modu­lated neurons' discharges and sympathetic rhythms indicates that if these neurons are functionally related to sympathetic discharge, the relationship must be through diffuse synaptic connections that are effective only on the time scale of the respira­tory cycle. On the basis of the rarity of HFOs in pontine I neurons' discharges, a similar conclusion has been reached about the relationship between these neurons and the central I pattern generator (Shaw et al. 1989).

14 M.1. COHEN et al.

Although the types of relations shown in Fig. 3 were rare, the results are of methodological interest, since they suggest a possible means of distinguishing whether different brainstem neurons are involved in respiratory or sympathetic pattern generation. HFOs in the range of 50-100 Hz are ubiquitous in the dis­charges of I motor nerves (phrenic, laryngeal, hypoglossal; Cohen et al. 1987 a) and of medullary I neurons (Christakos et al. 1988). The presence of such a coherent rhythm in discharge of an I-modulated neuron would indicate that it is closely linked to the I pattern generator. Similarly, since 2- to ii-Hz rhythms are ubiquitous in sympathetic discharges, the presence of such coherent rhythms in a neuron's discharge would indicate that it is closely linked to the sympathetic pattern generator.

References

Barman SM, Gebber GL (1976) Basis for synchronization of sympathetic and phrenic nerve discharges. Am J PhysioI231:1601-1607

Barman SM, Gebber GL (1980) Sympathetic nerve rhythm of brain stem origin. Am J PhysioI239:R42-R47

Barman SM, Gebber GL (1981) Brain stem neuronal types with activity patterns related to sympathetic nerve discharge. Am J Physiol 240:R335-R347

Christakos CN, Cohen MI, See WR, Barnhardt R (1988) Fast rhythms in the discharges of medullary inspiratory neurons. Brain Res 463:362-367

Christakos CN, Cohen MI, See WR, Barnhardt R (1989) Changes in frequency content of inspiratory neuron and nerve activities in the course of inspiration. Brain Res 482: 376- 380

Cohen MI, Gootman PM (1970) Periodicities in efferent discharge of splanchnic nerve of the cat. Am J PhysioI218:1092-1101

Cohen MI, See WR, Christakos CN, Sica AL (1987 a) High-frequency and medium-frequen­cy components of different inspiratory nerve discharges and their modification by vari­ous inputs. Brain Res 417:148-152

Cohen MI, See WR, Sica AL, Moss 1M (1987b) Influence of central nervous system state on inspiratory facilitation by pulmonary afferents. In: Von Euler C, Lagercrantz H (eds) Neurobiology of the control of breathing. Raven, New York, pp 251-256

Feldman 1L, Cohen MI, Wolotsky P (1976) Powerful inhibition of pontine respiratory neurons by pulmonary afferent activity. Brain Res 104: 341-346

Gebber GL (1980) Central oscillators responsible for sympathetic nerve discharge. Am J PhysioI239:H143-H155

Gootman PM, Cohen MI (1971) Evoked splanchnic potentials produced by electrical stim­ulation of medullary vasomotor regions. Exp Brain Res 13: 1-14

Gootman PM, Cohen MI (1983) Inhibitory effects on fast sympathetic rhythms. Brain Res 270: 134-136

Gootman PM, Cohen MI, Piercey MP, Wolotsky P (1975) A search for medullary neurons with activity patterns similar to those in sympathetic nerves. Brain Res 87: 395-406

Gootman PM, Feldman JL, Cohen MI (1980) Pulmonary afferent influences on respiratory modulation of sympathetic discharge. In: Koepchen HP, Hilton SM, Trzebski A (eds) Central interaction between respiratory and cardiovascular control systems. Springer, Berlin Heidelberg New York, pp 172-179

Hade JS, Mifflin SW, Donta TS, Felder RB (1988) Stimulation of parabranchial neurons elicits a sympathetically mediated pressor response in cats. Am J PhysioI255:H1349-H1358

Interrelations Between Slow and Fast Rhythms in Sympathetic Discharge 15

Haselton JR, Guyenet PG (1989) Central respiratory modulation of medullary sympathoex­citatory neurons in rat. Am J Physiol 256: R 739 - R 750

McAllen RM (1987) Central respiratory modulation of subretrofacial bulbospinal neurones in the cat. J Physiol (Lond) 388: 533 - 545

Mraovitch S, Kumada M, Reis DJ (1982) Role of the nucleus parabrachialis in cardiovascu­lar regulation in cat. Brain Res 232: 57 - 75

Shaw C-F, Cohen MI, Barnhardt R (1989) Inspiratory-modulated neurons of the rostrolat­eral pons: effects of pulmonary afferent input. Brain Res 485: 179-184

Common and Specific Sources of Regional Sympathetic Outflows in Cerebral Ischemia, Cushing Reaction, and Asphyxia * B. KOCSIS, G. L. GEBBER, and L. FEDINA

Introduction

Since Cannon [1] first described the so-called mass sympathetic reaction, in which all branches of the system are synergetically excited, the roles played by common versus specific central sources in the control of regional sympathetic outflow has been a subject of intense investigation. During the past two to three decades Cannon's original observations have been extended to include further similarities in the activity of widely separated sympathetic nerves (e.g., in reflexes evoked by afferent nerve stimulation [2, 3] and in the rhythmic structure of background activity [4-7]). In the same time span, it was also established that the central circuits controlling sympathetic nerve discharge (SND) may generate complex and highly differentiated patterns appropriate to different behavioral states, such as the defense reaction and desynchronized sleep [8, 9].

The current study was designed to separate and quantify the common and specific components in the discharges of different sympathetic nerves at rest and during perturbation of the system produced by severe and acute changes of CO 2

and O2 levels in the brain. The experiments were performed in baroreceptor­denervated cats anesthetized with chloralose and urethane. Recordings were made from postganglionic sympathetic nerves that innervate the heart (inferior cardiac nerve) and blood vessels in skeletal muscle (vertebral nerve) and the kidney (renal nerve). The experimental procedures are described in detail in a previous report [7].

Characterization of the Differential Responses of Sympathetic Nerves

Occlusion of the arteries supplying the brain, intracranial pressure (ICP) elevation, or systemic asphyxia produces an increase in SND throughout the body [10-13] and changes in its rhythmic structure [7, 11]. The sympathetic nerve responses to these perturbations start with an upward shift of the dominant frequency within the 2- to 1O-Hz range and an increase of burst amplitude (first phase). Later, the power in the 2- to 10-Hz band decreases in parallel with a rise in power of higher

* This research was supported by the Hungarian Academy of Sciences and by NIH grant HL 13187.

Common and Specific Sources of Regional Sympathetic Outflows 17

frequency components (second phase) [7]. A variant form of the second phase is characterized by increases in power in both the 2- to 10-Hz and higher frequency bands.

The two-phase response can vary in character from nerve to nerve and from time to time in the same nerve. In many cases the frequency shift (a) appears at different times in different nerves and (b) is more markedly expressed in the vertebral than in the cardiac or renal nerves. Typical examples are shown in Fig. 1, where simul­taneous recordings were made of the discharges of the vertebral, inferior cardiac and renal postganglionic sympathetic nerves during the Cushing response elicited by ICP elevation in three cats (A and B are from the same experiment). Each set of two tracings shows the same signal after low- ( < 50 Hz) and high-pass (> 50 Hz) filtering. The typical two-phase response was observed for the vertebral nerve in each of the three experiments, for the inferior cardiac nerve during trial B of the first experiment and in the second experiment (C), and for the renal nerve in the second experiment (C). The remaining responses of the inferior cardiac (A, D) and renal (A, B, D) nerves differed. In these cases the power in both the low- and high-frequency bands increased in parallel during the Cushing response.

The differential responses of the vertebral, inferior cardiac, and renal sympa­thetic nerves during two episodes of systemic asphyxia in the same cat are shown in Fig. 2. The "waterfall" displays of the power spectra were formulated from consecutive 2-s data blocks. The displays were similar for the three nerves under normocapnic conditions (Fig. 2, left column). Power was primarily distributed between 1 and 7 Hz. Asphyxia (produced by shutting off the artificial respirator)

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Common and Specific Sources of Regional Sympathetic Outflows 19

was accompanied by differential changes in the activity of the three nerves. During the first episode (Fig. 2, middle column), most of the power in vertebral and renal SND shifted to higher frequencies. In contrast, the shape of the power spectra of inferior cardiac SND was not much changed. During the second episode of as­phyxia (Fig. 2, right column) there was a shift to higher frequencies in all three nerves. However, power below 6 Hz was reduced appreciably in only the vertebral nerve.

Coherence Analysis

The common and specific components of the simultaneously recorded discharges of sets of postganglionic sympathetic nerves were distinguished using the coher­ence function. The coherence function provides a quantitative measurement of the strength of linear correlation between the two signals at each frequency [14]. A coherence value not significantly different from 0 denotes the absence of linear correlation between the two signals while a value of 1 denotes a perfect correlation. Coherence values significantly different from 0 but less than 1 indicate that the signals contain components from the same or linearly related sources, and compo­nents from specific or nonlinearly related sources [14].

Control Cushing

frequency (0-15 Hz) frequency (0-15 Hz) frequenoy (0-15 Hz)

Fig. 3. Uniform sympathetic nerve response pattern to ICP elevation. Renal (REN) and cardiac (CARD) sympathetic nerve auto spectra (normalized) and coherence function at rest (left) and during Cushing reaction (middle) in a baroreceptor-denervated cat: right, superim­position of the two plots on the right. Each plot is an average based on 50 (control) or 14 (Cushing) 5-s windows. Frequency resolution is 0.2 Hz. During Cushing reaction, total power (RMS value) in CARD and REN activity was increased to 140% and 117% of control, respectively

20 B. KOCSIS et al.

Control Asphyxia

frequency (0-15 Hz) frequenoy (0-15 Hz) frequency (0-15 Hz)

Fig. 4. Differential sympathetic nerve reaction during systemic asphyxia. Vertebral (VERT) and cardiac (CARD) sympathetic nerve autospectra (normalized) and coherence functions at rest (left) and during the first (middle) and second (right) halves of asphyxia in a barore­ceptor-denervated cat. Each plot is an average based on 14 (control), 10 (asphyxia first half) or 4 (asphyxia second half) 5-s windows. Frequency resolution is 0.2 Hz. During asphyxia total power (RMS value) in VERT activity changed to 110% (first half) and 81 % (second half) of control. RMS value for the CARD nerve changed to 103% (first half) and 72% of control

Sympathetic Nerve Response Patterns Involving a Common Source. Examples of a uniform sympathetic nerve response pattern to ICP elevation and of a differential response pattern to systemic asphyxia originating from a common source (or linearly related sources) are displayed in Figs. 3 and 4, respectively. A uniform response pattern is one in which the changes in shape of the power spectra are similar for both nerves. A differential response pattern is signified by nonuniform changes in the shape of the power spectra for both nerves. The response pattern to ICP elevation in Fig. 3 was characterized by a small and uniform upward shift in the peak frequency of inferior cardiac and renal SND (see superimposed power spectra in right column). This was accompanied by a corresponding shift of the coherence function (see superimposed coherence functions in bottom right panel).

Figure 4 shows an experiment in which asphyxia evoked differential responses in two postganglionic branches (vertebral and inferior cardiac) of the same stellate ganglion. The middle and right columns of this figure show the first and the second halves of the reaction. While changes in the frequency components of inferior cardiac SND were minor during asphyxia, the primary frequency component in vertebral SND increased from about 4 Hz (range 1-7.5 Hz) to 11 Hz (range, 7.5-14 Hz). The shape of the coherence function also changed; by the end of asphyxia when the power spectra of vertebral and inferior cardiac SND had

Common and Specific Sources of Regional Sympathetic Outflows 21

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markedly different shapes, the coherence function became bimodal. The first mode corresponded to the dominant frequency in the inferior cardiac nerve and the second to that in the vertebral nerve.

Sympathetic Nerve Responses Involving Specific Sources. Examples of uniform and differential sympathetic nerve response patterns involving specific sources of input are illustrated in Figs. 5 and 6. In Fig. 5, most of the power in vertebral and renal SND was contained below 4.5 Hz under normocapnic conditions (two upper panels in left column). ICP elevation markedly increased the power above 4.5 Hz in both signals (two upper panels in middle column). This occurred without a major change in power at the lower frequencies (see superimposed power spectra in right column). Thus the narrow-band signals in control were transformed into wide band signals. Although the power spectra of vertebral and renal SND were similar during the Cushing response, the coherence function was not markedly changed from control (bottom panel in right column). Thus, the higher frequency components added more or less uniformly to the two signals during the Cushing response appeared to arise primarily from sources that were specific to each nerve.

Figure 6 shows a differential (A) and a uniform (B) pattern of sympathetic nerve responses arising from specific sources. Two- to threefold reduction of the maximal coherence value and transition of the well-shaped coherence function into a vari-

22 B. KOCSIS et al.

Control' Asphyxia Control Asphyxia A B

frequency (0-15 Hz) frequency (0-15 Hz) frequenC7 (0-15 Hz) f'requenc7 (0-15 Hz)

Fig. 6A, B. Differential (A) and uniform (B) sympathetic nerve response patterns during systemic asphyxia in two baroreceptor-denervated cats. A Vertebral (VERT) and renal (REN) sympathetic nerve autospectra (normalized) and coherence functions at rest (left) and during asphyxia (right). Each plot is an average based on 50 (control) or 16 (asphyxia) 5-s windows. Frequency resolution is 0.2 Hz. During asphyxia, total power (RMS) changed to 129% (VERT) and 157% (REN) of control. B Same as in A for inferior cardiac (CARD) and REN nerve responses. Plots are averages of 50 (control) and 14 (asphyxia) 5-s windows. RMS values changed to 32% (CARD) and 46% (REN) of control

able rippling form in these experiments was indicative of the loss of the linear relationship between the networks generating the two signals [15]. Figure 6A shows a differential response in which most of the power in vertebral SND was shifted from O-Hz (peak at 1.5 Hz) to I-Hz (peak at 4 Hz) while that in renal SND was changed more dramatically from 1-Hz (peak at 3 Hz) to a wide band (with no clear peak) whose upper limit exceeded 15 Hz. In contrast, the loss of coherence in the experiment illustrated in Fig. 6 B occurred in the face of uniform changes in inferior cardiac and renal SND and the maintenance of narrow-band power spectra.

Discussion

Specificity of the Neural Networks Generating Regional Sympathetic Outflow. The discharges recorded under normocapnic conditions from pairs of postganglionic sympathetic nerves in baroreceptor denervated cats are statistically equivalent band-limited stochastic signals with strong but not perfect linear relationship between them. The SND auto spectra for different nerves usually extend from 1-2 Hz to 5 - 7 Hz, are unimodal, and overlap to a considerable extent.

Common and Specific Sources of Regional Sympathetic Outflows 23

Cerebral ischemia, brain compression and asphyxia evoked a change of mass activity (increase most often followed by a decrease, see RMS values in the figure legends) and the frequency components in SND. Spectral analysis revealed two kinds of alterations in the frequency components for individual postganglionic nerves: (a) an upward shift of the narrow-band spectra and (b) expansion toward higher frequencies without loss of the original lower frequency components. Re­garding the relationship between the discharges of two nerves, the system per­turbed by severe, acute brain hypoxia/hypercapnia was able to generate four basic response patterns: (a) uniform reaction with maintained high coherence, (b) differ­ential reaction with maintained high coherence, (c) uniform reaction with reduced coherence, and (d) differential reaction with reduced coherence. The response pattern observed for a particular set of postganglionic nerves in a given experiment could change from episode to episode of brain hypoxia/hypercapnia. Thus, the relationships between the discharges of two nerves were variable.

The results of the current study provide some clues concerning the central mechanisms involved in formulating uniform and differential patterns of spinal sympathetic outflow.

1. Two observations indicate that multiple neural circuits are responsible for the narrow-band (2- to 6-Hz) power spectra of SND. First, the shape of the power spectra for different sympathetic nerves could be virtually identical under condi­tions when the coherence values were low (Fig. 6 B). Second, the power spectra for different nerves on occasion had markedly different shapes and dominant frequen­cy bands (Fig. 4, right column). It follows that some degree of specificity exists in the relationships between particular neural circuits and individual postganglionic sympathetic nerves.

2. The multiple circuits responsible for the 2- to 6-Hz components in the dis­charges of different sympathetic nerves most often are coupled and act as a functionally common generator. The high coherence values at rest and in some cases during brain hypoxia/hypercapnia suggest that these circuits are linearly related. Linear coupling may be the result of interconnection of free-running oscillators or of a common stochastic input (most likely generated by a distributed system of brain stem reticular neurons) to multiple circuits that act as filters. When the coherence values fall, the circuits are either disconnected or nonlinearities predominate in their relationships.

3. Supraspinal networks play an important role in determining the nature of rhythmic SND. The selection of the actual working band of the oscillators (or filters) and their cooperative function is dependent on supraspinal networks since the response patterns observed were induced by local (cerebral ischemia or rcp elevation) as well as global (systemic asphyxia) changes in blood gases. Previous investigations, indicating that generation of the 2- to 6-Hz components in SND occurs in the brainstem [16,17], are consistent with this conclusion.

4. The mechanisms responsible for the occurrence of appreciable power in SND above 6 Hz remain unclear. Appearance of wide-band sympathetic nerve signals during brain hypoxia/hypercapnia may signify a degenerative change in state of the circuits responsible for 2- to 6-Hz activity. Alternatively, the higher frequency

24 B. KOCSIS et al.

components may be generated independently. This might occur in circuits that lie in parallel to those responsible for the 2- to 6-Hz component ofSND, or at a level closer to the output stages of the system generating 2- to 6-Hz activity.

Specific Sympathetic Control in Brain Hypoxia/Hypercapnia. The cerebral ischemic and Cushing reactions are considered to result from a coordinated effort of the cardiovascular system to restore blood supply to the brain. It has been suggested that a balanced cardiac response and a nonuniform vasoconstriction in different organ systems are committed to performing this task [18, 19].

In our experiments the high degree of variability of the reactions suggests that they are not governed in an entirely deterministic fashion. The variability encoun­tered in our experiments with severe, acute ischemic stimuli simulating real patho­logical situations might be explained by superimposition of well-shaped determin­istic response patterns on those arising from random hypoxic/hypercapnic-in­duced injury at different levels in the central networks controlling SND.

References

1. Cannon WB (1963) The wisdom of the body. Norton, New York, pp1-333 2. Koizumi K, Brooks CM (1972) The integration of autonomic system reactions: a discus­

sion of autonomic reflexes, their control and their association with somatic reactions. Ergeb Physiol 67: 1-67

3. Sato A, Schmidt RF (1973) Somatosympathetic reflexes: afferent fibers, central path­ways, discharge characteristics. Physiol Rev 53:916-947

4. Koizumi K, Seller H, Kaufman A, Brooks CM (1971) Pattern of sympathetic discharges and their relation to baroreceptor and respiratory activities. Brain Res 27: 281-294

5. Gootman PM, Cohen MI (1973) Periodic modulation (cardiac and respiratory) of spontaneous and evoked sympathetic discharge. Acta Physiol Pol 24:97-109

6. Gebber GL, Barman SM (1980) Basis for 2-6 cyc1ejs rhythm in sympathetic nerve discharge. Am J PhysioI239:R48-R56

7. Kocsis B, Fedina L, Pasztor E (1989) Two-phase change of sympathetic rhythms in brain ischemia, Cushing reaction, and asphyxia. Am J Physiol 256:R120-R132

8. Hilton SM (1982) The defence-arousal system and its relevance for circulatory and respiratory control. J Exp BioI 100: 159-174

9. Coote JH (1982) Respiratory and circulatory control during sleep. J Exp BioI 1 00: 223 - 244 10. Sadoshima S, Thames M, Heistad D (1981) Cerebral blood flow during elevation of

intracranial pressure: role of sympathetic nerves. Am J Physiol H78-H84 11. Matsuura S, Sakamoto H, Hayashida Y, Kuno M (1984) Efferent discharges ofsympa­

thetic and parasympathetic nerve fibers during increased intracranial pressure in anes­thetized cats in absence and presence of pressor response. Brain Res 305:291-301

12. Pasztor E, Fedina L, Kocsis B, Berta Z (1986) Activity of peripheral sympathetic efferent nerves in experimental subarachnoidal hemorrhage. I. Observations at the time of in­tracranial hypertension. Acta Neurochir (Wien) 79:125-131

13. Prabhakar NR, Mitra J, Van de Graaff W, Haxhiu MA, Cherniack NS (1986) Effect of focal cooling of central chemosensitive areas on cerebral ischemic response. Am J Physiol 251:R295-R302

14. Bendat JS, Piersol AG (1986) Measurement and analysis of random data. Wiley, New York, pp 1-390

Common and Specific Sources of Regional Sympathetic Outflows 25

15. Otnes RK, Enochson L (1978) Applied time series analysis, vol I: basic techniques. Wiley, New York, pp 1-428

16. Barman SM, Gebber GL (1980) Sympathetic nerve rhythm of brain stem origin. Am J PhysioI239:R42-R47

17. Gebber GL, Barman SM (1985) Lateral tegmental field neurons of cat medulla: a potential source of basal sympathetic nerve discharge. J Neurophysiol 54: 1498 -1512

18. Dampney RAL, Kumada M, Reis DJ (1979) Central neural mechanisms of the cerebral ischemic response: characterization, effect of brains tern and cranial nerve transections, and simulation by electrical stimulation of restricted regions of medulla oblongata in rabbit. Circ Res 45:48-62

19. Van Wylen DGL, D'Alecy LG (1985) Regional blood flow distribution during the Cushing response: alterations with adrenergic blockade. Am J Physiol 248: H98 - H108

Interrelationships Between the Respiratory and Sympathetic Rhythm Generating Systems in Neonates as Revealed by Alterations in Afferent Inputs *

P. M. GOOTMAN, A. L. SICA, A. M. STEELE, H. L. COHEN, B. W HUNDLEY, G. CONDEMI, M. R. GANDHI, L. EBERLE, and N. GOOTMAN

Introduction

Interactions between the respiratory (RESP) rhythm generator (RRG) and the sympathetic (SYMP) rhythm generating system (SRGS) have been of interest to one of us for more than 25 years (cf. [6-8]). Recently, we have begun to investigate these interactions in the developing swine model, Sus scrofa [4, 5, 10]. While some results are similar to those of earlier studies in adult cat, e.g., RESP and cardiac modulation of sympathetic discharge (see [7]), we have noted some age-re­lated differences. For example, high-frequency oscillations in phrenic (PHR) nerve discharge have been shown by us to be age-related, with peak frequencies higher than that usually reported for adult cat [2]. In addition, SYMP oscillations in neonatal swine have been found to range from 5 to 28 Hz [4, 7, 9]; these higher frequencies have been seen by us in adult cat cervical SYMP activity but not in adult cat efferent splanchnic activity [6,7]. In this paper we summarize some of our findings on the effects of alterations in pulmonary and baroceptor afferent inputs on the neonatal SRGS and RRG as monitored by recordings of efferent phrenic (PHR), recurrent laryngeal (RL), splanchnic (SPL) and cervical sympathetic (CS) activity.

Methods

Experiments were performed on piglets ( < 1 day to 50 days of age) lightly anes­thetized with Saffan, paralyzed, and artificially ventilated on 100% O2 with either an oscillating or cycle-triggered pump. Further details of methods have been given in earlier papers [2, 4, 12]. Recordings were made of nerve signals (monophasically recorded) simultaneously with aortic pressure (AoP), end-tidal CO2, intratracheal pressure (ITP), and EKG. Changes in baroceptor afferent (BA) input were elicited either by increases in AoP produced by bolus injection of phenylephrine (PE: 20 f.lg/kg) or by decreases in AoP elicited by bolus injection of Na nitroprusside

* Supported by NIH grants HL-20864 (P.M.G.) and HL-41 008 (A.L.S.)

Respiratory and Sympathetic Rhythm Generating Systems 27

(NP: 30 flg/kg) [5]. Changes in pulmonary afferent (PA) inputs were produced by different lung inflation tests: maintained lung inflation (MU), maintained no lung inflation, and no lung inflation for one ventilatory cycle (NU) [3, 8, 10]. Relative changes in SYMP, PHR, and RL activity were examined by integration proce­dures. Nerve activities were studied by auto- and cross-correlation methods, power spectral analysis and averaging techniques. Further details of data analyses are given in earlier papers.

Results

An example of the RESP modulation of SYMP activity can be seen in the traces of integrated CS and SPL activity simultaneously recorded with PHR activity (Fig. 1). Note the increase of SYMP activity during the PHR (inspiratory: I) burst. The importance of the relationship between the SRGS and the RRG can be seen in the power spectra of CS activity (Fig. 2). There is facilitation of SYMP activity during I as seen by an overall increase of power, especially at approximately 7 and 18 Hz. The I-related facilitation of SYMP activity is obvious in the threefold increase in power when CS activity was gated in 1. Similar changes in outputs of both the RRG and SRGS were obtained with alterations in BA inputs. Figure 3 shows the inhibition of both systems when a sudden increase in AoP was produced by injection of PE. The inhibition of SYMP activity lasted longer than did inhibi­tion of PHR activity. We found that inhibition of PHR discharge was more common and tended to last longer in the younger animals. On the other hand, NP

Fig. 1. Polygraph traces of simulta­neously recorded EKG, aortic pres­sure (AoP), inte­grated (100 ms t.c.) phrenic (Int. PHR), cervical sympathetic (Int. CS) and splanchnic (Int. SPL) activities from a 16-day-old piglet. ITP, intratracheal pressure

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28 P. M. GOOTMAN et al.

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Respiratory and Sympathetic Rhythm Generating Systems 29

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Fig. 5. Effect of a spontaneous increase in integrated (100 ms t.c.) phrenic inspiratory (Integ. PHR) activity on integrated (100 ms t.c.) cervical sympathetic activity (In­teg. CS) and aortic pressure (AoP) from a 5-day-old piglet ventilated on the cycle-triggered pump

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induced inhibition of BA inputs resulted in augmentation of outputs of both the RRG and SRGS (Fig. 4). However, the onset of increased PHR, RL, and SYMP activity was delayed relative to the change in AoP when compared to the onset of changes in PHR and SYMP activity in response to increased BA inputs. Fluctua­tions in the RESP modulation of SYMP discharge could be obtained by manipu­lating PA inputs, i.e., MLI and NLI tests, or by bilateral vagotomy [7, 10]. The effects of spontaneous changes in output from the RRG in an animal ventilated

30 P. M. GOOTMAN et al.

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Fig. 6. Left, effects of maintained lung inflation (see ITP, top trace) on lnt. PHR and lnt. CS activity in a 5-day-old piglet. Right, effect of no lung inflation test (monitored by ITP, top trace) on lnt. PHR (middle trace) and lnt. CS (bottom trace) activity in a 14-day-old piglet. See Fig. 1 for explanation of abbreviations

O.B

Fig.7. Top, each trace is an aver­age (n= 13) of integrated (100 ms t.c.) phrenic inspiratory bursts of activity. Bottom, each trace is an average (n = 13) of integrated (100 ms t.c.) cervical sympathetic activity. Solid line, control; dotted line, during no lung inflation tests. The 14-day-old piglet was venti­lated on the cycle-triggered pump. Traces start 100 ms before start of inspiratory phase

on the cycle-triggered pump is shown in Fig. 5. The prolongation of the I burst augmented the lung inflation (as would have occurred naturally); the effects of SYMP activity can be seen by the alterations in integrated CS activity. CS activity both increased and then showed a greater degree of inhibition, presumably reflect­ing greater PA input. During MLI (Fig. 6, left) SYMP activity generally started to decline but then increased. In very young piglets, SYMP discharge did not increase until termination of the MLI. In older piglets SYMP activity started to increase

Respiratory and Sympathetic Rhythm Generating Systems 31

during the latter half of the MLI. In the example of Fig. 6 (left) the SYMP oscillations during the MLI resembled those recorded prior to and following the inflation test, i.e., slow wave activity in phase with the PHR bursts. These oscilla­tions continued even in the absence of PHR output. On the other hand, the NLI test, which eliminated PA input for one ventilatory cycle, augmented both PHR and SYMP activity (Fig. 6, right). Averaging procedures verified the increase in duration of PHR burst (Fig. 7). The increase in CS activity during the NLI tests is shown in the bottom traces of Fig. 7. Note also that with elimination of PA inputs the increase in CS activity appeared earlier in the I phase.

Discussion

Our finding that SYMP discharge increased in power during I (Fig. 2) is similar to earlier reports of increased excitability of SYMP activity during I, for example, the lO/s periodicity seen in adult SPL auto correlations when gated in I [6]. On the other hand, even in adult cats, only 4 Hz was frequently noted when SYMP activity was gated in expiration [6]. Previously, we reported that correlation anal­yses revealed central respiratory modulation of neonatal SPL activity [4].

Our results also demonstrated that alterations in afferent inputs to the RRG and SRGS can have either similar or different effects on these systems. An example of the former was the response pattern to alterations in BA inputs [5], i.e., elevated AoP inhibited both rhythm generating systems (Fig. 3). Furthermore, there was a parallel increase in the outputs of these systems with an inhibition of BA inputs (Fig. 4). We did note, however, that the changes in outputs from the SRGS were more prolonged as compared to those of the RRG, as monitored by PHR and RL activities (Fig. 4). An example of the latter was that alterations in PA had more complex effects on the SRGS. While PHR activity was inhibited for the duration of the MLI test (Fig. 6, left), and while we have no way of knowing what is happening within the expiratory-generating network, the output from the SRGS was maintained or even increased. The integrated CS signal also suggested that the slow waves, which are normally entrained to the central RESP cycle, can continue to occur independently of at least the I-generating network. To the best of our knowledge, this has not been observed in adult mammals.

A spontaneous change in output from the RRG (Fig. 5) was seen as an increase in PHR burst. Since the animal was on the cycle-triggered pump, the lung inflation was greater than normal. The effects on the output of the SRGS were quite interesting: initially an increase in the integrated signal, followed by a greater than normal decrease in activity that paralleled the augmented lung inflation. Thus, this figure demonstrates both central and peripheral RESP modulation of SYMP activity.

The NLI test (Fig. 6, right) had parallel effects on the output of both the RRG and SRGS. PHR activity increased in duration while SYMP activity increased in amplitude. Furthermore, there was a shift in location of the peak of the SYMP burst, i.e., it occurred earlier in I, near the onset ofPHR activity (Fig. 7, bottom). This was not observed in adult mammalian SYMP discharge [8].

32 P. M. GOOTMAN et al.: Rhythm Generating Systems

Our results are not in conflict with the concept of a common cardiovascular-res­piratory rhythm generator [1, 11] or with the possibility of a network linking the RRG and SRGS. The finding of slow SYMP oscillations in the absence of PHR activity is not in conflict with either of the above hypotheses (Fig. 6, left), albeit contrast with results of Bachoo and Polosa [1]. Nevertheless, it is obvious from our studies that there is a close coupling between the RRG and SRGS in neonates. In conclusion, while the outputs of the neonatal RRG and SRGS resemble those of adult mammals, there are response patterns occurring in neonates that are not present in adult mammals. Furthermore, the absence of some responses in the developing mammal implies considerable postnatal maturation of the interactions between these two systems.

References

1. Bachoo M, Polosa C (1987) Properties of the inspiratory-related activity of sympathetic preganglionic neurones of the cervical trunk in the cat. J Physiol (Lond) 385: 545-564

2. Cohen HL, Gootman PM, Steele AM, Eberle LP, Rao PP (1987) Age-related changes in power spectra of efferent phrenic activity in the piglet. Brain Res 426: 179 -182

3. Cohen MI, Gootman PM, Feldman JL (1980) Inhibition of sympathetic discharge by lung inflation. In: Sleight P (ed) Arterial baroreceptors and hypertension. Oxford Med­ical Publications, London, pp 161-166

4. Gootman PM, Cohen HL, DiRusso SM, Rudell AP, Eberle LP (1984) Characteristics of spontaneous efferent splanchnic discharge in neonatal swine. In: Usdin E, Dahlstrom A, Engel J, Carlsson A (eds) Catecholamines: basic and peripheral mechanisms: Liss, New York, pp 369-374

5. Gootman PM, Cohen HL, Hundley BW, Condemi G, Eberle LP, Brust M (1989) Effects of alterations in baroceptor input on activity in sympathetic neurons arising at different segmental levels. FASEB J 3:.A:413

6. Gootman PM, Cohen MI (1974) The interrelationships between sympathetic discharge and central respiratory drive. In: Umbach W, Koepchen HP (eds) Central rhythmic and regulation. Hippokrates, Stuttgart, pp 195-209

7. Gootman PM, Cohen MI, DiRusso SM, Sica AL, Cohen HL, Eberle LP, Rudell AP, Gootman N (1987) Periodicities in spontaneous preganglionic sympathetic discharge. In: Ciriello J, Calaresu FR, Renaud LP, Polo sa C (eds) Organization of the autonomic nervous system: central and peripheral mechanisms. Liss, New York, pp 133-142

8. Gootman PM, Feldman JL, Cohen MI (1980) Pulmonary afferent influences on respira­tory modulation of sympathetic discharge. In: Koepchen HP, Hilton SM, Trzebski A (eds) Central interaction between respiratory and cardiovascular control systems. Springer, Berlin Heidelberg New York, pp 172-179

9. Gootman PM, Sica AL, Steele AM, Cohen HL, Griswold PG, Gandhi MR, Eberle LP, Hundley B (1988) Spontaneous efferent preganglionic sympathetic activity in neonatal swine. In: Dahlstrom A, Belmaker RH, Sandler M (eds) Progress in catecholamine research, Part A: basic aspects and peripheral mechanisms. Liss, New York, pp 449-453

10. Gootman PM, Sica AL, Steele AM, Gandhi MR, Cohen HL, Griswold PG (1987) Pulmonary afferent influences on efferent phrenic and sympathetic activities in develop­ing swine. Fed Proc 46: 1245

11. Koepchen HP, Klussendorf D, Sommer D (1981) Neurophysiological background of central neural cardiovascular-respiratory coordination: basic remarks and experimental approach. J Auton Nerv Syst 3:335-368

12. Sica AL, Steele AM, Gandhi MR, Donnelly DF, Prasad N (1988) Power spectral analyses of inspiratory activities in neonatal pigs. Brain Res 440:370-374

Identification of Postganglionic Thoracic Sympathetic Neurons: Cardiac and Respiratory Discharge Patterns * P. SZULCZYK and B. KAMOSINSKA

Introduction

The ongoing activity in the postganglionic sympathetic neurons innervating the cardiovascular effectors, the heart [2, 9, 10], and muscle resistance vessels [3, 6] is related to the cardiac and respiratory cycle. Since the temporal relationship be­tween the cardiac cycle and sympathetic activity is abolished when all baroreceptor afferents have been cut, it is assumed that it is a consequence of the baroreceptor reflex [3]. The respiratory modulation in the sympathetic neurones may depend on the input from cardiopulmonary receptors [12], the blood pressure changes sec­ondary to the mechanical movement of the thorax [6], and central coupling be­tween respiratory and sympathetic neurons [1, 2, 5, 6].

The primary aim of the present study was to demonstrate patterns of the cardiac and respiratory rhythmicities present in single postganglionic neurons which inner­vate thoracic viscera.

Methods

The experiments were performed on adult cats under standarized conditions: an initial intramuscular dose of 15 mg/kg ketamine hydrochloride followed by chlo­raloze anesthesia (70 mg/kg, intravenously), artificial respiration at a frequency of 30/min, immobilization with pancuronium bromide 0.1 mg/kg per hour, and body core temperature maintained at 37.5° ± 0.5 DC. Blood pressure was monitored from the right femoral artery. The heart rate was derived from the arterial pressure pulse. Arterial p02' pC02, and pH were measured in the arterial blood samples. These factors were maintained at a physiological level by means of ventilation adjustment and the administration of sodium bicarbonate. Both vagoaortic nerves were cut in the neck. Bilateral pneumothorax was performed. The thoracic white rami Ti-T5, the stellate ganglion, the postganglionic cardiac sympathetic nerves, and thoracic part of the vagus nerve were approached retropleurally on the right side [8, 13].

* This research was supported by grant C.F.B.P. PAN 06.02.III.1.6. and by the Warsaw Medical School.

34 P. SZULCZYK, B. KAMOSrNSKA

To determine of the cardiac rhythmicity a time histogram of the activity of single neurons was constructed by triggering the counter with pressure wave. Analysis time was 1000 ms, with bin width 10 ms, 100 bins, and 256 repetitions. Respiratory rhythmicity was evaluated by triggering the counter with a rising phase of the integrated phrenic nerve activity (time constant of the integrator was 20 ms). Analysis time was 10 s, with bin width 0.1 s, 100 bins, and 32 or 64 repetitions. Inspiration was defined as the time between the onset of the phrenic nerve activity and the beginning of decline, expiration as the remainder of the cycle [1].

Results

Localization of the Cardioaccelerator Postganglionic Sympathetic Axons. Electrical stimulation of the thoracic white rami (1 pulse, 0.2 ms, 15.0 V) evoked excitatory responses in the right inferior cardiac nerve (RICN; Fig. 1 A; B) and thoracic part of the right vagus nerve (RTVN; Fig. 1 A, C).

After the injection of atropine (1 mg/kg) to block any parasympathetic input to the heart, electrical stimulation of the peripheral stump of the RI CN and R TVN (15-s train, 30 Hz, 0.2 ms, 20.0 V) increased the heart rate from 153 to 219 beats/ min and from 154 to 241 beats/min, respectively. The increase in heart rate was significantly larger in the case of electrical stimulation of sympathetic fibers in the RTVN than when the sympathetic fibers in the RICN were stimulated (Fig. 2A). Electrical stimulation of the right cervical vagus after administering atropine did not increase the heart rate significantly above the control level (Fig. 2B). These

stimWR

+ B c 11 ______ _ f'-,..'"--__ _

"Ii 6.~g I ~ '" RICN J ....... ___ ---' '---

13 ~L -F-._

14

1 Ts

T516

~~ ~.. ~J'-,'__ __ _

VN -.JI..-. _________ _

Fig. 1. The responses in the right inferior cardiac nerve (B) and right thoracic part of the vagus nerve (C) evoked by electrical stimulation of the white rami from T 1 to T 5 and thoracic sympathetic trunk. A Anatomical arrangement of the stimulating (stirn.) and recording electrodes (ree.). RIeN, Right inferior cardiac nerve; VN, thoracic part of the vagus nerve. (B Modified from Szulczyk and Szulczyk [13]; C A. Szulczyk and P. Szulczyk, unpJblished data)

Identification of Postganglionic Thoracic Sympathetic Neurons 35

250.

HR/min 8 A

0.0.1

0.01

20.0.

150. ns.

cs cs cs cs cs RICN vagus a b c n =11 n =11 n=12 n=10 n =11

Fig. 2A, B. Control (e) and maximum (s) heart rate responses evoked by electrical stimula­tion of the postganglionic cardiac sympathetic nerves on the right side. In each case the vagus nerve was cut centrally and stimulated after the infusion of atropine. A Response evoked by electrical stimulation of the peripheral end of the right inferior cardiac nerce (RICN) and thoracic part of the vagus nerve (vagus). ANOYA, Fo=62.85, df=3.43, p= <0.001. B Response evoked by electrical stimulation of the right cervical (a), upper thoracic (b), and lower thoracic part of the vagus nerve. ANOYA, Fo = 18.43, df = 5.49; p<0.001. (Modified from Kamosinska et al. [8])

Fig.3A-D. Identification of single postganglionic sympa­thetic fibres dissected from the thoracic part of the right vagus nerve. T l' T 2' T 3 - white rami; TST - thoracic sympa­thetic trunk. A Positions of stimulating (stirn.) and recording (ree.) elec­trodes. B Electrical stim­ulation of the sympa­thetic branch between stellate ganglion and thoracic vagus evoked a single large action po­tential (three superim­posed sweeps). C Its shape was identical with the shape of the sponta­neously active fiber. D The fiber could be sweeps)

A TST

RICN

o

\/'-_2_5 _m_s_---'IE8 0.25 ms .

easily discriminated (expanded time base, five superimposed

results indicate that visceral thoracic sympathetic fibers on the right side in cats are located in the RICN and RTVN. The majority of the cardioaccelerator fibers were located in the RTVN.

Single postganglionic sympathetic fibers were isolated from the R TVN. The method of identifying single axons is indicated in Fig. 3. The fibers were identified

36 P. SZULCZYK, B. KAMOSrNSKA

B

IPhNA~

15n ] .n pJ

..t L..,JJ'l ... ~!t. o

o 500 ms1000 0 5 slO

Fig. 4A, B. Cardiac and respiratory rhythmicity in single postganglionic sympathetic fibers. A Cardiac rhythmicity. Upper trace, pulse pressure; lower trace, histogram of the cardiac rhythmicity. B Respiratory rhythmicity. Upper trace, integrated phrenic nerve activity; lower trace, histogram of the respiratory rhythmicity, 32 repetitions

A c 100[/\ f) )JV '-S6 V 'E

IPhNA~

~ B 4s

IPIt-lA~

~[ o 5 5 10

Fig. 5 A-D. Respiratory rhythmicity in single postganglionic sympathetic fibers. Two spon­taneously active fibers were present in the dissected bundle CA, B). The activity of the fibers was related to the activity of the phrenic nerve (B-D). C Integrated phrenic nerve activity. D Histogram of respiratory rhythmicity present in the activity of the larger fiber from A and B (64 repetitions)

by electrical stimulation of the sympathetic branch from the stellate ganglion to the RTVN (Fig. 3A). Only spontaneously active axons were analyzed. The electrical threshold for postganglionic axons was between 2.8 V and 19.5 V for O.2-ms pulse width (n=110) and between 1.4 and 9.8V for O.5-ms pulse width (n=19). The conduction velocity of the axons calculated on the basis of the distance between the stimulating and recording electrodes and the latency of the evoked spike was O.64±O.03 mls (n= 115).

Cardiac and Respiratory Discharge Patterns. Pronounced cardiac rhythmicity was present in the resting activity of 135 (91 %) neurons from a total number of 148 neurons (Fig.4A). In the remaining neurons (9%) the cardiac rhythmicity was absent. A histogram of the respiratory rhythmicity was constructed for 83 neu­rons with resting activity. Cardiac rhythmicity was present in the activity of all these neurons. In 52 neurons (62%), the activity increased from early (Fig. 5) or

Identification of Postganglionic Thoracic Sympathetic Neurons 37

middle expiration in incremental pattern. The peak of activity was reached before the phrenic nerve activity began to decline. Usually the neurons which increased their activity starting from early expiration disclosed some decrease in activity at the beginning or before the onset of inspiration (Fig. 5 D). In 13 (17%) neurons, the activity has been increasing since the end of expiration and lasted as long as the phrenic nerve activity (Fig. 4B). Two neurons fired during expiration. In 16 neurons (19%), the respiratory rhythmicity was barely noticeable or absent.

Discussion

Axons dissected from the R TVN were qualified as postganglionic sympathetic fibers for the following reasons: (a) they had high electrical threshold, (b) their conduction velocity was below 1.0 mis, (c) they were identified by electrical stim­ulation of the sympathetic branch from the stellate ganglion to the RTVN, (d) they had ongoing resting activity after the central incision of the vagus nerve, and (e) their resting activity was blocked by hexamethonium (Niemirowski and Szu1czyk, unpublished observation).

It seems that the heart is the major thoracic visceral effector, under a strict sympathetic control [8] although a sympathetic innervation of the lower esophageal sphincter [7] or pulmonary vasculature [4] was revealed. Most (91 %) of the sympathetic fibers dissected from R TVN had pronounced cardiac rhythmic­ity in their activity, which suggests that they supply the cardiovascular effector [3, 6, 12]. We have demonstrated that the majority of the cardioaccelerator sympa­thetic fibers were located in the RTVN. It was indicated that sympathetic fibers from the RTVN do not control the cardiac myocytes [11]. Taking the above data into account, we suggest that axons from RTVN with cardiac rhythmicity may supply the conducting system of the heart.

The experiments presented in this study were conducted on cats with both vagoaortic nerves incised in the neck and bilateral pneumothorax performed. This eliminated the input from cardiac and pulmonary vagal receptors and desynchro­nized blood pressure fluctuations evoked by the mechanical movement of the thorax from central respiratory cycle, indicated by phrenic nerve activity. It was suggested that in these conditions the respiratory rhythmicity present in the sym­pathetic neurones was related primarily to the central respiratory drive [5, 9]. In 81 % of the neurons a marked respiratory rhythmicity was present. In the majority of fibers the activity increased in incremental pattern beginning from early, middle, or late expiration and reached its peak during inspiration. This suggests that single postganglionic sympathetic neurons are switched on consecutively during the expiratory cycle.

Fibers without cardiac or respiratory rhythmicity seem to innervate noncardio­vascular thoracic effectors.

38 P. SZULCZYK, B. KAMOSINSKA: Postganglionic Thoracic Sympathetic Neurons

Summary

Single postganglionic sympathetic axons were dissected from the thoracic part of the right vagus nerve. Pronounced cardiac and respiratory rhythmicity was present in the resting activity of these fibers.

We suggest that the majority of the postganglionic sympathetic axons located in the thoracic part of the right vagus innervate the conducting system of the heart.

References

1. Bachoo M, Polo sa C (1986) The pattern of sympathetic neurone activity during expira­tion in the cat. J Physiol (Lond) 378:375-390

2. Bainton CR, Richter DW, Seller H, Klein JP (1985) Respiratory modulation of sympa­thetic activity. J Auton Nerv Syst 12: 77 -90

3. Blumberg H, Jiinig W, Rieckman C, Szulczyk P (1980) Baroreceptor and chemoreceptor reflexes in postganglionic neurones supplying skeletal muscle and hairy skin. J Auton Nerv Syst 2: 223 - 240

4. Fishman AP (1980) Vasomotor regulation of the pulmonary circulation. Annu Rev Physiol 42: 211-220

5. Gilbey MG, Numao Y, Spyer KM (1986) Discharge patterns of cervical sympathetic preganglionic neurones related to central respiratory drive in the rat. J Physiol (Lond) 378:253-265

6. Gregor M, Jiinig W, Wiprich L (1977) Cardiac and respiratory rhythmicities in cutaneous and muscle vasoconstrictor neurones to the eat's hindlimb. Pfliigers Arch 370:299-302

7. Gonella J, Niel JP, Roman C (1979) Sympathetic control of lower oesophageal sphincter motility in the cat. J Physiol (Lond) 287: 177 -190

8. Kamosinska B, Nowicki D, Szulczyk P (1989) Control of the heart rate by sympathetic nerves in cats. J Auton Nerv Syst 26:241-249

9. Koizumi K, Seller H, Kaufmann A, Brooks Ch McC (1971) Pattern of sympathetic discharges and their relation to baroreceptor and respiratory activities. Brain Research 27:281-294

10. Kollai M, Koizumi K (1980) Patterns of single unit activity in sympathetic postganglion­ic nerves. J Auton Nerv Syst 1:305-312

11. Phillips JG, Randall WC, Armour JA (1986) Functional anatomy of the major cardiac nerves in cats. Anat Rec 214: 365-371

12. Sundliif G, Wallin BG (1978) Effect of lower body negative pressure on human muscle nerve sympathetic activity. J Physiol (Lond) 278:525-532

13. Szulczyk A, Szulczyk P (1987) Spinal segmental preganglionic outflow to cervical sym­pathetic trunk and postganglionic cardiac sympathetic nerves. Brain Res 421: 127 -134

Species-Dependent Respiratory and Autonomic Nerve Activities: Respiratory-Sympathetic Synchronization and Autonomic Nerve Responses to Hypoxia and Hypercapnia in the Rat A. TRZEBSKI

Introduction

Biological evolution creates diversity. This principle has often been overlooked in studies dealing with circulatory and respiratory reflexes. Artificial conditions of anesthesia, needed for applying refined analytical techniques of recording, bias the way of thinking toward a simplified Sherringtonian paradigm of uniformity of basic reflex pathways in mammals. Yet, paradoxically, integrative physiology must search rather for diversity than uniformity in pursuing analysis of respiratory and autonomic control systems in individual species which express different behavioral patterns. By analyzing species-dependent diversities a fundamental problem may be approached: how the pattern of respiratory and autonomic nerve activities have been evolutionarily integrated with the species-specific motor and behavioral fac­tors. Insight into functional meanings for the whole system enables better under­standing of general mechanisms of biological adaptation emerging from behind diversities. Studying the phenomena of species specificity in this broader frame­work may find uniformity, yet of higher order, in which species-dependent differ­ences are nothing but manifestations of general biological principles.

Among experimental mammals the rat has attracted more attention recently as a species used for studying interactions between respiratory and autonomic ner­vous system activities. Both the obstacles in experimentation on large mammals and easy availability of genetically controlled strains with functional disturbances, for example, arterial hypertension, made the rat a suitable experimental model. Yet little has been known on respiratory-autonomic functional interactions in this species. Any extrapolation from other experimental animals such as cats, dogs, or rabbits may be misleading in view of well-known differences in behavioral pat­terns.

This review based on joint studies [3, 4, 6, 7, 10] focuses on results which have shown how far sympatho-respiratory synchronization (SRS) and the responsive­ness of autonomic nerve activities and adrenomedullary response to hypoxic and to hypercapnic stimuli in the rat prove different from those in other species.

40 A. TRzEBsKI

Methods

All experiments were performed on rats anesthetized with urethane (0.9-1.0 g/kg, i.v.) or alpha-chloralose (50 mg/kg, i.v.). In most experiments animals were para­lyzed, artificially ventilated, and bilaterally vagotomized. Activities of the cut right phrenic nerve, used as a monitor of central respiratory cycle, of cervical, cardiac, renal, adrenal, and/or lumbar sympathetic nerves were recorded, and either these were integrated, or multifiber spike frequencies were counted and recorded after window discrimination. For analysis of timing of sympathetic activities (SA) with­in each respiratory cycle the onset or end of the phrenic burst was used as a trigger for averaging SA for 16 respiratory cycles. Respiratory unit activities were record­ed extracellulary by tungsten microelectrode within the retroambigual area. Mi­croinjections of 20 nl 1 M monosodium glutamate were administered into the rostral ventrolateral medulla (RVLM) after drilling out the basal occipital bone and exposing the ventral surface. Hypercapnic and hypoxic stimuli or hyperoxia were induced by changing gas mixture into the input line of the respirator and continuously recording end-tidal O2 and CO2 concentration. Details of the meth­ods used have been published elsewhere [1, 3, 4, 6, 10].

Results

Synchronization of Sympathetic Activity Within the Respiratory Cycle (SRS). In cervical, renal, and lumbar sympathetic nerves the onset of inspiration correspond­ed to significant depression of the spontaneous mass activity. Recovery proceed-

"itt?; ~, ~*~ki ':-..Jl·· "."",)' .

O1s IntPhr

In!. Symp.

O1s BP [kPa] l!DO' a

MA.symp.

Rc.phr.

(

Fig. 1. A Spontaneous discharge in rat's cervical sympathetic trunk (MA symp.) averaged over 16 respiratory cycles triggered by the onset of phrenic burst (Re phr.). B The same triggered by the end of phrenic burst. C Integrated phrenic nerve activity (Int. Phr.) and integrated cervical trunk activity (Int. Symp.) recorded simultaneously. BP, Arterial blood pressure. (From Czyzyk et al. [6])

Species-Dependent Respiratory and Autonomic Nerve Activities 41

Fig. 2. A Averaged phrenic nerve activity (IPH), sympathetic cervical trunk activity (ICT), and renal nerve activity (IRN) in WKY rat in normoxia. B The same recording during systemic hypoxia. Carotid chemoreceptors and sinus nerves intact

Fig. 3. Rat artificially ventilated, vagotomized with both carotid si­nus nerves cut. IHP, Integrated phrenic nerve activity; ICT, inte­grated sympathetic cervical trunk activity; IP, tracheal pressure; ENA, activity of the retroambigual expira­tory neuron; lENA, integrated record of expiratory neuron activity

IPH

IP

ENA

lENA

WKY NORMOXIA

........ o 2s

1100 o Hz

ed slowly toward late inspiration, and a peak of sympathetic discharge occurred in the postinspiratory phase. Sometimes a second peak was observed at late (stage II) expiration (Fig. 1). This pattern was stable over a broad range of respiratory frequencies (20-80min-l) modulated by changes in core temperature, hypoxia (Fig. 2), or hypercapnia. The mechanism of respiratory cycle-related SA timing is of central origin as it remained unchanged after elimination of peripheral feed­backs from pulmonary receptors and arterial baro- and chemoreceptors in vagoto­mized rats with both carotid sinus nerves cut. Augmented sympathetic discharge was synchronized with the firing of expiratory neurones (Fig. 3) and with internal intercostal muscle activities in spontaneously breathing animals (Fig. 4).

Sites in the RVLM Influencing the Pattern of SRS. Among different sites in RVLM stimulated by glutamate microinjections which produced sympathoexcitatory and/

42 A. TRZEBSKI

WICY

PIIr.

C.T.

E..H.

I.E..H.

I. !'hr.

I.C.T.

- 5 -

s

Fig. 4. WKY rat. Phr, Phrenic nerve activ­ity; CT, cervical sympathetic nerve activ­ity; ExM, electromyogram of expiratory internal intercostal muscle. Below, the same record averaged over 16 respiratory cycles

PARA-AMBIGUAL AREA

Fig. 5. Schematic cross-section of the medulla at the level of upper part of nucleus ambiguus. Black points (left) , sites at which glutamate microinjections produced changes and inver sal of the temporal pattern of SRS timing. (From Baradziej and Trzebski [3])

Species-Dependent Respiratory and Autonomic Nerve Activities 43

A B AliA "" /"-\." r -v' ~V AliA

Fig. 6. A Control, averaged record of mass discharge of lumbar sympa­thetic nerve (ALSA) and phrenic nerve activity (APhA). B The same record after 20 nl L-glutamate mi­croinjection into parambigual area. Total inversal of the temporal pat­tern of SRS timing. (From Baradziej and Trzebski [3])

-fLfi • Ice APhA

hypoxia hypercapnia impi2s

cardiac A ft\ symp.n.~. ~ o ~~;~~ 200

~ 100 a

E ~ 60 ~ _...JIMr 40 ~ •. 20

o

cardiac BiiI\nJi!1~ vagaln. ~ .

F ~300 .l\"'''''~''''''''''_~ 200

~'",'"" ... --"' 100

o ~ rena!

symp. n.

10%C02. 2min

Fig. 7. Effect of systemic hypoxia (left) and hypercapnia (right) on the activity of the efferent cardiac sympathetic nerve (A, D), efferent cardiac vagal branches (B, E), and efferent renal sympathetic nerve (C, F). (From Fukuda et al. [10])

or phrenic neural activation or inhibition [3, 13, 14] a region beneath the rostral part of the nucleus ambiguus was identified (Fig. 5). Microinjections of glutamate into this parambigual area (PA) disturbed variably and reversibly the constant temporal SRS pattern. Inversion of the timing, that is an inspiratory facilitation and postinspiratory SA depression could be produced by glutamate application into PA (Fig. 6).

Cardiovagal and Sympathetic Nerve Responses to Graded Hypoxia and Hypercap­nia. Systemic hypoxia produced chemoreceptor dependent co-activation of effer­ent cardiac sympathetic and cardiac vagal nerve traffics (Figs. 7, 8). The effect was abolished by bilateral carotid sinus nerve section. In contrast, systemic hypercap­nia reduced and hypocapnia augmented efferent cardiac vagal nerve activity (Figs. 7, 8). The CO2 sympathoexcitatory effect is due to central and not to peripheral chemoreceptor stimulation, as remained unchanged in sinoaortic dener­vated rats. Heart rate significantly increased in systemic hypoxia (Fig. 9) in artifi­cially ventilated rats despite strong reflex activation of efferent cardiac vagal fibers (Fig. 8).

44 A. TRZEBSKI

hypoxia and hyperoxia hypocapnia and hypercapnia

%

250

200

150

100

D cardiac sy mp n. " *

~ ~

; Lim rllill

Fig.8A-F. Relative mean changes in efferent sympa­thetic and cardiac vagal nerve activities plotted against graded end-tidal CO2

concentration. Vertical bars, SE; asterisks, statistical sig­nificance. Rats artificially ventilated with carotid chemoreceptors intact. (From Fukuda et aL [10])

Sympathoadrenal Medullary Responses to Graded Hypoxia and Hypercapnia. Both hypoxia and hypercapnia stimulated adrenal sympathetic nerve activity (Fig. 10) and produced a parallel increase in the secretion rate of adrenaline and nor­adrenaline effluent in adrenal vein (Fig. 11). Although the ratio of resting secretion rate of adrenaline versus noradrenaline in the rat is selective, about 8: 1 [1], hypoxic and hypercapnic stimuli are nonselective as the percentage increase in both cate­cholamines secretion is equal (Fig. 11). The stimulatory effect of acute hypoxia is due to chemoreceptor reflex as carotid body denervation abolished it entirely. In contrast, effects of hypercapnic stimulation remained unchanged in chemodener­vated rats. The CO 2 effect is mediated mainly by central chemoreceptors and in small proportion also by a direct excitatory effect of CO2 upon adrenal medullary gland [4].

:F 20j ~ 0 <1-_20

hypoxia and hyperoxia

IIII!II,~

6 8 10 12 14 @' 90 F ET 02 ('10)

hypocapnia and hypercapnia

! I I I ! ! I I !

2 3 4 ® 6 7 8 9 10 F ET C02 ('101

Fig.9A-D. Changes in the heart rate and mean arterial pressure plotted against end-tidal 02 (left) and end-tidal CO 2 con­centration (right). Other symbols as in Fig. 8. (From Fukuda et aL [10])

Species-Dependent Respiratory and Autonomic Nerve Activities 45

A. hypoxia B. hypercapnia

adrenal !otlt -3 symp.n. ~ -~-3

................ j~ 110

10%C02 •. 2min

Fig. 10. A Responses of sympathetic adrenal nerve activity to hypoxia. B Response of sympathetic adrenal nerve activity to hypercapnia. FC02 (%) record of end-tidal CO2 •

F02 (%) record of end-tidal O2 • Rat artificially ventilated with carotid chemoreceptors intact. (From Biesold et al. [4])

Fig. 11. Mean relative changes of sympathetic adrenal nerve ac­tivity (A, D), adrenaline secre­tion rate (B, E), and nor­adrenaline secretion rate (C, F) plotted against end-tidal O2

concentration (left) and end­tidal CO2 concentration (right). Other symbols as in Fig. 8. (From Biesold et al. [4])

hypoxia

,'",,' 15~B.k .. · .. b .•••••.•.. a.. A symp. n. . .•••. 100 ••..•.• <:..< •• :..

Adr 150~ ~ a B

100~ ~50~a C NAdr

100 I I I I I

6 10 14 @ FET02 (%)

Discussion and Conclusions

hypercapnia

I I I I

® 6 8 10

FETC02 (%)

Several features of species-dependent characteristics have been demonstrated in rats. Most significant appears an almost inverted timing of SRS as compared to the pattern typical for cats. It has been suggested that different categories of respiratory-related neurons in the cat influence bulbar sympathoexcitatory neu­rons [2]. If so, the early inspiratory inhibition is seemingly more powerful in the rat than in the cat as SA depression extends over the most of the inspiratory phase. In contrast to cats, there seems to be no postinspiratory SA inhibition in the rat as postinspiratory period is characterized by peak sympathetic discharge. Paucity

46 A. TRZEBSKI

of data related to different categories of respiratory-related neurons in the rat makes any interpretation difficult. At least, neurons within PA seem to be impor­tant for setting the SRS temporal pattern. They may be mediating interneurons, a kind of interphase cells between different categories of respiratory-related neu­rons and premo to sympathetic neurons of RVLM.

It may be hypothesized that augmented SA during active expiration is of func­tional significance in the rat, a diving species (e.g., muskrat). Experimental diving apneic expiratory response in the rat is accompanied by strong sympathoexcitato­ry effect [6]. It must still be checked if other diving species exhibit similar SRS temporal pattern which would express oxygen-saving vasoconstrictor adaptation to specific behavioral factors.

This difference is, however, rather quantitative and not qualitative. Substantial percentage of cervical preganglionic sympathetic neurons in the rat fires also in the expiratory phase [11]. Some differences exist between different strains of rats. In Sprague-Dawley rats SRS timing varies in different sympathetic nerves [15]. The most interesting seems a striking difference between Wistar rats, including WKY rats, and spontaneously hypertensive rats (SHR). In the latter the timing of the SRS pattern is similar to that in the cat: a facilitation and not depression of SA during inspiration and a marked postinspiratory SA inhibition [7, 8]. As a similar SRS pattern is elicited in normotensive Wistar rats by glutamate microstimulation of parambigual neurons (Fig. 6), an attractive hypothesis is suggested that param­bigual neuronal population plays some role in the central mechanism of primary hypertension. Such a hypothesis is supported by our finding that glutamate mi­crostimulation of PA elicited long-lasting sympathoexcitatory and pressor re­sponse [3].

A second species-dependent difference consists in primary chemoreceptor reflex­induced cardioacceleration during acute hypoxia in artificially ventilated rats. This response is opposite to primary bradycardia observed in dogs and other species under controlled ventilation (see [9]). Coactivation of vagal and cardiac sympa­thetic efferents by hypoxic chemoreceptor stimulus, similar to that found in dogs [12], indicates that, un similar to dogs, heart rate is under dominant sympathetic control during hypoxia in the rat. One may hypothesize that during prolonged oxygen deficiency an active motor behavior supported by augmented heart activity is biologically more important in rats for survival than passive adaptation such as bradycardia, saving heart muscle oxygen consumption for brief hypoxic episodes in other species.

Third, one species-specific feature in the rat is a diminution of cardiac vagal efferent activity during hypercapnia (Figs. 7, 8). In other species, such as dogs, CO 2 stimulates cardiovagal efferent traffic and facilitates baroreceptor cardiova­gal reflex response [16].

Finally, hypoxia and hypercapnia stimulate adrenomedullary function in the rat in a nonselective fashion, equally for both catecholamines. In contrast, cats re­spond to chemoreceptor stimulation predominantly by reflex secretion of adrenaline [5]. Species-dependent, neurogenic response to acute hypoxia seems in the rat to be more vasoconstrictor and to a lesser extent mediated by vasodilator and metabolic influence of adrenaline. The rat, an animal of high metabolic rate,

Species-Dependent Respiratory and Autonomic Nerve Activities 47

has powerful mechanisms of nonneurogenic metabolic vasodilator responsiveness to tissue hypoxia unmasked in chemodenervated animals [10]. Thus, rather, nor­adrenaline secretion, amplifying neurogenic vasoconstrictor and heart rate con­trol, appears more appropriate for a fine tuning of the whole body adaptation to acute hypoxia than adrenaline .output which would excessively amplify otherwise strong local vasodilator effects of hypoxia in the rat.

Any hypothesis synthesizing these results and looking for the functional signif­icance of species-specific differences of respiratory and autonomic nervous system activities is only speculative at this stage. Further research, applying noninvasive methods which could correlate species-specific respiratory, autonomic nerve, and behavioral patterns of activities is needed to clarify the functional meaning of the species-dependent differences.

References

1. Araki T, Ito K, Kurosawa M, Sato A (1984) Responses of adrenal sympathetic nerve activity and catecholamine secretion to cutaneous stimulation in anesthetized rats. Neu­roscience 12:289-299

2. Bainton CR, Richter DW, Seller H, Ballantyne D, Klein JP (1985) Respiratory modula­tion of sympathetic activity. J Auton Nerv Syst 12:77-90

3. Baradziej S, Trzebski A (1989) Specific areas of the ventral medulla controlling sympa­thetic and respiratory activities and their functional synchronization in the rat. Prog Brain Res 81:193-204

4. Biesold D, Kurosawa M, Sato A, Trzebski A (1989) Hypoxia and hypercapnia increase the sympathoadrenal medullary functions in anesthetized, artificially ventilated rats. Jpn J PhysioI39:511-522

5. Critchley JAJH, Ellis P, Ungar A (1980) The reflex release of adrenaline and nor­adrenaline from the adrenal glands of cats and dogs. J Physiol (Lond) 298: 71-78

6. Czyzyk MF, Fedorko L, Trzebski A (1987) Pattern of the respiratory modulation of the sympathetic activity is species dependent: synchronization of the sympathetic outflow over the respiratory cycle in the rat. In: Ciriello J, Calaresu FR, Renaud LP, Polo sa C (eds) Organization of the autonomic nervous system. Central and peripheral mecha­nisms. Liss, New York, pp 143-152

7. Czyzyk-Krzeska MF (1988) Respiratory modulation of the spontaneous sympathetic activity in the rats with genetically controlled hypertension (SHR) (in Polish). PhD thesis, Warsaw Medical Academy, Warsaw

8. Czyzyk-Krzeska MF, Trzebski A (1987) Respiratory synchronization of the sympathetic activity (SA) in spontaneously hypertensive rats (SHRs). 2nd world congress of neuro­science (IBRO), Budapest, Hungary, S327, 981 (abstract)

9. Daly M de B (1983) Peripheral arterial chemoreceptors and the cardiovascular system. In: Acker H, O'Regan RG (eds) Physiology of the peripheral arterial chemoreceptors. Elsevier, Amsterdam, pp 325-394

10. Fukuda Y, Sato A, Suzuki A, Trzebski A (1989) Autonomic nerve and cardiovascular responses to changing blood oxygen and CO2 levels in the rat. J Auton Nerv Syst 28:61-74

11. Gilbey MP, Numao Y, Spyer KM (1986) Discharge patterns of cervical sympathetic preganglionic neurones related to central respiratory drive in the rat. J Physiol (Lond) 378:253-266

12. Kollai M, Koizumi M (1979) Reciprocal and non-reciprocal action of the vagal and sympathetic nerves innervating the heart. J Auton Nerv Syst 1:33-52

48 A. TRZEBSKI: Species-Dependent Respiratory and Autonomic Nerve Activities

13. Lawing WL, Millhorn DE, Bayliss DA, Dean JB, Trzebski A (1986) Excitatory and inhibitory effects on respiration of L-glutamate microinjected superficially into the ven­tral aspects of the medulla oblongata in cat. Brain Res 435: 322-326

14. McAllen RM (1986) Location ofneurones with cardiovascular and respiratory function at the ventral surface of the cat's medulla. Neuroscience 18:43-49

15. Numao Y, Koshiya N, Gilbey MP, Spyer KM (1987) Central respiratory drive-related activity in sympathetic nerves of the rat: the regional differences. Neurosci Lett 81:279-284

16. Zielinski A, Augustyniak M (1975) The effects of hypercapnia on the vagal component of the baroreflex. Acta Physiol Pol 26:223-228

Discussion on Respiratory Related and Non-related Rhythms in Sympathetic Efferents Moderator: M.1. COHEN

The discussion dealt with various aspects of power spectral analysis of sympathetic discharge. Karemaker emphasized some technical points in the interpretation of spectral distributions: one must be careful in the designation of peaks near the origin (i.e., near 0 Hz) as indicative of low-frequency spectral components, since prior filtering of the signal may have resulted in reduction of the lowest frequency components. Cohen and Gootman replied that this situation did not apply to the spectra shown in their papers, since the lowest frequency peaks were located at 4 Hz rather than 1 Hz. Several other speakers (Trzebski, Datta, and Zwiener) inquired about the meaning of zero coherence between two signals. Cohen replied that a significant coherence between two signals indicates that there is a linear correlation between the signals; however, the absence of coherence implied only that any possible correlation was not detected. In principle, a nonlinear correlation might exist but not be detected by linear spectral analysis.

Several speakers discussed the possible unitary origin of rhythmic population (nerve) activity. McAllen inquired whether the observed changes of sympathetic rhythms with time in the respiratory cycle could arise from recruitment of sympa­thoexcitatory neurons in the ventrolateral medulla. Cohen then emphasized that such relations should be investigated by spectral analyses of these medullary neurons' activities and especially the analysis of coherence between unit activities and peripheral sympathetic activity. Langhorst suggested possible relations of state changes in reticular neuron activities, as reported in his earlier papers, to changes of sympathetic rhythms. Cohen and Gootman agreed on the importance of such relations, as indicated by their observations on modification of sympathetic rhythms by several types of input, for example, lung inflation, CO2 level, and anesthesia.

Neurochemical Characterization of Cardiovascular and Respiratory Control Systems

CO2-Induced Depolarization of Neurons in Nucleus Tractus Solitarii: A Potential Substrate for Central Chemoreceptors * J. B. DEAN and D. E. MILLHORN

Introduction

Although hypercapnia typically inhibits neural activity in most regions of the central nervous system [3], certain neurons in the medulla oblongata are stimulated during exposure to CO 2 and/or H+. Such neurons may serve as chemoreceptors and mediate increased cardiorespiratory activity during hypercapnia and acidosis. Present dogma states that the central chemoreceptors are located exclusively in discrete areas along the ventrolateral surface of the medulla oblongata (VLM) [2, 16]. Results from several laboratories, however, dispute this claim and suggest that other regions of the brainstem may also contain chemosensitive neurons [1, 4, 6, 7, 14, 15, 17 -19]. Thus, the precise locations of chemosensitive neurons have not been established [19].

One potential site of central chemosensitivity is the nucleus tractus solitarius (NTS), located in the dorsal aspect of the medulla oblongata. The NTS subserves a variety of functions including cardiorespiratory control [8]. Recent single-unit studies [1, 18] have shown that units in NTS increase their firing rate during exposure to acid and hypercapnia. It was not determined, however, whether these excitatory responses were inherent to these units, or simply the result of impinging synaptic connections from chemosensitive neurons located elsewhere, i.e., the putative chemosensitive neurons of VLM.

The present study tests the hypothesis that neurons in NTS are depolarized by CO2 , and that this response is not dependent on chemical synaptic input. To accomplish this, intracellular recordings were made during hypercapnia in medullary slices in which the ventral half of the medulla, the putative site of central chemoreceptors, was removed. This preparation which we call "dorsal slice" elim­inates any possibility that responses were driven synaptically from cells in VLM. To determine whether CO 2 chemosensitivity is an inherent membrane property of recorded neurons in NTS, CO 2 responses were studied in high Mg2+ and low Ca2 + medium which blocks chemical synaptic transmission.

Additional characteristics of some of the neurons included in this study have been reported elsewhere [6, 7].

* This research was supported by grants from the National Institutes of Health (HL 33831, HL 07398, NS 07166), National Science Foundation (BNS-9021470) and the American Heart Association (881108, 198889-A-09). D.E.M. is a Career Investigator of ALA.

54 J. B. DEAN, D. E. MILLHORN

Methods

Transverse medullary slices were prepared from male Sprague-Dawley rats as described previously [6, 7]. Slices were bisected and the ventral half discarded. The resulting dorsal slices were maintained in a standard perfusion-interface chamber and nutrient medium [5, 13]. Control conditions were 5% CO2/95% °2, pH 7.4, and 35-37°C. Intracellular recordings (micro electrodes filled with 3-M K + -ace­tate, tip DC resistances of 70 -150 MQ) were· made using standard techniques [6, 7]. To test for chemosensitivity the gas mixture saturating the medium and air was changed to 7%, 10%, or 15% CO2 (balance 02) for 2-4 min, which decreased medium pH by 0.1,0.24, or 0.47 units, respectively. Inherent chemosensitivity was tested during perfusion with high Mg2+ (11.4 mM) and low Ca2+ (0.2 mM) synap­tic blockade medium [5]. Tetrodotoxin (TTX} 1 )lMwas added to regular medium in some experiments to block voltage-gated Na + channels and prevent occurrence of action potentials. Hyperpolarizing current pulses were administered via the recording microelectrode in bridge-balanced mode or discontinuous current clamp to measure input resistance (RN) or, membrane conductance (= 1/RN), during hypercapnia.

Results

CO 2 Depolarizes Neurons in NTS. Intracellular measurements were made in 40 NTS neurons in dorsal slices before, during, and after exposure~to CO2, Seventeen neurons were insensitive to hypercapnia, and four neurons were hyperpolarized by hypercapnia. Nineteen neurons, however, were depolarized and increased their firing rate during exposure to increased CO2, Hypercapnia stimulated chemosen­sitive neurons by depolarizing the membrane and/or decreasing the amplitude/du­ration of the afterhyperpolarization (AHP). For example, Fig. 1 A shows super­imposed membrane potential records of a neuron that did not fire spontaneously under control conditions (5% CO2), When CO2 was increased from 5% to 7%, the neuron depolarized by 12 to 15 mY, and began firing. These effects of hypercapnia were reversible. Another type of excitatory response measured during exposure to 7% CO2 is shown in Fig. 1 B. This neuron fired spontaneously at approximately 1 impulse/s under control conditions. Hypercapnia led to an increase in firing rate (approximately 8 impulsesjs) that was associated with depolarization of the mem­brane and a decrease in the amplitude and duration of the AHP. These effects of hypercapnia on membrane potential and waveform were reversed after returning to 5% CO2 ,

Inherent CO 2 Chemosensitivity. Responses to hypercapnia were studied during high Mg2 + and low Ca 2+ synaptic blockade perfusion to determine whether chemosensitivity was an inherent property of some neurons in NTS. Figure 2A shows superimposed single traces taken before and after 2 min of exposure to 15% CO2 in regular nutrient medium. In this case, hypercapnia did not induce a large

CO 2-Induced Depolarization of Neurons in Nucleus Tractus Solitarii 55

Fig. 1 A, B. Depolarizations in A two NTS neurons in dorsal slices resulting from 2 % increase in CO2. A Membrane potential -68 mY; 5% CO2, control and recovery; 7% CO 2 , 4 min of 7%C02

hypercapnia. This same neuron I ~=::';:::=::::~~~~~;;~~ also depolarized in response to 20 m V ~ 7% CO2 in the presence of i 1 J.lM TTX (see Fig. 3 A). 5%C02

B Superimposed action poten-tials (truncated) measured in 5% CO2 (membrane potential B - 50 m V) and after approxi-mately 1 min of 7% CO 2 . Re­covery in 5% CO2 was complete but is not shown. Each condi­tion is represented by two (B) or three (A) traces reproduced from a storage digital oscillo­scope 100ms

5ms

depolarization, but it did cause a decrease in the amplitude of the AHP causing membrane potential to reach threshold sooner. Figure 2 B shows superimposed records taken from the same neuron during high MgZ + and low Ca Z + synaptic blockade. In addition to blocking synaptic transmission, low Ca2+ medium also has other effects on neurons [7], which include spike broadening (not evident at this time scale) and a reduced AHP (small arrows indicate magnitude of AHPs in Fig.2A). Despite these direct effects of low Ca2+ medium, the neuron still re­sponded to 15% COz as it had in regular medium. Thus, chemosensitivity was not dependent on intact local synaptic circuits in the dorsal medulla. The majority of neurons studied during synaptic blockade retained their COz chemosensitivity [7].

Fig. 2 A, B. CO 2 responses measured in NTS neuron be­fore and during high Mg2 +

and low Ca2+ synaptic blockade. A Single traces showing action potentials (truncated) in 5% CO2

(membrane potential - 60 m V) and after 2 min of 15% CO2. B As in A, but during 15-18 min of synap­tic blockade. Effects of hy­percapnia in each medium were reversible (not shown). Small arrows, magnitude of AHP before and during hy­percapnia in regular medium

A

B

56 J. B. DEAN, D. E. MILLHORN

A Control 5 %

Hypercapnia Recovery 7 0 /0 5°/0

r~~ 7"10

~(lUM TTX)

-60 t ---U-ir-"1r1 ..,...,.....,....-r-rl-'T lr:jTTTT11 iJiT1TmT -100 _

500ms 2s

Fig. 3A, A', B. Polygraph records of RN in two NTS neurons before, during, and after hypercapnia. A CO2-excited neuron tested with 7% CO2 (action potentials truncated). Downward pen deflections show voltage trajectory resulting from a hyperpolarizing current pulse ( - 0.2 nA, 200 ms). Same neuron as shown previously in Fig. 1 A. A' Same as in A, but during exposure to 24-40 min of 1 11M TTX. B CO2-insensitive neuron tested in 10% CO2

(action potentials truncated). Bars, lower left and right, 500 ms and 2 s, respectively. Mea­surements were made after 3-4 min of hypercapnia and 5-10 min of recovery in 5% CO2

Possible Mechanism: CO 2 Decreases Membrane Conductance. In order to under­stand how neurons in NTS are depolarized by CO2 , we made measurements of RN before, during, and after hypercapnia in several CO2-excited neurons. Figure 3 A shows RN measurements made in a CO2-excited neuron. Increasing the CO2 from 5% to 7% depolarized the neuron, induced spiking, and increased RN from 155 to 260 MQ. These effects were reversed during recovery in 5% CO2 (RN = 150 MQ). The experiment was repeated with 1 11M TTX added to the medium (Fig. 3 A'), and a similar response was observed; 7% CO2 depolarized the neuron and increased RN from 123 to 187 MQ. Returning to 5% CO2 completely reversed the effects of hypercapnia on membrane potential and RN (= 120 MQ). Figure 3 B shows the same measurements made before, during, and after exposure to 10% CO 2 for a neuron located in NTS that was not CO2 chemosensitive. No change was noted in either membrane potential (approximately -55 mY) or RN (143-147 MQ) during exposure to hypercapnia.

Measurement of RN in other CO2-excited neurons showed that hypercapnia increased RN by a range of 20 -11 0 MQ [6]. In other words, membrane conduc­tance was lower during hypercapnia, suggesting that COrinduced depolarization may be due to a decreased K + conductance.

57

Discussion

Our previous study of single-units (n = 82) in dorsal medullary slices had shown that most units (71 %) in NTS were either insensitive to or inhibited by hypercap­nia. Of the units tested in NTS, however, 29% had increased their firing rate during hypercapnia [7]. Our present findings show that a subset of neurons in NTS are indeed depolarized by brief (:?: 30 s) exposure to hypercapnia. Increasing CO 2

by as little as 2% (i.e., a decrease of 0.1 medium pH unit) was sufficient to depolarize these neurons and increase firing rate. The increased firing rate was associated with depolarization and/or a decrease in the magnitude of the AHP.

Our findings in dorsal slices show that CO 2-induced depolarization and in­creased firing rate measured in NTS neurons were not the result of synaptic input from putative chemoreceptor sites in VLM. Previous studies [1, 18] had not ruled out this possibility. In addition, our results establish that certain neurons in NTS are inherently CO 2 chemosensitive. Under conditions of synaptic blockade or TTX perfusion most chemosensitive neurons continued to be depolarized by hy­percapnia. This is an important and new finding since it is believed that neurons in VLM are not inherently chemosensitive [10, 11] but derive their chemosensitivity from H+ -modulated cholinergic synaptic transmission [12, 16].

We did not attempt to determine whether CO 2 or the resulting change in pH mediates the depolarization. Nevertheless, our findings show that a CO2 -related stimulus has a direct facilitatory effect on these neurons. The CO 2-related stimulus appears to decrease outward conductance, possibly by blocking K + channels, which depolarizes the neuron and increases firing rate [6]. Prior intracellular studies [9, 10] had demonstrated that only "silent," nonexcitable glia cells in ventral and dorsal medulla oblongata were depolarized by acid, without concomi­tant changes in membrane conductance.

Depolarization by CO2 contrasts with the nonspecific inhibitory effect that hypercapnia has on neurons in many other regions. Carpenter et al. [3] suggested that CO 2-induced depolarization is indicative of a specialized function, possibly one pertaining to chemoreception. Although the function of the neurons that we report here is unknown, their inherent chemosensitivity and their location within a cardiorespiratory-related region [8] strongly suggest that they belong to the neural substrate that corresponds to the central chemoreceptors. This proposal, however, is in disagreement with the current theory [2,16] that CO2 /H+ chemore­ceptors are located exclusively in two discrete areas of VLM. The widespread acceptance of the VLM as the sole site of chemosensitivity in the CNS is surprising considering reports indicating that acidic, hypercapnic, or anesthetic solutions applied directly to the ventral surface have slight or no effect on respiration [4, 14, 15,17, 19]. Moreover, there is no evidence that neurons in VLM are inherently responsive to CO 2/H+ [10, 11].

A recent review [2] on this topic stated that, "The fraction of cells that apparent­ly respond to the H+ concentration of extracellular fluid is greater in the region of the ventral medullary shell than at other medullary sites." This view is not supported by recent electro physiological studies [1, 6, 7, 15, 18]. For example, Miles [18] reported that the proportion of chemosensitive single units in VLM was

58 J. B. DEAN, D. E. MILLHORN

no different from that found in other areas of the brainstem. In fact, the greatest proportion of chemosensitive single units was found in NTS. Although Miles [18] did not determine whether these NTS units were inherently chemosensitive, it still emphasizes that other brainstem areas might sub serve central cardiorespiratory chemosensitivity.

We believe that the question of central chemoreceptor location is unresolved. Accumulating evidence supports our contention that central cardiorespiratory chemosensitivity cannot be attributed completely to VLM neurons [1, 4, 6, 7, 14, 15, 17 -19]. We are not suggesting that neurons in VLM lack chemosensitivity, nor are we suggesting that NTS is the sole site of central chemosensitivity. We are proposing, however, that central chemosensitivity may be a more dispersed system than previously believed. This would be expected if the dominant function of the central chemoreceptors is not strictly cardiorespiratory control, but, instead, the maintenance of brain pH homeostasis which is accomplished via these systems. Chemosensitive neurons, regardless of their location, could contribute to brain pH regulation as long as they projected to cardiorespiratory control areas. Although the function of CO2-excited neurons in NTS cannot be determined at present, we propose that inherently chemosensitive neurons in NTS represent components of the neural network responsible for brain pH homeostasis and central cardiores­piratory control.

Acknowledgements. We wish to thank D.A. Bayliss, J. T. Erickson, and Dr. WL. Lawing for their participation in early experiments, and Lusia Klingler for her excellent technical assistance.

References

1. Arita H, Kogo N, Ichikawa K (1988) Locations of medullary neurons with non-phasic discharges excited by stimulation of central and/or peripheral chemoreceptors and by activation of nociceptors in cat. Brain Res 442: 1-10

2. Bruce EN, Cherniack NS (1987) Central chemoreceptors. J Appl Physiol 62:389-402 3. Carpenter DO, Hubbard JH, Humphrey DR, Thompson HK, Marshall WH (1974)

Carbon dioxide effects on nerve cell function. In: Nahas G, Schaefer KE (eds) Carbon dioxide and metabolic regulations. Springer, Berlin Heidelberg New York, pp 49-62

4. Cozine RA, Ngai SH (1967) Medullary surface chemoreceptors and regulation of res pi­ration in the cat. J Appl Physiol 22: 117 -121

5. Dean JB, Boulant JA (1989) Effects of synaptic blockade on thermosensitive neurons in rat diencephalon in vitro. Am J Physiol 257 (Regulatory Integrative Comp Physiol 26):R65-R73

6. Dean JB, Lawing WL, Millhorn DE (1989) CO2 decreases membrane conductance and depolarizes neurons in the nucleus tractus solitarii. Exp Brain Res 76:656-661

7. Dean JB, Bayliss DA, Erickson JT, Lawing WL, Millhorn DE (1989) Stimulation of neurons in nucleus tractus solitarii by carbon dioxide does not require chemical synaptic input. Neuroscience 36:207-216

8. Feldman JL, Ellenberger HH (1988) Central coordination of respiratory and cardiovas­cular control in mammals. Annu Rev Physiol 50: 593-606

9. Fukuda Y, Honda Y (1975) pH-sensitive cells at ventro-lateral surface of rat medulla oblongata. Nature 256:317-318

CO 2-Induced Depolarization of Neurons in Nucleus Tractus Solitarii 59

10. Fukuda Y, Honda Y, SchIMke ME, Loeschke HH (1978) Effect ofH+ on the membrane potential of silent cells in the central and dorsal surface layers of the rat medulla in vitro. Pflugers Arch 376:229-235

11. Fukuda Y, Loeschcke HH (1977) Effect of H + on spontaneous neuronal activity in the surface layer of the rat medulla oblongata in vitro. Pflugers Arch 371: 125 -134

12. Fukuda Y, Loeschcke HH (1979) A cholinergic mechanism involved in the neuronal excitation by H+ in the respiratory chemosensitive structures of the ventral medulla oblongata of rats in vitro. Pflugers Arch 379: 125-135

13. Kelso SR, Nelson DO, Silva NL, Boulant JA (1983) A slice chamber for intracellular and extracellular recording during continuous perfusion. Brain Res Bull 10:853-857

14. Kiley JP, Eldridge FL, Millhorn DE (1985) The roles of medullary extracellular fluid pH in control of respiration. Respir Physiol 59: 117 -130

15. Lipscomb WT, Boyarsky LL (1972) Neurophysiological investigations of medullary chemosensitive areas of respiration. Respir Physiol 16: 362 - 376

16. Loeschcke HH (1982) Central chemosensitivity and the reaction theory. J Physiol (Lond) 332: 1-24

17. Malcolm JL, Sarelius IH, Sinclair JD (1980) The respiratory role of the ventral surface of the medulla studied in the anaesthetized rat. J Physiol (Lond )307:503-515

18. Miles R (1983) Does low pH stimulate central chemoreceptors located near the ventral medullary surface? Brain Res 271:349-353

19. Millhorn DE, Eldridge FL (1986) Role of ventrolateral medulla in regulation of respira­tory and cardiovascular systems. J Appl Physiol 61: 1249-1263

Organization of Respiratory Reflexes in the Caudal Region of the Nucleus of the Tractus Solitarius G. D. HOUSLEY, S. BREW, D. DE CASTRO, and 1. D. SINCLAIR

Introduction

The nucleus of the tractus solitarius (NTS) extends through the medulla to the cervical region of the spinal cord. In terrestrial vertebrates it represents the major site of synapse of visceral afferent fibers. Cottle (1964) reviewed suggestions dating from the nineteenth century that function is localized in the nucleus, with afferents involved in the regulation of different systems connecting in separate regions. Comparative physiologists of the 1930s proposed that the appearance of the caudal NTS in air-breathing vertebrates was related to respiratory function. Cot­tle's histological studies, which used degeneration techniques, led her to propose that oral-gustatory function must be organized in the rostral component of the nucleus and cardiovascular function in the intermediate region just rostral to obex (anatomical obex corresponds to the point where the NTS first crosses over the central canal). The development of tracer studies, especially those based on trans­ganglionic transport of horseradish peroxidase, provided substantial further de­tail. Hamilton and Norgren (1984) confirmed that the rostral half of the nucleus was devoted to gustatory and somatosensory afferent information from the oral cavity. They found only crude somatotopy retained in the nucleus and favoured a function-by-function basis for its organization.

The distribution of synapses in NTS, the variations among species, and the correlated electrophysiological phenomena have been reviewed recently by Jordan and Spyer (1986). Most studies have been conducted on the cat, but there is much in common among mammalian species. There is minimal evidence of viscerotopic organization. The distribution of first synapses is diffuse, even from the isolated branches of cranial nerves IX and X such as the carotid sinus (CSN) and aortic depressor nerves (ADN). The arrangement of neural connections provides for the divergence and convergence of inputs which represents the substrate for functional organization. This proposal is supported by findings from electrophysiological mapping (Donoghue et al. 1984) which show that baroreceptor afferents project to a region of NTS rostral to obex while identified chemoreceptor afferents project to regions medial, further caudal and bilateral. Jordan and Spyer (1986) again raise the possibility of the integration of afferent inputs in the NTS which would allow the shaping there of appropriate physiological responses.

Here we review a series of studies on the rat which provide evidence that in this species the chemoreceptor input from the carotid body is organized in the NTS at a site distinctly separate from that at which circulatory inputs are organized.

Contrast of Circulatory and Respiratory Influences of the Carotid Sinus and Aortic Depressor Nerves

61

The contribution of the baroreceptor and chemoreceptor reflexes made by the input of the CSN and ADN to the NTS of the rat were assessed using the reflex bradycardia evoked by pressor doses of phenylephrine and the ventilatory re­sponse to an hypoxic stimulus regime (15.5%,13.3%,11.2% and 9.8% O2 balance N 2) during halothane anaesthesia. These were studied in the intact and then the successively right CSN sectioned, left CSN sectioned and bilateral ADN sectioned states.

Cardiovascular Studies. In seven cases, phenylephrine was injected into a femoral vein catheter following the procedure used in studies of the cat by Cechetto and Calaresu (1984). Transient hypertension induced by peripheral vasoconstriction using this technique excites baroreceptors (Gebber and Snyder 1970). Blood pressure was measured via femoral arterial cannulation. A dose of 4 Ilgjkg phenylephrine injected in 0.1 ml heparinized isotonic saline elicited transient hy­pertension with associated reflex bradycardia. A control dose of 0.1 ml heparinized saline failed to evoke any change in blood pressure or heart rate.

The reflex bradycardia (typical duration 30 s) elicited by the pressor dose of phenylephrine was maximal simultaneous with the peak in blood pressure and returned to pre-test levels over approximately 2 min. A - 16.6±2.75 beats min - 1

(n = 3) reflex bradycardia was associated with a phenylephrine-induced 38.0 ± 3.46 mmHg increase in mean arterial pressure (MAP) in the unilateral CSN-sec-

A

".'L ....

Es

t .. ---------------------------

.~------- .. - .w-_____ ,' .... --

~~------------------~----~~

------~-----------------------------------------.(

.... - .--~.,...~ t"'·,.., .. ___ _

-110

-,..

Fig. 1. The baroreflex-mediated bradycardia elicited by a pressor dose of phenylephrine (4 Jlgjkg) remains intact after bilateral eNS section (A) but is eliminated by bilateral ADN section (B), producing a prolonged pressor response. In the absence of the reflex bradycardia a tachycardia is present for the period of elevation in blood pressure

62 G. D. HOUSLEY et al.

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Fig. 2. Ventilatory response to hypoxia was reduced by uni­lateral CSN section (U.CSNX), due to a halving of the VT re­sponse, although the frequency (f) re­sponse was slightly elevated. Bilateral CSN section (B.CSN) removed all but a small fre­quency response during mild hyp­oxia. Data represent mean±S.E.; n=lO

tioned state, while a -14.4 ± 4.76 beats min -1 bradycardia occurred after bilateral CSN section during a 32.0 ± 4.00 mmHg increase in MAP in the bilateral CSN-sec­tioned state. These baroreflex responses did not differ significantly from the intact control state (p > 0.05; two-sample t test). In contrast, bilateral ADN section (by cutting the superior laryngeal nerves) eliminated the reflex bradycardia, producing a tachycardia ( + 14.4 ± 7.20 beats min -1) in assocation with the pressor response to phenylephrine. Consequently the increase in MAP was significantly greater after ADN section (50.0±5.29 mmHg; p<0.01, two-sample t test) than in the preceding CSN-sectioned states (Fig. 1).

Respiratory Studies. The ventilatory response to hypoxia was determined using pneumotachography. The procedure followed that previously described in detail

Organization of Respiratory Reflexes in the Caudal NTS 63

30 .-. INTACT

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Fig. 3. An example showing the change in breathing frequency response to hypoxia during progressive denervation of the CSN and ADN. As in Fig. 2, unilateral CSN section (U. CSNX) produced an increase in frequency response which was abolished by bilateral CSN section (B.CSNX). Bilateral section of the ADN (B.ADNX) failed to produce any additional change in breathing response to hypoxia, despite elimination of the baroreceptor reflex at this point (Fig. 1)

(Housley and Sinclair 1988). Briefly the intubated, halothane anaesthetized rats were exposed to brief changes in inspired O2 levels, and their tidal volume (V T), frequency of breathing, and minute ventilation (\1 E) responses were recorded. The right and left CSN and then the ADN (bilateral) were sectioned sequentially with hypoxic tests performed between treatments. Unilateral CSN section reduced the tidal volume (V T) response to hypoxia by approximately 50%, while bilateral CSN section eliminated it completely. Breathing frequency response to hypoxia was slightly enhanced at low to moderate levels of hypoxia after unilateral CSN section compared with the intact state and showed a marked depression after bilateral section. This resulted in approximately a 35% reduction in minute ventilation (\1 E) response to hypoxia after unilateral and a further 70% reduction after bilateral CSN section (Fig. 2). Subsequent bilateral section of the ADN input failed to produce additional changes as typified by the frequency response data shown in Fig. 3. The insignificant reduction in the reflex bradycardia produced by bilateral CSN section indicates that the major baroreceptor afferent input to the NTS in the rat is from the ADN. Functional cardiovascular influence exerted by carotid sinus baroreceptor fibres may not have been apparent because of the dynamic reserve response of the ADN. In contrast, the ADN served no significant chemoreceptor drive for respiration as has been previously established in the rat in both anaes­thetized and awake respiratory studies (Sapru et al. 1981; Martin-Body et al. 1985).

Central Projections of the Carotid Sinus Nerve Afferent Fibres

Given the apparent dichotomy of responses of the CSN and the ADN, it is interesting to note that the rat ADN afferent (baroreceptor) fibre projections are

64 G. D. HOUSLEY et al.

localized primarily to the ipsilateral interstitial and dorsolateral NTS (Ciriello 1983) and dorsomedial NTS (Higgins et al. 1984) just rostral to the obex. These findings are in agreement with electrophysiological evidence for baroreceptor afferent projections within the NTS (Lipski et al. 1975; Garcia et al. 1979; Donoghue et al. 1984). Our own investigations of the CSN afferent (baro- and chemoreceptor) fibre projections (Housley et al. 1987) indicated that there are two concentrations of afferent fibre synapses in the NTS (Fig. 4). Using wheat-germ

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Fig. 4. A schematic representation of the extent of CSN afferent fibre projections within the NTS of the rat revealed by anterograde transport of horseradish peroxidase. Rostral to the obex, CSN afferent fibres project principally to the dorsolateral NTS region. The density and distribution of reaction product was diminished at obex, but a second major aggregation of reaction product occurred caudal to the obex within the ventrolateral and commissural NTS regions. This caudal region represents the proposed site of carotid chemoreceptor termina­tion in the NTS responsible fo r the ventilatory response to hypoxia. AP, Area postrema; cc, central canal; Cu, cuneate nucleus; Gr, gracile nucleus; n.Comm., commissural subnucleus of NTS; sol, solitary tract; SolD, dorsal subnucleus of NTS; SoIM, medial subnucleus of NTS; SoIVL. ventrolateral subnucleus of NTS

Organization of Respiratory Reflexes in the Caudal NTS 65

agglutinin horseradish peroxidase as a transganglionic anterograde tracer, CSN afferent fibres were shown to enter the rat brainstem as a component of the glossopharyngeal nerve approximately 1.5-2.0 mm rostral to obex. However ex­traperikaryal reaction product characteristic of terminal and pre-terminal process­es was localized within the NTS as two aggregations; the first of these was 0.9-0.1 mm rostral to the obex and is similar in location to that described by Ciriello (1983) for the ADN. The second aggregation of CSN afferent termination lies from -0.2 to -0.8 mm caudal to the obex, within the lateral and ventrolateral commissural subnuclear regions. If the rostral site represents the region of input of baroreceptor afferent fibres to the NTS (and possible chemoreceptor input to cardiac regulation), then the more caudal site may receive the peripheral chemore­ceptor afferent projection which regulates the ventilatory response to hypoxia.

Neurotoxic Lesioning of the Carotid Sinus Nerve Termination in the NTS

To test the hypothesis that the more caudal aggregation of CSN afferent termina­tion represents chemoreceptor input, the neurotoxin kainic acid (KA) was mi­croinjected into the centre of either the rostral or caudal concentrations of CSN termination in NTS. The advantage of using KA rather than conventional elec­trolytic techniques to produce a lesion is that the agent works post-synaptically, leaving the axons of passage intact (Coyle 1983). Thus chemoreceptor afferent fibres travelling down the NTS through the rostral site would be unaffected by a rostral lesion, whereas a caudal lesion would destroy the neurones receiving the afferent termination and therefore reduce the ventilatory response to hypoxia. Unilateral CSN section and ventilatory testing to establish hypoxic chemosensitiv­ity was carried out in 20 rats. KA was then microinjected unilaterally into the contralateral NTS at either of the sites of concentration of the CSN afferent fibre termination (Fig. 5). The ventilatory response to hypoxia was then retested after a period of recovery (24 h), and finally the remaining CSN was sectioned and the residual hypoxic chemosensitivity measured. The results of these experiments have been described in detail (Housley and Sinclair 1988). Briefly, the caudal lesions produced significantly larger reductions in the ventilatory response to hypoxia than lesions at the rostral site (p < 0.001; split-plot two-way ANOVA). Caudal lesions reduced the ventilatory response to hypoxia by approximately 67%, mea­sured by reduction in the change in V E' Lesions at the rostral site, however, had an apparently non-specific effect on V T resulting in a small (18%) reduction in V E

response. The frequency response to hypoxia was totally unaffected by the rostral lesions but was reduced by 60% after caudal lesions.

Selective KA lesion of the neurones receiving CSN afferent fibres supports the localization of the carotid chemoreceptor input to respiration at the caudal NTS site (Housley and Sinclair 1988). A topographical organization of visceral afferent input to cardiorespiratory control pathways in the NTS is further supported by

66 G. D. HOUSLEY et al.

Fig. 5. A schematic representation of the dorsal rat hindbrain. The rostral and cau­dal sites (r.s. and c.s., hatched) repre­sent the topographi­cal location of the two principal aggre­gations of CSN synaptic processes in the NTS. V4 ,

Fourth ventricle; AP, area postrema; coordinates in mil­limetres relative to the calamus scripto­ri us (obex lies ap­proximately 0.3 mm rostral to 0,0)

comparable cardiovascular control studies carried out in rats by Doba and Reis (1973) who abolished baroreflexes by electrolytically lesioning a site closely corre­sponding to the rostral site of our KA study.

Localization of Function by Use of Excitatory Amino Acids and Antagonists

The injection of sub-toxic amounts of KA into the caudal NTS has a selective action on respiration. Instant increases of V T were elicited by stereotaxically controlled injections of KA (10-20 ng; 1 ng/nl) made directly into the exposed dorsal hindbrain 0.3 mm caudal to obex, 0.7 mm laterally and 0.5 mm deep during nembutal-ketamine anaesthesia. The increase in V T and an overall increase in V E

were stable, but any increase in breathing frequency quickly reversed. Typical responses are illustrated in Fig. 6. The arterial pressure fell briefly, but the respira­tory effects were prolonged. Injection of carrier, which included rhodamine B as a dye marker, did not produce respiratory or circulatory change. Comparable microinjections under halothane anaesthesia showed similar responses but exhib­ited a pronounced transient increase in breathing frequency which lasted 15 - 30 s. When the respiratory pattern had returned to normal, the ventilatory response to hypoxia was temporarily enhanced, the effect being dependent upon an intact ipsilateral CSN connection and hence carotid body input.

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Organization of Respiratory Reflexes in the Caudal NTS 67

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•• Fig. 6. Chart records of two cases showing respiratory responses to a 10-nl (1 ng/nl) kainic acid microinjection into the caudal NTS site (arro w) during nembutal-ketamine anaesthesia. Note the immediate and prolonged increase in tidal volume and reduction in breathing frequency while changes in blood pressure were transient. A brief increase in breathing frequency is evident in case K3

These effects of KA contrast sharply with those of comparable injections rostral to obex (Talman et al. 1981). At this site, KA (up to 6 ng) produces hypotension, mimicking the effect of the arterial baroreflex; in larger amounts KA blocks the reflex and produces hypertension. Our injections at the caudal site stimulated respiration, mimicking the chemoreflex in a way which seems comparable to the Talman study which mimicked the baroreflex. The prolonged slowing of breathing which we observed could be attributed to depolarization blockade of respiratory control neurones at the caudal site; recovery from this blockade with concomitant enhancement of the carotid chemoreflex is consistent with the enhanced responses to excitatory synaptic inputs observed under similar circumstances in hippocampal neurones (Collingridge and McLennan 1981).

Because KA acts through glutamate mechanisms, the observations led to further exploration of the caudal NTS region using glutamate and its antagonists (Brew, de Castro, Housley and Sinclair, 1990). In the first experiments, 50 nl injections were made into the exposed rat hindbrain 0.3 mm caudal and 0.4 mm lateral to obex and 0.5 mm deep. Injections were unilateral and followed sectioning of the contralateral CSN. Glutamate at 3 pmol - 1.6 nmol (0.2 ng = 1 pmol) immediately increased respiratory frequency and V T, both of which reached a maximum a few

68 G. D. HOUSLEY et al.

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Fig. 8. Chart records showing the abolition of a ventilatory response to hypoxia (change from 100% inspired O2 to 10% inspired 02) by microinjection of kynurenic acid (63 pmol) into the caudal NTS site (arrowhead). Identical injections into the contralateral site, which had the CSN input cut, failed to block the ventilatory response to hypoxia. The increases in bradycardia with kynurenic acid injection reflects the increase in hypoxia produced by the diminished ventilatory response. The effect of the kynurenic acid was reversible with time

seconds after injection and lasted for 15 - 30 s. These responses were generally dose dependent (Fig. 7). Histological studies demonstrated that marker dye in the injectate spread through a region extending approximately 0.7 mm rostrocaudally by 0.5 mm deep and wide. It is evident that injected glutamate reached most of the dense CSN synapses in the caudal but not contralateral NTS and was most unlikely to have reached the region of rostral ipsilateral CSN synapses in NTS. The effect of the glutamate on arterial blood pressure was negligible and brief. Gluta-

Organization of Respiratory Reflexes in the Caudal NTS 69

mate injected in grid fashion to surrounding areas did not produce noteworthy changes of respiration or circulation. Glutamate injected rostrally produces abrupt reduction of blood pressure (Talman et al. 1980, 1984). Thus the respiratory mech­anism appears to be strictly localized.

Either of two glutamate antagonists were injected into the caudal area of CSN projection to the NTS. The CSN of the opposite side was cut first to localize carotid body input to the experimental side. The drugs, glutamic acid diethyl ester (GDEE) and kynurenic acid, had no effect on respiration in rats lightly anaes­thetized with halothane and breathing 100% oxygen. However, tests of the venti­latory response to hypoxia (10% inspired oxygen) after injection demonstrated reduction or loss of response (Fig. 8). The response was reduced by 50% - 80% of the control value by 5 nmol GDEE and was eliminated by 200 nmol. With kynurenic acid it was reduced by 15% -45% with 80 pmol and eliminated by 150 pmol. Injection of the drugs on the other side, where the CSN had been sectioned, did not affect the response.

Conclusions

The above series of experiments show that in the rat the sensory fibres of the CSN project mostly to two distinct ipsilateral regions of the NTS, one rostral to obex within the dorsolateral-dorsomedial subnuclear regions and the other caudal to obex within the ventrolateral and commissural subnuclear regions. Neurotoxic lesioning at the caudal site substantially disrupts hypoxic stimulation of breathing, the injection of KA and glutamate in picomolar quantities stimulates breathing, and the injection of glutamate antagonists can totally block the ventilatory re­sponse to hypoxia without affecting normal breathing or arterial pressure.

We interpret the experiments as providing a histological basis for separating sensory inputs concerned with circulation from those concerned with respiration. The experiments involving KA lesioning, injection of excitatory amino acids, and particularly glutamate antagonists, suggest that glutamate is involved in the trans­mission of the hypoxic drive to breathing and consistently support the proposal that respiratory regulation is organized in a relatively restricted region of the NTS.

Our studies at the caudal NTS site have so far centred on the demonstration that it is the significant relay site of hypoxic inputs to respiration. The remaining question concerns the extent to which various inputs may be integrated at this site to produce variable but appropriate alterations of respiratory patterns. Few of the necessary data are yet available. A convergence of multiple chemoreceptor inputs to this region would support the hypothesis, but the projections from the sec­ondary peripheral chemoreceptors of the rat (Martin-Body et al. 1986) have not yet been identified. The relevant nerves, namely the pharyngeal branch of IX (excluding CSN input) and the abdominal vagus, do project to the area concerned (Housley et al. 1987; Leslie et al. 1982). Further, Bonham and McCrimmon (1989) have shown that pulmonary stretch receptors project to the site. The full role of the respiratory mechanisms organized at the site therefore await elucidation.

70 G. D. HOUSLEY et al.: Organization of Respiratory Reflexes in the Caudal NTS

References

Bonham AC, McCrimmon DR (1989) Excitatory amino acid transmission is required for the Breuer-Hering (HB) reflex in rats. FASEB J A403

Brew S, de Castro D, Housley GD, Sinclair JD (1990) The role of glutamate in the transmis­sion of the hypoxie input to respiration through the nucleus of the tractus solitarius. In: Acher H, Trzebski A, Regan RO (eds) Chemoreceptors and chemoreceptor reflexes. Plenum, New York, pp 331-338

Cechetto DF, Calaresu FR (1984) Units in the amygdala responding to activation of carotid baro- and chemoreceptors. Am J Physiol 246: R832- R836

Ciriello J (1983) Brain stem projections of aortic baroreceptor afferent fibres in the rat. Neurosci Lett 36: 37 -42

Collingridge GL, McLennan HM (1981) The effect of kainic acid on excitatory synaptic activity in the rat hippocampal slice preparation. Neurosci Lett 27:31-36

Cottle MK (1964) Degeneration studies of primary afferents ofIXth and Xth cranial nerves in the cat. J Comp Neurol 122: 329 - 343

Coyle JT (1983) Neurotoxic action ofkainic acid. J Neurocytochem 41:1-11 Doba N, Reis DJ (1973) Acute fulminating neurogenic hypertension produced by brainstem

lesions in the rat. Circ Res 32: 584- 593 Donoghue S, Felder RB, Jordan D, Spyer KM (1984) The central connections of carotid

baroreceptors and chemoreceptors in the cat: a neurophysiological study. J Physiol (Lond) 347:397-409

Garcia M, Jordan D, Spyer KM (1979) The central projections of single vagal afferent neurones in the rabbit. J Physiol (Lond) 289:42-43P

Gebber GL, Snyder DW (1970) Hypothalamic control of baroreceptor reflexes. Am J Physiol 218:124-131

Hamilton RB, Norgren R (1984) Central projections of gustatory nerves in the rat. J Comp NeuroI222:560-577

Higgins GA, Hoffman GE, Wray S, Schwaber JS (1984) Immunoreactivity within barorecep­tive portions of the NTS and the dorsal vagal nucleus of the rat. J Comp Neurol 226:155-164

Housley GD, Sinclair JD (1988) Localization by kainic acid lesions ofneurones transmitting the carotid chemoreceptor stimulus for respiration in rat. J Physiol (Lond) 406:99-114

Housley GD, Martin-Body RL, Dawson NJ, Sinclair JD (1987) Brain stem projections of the glossopharyngeal nerve and its carotid sinus nerve branch in the rat. Neuroscience 22:237-250

Jordan D, Spyer KM (1986) Brainstem integration of cardiovascular and pulmonary afferent activity. Prog Brain Res 67:295-314

Leslie RA, Gwyn DG, Hopkins DA (1982) The central distribution of the cervical vagus nerve and gastric afferent and efferent projections in the rat. Brain Res Bull 8:37-43

Lipski J, McAllen RM, Spyer KM (1975) The sinus nerve and baroreceptor input to the medulla of the cat. J Physiol (Lond) 251:61-78

Martin-Body RL, Robson GJ, Sinclair JD (1985) Respiratory effects of sectioning the carotid sinus, glossopharyngeal and abdominal vagus nerves in the awake rat. J Physiol (Lond) 361:35-45

Martin-Body RL, Robson GJ, Sinclair JD (1986) Restoration of hypoxic respiratory resonses in the awake rat after carotid body denervation by sinus nerve section. J Physiol (Lond) 380:61-73 .

Sapru HN, Gonzalez E, Krieger AJ (1981) Aortic nerve stimulation in the rat: cardiovascular and respiratory responses. Brain Res Bull 6: 393 - 398

Talman WT, Perrone MH, Reis DJ (1980) Evidence for L-glutamate as the neurotransmitter of baroreceptor afferent fibres. Science 209:813-815

Talman WT, Perrone MH, Reis DJ (1981) Acute hypertension after the local injection of kainic acid into the nucleus tractus solitarii of rats. Circ Res 48:292-298

Talman WT, Granata AR, Reis DJ (1984) Glutamatergic mechanisms in the nucleus tractus solitarius in blood pressure control. Fed Proc 43: 39-44

The Role of the Nucleus Raphe Magnus in the Control of Cold Shivering and Respiratory Evaporative Heat Loss P. HINCKEL

Introduction

In previous studies two lower brain stem centres have been shown to be part of thermoafferent pathways: the nucleus raphe magnus (NRM; Dickenson 1977; Taylor 1982; Hinckel and Persche11987) and an area situated ventrally to the locus coeruleus, the subcoeruleus region (Bruck and Hinckel 1980; Hinckel and Schroder-Rosenstock 1981).

Electrical stimulation of the nucleus subcoeruleus is known to cause excitatory metabolic responses in unanaesthetized guinea pigs (Bruck and Hinckel 1980). Circumscribed electrolytic lesion of the subcoeruleus area caused a decrease in shivering threshold temperature and reduced shivering activity, while the respira­tory evaporative heat loss threshold remained unchanged (Bruck and Hinckel 1982). This region, which receives afferents from abdominal, thoracic, and leg skin cold receptors, seems to be important for autonomic cold defence reactions (Hincke1 and Schroder-Rosenstock 1981).

Electrical stimulation of the NRM inhibited shivering in unanaesthetized guinea pigs. Interruption of either only the ascending or the descending NRM efferents did not have an appreciable effect on the inhibition response. It is concluded from these microcut experiments that inhibition of shivering caused by NRM stimula­tion is mediated partly by ascending and partly by descending efferents of the NRM (Hinckel et al. 1983). The ascending thermal pathways terminate mainly in the hypothalamus, thalamus and cortex (Hellon and Taylor 1982; Ishikawa and Hinckel1985), and the descending pathways project mainly to motoneuron pools and dorsal horn cells of the spinal cord (Light 1985). The inhibitory effects of NRM stimulation are in accordance with the improvement of cold defence follow­ing electrolytic lesions of the NRM (Szelenyi and Hinckel 1987). In addition, electrolytic lesion of the NRM caused a displacement of the vasoconstriction threshold (Szelenyi and Hinckel1987).

The present study was intended to analyse the role of the NRM in the control of heat defence reactions, such as respiratory evaporative heat loss (REHL). Moreover, the aim of the study was to examine whether the descending NRM efferents are involved in the control of REHL, especially after interruption of the ascending pathways.

72 P. HINCKEL

Methods

Electrical Stimulation and Microcut Experiments. Selective electrical stimulations were applied to different sites in the lower brain stem in 28 experiments on 14 guinea pigs before and after interruption of the ascending efferents of the NRM. The interruptions were made by micro knife cuts. For microknife cuts and chronic implantation of the stimulating electrodes, the guinea pigs were kept under halothane inhalation anaesthesia. Shortly before the experiments, electrodes were inserted into thigh and masseter muscles and thermocouples were inserted into the interscapular brown adipose tissue, the colon, and under the skin of the back and the abdomen.

The subsequent measurements were made with the animals in a fully awake state. The animals were placed in a metabolic chamber in which the air and wall temperatures could be kept constant in a range between 5° and 45°C. Oxygen consumption, the four body temperatures and the integrated electrical activity in the two muscle groups were continuously recorded. For the stimulations four-pole electrodes were used similar to those described in a previous study (Bruck and HinckeI1980). The positions of the electrodes were marked using the Prussian blue technique. Two types of stainless steel microknives were used, the first one for frontal and lateral and the second, a right angle bent one, for horizontal cuts (Hinckel and Perschel1987). The locations of the cuts were ascertained according to methods previously described (Hinckel et al. 1983; Hinckel and Perschel1987).

Single Unit Recordings. In these experiments 54 guinea pigs 6-8 weeks old and weighing 320-380 g were used. The animals were kept at normal room tempera­tures (ca. 21 QC). During the experiments they were anaesthetized with urethane (1.2 gjkg i.p.) and placed in a stereotaxic apparatus. The heads were fixed accord­ing to the coordinate system of Tindal (1965) and the coordinates scaled up according to the size of the animal (Rossner 1965).

Single-unit activity from the NRM was recorded extracellularly with stainless steel or tungsten electrodes (F. Haer, USA, impedance range 4-8 MQ at 1 KHz and 1 nA), amplified by means of a DAM 5-A amplifier (WPI, USA), discriminat­ed on a three-channel spike amplitude discriminator, and displayed on a storage oscilloscope (Tektronix, USA) and by an interface (CED, UK) on a computer. The spike frequency was recorded continuously together with the abdominal skin temperature and the colon temperature (at a depth of 10 cm).

The abdominal skin temperature was measured with an intracutaneously insert­ed thermocouple and changed with the temperature of the water-perfused support plate of the stereotaxic apparatus. The general temperature was changed by the independently perfused double-walled Perspex jacket that was placed on the top of the support plate. With a system of four thermostat-controlled water sources constant stimulus temperatures could be chosen within a range of _5° to + 60°C.

As the general search procedure, electrode penetrations were made at abdominal skin temperatures 0[34° - 35 °C to facilitate the finding of cold, warm, or non-ther­moresponsive neurons. This range may be considered as thermally neutral for thermoresponsive lower brain stem units as the activity of both cold- and warm-re-

Role of the Nucleus Raphe Magnus in the Control of Cold Shivering 73

sponsive units is diminished but not absent (Dickenson 1977; Hinckel and Schroder-Rosenstock 1982; Hinckel and Perschel 1987). The recorded neurons were tested for their steady state responses to thermal stimulation of the skin and to mechanical stimulations of different skin areas and the chest wall.

For random controls of the recording sites several electrode locations (steel electrodes) were marked by depositing iron at the recording sites (marker current 15 ~A, 1.5 V for 10 s) and made visible with the Prussian blue technique. These controls were in accordance with the stereotaxic coordinates.

Results and Discussion

Electrical Stimulation and Microcut Experiments. In previous experiments electri­cal stimulation of the NRM caused inhibition of cold-induced shivering and non-shivering thermogenesis in unanaesthetized guinea pigs (Bruck and Hinckel 1980; Bruck and Hinckel1982; Hinckel et al. 1983). The inhibitory effects ofNRM stimulation are in accordance with the improvement of cold defence following electrolytic lesions of the NRM. Those lesions caused an increase of shivering

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74 P. HINCKEL

threshold temperature, whereas sham operation or control lesions in surrounding areas had no effect on shivering during acute cold exposure (Szelenyi and Hinckel 1987).

In addition, electrical stimulation of the NRM caused inhibition of REHL, even after interruption of the dorsal and rostral connexions of the NRM (Figs. 1, 2). It seems that mainly the descending efferents of the NRM are involved in the inhib­itory mechanisms. These results are in accordance with the improvement in heat defence reactions following electrolytic lesions of the NRM (Szelenyi and Hinckel 1987). The improved heat defence as evidenced by the reduction of vasoconstric­tion threshold and by slope decrease in the REHL response can be interpreted as a sign of activation of the measured heat loss effects during acute heat exposure. Although the REHL threshold was not significantly influenced by the NRM lesions, the decrease in vasoconstriction threshold and the slope decrease in the REHL response indicated at least an improvement in heat defence, which seems compatible with a role of the NRM in limiting heat loss responses to hyerthermia.

By contrast, the subcoeruleus region, another area in the lower brain stem seems to be involved only in cold defence. Electrical stimulation of this area caused excitatory metabolic responses, whereas the REHL remained unchanged. Inter­ruption of the ascending efferents of the nucleus subcoeruleus did not influence the

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.!I! 39 ~ 350 c 0 c . "0 0 D u38 . E >: .. 20 300

250

200 ., E

~150

~ f'OO .2 50 e ~ 8! 0

NRM 8

" ~6 .. .!

E

~ ,

"J ..

~2 oe ~20

:; -'I: .. 60

>: w 0

1~ ~ 1

"'0

r-- .... -----------

.......... ~~=::.:.:.;y~/ ............................. .

_~~oL _____ -/

...... 2min

_/ ,-

" ........ - ...

guinea -pig adapted to co. 21 "C. descending NRM efferents interrupted

>-----< eLstim

......... / ... \ ...

Fig. 2. Thermoregulatory, metabolic and respiratory responses to electrical stimulation (el. stirn.) of the nucleus raphe magnus (NRM) and to external warming in a guinea pig after interruption of the descending NRM efferents. Respiratory frequency (RF), respiratory evaporative heat loss (REHL), skin temperature (Tsk ), and colonic temperature (Teol ) are increased during warm exposure (Ta, ambient temperature). Electrical stimulation of the NRM caused reduction in RF and REHL, whereas oxygen uptake (0. U.) was increased and electrical muscle activity (EMA) unchanged

Role of the Nucleus Raphe Magnus in the Control of Cold Shivering 75

metabolic activation. Electrolytic lesion of the subcoeruleus caused a decrease in shivering threshold temperature and reduced shivering activity, whereas the REHL threshold and the REHL slope remained unchanged (Bruck and Hinckel 1982).

Single- Unit Recordings and Microiontophoretic Applications. Out of the total of 102 recorded NRM neurons in 54 guinea pigs 43 units responded to thermal stimula­tion of the skin: 24 were warm responsive, 5 were cold responsive, 10 units had phasic activity and were either warm or cold responsive, and 4 neurons responded according to a constant pattern to the thermal stimulation but without being warm or cold responsive. The frequencies of the remaining neurons were either indepen­dent of thermal skin stimuli or their changes were not clearly elicited by thermal skin stimuli.

Some of the warm-responsive neurons had temperature-frequency characteris­tics that are somewhat bell shaped and others that are more steplike. The warm units reached maximum frequencies at skin temperature levels between 38 0 and 46 dc. The cold-responsive units were bell shaped and had maximum spike rates at temperature levels between 220 and 32°C.

The phasic active neurons exhibited a respiratory rhythm: 6 units had peak activities in the cold range between 23° and 33 °e and 4 units in the warm range between 39° and 43°C. The active phases of the warm units were shorter and had lower frequencies at lower skin temperature levels. Below 37 DC there was no active phase. The pattern of the phasic neurons seemed to be truly a respiratory rhythm and not associated with movements of the chest wall and diaphragm (i.e., motion artefacts).

Microiontophoretic applications of different putative neurotransmitters and blocking agents on thermoresponsive NRM units showed that serotonin and substance P may be involved in the thermal transmission in neuronal networks of the lower brain stem.

References

Bruck K, Hinckel P (1980) Thermoregulatory noradrenergic and serotonergic pathways to hypothalamic units. J Physiol (Lon d) 304: 193 - 202

Bruck K, Hinckel P (1982) Thermoafferent systems and their adaptive modifications. Phar­macol Ther 17:357-381

Cabot JB, Goff DM, Cohen DH (1981) Enhancement of heart rate responses during condi­tioning and sensitization following interruption of raphe-spinal projections. J Neurosci 7:760-770

Dickenson AH (1977) Specific responses of rat raphe neurones to skin temperature. J Physiol (Lond) 273:277-293

Hellon RF, Taylor DCM (1982) An analysis of a thermal afferent pathway in the rat. J Physiol (Lond) 326: 319 - 328

Hinckel P, Perschel WT (1987) Influence of cold and warm acclimation on neuronal respons­es in the lower brain stem. Can J Physiol Pharmacol 65: 1281-1289

Hinckel P, Schroder-Rosenstock K (1981) Responses of pontine units to skin-temperature changes in the guinea-pig. J Physiol (Lond) 314:189-194

76 P. HINCKEL: Role of the Nucleus Raphe Magnus in the Control of Cold Shivering

Hinckel P, Schroder-Rosenstock K (1982) Central thermal adaptation oflower brain stem units in the guinea-pig. Pflugers Arch 395:344-346

Hinckel P, Cristante L, Bruck K (1983) Inhibitory effects of the lower brain stem on shivering. J Therm BioI 8: 129-131

Ishikawa Y, Hinckel P (1985) The role of serotonin and noradrenaline in thermo afferent pathways to the hypothalamus. Pflugers Arch 405 [SuppI2]:R70

Lalley PM (1986) Serotoninergic and non-serotoninergic responses of phrenic motoneurones to raphe stimulation in the cat. J Physiol (Lond) 380:373-385

Light AR (1985) The spinal terminations of single, physiologically characterized axons originating in the pontomedullary raphe of the cat. J Comp Neurol 234: 536-548

Rossner W (1965) Stereotaktischer Hirnatlas vom Meerschweinchen. Pallas, Munich Szelenyi Z, Hinckel P (1987) Changes in cold- and heat-defence following electrolytic lesions

of raphe nuclei in the guinea-pig. Pflugers Arch 409: 175-181 Taylor DCM (1982) The effects of nucleus raphe magnus lesions on an ascending thermal

pathway in the rat. J Physiol (Lond) 326:309-318 Tindal JS (1965) The forebrain of the guinea-pig in stereotaxic coordinates. J Comp Neurol

124:259-266

Gene Expression for Neuropeptides in the Ganglia of the Vagus (Nodose) and Glossopharyngeal (Petrosal) Nerves * D. E. MILLHORN, M. F. CZYZYK-KRZESKA, D. A. BAYLISS, and K. B. SEROOGY

Introduction

The nodose and petrosal ganglia contain the cell bodies of primary sensory fibers of the vagus and glossopharyngeal nerve, respectively, that innervate visceral receptors of various modality, including baro- and chemoreceptors of the carotid sinus and aortic arch, stretch and irritant receptors in the heart, larynx and bronchi, and mechano- and chemoreceptors of the gastrointestinal tract. Because of their importance in autonomic regulation, there is much interest in identifying the chemical messengers (classical neurotransmitters and neuropeptides) employed by these neurons to transmit sensory information to second order neurons in the nucleus of the solitary tract (NTS) in the medulla oblongata. In this regard, numerous peptides have been identified immunohistochemically in vagal and glossopharyngeal fibers that innervate peripheral tissue (Lundberg et al. 1978, 1979; Helke et al. 1980) and that project to NTS (Ljungdahl et al. 1978; Cuello and Kanazawa 1978). Moreover, perikarya immunoreactive for a number of neu­ropeptides, including substance P (SP), neurokinin A (NKA), calcitonin gene-re­lated peptide (CGRP), somatostatin (SOM), vasoactive intestinal polypeptide (VIP), and cholecystokinin (CCK), have been found in the nodose and petrosal ganglia (Helke et al. 1980; Helke and Hill 1988; Kummer 1988; Lundberg et al. 1978).

Although immunohistochemical findings such as these provide important infor­mation concerning the identity of posttranslational products (e.g., peptides) in individual neurons, they do not provide information concerning the ability of the neurons to actually synthesize transmitter compounds, or more importantly, the dynamics of transmitter biosynthesis during different physiological and patholog­ical conditions. One approach for obtaining information of this type is to identify and determine the level of expression of the molecular substrate, i.e., messenger RNA (mRNA), for the compound of interest. In the present study, in situ hy­bridization was used to identify individual cells in the nodose and petrosal ganglia

* This work was supported by NIH grant HL 33831 and a grant from the American Heart Association (88-1108). D.E.M. is a Career Investigator of the American Lung Association. M.C.-K. was supported by a cooperative agreement with the United States Environmental Protection Agency. K.B.S. is a NRSA postdoctoral fellow (NS 08525). D.A.B. is supported by a fellowship from Glaxo Pharmaceutical Co., Research Triangle Park NC, USA, and an NSERC scholarship (Canada).

78 D. E. MILLHORN et al.

that express mRNA for several peptides (substance P/neurokinin A, CGRP, SOM, NPY, and CCK). In some cases, we were able to discern different concentrations of mRNA for a given peptide based on the intensity of cellular labeling.

Methods and Procedures

Male Sprague-Dawley rats (200-400 g) were anesthetized, and the nodose and petrosal ganglia were removed and frozen. The ganglia were cut (10 11m) in a cryostat, thaw-mounted onto gelatin-coated slides and processed for in situ hy­bridization.

Oligodeoxyribonucleotide "probes" complementary to the messenger RNA (mRNA) of the peptides of interest were synthesized on an Applied Biosystems DNA synthesizer. The probe for preprotachykinin A (PpTA), which encodes both SP and NKA, was 33 bases long (i.e., 33 mer) and complementary to bases 171-204 of rat ppTA precursor mRNA (Krause et al. 1987). The CGRP probe (30 mer) was complementary to nucleotides 313-342 of the coding region of CGRP mRNA in rat (Amara et al. 1982). The SOM probe (33 mer) was comple­mentary to bases 403 -435 of rat preproSOM (PpSOM) coding region (Goodman et al. 1982; Shen et al. 1982). The probe for preproCCK (PpCCK) was complemen­tary to bases 276-306 of rat CCK mRNA (Deschenes et al. 1984). The probe used for hybridization with neuropeptide Y (NPY) mRNA was complementary to nucleotides 207-233 of human NPY mRNA (Minth et al. 1984).

Probes were labeled at either the 3' end using lX-thio[35S]deoxyadenosine triphos­phate (dATP; New England Nuclear) and terminal deoxynucleotidyl transferase (Bethesda Research Laboratories, BRL) or the 5' end with T4 polynucleotide kinase (BRL) and y_[32p]ATP to specific activities of 2-6 x 106 cpm/pmol. Prior to hybridization with labeled probe, the tissue was warmed to room temperature, fixed in 4% paraformaldehyde, rinsed twice in 0.1 M phosphate-buffered saline (PBS), once in PBS with glycine (2 mg/ml), and twice again in PBS. Following dehydration and delipidation by washes in different concentrations of ethanol and in chloroform, the tissue sections were hybridized to 0.5-3.0 x 106 cpm of probe in hybridization buffer. The hybridization buffer contained 50% deionized for­mamide, 4 x SSC (1 x SSC is 0.15 M NaCl/0.015 M sodium citrate, pH 7.2) 10% dextran sulfate, 0.02% each of Ficoll, polyvinylpyrrolidone and bovine serum albumin, 500llg/ml denatured salmon sperm DNA, 250 Ilg/ml yeast tRNA, and 100 mM dithiothreitol.

The sections were covered with 100 III buffer containing labeled probe and allowed to incubate for 20-24 h at 37°C. The sections were then washed (1 x SSC, 10 mM sodium thiosulfate) at 45° -55°C and dipped in auto radiographic emulsion (NTB2 or NTB3, Kodak). The slides were developed after 1-3 weeks, counter­stained with toluidene blue (0.25%) and analyzed with a fluorescence microscope equipped with light and dark field condensors.

Gene Expression for Neuropeptides 79

Fig. lA-D. Photomicrographs showing neurons in the nodose ganglia that express mRNA for preprotachykinin A (ppTA) which encodes for both substance P and neurokinin A. A, C Low-power dark-field micrographs which show that numerous cells contain this mRNA. B Enlargement of the area shown inside the dashed line in A. Large clusters of silver grains in B indicate cells that express ppTA (thin arrows). Thick arrow a cell body that does not contain ppTA mRNA. In a given section individual neurons often showed different concentrations of ppTA mRNA as indicated by the density of silver grains over the cell; an example of this is shown in C. Here some perikarya are heavily labeled (straight arrows) whereas others show a less dense labeling (curved arrows). D High-power light-field micrograph that shows the cellular resolution of the labeling. (Calibration bars in A, C, 50 11m; in B, D, 25 11m)

80 D. E. MILLHORN et al.

Results

Perikarya containing mRNAs for ppTA (SP/NKA), CGRP, and SOM were found in both the nodose and petrosal ganglia. A new finding was that neurons of both nodose and petrosal ganglia contain mRNA for NPY In addition, we found no evidence for CCK mRNA in either the nodose or petrosal ganglia of rat.

Cell bodies containing ppTA mRNA were numerous in nodose ganglia (Fig. 1). Although these cells were located variously throughout the ganglia, they appeared to be more numerous at the central end (near the exit point of the nerve; see Fig. 1 a). Figure 2 shows cells in the petrosal ganglia that were labeled after hybridization for ppTA mRNA. Again, a large number of perikarya contained ppTA mRNA. We found that individual somata within a ganglia (nodose or petrosal) showed different silver grain densities indicating differences in ppTA mRNA concentration. It is important to realize that we cannot discriminate the three alternatively processed forms of ppTA mRNA (i.e., IX, {3, y). Since the {3 and y forms encode both SP and NKA, cells labeled with the ppTA probe may produce either or both of these peptides.

Perikarya containing mRNA for CGRP were also numerous in both the nodose (Fig. 3 A) and petrosal (Fig. 3 B) ganglia; their distribution was similar to that observed for ppTA. In fact, the distribution of cells containing mRNA for CGRP and ppTA overlapped (observed in adjacent sections from same ganglion), and on several occasions we observed coexistence of both species of mRNA in individual cells (not shown). Again, different densities of silver grains were observed over individual cell bodies of a ganglia thereby indicating different concentrations of CGRP mRNA in these cells.

Fig. 2. Dark (A) and light (B) field photomicrographs of cells in the petrosal ganglia that contain mRNA for preprotachykinin A (ppTA). Within a given section, somata containing high (straight arrows) and low (curved arrows) concentrations of ppTA mRNA were noted. The light-field micrograph in B attests to the cellular resolution of the labeling. Thin arrow, a cell that contains ppTA mRNA; thick arrow, a cell that does not contain ppTA mRNA. (Calibration bar in A, 50 11m; in B, 25 11m)

Gene Expression for Neuropeptides 81

Fig. 3. Light-field photomicrographs showing cells in the nodose (A) and petrosal (B) that contain mRNA for CGRP (thin arrows) and examples of cells that do not contain this mRNA (thick arrows). The cellular resolution of the labeling is evident from the micrograph. (Calibration bar, 25/lm)

The mRNA encoding ppSOM was found in relatively fewer cells of both the nodose and petrosal ganglia than described above for ppTA and CGRP. Examples of ppSOM mRNA in cells of the nodose (Fig. 4A) and petrosal (Fig. 4B) are shown. There was no obvious topographical localization of these cells within either the nodose or petrosal ganglia. In addition, the concentration of silver grains in ppSOM cells appeared to be less variant than that observed for either ppTA or CGRP.

ppNPY mRNA was found in only a few cells of the nodose and petrosal ganglia. An example of a cell containing ppNPY mRNA in a petrosal ganglion is shown in Fig. 4C. The neurons of the petrosal ganglion that express ppNPY mRNA were situated at the point where the glossopharyngeal nerve enters the ganglion (i .e., the

82 D. E. MILLHORN et al.

Fig. 4. Dark-field photomi­crographs showing perikarya in the nodose (A) and pet­rosal (B, C) ganglia that contain preprosomatostatin (PpSOM) mRNA (A, B) and preproneuropeptide Y (ppNPY) mRNA (C) . (Calibration bar in A, 50 Ilm; in C 251lm)

peripheral end ofthe ganglia). To our knowledge, this is first evidence for expres­sion of NPY mRNA in either petrosal or nodose ganglia in rat.

We failed to detect cells in either the nodose or petrosal ganglia that contained ppCCK mRNA (Fig. 5A). However, in sections of ventral mesencephalon pro­cessed in parallel, we found numerous perikarya that contained ppCCK mRNA (Fig. 5 B).

Discussion

In situ hybridization was used in the present study to demonstrate that neurons in the nodose and petrosal ganglia contain mRNA for several peptides including SP/NKA, CORP, and SOM. Thus, these findings corroborate results from im­munohistochemical studies in which cells of the nodose and petrosal ganglia were shown to contain these peptides. In addition, cells in both the nodose and petrosal ganglia were found to express mRNA for NPY.

Gene Expression for Neuropeptides 83

Fig.S. Photomicrographs of sections from a nodose ganglia (A) and the ventral tegmental area of the mesencephalon (B) that were hybridized with a probe directed against mRNA for preprocholecystokinin (ppCCK). We failed to detect cells in either the nodose or petrosal ganglia that express ppCCK mRNA (A). However, we found many cells in the ventral mesencephalon (B, arrows), an area rich in CCK, that express this mRNA. (Calibration bars, 50 f!m)

The finding that cells in nodose and petrosal ganglia express mRNA for NPY is new. NPY is traditionally associated with sympathetic ganglia where it often coexists with tyrosine hydroxylase, the rate-limiting enzyme for catecholamine synthesis. We have not observed coexistence for these two compounds in the petrosal ganglia although tyrosine hydroxylase (TH) has been localized immuno­histochemically in the same region of the petrosal ganglia, i.e., at the entrance of the glossopharyngeal nerve (most peripheral part of the ganglion) (Katz and Black

84 D. E. MILLHORN et al.

1986). NPY is not limited only to the petrosal ganglion. Findings from immuno­histochemical studies indicate that NPY is also located in other primary sensory neurons, for example, in the dorsal root ganglion (Lindh et al. 1989).

We failed to detect ppCCK mRNA in cell bodies of either the nodose or petrosal ganglion. However, attesting to the specificity and sensitivity of the probe, we did detect numerous perikarya containing mRNA for CCK in the ventral mesen­cephalon, a region previously shown to be rich in CCK and its mRNA (Seroogy et al. 1989b). Thus, our results indicate that CCK may not be present in either the nodose or petrosal ganglia and are in disagreement with immunohistochemical studies (Lundberg et al. 1979; Manyth and Hunt 1984; Helke and Hill 1988). Because preadsorption of CCK antibody with high concentration of CGRP elim­inates CCK immunostaining (Helke and Hill 1988), we believe that the immuno­histochemical findings are inconclusive. Similarly, studies of dorsal root ganglia have shown that COOH-terminal directed CCK antisera often cross react with CGRP (Ju et al. 1986, 1987; H6kfelt et al. 1988). Interestingly, although primary sensory afferents of rat do not appear to contain CCK, there is evidence that CCK is expressed in primary sensory ganglia of certain other species (e.g., guinea pig; Seroogy et al. 1989a).

The functional significance of pep tides found in the nodose and petrosal ganglia is unknown. The presence of peptides in afferent fibers in peripheral tissue, e.g., SP in sensory fibers that innervate the carotid body (Jacobowitz and Helke 1980), implies that the peptide may in some way modulate the activity of the receptor. The presence of peptides in primary afferent fibers (Helke and Hill 1988) and in terminal varicosities in NTS suggests that there may be release from axon termi­nals and serve as a transmitter or modulator at central synapses.

Acknowledgements. The authors express gratitude to Luisa Klingler for her excel­lent technical assistance. (NIH 33831)

References

Amara SG, Jonas V, Rosenfeld RM, Dng ES, Evans RM (1982) Alternative RNA processing in calcitonin gene expression generates mRNAs encoding different polypeptide products. Nature 298:240-244

Cuello AC, Kanazawa I (1978) The distribution of substance P immunoreactive fibers in the rat central nervous system. J Comp Neuro1178: 129-156

Deschenes RJ, Lorenz LJ, Haun RS, Roos BA, Collier KJ, Dixon IE (1984) Cloning and sequence analysis of a cDNA encoding rat preprocholecystokinin. Proc Natl Acad Sci USA 81:726-730

Goodman RH, Jacbos JW, Dee PC, Habener JF (1982) Somatostatin-2P encoded in a cloned cDNA obtained from a rat medullary thyroid carcinoma. J BioI Chem 257:1156-1159

Helke CJ, Hill KM (1988) Immunocytochemical study of neuropeptides in vagal and glossopharyngeal afferent neurons in the rat. Neuroscience 26: 539-551

Helke CJ, D'Donohue TL, Jacobowitz DM (1980) Substance P as a baro- and chemoreceptor afferent neurotransmitter: immunocytochemical and neurochemical evidence in the rat. Pep tides 1: 1-9

Gene Expression for Neuropeptides 85

Hokfelt T, Herrera-Marschitz M, Seroogy K, Ju G, Staines WA, Holtes V, Schalling M, Ungerstedt U, Post C, Rehfeld JF, Frey P, Fischer J, Dockray G, Hamaoka T, Walsh JH, Goldstein M (1988) Immunohistochemical studies on cholecystokinin (CCK)-im­munoreactive neurons in the rat using sequence specific antisera and with special refer­ence to the caudate nucleus and primary sensory neurons. J Chern Neuroanat 1: 11-52

Jacobowitz DM, Helke CJ (1980) Localization of substance P immunoreactive nerves in the carotid body. Brain Res Bull 5:195-197

Ju G, Hokfelt T, Fischer JA, Frey P, Rehfeld JF, Dockray GJ (1986) Does cholecystokinin­like immunoreactivity in rat primary sensory neurons represent calcitonin gene-related peptide? Neurosci Lett 68:305-310

Ju G, Hokfelt T, Brodin E, Fahrenkrug J, Fischer JA, Frey P, Elde RP, Brown JC (1987) Primary sensory neurons of the rat showing calcitonin gene-related peptide (CGRP) immunoreactivity and their relation to substance P-, somatostatin-, vasoactive intestinal peptide- and cholecystokinin-immunoreactive ganglion cells. Cell Tissue Res 247:417-431

Kalia M, Fuxe K, Hokfelt T, Johansson 0, Lang R, Ganten D, Cuello C, Terenius L (1984) Distribution ofneuropeptide immunoreactive nerve terminals within the subnuclei of the nucleus of the tractus solitarius of the rat. J Comp Neurol 222:409-444

Katz DM, Black IE (1986) Expression and regulation of catecholaminergic traits in primary sensory neurons: relationship to target innervation in vivo. J Neurosci 6:983-989

Krause JE, Chirgwin JM, Carter MS, Xu ZS, Hershey AD (1987) Three rat preprotachykinin mRNAs encode the neuropeptides substance P and neurokinin A. Proc Natl Acad Sci USA 84:881-885

Kummer W (1988) Retrograde neuronal labelling and double-staining immunochemistry of tachykinin- and calcitonin gene-related peptide-immunoreactive pathway in the carotid sinus nerve of the guinea pig. J Auton Nerv Syst 23: 131-141

Lindh B, Lundberg JM, Hokfelt T (1989) NPY-, Galanin-, VIP/PHI-, CGRP- and substance P-immunoreactive neuronal subpopulations in cat autonomic and sensory ganglia and their projections. Cell Tissue Res 256:259-273

Ljungdahl A, Hokfelt T, Nilsson G (1978) Distribution of substance P-like immunoreactivity in the central nervous system of the rat I. Cell bodies and nerve terminals. Neuroscience 3:861-943

Lundberg JM, Hokfelt T, Nilsson G, Terenius L, Rehfeld J, Elde R, Said S (1978) Peptide neurons in the vagus, splanchnic and sciatic nerves. Acta Physiol Scand 104:499-501

Lundberg JM, Hokfelt T, Kewenter J, Pettersson G, Ahlman H, Edin R, Dahlstrom A, Nilsson G, Terenius L, Uvnal-Wallensten K, Said S (1979) Substance P-, VIP-, and en kephalin-like immunoreactivity in the human vagus nerve. Gastroenterology 77:468-471

Mantyh PW, Hunt SP (1984) Neuropeptides are present in projection neurones at all levels in visceral and taste pathways: from periphery to sensory cortex. Brain Res 299: 297 - 311

Minth CD, Bloom SR, Polak JM, Dixon JE (1984) Cloning, characterization, and DNA sequence of human cDNA encoding neuropeptide tyrosine. Proc Nat! Acad Sci USA 81:4577-4581

Seroogy K, Mohapatra NK, Rethelyi M, McGehee DS, Lund PK, Perl ER (1989a) Species­specific expression of cholecystokinin messenger RNA in rodent dorsal root ganglia. Mol Brain Res (in press)

Seroogy K, Schalling M, Brene S, Dagerlind A, Chai SY, Hokfelt T, Persson H, Brownstein M, Huan R, Dixon J, Filer D, Schlessinger D, Goldstein M (1989 b) Cholecystokinin and tyrosine hydroxylase messenger RNAs in neurons of rat mesencephalon: peptide/ monoamine coexistence studies using in situ hybridization combined with immunocyto­chemistry. Exp Brain Res 74: 149-162

Shen L-P, Pictet RL, Rutter WJ (1982) Human somatostatin I: sequence of the cDNA. Proc Natl Acad Sci USA 79:4575-4579

Specificity and I or Non-Specificity in Brainstem Cardiorespiratory Networks

The Rostral Ventrolateral Medulla: Anatomical Substrates of Cardiopulmonary Integration D.A. RUGGIERO, R.E. GOMEZ, S.L. CRAVO, E. MTUI, M. ANWAR, and D.l RBIS

Introduction

The nucleus reticularis rostroventrolateralis (RVL) integrates the tonic and reflex control of cardiopulmonary function. R VL neurons are essential in the expression of somatosympathetic (Stornetta et al. 1989) and baroreceptor reflexes (Granata et al. 1985; McAllen et al. 1982), neurogenic hypertension (Benarroch et al. 1986a), the defense-arousal response (Hilton and Smith 1984; Hilton et al. 1983), the cerebral ischemic reflex (Dampney and Moon 1980; Guyenet and Brown 1986), and the cerebrovasodilation to hypoxia (Underwood et al. 1986). The RVL har­bors neurons synthesizing adrenaline (the C1 area; Ruggiero et al. 1985b) which project exclusively to the intermediolateral (IML) and intermediomedial (IMM) cell columns, admixed with nonadrenergic cells projecting to the IML and other spinal laminae, including respiratory lower motoneurons (Cravo et al. 1988; Ellen­berger and Feldman 1988; Ross et al. 1984a). The RVL (including the C1 area) transmits somatic and visceral afferents from the spinal cord (Ruggiero et al. 1989a; Stornetta et al. 1989) and cardiopulmonary afferents from the nucleus tractus solitarii (NTS) and ventral surface including those of chemoreceptors located peripherally in the carotid body and centrally (theoretically) near the ventral medullary surface (VMS; Donoghue et al. 1984; Millhorn et al. 1982; Ruggiero et al. 1989b; SchHitke et al. 1979). The RVL overlies the intermediate area (IA) of the VMS and may mediate the repertoire of cardiopulmonary respons­es to stimulating the VMS (Benarroch et al. 1986b; Gatti et al. 1985).

Nucleus Reticularis Lateralis: An Autonomic Subnucleus

Efforts to map the microcircuitry of the ventrolateral medulla (VLM) led to the discovery of a new reticular substructure concerned with visceral integration - the nucleus RVL et caudoventrolateralis (CVL; see review, Ruggiero et al. 1989a). The nucleus RVL and its caudal extension, the nucleus CVL form an autonomic cell column in the VLM defined by its unique cytoarchitecture, neurochemical organization, and specific interconnections with autonomic-limbic substructures and was termed after the nucleus reticularis lateralis (rostroventro- and caudo­ventro- were prefixed) of Meessen and Olszewski (1949). The boundaries of the subnuclei RVL and CVL are more discretely localized than the nucleus paragigan-

90 D. A. RUGGIERO et al.

tocellularis lateralis (NPGCL) which is a composite structure, extrapolated from human (Olszewski and Baxter 1954) to rat (Andrezik et al. 1981) and defined arbitrarily primarily by analyzing tissues stained for Nissl substance. In contrast to the nuclei R VL and CVL, the boundaries of NPGCL are inconsistent with connectivity data and more extensive medially, including parts of the lateral wings of the raphe and rostrally including a paraolivary nucleus concerned with auditory feedback control (Andrezik et al. 1981).

The nucleus R VL is bordered dorsally by the compact division of the nucleus ambiguus (NA) (Bieger and Hopkins 1987) and ventrally by the ventral subpial surface of the rostral VLM, coinciding with the glutamate-sensitive area (the area intermediate) of SchiMke and Loschcke (1967), medially by the lateral wings of nucleus raphe magnus (parapyramidal area: see Helke et al. 1989) and laterally by a ventral extension of nucleus reticularis parvocellularis and at rostral most levels the caudal pole of the facial nucleus.

The nucleus RVL is composed of three principal cell groups (Cravo et a!. 1988; Ruggiero et a!. 1989 a): (a) a small longitudinal column of multipolar cervical premo tor cells adjacent to the compact (retrofacial) division of NA admixed with morphologically similar cholinergic cells of the external division ofNA (Bieger and Hopkins 1987, Ruggiero et al., 1990); (b) the C1 area - the main body (vasomotor area) of RVL identified (in rat) by a horizontally elongate cluster of mostly fusiform and multipolar cells extending approximately 150-600 /lm dorsally to the VMS (Ruggiero et al. 1985 b); and (c) a ventral subpial group of predominantly fusiform cells lining or in close proximity to the VMS (Amendt et al. 1978; Ross et al. 1981 b). The nucleus CVL is composed of two main cell groups: (a) the nucleus retroambiguus (nRA), a diagonally elongate arc of cells oriented dorso­medially towards the NTS and representing a caudal extension of non-cate­cholaminergic neurons of nucleus RVL; and (b) the C1-A1 and A1 areas composed primarily or exclusively of noradrenergic neurons at respective intermediate and caudal levels of the medulla oblongata. Small to medium-sized neurons scattered throughout the nuclei RVL and CVL and extending into neighboring areas of the raphe, lateral tegmental field (and the nucleus tractus solitarii, NTS), contain glutamic acid decarboxylase (GAD) the enzyme biosynthesizing GABA (Meeley eta!. 1985; Ruggiero eta!. 1985a).

The Cl Area of the RVL is a Vasomotor Center

Physiological evidence suggests that neurons in the C1 area of nucleus R VL are sympathoexcitatory and tonically active. First, our initial observations revealed a tight correspondence between sites eliciting the largest increases in arterial blood pressure (AP) obtained with low-current stimulation and two regions of the medul­la: an area of the rostral VLM containing neurons staining immunocytochemically for the adrenaline-synthesizing enzyme, phenylethanolamine N-methyltransferase (PNMT) (the C1 area of nucleus RVL), and a region of the dorsomedial tegmen­tum through which axons of C1 neurons (the C1-reticulospinal tract) descend to

The Rostral Ventrolateral Medulla 91

the spinal IML and IMM (Ross et al. 1984a, b). Second, microinjections of small volumes of the perikaryal-selective excitatory amino acid L-glutamate (1 nmol; 15 nl; pH 7.4) in urethane-anesthetized rats, evoke dose-related pressor responses restricted to a longitudinal column of RVL (1.8-2.2 mm lateral to the midline; encompassing an area extending 450 11m posterior to the caudal pole of the facial nucleus) corresponding to the rostral one-third of the C1 area harboring sympa­thoexcitatory reticulospinal neurons (Cravo et al. 1988). In contrast to previous electrical and chemical stimulation studies (Dampney et al. 1982; Ross et al. 1984 b), micropipettes were introduced into the VLM through the ventral surface in order to limit diffusion of the agent (Cravo et al. 1988). Weak or no arterial pressor responses occurred at midmedullary levels, whereas depressor responses (see Day and Renaud 1983) were obtained from the nRA at caudal medullary levels; in contrast with the arterial pressor responses provoked by electrical stim­ulation (Loschcke et al. 1970; Ross et al. 1984 b). The cardiovascular-active area of RVL corresponds to the location of neurons activated antidromically by stimuli applied to the IML of the thoracic cord whose tonic activity is synchronized to the spontaneous bursts of splanchnic sympathetic nerve discharge and inhibited by elevations in AP that stimulate baroreceptors (Barman and Gebber 1985; Brown and Guyenet 1984; Morrison et al. 1988). An anatomical basis implicating the C1 area in autonomic regulation is suggested in part by a pool of immunochemically identified C1 (PNMT-immunoreactive) neurons which give rise to a transtegrnen­tal arc ofaxons coalescing in the dorsal medulla and descending through the spinal lateral funiculus. In the spinal cord adrenergic terminals are restricted to the IML and IMM throughout all levels of the thoracic cord, whereas some fibers also innervate preganglionic neurons at upper lumbar and sacral levels (Ruggiero et al. 1989 a). That a large proportion of the bulbospinal neurons in the cardiovascular­active area are adrenergic was demonstrated by quantitative dual-labeling experi­ments combining retrograde transport techniques with immunocytochemistry of catecholaminergic enzymes (Cravo et al. 1988). Whereas cells transporting retro­grade tracers (Fluoro-Gold, rhodamine-labeled microbeads, or wheat germ agglu­tinin - horseradish peroxidase, WGA-HRP) injected into the thoracic spinal cord were found throughout the VLM, dually labeled cells were restricted to the C1 area and coincided with the cardiovascular-active area of RVL. Adrenergic reticu­lospinal neurons demonstrated a dramatic peak within the glutamate-sensitive site of nucleus RVL, spanning an area 450 11m caudal to the facial nucleus, and a sharp decline at unresponsive midmedullary levels of the VLM. Noncatecholaminergic neurons projecting to the cord showed a more even distribution throughout the VLM (see Ruggiero et al. 1989a).

The identity of the central neurotransmitter responsible for the tonic back­ground excitation of thoracic preganglionic sympathetic neurons is unknown. Quantitative topographic analysis of adrenergic and nonadrenergic reticulospinal projections of the vasomotor area indicated that within the cardiovascular-active area of nucleus RVL greater than 70% of the total number of cells backfilled from the thoracic cord also contained the adrenaline-synthesizing enzyme PNMT (Cravo et al. 1988; Ruggiero et al. 1989 a). Since adrenergic terminals are restricted to spinal autonomic cell columns and synapse on preganglionic neurons in the

92 D. A. RUGGIERO et al.

thoracic cord, these data extend our previous findings (Ross et al. 1981 a; 1984a) and suggest that the majority of thoracic reticulospinal neurons in the vasomotor area of the nucleus RVL are adrenergic. The RVL, however, also harbors cell bodies containing glutaminase and glutamate, GABA, acetylcholine, and several neuropeptides including substance P and enkephalin (Guliano et al. 1989; Hokfelt et al.1979; Milner et al.1988; Ruggiero et al.1985a, 1989a, 1990a, b). Retrograde transport studies reveal numerous noncatecholaminergic reticulospinal neurons within or neighboring the RVL including those cosynthesizing serotonin and GABA (Millhorn et al. 1987 a) and somatostatin and enkephalin (Millhorn et al. 1987 b). Sun et al. (1988 a, b) suggest that, in the presence of a glutamate receptor antagonist, neurons with intrinsic pacemaker properties within a corresponding area of the rostral VLM are the origin of basal sympathetic tone, non adrenergic and, perhaps, use glutamate as a neurotransmitter. Gebber et al. (1989) refute the above hypothesis and further suggest that sympathoexcitatory neurons in the nucleus RVL are driven by antecedent input from the dorsal tegmental field (Barman and Gebber 1987). Afferents from the nucleus reticularis dorsalis to nucleus R VL were confirmed by retrograde transport studies (Ruggiero et al. 1989a).

Our anterograde tracing studies, however, suggest that a large percentage of the nonadrenergic reticulospinal neurons in the VLM backfilled from midcervical or thoracic levels, are respiratory and project to phrenic and intercostal lower motor neurons in the ventral horn (Cravo et al. 1988; Gomez et al. submitted). The distribution of functionally identified cervical and thoracic projection neurons in the nucleus RVL and in the nRA, respectively, correspond to the Botzinger com­plex at rostralmost levels and an extension of the ventral respiratory group (VRG) at caudal levels. The VRG also includes neurons with inspiratory activity concen­trated at midmedullary levels rostral to obex and another pool of predominantly expiratory neurons at the level of and caudal to the obex (see Ellenberger and Feldman 1988; Ezure et al. 1988; Feldman et al. 1985; Yamada et al. 1988).

The RVL and CVL Are Members of an Integrative Reflex Center

The nuclei RVL and CVL, a column of autonomic representation in the brainstem reticular formation are members of an integrative reflex center. Their efferent projections are restricted to autonomic nuclei including those receiving primary or higher order visceral afferents or projecting to preganglionic or respiratory spinal lower motoneurons. Afferents to the nuclei RVL and CVL, in part, reciprocate their outputs. Afferent projection fields also define their nuclear boundaries and are derived from: (a) the spinal dorsal horn and lateral tegmental field; (b) NTS; (c) pontine cardiorespiratory groups in the nucleus of Koelliker-Fuse and parabrachial complex; (d) periventricular gray; (e) hypothalamus; and (f) areas of cerebral cortex and basal forebrain associated with conditioned cardiopulmonary adjustments to exercise, the sleep-wake cycle and emotionally arousing stimuli (Dampney et al. 1982; 1987; Ruggiero et al. 1987; 1989a).

The Rostral Ventrolateral Medulla 93

Our studies, focused on cardiovascular reflexes, have demonstrated that neu­rons in the RVL playa crucial role in somatosympathetic and baroreceptor reflexes. Two bits of evidence suggest that R VL neurons represent an anatomical substrate for the arterial pressor response elicited by stimulating peripheral nerves. (a) In the medulla, spinal afferents from the dorsal horn project bilaterally and predominantly contralaterally to the nucleus R VL, nRA, and the lateral reticular precerebellar nucleus (LRN) and other brain stem autonomic substructures (Rug­giero et al. 1986). (b) The pressor responses to stimulation of the sciatic or sural nerves were abolished by electrolytic lesions or microinjection of the perikaryal selective excitotoxic agent kainic acid (KA) centered on the contralateral (but not ipsilateral) C1 area of the nucleus R VL. The responses were not affected by control injections of KA into the LRN and nRA, or by electrolytic lesions of other spinal-autonomic projection fields (e.g., A5 area, parabrachial complex) or after midcollicular transections interrupting spinal afferents to the forebrain (Stornetta et al. 1989).

The nuclei RVL and CVL were also found to integrate baroreceptor and other cardiopulmonary reflexes mediated by the NTS. Direct or indirect projections from the NTS to the nucleus RVL (Cravo et al. 1989; Gomez et al. 1989; Ross et al. 1985) may represent an anatomical basis for baroreceptor-mediated sympathoin­hibition: (a) Chemical inactivation of neurons in the C1 area of RVL or destruc­tion of the C1-spinal projection tract in the dorsal medulla, in contrast to controls, abolish the vasodepressor responses to vagal stimulation or carotid sinus stretch (Granata et al. 1985) and the enhanced sympathetic activity (and hypertension) produced by lesions of the NTS (Benarroch et al. 1986a); (b) the spontaneous discharge of cells in RVL activated antidromically from the thoracic cord are entrained to the cardiac cycle and inhibited by arterial baroreceptors; and (c) baroreceptor-evoked inhibition of vasomotor neurons in the nucleus R VL is trans­mitted by sympathetic nerves since sympathetic discharge and firing of RVL neurons are coupled with the parallel suppression of sympathetic nerve activity and RVL unit discharge (Barman and Gebber 1985; Brown and Guyenet 1984; Morrison et al. 1988).

The following evidence suggests that interneurons in the VLM may playa role in the baroreceptor reflex. Injections of KA into midmedullary regions (0.8-1.8 mm rostral to obex) of the VLM projecting to RVL (Gomez et al. 1989; sub­mitted; Ross et al. 1985; Ruggiero et al. 1989a; see below) block the vasodepressor response to baroreceptor afferent stimulation and the cardiac-related rhythm of sympathetic nerve activity (Cravo et al. 1989).

Since neurons in the VLM are involved in integrating cardiopulmonary reflexes, we sought to reevaluate the organization of NTS projections to VLM and to determine whether the afferents are organized viscerotopically (Gomez et al. 1989; Fig. 1). In an initial series of studies, the anterograde tracer Phaseolus vulgaris leukoagglutinin (PHA-L) was iontophoresed into cardiopulmonary divisions of the NTS and the distributions of terminals were mapped in the VLM. Punctate varicosities resembling terminal boutons defined the pyramidal-shaped substruc­ture of the nucleus RVL including the dorsal subambigual region, the C1 area and a thin strip lining the ventral subpial surface. Labeled processes extended through

94 D. A. RUGGIERO et al.

o Adrenergic neuron • Non-adrenergic neuron

Fig. 1. Summary of autonomic reflex arcs described in the text. Left, neurons in regions of the nucleus tractus solitarii (NTS) which receive first-order afferents from the lung, larynx, and tracheobronchial tree (v, ventral; vi, ventrolateral; is, interstitial; i, intermediate subnu­clei) project to the Botzinger complex (Be) in the apex of the rostral ventrolateral reticular nucleus (RVL) and to the nucleus retroambiguus (RA) at middle and caudal medullary levels. Neurons in the BC and nRA selectively project to phrenic and intercostal lower motorneu­rons in the cervical and thoracic ventral horns. Right, neurons in regions of the NTS which receive first-order afferents primarily from baroreceptors and the heart (dorsal, lateral, and - not illustrated - commissural subnuclei) and from receptors in the gastrointestinal tract (medial, subpostremal, and commissural subnuclei) project to the Cl area of the nucleus RVL, which in turn projects to autonomic preganglionic cell bodies in the intermediolateral (IML) and intermediomedial cell columns. Projections from the NTS to the ventrolateral medulla appear to be organized viscerotopically. Ai, Noradrenergic perikarya; C5, fifth cervical spinal segment; CVL, nucleus reticularis caudoventrolateralis; LRN, lateral precere­bellar reticular nucleus; T2, second thoracic spinal segment

intermediate and caudal levels of the VLM, where the terminal fields formed two columns: the Ai column defined by perikarya containing the rate-limiting cate­cholaminergic enzyme, tyrosine hydroxylase (TH), and the retroambigual cell group. Solitario-reticular fibers extend into the lateral funiculus of the spinal cord and terminate in the phrenic motor nucleus, IML and lamina VII and X (Mtui et al. 1990).

We next sought to determine whether cells in the NTS that project to the Cl vasomotor area of RVL differed in distribution from those projecting to the apex of nucleus RVL or the nRA (i.e., where respiratory spinal premotor neurons are concentrated; Gomez et al. 1989; submitted). Retrograde transport studies re­vealed that the projections from the NTS to the VLM are organized topographically. After injections of fluorescent dyes, Fluoro-Gold, or rhodamine-labeled mi-

The Rostral Ventrolateral Medulla 95

crobeads into the sympathoexcitatory area of nucleus RVL, most neurons were backfilled in subdivisions of the NTS and contiguous loci of the commissural nucleus (CNX) that receive primary cardiac and baroreceptor (dorsal, lateral, and interstitial divisions), chemoreceptor (caudal commissural divisions), and gas­trointestinal (subpostremal, medial, and ventromedial divisions) afferents (Ciriello 1983; Donoghue et aI. 1984; Kalia and Mesulam 1980; Norgren and Smith 1988; Ruggiero et aI. 1989 b; Shapiro and Miselis 1985). By contrast, injections into the apex of nucleus RVL (dorsal to the Cl area) or nRA (dorsomedial to the Ai area) labeled cells that were skewed laterally and ventrally in subnuclei of the NTS (intermediate, ventral, ventrolateral, and interstitial divisions) and contiguous loci of the commissural nucleus that receive first-order afferents from the lung, larynx, and tracheobronchial tree (Kalia and Richter 1985; Kalia and Mesulam 1980). We next sought to determine whether afferents from the NTS to the Cl vasomotor or phrenic premotor regions of the VLM are integrated by interneurons in the retic­ular formation (Gomez et aI., submitted) as suggested by our earlier studies (Ross et aI. 1985). Putative disynaptic connections between the NTS and nucleus RVL were revealed by the following "double-tracer" experiment. In the same animal the anterograde tracer PHA-L was iontophoresed into the caudal NTS and a retro­grade tracer (e.g. WGA-HRP) injected into the Cl area. On tissues processed for both tracers, neurons in the VLM were backfilled in a longitudinal column extend­ing caudally from the injection site in the nucleus RVL to the retroambigual area at the level of the calamus scriptorius. In the nRA, cells that were retrogradely labeled from the Cl area of R VL were surrounded by punctate varicosities (puta­tive terminals) derived from the NTS. Additional proof was obtained by demon­strating that iontophoresis of PHA-L into the nRA anterogradely labeled termi­nals in a restricted region of RVL identified by PNMT -immunoreactive adrenergic perikarya. These data (taken with the above findings) suggest that neurons in the nRA which receive afferents predominantly from respiratory subnuclei of the NTS project to the vasomotor area of nucleus R VL.

In summary, neurons in dorsal and lateral regions of NTS and the CNX inner­vated by first-order baroreceptor and chemoreceptor afferents and medial and subpostremal divisions receiving primary gastrointestinal afferents project to the Cl area of the nucleus RVL. Adrenergic or nonadrenergic (perhaps glutamatergic) neurons in the Cl area project to spinal preganglionic neurons in the thoracic cord and, along with antecedent inputs from the dorsal reticular formation (Barman and Gebber 1987), contribute to basal sympathetic nerve discharge. In contrast, neurons in ventral divisions of the nTS that receive afferents primarily from the lung and tracheobronchial tree project to regions surrounding the apex of the nucleus R VL and the nRA which, in turn, project selectively to phrenic and intercostal lower motoneurons. The projection fields from the NTS to the VLM are, therefore, organized viscerotopically. The overlap of different primary visceral afferents in the NTS (e.g., cardiopulmonary inputs to the dorsal subnucleus; Kalia and Mesulam 1980; Kalia and Richter 1988) along with intrareticular connections within the VLM might playa role in autonomic integration as, for example, the respiratory modulation of sympathetic nerve discharge (see e.g., Gootman et aI. 1980; Millhorn 1986).

96 D. A. RUGGIERO et al.

Bidirectional "Collateral" Projections Between the Nucleus Tractus Solitarii and the Ventrolateral Medulla and Spinal Cord

Data from the preceding studies also revealed that neurons in the nuclei RVL and CVL project dorsally to the NTS. PHA-L iontophoresed into either the C1 area of nucleus RVL or the nRA of CVL transported to terminals concentrated in cardiopulmonary divisions of the NTS and within sympathetic or respiratory cell columns in the spinal cord, respectively. Evidence that adrenergic neurons in the VLM send collaterals to both the NTS and spinal cord was obtained by injecting two different fluorescent tracers into either the midcervical or thoracic spinal cord and caudal divisions ofNTS and subsequently processing the tissues immunocyto­chemically for TH (or PNMT; Mtui et al. 1989). The experimental results demon­strated the following circuitry: (a) TH-immunoreactive cell bodies in the VLM were backfilled from the NTS and concentrated within the nucleus R VL; some TH-immunoreactive cells contained both fluorescent markers and therefore sent collaterals to the NTS and spinal cord; (b) adrenergic neurons that project to the NTS or to both NTS and spinal cord comprised a relatively small percentage of the total number of double-labeled cells, and (c) NTS-projection neurons within the nRA were primarily noncatecholaminergic and a separate population from those projecting to the spinal cord. The neurotransmitters of most NTS-projection cells in the VLM are unknown although perikarya containing somatostatin and methionine enkephalin were found to project to the NTS (Millhorn et al. 1987b). Whether the same neurons also project to the spinal cord is unknown. In conclu­sion, adrenergic and primarily nonadrenergic NTS-projection neurons in the VLM provide anatomical substrates.for electrophysiologically identified feedback cir­cuits that potentially modulate both cardiopulmonary reflex excitability and sym­pathetic tone (Ciriello and Caverson 1986; Lipski et al. 1984).

The Nucleus RVL Is a Component of the Chemoreceptor Reflex Arc

The nucleus R VL may also integrate the cardiopulmonary responses to pharmaco­logic and chemoreceptor stimulation of the ventral surface (Fig. 2). The following data suggest that the nucleus RVL may harbor chemosensory neurons and con­tribute to the increases in sympathetic nerve discharge (following systemic hyper­capnia) or superfusion of the VMS (with hypercapnic or acid solutions) after denervating peripheral chemoreceptors (Hanna et al. 1979; Lioy and Trzebski 1984; Trzebski et al. 1974). Clusters of adrenergic and noncatecholaminergic bul­bospinal projection neurons within the nucleus R VL (Ruggiero et al. 1989 a) lie in close proximity to the IA of the VMS where cooling, pharmacologic blockade (e.g., glycine) or lesions produce a loss of central chemosensitivity and a reduction in ventilation, arterial pressure, and both phrenic and sympathetic nerve discharge

T2

The Rostral Ventrolateral Medulla 97

o Adrenergic 'Gl' neuron • Non-adrenergic neuron

Sympathetic ganglia

Blood vessels

Fig. 2. Potential neuroanatomical circuits that integrate chemoreceptor and other cardiopul­monary reflexes in the ventrolateral medulla. Chemoreceptor afferents from the carotid body selectively project to medial and dorsal loci of the commissural nucleus of the vagus (CNX). Neurons in the CNX project to cardiorespiratory areas of the nucleus RVL and nucleus retroambiguus (nRA) and to a thin strip on the ventral medullary surface (VMS) correspond­ing to the intermediate glutamate-sensitive area (fA) ofRVL. Projections from the chemore­ceptor fields of CNX terminate in the apex and base of RVL. RVL neurons, in turn, project to the VMS and directly to preganglionic neurons in the intermediolateral cell column or to the phrenic motor nucleus. Intrareticular projections from the nRA, a source of respiratory­spinal premo tor neurons, to the RVL which contains sympathetic-spinal premotor neurons may represent anatomical substrates for cardiorespiratory integration. Ai, Noradrenergic perikarya; BC, Botzinger-complex; C5, fifth cervical spinal segment; CVL, nucleus reticularis caudoventrolateralis; LRN, lateral precerebellar reticular nucleus; RVL, nucleus reticularis rostroventrolateralis; T2, second thoracic spinal segment

(Cherniack et al. 1979; Feldberg and Guertzenstein 1972, 1976; Schliitke and Loschcke 1967). Our functional studies have shown that neurons within the RVL mediate the cardiovascular responsivity of the intermediate glycine-sensitive area of the VMS (Benarroch et al. 1986b): (a) a tight correspondence between adrener­gic neurons in the C1 area overlying the IA and sites along the rostral VMS from which changes in arterial blood pressure and heart rate were obtained by electrical or pharmacologic stimulation; (b) the pressor responses provoked by stimulating the IA were abolished by unilateral lesions involving the C1 area or the Cl-spinal tract in the dorsal medulla; and (c) control lesions of other brain stem autonomic nuclei projecting to the spinal IML, the Ai area or areas adjacent to the Cl-spinal tract did not alter the responses. An anatomical basis for ventral surface reflexes is suggested by the following data (Ruggiero et al. 1989 b). First, retrograde tracers applied to the IA combined with immunocytochemistry backfill adrenergic and noncatecholaminergic neurons in areas of R VL where bulbospinal projections to

98 D. A. RUGGIERO et al.

preganglionic and respiratory lower motor neurons originate (Ellenberger and Feldman, 1988; Ruggiero et al. 1989a). Second, iontophoresis of the plant lectin, PHA-L, into sympathetic- or respiratory-spinal premotor areas of the nucleus R VL anterogradely labels a thin sheet lining the glutamate-sensitiv~ area of the VMS. Third, adrenergic processes (dendrites and axons immunolabeled for TH and PNMT) derived from C1 neurons surround intraparenchymal microvessels and directly contact the ventral subpial surface subjacent to the RVL (see Rug­giero et al. 1985b). Fourth, ultrastructural data reveal, within 0.5 11m of the VMS, PNMT-immunoreactive dendrites ensheathed by astrocytic processes and both symmetric and asymmetric synapses between PNMT-immunoreactive adrenergic terminals and unlabeled dendrites.

Finally, our anatomical data support the idea that neurons in the nucleus RVL may integrate cardiopulmonary responses to chemoreceptor stimulation of the carotid body and the VMS (Millhorn et al. 1982). The following circuits suggest that afferents from peripheral and central chemoreceptors converge and are pro­cessed in the nucleus R VL. First is the demonstration that trans ganglionic trans­port of WGA-HRP injected into the carotid body of the rat labels punctate varicosities restricted to caudal divisions of the NTS involving medial and dorsal aspects of the CNX; thus confirming and extending antidromic mapping studies in the cat (Donoghue et al. 1984). Second, direct application ofWGA-HRP onto the VMS lining the nucleus R VL retrogradely labels cells in the same subnuclei of the NTS receiving inputs from the carotid body: i.e., backfilled cells were consis­tently restricted to the CNX in contrast to the more widespread localization of cells in the NTS projecting to the C1 area and apex of the nucleus R VL. More direct evidence that the chemoreceptor field in the CNX projects to the VMS was demonstrated by anterograde transport from an iontophoretic deposit of PHA-L in the CNX to a thin strip lining the IA of the VMS and overlapping the RVL-pro­jection field as desribed above. When mapped on serial sections, the distributions of processes projecting from the NTS and R VL onto the VMS (Ruggiero et al. 1989b) demonstrate a striking overlap with sites eliciting pressor responses to L-glutamate applied to the IA (Benarroch et al. 1986 b). Preliminary scanning electron micrographs (Gomez and Ruggiero, in preparation) reveal a sheet on the rostral VMS, composed of extremely small "glial-like" cells resembling neurons and processes. These elements overlapped the IA and an area analogous to the caudal extent of Mitchell's area in the cat (Mitchell et al. 1963) and were differen­tiated from the folds and ridges characterizing adjacent unresponsive sites.

In summary, the above data suggest that the central chemoreceptor, perhaps a neural/glial complex on the VMS, may link substructures in the NTS and reticular formation driving respiration with those maintaining arterial blood pressure.

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Loeschcke HH, Lattre J, Schliifke ME, Trouth CO (1970) Effects on respiration and circula­tion of electrically stimulating the ventral surface of the medulla oblongata. Respir PhysioI10:184-197

McAllen RM, Neil JJ, Loewy AD (1982) Effects ofkainic acid applied to the ventral surface of the medullary oblongata on vasomotor tone, the baroreflex and hypothalamic auto­nomic responses. Brain Res 238:65-76

Meeley MP, Ruggiero DA, Ishitsuka T, Reis DJ (1985) Intrinsic GABA neurons in the nucleus of the tractus solitarius and the rostral ventrolateral medulla in the rat: an immunocytochemical and biochemical study. Neurosci Lett 58:83-89

The Rostral Ventrolateral Medulla 101

Meessen H, Olszewski J (1949) A cytoarchitectonic atlas of the rhombencephalon of the rabbit. Karger, Basel

Millhorn DE (1986) Neural respiratory and circulatory interaction during chemoreceptor stimulation and cooling of ventral medulla in cats. J Physiol (Lond) 370:217-231

Millhorn DE, Eldridge FL, Waldrop TG (1982) Effects of medullary area I, cooling on respiratory response to chemoreceptor inputs. Respir Physiol 49: 23 - 39

Millhorn DE, Hi.ikfelt T, Seroogy K, Oertel W, Verhofstad A, Wu J-Y (1987 a) Immunohis­tochemical evidence for colocalization of gamma-aminobutyric acid (GABA) and sero­tonin in neurons of the ventral medulla oblongata projecting to the spinal cord. Brain Res 410:179-185

Millhorn DE, Seroogy K, Hi.ikfelt T, Schmued LC, Terenius L, Buchan A, BrownJC (1987b) Neurons of the ventral medulla oblongata that contain both somatostatin and enkephal­in immunoreactivities project to nucleus tractus solitarii and spinal cord. Brain Res 424:99-108

Milner TA, Pickel VM, Abate C, Joh TH, Reis DJ (1988) Ultrastructural characterization of substance P-like immunoreactive neurons in the rostral ventrolateral medulla in relation to neurons containing catecholamine-synthesizing enzymes. J Comp Neurol 270: 427 -445

Mitchell RA, Loeschcke HH, Massion MH, Severinghaus JW (1963) Respiratory responses mediated through superficial chemosensitive areas on the medulla. J Appl Physiol 18(3): 523-533

Morrison SF, Milner TA, Reis DJ (1989) Reticulospinal vasomotor neurons of the rat rostral ventrolateral medulla: relationship to sympathetic nerve activity and the Cl adrenergic cell group. J Neurosci 9: 1286-1301

Mtui EP, Gomez RE, Ruggiero DA, Anwar M, Reis DJ (1989) Afferents from ventrolateral medulla to nucleus tractus solitarii and spinal cord. Soc Neurosci Abstr 15:812

Mtui EP, Ruggiero DA, Anwar M, Aicher S, Gomez R, Reis DJ (1990) Autonomic reflex arcs from the nucleus tractes solitarii to the spinal cord. Soc Neurosci Abstr. 16: 12

Norgren R, Smith GP (1988) Central distribution of subdiaphragmatic vagal branches in the rat. J Comp Neurol 273:207-223

Olszewski J, Baxter D (1954) Cytoarchitecture of the human brainstem. Karger, Basel Ross CA, Armstrong DM, Ruggiero DA, Pickel VM, Joh TH, Reis DJ (1981 a) Adrenaline

neurons in the rostral ventrolateral medulla innervate thoracic spinal cord: combined immunocytochemical and retrograde transport demonstration. Neurosci Lett 25: 257-262

Ross CA, Ruggiero DA, Joh TH, Park DH, Reis DJ (1984a) Rostral ventrolateral medulla: selective projections to the thoracic autonomic cell column from the region containing Cl adrenaline neurons. J Comp Neurol228:168-184

Ross CA, Ruggiero DA, Park TH, Sved AF, Fernandez-Pardal J, Saavedra JM, Reis DJ (1984 b) Tonic vasomotor control by the rostral ventrolateral medulla: effect of electrical or chemical stimulation of the area containing Cl adrenaline neurons on arterial pres­sure, heart rate and plasma catecholamines and vasopressin. J Neurosci 4:479-494

Ross CA, Ruggiero DA, Reis DJ (1985) Projections from the nucleus tractus solitarii to the rostral ventrolateral medulla. J Comp Neurol 242: 511- 534

Ross CA, Ruggiero DA, Reis DJ (1981 b) Projections to the spinal cord from neurons close to the ventral surface of the hindbrain of the rat. Neurosci Lett 21:143-148

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102 D. A. RUGGmRO et al.: The Rostral Ventrolateral Medulla

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Descending Projections of Hypothalamic Sympathoexcitatory Neurons in the Cat * S.M. BARMAN

Introduction

Spike-triggered averaging has been used to identify hypothalamic (HYP) neurons with activity synchronized to the 2- to 6-Hz component of sympathetic nerve discharge (SND) in baroreceptor-innervated and -denervated cats [1, 13]. Such a temporal correlation suggests that either these neurons are elements of an efferent pathway involved in the control of SND, or they are receiving input from brain­stem neurons that generate this rhythmic discharge pattern in SND. Two observa­tions support the former view. One, stimuli applied through the recording mi­croelectrode often induces an excitatory sympathetic nerve response whose tempo­ral characteristics are similar to the corresponding spike-triggered average of SND [1, 13]. Two, lesions of HYP regions containing such neurons prevent the fall in blood pressure and SND caused by midcollicular decerebration [8]. Therefore, it should be possible to identify HYP neurons with sympathetic nerve-related activ­ity whose axons project to brain stem and/or spinal regions that are involved in the control of SND. Retrograde and anterograde transport studies in the cat [6, 7, 10-12] show that HYP neurons project to the thoracolumbar intermediolateral nucleus (IML) as well as to several brain stem nuclei that are involved in the control of cardiovascular function. These include the medullary lateral tegmental field (LTF), rostral ventrolateral medulla (RVLM), and mesencephalic periaqueductal gray (PAG). Since these brain stem nuclei are functionally heterogeneous, it re­mains to be determined which of these pathways mediate HYP-induced influences on SND.

The current study used the techniques of antidromic and synaptic activation to identify central nervous system pathways involved in relaying information from the hypothalamus to sympathetic nerves in the cat. Antidromic mapping was used to trace the axonal trajectories of HYP neurons with sympathetic nerve-related activity. Synaptic activation was used to determine whether LTF- and RVLM­sympathoexcitatory (SE) neurons are involved in mediating HYP-induced changes in SND.

* This work was supported by National Institutes of Health grant HL 33266.

104 S. M. BARMAN

Methods

Experiments were performed on cats anesthetized with sodium pentobarbital (35 mg/kg, i.p., initial dose), paralyzed with gallamine triethiodide, and artificially respired. Details of experimental procedures can be found in other publications from this laboratory [1-3]. Cats were placed into a spinal investigation unit (David Kopf Instruments) and stereotaxic head holder. Bipolar platinum electrodes were used to record from the left inferior cardiac postganglionic sympathetic nerve (bandpass, 1-1000 Hz). One barrel of a three-barrel glass micropipette was filled with 2 M NaCl to record from LTF or RVLM neurons or from neurons in the lateral, dorsomedial, posterior, and anterior portions of the hypothalamus. The other barrels were filled with 1 M L-glutamate (PH 8) and 2 M NaCl for ion­tophoresis and current balancing. A coaxial electrode (Rhodes Model SNE-l00) was used to stimulate the posterior or lateral hypothalamus and tungsten mi­croelectrodes (tip impedance, 10-30 kQ) were used to stimulate the PAG, LTF, and RVLM. All stimuli were ipsilateral to the recording sites. Stimulating current was monitored by measuring the voltage drop across a 100-Q resistor in series with the anode (an alligator clip attached to neck muscle). Electrodes were stereotaxi­cally positioned into the HYP and PAG. Stimulating and recording electrodes were positioned into the LTF and RVLM by using the midline and dorsal surface of the medulla as reference points.

Synaptic Activation of LTF-SE and RVLM-SE Neurons by Hypothalamic Stimulation

The first project tested the hypothesis that both LTF-SE and RVLM-SE neurons mediate HYP-induced influences on SND and that the synaptic activation of RVLM-SE neurons is dependent on their input from LTF-SE neurons. LTF-SE and RVLM-SE neurons were identified by using criteria established in other studies from this laboratory [2, 3]. First, spike-triggered averaging showed that their activity was synchronized to the cardiac-related component in inferior car­diac SND. Second, their firing rate decreased in parallel to SND during barorecep­tor reflex activation. Single shocks (70-500 jJA; 1 ms) applied once every 1.5 s to the posterior or lateral hypothalamus induced an excitatory response in inferior cardiac SND whose onset latency was 79 ± 2 ms and whose peak was 128 ± 4 ms (n=26). This stimulus synaptically activated 22 LTF-SE and 10 RVLM-SE neu­rons. All three RVLM neurons tested could be arttidromically activated by tho­racic IML micro stimulation. The neurons responded once or twice to a stimulus. Poststimulus histograms were constructed for 16 LTF-SE and 10 RVLM-SE neu­rons to determine the modal onset latency of synaptic activation. These values were 36±7 ms and 36±10 ms, respectively. Figure 1 shows an example of a poststimulus histogram for an LTF-SE neuron and the accompanying average of inferior cardiac SND. Synaptic activation of the neuron is indicated by the vari­ability in response onset latency, which in this case ranged from 20-58 ms (the

Descending Projections of Hypothalamic Sympathoexcitatory Neurons 105

15 Stim.-> Unit

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0

Stim.->SND

o 500 Interval, ms

Fig. 1. Sympathoexcitatory responses to electrical stimulation of the posterior hypothala­mus. Single shocks (350/JA; 1 ms) were applied once every 1.5 s. Top trace, poststimulus histogram (bin width, 2 ms) showing synaptic activation of a lateral tegmental field sympa­thoexcitatory neuron. Bottom trace, poststimulus average (bin width, 1 ms) showing the excitatory evoked response in inferior cardiac sympathetic nerve discharge (SND). Analyses are based on 225 stimuli

period in the histogram when the counts are above background level). The differ­ences between minimum and maximum latencies of activation of LTF and RVLM neurons were 32 ± 5 ms and 36 ± 6 ms, respectively.

The results from this project suggest that both LTF-SE and RVLM-SE neurons relay HYP-induced influences on SND. However, the data do not support the hypothesis that the response of RVLM-SE neurons was secondary to the activa­tion of their antecedent LTF-SE neurons. In this regard Barman and Gebber [3] showed that conduction time in the SE pathway from the LTF to the RVLM is approximately 30 ms. Thus, either RVLM-SE neurons are activated by an axonal collateral of the neurons controlling LTF-SE neurons, or they are activated by an independent pathway. One might have expected RVLM-SE neurons to show a secondary activation due to their input from LTF-SE neurons. In two cases the poststimulus histograms for R VLM -SE neurons were bimodal, with the second peak occurring 18 and 38 ms after the first. This may reflect input from the LTF. Failure to see such a response consistently could indicate that following their synaptic activation by another pathway RVLM-SE neurons are refractory to their input from the LTF.

106 S. M. BARMAN

Antidromic Activation of HYP-SE Neurons by LTF and RVLM Microstimulation

The second project tested the hypothesis that HYP-SE neurons project directly to the LTF and RVLM. In some experiments stimulating microelectrodes were posi­tioned into both of these regions to determine whether the same or different HYP neurons innervate the LTF and RVLM. Microstimulation of the region of the LTF that contains SE neurons induced an antidromic response in eight of 27 HYP neurons with sympathetic nerve-related activity. The axons of five of these neurons (19% of the total) either branched or terminated in the LTF. Three of these neurons were classified as SE since their firing rate decreased in parallel to SND during baroreceptor reflex activation (i.e., during the pressor response produced by aortic obstruction), whereas one was classified as sympathoinhibitory (SI) since baroreceptor reflex activation increased its firing rate. Branching or termination was suggested by the results of antidromic mapping. Figure 2 shows an example for a HYP-SE neuron. The stimulating microelectrode was moved in 200-)lm increments from the dorsal to ventral surface in tracks separated by 0.4~2 mm in the rostral-caudal or medial-lateral direction. At each site stimulated, I recorded the onset latency of antidromic activation and the threshold current for eliciting the response. As shown by the depth-threshold curves in Fig. 2, marked differences in antidromic latency were elicited when threshold current was applied at different levels of the medulla (track A versus track C) or to different sites in the same track (track C). Microstimulation of a few sites in track A elicited an antidromic re­sponse whose onset latency was 41 ms. Conduction velocity between the recording site in the hypothalamus and stimulating site in the LTF (a distance of approxi­mately 23 mm) was estimated to be 0.56 m/s. When the stimulating microelectrode was moved 1 mm more caudally, but at the same depth below the medullary surface (track C) the onset latency of antidromic activation was increased to 52 ms.

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Fig. 2. Depth-threshold curve for antidromic activation of a hypothalamic sympathoexcita­tory neuron. Square, circle (in medullary cross section), sites requiring the least stimulus current to elicit antidromic responses with onset latencies of 52 and 70 ms, respectively. Calibration, 1 mm. Depth refers to the distance below the dorsal surface

Descending Projections of Hypothalamic Sympathoexcitatory Neurons 107

Conduction velocity between these two medullary levels was estimated to be 0.09 m/s. A further decrease in conduction velocity (0.06 m/s) occurred between the sites requiring the least stimulus current to elicit the longest latency (70 ms) and shortest latency (52 ms) responses in track C. Both of these sites were in the region of the LTF containing SE neurons. These differences in axonal conduction velocity indicate that the different latencies of antidromic activation cannot be accounted for on the basis of stimulating various points on the main axon. Rather the data are supportive of activation of an axonal branch or terminal in the LTF. Only very high intensity stimuli applied in tracks Band D could induce an antidromic response in this HYP-SE neuron.

A marked shortening in onset latency of antidromic activation could often be produced by raising stimulus current at a single site of stimulation within the LTF. Current-dependent changes in onset latency of antidromic activation suggest the existence of axonal branching in the region of stimulation (see [2]). The shorter latency response presumably results from current spread to a more central point on the branch or to the main axon. Changes in onset latency of antidromic activation produced by raising stimulus current at a single site of stimulation or by applying threshold current to different sites in the medulla averaged 7.9 ± 3.2 ms for five HYP neurons that likely terminated in the LTF. Such differ­ences can occur because conduction velocity in axonal branches or terminals can be ten times slower than in the main axon [9]. The longest latency responses (37.3 ± 11.2 ms) were elicited with threshold currents of 133 ± 60 /lA. LTF micro­stimulation appeared to activate only the main axon of two HYP neurons with sympathetic nerve-related activity. The two neurons tested were classified as SE. For another neuron the data analysis was not sufficient to make a decision con­cerning the element activated by the stimulus.

Sixteen of 56 HYP neurons with sympathetic nerve-related activity were an­tidromically activated by microstimuli applied to the region of the RVLM that contains SE neurons whose axons innervate the thoracic IML. All eight neurons tested were classified as HYP-SE neurons based on their response to baroreceptor reflex activation. Twelve HYP neurons with sympathetic nerve-related activity (including seven HYP-SE neurons) were studied sufficiently to make a decision concerning the element of the neuron likely activated by RVLM stimulation. The data obtained indicated that in all except three cases the response produced by RVLM microstimulation (threshold current, 59 ±20 /lA) likely monitored activa­tion of the main axon of HYP neurons en route to a more caudal site. Small changes in onset latency produced by stimuli applied to different sites in the medulla could be accounted for on the basis of activation of different points along the main axon. Raising stimulus current above threshold did not shorten the onset latency of antidromic activation. Conduction time from the hypothalamus to the RVLM averaged 6.3 ± 1.9 ms for the nine HYP neurons that did not appear to terminate in the RVLM. Axonal conduction velocity was estimated to be 3.7 ± 0.5 m/s. The longest latency response of the three neurons that appeared to branch in this area was 18.5 ± 6.5 ms.

The LTF was also stimulated while recording from six HYP neurons that were antidromically activated by RVLM micro stimulation (including one that likely

108 S. M. BARMAN

branched in this region). The axons of none of these neurons passed through or branched in the LTF.

The results from the second project support the view that at least a component of the synaptic activation ofLTF-SE neurons by HYP stimulation results from the activation of HYP-SE neurons that innervate the LTF. This is strongly supported by the similarity in onset latencies of synaptic activation of LTF-SE neurons by HYP stimulation and the latencies of the antidromic responses of HYP neurons induced by LTF microstimulation. However, the activation of RVLM-SE neurons by HYP stimulation may be mediated primarily over a polysynaptic pathway. Alternatively, these responses may result from stimulation of fibers of passage in the hypothalamus.

Antidromic Activation of HYP-SE Neurons by PAG Microstimulation

The third project was designed to test the hypothesis that a component of the SE response produced by HYP stimulation is mediated by a pathway that synapses in the PAG. This was suggested since (a) the PAG receives a particularly rich inner­vation from the hypothalamus [7], (b) chemical stimulation of the PAG induces the cardiovascular and behavioral components of the defense reaction, including an increase in blood pressure [4], and (c) PAG neurons project to regions of the brainstem that are involved in the control of SND, including the RVLM [5, 10]. Of 62 HYP neurons with sympathetic nerve-related activity 31 were antidromically activated by PAG micro stimulation primarily at stereotaxic planes A2-A3. Stim­uli applied to these sites induced an excitatory response in the inferior cardiac sympathetic nerve (onset latency, 71±4ms; peak response, 127±6ms). Of 15 neurons tested 13 were classified as HYP-SE since their firing rate decreased during baroreceptor reflex activation. The other two were classified as SI since their firing rate increased during the pressor response to aortic obstruction. The data obtained support the view that 22 HYP neurons with sympathetic nerve-re­lated activity (including eight that were identified as HYP-SE neurons and two HYP-SI neurons) likely innervated the PAG. This accounts for 35% of the HYP neurons with sympathetic nerve-related activity identified in these experiments. This was demonstrated by using antidromic mapping and by producing marked shortening in antidromic latency when raising stimulus current above threshold. Differences in latency produced by moving the electrode or by raising stimulus current averaged 6.8 ± 1.2 ms for the 22 HYP neurons that likely innervated the PAG. Threshold current for eliciting the longest latency responses (25.6±2.3 ms) was 109±21 !lAo The antidromic activation of three HYP neurons with sympa­thetic nerve-related activity (including two HYP-SE neurons) likely resulted from activation of the main axon passing through the PAG. Axonal conduction velocity was estimated to be 1.3 ± 0.2 mls for these three neurons. The data analysis for the other six HYP neurons with sympathetic nerve-related activity was not complete enough to make a decision concerning the element of the neuron activated by PAG

Descending Projections of Hypothalamic Sympathoexcitatory Neurons 109

microstimulation. The LTF or RVLM was also stimulated while recording from 23 HYP neurons that were antidrornically activated by PAG stimulation. None of these neurons responded to medullary stimulation.

The results of the third project are consistent with the view that a component of the SE response produced by electrical stimulation of the hypothalamus is mediated via a pathway that synapses in the PAG. This pathway is distinct from that which innervates the LTF or projects through the RVLM.

Summary and Conclusions

The schematic in Fig. 3 summarizes the results obtained from these three projects concerning the pathways that may relay information from the hypothalamus to sympathetic nerves in the cat. Since most of the HYP neurons tested in this study were identified as SE, the connections are presumed to be involved in mediating increases in SND. 1. Fifty-three HYP neurons with sympathetic nerve-related activity were an­

tidromically activated by electrical stimulation of either the mesencephalic PAG, medullary LTF, or RVLM. This accounted for 65% of the HYP neurons with sympathetic nerve-related activity identified in these experiments.

2. The PAG appears to be a major projection site ofHYP neurons with sympathet­ic nerve-related activity. When stimulating SE sites in this nucleus, 35% of the HYP neurons studied appeared to innervate the PAG. Whether PAG neurons that receive HYP input innervate SE neurons in the brainstem or spinal cord remains to be determined. Based on anatomical studies [5, 10], the RVLM is a potential target.

Fig. 3. Schematic showing descending pathways that may mediate sympathoexcitatory influences from the hypothalamus. Width of the lines from the hypothala­mus denote the relative number of neurons comprising a given pathway. Connections from LTF to RVLM and from R VLM to IML are based on earlier studies from this laboratory [2, 3]. IML, Intermediolateral nu­cleus; HYP, hypothalamic nuclei; LTF, medullary lat­eral tegmental field; PAG, periaqueductal gray; RVLM, rostral ventrolateral medulla

RVLM

?

IML

'l,-_L_T_F--,

110 S. M. BARMAN: Descending Projections of Hypothalamic Sympathoexcitatory Neurons

3. The second greatest projection ofHYP neurons with sympathetic nerve-related activity (19%) was to the LTF. Since LTF-SE neurons were synaptically activat­ed by HYP stimulation, it is reasonable to assume that this projection plays a role in mediating HYP-induced increases in SND and blood pressure.

4. Very few «5%) HYP neurons appear to innervate the RVLM. It remains to be determined whether the synaptic response of RVLM-SE neurons induced by HYP stimulation is due to activation of fibers of passage or activation of a polysynaptic pathway originating in the hypothalamus. Their input from TLF­SE neurons does not appear to be a major component of this pathway since the modal onset latency of synaptic activation of the two groups of neurons was similar. Conduction time from LTF-SE to RVLM-SE neurons is approximately 30 ms [3].

References

1. Barman SM, Gebber GL (1982) Hypothalamic neurons with activity patterns related to sympathetic nerve discharge. Am J Physiol 242 (Regulatory Integrative Comp Physiol 11):R34-R43

2. Barman SM, Gebber GL (1985) Axonal projection patterns of ventrolateral medul­lospinal sympathoexcitatory neurons. J NeurophysioI53:1551-1566

3. Barman SM, Gebber GL (1987) Lateral tegmental field neurons of cat medulla: a source of basal activity of ventrolateral medullospinal sympathoexcitatory neurons. J Neuro­physiol 57: 1410-1424

4. Carrive P, Dampney RAL, Bandler R (1987) Excitation of neurones in a restricted portion of the midbrain periaqueductal grey elicits both behavioral and cardiovascular components of the defence reaction in the unanaesthetised decerebrate cat. Neurosci Lett 81:273-278

5. Carrive P, Bandler R, Dampney RAL (1988) Anatomical evidence that hypertension associated with the defence reaction in the cat is mediated by a direct projection from a restricted portion of the midbrain periaqueductal grey to the subretrofacial nucleus of the medulla. Brain Res 460:339-345

6. Dampney RAL, Czachurski J, Dembowsky K, Goodchild AK, Seller H (1987) Afferent connections and spinal projections of the pressor region in the rostral ventrolateral medulla of the cat. J Auton Nerv Syst 20:73-86

7. Holstege G (1987) Some anatomical observations on the projections from the hypothal­amus to brains tern and spinal cord: an HRP and autoradiographic tracing study in the cat. J Comp NeuroI260:98-126

8. Huang Z-S, Varner KJ, Barman SM, Gebber GL (1988) Diencephalic regions contribut­ing to sympathetic nerve discharge in anesthetized cats. Am J Physiol 254 (Regulatory Integrative Comp Physiol 23):R249-R256

9. Lipski J (1981) Antidromic activation ofneurones as an analytic tool in the study of the central nervous system. J Neurosci Methods 4: 1-32

10. Lovick TA (1985) Projections from the diencephalon and mesencephalon to nucleus paragigantocellularis lateralis in the cat. Neuroscience 14: 853-861

11. Saper CB, Swanson LW, Gowan WM (1978) The efferent connections of the anterior hypothalamic area of the rat, cat and monkey. J Comp NeuroI182:575-600

12. Saper CB, Swanson LW, Cowan WM (1979) Some efferent connections of the rostral hypothalamus in the squirrel monkey (Saimiri sciureus) and cat. J Comp Neurol 184:205-242

13. Varner KJ, Barman SM, Gebber GL (1988) Cat diencephalic neurons with sympathetic nerve-related activity. Am J Physiol 254 (Regulatory Integrative Comp Physiol 23): R257 - R267

Mechanism of the Modulatory Effect of Somatic Nerve Input on Abnormal Cardiovascular Function * P. Lr

Introduction

Although clinical experience indicates that certain forms of hypertension, hypoten­sion, arrhythmia, and coronary heart disease may be improved or even cured by acupuncture, little experimental analysis has been done in this field. In 1981 Li et al. [10] reported that in conscious dogs, acute hypertension induced by intravenous infusion of noradrenaline could be inhibited by electroacupuncture. In urethane­chloralose anesthetized rabbits arrhythmia evoked by hypothalamic or midbrain defense reaction areas could be inhibited by stimulation of deep peroneal nerve (DPN) or median nerve. Yao et al. [16] also demonstrated a long-lasting depressor effect of sciatic nerve stimulation in unanesthetized spontaneously hypertensive rats.

All these inhibitory effects were believed to be due to the activation of group III somatic afferent fibers which promote the release of opioids and serotonin in the central nervous systems to inhibit the defense reaction and chemoreceptor reflex. In contrast, stimulation of group IV somatic afferent fibers could activate the central cholinergic system, reset the baroreceptor reflex, increase sympathetic outflow, decrease renal blood flow, improve the cardiac output, and produce a pressor effect in hypotensive animals. These studies clearly suggested that somatic afferent inputs can modulate the abnormal cardiovascular function. In the present study we have tried to analyze the central mechanism which may be involved in these modulatory effects. Experiments were performed on rabbits of both sexes, anesthetized intravenously with urethane (700 mg/kg) and IX-chloralose (35 mg/kg), immobilized by gallamine triethiodide (Flaxedil; 4-5 mg/kg) and respired artifi­cially. The CO2 concentration of the expired air was kept within the normal range.

Inhibition of the Defense Reaction by Stimulation of DPN

The first series of experiments was designed to ascertain whether the cardiovascu­lar components of defense reaction could be inhibited by DPN stimulation and to analyze its central mechanism. Stimulation of hypothalamic (HYP) and midbrain

* This research was supported by the National Natural Science Fund of China and by the Science Fund of Ministry of Health.

112 P. Lr

periaqueduct gray (pAG) defense areas with a current of 100-300 IlA (0.5 ms, 70 Hz for 3 s) produced a pressor response accompanied by tachycardia, increase in left ventricular pressure (LVP) and dp/dtmax , increase in femoral blood flow, and conductance. These responses could be inhibited by DPN stimulation (400IlA, 0.5 ms, 10 Hz for 10-20 min) or local application of morphine (50 mg/ml) onto the ventral surface of the rostral ventrolateral medulla (rVLM). This inhibitory effect was blocked by rnicroinjection of naloxone (1 Ilg/ Ill) into rVLM. On the other hand, the pressor response induced by rVLM stimulation could not be

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65

o 50 100ms

Fig. 1. Defense reaction related neurones in rVLM inhibited by DPN stimulation. A, Distri­bution of recording sites. e, neurones inhibited by DPN stimulation; 0, neurones unaffected by DPN stimulation. B, Influence of DPN stimulation and iontophoresis of drugs to dis­charges of a rVLM neurone evoked by PAG activation. Left, oscilloscope recordings, single sweep. Middle, ten superimposed sweeps; time scale, 10 ms; voltage scale, 100IlY. Right, poststimulus histograms of 40 sweeps; 1 ms/bin. BJ, Before DPN stimulation; B2, during DPN stimulation; B3, 30 min after DPN stimulation; B4, iontophoresis of morphine; B5, iontophoresis of naloxone immediately after B 4

Mechanism of the Modulatory Effect of Somatic Nerve Input 113

inhibited by DPN stimulation. It is therefore suggested that the rVLM might be a key area for the inhibitory effect of DPN stimulation and opioids might midiate this inhibitory process [5].

When the evoked neuronal discharges in rVLM were recorded, 35 (64%) of the 54 units excited by single or double shocks applied to PAG could be inhibited by DPN stimulation or iontophoresis of morphine (50 mM). This inhibitory effect was blocked by iontophoretical administration of naloxone (50 mM; Fig. 1).

In 24 defense reaction related neurones in rVLM, the latencies of antidromic response evoked by stimulation of lateral horn of thoracic spinal cord (T 10) were measured. In 17 of them the latencies were prolonged by stimulation of DPN [6]. These results indicate that the DPN input could inhibit defense reaction by activa­tion of opiate receptor in the rVLM. Further experiments with electrophysiologi­cal and pharmacological techniques showed that the inhibitory effect was due to activation of a pathway from nucleus arcuatus (ARC) to neurones in the ventral PAG of midbrain which in turn project to nucleus raphe obscurus (NRO) in the medulla. From the NRO inhibitory projections were sent to spinally projecting sympathoexcitatory neurones in the nucleus paragigantocellularis lateralis of the rVLM [7-9].

Pressor Effect of Superficial Peroneal Nerve Stimulation

In the second group of rabbits, we analyzed the central cholinergic mechanism which takes part in the pressor response induced by stimulation of the superficial peroneal nerve (SPN) in normotensive and hypotensive animals. We first observed the influence of the central cholinergic system on the animals with shock. This shock was induced by occlusion and reperfusion of the superior mesenteric artery. Their mean blood pressure (MBP) dropped from 104 ± 5 mm Hg to 68 ± 5 mm Hg, and two-thirds of the rabbits died within 2 h. Intracerebroventricular injection (icv) of neostigmine (100 Ilg in 200 Ill) induced a significant pressor effect. The MBP was measured at 77 ± 5 mmHg 20 min later, and the survival time of the animals was prolonged. No rabbits had died within 2 h of treatment with neostig­mine. In normotensive rabbits, icv neostigmine also produced a pressor effect. The MBP was raised from 100± 10 mmHg to 135± 6 mmHg within 10 min, accompa­nied by a marked increase of LVP and renal nerve discharge [15].

Stimulation of the SPN (50-100 IlA, 0.5 ms, 10 Hz for 15 min) or microinjec­tion of neostigmine (0.25Ilg) or carbachol (30llg) into the rVLM produced a similar pressor response. The pressor response induced by PAG stimulation was also facilitated during this period. When atropine (0.25 Ilg) was microinjected into rVLM bilaterally, the resting blood pressure was reduced, and the pressor effect of icv neostigmine and the facilitatory effect of SPN stimulation were all abolished [14].

These results showed that the SPN input may exert a pressor effect on nor­motensive and hypotensive animals via activation of cholinergic mechanisms in the central nervous system, especially cholinergic receptors in the rVLM.

114 P. Lr

Inhibition of the Baroreflex by Stimulation of SPN

The third series of experiments was designed to detect the effect of SPN stimula­tion on the bradycardia induced by baroreflex input and to analyze its mechanism. It was confirmed that bradycardia induced by stimulation of the aortic depressor nerve or nucleus tractus solitarius (NTS) could be blocked by SPN stimulation (50-150 IlA). This effect was not changed significantly after decortication or postcollicular transection [11] but was greatly reduced following bilateral elec­trolytic lesion of rVLM or microinjection of lidocaine or atropine into the same region. In high spinal (C 1 transection) animals the bradycardiac responses to aortic nerve stimulation were also partially blocked by administration of DL-ho­mocysteic acid (DLH) or physostigmine into the rVLM. This indicates that the inhibitory effect on the baroreflex by stimulation of SPN may be mediated by activation of rVLM [12].

Electrophysiological evidence showed that the excitatory responses of barore­ceptor sensitive neurones within NTS were suppressed by prolonged stimulation of SPN or a conditioning stimulation of SPN or a conditioning stimulus delivered to the ipsi- or contralateral rVLM. Similar results were also observed after appli­cation of DLH into rVLM in baroreceptor denervated animals. These results suggest that stimulation of the SPN inhibit the evoked baroreflex by activation of a cholinergic mechanism in the rVLM (Fig. 2).

B

..... , ........

j 1,ll.~A,. A t

4; II, IL I

1 .. ~ ... ,1hl , r. ...... 1mm

Fig. 2. The inhibition of excitatory responses of vagal cardiomotor neuron within the dorsal vagal nucleus to aortic nerve stimulation. A, Poststimulus time histograms (89 sweeps, 1 ms/bin) of a unit responding to electrical stimulation of aortic nerve. Traces from top to bottom: testing stimulus of aortic nerve; testing stimulus preceded by a conditioning stimulus to the rVLM, 2 min during SPN stimulation, 8 min after SPN stimulation. t, Test stimulus; ., conditional stimulus. B, The location of electrical stimulation and DLH injection site. e, Inhibitory sites by electrical stimulation. 0, noninhibitory sites by electrical stimulation. Figures, the distance from the obex. VII, Retrofacial nucleus. [0; inferior olive; V, trigeminal tract

ACh

Mechanism of the Modulatory Effect of Somatic Nerve Input 115

Psychosocial

stress ~.--_Sl~,-ep_..,

~P MC

[

defenr area t dorsal ventral ~G ~G

t Opioids t rVLM ------- NRO

I I I I I I I I I I I

Spinal sympath. neurons

Cardiac mechano­receptor

L __ _ ------ Heart

f---__ Vessels

~----I-- Kidney

'------ IV -------- Somatic Nerve Input ------ III -------' I Acupuncture)

Fig. 3. Summary diagram to illustrate the modulatory effect of somatic afferent nerve inputs on cardiovascular center. -- Excitatory pathway; --- inhibitory pathway

Discussion

It is well established that neurones in the rVLM playa key role in setting the resting level of vasomotor tone, integrating the defense reaction, baroreceptor and chemoreceptor reflexes, etc. A number of neuroactive substances exist in this area, such as catecholamine, serotonin, acetylcholine, substance P, met- and leu­enkephalin, etc. [2]. Akaike et al. [1] showed that analgesia could be induced by microinjection of morphine into the rVLM of rat. Our present work indicates that the afferent input from group III fibers induced by DPN stimulation could inhibit the spinally projecting and defense reaction related neurones in rVLM by activating opiate receptors in the ARC-ventral PAG-NRO pathway to decrease the

116 P. LI

sympathetic outflow and produce a depressor effect or inhibitory effect on arrhythmia. Futoro-Neto and Coote [3] found that stimulation of the NRO in cat could induce a de synchronized sleeplike pattern of sympathetic activity which is just contrary to the pattern characteristic of defense arousal reaction. Guillemin et al. [4] reported that the endogenous opioids are released during stress. It seems that during the defense reaction the ARC-ventral PAG-NRO system may also be activated and opioids are released. This might be another important negative feedback mechanism other than the baroreflex in the regulation of the defense reaction (Fig. 3).

In modem society there is much psychosocial stress, and the cardiovascular responses to this are very much like those of the defense reaction in animals. It may be that the exaggerated sympathoexcitatory drive which occurs in psychosocial stress is the first sign of a developing hypertension and coronary heart disease. Furthermore, insomnia and reduced somatic nerve input due to lack of physical exercise decreases the activity of the ARC-ventral PAG-NRO system and the release of opioids into the central nervous system. Thus it is easier to develop hypertension and coronary heart disease. Acupuncture, which increases the deep somatic input may facilitate this negative feedback mechanism by releasing more endogenous opioids in the central nervous system, so alleviating some kinds of hypertension, arrhythmia, and coronary ischemia. Willete et al. [13] reported that the pressor response and tachycardia produced by microinjection of L-glutamate, physostigmine, or carbachol into rVLM bilaterally could be blocked by intra­venous injection of atropine. In our study, when the SPN was stimulated to excite group IV afferent fibers, cholinergic receptors in rVLM were activated to induce a pressor effect and the bradycardia evoked by baroreceptor input was blocked. So the rVLM is also a key area for the modulatory effect of somatic nerve stimulation or acupuncture. Further experiments are being carried on to determine whether different somatic inputs can converge on the same neurones in the rVLM to modulate its excitability.

Acknowledgement. I wish to express my appreciation to Dr. T. A. Lovick for her helpful discussion and correction of this paper.

References

1. Akaika A, Shibata T, Satoh M, Takagi H (1978) Analgesia induced by microinjection of morphine into, and electrical stimulation of the nucleus reticularis paragigantocellularis ofrat medulla oblongata. Neuropharmacology 17:775-778

2. Ciriello J, Caverson MM, Polosa C (1986) Function of the ventrolateral medulla in the control of circulation. Brain Res Rev 11:359-391

3. Futuro-Neto HA, Coote JH (1982) Desynchronized sleeplike pattern of sympathetic activity elicited by electrical stimulation of sites in the brain stem. Brain Res 252: 269-27 6

4. Guillemin R, Vargo TM, Tossier J, Mimik S, Ling N, Rivier C, Bloom FE (1977) f3-Endorphin and adrenocorticotropin are secreted concomitantly by the pituitary. Sci­ence 197:1367-1369

5. Huangfu DH, Li P (1985) The inhibitory effect of the deep peroneal nerve inputs on defence reaction elecited by brain stem stimulation. Chin J Physiol Sci 1: 176-184

Mechanism of the Modulatory Effect of Somatic Nerve Input 117

6. Huangfu DH, Li P (1986) Effect of deep peroneal nerve input on ventral medullary defence-reaction-related neurones. Chin J Physiol Sci 2: 123 -131

7. Huangfu DH, Li P (1987) The role of nucleus arcuatus in the inhibitory effect of deep peroneal nerve inputs on defence reaction. Chin J Physiol Sci 3:37-46

8. Huangfu DH, Li P (1988) Role of nucleus raphe obscurus in the inhibition of defence reaction by deep peroneal nerve stimulation. Chin J Physiol Sci 4:77-83

9. Huangfu DH, Li P (1988) The inhibitory effect of ARC-PAG-NRO system on the ventrolateral medullary neurons in the rabbit. Chin J Physiol Sci 4:115-125

10. Li P (1985) Modulatory effect of electroacupuncture on cardiovascular functions. J Tradit Chin Med 5:211-214

11. Wang Q, Li P (1987) The blocking effect of somatic inputs on bradycardia induced by stimulation of the nucleus tractus soliterius in rabbits. Chin J Physiol Sci 3: 366-373

12. Wang Q, Li P (1988) Stimulation of the ventrolateral medulla inhibits the baroreceptor input in the nucleus tractus solitarius. Brain Res 473:227-235

13. Willete RN, Pennen S, Kreiger AJ, Sapru HN (1984) Cardiovascular control by choli­noceptive mechanism in the rostral ventrolateral medulla. J Pharmacol Exp Ther 231:457-463

14. Xia QG, Lu L, Li P (1989) Role of rostral ventrolateral medulla in the pressor response to intracerebroventricular injection of neostigmine. Acta Physiol Sin 41:19-29

15. Xiao YF, Xia QG, Zong GQ, Li P (1989) Pressor response to intracerebroventricular injection of neostigmine in normotensive and hypertensive rabbits. Chin J Physiol Sci 5:26-32

16. Yao T, Anderson S, Thoren P (1982) Long lasting cardiovascular depressor response following sciatic stimulation in spontaneously hypertensive rats. Evidence for the in­volvement of central endorphin and serotonin systems. Brain Res 244:295-303

On the Existence of a Common Cardiorespiratory Network * D.W RICHTER, K.M. SPYER, M.P. GILBEY, E.B. LAWSON, C.R. BAINTON, and Z. WILHELM

Introduction

The primary function of the respiratory system, the gills and/or lungs, is to ex­change gases between the external environment and the internal milieu of the organism. The cardiovascular system assists this function by transporting blood gases - O2 from the gill/lung capillaries to the tissue capillaries and CO2 from the tissue capillaries to the gill/lung capillaries. These processes of gas exchange and gas transportation are adjusted to maintain homeostasis in varying physiological circumstances - this is the basic cardiorespiratory function.

The adjustment of "respiratory" and "cardiovascular" processes dictates the levels of motor activities, such as the respiratory pump, the heart pump and the smooth muscle tone of the vessels supplying the lung, heart, skeletal muscles, skin, brain and other organs. This demands a high degree of coordination between the nervous substrates that control these motor activities. How is this achieved? Is it simply a balancing of pump outputs determining the flow rates of air/water through the gills/lungs and the flow rate of blood through the vessels? This could be achieved easily by regulation of efferent nervous activities through a common tonic drive to independent control networks. Or is there a need for a more refined adjustment, and does cooperation mean that every single uptake/inhalation of water/air must be accompanied by an increase in stroke volume, heart rate and vasoconstriction and, conversely, every expulsion of water/exhalation of air be followed by a decrease in stroke volume, heart rate and vasodilatation? The latter seems to be true, as we know from the work of Hering (1869). Interaction between the respiratory, sympathetic and parasympathetic networks is seen during each respiratory cycle (for review see Feldman and Ellenberger 1988; Janig 1985; Richter and Spyer 1990; Spyer 1984). The most generally accepted explanation for this is a respiratory network modulation of cardiovascular - sympathetic and parasympathetic - activities either by irradiation (Schweitzer 1937) of respiratory activity to the vasomotor centres or by synaptic inputs to the cardiovascular network(s) within the brainstem and/or the spinal cord. Both mechanisms, howev­er, imply a separation of at least two i.e. respiratory and cardiovascular (sympa­thetic and parasympathetic), networks which are otherwise controlled indepen­dently.

* This research was supported by the DFG and MRC.

On the Existence of a Common Cardiorespiratory Network 119

In this contribution, we contest this interpretation and try to indicate how during phylogeny biological organisms have developed an optimized common cardiorespiratory network that controls a single cardiorespiratory function.

Phylogeny of Respiratory Motor Control and Central Respiratory Rhythm

In decapodian crayfish, bony fish, adult amphibians, reptiles, snails, birds and mammals, gas exchange is organized similarly. In the fish and larvae of amphibi­ans, fresh water flows through the gills to allow Oz uptake and then leaves the gills with a high content of COz. With the development of lungs in adult amphibians, reptiles, snails, birds and mammals, air is pumped or swallowed into the lungs, resulting in the inhalation of Oz-enriched air and exhalation of COz-enriched air.

Different muscles are used to drive water or air through the different gas exchangers. In crayfish, rhythmic movements of the second maxilla produces a flow of water through the chamber containing the gills, whilst in bony fish fresh water is swallowed and then forced through the gills by active movements of the gill plate (Ishihara 1907). In adult amphibians, respiratory movements consist of expulsions of COz-enriched air out of the lungs into the larynx where the air is then refreshed through the open nose and then sucked into the lungs again (Babak 1913). In snails, reptiles, birds and mammals, however, air flow is controlled by muscle contractions which directly move the thorax and/or the lung (see Euler 1986; Feldman 1986).

In all animal species, respiratory motor movements are controlled by nervous substrates in the supraspinal brain (Babak 1913; Heymans and Heymans 1927; Ishihara 1907). These produce rhythmic activity that leads to periodic contractions of gill, pharyngeal, laryngeal, lung or, in cooperation with spinal motoneurones, thoracic, abdominal, and diaphragmatic muscles. The demands for nervous con­trol become more complex the later the animal species appear during phylogeny. In bony fish, the nervous control is relatively simple. Mutual rhythmic discharges are required to control the movement of the gill plate (gill breathing). Different patterns of respiratory activity, however, became necessary once lungs have devel­oped. A typical example is the respiration of adult amphibians. Here, breathing movements consist of two rhythmic events that are controlled from different brain structures (Babak 1913). There are infrequent and irregular muscular contractions which compress and depress the lungs (lung breathing) and more frequent, regular and stable oscillations of laryngeal muscles which refresh the air in the larynx and pharynx (laryngeal breathing). Lung and laryngeal breathing are present also in the mammal, but the two rhythmic nervous activities are then synchronized (Fig. 1; Harding 1984). This occurs in three neutral phases: inspiration, postinspi­ration and expiration. The phases can be discerned in the motor outflow of the phrenic, intercostal and in the laryngeal nerves. The lungs are filled with fresh air during the inspiratory phase (I phase) by expansion of the thorax resulting from a steadily augmenting contraction of the diaphragm and inspiratory intercostal

120 D. W RICHTER et al.

~ I, , _____ ...........;.1 ' , ! '-~'"'---PN Int

PN mVO.1] ___ ....

RLN lnt

RLN mVO.05] ••••

TP

BP

RAT

Fig. 1. Respiratory-related discharges in an urethane-anaesthetized and artificially venti­lated rat. For further description see text. PN, Phrenic nerve discharge in original and in integrated form (index, Int); RLN, recurrent laryngeal nerve discharge in original and in integrated form (index, Int); TP, tracheal pressure; BP, blood pressure

muscles. This is seen as a crecendo-type increase of the inspiratory discharge in the phrenic nerve. Laryngeal muscles are also active during inspiration and dilate the larynx. Exhalation of air from the lungs is achieved passively by the recoil forces of the lung tissue and the thorax. During this initial phase of exhalation there are no active expiratory movements. Lung volume is rather held by active contractions of the diaphragm which is determined by a postinspiratory "after-discharge" in the phrenic nerves (Remmers and Bartlett 1977). During the postinspiratory (p-I) phase, expiratory airflow is retarded by contraction of laryngeal adductor muscles, and this activation is visible in the enhancement of the recurrent laryngeal nerve discharge (Fig. 1). Under certain conditions, such as during work, expiratory airflow is enforced by an active contraction of expiratory intercostal and abdom­inal muscles. This phase is termed the secondary phase of expiration (E2 phase) as it follows the initial p-I phase. During quite breathing, and also under states of excitement or severe pain (rapid shallow breathing) or in some animals during thermoregulation (panting), the E2 phase is often absent (Richter and Spyer 1989). Under such conditions, rhythmic breathing occurs in just two phases - inspiration and postinspiration.

In mammals, the efferent nervous outflow to the various respiratory muscles is ultimately controlled by neurones localized in the lower brain stem (for review see Euler 1986; Feldman 1986; Richter et al. 1986). These comprise early-inspiratory (e-I), ramp inspiratory (r-I), late -inspiratory (1-1), p-I and E2 neurones. There is an essential difference in the discharge of these neurones: e-I and p-I neurones have a rapid onset and then declining pattern of action potential discharge, whereas r-I, I-I, and Ez neurones show an augmenting pattern of action potential discharge.

On the Existence of a Common Cardiorespiratory Network 121

A rapid onset followed by a declining discharge is also typical of laryngeal and glossopharyngeal motoneurones innervating the muscles of branchial origin. We would, therefore, speculate that a declining pattern of discharge is a characteristic of the bulbar "branchial/gill system", whilst an augmenting pattern is typical for the bulbospinal "lung system". Thus we believe that in the mammal, the phyloge­netic younger nervous substrate for lung breathing has developed from the phylo­genetically older nervous substrate that regulates gill/laryngeal breathing.

Respiratory Activity Patterns in the Autonomic Discharge

There is evidence that the outflow to the cardiovascular system from the autonom­ic nerves is modulated by central respiratory activity but not simply as a conse­quence of irradiation of inspiratory activity. Renal, cardiac, cervical and abdom­inal sympathetic nerves of many species and vagal cardioinhibitory efferents show respiratory related patterns of discharge and it is this powerful respiratory dis­charge which suggests their cardiovascular function (see:'Janig 1985). All of these activity patterns become evident in newborn and adult mammals (Lawson et aI. 1989a; Richter 1982; Richter et aI. 1986). The discharge of cervical, cardiac and renal pre- and postganglionic sympathetic neurones in the cat show not just activation during late inspiration, but also depression during early and postinspi­ration (Fig. 6 B). A complementary, though opposite behaviour is seen in some sympathetic nerves of the rat (Fig. 2). In the cervical sympathetic nerve, there is e-I depression and p-I augmentation of discharge, and cardiac, splanchnic, adrenal and renal nerves show e-I augmentation (see: Czyzyk et aI. 1987; Gilbey et aI. 1986; Haselton and Guyenet 1989; Numao et aI. 1987).

Most observations on respiratory modulation of sympathetic activity have in­volved whole nerve recordings, with the inherent problem of simultaneous dis­charge of a heterogenous mixture of functional elements, many of which are not associated with the cardiovascular system. The more precise data, therefore, come from studies on single pre- and postganglionic neurones (see Polo sa et aI. 1980; Gilbey et aI. 1986; Janig 1985). But also here, the various patterns of respiratory activity can be discerned to different degrees in the discharge of single neurones. In addition, these neurones also receive tonic inputs independent of respiratory activity. As an example, Fig. 3 gives evidence from intracellular recordings that some thoracic preganglionic sympathetic neurones of the cat are depolarized during inspiration (see also Dembowsky et aI., personal communication).

In this respect, these preganglionic sympathetic neurones behave similarly to respiratory neurones, such as phrenic motoneurones (Fig. 3 A) and to some extent as bulbospinal inspiratory neurones. However, there are no clear signs of e-I and p-I inhibition in the single neurone (see also: Dembowsky et aI., this meeting) which would be comparable to that seen in propriobulbar I-I neurones (Fig. 3B). Depression of activity in the whole cardiac sympathetic nerve indicates that pre­ganglionic sympathetic neurones receive an excitatory input from bulbospinal neurones whose discharge is modulated by various forms of synaptic inhibition from propriobulbar neurones (see below).

122 D. W RICHTER et al.

A B

CeSN mvllO']~._IJ."I"IP •• t._.,,,, ]~.-+~ ........ ~ Int. CeSN

PN mYOD5] --.----~.I_.--" " C D

CeSN mYoo.] !~'_~ •. I~.I_'II."~II~ ] ... tIIJ •• ' '-"'1 •• • ~ ':.1,', Int. CeSN ,))~,jJ~,~\~,It~~~M~1,,~,~'/l..,!,4~~I~~1PN~~~ ~\~'~"J;\}~~~~~J.I,.,,,,"-i,l,,}\.-"))I).J#~''w).:'-PN mvO.05] ____________ _

" Fig. 2. Changes of respiratory activity patterns in the cervical sympathetic nerve of an urethane-anaesthetized and artificially ventilated rat during control (A), isocapnic hypoxia (B), hypoxia induced "central apnea" (C) and during recovery (D). For further description see text. CeSN, Cervical sympathetic nerve discharge in original and in integrated form (Int. CeSN); PN, phrenic nerve discharge. Peripheral baroreceptors were left intact. The arterial blood pressure did not reveal respiratory pressure waves

A

5mv[)\JlJl

C MPPN

MPSymp

PN 0.1 mV[ .,. ... " B PN (t) ~

PN 0.1 mv[ ,

10mv[~ 200[

MPbulb BP mmHg .-mN

100

2s' L...--....J 2,5 s

Fig. 3. Comparison of the membrane potential trajectories of a C5 phrenic motoneurone (A), bulbar late-inspiratory neurone (B) and thoracic preganglionic sympathetic neurone (C) in pentobarbital-anaesthetized and artificially ventilated cats. MP, Membrane potential; PN, phrenic nerve discharge in original and its integrated form; BP, arterial blood pressure. For further description see text

On the Existence of a Common Cardiorespiratory Network 123

Fig. 4. Comparison of the membrane potential trajecto­ries of a vagal cardioin­hibitory neurone (A) and a bulbar postinspiratory neu­

A

PN rone (C) at control and after IPSP reversal following in­tracellular chloride injection C (B, D, respectively) in pento­barbital-anaesthetized and artificially ventilated cats. MP, Membrane potential; PN, phrenic nerve discharge. For further description see text

PN

l......J

4 s

'-' 1 s

B

5

D

.-The most clear evidence for respiratory patterning in autonomic discharge has

come from studies on vagal cardioinhibitory neurones in the cat. Here, there is evidence of e-I and Ez inhibition (Gilbey et al. 1984). The similarity of synaptic events in vagal cardiomotor neurones to those in propriobulbar postinspiratory and laryngeal adductor neurones is illustrated in Fig. 4. Vagal cardioinhibitory neurones show also membrane depolarization at the onset of the p-I phase the steepness of which is greatly reduced when the inhibitory postsynaptic potentials are reversed by intracellular chloride injection (Fig. 4 B, D). This indicates the significance of rebound excitation following release from membrane hyperpolar­ization (Richter and Spyer 1989; Richter et al. 1986).

Afferent Control of Cardiorespiratory Activities

Whilst certain of these patterns of discharge in the autonomic outflows may be dependent on reflex inputs arising from the arterial baroreceptors and chemore­ceptors, they are clearly in part independent of these since the various phases of excitation and inhibition - albeit in modified form - remain in the sinoaortically denervated animal. Equally, the efficacy of these reflexes are modified by respira­tory activity, and they affect respiratory activity.

Baroreceptor Afferents. The tonic input to preganglionic sympathetic neurones of the dog and the cat is more sensitively controlled by the afferent discharge of

124 D. W RICHTER et al.

arterial baroreceptors than are the respiratory discharge components. Stimulation of baroreceptor afferents blocks the tonic component, leaving an almost pure respiratory discharge pattern with inspiratory augmentation and postinspiratory depression (see Seller et al. 1968). Also the baroreflex itself reveals respiratory modulation in its effectiveness (Koepchen et al. 1961; Seller et al. 1968). The reflex is most effective during the p-I phase and almost ineffective during e-I. This demonstrates that medullary integration of the baroreceptor afferent activity is affected by the bulbar respiratory network. Respiratory neurones themselves are affected by baroreceptor afferent activity. Inspiratory neurones are inhibited, and Ez neurones reveal disinhibition during the time they are inhibited by e-I neurones (Ballantyne and Richter 1986; Richter and Seller 1975). These findings indicate that baroreceptor afferents affect both e-I and p-I neurones, populations of neu­rones known to be directly involved in respiratory rhythm generation (Richter et al. 1986).

Chemoreceptor Afferents. The activity of respiratory neurones is sensitively con­trolled by the afferent discharge of peripheral chemoreceptors. With the exception of e-I neurones, which are inhibited, r-I, p-I and Ez neurones are excited (Lawson et al. 1989 b). Corresponding influences are observed in the sympathetic discharge. In the cervical sympathetic nerve of the rat, inspiratory depression and p-I activa­tion become more pronounced during hypoxia or asphyxia (Fig. 2 B). During hypoxic apnea, there is no such modulation of the sympathetic discharge (Fig.2e).

Specificity of Rhythmic Activities

The most obvious rhythmic activity patterns in autonomic nerve discharge are activity fluctuations which occur in synchrony with the arterial blood pressure and breathing movements. Pulse rhythmic variations in their discharge disappear after denervation of arterial baroreceptors and are therefore explained by the barore­flex. Respiratory modulated activity becomes more evident after barodenervation and remains as long as there is a respiratory output in the phrenic nerve. Oscilla­tions of a similar frequency range are found to persist in the sympathetic output even when phrenic nerve discharge is temporarily stopped by different manoeuvres (Barman and Gebber 1976; Koepchen 1962; Koepchen et al. 1987). This is taken as evidence for the existence of a "central oscillatory process" which activates both the sympathetic system (at a lower threshold of activation) and respiratory system (at a higher threshold of activation; Koepchen et al. 1981, 1987; Trebski and Kubin 1981).

We want to add two further observations to complete these descriptions of central cardiorespiratory oscillations. The first deals with the state of the respira­tory network during a presumed central apnea as indicated by the silence in the phrenic nerve output. Here rhythmic activity often persists in the laryngeal net­work. This is illustrated in Fig. 5, where phrenic nerve discharge was effectively blocked by stimulating laryngeal receptors in a newborn minipig.

On the Existence of a Common Cardiorespiratory Network 125

Fig. 5A, B. Specificity of A B contrOl rhythmic activity patterns.

~ A The respiratory rhythm ~ + as determined by recur- RLN rent laryngeal (RLN) and

~~ M'''~_.~l phrenic nerve discharges RLN InI (PN) during "central ap-nea" evoked by smoke PN rll '-..!~.

~ i,!~~: stimulation of laryngeal PN afferents in a pentobarbi- TP D .1JJ Ii.1.1 J.JJ J.1.1,

tal-anaesthetized and 2% halothane

artificially ventilated 19- .. 4 .-day-old minipig. TP, Tra-cheal pressure. B Block-ade of cervical sympa- ~vvJo/I.W thetic nerve discharge .----.:_ \. (eN) and persistence of [ "11111~"I"II"II~"I"'l'''''I' I "''' ' ''''''''''"' phrenic nerve discharge :_. __ . lL'.1.' L·,iJ.1 11 111111 Ululllimllijl ~11Iilllllll ' l1hIIIlU IIII (PN) after inhalation of 2% halothane in a pento- recovery

barbital-anaesthetized PN • • • • and artificially ventilated 0.1 mY [

cat. The arterial blood

'NJv,~",.JJ. WI\} 111",:.,,,,,,11". pressure was held at con- eN trol levels by intravenous

I : : .. ' r ' infusion of dopamine. ------ --. - - ~

0.2mY [ BP, Arterial blood pres-

6mmHg [ .: ';:;' ':::. sure. For further descrip-tion see text Ss

Smoke inhalation in the larynx blocked phrenic nerve discharge, but the recur­rent laryngeal nerves continued to discharge rhythmically after a transient period of tonic discharge. This laryngeal rhythm was quite regular and not dissimilar to the frequency of the preceding rhythmic discharge in the phrenic nerve. Later rhythmic phrenic activity reappeared but was coupled with the laryngeal rhythm in a 1 : 3 or 1 : 4 relationship before it again became more regular and coupled in with a 1 : 1 relationship. Hence, arrest of the respiratory spinal output revealed the persistence of a laryngeal rhythm that appears to underly the bUlbospinal lung rhythm (as determined by the phrenic nerve discharge).

The second observation deals with the notion of a differential threshold for activation in the cardiovascular and respiratory systems. The example illustrated in Fig. 6 B shows that the sympathetic output is more sensitive to blockade by anaesthetics than the respiratory output. Cardiac sympathetic nerve discharge is completely blocked by halothane inhalation whilst the phrenic nerve discharge persists when the arterial blood pressure is held at control levels by dopamine infusion, It is noteworthy that respiratory related modulations of discharge were seen from the very onset of recovery of sympathetic discharge.

126 D. W. RICHTER et al.

A Common Cardiorespiratory Network

We have described comparable activity patterns in (a) e-I interneurones and laryn­geal abductor motoneurones with a similar pattern of inhibition/depression in I-I neurones, p-I neurones, laryngeal adductor motoneurones and in the sympathetic nerve discharge, (b) p-I interneurones, laryngeal adductor motoneurones and in vagal cardiomotor neurones with a similar pattern of inhibition/depression in e-I neurones, r-I neurones, I-I neurones, laryngeal abductor motoneurones, and in sympathetic nerve discharge, and (c) inspiratory ramp interneurones, inspiratory bulbospinal neurones, phrenic neurones and preganglionic sympathetic neurones (compare Bainton et al. 1985; Gilbey et al. 1986; McAllen 1987). This striking similarity in the central control of respiratory and cardiovascular functions en­forces our belief that it is inappropriate to consider the control of one of the two systems separately.

We now try to explain how we believe the adjustment of the cardiorespiratory function is controlled by one common "cardiorespiratory network" that deter­mines ventilation, cardiac output and peripheral blood flow. Our observations of a high degree of similarity in the central control of respiratory and cardiovascular outputs together with the conclusion that a laryngeal rhythm underlies the lung rhythm and phylogenetic considerations have led us to speculate about the neu­ronal principles of cardiorespiratory control.

We propose a two-component cardiorespiratory network that has developed successively during phylogeny: In the fish, there was need for a network that elicited a rhythm for opening and closing the gills. Later, during phylogeny as in the adult amphibian, we see the development of two rhythms. The former gill rhythm controls regular laryngeal breathing, and a more irregular rhythm is developed for lung breathing. The neuronal network for gill/laryngeal breathing must contain excitatory components which activate output elements projecting to the gill/laryngeal muscles, and inhibitory elements which transmit mutual inhibi­tion to the antagonistic population of neurones (Fig. 6 B). This gill/laryngeal net­work must also possess a stable rhythm-generating mechanism which we believe becomes visible in the characteristic discharge behaviour of the neurones that have a rapid onset of activity (probably involving rebound excitation) and a pro­nounced degree of adaptation that leads to the steady decline of discharge (Fig.6A). This phylogenetically older network is probably still present in the mammal (Heymans and Heymans 1927) and performs the same task - the primary rhythm generation of respiration. In the mammal this involves e-I and p-I neu­rones, which are termed on the basis of the temporal relationship of their discharge to the spinal respiratory output. A similar activity pattern is seen in laryngeal motoneurones/muscles which control expiratory airflow during normal breathing (Harding 1984).

The autonomic nervous system must also be controlled by this primary rhythm generator. And indeed, in the sympathetic system this becomes evident in the e-I and p-I depression of its discharge. The vagal cardiomotor control elements within the brainstem of cats, are inhibited by e-I neurones and are possibly activated by p-I neurones.

A

Insp.

On the Existence of a Common Cardiorespiratory Network 127

Post-I. EXP2

IP$Ps

B Gill Rhythm

t.ung Patlern

cxdlalory inhibilllry

[J={> Fig. 6. Scheme of a common cardiorespiratory network within the brainstem of cats (B) and membrane potential trajectories of its principle neurones (A). The scheme indicates a cir· cuitry of synaptic interconnections between the neurones of a system responsible for the gill rhythm and a system controlling lung breathing. Stage 2 expiratory neurones are not consid­ered in this scheme. LAR, Laryngeal adductor (index, ad) and abductor (index, ab) motoneu­rones; SYM, sympathetic output; CVM, cardiomotor vagal output; Ibs , inspiratory bul­bospinal output; e-I, early-inspiratory neurones; p-I, postinspiratory neurones; r-I, ramp-in­spiratory neurones; /-1, late-inspiratory neurones. A fuller description is provided in the text

The phylogenetically younger component of the network developed together with lung breathing and required a mechanism for augmenting inspiratory activity to produce a steadily increasing lung volume. Augmenting inspiratory activity (Fig. 6 A) is most probably produced by recurrent excitation within a population of r-I neurones (see Euler 1986; Feldman 1986). The population of inspiratory neurones therefore becomes part of a pattern generator for lung breathing. The output of this lung system are the bulbospinal inspiratory neurones which finally activate spinal inspiratory motoneurones for lung breathing. E2 neurones are part of the pattern generating network (not illustrated in Fig. 6). Their discharge pat­tern seems to result from intrinsic or extrinsic tonic activation and periodic inhibi­tion during e-I and p-I (Ballantyne and Richter 1986).

The older and obviously more dominant rhythm generating network exerts an essential influence upon this younger pattern-generating network (see below; Fig. 6), and some reciprocal influences are fed back to this network during the E2 phase. The most essential coupling between the rhythm generator and the pattern

128 D. W RICHTER et al.

generator becomes evident in the timing process of the inspiratory off-switch mechanism. r-I neurones of the pattern-generating network activate I-I neurones which are assumed to produce the first and still reversible inspiratory off-switch (see Euler 1986). The onset of discharge of these 1-1 neurones, however, depends on the rate of adaptation of e-I neurones. Inspiration is then irreversibly brought to an end by p-I inhibition when the primary rhythm generator has moved to its antagonistic phase. The discharge of E2 neurons is also controlled by e-I and p-I inhibition. As seen during rapid shallow breathing or panting, E2 neurones can be totally blocked by the rhythm generator. The respiratory rhythm is then limited to two phases.

The pattern generating network exerts an excitatory influence upon the bul­bospinal sympathetic outflow. This is seen in an augmentation of activity during inspiration and possibly during stage 2 expiration (see Bainton et al. 1985). The vagal cardiomotor system does not seem to be affected by the pattern generator.

Comments

Of particular significance is the consequence of disturbance of the cardiorespira­tory rhythm. Any direct disturbance of the rhythm generator, or of its interaction with the pattern generator must have pathophysiological significance. This might occur at birth when the newborn has initiated regular lung breathing under isocap­nic and normoxic conditions, or whenever peripheral afferents from the larynx are activated (see Remmers et al. 1986; Richter et al. 1987). Whatever processes stabilize e-I and p-I phases of the rhythm generator must also depress the pattern generator and consequently lung breathing. The former would result in an apneu­sis accompanied by tachycardia and the latter in an apnea with bradycardia.

References

Babak E (1921) Die funktionelle Charakterisierung des Kehlatemzentrums der Amphibien. Fischer, Jena (Handbuch der vergleichenden Physiologie, Vol I)

Bainton CR, Richter DW, Seller H, Ballantyne D, Klein JP (1985) Respiratory modulation of sympathetic activity. J Auton Nerv Syst 12:77-90

Ballantyne D, Richter DW (1986) The non-uniform character of inhibitory synaptic activity in expiratory bulbospinal neurones of the cat. J Physiol (Lond) 370:433-456

Barman SM, Gebber GL (1976) Basis for synchronization of sympathetic and phrenic nerve discharges. Am J PhysioI231:1601-1607

Czyzyk MF, Fedorko L, Trzebski A (1987) Pattern of the respiratory modulation of the sympathetic activity is species dependent: synchronization of the sympathetic outflow over the respiratory cycle in the rat. In: Cinello J, Cateresu FR, Renaud LP, Polosa C (eds) Organization of the autonomic nervous system: central and peripheral mechanisms. Liss, New York, pp 143-152

Euler C von (1986) Brain stem mechanisms for generation and control of breathing pattern. In: Fishman AP, Cherniack NS, Widdicombe JG, Geiger SR (eds) The respiratory system, American Physiological Society, Bethesda, pp 1-67 (Handbook of physiology, sect III)

On the Existence of a Common Cardiorespiratory Network 129

Feldman JL (1986) Neurophysiology of breathing in mammals. In: Bloom FE (ed) The nervous system, American Physiological Society, Bethesda, pp 463-524 (Handbook of physiology, sect I)

Feldman JL, Ellenberger HH (1988) Central coordination of respiratory and cardiovascular control in mammals. Annu Rev Physiol 50: 593 -606

Gilbey MP, Jordan D, Richter DW, Spyer KM (1984) Synaptic mechanisms involved in the inspiratory modulation of vagal cardio-inhibitory neurones in the cat. J Physiol (Lond) 356:65-78

Gilbey MP, Numao Y, Spyer KM (1986) Discharge patterns of cervical sympathetic pregan­glionic neurones related to central respiratory drive in the rat. J Physiol (Lond) 378:253-265

Harding R (1984) Function of the larynx in the fetus and newborn. Annu Rev Physiol 46:645-659

Haselton JR, Guyenet PG (1989) Central respiratory modulation ofmeduilary sympathoex­citatory neurons in rat. Am J Physiol 256:R739-750

Hering E (1869) Uber den EinfluJ3 der Atmung auf den Kreislauf. 1. Uber Athembewegungen des GefiiJ3systems. Sber Akad Wiss. Vienna, Math Nat Kl II Abtl 60: 829-856

Heymans JF, Heymans C (1927) Sur les modifications directes et sur la regulation reflexe de l'activite du centre respiratoire de la tete isolee du chien. Arch Int Pharmacodyn 33:273-370

Ishihara M (1907) Bemerkungen tiber die Atmung der Fische. Zentralbl PhysioI20:157-169 Jiinig W (1985) Organization of the lumbar sympathetic outflow to skeletal muscle and skin

of the cat hindlimb and tail. Rev Physiol Biochem Pharmacol 102: 119 - 213 Koepchen HP (1962) Die Blutdruckrhythmik. Steinkopff, Darmstadt Koepchen HP, Wagner P, Lux HD (1961) Uber die Zusammenhiinge zwischen zentraler

Erregbarkeit, reflektorischem Tonus und Atemrhythmus bei der nerv6sen Steuerung der Herzfrequenz. Pflugers Arch 273:443-465

Koepchen HP, KliiJ3endorfD, Sommer D (1981) Neurophysiological background of central neural cardiovascular-respiratory coordination. Basic remarks and experimental ap­proach. J Auton Nerv Syst 3:336-368

Koepchen HP, Abel HH, KltiJ3endorf D (1987) Brain stem generation of specific and non­specific rhythms. In: Cinello J, Caleresu FR, Renaud LP, Polosa C (eds) Organization of the autonomic nervous system: central and peripheral mechanisms. Liss, New York, pp 179-188

Lawson EE, Richter DW, Ballantyne D, Lalley PM (1989 a) Peripheral chemoreceptor inputs to medullary inspiratory and postinspiratory neurons of cats. Pflugers Arch 414: 523-533

Lawson EE, Richter DW, Bischoff A (1989b) Intracellular recordings of medullary respira­tory neurones in the lateral medulla of piglets. J Appl Physiol 66:983-988

McAllen RM (1987) Central respiratory modulation of subretrofacial bulbospinal neurones in the cat. J Physiol (Lond) 388:533-545

Numao Y, Koshiya N, Gilbey MP, Spyer KM (1987) Central respiratory drive-related activity in sympathetic nerves of the rat: the regional differences. Neurosci Lett 81: 279-284

Polo sa C, Berber U, Schondorf R (1980) Central mechanisms of interaction between sympa­thetic preganglionic neurones and the respiratory oscillator. In: Koepchen HP, Hilton SM, Trzebski A (eds) Central interaction between respiratory and cardiovascular control systems. Springer, Berlin Heidelberg New York, pp 137-143

Remmers JE, Bartlett D J r (1977) Reflex control of expiratory airflow and duration. J Appl Physiol (Respirat Environ Exercise Physiol) 42: 80-87

Remmers JE, Richter DW, Ballantyne D, Bainton CR, Klein JP (1986) Reflex prolongation of the stage I of expiration. Pflugers Arch 407: 190-198

Richter DW (1982) Generation and maintenance of the respiratory rhythm. J Exp Bioi 100:93-107

Richter DW, Seller H (1975) Baroreceptor effects on medullary respiratory neurones of the cat. Brain Res 86:168-171

130 D. W RICHTER et al.: On the Existence of a Common Cardiorespiratory Network

Richter DW, Ballantyne D, Remmers JE (1986) Respiratory rhythm generation: a model. NIPS 1:109-112

Richter DW, Ballantyne D, Remmers JE (1987) The differential organization of medullary post-inspiratory activities. Pflugers Arch 410:420-427

Richter DW, Spyer KM (1980) Cardio-respiratory control. In: Loewy AD, Spyer KM (eds) Central regulation of autonomic functions. Oxford University Press, New York, pp. 189-207

Schweitzer A (1937) Die Irradiation autonomer Reflexe. Karger, Basel Seller H, Langhorst P, Richter D, Koepchen HP (1968) Uber die Abhangigkeit der pressore­

ceptorischen Hemmung des Sympathicus von der Atemphase und ihre Auswirkung in der Vasomotorik. Pflugers Arch 302:300-314

Spyer KM (1984) Central control of the cardiovascular system. In: Porter PF (ed) Recent advances in physiology. Raven, Edinburgh, pp 163-200

Trzebski A, Kubin L (1981) Is the central inspiratory activity responsible for pC02-depen­dent drive of the sympathetic discharge. J Auton Nerv Syst 3:401-420

Cooperativity in Distributed Respiratory and Cardiovascular-Related Brainstem Neural Assemblies: Insights from Many-Neuron Recordings * B. G. LINDSEY, Y M. HERNANDEZ, and R. SHANNON

Introduction

Baroreceptor stimulation causes a decline in respiratory frequency and tidal vol­ume [16]. The brainstem mechanisms that mediate these changes are not well understood [2,3,16]. Neurons distributed in the nucleus tractus solitarius and both midline and ventrolateral regions of the medulla have been implicated in the regulation of breathing and cardiovascular control [1-5, 12, 13, 15, 18]. Our working hypothesis is that the bulbospinal projections that control the muscles of breathing and the sympathetic innervation of the cardiovascular system are regu­lated by a shared, dynamically organized, distributed neural network. The study of emergent network properties and processes requires the ability to represent the state (e.g., degree of synchrony) of subsets of neural assemblies as they interact. Traditional methods lack the spatial and temporal resolution needed for this task. In this preliminary report, we describe the use of many-neuron recordings and quantitative analytical methods to detect and evaluate functional connectivity and cooperative behavior among brainstem cardiorespiratory neurons. The data ob­tained with this approach support the hypothesis that a distributed neural network in the midline of the medulla contributes to both the stability of the breathing pattern and to changes in that pattern associated with altered baroreceptor activ­ity.

Methods

Twelve cats were anesthetized (Dial), bilaterally vagotomized, paralyzed, ventilat­ed, and maintained as previously described [10, 19]. Spike trains of 80 groups of 4-12 simultaneously monitored neurons were recorded with multiple arrays of tungsten microelectrodes placed in the region of the retrofacial nucleus in the rostral ventrolateral medulla, the region of nucleus raphe obscurus, and the mid­line at the pontine-medullary border. Timing pulses derived from "integrated" efferent phrenic nerve activity and arterial blood pressure were used to define

* This research was supported by NIH grant NS 19814.

132 B. G. LINDSEY et al.

changes in mean firing rates synchronized with either the respiratory or cardiac cycle. Arterial blood pressure was increased by inflation of an embolectomy catheter placed in the descending aorta [1]. Xscope (Miller and Lindsey, unpub­lished), an interactive computer graphics program that runs under the X 11 win­dow system, was used (a) to screen spike train data sets for concurrent long-time scale (seconds-minutes) alterations in neuronal activities and respiratory rhythm associated with changes in blood pressure and (b) to segment data according to respiratory phase or stimulus conditions for subsequent evaluation with cross-cor­relation and gravitational clustering analysis [7, 10, 11].

Results

Figure 1 shows an Xscope representation of nine simultaneously monitored medullary neuron spike trains, arterial blood pressure, and integrated phrenic activity. Impulses of neurons (codes) 1, 3, 5, and 8 were recorded in the rostral ventrolateral medulla. Neurons 2 and 9 were monitored 3.0 mm rostral to the obex in the region of nucleus raphe obscurus; cells 11, 13, and 14 were recorded in the midline at the pontine-medullary border. Neurons 8, 11, 13, and 14 exhibited reductions in activity associated with periods of increased arterial pressure: the firing rate of cell 2 increased. These changes were accompanied by a reduction in the amplitude of integrated phrenic efferent activity and a reduction in respiratory frequency. In this sample, none of the neurons exhibited short-time scale correla­tions with any other cell.

Elements of other groups similarly tested exhibited evidence for shared inputs and/or paucisynaptic cross-connections (not shown). For example, two neurons recorded simultaneously in the region of nucleus raphe obscurus (3 mm rostral to the obex) responded oppositely to an increase in blood pressure. The firing rate of the neuron with an augmenting expiratory (E) discharge pattern increased when the blood pressure increased. The respiratory modulated discharge pattern of this cell was similar to that of neurons previously postulated to inhibit neurons in the region of the retrofacial nucleus on the basis of spike train cross-correlation data [9]. A second neuron with an augmenting inspiratory (I) discharge pattern re­sponded to the increase in blood pressure with a decrease in activity. The results of cross-correlation of the two spike trains were consistent with inhibition of the expiratory modulated neuron by the inspiratory modulated cell. This interpreta­tion would suggest that disinhibition played a role in generating the increased activity in the expiratory modulated cell.

The data in Figs. 2, 3, and 4 document evidence for cooperative interactions among another distributed group of brainstem midline cardiorespiratory neurons. Neurons 2, 6, and 7 were monitored at the pontine-medullary border; cells 1, 3, 4, 5, and 8 were recorded in the region of nucleus raphe obscurus, 5 mm caudal to the other neurons. The stack of cycle triggered histograms (Fig. 2) shows that the mean firing rates of cells 6 and 7 declined around the I -E phase transition; the other six neurons were classified as "not respiratory related".

Cooperativity: Insights from Many-Neuron Recordings 133

u

l'

[ur •• ", r •• t tt ... : Su· ••• W.dth: .......

Fig. 1. Copy of computer graphics display showing Xscope representation of nine simulta­neously recorded brains tern neuron spike trains, arterial blood pressure (second from bottom), and integrated phrenic activity (bottom trace). Screen width, 40.0 s

Spike train data from the eight neurons recorded during the E-I and I-E phase transitions were analyzed separately [7, 11]. Figure 3 (left) shows the projected trajectories of eight particles representing the neurons during the E-I phase transi­tion. Note two groups of particles clustered as the analysis progressed: particles 6 and 7 formed one group, and particles 2, 3, 5 and 8 formed the other. During the I-E phase transition, some neurons had different relationships (Fig. 3, right). Particles 3 and 5 (arrows) did not aggregate, indicating a change in the syn­chronous activity in the corresponding neurons.

Additional data provided evidence for antagonistic reciprocal connections between the two groups of neurons represented by the identified clusters. The cross-correlogram (Fig. 4, left) calculated for spike trains represented by particles 5 and 6 documents a transient decrease in the firing rate of neuron 6 following spikes in cell 5 and an increase in the firing rate of cell 5 following spikes in cell 6 (as seen by "reading" the histogram from right to left). Such spike-triggered averages of changes in firing rate often revealed evidence for synaptic actions of midline neurons with durations of tens of milliseconds.

134 B. G. LINDSEY et al.

38 . Spikes per Seco,..,d

Tuna (sec,)

E-I Phase Transition

129.3 sec.

Fig. 2. A "stack" of cycle triggered his­tograms generated from eight simulta­neously monitored single neurons (1 - 8) and many-neuron efferent phrenic ac­tivity. Bin widths, 40 ms; 480 cycles averaged

I-E Phase Transition

129.3 sec.

Fig. 3. Gravitational representation of spike train from eight simultaneously monitored single neurons. Both panels show the "trails" of particle trajectories during analysis of the two 129.3-s samples. In each case, spikes recorded during a particular phase of 140 successive respiratory cycles were selected and concatenated for gravity analysis [7, 11]. Left panel, generated from spikes monitored during the expiratory-inspiratory (E-1) phase transition, indicates (a) aggregation of particles into two clusters and (b) movement of the two clusters toward each other. Right panel, from spikes recorded during the inspiratory-expiratory (I-E) phase transition, indicates the failure of one group to cluster. The same parameters were used for both calculations: Acceptor decay forward; effector decay backward; charge decay time constant 6.0 ms; no rate normalization

Cooperativity: Insights from Many-Neuron Recordings 135

-275.0

5~6

5.8 SPIKE?/SEC

275.0 MS

Mechanism for Ensemble Stability

Excitatory --0 Inhibitory -e

Fig. 4. Left, cross-correlogram with shift predictor control calculated for a pair of neurons consisting of one cell from each "group" defined by the gravity representation. Bin width, 5.5 ms. A peak with a negative lag with respect to the origin and a trough with a positive lag are apparent. Both features are significant according to several criteria [10]. Right, scheme summarizing inferred antagonistic reciprocal connectivity between the two distributed groups of synchronously discharging neurons. See text

In another case, cross-correlation analysis of respiratory-phase segmented spike train data from midline inspiratory neurons revealed that the short-time scale synchrony of the neurons was similar during both inspiration and expiration. This result suggests that the synchrony was not dependent upon a shared influence active only during the inspiratory phase of the respiratory cycle.

Discussion

The results provide evidence for mutually dependent cooperative behavior be­tween spatially distributed neural assemblies; they are consistent with several hypotheses.

1. Antagonistic reciprocal functional links between elements of different sub­assemblies may operate to stabilize the activity of the group as a whole. This arrangement promotes the maintenance of a particular state or level of activity: a tendency for one subassembly to change will be countered by the resultant effect on the other (Fig. 4, right). Such a mechanism could contribute to the stability of the breathing rhythm and help to define a set point for tonic sympathetic activity in cardiovascular regulation. This scheme also allows various normal or patholog­ical biasing mechanisms to alter the activity of the assembly.

2. We have previously reported evidence for network interactions among ipsilat­eral neurons within the ventrolateral medulla that are appropriate for roles in the development, termination, and modulation of each phase of breathing [8, 10, 19]. Subsequent work suggested functional connections for (a) synchronization of bilaterally distributed rhythm-generating processes, (b) shaping respiratory modu-

136 B. G. LINDSEY et aJ.

Mldll •• Ro,.ral

V,.uol.cenl ".td.lI.

N .. -."­V To.le Cardlo-

'.hlbU." --e r .. lplra:lory Nt .. l'o_

.aroRuplo, Exdlalloa or DIII.hlblelolll

Fig. 5. Scheme sum­marizing several ob­servations consistent with parallel paths for the influence of baroreceptors on the breathing pattern. See text

lated discharge patterns of midline medullary neurons by both lateral respiratory neurons and other midline cells, and (c) modulation of ventrolateral medullary neurons by midline cells [6, 9]. When considered together with the results of others [12, 17], these data suggest sites of inhibitory control of inspiratory and expiratory neurons in the ventrolateral medullary network appropriate for regulating both tidal volume and respiratory frequency (Fig. 5).

3. Neurons that increase their activity in response to a rise in blood pressure may be sympathoinhibitory, whereas cells that decrease their activity may be sympa­thoexcitatory. The present results support the view that such neurons have actions within the brain stem as well as at spinal levels [1 , 15].

4. Changes in synchrony within a group of neurons without a concomitant change in mean firing rate of each cell allows differential control of parallel channels from the group to its targets. For example, a change in population synchrony with little or no change in mean rate could alter spatial summation at one target without changing frequency dependent cotransmitter release at the synapses of the individual neurons at another target site [14] .

5. The short-time scale changes in firing rate revealed by spike triggered averag­ing suggest (a) changes in conductance at individual synapses lasting tens of milliseconds and/or (b) significant presynaptic synchronization.

Acknowledgements. We thank J. Gilliland and C. Orsini for technical assistance and K. Morris and J. Miller for software development.

Cooperativity: Insights from Many-Neuron Recordings 137

References

1. Barman SM, Gebber GL (1989) Lateral tegmental field neurons of the cat medulla: a source of basel activity of raphespinal Sympathoinhibitory neurons. J Neurophysiol 61:1011-1024

2. Ciriello J, Caverson MM, Polo sa C (1986) Function of the ventrolateral medulla in the control of the circulation. Brain Res Rev 11: 359-391

3. Feldman JL, Ellenberger HH (1988) Central coordination of respiratory and cardiovas­cular control in mammals. Annu Rev Physiol 50:593-606

4. Holtman JR Jr, Dick TE, Berger AJ (1986) Involvement of serotonin in the excitation of phrenic motoneurons evoked by stimulation of the raphe obscurus. J Neurosci 6:1185-1193

5. Lalley PM (1986) Serotoninergic and non-serotoninergic responses of phrenic motoneu­rones to raphe stimulation in the cat. J Physiol (Lond) 380:373-385

6. Lindsey BG, Hernandez Y, Shannon R (1989) Functional connectivity among respirato­ry related midline medullary neurons. Proc Int Union Physiol Sci 27: 305

7. Lindsey BG, Hernandez YM, Shannon R, Gerstein GL (1989) Respiratory and cardiac related brain stem neural assemblies: dynamic functional connectivity. Soc Neurosci Abst 15: 1191

8. Lindsey BG, Segers LS, Shannon R (1987) Functional associations among simultaneous­ly monitored lateral medullary respiratory neurons in the cat. II. Evidence for inhibitory actions of expiratory neurons. J Neurophysiol 57: 1101-1117

9. Lindsey BG, Segers LS, Shannon R (1987) Functional associations of ventral respiratory group neurons with midline and contralateral respiratory-modulated brainstem neurons. Soc Neurosci Abst 13:1586

10. Lindsey BG, Segers LS, Shannon R (1989) Discharge patterns ofrostrolateral medullary expiratory neurons in the cat: regulation by concurrent network processes. J Neurophys­ioI61:1185-1196

11. Lindsey BG, Shannon R, Gerstein GL (1989) Gravitational representation of simulta­neously recorded brainstem respiratory neuron spike trains. Brain Res 483: 373 - 378

12. McAllen RM (1987) Central respiratory modulation of subretrofacial bulbospinal neu­rons in the cat. J Physiol (Lond) 388: 533-545

13. Millhorn DE (1986) Stimulation of raphe (obscurus) nucleus causes long-term potenti­ation of phrenic nerve activity in cat. J Physiol (Lond) 381:169-179

14. Millhorn DE, Hokfelt T (1988) Chemical messengers and their coexistence in individual neurons. NIPS 3:1-5

15. Morrison SF, Gebber GL (1984) Raphe neurons with sympathetic-related activity: baroreceptor responses and spinal connections. Am J Physiol 246: R338 - R348

16. Nishino T, Honda Y (1982) Changes in pattern of breathing following baroreceptor stimulation in cats. Japn J PhysioI32:183-195

17. Remmers JE, Richter DW, Ballantyne D, Bainton CR, Klein JP (1986) Reflex prolonga­tion of stage I of expiration. Pflugers Arch 407: 190-198

18. Richter DW, Seller H (1975) Baroreceptor effects on medullary respiratory neurones of the cat. Brain Res 86:168-171

19. Segers LS, Shannon R, Saporta S, Lindsey BG (1987) Functional associations among simultaneously monitored lateral medullary respiratory neurons in the cat. 1. Evidence for excitatory and inhibitory actions of inspiratory neurons. J Neurophysiol 57: 1078-1100

Polymorphic Nature of Central Networks Controlling Sympathetic Nerve Discharge * G. L. GEBBER, B. KOCSIS, S. M. BARMAN, and M. J. KENNEY

Introduction

The brain stem networks that regulate sympathetic nerve discharge (SND) and thus blood pressure are concerned primarily with two functions. The first is to generate the background activity of the system while the second is to formulate complex and highly differentiated patterns of spinal sympathetic outflow that help to support specific behavioral states. The present paper summarizes our most recent efforts to understand how these tasks are performed.

The primary frequency in the background discharges of pre- and postganglionic sympathetic nerve bundles is that of the heart rate in cats with functioning barore­ceptor reflexes [1, 2]. The cardiac-related rhythm in SND is transformed into a quasiperiodic pattern after section of the baroreceptor and vagus nerves. As demonstrated with power density spectral analysis [1, 2], most of the power in SND is contained between 2 and 6 Hz after baroreceptor denervation. This quasiperiodic pattern is referred to here as the 2- to 6-Hz rhythm. The 2- to 6-Hz rhythm is ubiquitons to all postganglionic sympathetic nerves with cardiovascular function [2], and it persists in SND after midcollicular decerebration [2]. The precipitous fall in blood pressure produced by high spinal transection is accompa­nied by loss of this sympathetic nerve rhythm [3]. On the basis of these observa­tions, we have proposed that the 2- to 6-Hz rhythm is representative of the fundamental organization of brain stem networks responsible for a significant component of SND and thereby cardiovascular tone (see review [4]).

A primary goal of our laboratory is to understand how the 2- to 6-Hz rhythm is generated. Whereas we have been successful in identifying some of the cell types that comprise the brain stem rhythm generator [5-7], very little information is available concerning their functional interconnections. As discussed by Getting [8], functional connectivity reflects the relative strengths of synaptic connections with­in a network. The strengths of the synaptic connections determine the activity pattern of the network at any point in time, and changes in strength can switch its mode of operation within the constraints dictated by the anatomical connectivity. In this paper, we present evidence that the 2- to 6-Hz rhythm in SND is generated by multiple circuits of brain stem neurons, and that the functional connectivity between and/or within these circuits can be reordered to form different patterns of spinal sympathetic outflow.

* This study was supported by National Institutes of Health grant HL 13187.

Patterns of Relationship Between the Discharges of Sets of Sympathetic Nerves

139

We used auto spectral, coherence, and phase spectral analysis [9, 10] to study the relationships between the simultaneously recorded discharges of sets of two post­ganglionic sympathetic nerves in baroreceptor-denervated cats anesthetized with either diallylbarbiturate and urethane or alpha-chloralose. The two most elemen­tary patterns of relationship observed are illustrated in Fig. 1. The recordings were made from the inferior cardiac (lCN) and renal (RN) nerves in this experiment. The autospectra ofICN and RN activity under normocapnic conditions are shown in the top two panels of Fig. 1 A. Note that most of the power in SND was contained between 2 and 6 Hz. The coherence function shown immediately below the auto spectra is the normalized cross-spectrum. It provides a quantitative mea­sure of the strength of linear correlation of the two signals in the frequency domain. A coherence value not significantly different from zero signifies the absence of correlation while a value of 1 denotes perfect correlation. Coherence values greater than 0.2 were considered to be significantly different from zero [10]. In the case shown, activity in the two postganglionic nerves was coherent over a bandpass of 0.5-7.5 Hz. The phase spectrum (bottom panel in Fig. 1 A) is a plot ofthe difference in the phase (degrees) of the two signals as a function offrequen­cy. The phase spectrum was linear in the coherent frequency band and showed random fluctuations elsewhere. The slope of the line reflects the difference in conduction times from the generator(s) of the 2- to 6-Hz rhythm to the two nerves [9]. The interval (ms) between activity in the two nerves is constant over the coherent frequency band when the Y -intercept (obtained by extrapolation) of the linear portion of the phase spectrum is 0°. This was basically the case under normocapnic conditions in the experiment illustrated in Fig. 1. Activity in the RN lagged behind that in the ICN by a value near 55 ms at each coherent frequency (Fig. 1 C). The interval at each frequency was derived from the corresponding difference in phase angle.

The pattern of constant interval between activity in the ICN and RN implies that both nerves are governed by the same circuit of brain stem neurons (Le., a common 2- to 6-Hz rhythm generator). However, this seems unlikely since the pattern could be changed to one in which the interval between activity in the two nerves was frequency dependent. In the experiment shown, this was accomplished by adjusting the artificial respirator to raise end-tidal CO2 from 4.5% to 6.3%. The pattern of relationship observed under hypercapnic conditions is shown in Fig. 1 B. Hypercapnia was accompanied by a modest reduction of the peak coher­ence value from 0.6 to 0.5. The phase spectrum, however, was more dramatically altered. While the slope of the linear portion of the phase spectrum was basically unchanged, the line itself was offset so that its Y-intercept approached 180°. Under this condition, activity in the RN led that in the ICN at frequencies below 4.5 Hz and lagged at higher frequencies (Fig. 1 C). These results demonstrate that the two nerves were not controlled by a common brain stem generator. Rather, the pat­terns depicted in Fig. 1 indicate that the 2- to 6-Hz rhythm in SND is generated

140 G. L. GEBBER et al.

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Polymorphic Nature of Central Networks 141

by multiple circuits of brain stem neurons whose functional relationships can be reordered. We have not yet systematically defined the experimental variables that determine the pattern of relationship. The pattern induced by hypercapnia in Fig. 1 has also been observed under normocapnic conditions in other animals. Both patterns (constant versus frequency-dependent interval) were also observed for other sets of postganglionic sympathetic nerves. These included the vertebral and inferior cardiac branches of the same stellate ganglion, and the vertebral and renal nerves.

Models of Central Organization

Figure 2 presents two models of central organization that are consistent with the results shown in Fig. 1. The models are oversimplified, and they cannot be distin­guished on the basis of the currently available data. They should be considered solely as guides to future investigation. In the first model (A), the brain stem network controlling SND is viewed as a system of coupled oscillators, each of which can independently generate a 2- to 6-Hz rhythm. Under conditions of strong coupling, the interval between activity in different nerves would remain constant over the coherent frequency band. This state is analogous to type 0 resetting of a rhythm by a single-pulse perturbation when the onset of the next cycle is advanced or delayed to the same point in time independent of the position in the cycle at which the pulse is applied [11, 12]. The state of strong coupling would explain those cases when the linear portion of the phase spectrum has a Y-intercept of 0°. Under the conditions of weaker coupling, the interval between activity in different sympa­thetic nerves would change over the coherent frequency band since a change in frequency of the driving oscillator causes its output to reach the driven oscillator at a different phase of its cycle [13]. This state is analogous to type 1 resetting of a rhythm by a single-pulse perturbation [11, 12] and would explain those cases when the Y-intercept of the phase spectrum is offset from zero.

In the second model (B), the central network controlling SND is viewed as a system of parallel filters that share stochastic input most likely from a widely distributed network of brain stem reticular neurons. Under conditions when the bandpasses and, thus, transfer functions (input/output delays) of the filters are the same, the interval between activity in different sympathetic nerves would remain constant over the coherent frequency band. Under other conditions when the bandpasses and transfer functions of the filters are not the same, the interval between activity in different nerves would change from frequency to frequency in the coherent band .

.. Fig. 1. Inferior cardiac (leN) and renal (RN) sympathetic nerve autospectra, ICN ---> RN coherence function, and phase spectrum during normocapnia (A) and hypercapnia (B) in a baroreceptor-denervated cat. Each plot is based on 42 5-s windows; frequency resolution is 0.2 Hz; relative power x 100 in the auto spectra equals the percentage of total power in the bin containing the peak frequency; the difference in phase (degrees) of activity in the ICN and RN has been converted to an interval (ms) and plotted against frequency in C

142 G. L. GEBBER et al.

A Coupled Oscillators

B Parallel Filters Stochastic Input

Fig. 2A, B. Models of central organization of the cir­cuits generating the 2- to 6-Hz rhythm in sympa­thetic nerve discharge. A Coupled oscillators, 0 1 and O2 , B Parallel filters, F 1 and F 2

Relationships Between Brain Stem Unit Activity and SND

To gain further insight into the organization of the brain stem networks responsi­ble for the 2- to 6-Hz rhythm, we have begun to investigate the relationships between the discharges of single brain stem neurons and SND in the frequency domain. The neurons studied were located in three medullary regions of barore­ceptor-denervated cats: the lateral tegmental field, the rostral ventrolateral medul­la, and the raphe. These neurons were shown to have sympathetic nerve-related activity with spike-triggered averaging. The neurons in the rostral ventrolateral medulla and raphe send their axons to the intermediolateral nucleus of the thoracic spinal cord [14,15] while those in the lateral tegmental field have axons that project to the rostral ventrolateral medulla or raphe [6, 7].

The relationships shown in Fig. 3 are representative of those observed for neu­rons in each of the three medullary regions. The spike-triggered averages in panel A demonstrate that the naturally occurring discharges of this raphespinal neuron were correlated to activity in both the ICN and RN. The averages show ICN and RN activity that preceded and followed unit discharge at time zero. Clearly, the activity of this brain stem neuron was correlated to a 2- to 6-Hz rhythm in SND. Furthermore, the period of the rhythm appears to be the same for the two nerves. Indeed, the peak frequencies in the ICN -4 unit and RN -4 unit coherence functions were the same in this experiment (see bottom two panels on right side of Fig. 3 B). This observation suggests that each of the brain stem circuits generating the 2- to 6-Hz rhythm influences more than one sympathetic nerve. The influences exerted by each brain stem circuit on different nerves need not be uniform. This was suggested by two observations. First, in some cases brain stem unit activity co­hered more strongly to that in one of the two nerves (Fig. 3). Second, the relative power at particular frequencies in the coherent band could be greater in one nerve than another. Future studies should consider the possibility that the nonuniform actions exerted by brain stem circuits on different sympathetic nerves playa role

Polymorphic Nature of Central Networks 143

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Fig. 3 A, B. Relationships between raphe spinal unit activity and the discharges of the infe­rior cardiac (leN) and renal (RN) sympathetic nerves in a baroreceptor-denervated cat. A Spike-triggered average of activity in ICN and RN. Bin width is 5 ms, and 2598 spikes were used to construct the averages; vertical calibration is 60 mV for amplified ICN activity and 40 m V for amplified RN activity. B Left, from top to bottom, the autospectra for the ICN, RN, and raphe neuron. Right, the ICN -> RN, ICN ->unit, and RN ->unit coherence func­tions. Each plot is based on: 85 5-s windows; frequency resolution is 0.2 Hz; relative power in the autospectra is as defined in Fig. 1

144 G. L. GEBBER et al.

in the formulation of complex and highly differentiated patterns of spinal sympa­thetic outflow.

Coherence analysis provided additional important information. First, the fre­quency band over which brain stem unit activity and SND cohered was narrower than that for the two nerves (Fig. 3 B). This observation strongly suggests that each sympathetic nerve is influenced by more than one brain stem generator of the 2-to 6-Hz rhythm. Second, the auto spectrum of the activity of the brain stem neurons with sympathetic nerve-related activity generally was "noisy" in most cases (Fig. 3). That is, the rhythm represented by the relatively sharp peak in the sympathetic nerve--+unit coherence functions was not clearly represented in the auto spectrum of brain stem unit activity. In the example shown in Fig. 3, the autospectrum of brain stem unit activity contained near equal power at frequencies between 4.5 and 15 Hz. Autospectra of unit activity were constructed as described by Christakos et al. [16]. Digital low-pass filtering (cut off at 250 Hz) of neuron spike trains was performed by convolving the trains with a sine function (defined as sin x/x) whose parameters were such that the information in the autospectra primarily reflected the interspike intervals rather than the shape of the unit action potentials.

Rhythm Generation by a Probabilistic Network Oscillator

The "noisy" auto spectra of brain stem unit discharges prompted us to characterize in greater detail the spike trains of neurons with sympathetic nerve-related activity. The results of a typical experiment are shown in Fig. 4. The neuron studied in this experiment was located in the medullary lateral tegmental field of a baroreceptor­innervated cat. Inferior cardiac SND contained a strong cardiac-related rhythm in this experiment. This is evident from the post-R wave average of SND in panel C and the auto spectrum of SND in panel D. The activity of the brain stem neuron was correlated to the cardiac-related rhythm in SND as demonstrated with spike­triggered averaging (panel B) and post-R wave analysis (panel c). However, this relationship was probabilistic in nature. First, the neuron did not discharge in every cardiac-related cycle of SND (panel A). Second, when the neuron did fire, it discharged a variable number of times in a cycle of SND. As a consequence, the auto spectra of brain stem unit activity (panel E) contained only a hint of the sharp peak appearing at the frequency of the heart rate in the auto spectrum of SND (panel D). Moreover, the interspike interval histogram of unit activity was unimo­dal and dispersed (panel F). Thus, the rhythm in SND is not clearly reflected in the spike trains of individual brain stem neurons that we believe are contained in or receive input from the generator of 2- to 6-Hz activity [5-7, 14, 15]. These observations lead us to propose that the rhythm in SND is an emergent property of a network comprised of neurons that function in a probabilistic fashion. It appears that each cycle of SND is generated by a subset of the network, and that the composition of the subset changes from cycle to cycle.

Polymorphic Nature of Central Networks 145

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Summary

1. The 2- to 6-Hz rhythm in SND is generated by multiple brain stem circuits. 2. Each of these brain stem circuits affects more than one sympathetic nerve, and

each sympathetic nerve receives input from more than one circuit. 3. The functional connectivity between and/or within these brain stem circuits

can be reordered to produce different modes of operation. One mode is character­ized by tight synchronization of the activity of all circuits with constant time delay over the coherent frequency band. A second mode of operation is characterized by a frequency-dependent interval between the activity in different circuits. This mode might be involved in formulating complex and highly differentiated patterns of spinal sympathetic outflow.

146 G. L. GEBBER et al.: Polymorphic Nature of Central Networks

4. The 2- to 6-Hz rhythm appears to be an emergent property of a network whose elements function in a probabilistic fashion. Each cycle of sympathetic nerve activity is generated by a subset of the network. The composition of the subset changes from cycle to cycle.

Acknowledgement. The authors are grateful to Ms. Diane Hummel for typing the manuscript.

References

1. Barman SM, Gebber GL (1980) Sympathetic nerve rhythm of brain stem origin. Am J PhysioI239:R42-R47

2. Gebber GL, Barman SM (1980) Basis for 2-6 cycle/s rhythm in sympathetic nerve discharge. Am J PhysioI239:R48-R56

3. Ardell JL, Barman SM, Gebber GL (1982) Sympathetic nerve discharge in chronic spinal cat. Am J PhysioI243:H463-H470

4. Gebber GL (1984) Brain stem systems involved in cardiovascular regulation. In: Randall WD (ed) Nervous control of cardiovascular function. Oxford University Press, New York, pp 346-368

5. Gebber GL, Barman SM (1985) Lateral tegmental field neurons of cat medulla: a potential source of basal sympathetic nerve discharge. J Neurophysiol 54: 1498-1512

6. Barman SM, Gebber GL (1987) Lateral tegmental field neurons of cat medulla: a source of basal activity of ventrolateral medullospinal sympathoexcitatory neurons. J Neuro­physiol 57: 1410-1424

7. Barman SM, Gebber GL (1989) Lateral tegmental field neurons of cat medulla: a source of basal activity of raphespinal sympathoinhibitory neurons. J Neurophysiol 61: 1011-1024

8. Getting PA (1989) Emerging principles governing the operation of neural networks. Annu Rev Neurosci 12:185-204

9. Jenkins GM, Watts DG (1968) Spectral analysis and its application. Holden-Day, San Francisco, pp 1-523

10. Cohen MI, See WE, Christakos CN, Sica AL (1987) High-frequency and medium-fre­quency components of different inspiratory nerve discharges and their modification by various inputs. Brain Res 417:148-152

11. Winfree AT (1980) The geometry of biological time. Springer, Berlin Heidelberg New York, pp 25-39

12. Glass L, Mackey MC (1988) From clocks to chaos: the rhythms of life. Princeton University Press, Princeton, pp 98-118

13. Pavlidis TC (1973) Biological oscillators: their mathematical analysis. Academic, New York, pp 71-98

14. Morrison SF, Gebber GL (1985) Axonal branching patterns and funicular trajectories of raphespinal sympathetic neurons. J Neurophysiol 53:759-772

15. Barman SM, Gebber GL (1985) Axonal projection patterns of ventrolateral medul­lospinal sympathoexcitatory neurons. J Neurophysiol 53: 1551-1566

16. Christakos CN, Cohen MI, See WR, Barnhardt R (1987) Fast rhythms in the discharges of medullary inspiratory neurons. Brain Res 463: 362- 367

Interrelation and Superposition of Respiratory and Cardiovascular Rhythms in EEG and Brainstem Reticular Unit Activity as Studied by Quantitative Spectral Analyses * T. HUKUHARA JR., K. TAKANO, N. KIMURA, and F. KATO

Introduction

The main part of the neural networks responsible for the production of the respiratory rhythms [4, 5, 9-11, 14, 25] and several types of the cardiovascular rhythms [3, 7, 18,20] may be located in the neural organizations of the brain stem. Both rhythms propagate caudally to the respiratory motoneuron pools and to the autonomic control mechanisms in the lower part of the neuroaxis, mainly to the respiratory motoneuron pools, preganglionic sympathetic neuron pools, and pre­ganglionic vagal cardiomotor neuron pools [4-6, 9, 12, 14,23]. There is qualitative evidence that the brain stem respiratory neural networks send not only caudally but also rostrally the respiratory related rhythms to the higher central mechanisms, including many areas of the cerebral neocortex, some regions of the hypothalamus, thalamus, basal ganglia, and the limbic system [4, 8,9, 14, 20, 26, 29] (Fig. 1). In addition, spontaneous periodic variations with different periods were also de­scribed in the EEG in neocortical areas of the cat [7, 9, 20, 26] (Fig. 2) and dog [3].

To obtain basic information concerning the mechanisms of the possible intra­central propagation of the respiratory [4, 9, 14, 20, 26, 29] and cardiovascular rhythms [3, 7, 20] and to ascertain the potential interactions between the both rhythms [6, 17, 20, 23, 24, 27], the periodicity properties of these two periodic ECoG fluctuations was studied quantitatively by means of pulse weight auto- and cross-correlation analyses [2, 3, 17 - 21, 27] in combination with the transection technique of the brain stem [9 -11, 19] in the cat. Furthermore, the possibility was examined in the rabbit as to whether some superposition of both the respiratory and cardiovascular rhythms in the brain stem neuronal activity may exist.

The experimental results revealed that both the cardiovascular-related and res­piratory rhythms produced in the part of the neuroaxis caudal to the midpontine level propagate rostrally to the neural organization responsible for ECoG activity in the anterior and posterior sigmoid gyrus.

* This research was supported in part by the Ministry of Education, Science, and Culture of Japan, grant-in-aid for scientific research no. 60304044 and grant-in-aid for developmen­tal scientific research no. 01870012 and by the Science Research Promotion Fund 1988 from the Japan Private School Promotion Foundation.

148 T.HUKUHARAJR. et al.

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Fig. 1. Periodic fluctuation in EEG pattern corresponding to phrenic nerve volleys. Vagoto­mized, anesthetized, paralyzed, and artificially ventilated cat. t, Phrenic nerve activity; 2, anterior sigmoid gyrus; 3, suprasylvian gyrus; 4, caudate nucleus; 5, hippocampus; 6, midbrain reticular formation

Fig. 2. Spontaneous periodic fluctuation in electrocorticogram in the anterior sigmoid gyrus (1), phrenic (2), and renal sym­pathetic nerve activities (3) syn­chronized with Mayer's wave in arterial blood pressure. Vagoto­mized, anaesthetized, paralyzed, and artificially ventilated cat. 4, Femoral blood pressure; horizontal short bars (right), cali­bration for 200 (upper) and 100 mmHg (lower). The period of Mayer's wave was approxi­mately 180 s. The time interval between the left and right panel was 21.05 s

Vagotomized cats and rabbits were anesthetized with diethyl ether, paralyzed with gallamine, and maintained by artificial respiration under monitoring end-tidal CO2 and O2 levels and femoral arterial blood pressure. Rectal temperature was kept around 37 DC. Spontaneous efferent discharges of the renal sympathetic nerve and neocortical ECoG were recorded simultaneously together with the phrenic nerve activity. According to the standard methods [2], the autocorrelation func-

Interrelation and Superposition of Respiratory and Cardiovascular Rhythms 149

tions of neocortical ECoG in the anterior and posterior sigmoid gyrus, phrenic and renal sympathetic nerve activities, and the cross-correlation functions between ECoG and the phrenic or renal sympathetic nerve activity were computed. In the rabbit respiratory unitary discharges were recorded in the bulbar reticular forma­tion by standard methods. Statistical comparisons were made by means of Stu­dent's t-test, with p < 0.05 considered significant.

Results

Identification and CO 2 Response of Periodic ECoG Fluctuations by Means of Cor­relation Analyses in the Cat. Two types of periodic fluctuation were identified in the neocortical ECoG in the anterior and posterior sigmoid gyrus by correlation analyses in cats in which bilateral vagosympathetic trunks and carotid sinus nerves were cut in the neck. The period (mean, 3.27 s; range, 1.4-6.2 s) of one type of the rhythmic ECoG variation (short-term fluctuation) in the anterior sigmoid gyrus synchronized with that of the respiratory cycles (mean, 3.27 s) as measured by autocorrelogram and correlation coefficient of autocorrelation of ECoG and the phrenic discharges respectively (Table 1). The correlation coefficient of cross-cor­relation (CCC) between ECoG and phrenic discharges ranged from 0.06 to 0.48 (mean, 0.22; Fig. 3A, left). The period (mean, 27.55 s; range, 14.3-180.0 s) of the second type of ECoG fluctuation synchronized with that (mean, 27.48 s) of a long-term periodic fluctuation of the renal sympathetic nerves in synchronization with the rhythmic fluctuation of arterial blood pressure (Table 1). The CCC between ECoG and the renal sympathetic nerve activities (RNA) ranged from 0.092 to 0.230 (mean 0.16; Fig. 3 B, left). These two ECoG fluctuations were

Table 1. Period and correlation coefficient (CC) of autocorrelation of spontaneous rhythmic fluctuations in BCoG, phrenic nerve activity (PNA) and renal sympathetic nerve activity (RNA) before and after midpontine transection of the brain stem: vagotomized, anesthetized, paralyzed, and artificially ventilated cats in which bilateral carotid sinus nerve was cut (n=10)

Before Period (s) CC Autocorrelation

After Period (s) CC Autocorrelation

" n=8 b p<O.OOl

PNA

Respiratory rhythm

3.27±1.96 0.60±0.16

11.57 ± 7.48 0.28±0.13 b

BCoG

Respiratory Long-term rhythm rhythm

3.27 ± 1.91 0.19±0.04

27.55 ± 15.43 0.19± 0.04

21.89±15.36 0.21 ± 0.07

RNA

Long-term rhythm

27.48 ± 14.32 0.16± 0.02"

32.16± 1.96 0.17± 0.02"

150 T. HUKUHARAJR. et al.

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Fig, 3. Autocorrelograms show­ing effects of midpontine brain stem transection on ECoG (A i and B i), phrenic nerve activity CA2), and renal sympathetic nerve activity (B2). Anes­thetized, paralyzed, and artifi­cially ventilated cat in which the bilateral vagosympathetic trunks and carotid sinus nerves were cut. Left CA, B), autocorrelo­grams before midpontine tran­section; right, after. Abscissa, delay time; each division, 900 ms in Ai, A2 on left and 5.775 s in Ai, A2 on right and in B. The numbers in the upper-right part of each autocorrelogram repre­sent the mean value of the pe­riod of rhythmic fluctuations in seconds as measured by each autocorrelogram

observed after a short-term cessation of artificial ventilation during immobiliza­tion, The short-term ECoG fluctuation was observed as a periodic burst activity with the respiratory period in most experiments. The phase of the respiratory ECoG burst activity was different from preparation to preparation and varied even in an animal in the course of experiment.

Changes in the CCC between ECoG and phrenic or renal sympathetic nerve activity were observed by hyper- and hypocapnia induced experimentally by alter­ations of the rate of artificial ventilation (Table 2; Fig. 4). The CCC between ECoG and phrenic discharges correlated (r=0.36, p<0.05) with the CO2 concentration in expired air within a range from 2.4% to 6.5%,

Effects of Midpontine Brain Stem Transection on Rhythmic Fluctuations of EeoG, Phrenic and Renal Sympathetic Nerve Activities. To determine the basic functional organization of the possible mechanism responsible for the production of both the

Interrelation and Superposition of Respiratory and Cardiovascular Rhythms 151

A

B

-77

Fig. 4A, B. Cross-correlograms showing effects of hypo- (2) and hyperventilation (3) on cross-correlation function between ECoG in the anterior sigmoid gyrus and the phrenic nerve activity in A and between ECoG and the renal sympathetic nerve activity in B. Bilateral vagosympathetic trunks and carotid sinus nerves were cut. Abscissa, delay time; each divi­sion, 900 ms in A and 5.775 s in B. Ordinate, cross-correlation function. Vertical bar (left of each cross-correlogram) indicates the calibration for the correlation coefficient as 0.5 in A and 0.25 in B. Note an increase of the positive peak of cross-correlation function in 2 and a decrease of those in 3

Table 2. Effects of change in ventilation on correlation coefficient of cross-correlation be­tween ECoG and PNA and between ECoG and RNA

Normoventilation Hypoventilation Hyperventilation

a p<O.Ol

ECoG-PNA (n=14)

0.22±0.12 0.30±0.15 0.10±0.04 a

ECoG-RNA (n=10)

0.16±0.04 0.19±0.05 0.11±0.09

short-term (respiratory) and long-term rhythmic activity in the neocortical ECoG, the period and autocorrelation coefficient of the rhythmic activity of ECoG and the phrenic and renal sympathetic nerves were computed before and after brain stem transection at the midpontine pretrigeminal level. This was carried out by means of correlation analyses in ten cats in which the vagosympathetic trunks and carotid sinus nerves were cut bilaterally in the neck. Figure 5 shows the changes in ECoG, PNA, and RNA patterns in an experiment.

After midpontine transection both of these two types of the rhythmic ECoG fluctuation were abolished and replaced by a single and new rhythmic ECoG fluctuation having a period (mean, 21.89 s) different from both the periods of spontaneous respiratory volleys of the phrenic (mean, 11.57 s) and of the long­term fluctuation of the renal sympathetic nerve activities (mean, 32.16 s) as mea­sured by autocorrelograms after the brain stem transection (Table 1). Both corre­lation coefficients of autocorrelation (ACC) of ECoG and of RNA in terms of

152 T. HUKUHARAJR. et al.

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Fig. 5. Activity patterns of ECoG in the anterior sigmoid gyrus (1), phrenic (2), and renal sympathetic nerve activities (3) before (A) and after midpontine transection (B). Anes­thetized, paralyzed, and artificially ventilated cat in which the bilateral vagosympathetic trunks and carotid sinus nerves were cut. 4, Arterial blood pressure (mmHg). Note the maintained and considerably regular rhythmicity of the phrenic nerve activity although the period of respiratory volleys was prolonged

Table 3. Arterial blood pressure, end-tidal CO2 , and O2 concentration before and after midpontine transection (n = 10)

Before After

Blood pressure (mmHg)

92.9±20.5 76.6±16.8

End-tidal CO 2 (%)

4.4±0.7 4.4±0.8

End-tidal O2 (%)

15.3 ± 1.1 15.4 ± 1.3

respective rhythmic fluctuation remained almost unchanged after brain stem tran­section (Table 1), while ACC of the phrenic nerve activity decreased significantly (p < 0.01) after the brain stem transection (Table 1; Fig. 3 A 2, right). At the same time, the value of total power of the renal sympathetic and phrenic nerve activity decreased to 47.1%±36.0% (n=10,p<0.01) and 76.0%±42.7% (n=1O) of the control value, respectively. Total power value of the slower activity (1.0-3.5 Hz) of ECoG decreased to 57.3%±32.3% (n=10,p<0.01) of the control, while that of the faster activity (3.5-30 Hz) showed a tendency to increase. Arterial blood pressure, end-tidal CO2 and O2 concentrations before and after brain stem tran­section are summarized in Table 3.

Interrelation and Superposition of Respiratory and Cardiovascular Rhythms 153

Fig. 6. Superposition of respiratory rhythm and long-term fluctuation in spontaneous unitary dischages. Vagotomized, anesthetized, paralyzed, and artificially ventilated rabbit. Bilateral depressor nerves were cut. i, Arterial blood pressure; 2, 3, two different bulbar expiratory units; 4, phrenic nerve activity; O2 and CO2 concentration in expired air were 16.2% and 3.60%, respectively. Note fluctua­tions of both the expiratory unit activities synchronized with periodic long-term arterial blood pressure waves

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Respiratory Units Displaying a Rhythmic Fluctuation in Activity in Synchrony with Long-Term Arterial Blood Pressure Waves in the Rabbit. Spontaneous discharges of reticular respiratory units, which are reticular units having respiratory rhythms, exhibited both short- and long-term fluctuations of stability as measured by the coefficient of variation (CV) of the period of their burst, even at constant end-tidal O2 and CO 2 levels. In 23 out of 29 respiratory units, a rhythmic fluctuation of CV was observed (period, mean±S.D., 56.1 ±27.8 s; range, 25-130 s), in synchrony with rhythmic fluctuation of systemic arterial blood pressure in rabbits in which the vagus, depressor, cervical sympathetic, and carotid sinus nerves were cut bilaterally (Fig. 6).

Discussion and Conclusion

These experimental results indicate the existence of ascending efferent projections from the rhythm-generating mechanisms for the periodicity of respiration [4, 5, 9, 11,14,25] and Mayer's blood pressure waves [20, 23, 28]. Moreover, they strongly suggest that the rhythmic activities in the neural organization of the neuroaxis caudal to the midpontine level play an essential role in the production of the long-term periodic fluctuations relating to the cardiovascular control and respira­tory variation in ECoG [9,14,20,26]. Hence, an inherent rhythm of periodic burst or modulatory activity in the different regions of the higher central mechanisms may become entrained to the conceivable long-term cardiovascular rhythm pro­duced by periodic fluctuations of the overall activity level of the brain stem cardiovascular control mechanisms [3, 7, 12, 15,27,28] and to respiratory rhythm by ascending efferent projections from the respiratory rhythm-generating neural organizations in the bulbar respiratory mechanisms [9-11,14]. Thus, the neocor­tical respiratory rhythms and the neocortical long-term rhythms, may be elicited as the result of the different entrainments driven by the respective oscillatory networks caudal to the midpontine level.

There have been reported the following observations in cats and rabbits with respect to the neuronal organization of the brain stem rhythm-generating mecha-

154 T. HUKUHARAJR. et al.

-1~

-11

Fig. 7. The proximity of identified "respiratory" (right half) and car­diovascular neurons (left half) in a series of transverse sections of the cat brain stem. Levels of sections in numbers -12, -11 and - 8 (mm) indicate the respective stereotaxic frontal sections. Dots, locations from which single-unit activity of neurons was recorded. Horizontal bar (lower-left), 3 mm

nisms and to the possible interrelation between the respiratory and cardiovascular control mechanisms [1, 4, 6, 12, 13, 17, 20, 23, 24, 27] at the neuronal level.

1. Respiratory or respiration-related neurons are identified on the basis of significant correlation with efferent phrenic nerve activity as measured quantita­tively by auto- and cross-correlation analyses besides the conventional criteria for identification [13, 14,18,21]. Unit activities of reticular respiratory neurons corre­late to different degrees to the phasic fluctuation of the phrenic nerve activity, suggesting the functional heterogeneity of these neurons in the central respiratory mechanisms [9, 12-14, 16, 19, 21].

2. Cardiovascular neurons in the brain stem reticular formation were identified by means of a newly applied quantitative identification method using correlation analyses between unitary discharges and the phasic and nonrhythmic fluctuations of the renal sympathetic nerve activity. There were found different degrees of the correlation mentioned above, suggesting that the population of the reticular car­diovascular neurons may be also a functionally heterogenous neuron group [13,

Interrelation and Superposition of Respiratory and Cardiovascular Rhythms 155

18], and that some of them together with some neurons in (1) described above might sub serve the interrelation between the central respiratory and cardiovascu­lar control mechanisms.

3. Anatomical distribution of the members ofthe respiratory and cardiovascular neuron populations are scattered and intermingled in the brain stem reticular formation and the specified regions in the brain stem [9, 12, 14, 15, 18, 22, 26] (Fig. 7).

4. Spontaneous unit activity of some respiratory neurons exhibited a rhythmic fluctuation in relation to a type oflong-term cardiovascular rhythms with a period in synchrony with rhythmic systemic arterial blood pressure fluctuations [12, 13, 15].

5. A total of21 % [18] to 25% [15] of identified reticular cardiovascular neurons displayed spontaneous discharge patterns with respiratory rhythms.

Substantial evidence now exists that there may be multiple neural processes of interactions between tonic and rhythmic neural functional units of neural net­works of the respiratory and cardiovascular control mechanisms. These are as follows; entrainment, hierarchical innervation or hierarchical functional organiza­tion, dominance or priority over other neural networks [9,11,14,21], intracentral convergence and divergence, and tone rhythm conversion [23]. Further intensive investigations for the interrelation between the respiratory and cardiovascular control mechanisms to improve our understanding.

References

1. Baust W, Niemczyk H, Schaefer H, Vieth J (1962) Uber ein pressosensibles Areal im hinteren Hypothalamus der Katze. Pfliigers Arch 274:374-384

2. Bendat JS, Piersol AJ (1971) Random data: analysis and measurement procedures. Wiley, New York, pp 14-31

3. Camerer H, Stroh-Werz M, Krienke B, Langhorst P (1977) Postganglionic sympathetic activity with correlation to heart rhythm and central cortical rhythms. Pfliigers Arch 370:221-225

4. Cohen MI (1979) Neurogenesis of respiratory rhythm in the mammal. Physiol Rev 59:1105-1173

5. Euler C von (1986) Brain stem mechanisms for generation and control of breathing pattern. In: Cherniack NS, Widdicombe JG (eds) The respiratory system. American Physiological Society, Bethesda, pp 1-67 (Handbook of physiology, sect 3, vol II, part 1)

6. Feldman JL, Ellenberger HH (1988) Central coordination of respiratory and cardiovas­cular control in mammals. Annu Rev Physiol 50: 593-606

7. Gebber GL, Barman SM (1980) Basis for 2-6 cycles rhythm in sympathetic nerve discharge. Am J Physio1239:R48-R56

8. Hugelin A (1986) Forebrain and midbrain influence on respiration. In: Cherniack NS, Widdicombe JG (eds) The respiratory system. American Physiological Soeciety, Bethes­da, pp 69-91 (Handbook of physiology, sect 3, vol II, part 1)

9. Hukuhara T Jr (1973) Neuronal organization of the central respiratory mechanisms in the brain stem of the cat. Acta Neurobiol Exp (Warsz) 33:219-244

10. Hukuhara T Jr (1974) Functional organization of brain stem respiratory neurons and rhythmogenesis. In: Umbach W, Koepchen HP (eds) Central rhythmic and regulation. Hippokrates, Stuttgart, pp 35-49

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11. Hukuhara T (1976) Functional organization of brain stem respiratory neurons and its modulation induced by afferences. Coil Inst Nat! Sante Rech Med, INSERM 59:49-53

12. Hukuhara T Jr (1980) Spontaneous activity pattern, anatomical distribution of brain stem reticular neurons with correlation to the phrenic and renal sympathetic nerve activities and their responses to electrical stimulation of the spinal cord. In: Koepchen HP, Hilton SM, Trzebski A (eds) Central interaction between respiratory and cardiovas­cular control systems. Springer, Berlin Heidelberg New York, pp 21-29

13. Hukuhara T Jr (1984) Discharge properties of respiratory modulated brainstem reticular neurons and their relation to slow arterial pressure fluctuations in the rabbit. In: Miyakawa K, Koepchen HP, Polosa HP (eds) Mechanisms of blood pressure waves. Springer, Berlin Heidelberg New York, pp 305-316

14. Hukuhara T Jr (1988) Organization of the brain stem neural mechanisms for generation of respiratory rhythm-current problems. Jpn J Physiol 38:753-776

15. Hukuhara T Jr, Takeda R (1975) Neuronal organization of central vasomotor control mechanisms in the brain stem of the cat. Brain Res 87:419-429

16. Hukuhara T Jr, Goto K, Kiguchi Y, Takano K (1979) Unterschiedliche Stabilitiit re­spiratorischer Einzelneuronenaktivitiit im Hirnstamm der Katze. Jikeikai Med J 26: 245-261

17. Hukuhara T Jr, Kimura N, Takano K, Fu W-J (1986) Cross-correlation analysis of phase relation between respiratory volleys in the phrenic, vagus and sympathetic nerve activities. J Auton Nerv Syst [Suppl]:281-284

18. Hukuhara T Jr, Nishikawa Y, Takano K, Kimura N (1986) Functional organization of brain stem reticular neurons in relation to the central cardiovascular control mechanisms in the cat. In: Nakamura K (ed) Brain and blood pressure control. Elsevier, Amsterdam, pp 13-22

19. Hukuhara T Jr, Takano K, Kato F, Kimura N (1988) Medullary inspiratory neurons with respiratory rhythm and little correlation to phrenic high-frequency oscillation. Tohoku J Exp Med 156 [Suppl]:11-19

20. Hukuhara T Jr, Miyakawa M, Kimura N, Takano K, Kato F (1987) Periodic variaton of electro-corticogram in relation to respiratory rhythm and long-term periodic fluctua­tion of the renal sympathetic nerve activity. In: Sieck GC, Gandevia SC, Cameron WE (ed) Respiratory muscles and their neuromotor control. Liss, New York, pp 121-125

21. Hukuhara T Jr, Goto K, Takano K, Kiguchi Y, Hattanmaru Y, Kimura N (1983) A new aspect of the functional organization of respiratory neurons in the brain stem with respect to the rhythmogenesis of respiration. Different stabilities of reticular respiratory neurons in the rabbit. In: SchiMke ME, Koepchen HP, See WR (eds) Central neurone environment and the control systems of breathing and circulation. Springer, Berlin Heidelberg New York, pp 185-196

22. Hukuhara T Jr, Saji Y, Kumadaki N, Kojima H, Tamaki H, Takeda R, Sakai F (1969) Die Lokalisation von atemsynchron entladenden Neuronen in der retikularen Forma­tion des Hirnstammes der Katze unter verschiedenen experimentellen Bedingungen. Naunyn - Schmiedebergs Arch PharmacoI263:462-484

23. Koepchen HP (1983) Respiratory and cardiovascular "centres": functional entirety or separate structures? In: SchiMke ME, Koepchen HP, See WR (eds) Central neurone environment and the control systems of breathing and circulations. Springer, Berlin Heidelberg New York, pp 221-237

24. Koepchen HP, Kliissendorf D, Sommer D (1981) Neurophysiological background of central neural cardiovascular respiratory coordination: basic remarks and experimental approach. J Auton Nerv Syst 3:335-368

25. Koepchen HP, Kliissendorf D, Lazar H, Hukuhara T Jr (1985) Conclusions on respira­tory rhythmogenesis drawn from lesion and cooling experiments predominantly in the region of ventrolateral nucleus of solitary tract (vINTS). In: Bianchi AL, Denavit-Saubie M (eds) Neurogenesis of central respiratory rhythm. MTP Press, Lancaster, pp 77 -80

26. Kumagai H, Sakai F, Sakuma A, Hukuhara T Jr (1966) Relationship between activity of respiratory center and EEG. Prog Brain Res 21A:98-111

Interrelation and Superposition of Respiratory and Cardiovascular Rhythms 157

27. Langhorst P, Schulz G, Lambertz M (1984) Oscillating neuronal network of the 'com­mon brain stem system'. In: Miyakawa K, Koepchen HP, Plosa C (eds) Mechanisms of blood pressure waves. Springer, Berlin Heidelberg New York, pp 257-275

28. Polosa C (1984) Rhythms in the activity of the autonomic nervous system: their role in the generation of systemic arterial pressure waves. In: Miyakawa K, Koepchen HP, Polo sa C (eds) Mechanism of blood pressure waves. Springer, Berlin Heidelberg New York, pp 27-41

29. Vibert IF, Caille D, Bertrand F, Gromysz H, Hugelin A (1979) Ascending projection from the respiratory centre to mesencephalon and diencephalon. Neurosci Lett 11: 29-33

Functional Organization of the Common Brainstem System to Different States at Different Times F. EBINGER, M. LAMBERTZ, and P. LANGHORST

The investigation of the way in which the central nervous system is organized has been dominated by two concepts. The holistic concept considers the brain as a single dynamic whole. The localization concept relegates functions to different isolated parts of the brain (see [6, 20]). For technical reasons most experiments, until the middle of our century, were done from a morphological aspect and supported the localization concept. This experimental approach dominated the manner of thinking and even led to the use of morphological terms for functional systems, e.g. reticular system or limbic system.

With the development of microrecording techniques, more precise electrophys­iological data became available, but a definite decision in favour of one of the two concepts was still not possible [7, 8]. With the aid of stimulatioQ reaction analysis, it is possible to investigate the influence of only a small number out of thousands of synapses converging on one neurone. Electrical stimulation leads to a discharge which is dominated by the same restricted number of inputs, disregarding the effectiveness of all other inputs [11, 12]. Under physiological conditions adequate stimulation, provoking naturally occurring input patterns allows investigation of the individual afferent spectra of the single neurones. A more detailed comparative evaluation of the discharge behaviour of neurones is meaningful only when the activities of the neurones are recorded under identical physiological conditions. The simultaneous recordings of the different neurones in our experiments accom­plish this prerequisite [9, 15, 25, 26].

The importance of the neurones in the reticular formation of the lower brain­stem for somatovisceral integration and regulation is well accepted. Hess has postulated the existence of a "common brainstem mass" for the "leistungsorien­tierte Integration" of somatomotor and visceral systems [3-5]. Our investigations of simultaneously recorded neurones clearing up neurophysiological properties of the reticular neuronal network support Hess' hypothesis. As a result of comparing the discharge patterns of reticular neurones with similar patterns in cardiovascu­lar, respiratory and somatomotor systems, and the EEG, we were able to establish the concept of a common brainstem system (CBS) which integrates and regulates the different effector systems. In this multifunctional CBS, the basic activity for somatomotor, cardiovascular and respiratory regulation is generated [13, 18]; the CBS has a mainly homoeostatic function. A functional part of the CBS, the ascending reticular activating system [23], is necessary for the maintenance of consciousness. By integrating the various peripheral afferents and higher brain structure commands, the CBS regulates the degree of activity required in the organ systems of the body for the actual on-going mode of behaviour.

Functional Organization of the Common Brainstem System 159

The methods applied have been more precisely described in earlier papers [9,15, 25, 26]. A short summary is given here. Several neurones were extracellularly recorded by placing one or two electrodes in the reticular formation of the medulla oblongata of anaesthetized dogs. The action potentials of the different neurones, recorded with one electrode at the same time, were separated by window discrim­inator circuits. The neuronal activities, EEG, ECG, EMG, arterial blood pressure, interpleural pressure, sympathetic nerve activity and phrenic nerve activity were recorded simultaneously and stored on magnetic tape. Signal analysis was per­formed off-line on a digital computer. When considering the electrophysiological properties of the individual reticular neurones, it must be emphasized that one and the same neurone can be labelled as a cardiovascular one, a respiratory one,a somatomotor one, and one which regulates the general degree of activity.

Each CBS neurone is influenced by somatosensory afferents as well as by afferents from visceral receptors. However, the somatomotor and visceral afferents determine the discharging of the CBS neurones in different ways.

Baroreceptor afferents exhibit a generally activity-decreasing influence on the various central and peripheral effector systems via the CBS neurones. In the same way, the generally activity-increasing effect of chemoreceptors and nociceptors is mediated by neurones of the CBS. The influence that baroreceptor and chemore­ceptor activities have on the discharging of nearly all CBS neurones has been established [15]. An isolated influence ofbaroreceptors, chemoreceptors and noci­ceptors on single functional systems is not possible from a functional point of view [14]. Chemoreceptor afferents guarantee the maintenance of basic activity even during normoxia and normocapnia. During hypoxia and hypercapnia an extreme increase of chemoreceptor afferents induces a high degree of activity in the CBS, leading to an increase in the activity of the effector systems in the sense of an "emergency reaction" [2]. In consequence of an activity increase in the CBS the arterial blood pressure is elevated, which in turn enhances baroreceptor activity. This baroreceptor feedback system stabilizes and dampens the CBS activity level by counteracting the influence of chemoreceptors, nociceptors and higher brain structures [17]. This means that the CBS neurones are the central part of a man­ifold feedback system. A single neurone is not only the source of efferent activity patterns but also the target of various afferents often originating in the effector systems influenced by the neurones. Thus, the discharge frequency of the reticular neurones fluctuates according to the ever-changing activity in the single afferent and the permanently changing combination of afferents simultaneously reaching each neurone. In this manifold feedback system, cause and effect are usually indistinguishable.

An example of the dynamic reactions of two neighbouring reticular neurones to the same input is given in Fig. 1. By means of a rhythmically inflatable balloon inserted into the descending aorta, blood pressure waves were created which rhythmically increase the activity of pressoreceptor afferents. This led to a decrease in the activities of the neighbouring neurones and in respiratory depth and fre­quency. Fifty-six minutes later, the same manoeuvre affected neither the activities of the same neurones nor respiration. Other afferents had greater effect at that moment and cancelled the activity increasing input.

160 F. EBINGER et al.

. rr

E'ed- 1"',1 _N08A -j~~""'. ~y J

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Fig. 1. Polygraph registrations of the activity of two neighbouring neurons, simultaneously recorded with one electrode at two different times during the monitoring. These neuronal discharges are represented by standardized impulses and frequency curves together with arterial blood pressure and intrapleural respiratory pressure. As a result of rhythmic infla­tions of a balloon in the abdominal aorta (20-s periods), the blood pressure oscillates between 90 and 170 mmHg (left) and between 80 and 160 mmHg (right). Left, the activity of both neurones decreases as blood pressure rises. Note the reduction in depth and frequency of respirattion during elevated blood pressure. Right, 56 min later the same manoeuvre does not affect neuronal activities

Troughs in R-wave triggered post-event time histograms of spontaneous CBS neurone activity demonstrate the generally activity decreasing influence of barore­ceptors under physiological conditions (Fig. 2). The R-wave triggered histograms of two CBS neurones, recorded with one electrode, exhibit such troughs approxi­mately 130 ms after the R-wave (Fig. 2, left). In another registration period of the activity of the same neurones, an activity decreasing influence of baroreceptors is no longer visible (Fig. 2, right). The troughs in both histograms have disappeared. This shows that the influence of baroreceptor afferents for momentary discharge in the activity not influenced experimentally has changed spontaneously.

The other quality of inputs to CBS neurones originates in somatosensory recep­tors. All CBS neurones investigated received additional afferents from somatosen­sory receptors besides inputs from visceral receptors. The number of somatosenso­ry afferents converging on such neurones is restricted. Figure 3 shows a typical convergence of heterosensory and heterotopic somatic afferents onto neighbour­ing neurones. A slight touch of the left groin caused an increase in the discharge frequency of neurone 8 A and a decrease in the discharge frequency of neurone 8 B. Passive movements of the right hind leg excited both neurones, and a touch of the left ear inhibited both neurones. Touching the left ear again with the same strength several minutes later only had an effect on neurone A. Later during the recording, both neurones reacted as before. This demonstrates again that the responsiveness of CBS neurones to repeated physiological stimulation of the same receptive field changes in the course of time, depending on the actual combination of effective inputs.

Functional Organization of the Common Brainstem System 161

Neuron Nr. 21 A

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Fig. 2. R wave-triggered post-event time histograms of the activities of two neighbouring neurones, recorded at the same time with one electrode, and distributions of the trigger signal intervals. The histograms to the left and right were computed from the activities of the same neurones during different periods of the recordings. Bin width of the histograms, 4 ms. Ordinate, number of counts/bin; abscissa, time in milliseconds after the trigger signals. Left, histograms computed for 733 cardiac cycles, mean duration of heart cycle 317 ms. Left histograms, both neurones have a trough between 120 and 180 ms after the beginning of the heart cycle, indicating reduced activity of the neurons at this time. Right histograms, trough are hardly visible, indicating that at another time during the recording, no pulse-rhythmical modulations of the neuronal discharges could be detected

The composition of somatosensory afferent impingements does not change completely from neurone to neurone as had been concluded from single cell recordings [7, 8, 11, 12]. Neighbouring neurones have very similar somatosensory afferent spectra. If the somatosensory receptor inputs determine neuronal dis­charging, neighbouring neurones with similar afferent spectra are determined by the same inputs and are thereby functionally coupled to subpopulations. This organization in sUbpopulations is subject to temporal changes.

These findings verify that the network is not diffusely organized. Depending on the momentary influence of the different inputs on reticular neurones, different states of functional organization can be observed in recordings over a longer period of time.

162 F. EBINGER et al.

A

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Fig. 3A, B. Example of convergence of heterosensory and heterotopic somatic afferents on two neighbouring neurons and changing responsiveness to repeated stimulation of the same receptive field. A A slight touch of the left groin causes an increase of the discharge frequency of neurone 8 A and a decrease in neurone 8 B. Passive movements of the right hind leg excite both neurones. B Touching the left ear inhibits both neurones and touching it again several minutes later with the same strength has an effect only on neurone A; neurone B is unaffected

If the discharge is dominated by baroreceptor afferents, the CBS is stabilized and often characterized by rhythmical patterns. During such periods, the rhythms visible in the activity of most CBS neurones dictate the activity of the effector systems influenced by these neurones. The neuronal network CBS is organized at that time as a whole. Its neurones are mainly influenced by other neurones of the network. The responsiveness to inputs coming from outside the CBS is reduced. This state of functional organization corresponds to the behaviour pattern of relaxed wakefulness [16, 19, 22]. If the neuronal network is dominated by so-

Functional Organization of the Common Brainstem System 163

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Fig. 4. A Interval histograms sampled for 320 s, counted in bins of 2 ms, Abscissa scaled in milliseconds; ordinate in counts/bin. The histogram shows a multimodal distribution of intervals. These histograms are typical for neurones discharging in burst with a low mean frequency. B Interval histograms sampled for 300 s, counted in bins of 1 ms; 9 min after the end of the registration period for the histograms shown in A. Discharge rates of both neurones had increased. The histograms are of a more symmetrical shape

matosensory afferents, neighbouring reticular neurones are functionally coupled to small subpopulations. Only this state of neuronal organization enables differen­tiated regulation procedures. If the dischm;ge is determined by inputs producing emergency reactions such as arterial chemoreceptor or nociceptor afferents, ex­tremely high discharge frequencies result without coordinated discharges and regulation or relaxation is not possible.

These three different states of functional organization can be recognized by characteristic discharge levels and characteristic interval histograms. Bimodal in­terval distributions appeared when the discharge frequencies were between 0.3 and 7 impulses per second. Figure 4 shows. the interval histograms computed from two different parts of the registration of simultaneously recorded neurones. The his­tograms in Fig. 4A reveal the bimodal distribution of intervals typical for neu­rones discharging in bursts with a low mean frequency and corresponding to the

164 F. EBINGER et al.

functional state of relaxed wakefulness. Nine minutes later the discharge rates of both neurones had increased, and the histograms then had a symmetrical shape. These interval histograms are typical for neurones discharging with more than 16 impulses per second and correspond to the functional state of emergency reaction. In the frequency range between 8 and 16 impulses per second, a nonrhythmical discharging of the CBS neurones is characteristic. In this state, neighbouring neurones can be organized into subpopulations, and regulation manoeuvres are possible. The interval histograms are of an exponential form (Fig. 5).

The three states of functional organization described above are characterized not only by mean discharge frequencies and different types of interval histograms but also by the temporal coordination of action potentials in simultaneously recorded neurones. These temporal relations were investigated by studying cross­covariance histograms of neuronal activities. In the rhythmical discharging state, pairs of simultaneously recorded neurones reveal a rhythmical form of coupling. In Fig. 6, both auto-covariance histograms reveal a distinctive rhythm with a period of 300 ms. The cross-covariance histograms do not manifest phase shifts between the rhythmical signals but do reveal the same functional relation between both neurones and a common source of the rhythm.

There are also rhythmic correlations between neuronal activities recorded simul­taneously with two electrodes in the right and left half of the medulla. The rhythm, given as an example, corresponds to the delta-theta rhythm of the EEG. CBS neurones tend to exhibit various rhythmical phenomena in spontaneous activity. These phenomena in different frequency ranges often occur during the same discharge period. Thus, both nonrhythmic and rhythmic discharge patterns play a part in the coordination of the partial systems during regulatory processes [9,10, 16].

Neuron No 206 A

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Fig. 5. First-order histograms for the discharges of a neurone pair, simultaneously recorded, firing in a frequency range of 8-16 impulses/so Intervals were sampled for 380 s in bins of 1 ms. Abscissa scaled in milliseconds; ordinate in counts/bin. Both histograms show exponen­tial distributions

Functional Organization of the Common Brainstem System 165

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Fig. 6. Analyses of temporal relations of discharges of neurones recorded simultaneously with one electrode using auto- and cross-covariance histograms (ACH, CCH). ACHs and CCHs of the activities of a neurone pair discharging rhythmically coupled. Both ACHs reveal a distinct rhythm with a duration of 300 ms. Bottom right, the CCH with rhythmic course for positive and negative lags. The frequency of this rhythm is identical to that of the ACHs. Top right, the broad symmetric maximum on both sides of the ordinate indicates no phase shifts between the rhythms in the two signals

In the other neuronal discharging state with a tonic pattern in mean frequency ranges of 8-16 impulses per second and with exponential interval distributions, the discharges of neighbouring neurones often had a strong short-term correlation (Fig. 7). The cross-covariance histogram then had a sharp peak to the right of zero lag. This peak was not repeated in longer lags. A peak to the right of the ordinate demonstrates that neurone B discharges preferentially 1-5 ms after neurone A. The strong correlation could only be observed between the activities of neighbour­ing neurones. It could be experimentally induced by activating the somatosensory afferents physiologically, for example, by bending the legs.

This type of strong coupling is induced by common inputs to the neighbouring neurones and not by their direct synaptic connections (Fig. 8). The cross-covari­ances again exhibit a sharp peak to the right of zero lag which is not repeated in longer lags. Both auto-covariances have repeated maxima, demonstrating different rhythms in the activity of neurone A compared to that of neurone B. Rhythms are not visible in the cross-covariance histogram. The power spectra in Fig. 8, the

166 F. EBINGER et al.

Neuron N0 44/2 AlB

AUTOCOVARIANCE 182

CROSSCOVARIANCE

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Fig.7. Another example of a neurone pair simultaneously recorded with short-term syn­chronization of their discharges. Both neurones have interval distributions of exponential shape. The ACHs do not show periodicities. The CCH exhibits a strong peak to the right of zero lag

neurone A maximum being at 17 Hz and the neurone B other maxima being at 30 Hz and 60 Hz, and the cross-power spectr.um with no preferred common rhythm confirm these findings. They indicate that the coupling of the two neurones is not caused by a direct synaptic connection. In that case, the rhythmic pattern of the putative presynaptic neurone would appear as a secondary effect in the cross-covariance histogram and in the cross-power spectrum. Therefore, it must be concluded that the short-term coupling of the neuronal discharging is evoked by the common inputs from somatosensory receptors which simultaneously influence neighbouring neurones at that time.

Thus, as a reaction to common somatosensory afferents, neighbouring neurones are functionally organized into subpopulations, which are defined by the afore­mentioned characteristics: mean discharge frequencies between 8 and 16 impulses per second, exponential shape of the histograms, and short-term coupling.

When the neurones discharged with mean frequencies higher than 16 impulses per second and the interval histograms had a symmetrical shape, no correlation

Functional Organization of the Common Brainstem System 167

between the discharging of neighbouring neurones could be observed (Fig. 9, cross-covariance histogram).

The different types of functional organization form the neurophysiological background of three different behaviour patterns [10,12, 13, 16, 18,27]. The state of mainly low-frequency discharge, bimodal interval distribution, and rhythmical coupling of reticular neuronal activity corresponds to relaxed wakefulness. The nonrhythmical discharge pattern with an exponential interval histogram and short-term coupling corresponds to alertness which demands quick regulation procedures. The state of nonrhythmical, high-frequency discharging with an expo­nential interval distribution and uncorrelated discharging of neighbouring neu­rones represents the emergency reaction.

The permanent changes in behaviour patterns in normal life require changes in activity and therefore changes in the functional organization of the CBS. In Fig. 10, the neurone pair number 15 discharged rhythmically coupled, uncorrelat­ed, and rhythmically plus strongly coupled. The neurone pair number 1 discharged uncorrelated, strongly correlated, and uncorrelated plus strongly correlated. It is noteworthy that the maxima in the cross-covariance histogram switched from the left to the right of the ordinate. This means that during the registration period of the neuronal activity from which the second histogram was calculated, neurone B discharged before neurone A. Later, neurone A discharged before neurone B (fourth histogram, second line). The neurone pair number 7 discharged with a combination of strong and rhythmic coupling, rhythmically coupled, uncorrelated and again with a combination of strong and rhythmic coupling.

Whenever strong couplings were detected, the neurone with the higher discharge rate fired shortly before the neurone with the lower discharge rate. When the sharp maximum of the cross-covariance histogram moved from one side of the ordinate to the other, analogous changes in the discharge rates of the two neurones were seen.

The transition from one type of discharge coupling to another one is an expres­sion of a change in the functional state of the organism, requiring another form of regulation and therefore another form of CBS organization.

The three states of functional organization, to which three states of behaviour correspond, are characterized by mean discharge frequencies [21], types of interval histograms and types of couplings. In each of the three functional states presented in Fig. 11, one type of input dominates neuronal discharging. These examples of functional organization represent physiological states which usually change from one to another because of the transient nature of the input patterns. Our investi­gations show that reticular neurones are able to discharge in a well-coordinated fashion and with different discernible patterns. Three levels of activity are accom­panied by three different forms of functional organization of the neurones in the reticular network, and the CBS influences its effector systems with three different patterns. Conversely, the effector systems influence the CBS via three different forms of inputs. The functional state is sustained in these feedback systems until it is changed by new peripheral inputs or central commands.

With low-level activity, the entity of the neuronal network CBS acts as a stabi­lizer, guaranteeing the resting activity of the nervous system by its rhythmic

168 F. EBINGER et al.

Neuron NQ 1 AlB

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T ms

Fig. 8. A ACHs and CCHs of the activities of neighbouring neurones with strongly corre­lated discharges. Top right, the CCH has a sharp peak to the right of zero lag. Bottom right, the peak in the second bin is not repeated in longer lags. "Secondary effects", indicating the coupling mechanism, are not visible. Both ACHs have repeated maxima of different frequen­cies. The repeated maxima in both ACHs show that the neurones discharge in different rhythms

activity patterns [1]. In 1972 Moruzzi reported that each instinctive behaviour pattern requires the adequate degree of activity in the "reticular formation" [22]. In this functional state, for instance, the influence of central amygdala neurones on arterial blood pressure via the CBS reflects the rhythmical properties of this state [19]. Figure 12 shows the cross-covariance histograms of the activities of four different neurones of the right amygdala centralis together with arterial blood pressure in quiet wakefulness. In relation to the neuronal activity, blood pressure shows increases and decreases every 8-13 s. This rhythm often dominates the discharge patterns of CBS neurones [10, 16].

During the intermediate level of activity, the network exerts different influences on different effector systems at the same time. In this state, it does not act as a whole, as had been thought by Moruzzi [22]. The neurones are organized into subpopulations by the common afferents from somatosensory receptors. Neu-

Functional Organization of the Common Brainstem System 169

Neuron NQ 1 AlB

Fig. 8. B Power and cross-power spectra of the analysis of common rhythmic patterns in the discharge sequences of neurone pair num-ber 1. Abscissa scaled in hertz, fre­quency bin is 0.99 Hz; ordinates scaled in relative figures. The power spectrum of neurone A has maxima at 17 Hz and integer multiples of this frequency. The power spectrum of neurone B has maxima at 30 and 60 Hz. In the cross-power spectrum, no preferred common frequency of both neurones is visible B

1225

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198

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198

19

Power spectra

1~~

99.7 Y·S

l~ 99.7Y·S

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rones displaying strongly coupled discharges belong to the same subpopulation of the network. The configuration of each subpopulation is given by number, type, and discharge pattern of the afferents acting on this set of neurones and by the intrinsic activity of the network. The configuration of the sUbpopulation is not static. It changes when the composition of effective afferents changes.

The activity patterns of different subpopulations have different functions within this distributed system [24]. The population receiving the most "essential" infor­mation for the ongoing pattern of behaviour dominates the functional organiza-

170 F. EBINGER et al.

Neuron NQ 1 AlB

~ AA e T) Autocovariances

'11~ 4>BB(T)

835

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I

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CrOS$covariances of different parts of the registration of:

Neuron A--B

Neuron A---B

Neuron A-B

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~ AB eT) Crosscovariances

512 ~s

Functional Organization of the Common Brainstem System 171

activity dominating type of type of regulation interval cross covanance hi stogram coupl ing hi stogram abil ity

level inputs

~~ rhythmical V' rhythmica 1 coupl ing +. coordination

low pressoreceptor at rest

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, Deal sub-

~- popul ations

~L ~_ no coupl ings

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Fig. 11. Schematic representation of the characteristics of the three states of functional organization

tion of the system. The dynamic organization of the CBS in subpopulations and the coordination of the activity of these populations is the mechanism which produces the prerequisites for coordinated regulation of respiratory, cardiovascu­lar and somatomotor systems for the adaptation of the organism to different tasks. Regulation in the sense of adaptation of all organ systems in the body is not realized by circumscribed centres but by the different dynamically interconnected plurifunctional and dynamically configurated subpopulations. Therefore, from the functional point of view it is not meaningful to consider cardiovascular or respira-

Fig. 9. Histograms have a bin width of 1 ms. Abscissa scaled in milliseconds; ordinate in counts/bin related to bin width and time of evaluation. Left, ACHs of the two neurones. Both histograms have the same form. The amplitudes of the maxima decline rapidly, indicating regular discharges with fluctuating intervals. Right, CCHs plotted for - 50 ms < T < + 50 ms (top) and for -512 ms <T < + 512 ms (bottom). The bin counts fluctuate around the abscissa (expected value) without reaching an obvious maximum

Fig. 10. CCHs of three pairs of neurones displaying different types of correlated discharges in different parts of the recordings. Pairs were recorded in three different experiments. In each case, the neurones of one pair were recorded with a single electrode. Upper line (left to right): neurone pair number 15 discharges rhythmically coupled, uncorrelated, rhythmically coupled, and rhythmically plus strongly coupled. Middle line (left to right): neurone pair number 1 discharges uncorrelated, strongly correlated, uncorrelated and strongly correlated. Note that the maxima in the CCHs have switched from the left of to the right of the ordinate. Lower line (left to right): neurone pair number 7 discharges with a combination of strong and rhythmical coupling, rhythmically coupled, uncorrelated, and again with a combination of strong and rhythmic coupling

172 F. EBINGER et al.

w

REM

NEUROO 233-BP

t S

NEURON 261-BP 0052

w NElffiN 241-BP 0059

REM NEURON 72 - BP 0.094

Fig. 12. Four CCHs computed from the discharge sequences of four neurones of the right amygdala centralis and arterial blood pressure

tory regulation. The regulation during physical work serves the optimal supply and scavenging of the working muscles which are thereby enabled to fulfil definite movements within the framework of behaviour patterns. The responsible nervous structures organize the control of vigilance, muscle tone, as well as cardiovascular and respiratory systems.

In the state of high discharge frequencies, the CBS is organized by central commands or peripheral inputs into the functional state of emergency reaction. With this type of organization, which has a high degree of activity in all output pathways, a regulation with different peripheral nerves cannot be realized.

In the morphological matrix "reticular formation of the lower brainstem", the principles of functional interactions between single elements of this distributed system change. Both the holistic and the localization concepts can be temporarily realized. The realization is determined by the behaviour pattern which is actually demanded by the environmental processes.

Acknowledgements. The authors wish to thank Mrs. G. Braunig and Mrs. C. Heidelmeyer for preparing the manuscript, Mr. P. Holzner for preparing the figures, and the staff of the Institute's workshops for constructing the special equipment.

173

References

1. Bremer F, Terzuolo C (1954) Contribution a l'etude des mecanismes physiologiques du maintain de l'activite vigile du cerveau. Interaction de la formation reticule et de l'ecorce cerebrale dans Ie processus du reveil. Arch Int Physiol 62: 157 -178

2. Canon WB (1929) Bodily changes in pain, hunger, fear, and rage. Appleton Century Crofts, New York

3. Hess WR (1938) Das Zwischenhirn und die Regulation von Kreislauf und Atmung. Thieme, Leipzig

4. Hess WR (1948) Die funktionelle Organisation des vegetativen Nervensystems. Schwabe, Basel

5. Hess WR (1949) Das Zwischenhirn. Syndrome, Lokalisation, Funktionen. Schwabe, Basel

6. Jackson JH (1958) Evolution and dissolution of the nervous system. In: Taylor J (ed) Selected writings of J. H. Jackson. Basic Books, New York

7. Koepchen H-P, Langhorst P, Seller H, Polster J (1961) Beeinflussung der Aktivitiit einzelner Neurone im Rhombencephalon des Hundes durch Variation des arteriellen Druckes. Pfliigers Arch Gesamte PhysioI274:53-65

8. Koepchen H-P, Langhorst P, Seller H (1975) The problem of identification of autonomic neurons in the lower brain stem. Brain Res 87: 375-393

9. Lambertz M, Schulz G, Langhorst P (1985) Reticular formation of the lower brain stem. A common system for cardiorespiratory and somatomotor functions. Considerations by the aid of computer stimulations. J Auton Nerv Syst 12:63-75

10. Lambertz M, Kluge W, Schulz G, Langhorst P (1986) Principles offunctional organiza­tion of a common system in the reticular formation for cardiorespiratory and somatomo­tor regulation. Computer simulations based on physiological data. J Auton Nerv Syst [Suppl]:269-274

11. Langhorst P (1968) Untersuchungen neuronaler Hirnstammaktivitiit zur Frage der Spezifitiit oder Unspezifitiit der Kreislaufzentren. Inauguraldissertation, University of G6ttingen

12. Langhorst P, Werz M (1974) Concept of functional organization of the brain stem cardiovascular center. In: Umbach W, Koepchen H-P (eds) Central rhythmic and regu­lation. Hippocrates, Stuttgart, pp 238-255

13. Langhorst P, Schulz B, Lambertz M, Schulz G, Camerer H, Stroh-Werz M (1980) Dynamic characteristic of the unspecific brain stem system. In: Koepchen H-P, Hilton SM, Trebski A (eds) Central interactions between respiratory and cardiovascular control systems. Springer, Berlin Heidelberg New York, pp 30-39

14. Langhorst P, Schulz G, Lambertz M, Stroh-Wertz M, Krienke B, Keyserlingk D von (1980) Is there an influence of discharge patterns of neurons of the common brain stem system on neuronal activity in the dorsomedial part of the NTS. In: Koepchen H-P, Hilton SM, Trebski A (eds) Central interactions between respiratory and cardiovascular control systems. Springer, Berlin Heidelberg New York, pp 116-124

15. Langhorst P, Schulz B, Schulz G, Lambertz M (1983) Reticular formation of the lower brain stem. A common system for cardiorespiratory and somatomotor functions. Dis­charge patterns of neighbouring neurons influenced by cardiovascular and respiratory afferents. J Auton Nerv Syst 9:411-432

16. Langhorst P, Lambertz M, Schulz G (1986) Assessment of rhythmicity in the visceral nervous system. In: Lown B, Malliani A, Prosdocimi M (eds) Neural mechanisms and cardiovascular disease. Liviana, Padova, pp 133 -144 (Fidia research series 5)

17. Langhorst P, Schulz G, Lambertz M (1986) Physiological and pathophysiological as­pects of the multifunctional system for arousal, somatomotor, cardiovascular and res­piratory regulation. In: Kunze K, Zangemeister WH, Arlt A (eds) Clinical problems of brainstem disorders. Thieme, Stuttgart, pp 162-170

18. Langhorst P, Schulz G, Lambertz M (1986) Integrative control mechanisms for car­diorespiratory and somatomotor functions in the reticular formation of the lower brain

174 F. EBINGER et al.: Functional Organization of the Common Brainstem System

stem. In: Grossman P, Janssen KH, Vaitl D (eds) Cardiorespiratory and cardiosomatic psychophysiology. Plenum, New York, pp 9-39

19. Langhorst P, Lambertz M, Schulz G, Stock G (1987) Role played by amygdala complex and common brainstem system in integration of somatomotor and autonomic compo­nents of behaviour. In: Ciriello J, Calaresu FR, Renaud LP, Polosa C (eds) Organization of the autonomic nervous system: central and peripheral mechanisms. Liss, New York, pp 347-361

20. Luria AR (1984) Higher cortical functions in man, 2nd edn. Basic Books, New York 21. Moruzzi G (1958) The functional significance of the ascending reticular system. Arch Ital

Biol96:17-28 22. Moruzzi G (1972) The sleep-waking cycle. Ergeb Physiol 64: 1-165 23. Moruzzi G, Magoun HW (1949) Brain stem reticular formation and activation of the

EEG. EEG Clin Neurophysiol1:455-473 24. Mountcastle VB (1979) An organizing principle for cerebral functions. The unit module

and the distributed system. In: Schmitt FO, Worden FG (eds) The neurosciences fourth study program. MIT Press, Cambridge, pp 21-42

25. Schulz B, Lambertz M, Schulz G, Langhorst P (1983) Reticular formation of the lower brainstem. A common system for cardiorespiratory and somatomotor functions: dis­charge patterns of neighbouring neurons influenced by somatosensory afferents. J Auton Nerv Syst 9:433-449

26. Schulz G, Lambertz M, Schulz B, Langhorst P, Krienke B (1985) Reticular formation of the lower brainstem. A common system for cardiorespiratory and somatomotor functions: cross-correlation analysis of discharge patterns of neighbouring neurons. J Auton Nerv Syst 12:35-62

27. Siegel GM (1979) Behavioral functions of the reticular formation. Brain Res Rev 1:69-105

Discussion on Coordinated Activity in Brainstem Reticular Networks Moderator: G. L. GEBBER

Much of the Discussion focused on the neural mechanisms responsible for the coordinated activity of the effector systems controlled by the reticular formation. Langhorst reiterated the view expressed in his formal presentation that individual neurons of the brainstem reticular formation are multifunctional in character. He believes that one and the same neuron influences cardiovascular, respiratory, somatomotor, and cortical targets, and that selective gating of the outputs of these neurons to their different targets provides in part the substrate for the formulation of complex behavioral response patterns. Gebber asked whether Langhorst'S con­cept of the multifunctional reticular neuron includes those with spinal axons. Langhorst replied that although he suspects that this is the case, he is not as much interested in the details of the circuitry comprising the "common brainstem sys­tem" as he is in describing its dynamical properties. Cohen pointed out that at least some brainstem neurons must be cosidered as functionally specific. He argued that while phrenic, sympathetic, and cortical activity contain common rhythms on the time scale of the respiratory cycle, more rapid rhythms can be used as markers of components specific to each system. For instance, inspiratory neurons of the dorsal respiratory group show a high-frequency oscillation (HFO) greater than 50 Hz while bulbospinal neurons innervating the intermediolateral sympathetic spinal nucleus do not. The discharges of the latter neurons contain a 2- to 6-Hz or 10-Hz rhythm in place of the HFO. Gebber added that it is also possible to distinguish brainstem neurons with similar rhythms. For instance, under barbitu­rate anesthesia, one can find cat medullary neurons with activity correlated to the 2- to 6-Hz rhythm in sympathetic nerve discharge but not the delta-theta rhythms in the EEG. Medullary neurons with activity related to the EEG but not sympa­thetic activity can be found in the same preparations. In contrast, in the chloralose­anesthetized cat, medullary neurons with activity correlated both to EEG and sympathetic activity (in the 2- to 6-Hz frequency band) are commonly encoun­tered. On this basis, Gebber suggested that dynamic coupling and uncoupling of systems of "target-specific" neurons might explain many of the observations made in Langhorst's laboratory. Langhorst responded that while the models of reticular organization proposed by various laboratories differ in detail, there is general agreement on the dynamic capabilities of the system that help to formulate and support complex and highly differentiated behavioral response patterns. Richter remarked that both the model of the multifunctional neuron and that of the dynamic coupling of circuits comprised of "target-specific" neurons have their strong points. He suggested that the reticular formation likely contains both

176 G.L.GEBBER

"target-specific" and "target-unspecific" neurons. In reviewing the salient points of his formal presentation, Richter reminded the audience that his data are consis­tent with the view that early-inspiratory, postinspiratory, ramp-inspiratory, and some expiratory neurons comprise a common cardiorespiratory network. Never­theless, he assumes that the outputs of this network are distributed to other reticular neurons that are "target-specific." Indeed, the spinal axonal conduction velocities of respiratory neurons are at least tenfold that of bulbospinal sympa­thoexcitatory neurons of the rostral ventrolateral medulla. Hukahara pointed out that his data require that the substrate for cardiorespiratory and cortical coordina­tion be expanded to include the coupling of oscillators that are self-contained in the forebrain as well as the brainstem. His suggestion arises from the observation that while locked in cats with an intact neuraxis, slow rhythms with a period between 20 and 30 s persist in the EEG, renal nerve activity, and blood pressure after midbrain transection. Kelso remarked that perhaps too little attention has been paid to the dynamical behavior of the systems responsible for cardiorespira­tory and motor coordination. He reminded us that even simple dynamical systems such as a dripping faucet can display complex and creative behavior. Kelso sug­gested that more emphasis be placed on a description of state transitions and less on the details of the circuitry, a view also espoused by Langhorst.

The discussion also focused on several computational techniques that have recently been applied to the analysis of cardiorespiratory networks. Trzebski was impressed with the gravity and snowflake methods used by Lindsey to study the synaptic interactions of three or more neurons whose discharges are simultaneous­ly recorded. These methods undoubtedly will be widely used in the future to visualize the dynamic associations among neurons comprising cardiorespiratory networks. Feldman, however, voiced a note of caution. He pointed out that such methods still leave us in a quandary regarding the problem of distinguishing causality from correlation.

Trzebski asked whether it is appropriate to use the coherence function when the system being analyzed exhibits nonlinearities. Kocsis explained that coherence analysis can be used under this circumstance. As described by Bendot and Piersol (Measurement and Analysis of Random Data, Wiley, New York, 1966), the coher­ence function is unity in the ideal case of a linear system in which signal transmis­sion is undisturbed by noise. In such a system, the coherence function is zero if the two signals are completely unrelated. If the coherence value is significantly differ­ent from zero but less than unity, one or more of the following situations can be assumed to exist: (a) the two signals arise from common as well as uncommon inputs; (b) noise is present in the signals; (c) the system relating the two signals is not linear. The third situation is most important with regard to Trzebski's ques­tion. Nonlinearities reduce the coherence but do not necessarily obscure a signif­icant relationship between the two signals.

Relative to Gebber's presentation, Langhorst stated that is not possible to distin­guish a true lead from a lag in a phase spectrum. Cohen concurred with Langhorst'S view. Gebber agreed as well, but considered this a technicality. He reminded the audience that phase spectral analysis revealed frequency-dependent changes in the interval between activity in two sympathetic nerves. This observation is consistent

Discussion on Coordinated Activity in Brainstem Reticular Networks 177

with either of two models (coupled oscillators versus parallel filters) of central organization of the system generating the 2-: to 6-Hz rhythm. Sinclair asked for clarification on what is meant by a neural bandpass filter. Kocsis explained that the 2- to 6-Hz sympathetic rhythm might result from the filtering properties of a neural network subjected to a stochastic input. The bandpass of the filter would be determined, in part, by conduction time in feedback loops (e.g., recurrent inhibi­tion) of the filtering circuit. Feldman remarked that all oscillating circuits likely require a source of tonic drive. His statement implies that the difference between a neural oscillator and a neural filter is seman tical. Richter suggested that the shape and perhaps frequency of the 2- to 6-Hz slow wave in sympathetic nerve discharge might be determined, in part, by the intrinsic properties (e.g., calcium-activated potassium conductance) of pre- and postganglionic neurons. Gebber responded that while the final shape of the slow wave might indeed be dependent upon such factors, the observed relationships between the activity of brainstem neurons and sympathetic nerves left little doubt that rhythm generation occurs at a supraspinal locus and involves network interactions. Koepchen ended the session by encourag­ing the audience to continue these fruitful discussions both during and after the Symposium.

Peripheral and Central Interrelation Between Cardiorespiratory and Motor Control

Somato-Vegetative Interaction at the Peripheral Level: Possible Effects on Motor Performance * M. PASSATORE and C. GRASSI

Introduction

Data are available in the literature that demonstrate the relationship between skeletal muscle function and sympathetic nervous system activity. Exercise has been associated with an increase in sympathetic outflow, which is aimed at adjust­ing cardio-respiratory parameters to meet the requirements of the active muscle. Such an increase can be obtained either through the coactivation of sympathetic and motor systems, induced by central parallel commands (Krog and Lindhard 1913; Goodwin et al. 1972; McCloskey 1981) or through a reflex activation medi­ated by group III and group IV muscle afferents excited by the muscular contrac­tion (McCloskey and Mitchell 1972; Iwamoto et al. 1991). These changes of the sympathetic command could sub serve motor performance not only by modifying the vegetative functions but also by affecting the activity of skeletal muscle through an action exerted at both central and peripheral levels. The present paper does not deal with the role played by the central monoaminergic pathway in motor control but focuses on the possibility that an increase in sympathetic outflow affects skeletal muscle function by acting at the peripheral level. In particular, the possibility is considered that an increase in the sympathetic outflow induces (a) a change in muscle spindle information thereby reflexly modifying muscle function and (b) a change in muscular contraction.

The existence of a direct action exerted by the sympathetic nervous system on muscle spindle information has long been investigated by several authors (refer­ences in Bowman 1981 and in Passatore et al. 1985b). However, the changes in spindle afferent discharge observed in these studies, usually performed on cat hindlimb muscles, were scarce and were therefore interpreted as functionally in­significant (Hunt 1960; Hunt et al. 1982).

We have reinvestigated the sympathetic action on muscle spindles belonging to jaw closing muscles, a muscular district which has recently been described as receiving abundant adrenergic innervation (Barker and Saed 1987). In these exper­iments performed on anaesthetized and paralysed rabbits, sympathetic stimulation at physiological frequencies proved able to increase significantly the firing rate. in a large percentage of spindle afferents (Passatore et al. 1985a, b). The average

* This research was supported by CNR and MURST grants. The generous financial assis­tance of the Istituto Bancario S. Paolo di Torino and of the Cassa di Risparmio di Torino is gratefully acknowledged.

182

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M. PASSATORE, C. GRASSI

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Time (s)

Fig. 1. Effect of sympathetic stimulation at 10 Hz (bars) on the discharge fre­quency of three spindle afferents belong­ing to jaw closing mus1ces of anaes­thetized and curarized rabbits. (From Passatore et al. 1985 a, by permission of publisher)

enhancement ofthe discharge frequency ranged from 20 to 30 impulses per second, reaching increments up to 65 per second (Fig. 1). These effects were attributed to a direct action of the adrenergic mediator on muscle spindles, even though the exact site of action (spindle endings or intrafusal muscle fibres) could not be ascertained in the intact animal preparation. In the same experimental model, sympathetic stimulation was also found to affect spindle sensitivity to muscle length changes, evaluated under both static and dynamic conditions (Passatore et al. 1985 a, b; Passatore and Grassi 1989).

In the present study we investigated whether an activation of the sympathetic nervous system, besides modifying muscle spindle information, can also influence motor function by affecting the mechanism of muscular contraction. There is much information in the literature on the different actions exerted by cate­cholamines on skeletal muscles (references in Bowman 1981). Regarding contrac­tion of nonfatigued muscles, adrenaline was found to increase both the amplitude and the duration of maximal twitches in fast-contracting muscles, while opposite effects were detected in slow-contracting muscles. However, these effects were observed under experimental conditions creating blood concentrations of cate­cholamines higher than any that occur even in extreme stress.

Since both anatomical (Barker and Saed 1987) and functional data (Passatore et al. 1985a, b) suggest that masticatory muscles are provided with a rich adrener­gic innervation, we chose this muscular district to reinvestigate the effects of sympathetic stimulation at frequencies within the physiological range.

Methods

The experiments were performed on albino rabbits (weight 2.5-3.5 kg) anaes­thetized with urethane (1.2 g/kg, i.v.). The animal's skull was blocked in a stereotaxic frame, and the mandible was fixed by cementing together upper and lower incisor teeth. The digastric muscle (DM) was directly stimulated through Ag-AgCI electrodes sewed to the muscle (0.1-250 Hz, 0.5 ms pulse duration, 8 -12 V), and the tension developed during the muscular contraction was mea­sured by a force transducer (Grass FT03) connected to the cut central tendon of

Somato-Vegetative Interaction at the Peripheral Level 183

the muscle. In all these experiments, stimulation voltages were employed that were adequate to elicit the contraction of the entire muscle. In some animals, contrac­tion of the DM was induced reflexly by stimulating, through bipolar concentric electrodes (0.1 Hz, 0.1 ms pulse duration, 20 - 300 llA), discrete areas of the caudal portion of the mesencephalic trigeminal nucleus. These areas contain the somata of afferent neurons innervating periodontal mechanoreceptors, which mediate the jaw opening reflex (for additional methodological details, see Passatore et al. 1983). The peripheral stump of the cervical sympathetic trunk (CST) was stimulat­ed through Ag-AgCI electrodes (1 - 10 Hz, 0.5 ms pulse duration, 8- 10 V). In some experiments rabbits were paralysed (pancuronium bromide, 0.3 mgjkg i.v., or tubocurarine, 0.25 mgjkg i.v., repeated when needed) and artificially ventilated to maintain the end-tidal CO2 levels near control values. Arterial blood pressure and heart rate were monitored throughout the experiment, and a femoral vein was cannulated for drug injection. In four animals, 11.- and ,B-adrenoreceptor blocking agents were administered (phentolamine, 3-4 mgjkg and propranolol, 1.0- 2.5 mgjkg) .

Results

In anaesthetized rabbits, electrical stimulation of the DM at 0.1 Hz induced twitch responses with stable amplitude and duration over time. Maximal twitch tension obtained at the optimal muscle length ranged from 60 to 110 g, depending on animal weight. Unilateral stimulation of the CST at 10 Hz consistently induced an increase in peak tension in the ipsilateral DM (Fig. 2A, B). This effect was detect­able 10- 20 s after starting the sympathetic stimulation and reached a maximum increment of 10% - 15% of control values within the next 1.5 - 3.0 min; larger enhancements, up to 30%, occasionally occurred. The tension augmentation was associated with an increase in the twitch duration, which usually appeared after 2.5- 3.0 min of CST stimulation. This change in the twitch time course was due to prolongation of the time from the start of the contraction to its peak and mainly to the increase in the time from peak tension to half-relaxation value (Fig. 2B). When sympathetic stimulation was ended, first the twitch magnitude and then the duration returned to control levels (1-3 min and 3- 5 min, respectively). The

A B

f\ j '

c

lOOms

Fig.2A-C. Effect of sympathetic stimulation on maximal twitches (A, B) and on incom­plete tetanic contractions (C) of the digastric muscle, directly stimulated at 0.1 and 30 Hz respectively. B, C contractions recorded during sympathetic stimulation (upper traces) are superimposed on controls

184 M. PASSATORE, C. GRASSI

A

B

c J50g 15s

Fig. 3. Effect of adrenoceptor blocking agents on twitch potentiation induced by sympathetic stimulation at 10 Hz (bars). A, Control; B, after p-adrenoceptor blockade; C, after IX-adrenoceptor blockade

described effects were already present, though smaller, upon CST stimulation at 3 Hz.

During repetitive stimulation of the DM, sympathetic fibre activation enhanced the fusion of incomplete tetanic contractions; therefore the sympathetically in­duced increase in the developed tension was, in percentage terms, larger than that observed in twitch responses (Fig. 2 C). Instead, stimulation of the CST failed to elicit any change in maximal tetanic contractions. The same effects were obtained in experiments in which neuromuscular junctions were blocked by paralysing agents (tubocurarine or pancuronium bromide).

All the CST stimulation trials were performed on maximal twitches of the DM. Therefore, the increase in the peak tension should be attributed to a positive inotropic effect rather than to any sympathetically induced increase in the number of muscle fibres activated by the electrical stimulus directly delivered to the muscle. This was further verified in some experiments in which DM was indirectly stimu­lated. However, since the stimulation of the intact muscle nerve is rather difficult due to the anatomical arrangement, and because cutting the nerve would interrupt the sympathetic supply to the DM, the motor supply to the muscle was activated by stimulating discrete areas of the mesencephalic trigeminal nucleus (see "Meth­ods"). Under these experimental conditions the effects produced by CST stimula­tion on DM contraction were similar to those obtained in direct stimulation trials.

The blockade of ,B-adrenergic receptors (propranolol, 1- 2. 5 mg/kg, i. v.) did not significantly modify the effects of CST stimulation, which were, instead, almost completely abolished by the administration of IX-adrenoceptor antagonists (phen­tolamine 3- 4 mg/kg i.v.; Fig. 3).

Discussion

Stimulation of the CST at physiological frequencies increases both peak tension and duration of maximal twitches as well as the fusion of incomplete tetanic contractions in nonfatigued digastric muscle. In fast-contracting hindlimb muscles similar responses had been observed following the injection of large, "non-physi­ological" doses of catecholamines, while sympathetic stimulation at physiological

Somata-Vegetative Interaction at the Peripheral Level 185

frequencies did not elicit any effect, after denervation of the adrenal glands (refer­ences in Bowman 1981). The difference in responses observed in the two muscular districts could be due to a particularly rich adrenergic innervation of the mastica­tory muscles (Barker and Saed 1987; Barker and Saito 1981); this could also account for the different magnitude of the sympathetic action exerted on muscle spindles located in hindlimb and injaw closing muscles (Hunt et al. 1982; Passatore et al. 1985a, b).

Regarding the mechanism responsible for the sympathetically induced increase in tension, the presence of such an effect in fully curarized animals shows that it is not dependent on an action exerted by the adrenergic mediator on the neuromus­cular junctions. Moreover, the time course of the response might be consistent with an effect secondary to vasomotor changes. However, it has been shown that the local reduction of the blood flow either decreases or has no effect on force produc­tion in skeletal muscles, depending on their fibre composition, but it has never been shown to increase the developed tension (Hobbs and McCloskey 1987). In addi­tion, data from experiments in which muscular contraction and muscle blood flow were recorded simultaneously (Bowman and Zaimis 1958), together with the pres­ence of muscle twitch potentiation in in vitro preparations (references in Bowman 1981; Arreola et al. 1987) exclude that the effects elicited by catecholamines are secondary to vasomotor changes.

All these findings suggest that the increase in force production induced by sympathetic stimulation at physiological frequencies is ascribable to an action exerted on the mechanism of muscular contraction. Consequently, the motor function could be changed through this action under those physiological and pathological conditions in which the sympathetic outflow is modified. Then it may be suggested that the activation of the sympathetic system occurring during exer­cise can improve motor performance not only through an adjustment of the cardio-respiratory parameters but also by directly acting on muscle contraction.

However, in order to define the global influence exerted by the sympathetic system on motor function, the complex interaction among all the sympathetically­modulated nervous signals converging on the rx motoneuron has to be taken into consideration. First of all it must be considered that the sympathetic system does affect muscle spindle information by enhancing the spindle afferent discharge and by decreasing the receptor sensitivity to muscle length changes (Passatore et al. 1985 a, b; Passatore and Grassi 1989). Such decrease in spindle sensitivity fits with the marked reduction of the tonic vibration reflex induced by sympathetic stimu­lation which has been found in jaw closing muscles (Grassi et al. 1991). The enhancement in the spindle afferent discharge would reflexly activate the rx motoneurons of the parent muscle. Then this effect would concur with that exerted on the contractile mechanism in potentiating the muscular contraction under conditions of sympathetic activation. In addition, the modifications of both the spindle sensitivity and the stretch reflex suggest that a parallel reduction of the feed-back control of muscle length also occurs.

Moreover, an increasing number of reports indicates that catecholamines can modulate the activity of several other somatic and visceral afferent inputs, effects of opposite sign having been reported on different receptors (references in Akoev

186 M. PASSATORE, C. GRASSI

1981; Passatore and Grassi 1989). In this context, recent data suggest that sensory information transmitted by certain populations of C group fibres is reduced by activation of the sympathetic system, due to the depressing action exerted on the transmission of the nerve impulses (Shyu et al. 1989). There is no doubt that this influence of catecholamines on the sensory information contributes in modifying the level of excitability of the IX motoneurons.

Finally the actions exerted by the central monoaminergic pathways on the nervous circuits controlling movement should be considered. In fact, numerous projections to the spinal cord, cerebellum and cerebral cortex have been found, originating from noradrenergic neurons located in locus coeruleris, subcoeruleus and parabrachialis (references in Holstege and Kuypers 1987). In addition, nor­adrenaline has been reported to decrease membrane conductance in spinal motoneurones (Engberg and Marshall 1971), and this has been considered a mechanism by which other synaptic inputs converging on the same cell could be potentiated (Grillner 1981). Therefore, in the central nervous system, noradrenergic fibres could playa role in controlling the gain of other synapses. In particular, these noradrenergic projections have been reported to facilitate both extensor and flexor monosynaptic reflexes (Chan et al. 1986), while they can depress transmis­sion from group II muscle afferents to a group of intemeurons intercalated in the reflex pathways to motoneurons (Bras et al. 1989). In addition, the same projec­tions are also directed to the dorsal hom, and they seem to exert an inhibitory effect on transmission of afferent information (Jones and Gebbart 1986; Mokha et al. 1986). These brainstem neurons, which receive numerous projections from the limbic structures, can be then considered important connections through which emotional conditions can strongly influence both sensory input and motor output.

In conclusion, all the results discussed above show that an activation of the sympathetic nervous system, occurring in numerous physiological conditions, sig­nificantly affects motor function by acting at both central and peripheral levels. The complex interactions among all the mentioned effects require further elucida­tion.

Acknowledgements. M. Passatore wishes to express her gratitude to Prof. Ermanno Manni for the warm hospitality in his laboratory, after her moving to Torino. She also wishes to thank Mr. R. Dalla Valle for his skillful technical work.

References

Akoev GN (1981) Catecholamines, acetylcholine and excitability ofmechanoreceptors. Pro­gress in Neurobiology 15:269-294

Arreola J, Calvo J, Garcia MC, Sanchez JA (1987) Modulation of calcium channels oftwitch skeletal muscle fibres of the frog by adrenaline and cyclic adenosine monophosphate. J Physiol (Lond) 393:307-330 '

Barker D, Saed HH (1987) Adrenergic innervation of rat jaw muscles. J Physiol (Lond) 391:114P

Barker D, Saito M (1981) Autonomic innervation of receptors and muscle fibres in cat skeletal muscle. Proc Roy Soc Lond [B]212:317-332

Somato-Vegetative Interaction at the Peripheral Level 187

Bowman WC (1981) Effects of adrenergic activators and inhibitors on skeletal muscles. In: Szekeres L (ed) Adrenergic activators and inhibitors. Springer Verlag, Berlin Heidelberg New York, pp 47 -128 (Handbook of experimental pharmacology, vol 54/2)

Bowman WC, Zaimis E (1958) The effects of adrenaline, noradrenaline and isoprenaline on skeletal muscle contractions in the cat. J Physiol (Lond) 144:92-107

Bras H, Jankowska E, Noga BR (1989) Depression of transmission from group II muscle afferents to spinal interneurones by descending monoaminergic pathways. Eur J Neu­rosci [Suppl] 2: 54.2

Chan JYH, Fung SJ, Chan SHH, Barnes CD (1986) Facilitation of lumbar monosynaptic reflexes by locus coeruleus in the rat. Brain Res 369: 103 -109

Engberg I, Marshall KC (1971) Mechanism of noradrenaline hyperpolarization in spinal cord motoneurones of the cat. Acta Physiol Scand 83: 142-144

Goodwin GM, McCloskey DI, Mitchell JH (1972) Cardiovascular and respiratory responses to changes in central command during isometric exercise at constant muscle tension. J Physiol (Lond) 226: 173 -190

Grassi C, Deriu F, Passatore M (1991) Effects of sympathetic nervous system activation on the tonic vibration reflex in jaw-closing muscles of the anaesthetized rabbit. J Physiol (Lond) 435: 59P

Grillner S (1981) Control oflocomotion in bipeds, tetrapods, and fish. In: Brooks VB (ed) Motor control. Am Physiol Soc, Bethesda, pp 1179-1236 (Handbook of physiology, sect 1, vol II)

Hobbs SF, McCloskey DI (1987) Effects of blood pressure on force production in cat and human muscle. J Appl PhysioI63:834-839

Holstege JC, Kuypers HGJM (1987) Brainstem projections to spinal motoneurons: an update. Neuroscience 23: 809-821

Hunt CC (1960) The effect of sympathetic stimulation on mammalian muscle spindles. J Physiol (Lond) 151: 332-341

Hunt CC, Jami L, Laporte Y (1982) Effects of stimulating the lumbar sympathetic trunk on cat hindlimb muscle spindles. Arch Ital Bioi 120: 371-384

Iwamoto GA, Waldrop TG, Bauer RM (1991) Brainstem mechanisms involved in reflex cardiovascular responses to muscular contraction. See in this book pp. 193-199

Jones SL, Gebhart GF (1986) Quantitative characterization of ceruleospinal inhibition of nociceptive transmission in the rat. J Neurophysiol 56: 1397 -1410

Krogh A, Lindhard J (1913) The regulation of respiration and circulation during the initial stages of muscular work. J Physiol (Lond) 47: 112-136

McCloskey DI (1981) Centrally-generated commands and cardiovascular control in man. Clin Exp Hypertens 3:369-378

McCloskey DI, Mitchell JH (1972) Reflex cardiovascular and respiratory responses originat­ing in exercising muscle. J Physiol (Lond) 224: 173 -186

Mokha SS, McMillan lA, Iggo A (1986) Pathways mediating descending control of spinal nociceptive transmission from the nuclei locus coeruleus (LC) and raphe magnus (NRM) in the cat. Exp Brain Res 61: 586-606

Passatore M, Filippi GM, Grassi C (1985 a) Cervical sympathetic nerve stimulation can induce an intrafusal muscle fibre contraction in the rabbit. In: Boyd lA, Gladden MH (eds) The muscle spindle. Macmillan, London, pp 221-226

Passatore M, Grassi C, Filippi GM (1985b) Sympathetically-induced development of ten­sion in jaw muscles: the possible contraction of intrafusal muscle fibres. Pflugers Arch 405:297-304

Passatore M, Grassi C (1989) Jaw muscle reflexes can be affected by sympathetic nervous system activation. In: van Steenberghe D, De Laat A (eds) EMG of jaw reflexes in man. Leuven University Press, pp 127 -145

Passatore M, Lucchi ML, Filippi GM, Manni E, Bortolami R (1983) Localization of neu­rones innervating masticatory muscle spindles and periodontal receptors in the mesen­cephalic trigeminal nucleus and their reflex actions. Arch Ital BioI 121: 117 -130

Shyu BC, Olausson B, Andersson SA (1989) Sympathetic and noradrenaline effects on C-fibre transmission: single-unit analysis. Acta Physiol Scand 137: 85-91

Muscular Activity and Cardiovascular Regulation * D.l. MCCLOSKEY and S.P. HOBBS

The present study began by asking whether different arterial blood pressures (BP), within the physiological range, could alter muscular force production.

Animal Experiments

We studied whether changes in arterial BP within the physiological range would alter blood flow and force production of isolated muscles which contain predom­inantly one type of muscle fibre. Experiments were carried out on cats anaes­thetized with pentobarbital sodium. The soleus, medial gastrocnemius, and caudo­femoralis muscles were studied. These muscles were chosen because of their differ­ent fibre compositions: soleus, 100% type I fibres (high oxidative enzyme levels, slowly fatiguing); medial gastrocnemius, 61 % type II a and 25% type I fibres (both high oxidative enzyme levels, slowly fatiguing); caudofemoralis, 91 % type II B fibres (glycolytic enzymes, fast fatiguing).

Each muscle was made to contract intermittently by delivering trains of square - wave stimuli to the end of its cut nerve through an isolated stimulator. Electrical pulses 0.5 ms in duration were delivered at 40 Hz for 320 ms every second. Supra­maximal voltage was used for eliciting maximal tetanic contractions. This stimula­tionprotocol follows that of Burke et al. (1973), who used it to study the fatiguabil­ity of different types of motor units. Soleus and medial gastrocnemius muscles were made to contract for prolonged periods of time (from 15 min to over 2 h) during which peak force at a given BP was well maintained, or fell only very gradually with time.

The effects of muscle perfusion pressure on muscle blood flow and developed force during contractions were studied when pressure was changed by partially occluding the terminal aorta or by withdrawing blood through a catheter in the common carotid artery. Muscle perfusion pressure was measured as the pressure in the contralateral femoral artery, through a cannula that was connected to a pressure transducer. With caudofemoralis the muscle on one side was made to contract when pressure was high and that on the other when it was low. With soleus and medial gastrocnemius, femoral arterial pressure was changed in steps

* This research was supported by the National Health and Medical Research Council of Australia.

Muscular Activity and Cardiovascular Regulation 189

between 70 and 135 Torr. Pressure was maintained at each level until a steady level of force was produced. The values of BP, muscle blood flow, and developed force used for analysis were measured during the periods when force production was steady.

A representative response of force production and blood flow of soleus to changes in BP between 125 and 75 Torr is shown in Fig. 1. Similar results were obtained in medial gastrocnemius. As can be seen, force changed in the same direction as BP. The change in force usually began within 5-10 s of the change in BP, and a new steady level of force was generally reached within a few minutes. Muscle blood flow was also directly related to BP, as shown in Fig. 1 (Hobbs and McCloskey 1987).

In contrast to forces produced by soleus and medial gastrocnemius, the force developed by caudofemoralis was not affected by changes in BP. In all caudo­femoralis muscles tested, force fell rapidly after the initiation of contractions such that after a few minutes of intermittent tetanic contraction less that 5% of the initial force was being produced. This rapid fall in force occurred over a similar time course whether BP was held at about 70 or 125 Torr.

Possible Physiological Significance. These findings indicated a marked dependence on perfusion pressure across the physiological range for muscles of type I and type IIa fibre types. These are fibres high in oxidative capacity, with high resistance to fatigue. In contrast, perfusion pressure had no apparent effect on the rapid fa­tiguability of muscle with low oxidative capacity, type IIb fibres.

Clearly, there was a critical dependence of developed muscular force on BP for the slowly fatiguing muscles. From this it would follow that the magnitude of descending motor command necessary to achieve a given level of muscle force would be less if BP were to rise. This point is important in considering cardiovas-

BP 150[ mm Hg

50

Force 600[ 300

9

o Blood flow

mil mini 1 OOgm

L--I 1min

Fig. 1. Representative response from an anaesthetized cat showing relation between force production and blood flow for different levels of arterial pressure. Upper trace, mean arterial pressure supplied to the soleus: changes were produced by graded occlusions of the abdom­inal aorta. Lower trace, force attained in repeated isometric tetanic contractions (40 Hz for 320 ms, every second). Numbers below force give blood flow per wet weight of muscle. (From Hobbs and McCloskey 1987)

190 D.1. MCCLOSKEY, S. F. HOBBS

cular control in exercise, as it is thought that one important determinant of the cardiovascular response in exercise is the magnitude of descending motor com­mand (Goodwin et al. 1972).

It may be, therefore, that if a muscle begins to fatigue while performing a given level of work, the extra motor command that is called up voluntarily to recruit the extra fibres needed to maintain the work level acts as a drive to the cardiovascular system and so raises BP (Goodwin et al. 1972; Hobbs 1982). This, in turn, increases muscle perfusion and work output in the manner just described for the cat exper­iments. A new steady state would be established when the motor command had increased muscle blood flow to a level that would sustain the required force level.

In this way, the motor command element of cardiovascular regulation, which might at first have seemed to be an open-loop, 'feed-forward' system, can be seen to be part of a control loop that involves an interaction of command signals, force production and cardiovascular variables.

It is clearly of interest to see whether there is evidence of this system acting in man.

Human Experiments

If a fall in BP were to reduce force production in normal human subjects, then presumably the level of muscle activation would have to increase in order to maintain force.

That this occurs in human subjects was demonstrated in electromyographic studies in subjects performing rhythmic contractions at low levels of force produc­tion (approximately 10% of maximal voluntary contraction). When such subjects began a series of contractions in a limb at heart level, and then continued the contractions as the limb was raised above heart level, the integrated EMG of the contracting muscles increased (Hobbs and McCloskey 1987). Presumably, the fall in arterial pressure within the elevated limb led to a reduction of muscle force production in a manner similar to that described above for the cat. The subject was able to maintain the contractions, however, by recruiting more motor units or by achieving a higher rate of activation of already active motor units. This increased level of activation was seen as an increase in the integrated EMG.

If the hypothesis of a control loop involving motor command, cardiovascular response and muscle force production as outlined above is correct, one would expect to find a larger cardiovascular response when a subject contracts an elevat­ed limb than when he/she contracts the same limb at heart level. This is because the lower perfusion pressure available to an elevated muscle reduces force output and so requires a larger motor command for the greater activation of motor units needed to continue a given task. This larger motor command would be expected to evoke a larger cardiovascular response.

This predicted pattern or response is readily demonstrable (Fig. 2). Normal subjects produce larger cardiovascular responses to the same low-level, rhythmic muscular work when that work is performed with the contracting muscles elevated

Muscular Activity and Cardiovascular Regulation 191

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~ 140 E ~

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~ 100

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Fig. 2. Arterial pressure and heart rate responses from two normal subjects. Each subject performed repetition handgrip contractions, lifting a load 10% of maximum voluntary contraction repeatedly from a rest up to a stop 5 cm away. Control contractions were performed with the hand and forearm supported at heart level. The cardiovascular responses to these are compared with similar contractions performed with the arm supported in an elevated position (elbow 40 cm above heart level, wrist 60- 70 cm above heart level)

than when they are at heart level. This finding does not, of course, rule out a role for reflexes based on sensory nerves in the contracting muscle. It serves, however, as an illustration of how a cardiovascular drive based on motor commands could serve to align muscle perfusion and muscle performance.

Conclusion

The interaction of muscle blood flow and force or work production of aerobic muscle fibres must affect both motor and cardiovascular control. The results reported here indicate that human subjects must increase muscle activation in order to maintain force output when perfusion pressure and, presumably, blood flow to contracting muscle are reduced. To increase muscle activation, the motor command would have to increase. This increase in motor command is associated with an increased cardiovascular response. This should increase muscle blood flow

192 D.1. MCCLOSKEY, S. F. HOBBS: Muscular Activity and Cardiovascular Regulation

by its effects on BP, heart rate and cardiac output. Thus, muscle blood flow during constant force or work exercise may be controlled by feedback that involves an interaction of centrally generated command signals, force or work production of active motor units and BP, heart rate and cardiac output.

References

Burke RE, Levine DN, Tsairis P, Zajac FE (1973) Physiological types and histochemical profiles in motor units of cat gastrocnemius. J Physiol (Lond) 234: 723-748

Goodwin GM, McCloskey DI, Mitchell JH (1972) Cardiovascular and respiratory responses to changes in central command during isometric exercises at constant muscle tension. J Physiol (Lond) 226: 173 -190

Hobbs SF (1982) Central command during exercise: parallel activation of the cardiovascular and motor systems by descending command signals. In: Smith OA, Galasy RA, Wass SM (eds) Circulation, neurobiology and behaviour. Elsevier, New York, pp 217-232

Hobbs SF, McCloskey DI (1987) Effects of blood pressure on force production in cat and human muscle. J Appl PhysioI63:834-839

Brainstem Mechanisms Involved in Reflex Cardiovascular Responses to Muscular Contraction * G.A. IWAMOTO, T.G. WALDROP, and R.M. BAUER

Introduction

The interdependence of cardiorespiratory and motor coordination is perhaps uniquely expressed in two contributions from our laboratories. It is clear that any type of somatomotor activity cannot be sustained without the adjustments made by the cardiovascular and respiratory apparatus. This integrated phenomenon is commonly called exercise. The neural circuitry which underlies these adjustments for this activity has been the primary focus of our laboratories.

There are two mechanisms underlying these compensations for exercise. Through one mechanism, which will be treated in another presentation from our laboratories, the cardiorespiratory and somatomotor systems are driven in parallel from higher brain centers. Through the second mechanism, the subject of this presentation, signals from the contracting muscles can drive the cardiorespiratory apparatus. In other words, we are dealing with a form of soma to sympathetic reflex. There is ample evidence to consider that both mechanisms playa role in the actual response and are not mutually exclusive of each other.

The Experimental Model

The model we have used to study the reflex mechanism is the acute anesthetized or decerebrate cat in which muscular contraction is induced (Fig. 1) by electrical stimulation of ventral roots (3 x motor threshold, O.1-ms pulses at 20-50 Hz). This stimulation evokes muscular contraction which consequently leads to increas­es in blood pressure, heart rate, appropriate changes in regional blood flow and increases in ventilation. Thus, we are studying a somatosympathetic reflex, but one originating from the actual contraction of muscle. We have sometimes referred to this response as the exercise pressor reflex, but it is important to remember that this reflex is only one part of the total contribution by the nervous system to exercise.

* This research was supported by NIH grants HL 06296, HL 38726, and HL 37400 and by the American Heart Association. G.A.I. is also supported by NIH Research Career Devel­opment Award K 04 01910. T.G.W is an established investigator of the American Heart Association. R.M.B. is supported by NIH training grant GM 07143.

194 G.A.lwAMOToet al.

Venlro'

l. , Sh:~~ljon L,

s,

AR TERIAL BLOOD

PRESSURE mmHq

MUSCL E TENSION

k9

200 - .......

150 - AI~"""Wafttw .... ~ 100 - --' - ~ 50 -

0 -

Fig. 1. Diagram showing the basic experimental preparation for studying the pressor re­sponse to muscular contraction as it is used for investigation of single cells. Lower left, the reflex paradigm. The L 7 and S 1 ventral roots are isolated in a cat following a laminectomy. Electrical stimulation is then applied, causing a static contraction of hindlimb muscles. The triceps surae is monitored for muscle tension. Upper left, the recording arrangements and the target areas studied. Right, a typical blood pressure response to static muscular contraction. (Modified from [10))

It might be thought that this response is strictly a pain response to artificially induced contraction. However, human subjects whose muscles are stimulated percutaneously, which evokes similar cardiovascular changes, do not report this as a painful experience [12].

The magnitude of the reflex is dependent on many variables which are associat­ed with the nature of muscular contraction [9]. This includes the amount of evoked tension and, within limits, the amount muscle mass which is activated. The reflex may be evoked from muscles of varying fiber type composition including mixed muscles composed of slow- and fast-twitch fibers as well as muscles composed solely of slow-twitch fibers.

The afferent arm of the reflex is known to be small-diameter muscular afferent fibers in the group III and IV range or A-delta and C fibers, respectively [14, 17]. The group III afferents characteristically increase their firing rapidly at the onset of muscular contraction and then show adaptation at varying rates in returning to baseline levels, indicating an apparent sensitivity to mechanical events. The group IV afferents, on the other hand, slowly increase their discharge during muscular contraction, a pattern consistent with sensitivity to chemical events occurring as a result of the contraction. As is explained below, these discharge patterns are also reflected at higher levels of neural integration.

195

Central Neural Integration of the Reflex

There are at least two basic central neural components to the reflex. One is mediated at spinal levels, and the other is dependent on the participation of the brainstem. We studied this organization in decerebrate nonanesthetized cats to ensure a maximum sensitivity by the preparation [13].

The response of C 1 spinally transected animals consists of only a small blood pressure response. A response which includes both blood pressure and heart rate changes requires that the medulla be intact. There is a supramedullary component involved, as the responses were somewhat diminished by transection 5 mm rostral to the obex. These experiments indicated that the central circuitry ofthe reflex was arranged much as any other soma to sympathetic reflex.

We then began to investigate which parts of the brainstem were most likely to participate in the response. A prior report by Ciriello and Calaresu [8] had indicat­ed that somatosympathetic reflex activity could be abolished by lesions in the caudal ventrolateral medulla, which was also known as a principal relay for information from higher threshold muscle afferents. We found that the response to muscular contraction could also be abolished by lesions in this region [11].

By this time, it had become clear based on the work of several laboratories that more than the caudal ventrolateral medulla was likely to be involved in the response. Based on many anatomical [8,18] and physiological studies [1, 5,8, 15, 18], the principal sympathetic outflow pathway from the ventrolateral medulla appeared to be most prominent at the rostral end.

As it was also clear that a suitable antagonist to the effects of excitatory amino acids was effective [21] in antagonizing some autonomic responses mediated through the ventrolateral medulla, we then extended our investigations. Kynurenic acid was delivered via micropipette to block the action of excitatory amino acids in both the caudal and rostral ventrolateral medulla. We found that injections of kynurenic acid into these ventrolateral medullary sites (Fig. 2) could greatly dimin­ish the pressor response evoked by muscular contraction [2].

Electrophysiological Studies

The cells in both rostral and caudal ventrolateral medulla were found to be responsive to muscular contraction [3, 10]. In chloralose and chloralose-urethane anesthetized cats, we found that units which could be classed as that of cells by standard electrophysiological criteria were responsive to muscular contraction. Virtually all of the cells that responded to muscular contraction which were subsequent tested were responsive to capsaicin, a selective small fiber agonist.

For a number of cases in which the cells were localized in or near the nucleus ambiguus [10], an inhibition resulted during muscular contraction (Fig. 3). While this was not the only inhibitory pattern observed, these results are consistent with a vagal withdrawal [17] or perhaps an inhibition of baroreflexes which is known to occur during static muscular contraction [20].

196 G.A. IWAMOTO eta!'

A D

• • .~~.

P 100 P 120

B E

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c F

P 116 P14.0

Fig.2A-R Summary of kynurenic acid injection sites in the ventrolateral medulla. Filled triangles, sites at which kynurenic acid attenuated the reflex pressor response to muscular contraction; open triangles, sites at which the injections had no effect. 5 SP, Alaminar spinal trigeminal nucleus; 5 ST, spinal trigeminal tract; FTG, gigantocellular tegmental field; FTM, magnocellular tegmental field; laD, dorsal accessory nucleus of the inferior olive; 10M, medial accessory inferior olive; LRI, lateral reticular nucleus, internal division; LRX, lateral reticular nucleus, external division; P, pyramidal tract; P P R, postpyramidal nucleus of the raphe; S, solitary tract; V 4, fourth ventricle. These drawings were adapted from Berman's stereotaxic atlas [4]. Anterior/posterior stereotaxic location is given at the bottom right of each drawing. (From [2])

In the caudal ventrolateral medulla [10], there were three excitatory patterns observed to static muscular contraction: (Fig. 3) 1) A brisk initial discharge followed by a gradual adaptation. Recalling the discharge signatures of the group III and IV primary afferents, it is clear that this pattern of discharge is associated with the signals from group III fibers. However, at least in the caudal ventrolateral medulla, there were no clear examples of the other type of discharge, that associ­ated with group IV fibers. 2) A brief burst of discharge in response to both, the onset and cessation of muscular contraction. 3) A somewhat atypical response of an initial inhibition followed by a sustained discharge.

In the more rostral ventrolateral medulla [3] we saw many of the same patterns of discharge, but a striking finding was the incidence of cells which responded in a manner similar to that of the group IV afferents. The cells did not fire with a burst at the onset of contraction, but rather there was a slow buildup in the firing frequency as if the cells were receiving input from the accumulation of metabolites in the muscle. Many of the cells were also responsive to baroreceptor stimuli.

Reflex Cardiovascular Responses to Muslcular Contraction 197

Left ( Ipsilateral)

+2mm

roD roM

+1mm

Obex

10M

5

5

5

Right (Contralateral)

10M

55T

55P

AMB

Fig. 3. Summary of cell responses to static muscular contraction according to recording site. Ipsilateral and contralateral are labeled with reference to hindlimb in which static muscular contraction was evoked. Each composite diagram is meant to encompass brain level indicat­ed ±0.5 mm. Dots, Cells responding with initial brisk discharge and gradual return toward control levels (slow adaptation); upward arrow heads, cells responding with an initial burst often accompanied by a burst at the cessation of contraction; minus signs, cells inhibited during contraction; plus signs, cells initially inhibited during contraction, later responding with slowly adapting discharge. CI, Nucleus centralis inferior; LRN, lateral reticular nucleus; AMB, nucleus ambiguus; FTL, lateral tegmental field; PR, paramedian reticular nucleus; CE, central canal; 12 N, hypoglossal nerve; other abbreviations as in Fig. 2. (From [10])

198 G. A. IWAMOTO et al.

While we were able to characterize cell responses to muscular contraction, it was not clear that these cells were associated with sympathetic outflow. While not conclusive evidence of a contribution to sympathetic outflow, we have recently [3] used techniques developed by Barman and Gebber [1] to correlate the spontaneous firing of the cells with the 2- to 6-Hz slow-wave component of sympathetic nerve activity. A high percentage (> 70%) of the cells responsive to muscular contraction also showed a temporal correlation with sympathetic nerve activity. The same types of activity patterns have also been observed in baroreceptor denervated animals, indicating that the rhythmicity of the cells is separate from input from the baroreceptors. A small percentage of these cells also respond during injections of phenylephrine, which provides a putative baroreceptor input.

Thus, a strong case can be made that cells in a relatively extensive area of the ventrolateral medulla can potentially participate in the pressor response evoked by muscular contraction. As numerous studies have shown, there is a spinal projec­tion from a part of this area to the intermediolateral cell column [1,5,8,18]. The medullary area also overlaps with the distribution of C 1 epinephrine containing cells [7, 16, 19] as well as with areas containing substance P [6, 16]. While some of the data about these cell groups are controversial [22], both of these substances are thought to mediate sympathoexcitation at the level of IML.

In summary, we have identified one area of the medulla which appears to exhibit characeristics consistent with participation in the reflex evoked by muscular con­traction. It remains to be seen whether other areas can also be implicated in this process.

References

1. Barman SM, Gebber GL (1985) Axonal projection patterns of ventrolateral medul­lospinal sympathoexcitatory neurons. J Neurophysiol 53:1551-1566

2. Bauer RM, Iwamoto GA, Waldrop TG (1990) Ventrolateral medullary neurons modu­late the pressor reflex to muscular contraction. Am J Physiol 259:R606-R611

3. Bauer RM, Iwamoto GA, Waldrop TG (1989) A cardiovascular reflex evoked by mus­cular contraction is modulated by neurons in the ventrolateral medulla. Proc XXXI. Int Cong Physiol Sci, Helsinki, p 302

4. Berman AL (1968) The brain stem of the cat. Madison, Wisconsin 5. Brown DL, Guyenet PG (1984) Cardiovascular neurons of brain stem with projections

to spinal cord. Am J PhysioI247:Rl009-1016 6. Ciriello J, Caverson MM, Calaresu FR, KrukoffTL (1988) Neuropeptide and serotonin

immunoreactive neurons in the cat ventrolateral medulla. Brain Res 440:53-66 7. Ciriello J, Caverson MM, Park DH (1986) Immunohistochemical identification of nor­

adrenaline and adrenaline synthesizing neurons in the cat ventrolateral medulla. J Comp NeuroI253:216-230

8. Ciriello J, Caverson MM, Polosa C (1986) Function of the ventrolateral medulla in the control of the circulation. Brain Res 11:359-391

9. Iwamoto GA, Botterman BR (1985) Peripheral factors influencing the expression of the pressor response evoked by muscular contraction. J Appl Physiol 58: 1676-1682

10. Iwamoto GA, Kaufman MP (1987) Characteristics of caudal ventrolateral medullary cells responsive to muscular contraction. J Appl Physiol 62(1): 149 -157

Reflex Cardiovascular Responses to Muslcular Contraction 199

11. Iwamoto GA, Kaufman MP, Botterman BR, Mitchell JH (1982) Effects of lateral reticular nucleus lesions on the exercise pressor reflex in cats. Circ Res 51:400-403

12. Iwamoto GA, Mitchell JH, Mizuno M, Secher NH (1987) Cardiovascular responses at the onset of exercise with partial neuromuscular blockade in cat and man. J Physiol 384:39-47

13. Iwamoto GA, Waldrop TG, Kaufman MP, Botterman BR, Rybicki KJ, Mitchell JH (1985) Pressor reflex evoked by muscular contraction: contributions by neuraxis levels. J Appl Physiol 59(2):459-467

14. Kaufman MP, Longhurst JC, Rybicki KJ, Wallach JH, Mitchell JH (1983) Effects of static muscular contraction on impulse activity of groups III and IV afferents in cats. J Appl Physiol 55:105-112

15. McAllen RM (1986) Location of neurones with cardiovascular and respiratory function, at the ventral surface of the cat's medulla. Neuroscience 18:43-49

16. Marson L, Loewy AD (1985) Topographical organization of substance P and monoamine cells in the ventral medulla of the cat. J Auton Nerv Syst 14:271-285

17. Mitchell JH, Schmidt RF (1983) Cardiovascular reflex control by afferent fibers from skeletal muscle receptors. In: Handbook of physiology. The cardiovascular system, peripheral circulation and organ blood flow (sect 2, vol III, chap 17). American Physi­ological Society, Bethesda, pp 623-650

18. Reis DJ, Granata AR, Joh TH, Ross CA, Ruggiero DA, Park DH (1984) Brainstem catecholamine mechanisms in tonic and reflex control of blood pressure. Hypertension 6 (Suppl 11):7-15

19. Ruggiero DA, Gatti P, Gillis RA, Norman WP, Anwar M, Reis DJ (1986) Adrenaline synthesizing neurons in the medulla of the cat. J Comp Neuro1252: 532-542

20. Streatfield KA, Davison NS, McCloskey DI (1977) Muscular reflex and baroreflex influences on heart rate during isometric contractions. Cardiovasc Res 11: 87 -93

21. Sun M-K, Guyenet PG (1986) Hypothalamic glutamatergic input to medullarysympa­thoexcitatory neurons in rats. Am J Physio1251:R798-810

22. Sun M-K, Young BS, Hackett JT, Guyenet PG (1988) Rostral ventrolateral medullary neurons with intrinsic pacemaker properties are not catecholaminergic. Brain Res 451:345-349

Simultaneous Suppression of Postural Tone and Respiration and its Functional Significance in the Respiratory-Motor Coordination * K. KAWAHARA, Y. NAKAZONO, Y. YAMAUCm, Y. MIYAMOTO, and S. KUMAGAI

Introduction

In acute precollicular-postmammillary decerebrate cats, stimulation of the dorsal part of the caudal tegmental field (DTF) in the pons along the midline results in long-lasting suppression of extensor muscle tone (Mori et al. 1982). During the course of our investigation on the coupling of the locomotor rhythm and the respiratory rhythm, we have recently found that DTF stimulation used for reduc­ing the decerebrate rigidity not only elicits suppression of hindlimb extensor muscle tone but also suppresses respiratory movements (Kawahara et al. 1988b). Suppressed tonic discharges of the hindlimb antigravity muscles caused by DTF stimulation persist for more than several minutes after the stimulation ends. In contrast, respiratory movements, once markedly suppressed by DTF stimulation, gradually recover in spite of the continuation of the stimulation.

The primary functional significance of respiration is to maintain blood-gas and acid-base homeostasis for life. This function is accomplished automatically by the respiratory control system with multiple feedback loops. However, the central nervous system must integrate respiratory movements with other body movements such as speech (Bunn and Mead 1971) and locomotion (Bramble and Carrier 1983; Kawahara et al. 1988a, 1989a, d).

This study reports first on the detailed properties of the DTF-elicited simulta­neous suppression of postural tone and respiration and second on its possible functional significance in the respiratory-motor coordination.

Methods

Experimental procedures have been described elsewhere in detail (Kawahara et al. 1988b, c, 1989a-c). In short, after surgical decerebration under halothane anes­thesia, the head of the animal was fixed in a stereotaxic frame, and the limbs were placed on the surface of a still treadmill belt. Electromyograms (EMGs) were recorded by implanting the bipolar electrode made of thin (70 /lm) copper wires insulated except just for their tips into the bilateral soleus muscles. EMGs of the

* This research was supported in part by a grant from the Ministry of Health and Welfare of Japan to K.K.

Simultaneous Suppression of Postural Tone and Respiration 201

diaphragmatic and external intercostal muscles were also recorded by implanting the copper wires. Bipolar recording of the hypoglossal nerve activity was per­formed by inserting thin copper wires completely insulated except at the tip into the nerve. The peripheral end of the nerve was then ligated and sectioned. End­tidal PC02 was monitored with an infrared gas analyzer and recorded on an FM data recorder. The EMGs of the diaphragm and external intercostal muscles as well as the hypoglossal neural activity were then integrated in terms of resistance and capacitance (time constant, 0.1 s) and recorded.

Glass-insulated microelectrode were used for stimulation of the DTF. DTF stimulation consisted of 0.2-ms rectangular pulses delivered at a frequency of 50 pulses per second with an intensity of 30-70 )lA. In some experiments, another microelectrode was penetrated into the rostral pontine brain stem and was used for recording of unit spikes and field potentials. The DTF was stimulated once per second, and antidromically activated spikes or field potentials were identified at the rostral pontine reticular formation. The body temperature was maintained at between 36.0 and 37.5 °C by an infrared lamp.

At the end of each experiment, the animals were deeply anesthetized (pentobar­bital sodium, i.v.), and the stimulating site was marked with an electrolytic lesion (DC current of 20 )lA, 20 s). The location of the electrode tip was determined with reference to the stereotaxic atlas of Snider and Niemer (1961).

Results

Simultaneous Suppression of Postural Tone and Respiration. The experiments were always begun at least 1.5 h after decerebration. In most of the tested animals, stimulation of the midline DTF elicited parallel suppression of hindlimb extensor muscle tone and respiration. Figure 1 shows examples of the results obtained from two different cats. In both animals, DTF stimulation markedly suppressed the

Fig. 1 A, B. Simultaneous sup­pression of diaphragmatic and soleus msucle activities. A and B are the results obtained from two different cats. Stimulus intensity was about 50 IlA both in A and B. Stimulus sites were P 5.5, LRO, H-4.5 in A and P5.0, LRO, H-4.0 in B. DIA. EMG, Diaphragmatic EMG; INT, integrated DIA. EMG; SOL. EMG, EMG of soleus muscle; DTF, dorsal tegmental field; L, left side; R, right side

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202 K. KAWAHARA et al.

tonic discharges of bilateral soleus muscles, and the abolished muscle activities did not appear after stimulation ended. DTF stimulation also depressed rhythmic diaphragmatic activity. However, the diaphragmatic activity, once depressed by stimulation, gradually resumed and became greater in amplitude in spite of the continuation of DTF stimulation, as is seen from the integrated diaphragmatic EMG. Immediately after the end of stimulation, rebound augmentation in the diaphragmatic activity occurred.

Neuronal Structures Responsible for Parallel Suppression of Postural Tone and Respiration. Systematic survey of the stimulating electrode in and near the midline dorsal tegmentum of the pons disclosed that the effective DTF region was restrict­ed within about 1 mm dorsoventrally (Fig. 2). In the animal in this figure, the dorsoventral extension of the effective DTF region, stimulation of which elicited parallel suppression of postural tone and respiration, was distributed from H-5.0 to H-5.5. When the electrode was penetrated more ventrally, the augmentation both in the diaphragmatic and in the soleus muscle activity started to appear at a depth of H-7.0. As to the mediolateral extension of the effective DTF region, the most lateral boundary was less than 1.0 mm from the midline. Rostrocaudal extension of the effective region was distributed from P4.0 to P7.0 and almost

A H- 45 B INT

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Fig.2A-H. Dorsoventral distri­bution of the stimulus effects at the midline. EMGs of diaphragm and soleus muscles were recorded while advancing the stimulus electrode at O.S-mm in­crements along the midline. Stimulus intensities were equal (40IlA) in A-H. Abbreviations are the same as those in Fig. 1. (Modified from Kawahara et al. 1988 b)

Simultaneous Suppression of Postural Tone and Respiration 203

Fig. 3. Recovering process of respiration during DTF stimulation. Bold solid line, the duration of stimulation, about 2 min. The stimulus site and stimulus intensity were P 5.5, LR 0, H-6.0, and 70 ~A . Hori­zontal broken line (PCOz record), the ap­proximate level of end-tidal PCOz before the start of stimulation. PCOz, CO2 ten­sion of expired air. (Modified from Kawa­hara et al. 1988 b)

DIA. EM G

IIMIIIII'. , I" '" III "'" I WIIIIIIIIII~IIII~II~II I*lIlllIl ll lj

coincided with that identified previously for long-lasting suppression of hindlimb muscle tone (Mori et al. 1982). Suppressive effects on hindlimb extensor muscle tone and on diaphragmatic activity could not be separated from each other. When the stimulation was given to the one site within the DTF, and the suppressive effects on respiration were elicited, the extensor activity was also suppressed, and vice versa.

Recovering Process of Respiration During Stimulation. The once-suppressed di­aphragmatic activity gradually recovered during DTF stimulation. The detailed recovery process of the respiratory movements was then analyzed (Fig. 3). DTF stimulation resulted in an apneic state for more than 30 s, as is seen from the lowest record (peOz). During stimulation, however, the rhythmic diaphragmatic activity reappeared, and the amplitude of the integrated diaphragmatic activity gradually became greater. About 1 min after the start of DTF stimulation, nearly stable respiration was established during stimulation. The characteristic features of such stable respiration during stimulation are: (a) the respiratory frequency is smaller than the prestimulus frequency; (b) the peak amplitude of the integrated diaphrag­matic EMG is greater than that before the stimulation, and (c) the end-tidal peoz is kept at almost the same value as the pre stimulus one. This result suggested that the minute ventilation, which was not measured in this study, was kept at almost the same value as that before the stimulation, although the respiratory frequency markedly decreased. Immediately after termination of DTF stimulation, rebound augmentation in respiratory movements occurred, and the cat hyperventilated. As a result, the end-tidal peoz decreased greatly. Thereafter, the end-tidal peoz gradually returned to the prestimulus level.

DTF-Elicited Depression of External Intercostal Muscle Activity. The rhythms between the diaphragmatic and external intercostal muscle activities were almost in phase before the start of stimulation (Fig. 4). DTF stimulation decreased the tonic discharges of the bilateral soleus muscles and the rhythmic diaphragmatic activity. The stimulation also suppressed the rhythmic external intercostal dis­charges. The suppressed rhythmic activity of the external intercostal muscle seemed difficult to recover during DTF stimulation, compared with the recovery process of the diaphragmatic activity. After termination of DTF stimulation, both

204 K. KAWAHARA et al.

DIA ft ...

EI • I • I •

, . _. _ -.:.. . : • Fig.4. Suppressive effects on IN TEl .• 1 diaphragm, external intercostal,

J\.J"\;.t\--''-''''--__ -'-_--''--:....-='''"'''·;:=- '''i -~-I;:J--I.J_I;I and soleus muscle activities.

(ll . lilfit ; I

5s

• _ J _~ i :j

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Stimulus site and stimulus inten­sity were P6.0, LRO, H-5.0, and 60/-lA. DIA, Diaphragmatic EMG; INT DIA , integrated DIA EMG; EI, external inter­costal EMG; INT EI, integrated ... (RL _. J

Peo! 20_ EI EMG; SOL, EMG of soleus I muscle. (Modified from Kawa­

hara et al. 1989c)

the diaphragmatic and external intercostal muscles showed vigorous bursting discharges. When the external intercostal muscle exhibited tonic as well as rhyth­mic discharges before the start of stimulation, the relatively weaker stimulation suppressed the tonic discharges but did not the rhythmic ones (Kawahara et al. 1989c). The once-suppressed tonic discharges of the muscle did not recover even after termination of the stimulation. This was similar to the DTF-elicited suppres­sion of the soleus muscle activity.

OIA

-let. I nt.It I~ • . tt.U"'­INT:. DIA, ; !,_ I I 1.11 ~ II IL. , ~

HYPO - -

~ ~ I i!1 I ,i. II I' ~~ ? INT HYPO I, -

~ Fig.5. Suppre,"" clIoc~ on hyp<>gio,,,,i n,urni octiv· ity. Stimulus site and stimulus intensity were P 5.0, LRO, H-4.8, and 60 /-lA. Horizontal broken line (INT HYPO record), an approximate tonic discharge level before the start of stimulation. HYPO, hypoglossal neural activity; INT HYPO, integrated hypoglossal ac­tivity; other abbreviations as in Fig. 1. (Modified from Kawahara et al. 1988c)

Simultaneous Suppression of Postural Tone and Respiration 205

DTF-Elicited Depression of Hypoglossal Neural Activity. Prior to DTF stimulation, the hypoglossal nerve exhibited almost tonic discharges (Fig. 5). DTF stimulation decreased the tonic activity of the nerve. The reduced tonic discharges persisted after stimulation ended, as is seen from the integrated hypoglossal nerve activity. This was similar to the DTF -elicited suppressive effects on the tonic discharges of the soleus and external intercostal muscles. In contrast, the nerve started to show rhythmic bursting discharges synchronized with the diaphragmatic activity during DTF stimulation. Immediately after the end of stimulation, rebound augmenta­tion in the diaphragmatic activity occurred. However, rebound augmentation in the hypoglossal neural activity was not observed. At present, the exact neural mechanisms responsible for this DTF-elicited differential suppressive effects on the diaphragmatic and hypoglossal nerve activities were not clear. These different suppressive effects may originate from the functional difference between the two kinds of activities in the regulation of respiration.

Discussion

This study demonstrated that stimulation of the DTF elicited concurrent suppres­sion of the bilateral soleus, diaphragmatic, and external intercostal muscle activi­ties in decerebrate cats. DTF stimulation also suppressed the hypoglossal neural activity.

The abolished tonic discharges of the antigravity muscles caused by DTF stim­ulation did not recover even after termination of the stimulation. The atonic state produced by DTF stimulation reminds us of postural atonia that occurs during REM sleep. Postural atonia is one of the most prominent features of REM sleep (Jouvet 1967). Previous studies have demonstrated that tonic intercostal muscle activity is virtually abolished during REM sleep, both in humans and in cats (Duron and Marlot 1980; Tabachnik et al. 1981). In addition, phasic intercostal muscle activity is also markedly reduced or absent during REM sleep (Parmeggiani and Sabattini 1972). This study showed that DTF stimulation depressed the tonic as well as rhythmic discharges of the external intercostal muscle. The decreased or abolished tonic discharges of the muscle persisted for several minutes after the end ofDTF stimulation (Kawahara et al. 1989c). DTF stimulation also suppressed the tonic as well as the rhythmic discharges of the hypoglossal nerve innervating genioglossus muscle. The tonic activity of the genioglossus muscle is depressed during REM sleep in humans (Sauerland and Harper 1976). Sauerland and Harper reported that the tonic activity of the genioglossus muscle reflects the effort of the muscle to counteract the relapse of the tongue due to its own gravity. Thus, suppression of hypoglossal neural activity caused by DTF stimulation seems very interesting from the point of view of the genesis of obstructive sleep apnea. These findings also remind us of spontaneous suppression that occurs during REM sleep.

The paucity of cell bodies at the effective site for DTF stimulation raises the possibility that DTF-elicited suppression of postural tone results from activation of descending axons passing through the DTF (Mori and Ohta 1986). We have

206 K. KAWAHARA et al.

recently found that antidromic spikes are recorded in and near the nucleus reticu­laris pontis oralis (PoO) by stimulation of the effective DTF site (Kawahara et al. 1988 c). Tonic electrical stimulation of this site, from which antidromic spikes are recorded by stimulatioin of the DTF, results in parallel suppression of postural tone and respiration, similar to the DTF-elicited suppression. Thus, DTF-elicited suppressive effects on postural tone and respiration may result from activation of the descending fibers originating from the neurons in the PoO. Microinjection of carbachol, a cholinergic agonist, into the pontine reticular formation around or near the PoO, produces postural atonia resembling that which occurs sponta­neously during REM sleep (Mitler and Dement 1974; Amatruda et al. 1975). Carbachol microinjection into the PoO results in parallel suppression of postural tone and respiration (Kawahara et al., unpublished observation). In addition, when cats are given bilateral lesions of the pontine tegmentum including the PoO, they display REM sleep without atonia (Henley and Morrison 1969). These results support the idea that DTF-elicited suppression, probably originating from the neurons in the PoO, is similar to the spontaneous suppression of various kinds of muscle activities that occurs during REM sleep.

DTF stimulation produced marked suppression of the rhythmic diaphragmatic activity. However, the diaphragmatic activity, once suppressed by stimulation, gradually recovered in spite of the continuation of DTF stimulation. During the latter part of DTF stimulation, the end-tidal PC02 was kept at almost the same level as that before the stimulation. Therefore, respiratory movements did not seem to receive any inhibitory influences by DTF stimulation, judging from appearances. The existence of the rebound augmentation in respiratory move­ments at the end of DTF stimulation suggested that suppressive effects on respira­tion were not abolished but continued to operate during the entire period of DTF stimulation. If so, the strong respiratory drives to overcome the exerted inhibitory influences must be brought about during DTF stimulation. At present, the origin of such respiratory drives is uncertain. However, the chemical drives, emerging as a consequense of the changes in arterial CO 2 tension or pH due to the preceding apneic state, may be the most probable origin of the respiratory drives. Therefore, dysfunction of the central chemosensitive mechanism, if present, may result in the long-lasting apneic state during the entire period of DTF stimulation. This idea seems interesting from the point of view of the genesis of dyspnea during sleep. Schlaefke (1981) reported that one of the causes for the pathogenesis of sleep apnea syndrome (Ondine's curse) may be malfunction of the central chemosensi­tive mechanism.

The preparations used in this study are decerebrate cats. Therefore, direct comparisons of the present results with those obtained from intact animals and from humans may be difficult. However, all the present findings and discussions lead us to conclude that there are close similarities between the DTF-elicited suppression of various kinds of muscle activities and the spontaneous suppression which occurs during REM sleep. Thus, the present preparations may enable one to analyze the brainstem neuronal structures responsible for the regulation of respiration during REM sleep and for the pathogenesis of obstructive and central sleep apnea.

Simultaneous Suppression of Postural Tone and Respiration 207

Acknowledgement. We express our sincere appreciation to Prof. Y. Honda, Depart­ment of Physiology at Chiba University, for his helpful discussions of the results.

References

Amatruda TT, Black DA, McKenna TM, McCarley RW, Hobson JA (1975) Sleep cycle control and cholinergic mechanisms: differential effects of carbachol injections at pon­tine brain stem sites. Brain Res 98:501-515

Bramble DM, Carrier DR (1983) Running and breathing in mammals. Science 219:251-256 Bunn JC, Mead J (1971) Control of ventilation during speech. J Appl PhysioI48:870-872 Duron B, Marlot D (1980) Intercostal and diaphragmatic electrical activity during wakeful-

ness and sleep in normal unrestrained adult cats. Sleep 3:269-280 Henley K, Morrison A (1969) Release of organized behavior during desynchronized sleep in

cats with pontine lesion. Psychophysiol 6: 245 Jouvet M (1967) Neurophysiology of the state of sleep. Physiol Rev 47: 117 -177 Kawahara K, Kumagai S, Nakazono Y, Miyamoto Y (1988a) Analysis of entrainment of

respiratory rhythm by somatic afferent stimulation in cats using phase response curves. BioI Cybern 58:235-242

Kawahara K, Kumagai S, Nakazono Y, Miyamoto Y (1989a) Coupling between respiratory and stepping rhythms during locomotion in decerebrate cats. J Appl Physiol 67 (to be published)

Kawahara K, Nakazono Y, Kumagai S, Yamauchi Y, Miyamoto Y (1988b) Parallel suppres­sion of extensor muscle tone and respiration by stimulation of pontine dorsal tegmentum in decerebrate cat. Brain Res 473: 81-90

Kawahara K, Nakazono Y, Kumagai S, Yamauchi Y, Miyamoto Y (1988c) Neuronal origin of parallel suppression of postural tone and respiration elicited by stimulation of mid­pontine dorsal tegmentum in the decerebrate cat. Brain Res 474:403-406

Kawahara K, Nakazono Y, Kumagai S, Yamauchi Y, Miyamoto Y (1989b) Inhibitory influences on hypoglossal neural activity by stimulation of midpontine dorsal tegmen­tum in decerebrate cat. Brain Res 479: 185-189

Kawahara K, Nakazono Y, Miyamoto Y (1989c) Depression of diaphragmatic and external intercostal muscle activities elicited by stimulation of midpontine dorsal tegmentum in decerebrate cats. Brain Res 491:180-184

Kawahara K, Nakazono Y, Yamauchi Y, Miyamoto Y (1989d) Coupling between respira­tory and locomotor rhythms during fictive locomotion in decerebrate cats. Neurosci Lett 103: 326-332

Mitler MM, Dement WC (1974) Cataleptic-like behavior in cats after micro-injections of carbachol in pontine reticular formation. Brain Res 68:335-343

Mori S, Kawahara K, Sakamoto T, Aoki M, Tomiyama T (1982) Setting and resetting of level of postural muscle tone in decerebrate cat by stimulation of brain stem. J Neuro­physiol48:737-748

Mori S, Ohta Y (1986) Proposed model of postural atonia in a decerebrate cat. Behav Brain Res 9:415-416

Parmeggiani PL, Sabattini L (1972) Electromyographic aspects of postural, respiratory, and thermoregulatory mechanisms in sleeping cats. Electroencephalogr Clin Neurophysiol 33:1-13

Sauerland EK, Harper RM (1976) The human tongue during sleep: electromyographic activity of the genioglossus muscle. Exp Neurol 51: 160-170

Snider RS, Niemer WTA (1961) Stereotaxic atlas of the cat brain. University of Chicago Press, Chicago

Schlaefke ME (1981) Central chemosensitivity: a respiratory drive. Rev Physiol Biochem Pharmacol 90: 171-244

Tabachnik E, Muller NL, Bryan AC, Levison H (1981) Changes in ventilation and chest wall mechanics during sleep in normal adolescents. J Appl PhysioI51:557-564

Hypothalamic Modulation of Cardiovascular, Respiratory and Locomotor Activity During Exercise * T.G. WALDROP, R.M. BAUER, G.A. IWAMOTO, and R.W STREMEL

Introduction

Exercise requires a coordinated interaction between the locomotor, respiratory, and cardiovascular systems since respiratory and cardiovascular function must increase in proportion to each other and to the increased metabolic needs assQci­ated with locomotor movements. The actual predominant mechanism responsible for this coordinated effort has not been elucidated despite over a century of investigation. It is known, however, that neural mechanisms must ultimately be involved in the linkage of the cardiorespiratory drive to locomotor activity.

Considerable evidence has mounted for the importance of two major central neural control mechanisms in regulating cardiorespiratory activity during exercise [7]. A reflex originating in contracting skeletal muscles provides afferent input to spinal and supraspinal circuits which modulate cardiorespiratory activity. In addi­tion, a "central command" mechanism involves projections to the cardiorespirato­ry areas in the brainstem from sites in the rostral brain that also send descending input to spinal locomotor circuits [6]. Thus increases in cardiovascular and respira­tory function occur simultaneously with the initiation of exercise. Evidence from this laboratory has demonstrated that both the reflex originating in contracting muscles and the central command mechanism involve neurons in the posterior hypothalamus.

Methods

All experiments were performed on adult cats anesthetized with a mixture of a-chloralose and urethane. Arterial pressure, heart rate, respiratory activity (ven­tilation and/or phrenic nerve activity) and locomotion (electromyographic activ­ity) were measured. Electrical stimulation (100-300 /lA, 70 Hz, 1 ms pulse dura­tion) and microinjections (50-200nl) of GABA antagonists were used to activate posterior hypothalamic neurons which alter cardiorespiratory and locomotor function. Destruction of hypothalamic neurons was performed by electrolytic lesions. Single-unit activity of hypothalamic neurons was recorded using mi-

* This research was supported by NIH grants HL 06296 and HL 38726 and by the American Heart Association. R.M.B. is supported by NIH training grant GM 07143.

Hypothalamic Modulation During Exercise 209

croelectrodes, and in some experiments the activity of these neurons was correlated with postganglionic sympathetic nerve activity using computer processing. In order to investigate the role of hypothalamic neurons in the feedback from con­tracting limb muscles, stimulation of the peripheral cut ends of the L7 and Sl ventral roots was used to elicit contraction of the triceps surae muscles. In addi­tion, some of these methods were utilized in experiments to determine medullary sites which modulate cardiorespiratory drive originating from activation of hypo­thalamic neurons. Details of all the above methods can be found in publications from this laboratory [2,8,10-15].

Results

Hypothalamic Involvement in the Central Command Mechanism. Electrical stimula­tion of the posterior hypothalamus elicits locomotor movements (walking and running) that are accompanied by proportional increases in cardiorespiratory activity [6, 14]. Furthermore, a concomitant redistribution of organ blood flows occurs that is similar to those changes which occur in awake, unanesthetized cats [5, 12]. These vascular changes include increased blood flow to the heart, di­aphragm, and skeletal muscles, decreased blood flow to the kidneys, and increased vascular resistance in abdominal viscera. All of the above responses persist after muscular paralysis.

A problem with the aforementioned studies is that electrical stimulation affects cell bodies as well as fibers of passage originating outside the hypothalamus. Thus, one cannot determine from these studies the actual neuroanatomical substrate responsible for the observed linkage between ventilation and locomotion. This problem was avoided in subsequent experiments by microinjecting antagonists to the inhibitory neurotransmitter GABA into the posterior hypothalamus [1, 10, 11]. Figure 1 demonstrates that these miocroinjections elicited rhythmic, alternating bursts of activity in limb muscles; this motor activity was accompanied by increas­es in respiratory and cardiovascular activity. In addition, sympathetic drive was elevated by the microinjections (Fig. 2) [10]. The microinjection sites were all within a distance of 1.2 mm from the centers of the dorsal and posterior hypotha­lamic areas [4]. The above experiments demonstrate that posterior hypothalamic neurons provide parallel activation of the cardiorespiratory and locomotor sys­tems, thus implying a role in the central command mechanism.

Hypothalamic Modulation of Feedback from Contracting Muscles. Experiments from this laboratory have also implicated the posterior hypothalamus in the modulation of the cardiorespiratory responses to feedback from contracting mus­cles. Bilateral lesions of this hypothalamic area altered the heart rate and respira­tory frequency response to contraction of hindlimb muscles [13]. In addition, static and rhythmic contractions of hindlimb muscles produce increased discharge fre­quency of single units recorded from the posterior hypothalamus (Fig. 3) [15]. Two types of excitatory neuronal responses were observed: (a) abrupt increases in

210 T. G . WALDROP et al.

Arter ial Pressure (mmHg)

A 200 [ ._-_ ...... , .. ",.'.' 100

Hearl Rate 300 [ (beals/min) ---------

180

Left Tr iceps Muscle Activity

Right Triceps Muscle Acl ivity

Tidal Volume (ml)

Respiratory 4200 [ Frequency ___ ~ ___ ~

(breaths/min)

40° [ A i,way PC02 ' IIWj~Iru~~~~~ (mmHg) VI

B c

Fig, 1 A -c, Cardiovascular, locomotor and respiratory effects of microinjecting a GABA antagonist into the posterior hypothalamus in an anesthetized cat. A Control conditions. B Microinjection of the GABA antagonist (picrotoxin) produced rhythmic, alternating bursts of triceps muscle activity as well as increases in cardiorespiratory activity. C These effects were reversed by microinjection of a GABA agonist (muscimol) into the same hypo­thalamic site. From Waldrop et al. [11]

A ARTERIAL 200[. ________ • PRESSURE

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HEART 2J RATE -------------

Imtn- I ) 1:;0

INTEGRATEO CERVICAL NERVE

ACTIVITY

AIRWAY PCO. (torr)

B c

us

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Fig. 2, Effects of a GABA antagonist upon the activity of cervical sympathetic nerve activ­ity. A Control conditions. B Microinjection of the antagonist into the posterior hypothala­mus evoked increases in the phasic amplitude and frequency of bursts of sympathetic activity with concomitant elevations in arterial pressure and heart rate. C Microinjection of a G ABA agonist into the same site reversed the effects. (From Waldrop and Bauer [10])

Hypothalamic Modulation During Exercise 211

A 30sec I------i

Arterial 175[ •• Pressure ... __ .... - ......... ___ _

(mmHg) 75

Heart 270[ Rate --------f----__ _ (beats/min) 150

Muscle 4 Tension

(kg) 0

Oischarge 11 Frequency ( Impulses/sec)

Tetanic Contraction

C

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B

q ,., ..

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Fig. 3. Effects of static (A) and rhythmic (B) contractions of hindlimb muscles on the discharge frequency of a neuron located in the posterior hypothalamus. Both types of contraction increased the firing frequency of this neuron. C Line drawing showing location of recording site (triangle). (From Waldrop and Stremel [15])

discharge frequency at the onset of contraction and (b) a delayed, more gradual increase in firing. These responses are similar to those recorded from medullary units and from spinal afferent fibers originating in contracting hindlimb muscles [3, 7]. Preliminary experiments have found that hypothalamic units which respond to muscular contraction have a temporal correlation with sympathetic discharge. Thus, neurons which are activated by afferent input from contracting muscles are likely to be involved in cardiovascular control.

Medullary Modulation of Hypothalamic Drive Related to Exercise. Hypothalamic projections to medullary sites involved in cardiorespiratory regulation are well documented [9]. Recent experiments have attempted to determine whether some of these projections are important in the hypothalamic modulation of exercise drive. A recent study from this laboratory examined the effects of lesioning the nucleus reticularis gigantocellularis on the cardiorespiratory responses to hypothalamic stimulation [8]. Augmented tidal volume, respiratory frequency and heart rate responses to hypothalamic stimulation were observed after bilateral lesions. How· ever, lesioning had no effects upon the magnitude of the responses to contraction of hindlimb muscles. Additional experiments have examined the role of an excita­tory amino acid mechanism in the cardiorespiratory responses to hypothalamic

212

A

B

T. G. WALDROP et al.

A.CONTROL

'oar Arterial

Pressure (mmHg)

7S

Muscle ] Tension (Kg)

B.AFTER KYN

Arteria I ISOr

Pressure

sol (mmHg)

Muscle '] Tension (Kg)

A. CONTROL

Arterial Pressure (mmHg)

Heart 270[ Rate (beats/min) 180

B. AFTER KYN

Arterial 200[ Pressure (mmHg)

100

Heart 270[ Rate (beats/min) 180

30 sec

(

n

Fig. 4. Effects of bilateral microinjections of an excitatory amino acid antagonist (kynurenic acid, KYN) into the ventrolateral medulla upon the response to hindlimb muscular contrac­tion (A) and posterior hypothalamic stimulation (B). The arterial pressure response to muscular contraction was diminished after microinjection of kynurenic acid. (From Bauer et al. [2])

Hypothalamic Modulation During Exercise 213

stimulation [2]. Microinjection of an excitatory amino acid antagonist into the ventrolateral medulla attenuated the pressor response to muscular contraction but did not alter the responses to hypothalamic stimulation (Fig. 4). Additional exper­iments are needed to determine the neurotransmitter(s) utilized by the hypotha­lamic projection to medullary neurons.

Discussion

The posterior hypothalamus has long been known to exert a stimulatory influence upon the cardiorespiratory systems. The experiments described above suggest that this area of the brain is involved in at least two of the mechanisms which regulate cardiorespiratory activity during exercise.

The first set of experiments provided evidence that neurons in the posterior hypothalamus can elicit cardiorespiratory responses which are proportional to the accompanying locomotion and are comparable to those seen during voluntary exercise in awake animals [5, 6, 10, 11]. Since these responses are not dependent upon feedback from contracting muscles, it appears that the hypothalamus is a site of central drive to the cardiorespiratory systems during exercise [6, 11]. Moreover, these responses can be evoked by chemical manipulation of cell bodies alone and, thus, are not due to stimulation of fibers of passage which originate outside the hypothalamus.

Microinjections of GABA antagonists into the posterior hypothalamus pro­duced locomotion with concomitant increases in cardiovascular and respiratory activities [1, 11]. These findings suggest that a GABAergic mechanism in the hypothalamus tonically inhibits neurons which modulate locomotion and car­diorespiratory function. Thus, disinhibition of these hypothalamic neurons pro­vides a potential neural mechanism responsible for the central command drive active during exercise.

Both central command and feedback from contracting muscles are mechanisms which regulate cardiorespiratory activity during exercise [6, 7]. Since both mecha­nisms probably function simultaneously during exercise, central integration of these two mechanisms and others must occur in order to produce the appropriate responses to exercise. Our findings suggest that the posterior hypothalamus may provide some of this central integration. The same area ofthe hypothalamus which was capable of producing locomotion and increases in cardiorespiratory activity contains neurons whose discharge is stimulated by feedback from contracting muscles [11,15]. Moreover, the full expression of the cardiorespiratory responses to hindlimb muscular contraction requires the integrity of the posterior hypothal­amus [13].

Our studies have also examined medullary sites which could integrate input from neural mechanisms involved in exercise regulation [2, 8]. These studies have identified two sites which modulate some of the mechanisms related to exercise regulation. However, additional experiments are needed to identify medullary sites and neurochemicals active during exercise.

214 T. G. WALDROP et al.: Hypothalamic Modulation During Exercise

References

1. Bauer RM, Vela MB, Simon T, Waldrop TG (1988) A GABAergic mechanism in the posterior hypothalamus modulates baroreflex bradycardia. Brain Res Bull 20:633-641

2. Bauer RM, Iwamoto GA, Waldrop TG (1989) Ventrolateral medullary neurons modu­late the pressor reflex to muscular contraction. Am J Physiol257: R1154-R1161, 1989

3. Bauer RM, Iwamoto GA, Waldrop TG (1989) A cardiovascular reflex evoked by mus­cular contraction is modulated by neurons in the ventrolateral medulla. Proc Int Union Physiol Sci XVII: 302

4. Berman AL, Jones EG (1982) The thalamus and basal telencephalon of the cat. Univer­sity of Wisconsin Press, Madison

5. Diepstra G, Gonyea WJ, Mitchell JH (1982) Distribution of cardiac output during static exercise in the conscious cat. J Appl Physiol 52:642-646

6. Eldridge FL, Millhorn DE, Kiley JP, Waldrop TG (1985) Stimulation by central com­mand oflocomotion, respiration and circulation during exercise. Respir Physiol59: 313-337

7. Mitchell JH (1985) Cardiovascular control during exercise: central and reflex neural mechanisms. Am J Cardiol 55:34D-41D

8. Richard CA, Waldrop TG, Bauer RM, Mitchell JH, Stremel RW (1989) The nucleus reticularis gigantocellularis modulates cardiopulmonary responses to central and periph­eral drives related to exercise. Brain Res 482:49-56

9. Saper CB, Loewy AD, Swanson LW, Cowan WM (1976) Direct hypothalamo-autonom­ic connections. Brain Res 117:305-312

10. Waldrop TG, Bauer RM (1989) Modulation of sympathetic discharge by a hypothalamic GABAergic mechanism. Neuropharmacology 28:263-269

11. Waldrop TG, Bauer RM, Iwamoto GA (1988) Microinjection of GABA antagonists into the posterior hypothalamus elicits locomotor activity and a cardiorespiratory activation. Brain Res 444:84-94

12. Waldrop TG, Henderson MC, Iwamoto GA, Mitchell JH (1986) Regional blood flow responses to stimulation of the subthalamic locomotor region. Respir Physiol64: 93 -1 02

13. Waldrop TG, Mullins DC, Henderson MC (1986) Effects of hypothalamic lesions on the cardiorespiratory responses to muscular contraction. Respir Physiol 66: 215 - 224

14. Waldrop TG, Mullins DC, Millhorn DE (1986) Control of respiration by the hypothal­amus and by feedback from contracting muscles. Respir PhysioI64:317-328

15. Waldrop TG, Stremel RW (1989) Muscular contraction stimulates posterior hypotha­lamic neurons. Am J PhysioI256:R348-R356

Approaches of Systems Theory to Cardiorespiratory and Motor Coordination

The Approach of Synergetics to the Study of Coordination of Rhythms

H. HAKEN

Introduction

Synergetics is an interdisciplinary field of research that deals with complex systems [1, 2]. These systems are composed of many parts or subsystems which possess substantial degrees of freedom. At this microscopic level a huge amount of infor­mation is necessary to describe the system adequately. However, the cooperation of the individual parts may produce macroscopic order or a high degree of coor­dination which may be described by few macroscopic degrees of freedom. Thus an enormous compression of information takes place. At the same time we may speak of an upconversion of order from the microscopic to the macroscopic level. Biol­ogy abounds with examples in the form of movements, locomotion, uptake of food, breathing, heart beats, blood circulation, perception, speech, etc. More generally, in all these cases a spontaneous formation of macroscopic structures via self-organization takes place.

More specifically, synergetics asks the question whether self-organization is governed by universal principles irrespective of the nature of the subsystems. Indeed, self-organization is found not only in the animate world but also in the inanimate. Over the past 20 years or so this question could be answered in the positive provided attention is focused on qualitative changes on macroscopic scales. Numerous examples could be found for the application of those principles, for instance, in physics where phenomena in fluids and lasers can be treated this way, in chemistry where the spontaneous formation of macroscopic spirals or ring patterns or oscillations occur, and in biology where models of morphogenesis and more recently of perception have been propagated along these lines.

Outline of the Mathematical Approach

Our approach is based on a rigorous mathematical treatment which we can indi­cate here only quite briefly. At the microscopic level the system is described by a state vector:

(1)

where the components may describe, for example, firing rates of neurones but equally well local concentrations of biochemicals, etc. Quite generally, the state

218 H. HAKEN

vector q obeys evolution equations of the form:

q=N(q,o:)+F(t) (2)

where q denotes the temporal derivative of the state vector, and N is a nonlinear function depending on q and on a set of control parameters 0:. This set describes external influences on the system or, in a complex system, possibly the influence of one major part of the system on the one under consideration. F are fluctuating forces which are present in any physical, chemical, or biological system. When the control parameters are changed beyond critical values, the behavior of the system described by the state vector q may change qualitatively. This is the situation studied by synergetics. It has been shown [1, 2] that at those points the whole dynamics is governed by few so-called order parameters ~u which again obey equations of the form:

(3)

with by now different nonlinear functions Nu and new fluctuating forces Fu. According to the slaving principle of synergetics, the behavior of the total system (1) at the macroscopic level is governed by the dynamics of the order parameters, i.e., q may be represented as a function of ~u:

q=f(~J· (4)

In this way a link between the microscopic level described by (1) and (2) and the macroscopic level described by (3) is established. When the laws (2) are explicitely given, the relations (3) and (4) can be derived. Of course in complex systems, such as in biology, neither (1) nor (2) are explicitely given. But we may conclude from the general mathematical results that also here the dynamics is governed by the few order parameters obeying equations (3). This leads us to the idea of first identifying the adequate order parameters of biological phenomena and then trying to model their equations [3]. I illustrate this below in a specific example.

The Same System Can Show Quite Different Behaviour

On the other hand, it is most useful to draw on the experiences that we have been able to establish for explicit equations which describe, for instance, processes in the light source laser [1]. Here it could be shown that with increasing the energy input (i.e., the control parameter 0:) into the system, the system can establish more and more order parameters which lend the system specific kinds of behavior (Fig. 1). At a weak pump level the laser emits light in the form of individual wave tracks, i.e., microscopic chaos is present. Beyond a first critical threshold a well-ordered sinusoidal laser wave emerges described by a single order parameter. At a still higher level of excitation, a variety of order parameters occur which lead to ultrashort but regular pulses. Under different conditions of energy input and output, deterministic chaos governed by three different order parameters is gener­ated. Thus, by the change of a single or a few control parameters, quite different kinds of behavior may emerge. This led me several years ago to the suggestion [4]

The Approach of Synergetics to the Study of Coordination of Rhythms 219

E E

t t

a b

E E

t t

c d

Fig. la-d. Different kinds of behavior of the same complex system (the laser). The electric field strength (E) as output of the laser is plotted versus time (t). a At a low pump rate the field strength consists of individual wave tracks so that microscopic chaos emerges. b At an increased pump rate suddenly a well-ordered coherent wave emerges. c At a still higher pump level regular short pulses are emitted. d Under other excitation and emission conditions deterministic chaos at a macroscopic level emerges

that coordination phenomena in biology, and especially their changes, such as the change of the gaits of horses, can be treated by the order parameter concept and the concept of nonequilibrium phase transitions introduced previously in synerget­ics.

An Example: Hand Movements

Experiments carried out by Kelso [5] on involuntary changes of human hand movements turned out to be an ideal testing ground for these concepts. Because I believe that these concepts are also of concern to the problems that this sympo­sium is concerned with, I shall briefly remind the reader of our approach. When test persons were told to move their fingers or hands in parallel and then to increase the frequency of their movement, suddenly an involuntary change from the parallel to the antiparallel (symmetric) movement occurred. Describing the elongations of the left and right finger by

and Xl (t)=rl cos(w t+c/Jl)

x 2(t)=r2 coS(Wt+c/J2)

(5)

(6)

220 H. HAKEN

respectively, we introduce the phases ¢1 and ¢2 and correspondingly the relative phase [6]:

(7)

Because the phase is a macroscopic quantity that undergoes a qualitative change, we identified it as the corresponding order parameter, which obeys a typical order parameter equation of the form:

oV </J= - o¢ +F(t). (8)

The potential function V can be deduced from symmetry considerations to have the form:

V= -a cos¢-h cos2¢. (9)

We may interpret the behavior of ¢ simply by identifying it with the coordinate of a ball which slides down the mountains of a landscape. This potential "landscape"

is plotted in Fig. 2. When the ratio ~ is considered as a control parameter which

depends on frequency, we encounter a change of the potential landscape as shown in this figure. When the system is initially prepared in the state with ¢ = 1C it eventually drops to ¢ = 0, indicating the involuntary transition between the two kinds of movements. When we now lower the frequency, the system rests in this state so that hysteresis can be predicted and was observed. When we admit for

V 1.000 V 0.875 V 0.750

\

...::\"'fL---\--+-I----3~ 4> -+--+-+-1---1-+4>

, -+---\-+-1---1-+ 4> --+-;--+--I--~4>

v 0.250 v 0.125 v 0.000

-+-~r---+-+--++4>

Fig. 2. Behaviour of the phase angle c/J when the control parameter is changed from 1.0 to 0.0. Note the change of the potential landscape with decreasing control parameter and the corresponding change of the stable states

The Approach of Synergetics to the Study of Coordination of Rhythms 221

fluctuating forces, the relative phase cjJ undergoes fluctuations that increase when the potential curve around cjJ=n becomes flatter and flatter. This phenomena is well known in synergetics as "critical fluctuations" and was found in all details in Kelso's experiments [7]. The theory [1] could predict a number of phenomena occurring in this transition including that of critical slowing down (for more examples see the contribution by Kelso, this volume). In the spirit of synergetics one may ask, what is the next lower hierarchical level for the description of the hand movements? This level is evidently that of the individual hands. The corre­sponding equations have the form [6]:

Xi +/1 (Xl , xl )=K12

xi + /z (xz, Xz) =KZl

(10)

(11)

where the coupling term K had to be chosen in a specific form in order to let (8) be derived, namely:

KZl = - K12 = (Xl -Xz) (cx+ P(Xl -xz)Z). (12)

It is of great interest that the coupling term must be nonlinear. I suspect that such nonlinear coupling terms are important for the description of mode-locking phe­nomena in cardiovascular and respiratory coordination phenomena.

Outlook

In conclusion, we may state that the possibility of modeling this specific system adequately consists in the study of a qualitative change. Koepchen reported of numerous transitions in the cardio-vascular and respiratory system (private com­munication). But even if no qualitative changes occur, in a number of cases the number of degrees of freedom may be estimated which then may lead to a model­ing of the system, possibly along the lines indicated above. In a first step, a time series analysis may be performed. In general, only the time series x=a(t) ofa single quantity, e.g., the blood pressure, or the EEG, or EeG is measured. In order to reconstruct a multivariable dynamics, following Ruelle [9] one introduces addi­tional variables by taking time shifts according to:

y(t)=a(t+T), z(t)=a(t+2T), ... ,

where, for instance, we attempt to describe the system by three variables. The variables x, y, z follow then a trajectory within a three-dimensional space in our example. We [9] then determine the correlation dimension by the Grassberger-Pro­caccio method [10], for which examples are given in Fig. 3. Because the dimension is rather low, it appears promising to model these processes by differential equa­tions with a few variables only. By means of such models it must then be proven self-consistently whether the determination of the dimension by the Procaccio­Grassberger method is justified. At any rate, we find here an access for an appro­priate modeling of the rather complex behavior in biological systems.

222 H. HAKEN

a

;§ ."

I C

abdominal respiration

o.5,-----------------,

0.4

0.3

0.2

0.1

0.0 H++-II+t\l-1-+++t+Ht-Il+Ht-\-\IHt+H'I-ttt~IH/-\-IttI-++H \ \ ~

-0.1

-0.20'-----~20--4~0 -~60--8~0-~10,--0 --'120

time in seconds

autocorrelation function of abdominal respiration

xlO-2

-2 0 10 12

lime in seconds

power spectrum of abdominal respiration

x 10'

b 0.05 0.10 0.15 0.20

frequeocyinhertz

abdominal respiration

~ .

I ~J .. //.////. :§ 7 ~ 5 ...... /

8~V:--d '= 3.3

d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 embedding dimension

0.40

Fig.3a-d. Analysis of the time series of abdominal respiration as measured by H. P. Koepchen, H. H. Abd and D. KliiBendorf. a The measured curve. b The power spectrum. c Autocorrelation function. d The correlation exponent from which the dimension d=3.3 may be deduced (after Schanz, Haken, and Koepchen)

In conclusion, we may observe that synergetics has introduced a novel method­ology into the study of complex systems. This methodology asks for the study of macroscopic qualitative changes. In such a situation, order parameters and their dynamics can be determined, as was first demonstrated by means of numerous examples in physics and chemistry. Our expectation that the study of biological coordination can be treated similarly has been fully substantiated by the beautiful experiments by Kelso and our common interpretation. I am convinced that this methodology will prove useful also in other cases of bological coordination, in particular those treated at this Symposium.

Acknowledgements. I wish to thank Prof. Koepchen, Mr. Lorenz, and Mr. Schanz for valuable discussions.

References

1. Haken H (1983) Synergetics, an introduction (3rd edn). Springer-Verlag, Berlin Heidel­berg New York Tokyo

The Approach of Synergetics to the Study of Coordination of Rhythms 223

2. Haken H (1987) Advanced synergetics (2nd edn). Springer-Verlag, Berlin Heidelberg New York Tokyo

3. Haken H (1988) Information and selforganisation. Springer-Verlag, Berlin Heidelberg New York Tokyo

4. Haken H (1983) Synopsis and Introduction. In: Basar E, Flohr H, Haken H, Mandell AJ (eds) Synergetics of the brain. Springer-Verlag, Berlin Heidelberg New York Tokyo, pp 3-25

5. Kelso JAS (1984) Phase transitions and critical behavior in human bimanual coordina­tion. Am J Psycho I 246:R1000-R1004

6. Haken H, Kelso JAS, Bunz HH (1985) A theoretical model of phase transitions in human bimanual coordination. BioI Cybern 51:347-356

7. Schoner G, Haken H, Kelso JAS (1986) A stochastic theory of phase transitions in human hand movement. BioI Cybern 53: 247 - 257

8. Schanz M, Haken H, Koepchen HP (to be published) 9. Eckmann JP, Ruelle DE (1985) Ergodic theory of chaos and strange attractors. Rev Mod

Physics 57: 1-47 10. Grassberger P, Procaccia I (1983) Measuring the strangeness of strange attractors.

Physica 9D: 189-208

Behavioral and Neural Pattern Generation: The Concept of Neurobehavioral Dynamical Systems * lA.S. KELSO

Prologue

The concept of neurobehavioral dynamical system (NBDS) is introduced as a unifying explanation of the following facts of neural and behavioral patterns generation, namely: 1) that numerous physical mechanisms are capable ofrealizing the same neural and behavioral patterns; 2) that the same network can produce multiple patterns, a feature known as multifunctionality; and, 3) that networks can switch flexibly and spontaneously from one configuration to another under certain influences. Synergetic phase transitions provide the methodological strategy through which to discover laws of neural and behavioral pattern generation. At transitions, patterns arise in a self-organized fashion, as collective states produced by coupled nonlinear dynamics. Identified laws: 1) possess so-called 'universal' properties, governing dynamical behavior on several scales of observation (e.g. individual neurons, neural networks, kinematics ... ) and in different systems (thereby accounting for fact * 1 above); 2) exhibit multistability and bifurcation depending on parameter values (fact * 2 above); and 3) are stochastic, fluctuations playing a key role in probing the stability of the pattern dynamics and promoting labile change (fact * 3). In a NBDS, it is not necessary to posit a separate pattern generator for each observed behavior. Rather, where the system "lives" in the parameter space of the law, determines whether ordered or irregular patterns are observed. Linkage among different levels of description is by virtue of shared dynamical laws, which incorporate both chance and choice.

Introduction

There is general agreement among neurobiologists that the neural basis of most, if not all, rhythmic behaviors is a central pattern generator (ePG), neural circuitry which, when activated, generates a rhythmic motor pattern. Indeed, the patterns of activity in neural networks are often sufficently well-defined that they are given

* This research was supported by NIMH (Neurosciences Research Branch) grant MH42900-01, U.S. Office of Naval Research contract N00014-88-J-1191, and NINCDS grant NS-24771.

Behavioral and Neural Pattern Generation 225

a name, such as "flight" CPG, "locomotor" CPG, "respiratory" CPG, "swim­ming" CPG, and so forth (e.g., Grillner 1977). It is often remarked that under­standing a CPG is difficult because identifying all the elements - neurons and interneurons - and their properties is difficult. Nevertheless, new techniques in anatomy, biochemistry, and electrophysiology have greatly enhanced neuron iden­tification, and clarified important details about connectivity, membrane proper­ties, synaptic transmission types, and so on. On the functional side, it is now becoming widely recognized that the outputs of pattern-generating neural net­works are intrinsically flexible: the same networks can produce multiple patterns (e.g., Mpitsos et al. 1988). Not so many years ago it was almost heresy to suggest - even though based on experimental observations (e.g., Kelso et al. 1984) - that "hard-wired" neural circuits are the exception rather than, as convention would have it then, the rule. Now it has become clear that ensembles of biological elements cooperate to produce stable, function-specific patterns on the one hand, yet can switch flexibly from one pattern to another (and even form novel patterns) under parametric influences.

Deeper knowledge of the "nuts and bolts" of CPGs gained through improved technology indicates that the mechanisms underlying rhythmic motor patterns are local to the particular species member under investigation. In a discussion of mechanistic descriptions for invertebrate CPGs, Selverston (1988) concludes: "Such descriptions are remarkable for their lack of common neuronal mechanisms despite the similarities between the motor patterns they generate" (p. 377; italics mine). This fact, that many physical mechanisms may instantiate the same pattern, hints strongly of universality, that some underlying law(s) or rule(s) govern pattern generation in the nervous system. A number of prominent neuroscientists have emphasized the need for, and bemoaned the lack of, principles of neuronal pattern generation. Getting (1989), for example, stresses the complementary goals of knowing the "nuts and bolts" involved in neuronal pattern generation as well as discovering principles of operation. However, methodological strategies are need­ed to find putative laws and principles as well as a language with which to express them.

As illustrated briefly in this paper, the theoretical concepts ofsynergetics (Haken 1977,1983), a theory of pattern formation and self-organization in open, nonequi­librium systems, combined with the tools and techniques of nonlinear dynamical systems provides a language for understanding behavioral and neural generation (see also Haken 1983). Because it marries neuroscience, behavior and dynamical systems, we shall refer to the entire approach and the object of study as a neuro­behavioral dynamical system (NBDS). The concept of NBDS emphasizes synerget­ic construction principles for patterns of neural and behavioral function and their dynamics (e.g. Kelso 1990; Kelso and Schoner 1987, 1988; Schoner and Kelso 1988 a). Recognizing that such patterns are supported by diverse structures includ­ing neural pathways, cells, synaptic processes, and so forth, the aim is to express the mechanisms underlying how these (multiple) patterns persist stably and change flexibly in a unified language. As we shall see, this language does not merely provide a compact description of neuronal and behavioral pattern generation but explains why certain patterns are observed (or "selected") what their features are,

226 J. A. S. KELSO

and what causes them to change or switch. At certain special entry points called phase transitions where patterns switch spontaneously, the theory predicts novel, but observable phenomena.

The introduction of the NBDS concept and the focus on identifying laws of pattern generation is intended to replace the older view of the central pattern generator as a hard-wired circuit that causes behavior. By way of contrast, the NBDS concept provides a theoretical but operational context for interpreting much recent evidence of multifunctionality in neural networks, that neural cir­cuitry can switch flexibly among functional states and can reconfigure itself Cself­assemble") according to current conditions (see e.g., Marder 1989; Selverston 1988). Theoretically, loss of stability - seen, for example, through enhanced fluc­tuations in collective patterned states - is the mechanism underlying switching. Thus, variability plays a central role in pattern selection in a NBDS, probing the stability of the pattern dynamics and facilitating labile change to new configura­tions.

The NBDS Concept

It is widely agreed: (a) that the operation of a neural network depends upon interactions among multiple nonlinear processes at cellular, synaptic, and network levels, and (b) that modulation of these processes can alter network operation (for an excellent review see Getting 1989). The combination of (a) and (b) somehow sculpts patterns of neural and behavioral activity. Let us try to conceive how such pattern formation may occur in the broader context of cooperative phenomena in nonequilibrium systems. Synergetics (Haken 1977, 1983), for example, typically deals with equations of the following form:

q = N (q, parameters, noise) (1)

Here q is a high dimensional state vector containing all relevant microscopic variables (in the present context, e.g., cellular, synaptic properties). N is a nonlin­ear function of the microscopic state vector and depends on a number of parame­ters (in the present context, e.g., neuromodulators, neurotransmitters or, more generally, chemical and electrically induced events may act as parameters) as well as biological noise acting at microscopic levels, but which is unaccounted for in the state vector, q.

In general, when parameters in (1) change continuously, the corresponding solutions of (1) also change smoothly. However, at critical values of parameters, solutions may change qualitatively or discontinuously. At these nonequilibrium phase transitions patterns form spontaneously or changes in patterns occur. Such stable patterns and pattern changes arise solely as a result of the collective dynam­ics of the system [the function N in (1)] with no specific ordering influence from the outside, and no explicit pattern generator inside. Putative control parameters in (1), such as the concentration of a neuromodulator (e.g., serotonin and oc­topamine effects on the pyloric rhythm of the lobster; see Selverston 1988) may

Behavioral and Neural Pattern Generation 227

contain no specific information about emerging spatiotemporal patterns. Thus, the latter are "self-assembled" or "self-organized," that is, patterns emerge spon­taneously in accordance, for example, with the current concentration of circulating hormones and neurotransmitters. The spontaneous formation of patterns in nonequilibrium systems may be understood as special solutions of the system's dynamics that allow for a much lower dimensional description. Emerging patterns are characterized by collective variables or order parameters whose dynamics possess attractors. Attractors of the order parameter (pattern) dynamics exist because nonequilibrium systems are dissipative: many independent trajectories with different initial conditions converge in time to a certain limit set or attractor solution. Stable fixed point, periodic limit cycle, and chaotic solutions are thus all possible in the same system [e.g., (1)], depending on parameter values. Several attractors with different basins of attraction may also coexist, a feature called multistability. Multistability, the coexistence of several states for the same value of the control parameter is an essential feature of a (nonlinear) NBDS.

The crucial link between patterns and attractors lies with the theoretical concept of stability. Such words as stability and (functional) state have crept into the CPG literature, but one may ask: how does one define the state of a complex, biological system? In a NBDS, stability is of the collective variable (order parameter) dynam­ics and can be measured in a variety of ways [e.g., time to relax to the attract or after a perturbation (relaxation time), fluctuations of the collective variable around the attract or states, and so on; for details see, e.g., Kelso, Schoner, Scholz, Haken 1987; SchOner and Kelso 1988a).

Switching in a NBDS, as mentioned earlier, is associated with loss of stability. As a control parameter crosses a critical point, the previously stable pattern becomes unstable, and one pattern switches to another pattern that is stable beyond the critical point. Enhancement of order parameter fluctuations and a strong increase in relaxation time (critical slowing down) are predicted as signs that patterns are about to switch. Another measure, the switching time (the time for a transient switch to occur during a phase transition) is determined by the relative stability of the different attractors of the pattern dynamics. When detected exper­imentally, these predicted features link loss of pattern stability to nonequilibrium phase transitions (Haken 1977, 1983). Whether loss of stability is observed or not depends crucially on time scale relations (see Kelso et al. 1987; Schoner and Kelso 1988 a for a readable account). For example, if the control parameter is changed too slowly, switching may occur before the actual instability. Loss of stability may not, therefore, be detected. In a NBDS where both slower acting neuromodulatory inputs and electrical stimulation (natural or artificial) are putative control parame­ters, the relevant time scales of such parameter change may be very different. The beauty of the theory is that time scale relations can be, and have been, directly measured in experimental systems (see, e.g., Kelso et al. 1987; Scholz et al. 1987; Mandell and Kelso, 1989).

To summarize briefly: The NBDS concept entails both a language for under­standing pattern generation and a strategy that affords understanding. The lan­guage is dynamical. Neuronal and behavioral patterns are characterized by attrac­tors of collective variables or order parameters. Control parameters influence

228 1. A. S. KELSO

(often in a nonspecific fashion) the layout of attractors, promoting switching, qualitative changes in patterns. Stability of a collective variable is the central concept and can be measured quantitatively. Loss of stability, a nonequilibrium phase transition, holds the key to understanding pattern stability and change. Multifunctionality, only now receiving its just attention in the neuronal pattern generation (CPG) literature, corresponds to multistability in a NBDS. Thus, mul­tifunctional neural networks constitute an ideal test field for the NBDS concept.

The Phase Transition Methodology

What form do attractors take for neuronal and behavioral pattern generation, and how do we find them? In NBDS terms, phase transitions hold the key to these questions constituting a special entry point for developing theoretical understand­ing. The reason is that qualitative change allows a clear distinction of one pattern from another, thereby allowing the identification of order parameters or collective variables for different patterns and the order parameter dynamics (stability, loss of stability, etc.) Around phase transitions or bifurcations, phenomenological description turns to prediction; the essential processes governing a pattern's stabil­ity, change, and even its selection can be uncovered. Well defined measures (fluc­tuations, relaxation times, switching times, time scale relations, and so forth) are available to elucidate these processes. In addition, the control parameters that promote instabilities can be discovered (for many examples, see Kelso 1990).

Our work has studied patterns of rhythmic coordination in humans as a window into principles of neural and behavioral organization (see Schoner and Kelso 1988 a; Jeka and Kelso 1989 for most recent reviews). Relative phase was identified as an order parameter or collective variable capturing the ordering relations among the individual oscillatory components. Multistability and transitions among phase-locked states were observed at critical values of a continuously changed control parameter, in this case, frequency (Kelso 1981, 1984). En route to these transitions, enhancement of fluctuations (Kelso and Scholz 1985; Kelso et al. 1986) and critical slowing down of the order parameter (Scholz et al. 1987; Scholz and Kelso 1989) were observed experimentally, both quantitatively predicted by theoretical modeling of the nonlinear dynamics (Haken et al. 1985; Schoner et al. 1986). Once the pattern dynamics were found (i.e., laws or equations of motion for the dynamic patterns observed), it was possible to synthesize them from other levels of description, thereby constituting a micro- to macro-relation. Specifically, it was possible to derive mathematically the order parameter (relative phase) dynamics for the patterns by cooperatively coupling the individual components (Haken et al. 1985). It is worth emphasizing that the coupling functions are quite unspecific to the patterns of coordination that result. Several functional forms give rise to the same phase-locked pattern. Moreover, changes in coordinative pattern can be effected by changing only the eigenfrequencies of the components and keeping the coupling function constant. Thus, the system's collective properties cannot be attributed to the coupling per se but to the coordinated system as a

Behavioral and Neural Pattern Generation 229

whole. There are, therefore, many "mechanisms" that can give rise to the same pattern (see also Tank 1989).

Remarkable parallels exist between this work on humans and rhythmic neuronal patterns in vertebrate and invertebrate neural networks. For example, of all the possible neuronal patterns that could be produced by these networks, only a few kinds of temporal order are actually observed. Temporal constraints reflect tremendous information compression often referred to as "degeneracy in the pattern code" (see Kristan 1980). Viewed as a NBDS, the reason why a limited number of temporal patterns occur is that only a few are stable. It is interesting in this respect that single neurons have been shown to display many of the features of neuronal patterns including multistability, period doubling bifurcations, and even deterministic chaos (e.g., Matsumoto et al. 1987). We suppose that this is because a single neuron may be accurately characterized as a nonlinear oscillator, or more generally, a NBDS. Thus, both the single neuron and the collective behavior of neurons can be understood in NBDS terms. The variables of temporal order (e.g., synchronization, phase and frequency-locking, phase plane variables) prove to be adequate, function-specific collective variables or order parameters for both brain and behavior.

In summary, experimental demonstrations of ordered spatiotemporal patterns, multistability, variability, switching, and bifurcation in neuronal, neuromuscular, and behavioral (kinematic) experimental systems, reflect the universality property of a NBDS. In a NBDS, the same patterns can be produced by very different mechanisms, and different patterns can be produced by the same mechanisms. Principles of pattern generation are thus level or system independent. The key concept concerns the stability of a collective state which is most clearly defined at phase transitions. At such transitions, self-organization becomes apparent: differ­ent patterns arise as stable states of the coupled nonlinear dynamics. Such coupled nonlinear dynamics may be shown to govern spatiotemporal behavior at several scales of observation from the single neuron up.

Laws and the Phase Attractive Circle map

Here we give an example of what form the laws may take for dynamic patterns in brain and behavior, NBDS laws, so to speak. Our particular focus concerns frequency and phase synchronization, spatiotemporal patterns which are ubiqui­tous in nature. To name only a few recent examples from biology: the 1:1 and 2:1 entrainment of membrane fluctuations by brief depolarizing pulses in lamprey (Wallen and Grillner 1985; Grillner et al. 1988); 3:1 mode locking and period-dou­bling bifurcations in periodically stimulated squid axons (Matsumoto et al. 1987; Guttman et al. 1980); phase and frequency synchronization even in distant cell populations of areas 17 and 18 in primary visual cortex (Eckhorn et al. 1988; Gray et al. 1989); locomotor-respiratory coupling which takes the form of simple integer frequency relationships in many locomoting creatures (Bramble and Carrier 1983); the interactions of the limbs in split-belt treadmill locomotion of mesencephalic

230 J. A. S. KELSO

cats (Kulugin and Shik 1970) and human infants (Thelen et al. 1987); and period­ically stimulated heart cells (Guevara et al. 1981).

Why are only a few, usually simple, space-time patterns observed in all these different experimental preparations? The answer, intuited by von Holst (1939, 1973) many years ago, lies in the fact that only a few forms of temporal organiza­tion are stable. It cannot be overemphasized that in synergeticjNBDS terms, stability refers to a collective state. We know stability is crucial because of com­pelling evidence (briefly reviewed in the two preceding sections) for nonequilibri­urn phase transitions, in which loss of stability plays a key role. Nonequilibriurn phase transitions are at the core of pattern formation and self-organization (later we will draw attention to the particular biological significance of this discovery).

For example, in the original Kelso experiments (in the previous section) only two forms of temporal patterning are stable, in-phase and antiphase, 1-to-1 fre­quency-locked. A bifurcation occurs as rate is increased: the antiphase pattern loses stability and a spontaneous switch to in-phase occurs. Haken et al. (1985) were able to determine the dynamics of relative phase, ¢, from a few basic postu­lates. The simplest mathematical form is

¢ = -oVjo¢ (2)

complying with periodicity and symmetry requirements, the potential

V(¢) = - a cos(¢) - b cos (2 ¢) (3)

has attractors corresponding to the observed patterns at ¢ = 00 and ¢ = 1800

and captures the bifurcation or phase transition, in that above the critical point (bja = 0.25) both patterns are stable (a condition called bistability); below it, only the in-phase mode is stable. Furthermore, after the transition is over, the system stays in the in-phase mode when the control parameter is reversed, a feature (also observed in the Kelso experiments) called hysteresis. Local measures of the in­phase and antiphase attractors allow for the easy determination of the a, b parame­ters whose ratio corresponds to driving frequency in the experiment (see, e.g., Schoner et al. 1986; Scholz et al. 1987).

The order parameter dynamics (2), (3) for ¢ can be derived by nonlinearly coupling the individual components. The latter are precisely mapped onto limit cycle attractors of the following functional form (whose parameters can again be determined by detailed experiments, see Kelso et al. 1981; Kay et al. 1987; 1991)

(4)

where y > 0, ill> 0, f3 > 0 and y > 0 are model parameters. Using the simplest nonlinear coupling between oscillators of type (4), Haken

et al. (1985) derived a closed form dynamics for the relative phasing patterns and transitions among them. A particularly salient point is that a pattern-forming, nonlinear phenomenon occurs, a phase transition, through change of a single parameter, the oscillation frequency, ill in (4).

What distinguishes the present theoretical approach from other related model­ing efforts (see, e.g., Kope1l1988; Rand et al. 1988)1 As emphasized in the previous section, the NBDS construct emphasizes the methodology of phase transitions in

Behavioral and Neural Pattern Generation 231

order to discover laws at a chosen level of observation. Once these are found, they can be derived from an adjacent, next lower down level. The issue is not macro­versus micro-, or "top-down" versus "bottom-up." Rather it is to find laws of pattern - stated in terms of order parameter dynamics - at one's chosen level of description. There is no ontological priority of one observational scale over anoth­er. What is "macro" at one level can be "micro" to another. It is the methodolog­ical strategy and the principles derived therefrom that do not change across levels of investigation. The aim is to obtain as complete a description as possible on any given level.

An emphasis on stability under certain nonspecific boundary conditions should not be taken to mean that a NBDS cannot adapt to specific requirements. The great benefit of the approach is that once the order parameter dynamics are found, it is possible to express specific behavioral requirements in terms of the order parameters. Such behavioral information, arising, for instance, due to the environ­ment, learning, memory or intention (see Schoner and Kelso 1988 b) is information only to the extent that it modifies the order parameter dynamics, i.e., alters the vector field specified in (3). Information is specific in the sense that it is expressed in the same language as the order parameters. Thus, information is not arbitrary with respect to the intrinsic pattern dynamics: it may cooperate or compete, depending on how close it is to the existing pattern dynamics (for examples see Schoner and Kelso 1988 b; Kelso and DeGuzman 1988; see below).

We can readily extend the basic coordination laws given in (2) and (3) to patterns that are not 1: 1 frequency locked. From the examples given at the beginning of this section, other forms of entrainment (e.g., 2:1, 3:1, 2:3) are possible. In this respect, it is useful to conceive the combined motions produced by dissipatively coupled nonlinear dynamics in terms of simple maps. For example, a generic description of coupled, multifrequency systems is a circle map (which maps the circumference of a circle onto itself). To see this, consider the relative phase dynamics (3), derived by assuming strongly attracting limit cycle solutions of the component dynamics. Relative phase, cPn, is calculated experimentally, as follows:

(5)

where Tn is the peak-to-peak period of oscillator 1 beginning at time to, and 'n is the interval between that event and the peak onset of oscillator 2. Normalizing amplitude, mode-locked solutions can take the form of a map:

(6)

A very simple theoretical prototype for this map is called the standard circle or sine circle map:

K G(cPn)=cPn+l=CPn+Q--sin(2ncpn), mod 1

2n (7)

which actually characterizes the coupled nonlinear dynamics of a bewildering array of natural phenomena (see Glazier and Libchaber 1988 for review). Exper­imentally, Q is defined as the ratio of two frequencies (e.g., an intrinsic membrane

232 J. A. S. KELSO

oscillation "driven" by an externally depolarizing pulse as in Grillner et al. 1988). The exact functional form, sin 2 n CPn' is not crucial: any function with a single cubic inflection point exhibits similar qualitative (and quantitative) behavior (Bak et al. 1984). The nonlinear term, K (such as the amplitude of the driving frequency) is what makes the map (7) interesting. As the strength of the nonlinearity K increases, the width of locked frequency-ratios increases. Plotted on the (K, Q) plane, these Arnold tongues (Arnold 1974) sprout up from K = 0, with the largest tongues (1: 1, 2: 1, 3: 1, etc.) representing the most stable, and attracting, fre­quency-locked regions (see Fig. 1 a). At K = 1, called the critical line, the map G(CPn) is no longer invertible; the tongues start to overlap, and a transition to chaos (including hysteresis) occurs. Above the critical line, the insides of the tongues exhibit rich dynamics, among which are period doubling cascades to chaos.

Although we cannot go into great detail here, it is easy to see, in principle, why only a few, low-integer frequency lockings are often seen in biological systems. The reason is that the widest, hence most attractive mode lockings are the low integer ones. If the experimental noise level is low, it may be possible to observe other locked states. However, the presence of noise can easily kick the systems into nearby, more stable attractors. On the other hand, below the critical line, K < 1, frequency ratios (Q) that are not exactly, say, 1: 1 or 2: 1 will be "sucked in" to tongues with 1: 1 and 2: 1 winding numbers.

Although the map (7) constitutes a generic description for coupled, nonlinear oscillators it does not account for (provide a precise description of) recent exper­imental observations (Kelso and DeGuzman 1988) of phase attraction to in-phase and antiphase modes and spontaneous jumps from one mode to the other within a frequency ratio that is not 1: 1. Nor, of course, does the sine circle map accom­modate the earlier experimental discovery of phase transitions. The addition of a phase attractive term to (7) enables us to transform the Haken-Kelso-Bunz (HKB) model [equations (2) and (3)] to a map

K <Pn+ 1 = <Pn + Q - - [1 + A cos(2n <PJ] sin(2n <PnL mod 1 (8)

2n

The "intrinsic" parameter A expresses the bistability of relative phase (coexis­tence of in-phase and antiphase) and behaves as the ratio 4b/a in the original Haken et al. (1985) model. Although detailed analysis and derivation of the so­called HKB map [equation (8)] is beyond the scope of the present paper (see DeGuzman and Kelso, 1991) let us draw attention to a few features. A most significant one is that the juxtaposition of only two parameters, A representing the intrinsic, phase attractive dynamics and K, an external driving parameter, deter­mines all observed temporal patterns. The mechanism for pattern selection is a competition between the "intrinsic" and "extrinsic" terms, A and K. (Note that the word "intrinsic" as used here refers to the order parameter dynamics that arise due to nonspecific changes in control parameters, as in our original experiments re­viewed in the previous section. Nothing about "hardwiring" is inferred, however.)

The HKB or phase attractive circle map (8) is a simple, low-dimensional law that accommodates experimental observations such as: (a) the shift from bistability to mono stability as driving frequency is increased; (b) jumps from less stable to more

Fig. la-c. Arnold tongues (color coded) for the map (7) and the HKB map (8). a A = O. The pattern of locked tongues is shown on the Q (x-axis) versus K (y-axis) plane for a few frequency ratios. Q and K are scaled from 0 to 1. Red, blue, green, etc. correspond to 1 : 1, 1 : 2, {1 :3, 2:3}, {1:4, 3:4}, . . . mode­locked regions. Black areas are quasiperiodic. Tongue width dimin­ishes as the size of the denominator increases. Note there is no overlap among the tongues below the criti­cal surface, K = 1 (see text for de­tails). b A=0.5. Description is the same as in a. Note that the tongues broaden and overlap for smaller values of K. Also, period doubling bifurcations are seen inside the tongues en route to irregular be­havior (black areas inside tongues). See text for details. c A = - 0.5. De­scription is the same as Fig. 1. In this parameter region the tongues are much thinner and do not over­lap until Kp 1 (see text for details)

Behavioral and Neural Pattern Generation 233

234 1. A. S. KELSO

stable modelockings, e.g., 4:3 to 1:1, 5:2 to 2:1; and (c) spontaneous transi­tions from one phase relation to another within low-integer modelockings.

A number of exciting consequences and predictions follow from the HKB map. One is that the relative strength of A and K determines the width of the Arnold tongues (stable, mode-locked regions). This means that irregular dynamics -quasiperiodicity and chaos - can be suppressed or accelerated depending on the value of A relative to K. For the experts, the parameter A shifts the critical surface given by K=1 in the sine circle map. For example, for A=0.5 the critical surface is depressed toward smaller K. Thus, the tongues overlap at smaller values of the driving term, K (Fig. 1 b). At the same time, the widths are broader, meaning that the biological system can stay in some mode-locked region even beyond the critical surface, that is, relative to the ordinary sine circle map (7). On the other hand, for A = - 0.5, the tongues are thinner, but the critical region is reached later (see Fig. 1 c). The drawback is that a small amount of noise can easily kick the system out of these narrow stability: regions, inducing quasiperiodicity and chaos.

In short, in a NBDS, where the system "lives" in parameter space, determines whether ordered frequency-locked patterns or irregular patterns are observed. It cannot be overemphasized that it is the same pattern-generating system, only the parameters are subject to change. It is exciting that the introduction of the biolog­ically significant feature of phase attraction adds a new dimension to the circle map, itself a characterization of certain universal properties in many dissipative physical systems. Not only is relative phase an order parameter or collective variable in the sense of synergetics, but the phasing itself is an ordering mechanism for multifrequency neural and behavioral patterns.

Finally, we note that our simple laws, stated either as differential equations (2-4) or as discrete maps of relevant variables (7,8) encapsulate von Holst's (1939, 1973) rules, themselves extracted from much empirical research. Somehow these rule-shave been largely ignored, perhaps because they appear overly descriptive. Von Holst's (1939, 1973) rule 4 (pp. 119, 120), for example, was that "the degree of stability increases with the simplicity of the frequency relationships. Increasing degree of complexity is accompanied by. decreasing stability." Our experiments cum theoretical modeling reveal that universal features of a system's ability to generate multifrequency patterns are governed by the differential stability of mode-locked states, as seen through the width of Arnold tongues. Viewed as an NBDS, pattern complexity is inversely proportional to tongue width, thereby rationalizing the relative difficulty of producing different patterns, as well as the frequency with which different pattems occur in a wide variety of experimental preparations, at many different scales of observation. Phase attraction sit inside these multifrequency patterns acting as an ordering mechanism, but it may - when the system is driven by external parameters - give way to irregular dynamics, and even chaos.

235

A Parting Note

With so much microscopic structure to explore and so much technology available, with which to explore it, we can lose sight of some deep issues. For the biologist, understanding the relation between structure and function has always been a high priority. Structural decomposition has held sway in physics, where it is quite appropriate to seek the ultimate (fundamental? elemental?) particles of matter. In the behavioral and brain sciences, however, functional decomposition is equally, if not more important. Here we are confronted with complexity, and ways must be found to identify relevant quantities, in systems where it is not possible (even if it were useful) to determine the detailed behavior of every degree of freedom. In this paper, following Haken's (1977, 1983) synergetics, we have emphasized the phase transition methodology as an operational way to identify relevant variables. Although phase transitions, by definition, refer to nonlinear, qualitative changes, our theoretical premise is that they allow for the clear demarcation of patterns and hence the definition of collective variables (order parameters) and their dynamics so essential to characterizing behavior in the linear range.

Not all change takes the form of phase transitions. Nevertheless, phase transi­tions reveal how pattern persistence and flexibility arise from the interplay between stability and fluctuations in a dynamical system. At transitions, self-organization becomes apparent; instability (often due to competing forces) creates new (or different) patterns and configurations among the components. The latter often arise as stable, phase-synchronized states of coupled nonlinear dynamics (see previous two sections).

Phase transitions not only provide an experimental strategy and predictive framework, they also implicate a language within which to express rules or laws for pattern generation. Understanding obviously depends on the language used. The present analysis of pattern generation qua neurobiological dynamical systems places the language of dynamics at the forefront, removing any ontological prior­ity of one scale of observation over another. The methodology and language apply uniformly on all scales. Our demonstration that once the order parameters for patterns and their dynamics (equations of motion) are found at one level they can be derived by cooperatively coupling the dynamics at an adjacent level, indicates that the linkage among levels of description is by virtue of shared dynamics. Note that this linkage between levels is possible only if a dynamic pattern description is available on both levels in question.

In short, the NBDS language and strategy cuts across scales of observation. We hope to have shown that the theoretical framework is rich enough to (a) identify relevant properties and modes of operation of neurons, neuronal networks, and behavior, (b) predict new features on the basis of well-tried observables, and (c) relate levels of description. The language is abtract, thereby covering a wide variety of cases, yet operational- sufficiently so that the pattern dynamics can be comput­ed in all cases. Indeed, the very fact that neuronal and behavioral patterns are realized by a diversity of physical mechanisms bodes well for computational "neural" networks. One benefit of finding laws for dynamic patterns - their

236 J. A. S. KELSO

stability, change, and modification - is that these laws may be said to take the form of natural computational principles for neurobiology and behavior.

Acknowledgements. I thank Tom Holroyd for his help in producing the figures, Betty Tuller for comments on the manuscript and David Engstrom for stressing the need for a bridging concept between neurons and behavior.

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The Applicability of Chaos Theory to Rhythmic Breathing Patterns * C. L. WEBBER, Jr. and J. P. ZBILUT

Chaos Theory

From molecular to organismic levels, life in this world is characterized by rhythms and oscillatory patterns within and between innumerable varieties of life forms. For the more complex species, life is sustained by major physiological systems, including the cardiovascular, respiratory, and nervous systems. In the face of challenging and life-threatening environmental conditions these control systems respond with impressive flexibility and creative variability. Not only is it impera­tive to know how individual rhythms arise, but we must come to understand the modulation of rhythmical processes which best suit the organism for any particu­lar physiological state. Pressing research questions relate to the stochastic versus deterministic nature of these systems and to the predictability of abnormal system dynamics.

Armed with the new and developing techniques of chaos science, it is now possible for investigators to glean a more comprehensive understanding of compli­cated dynamical processes in biological and physiological systems. By way of definition, and contrary to popular usage, chaos depicts any activity that appears to be random, but which is completely determined and ordered. Although com­mon statistical testing cannot distinguish between random noise and fully deter­ministic processes, systems characterized by nonlinear feedbacks are sure candiates for possessing chaotic attributes. Certain "turbulent" breathing patterns, for ex­ample, may be found to be chaotic instead of random.

This contribution seeks to provide specific examples of dynamical behavior of the respiratory system as viewed from the new perspective of chaos. The displayed data all come from the rat, either anesthetized or unanesthetized, asleep or awake. Several introductory texts, monographs, and papers on chaos in physical or phys­iological systems are available which require little [2, 4, 8, 11] to moderate [7, 19] mathematical expertise to understand.

* This research was supported by funding from the Potts Foundation.

240 C. L. WEBBER, J. P. ZBlLUT

Periodic Stimulation

One classic way in which to study any rhythmical system is to perturb the system and observe the poststimulus dynamics. The stimulus can be single pulses [16], periodic pulses [18], or even aperiodic pulses [14]. Depending upon the strength and timing of the stimulus, the pattern can be reset (strong type 0 resetting), not be reset (weak type 1 resetting), or be altered unpredictably and dysrhythmically (phase singularity) [25]. For the respiratory system lung inflation mayor may not phase-delay the next phrenic burst, and certain volume-timing combinations reveal points of phase singularity [16]. At such points the system displays extreme sensi­tivity to initial conditions, which is indicative of chaotic dynamics.

Periodic stimulation of an oscillatory network, although more complicated than single-pulse perturbations, can also elicit chaos-like behavior. Cyclic inflation of the lungs in vagus-intact cats, for example, results in distinct regions of stimulus: response couplings, but between the zones of phaselocking there exist regions of aperiodic dynamics [17, 18]. This principle is clearly illustrated in Fig. 1 from one of our rats which was anesthetized, immobilized, and artificially ventilated at selected frequency-tracheal pressure combinations. The vagus nerves were left intact, and phrenic activity was recorded in the cervical region. When inflation was withheld, the spontaneous frequency of phrenic bursting was about 60 per minute. During cyclic inflation, various regions of pump-to-phrenic couplings can be identified on the phase-locking diagram (solid symbols in Fig. 1). Between these Arnold tongues, however, the dynamics are irregular and unpredictable (open squares). For low tracheal pressures the two oscillators can be viewed as running independently, but at larger tracheal pressures such aperiodic couplings can be

20

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Fig. 1. Phase-locking diagram delineating the parameter space for tracheal pressure and pump frequency forcings of phrenic burst timings in an anesthetized, immobilized, and vagus-intact rat. Arnold tongues indicate regions of integer pump:phrenic couplings (solid symbols) and intermediate spaces designate regions of aperiodic dynamics (open squares)

The Applicability of Chaos Theory to Rhythmic Breathing Patterns 241

viewed as being chaotic. Indeed, if the frequency and pressure parameters were fine-grained, one would expect hysteresis and irregular dynamics in the regions where the Arnold tongues overlap at high tracheal pressures.

Network Bifurcations

It is well known that the phrenic neurogram of cats carries dual fast rhythms in the ranges of 20-50 Hz and 50-100 Hz [20, 24]. Our own work shows that the high-frequency oscillation (HFO) is present throughout the phrenic burst, but that the medium-frequency oscillation (MFO) is present only in the late discharge phase when central respiratory drive is the strongest [24]. The origin of the phrenic HFO is attributed to brainstem respiratory neurons which carry coherent HFO spectral lines [3], but the source of the MFO remains unknown. Possibly some other central nervous system site drives the phrenic MFO, but chaos theory offers an alternative interpretation.

Reiteration of a very simple quadratic difference equation reveals the character­istics of period bifurcation and ultimate chaos when a tuning parameter is in­creased stepwise [13]. The same principle is observed in the physical system of liquid helium flow which goes through a period-doubling route to turbulence (chaos) upon external heating (tunable parameter) [12]. Could such a situation exist for the phrenic population under different levels of central drive?

Figure 2 shows two power spectra from a rat phrenic neurogram recorded during high (upper panel) or low (lower panel) passive ventilation. In this situa­tion, end-tidal CO 2 , although not measured, can be considered to be the tuning parameter. With a relatively lower value of CO2 , the phrenic network produces a primary HFO; with a relatively higher value of CO 2 , the primary peak remains, but bifurcates into a secondary MFO. By this reasoning there is no need to postulate a separate descending MFO drive to the phrenic motoneuron pool; rather, the presence of MFOs may simply be an emergent property of the phrenic nucleus as tuned by the CO2 parameter. This new hypothesis deserves to be studied with great care since it suggests that excitation of the phrenic network may produce unanticipated dynamics in certain situations.

Attractors and Basins of Attraction

Chaos theory, in contradistinction to gaussian statistical theory, argues that it may be inappropriate to express breathing frequency as a simple mean. Traces of thoracic pressure in the unrestrained and unanesthetized rat as shown in Fig. 3, for example, reveal that much more information is carried by the period and ampli­tude of individual breaths, whether the animal is asleep or awake. In phase space these fluctuating pressure excursions with time translate into loops which are

242 C. L. WEBBER, J. P. ZBlLUT

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0 0 50

100

100

High Passive Ventilation

(low PAC02)

150 200

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bounded but not superimposed. In terms of attractors, One might say that the respiratory system is wobbling in the basin of a limit-cycle attract or, the wobbling due to intrinsic or extrinsic noise. It is interesting that when an intruder rat is introduced, the resident rat awakes and rapidly shifts attractors as evidenced by the altered geometry of the phase-space portrait in the awake state. More On the theory of limit-cycle oscillators, attractors, and multiple basins of attraction can be found elsewhere [6, 7, 9, 21].

The Applicability of Chaos Theory to Rhythmic Breathing Patterns 243

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'-q,. :z:

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1 see 10 em H:P

Fig. 3. Time series and phase-space portraits of thoracic pressure changes in an unanes­thetized rat asleep and awake, breathing at different spontaneous frequencies (j). Introduc­tion of a second rat (resident-intruder paradigm) wakes the test rat to a new physiological state. Altered geometry of the awake phase-space portrait may indicate an accompanying shift to a new attractor

Self-Organized Criticality

Systems which are characterized by dynamical minimal stability and spatial scaling are said to possess self-organized criticality. That is, perturbations at one scale induce a cascade of energy dissipation across all scales of time and/or distance such that the system manifests a robustness against external pertubations. This is the reason why the angle of repose of a sand pile or cone remains unchanged indepen­dent of its height and magnitude of perturbations (consider an hourglass). An important discovery has shown that systems possessing self-organized criticality also exhibit in the frequency domain a power-law behavior over widely different time scales [1, 15]. This process is identified as l/fl flicker noise as opposed to l/fo white noise or 1/f2 colored noise.

Figure 4 illustrates our analysis of power ~ jP for the unanesthetized rat introduced above. For both the asleep and awake states, 512 consecutive respira­tory periods (time domain) were measured and transformed by a fast-Fourier transform into period power spectra (frequency domain: left plots in Fig. 4). Then, a least-squares regression line was computed for each log-log transformed spectra, the slope of which was the exponent fJ of the power law (right plots). The results show that the asleep state follows a white noise process (1/~) whereas the awake state follows a flicker noise process (1/fl) ofself-organized criticality. This data may be interpreted to indicate that during sleep the raUs in a chaotic mode oflow attention, such that switching to an alternateattractor, if necessary, can be done swiftly and without searching (see [23]). When intruded upon, the rat immediately

244 C. L. WEBBER, J. P. ZBILUT

Period Power Spectra

60r-----------------------_.

o 45 ~ o

'-" ... 30 Q)

~ o c..

Asleep

60r-~A-w-ak~e-------------------.

,...... 45 :l C

":: 30 Q)

~ o

c.. 15

oWm.M~~~~~ o 23 47 70 94 117 141 164

Frequency (min-1)

L Q)

:t a ~

Log-Log Regressions

3.0,-.,..-,---------------------. Asleep l/f'

1.5

0.0

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

slope = -0.702 r = 0.468

l/f'

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Log (Frequency, min-I)

Fig. 4. Power spectra analysis of 512 consecutive respiratory periods of the unanesthetized rat in states of sleep and wakefulness. Linear spectra (left panels) and log-log spectra (right panels) plots are both illustrated. Linear regression analysis on the log-log data reveals a power scaling across a few decades of frequency for the awake state (liP) but not for the asleep state which follows a more random or chaotic distribution (l/jD). The r value is the regression coefficient, and the slope equals the fJ exponent of the function: power ~ jP

awakes and switches to a higher attention state which is robust against perturba­tions across several decades of frequency.

Images of Chaos

From the ergodic or probabilistic viewpoint (as opposed to the topological or geometric viewpoint), numerical methods have been devised to compute dynami­cal parameters from time series. These include the fractal dimension, entropy, Liapunov exponents, etc. [14]. All these methods depend upon the assumption that the original time series represents an autonomous dynamical system, but this may not be the case for biological systems that have adiabatic (slowly changing or drifting) parameters [22]. To overcome this limitation, graphical solutions to the problem have been suggested, such as recurrence plots of dynamical systems [5, 14]. As the name suggests, recurrence plots are delay contour maps that contain time autocorrelation information. Two graphical examples are plotted in Fig. 5 for the rat in the sleeping and awake states. Careful examination of the small-scale

The Applicability of Chaos Theory to Rhythmic Breathing Patterns 245

Asleep

Awake

Fig. 5. Graphical representation of time series from dynamical systems that possess adiabat­ic (slowly changing) parameters. Depending upon the physiological state, the recurrence plots exhibit different textures: aperiodic contours without banding (asleep); periodic con­tours with banding (awake). A detailed explanation of the horizontal and vertical axes is given elsewhere [14]

texture reveals significant differences between the two states, the lack of banding (upper panel in Fig. 5) being associated with aperiodic components (l /fo noise) and the banding (lower panel) being associated with periodic components (1 /f1 noise).

Future Directions

Rhythms of respiration represent a "Russian doll" situation in which mUltiple frequencies on different time bases (HFOs, minute ventilation, sleep/wake circadi-

246 C. L. WEBBER,J. P. ZBILUT

an and circannual rhythms) are superimposed. That this rhythm upon rhythm upon rhythm may best be described by a fractal relationship in the time and/or frequency domains remains to be demonstrated. Depending upon the magnitude of specific tuning parameters, chaos may develop at any level independently or in a cascadelike fashion across scales. In addition to the period-doubling scenario, the possible routes to chaos are numerous and include quasiperiodicity and intermit­tency. However, it is left for future experimenters to determine the critical parame­ters and pave the way for diagnosis and prediction of dynamical diseases. No longer can we dismiss complicated patterns as noise, but consideration of the possibility of chaos lurking in the patterns must be adequately addressed. Mecha­nisms of phase or state transitions promise to be of most interest to which concepts of synergetics and nonlinear oscillator theory are ideally suited [10].

Acknowledgements. We acknowledge the excellent technical assistance of Ms. Therese Kramer, Mr. Michael Cassidy, and Ms. Marian Asfeld.

References

1. Bak P, Tang C, Wiesenfeld K (1987) Self-organized criticality: an explanation of 1/f noise. Phys Rev Lett 59:381-384

2. Briggs J, Peat FD (1989) Turbulent mirror: an illustrated guide to chaos theory and the science of wholeness. Harper and Row, New York

3. Christakos CN, Cohen MI, See WR, Barnhardt R (1988) Fast rhythms in the discharges of medullary inspiratory neurons. Brain Res 463: 362-367

4. Crutchfield JP, Farmer JD, Packard NH, Shaw RS (1986) Chaos. Sci Am 255:46-57 5. Eckmann J-P, Kamphorst SO, Ruelle D (1987) Recurrence plots of dynamical systems.

Europhys Lett 4:973-977 6. Eldridge FL, Paydarfar D, Wagner PG, Dowell RT (1989) Phase resetting of respiratory

rhythm: effect of changing respiratory "drive." Am J Physiol 257: R271-R277 7. Glass L, Mackey MC (1988) From clocks to chaos: the rhythms of life. Princeton

University Press, Princeton 8. Gleick J (1987) Chaos: making a new science. Penguin, New York 9. Gwinn EG, Westervelt RM (1986) Fractal basin boundaries and intermittency in the

driven damped pendulum. Phys Rev Am 33:4143-4155 10. Haken H, Kelso JAS, Bunz H (1985) A theoretical model of phase transitions in human

hand movements. BioI Cybern 51:347-356 11. Kadanoff LP (1986) Chaos: a view of complexity in the physical sciences. In: The great

ideas today. Encyclopedia Britannica, Chicago, pp 62-92 12. Libchaber A, Maurer J (1982) A Rayleigh-Benard experiment: helium in a small box. In:

Riste T (ed) Nonlinearphenomena at phase transitions and instabilities. Plenum, New York, pp 259-286

13. May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261:459-467

14. Mayer-Kress G, Hiibler A (1989) Time evolution of local complexity measures and aperiodic perturbations of nonlinear dynamical systems. In: Abraham NB (ed) Quanti­tative measures of complex dynamical systems. Plenum, New York

15. Montroll EW, Shlesinger MF (1982) On 1/fnoise and other distributions with long tails. Proc Natl Acad Sci USA 79:3380-3383

16. Paydarfar D, Eldridge FL, Kiley IP (1986) Resetting of mammalian respiratory rhythm: existence of a phase singularity. Am J PhysioI250:R721-R727

The Applicability of Chaos Theory to Rhythmic Breathing Patterns 247

17. Petrillo GA, Glass L (1984) A theory for phase locking of respiration in cats to a mechanical ventilator. Am J Physio1246:R311-R320

18. Petrillo GA, Glass L, Trippenbach T (1983) Phase locking of the respiratory rhythm in cats to a mechanical ventilator. Can J Physiol Pharmaco161:599-607

19. Rensing L, Heiden U an der, Mackey MC (1987) Temporal disorder in human oscilla­tory systems. Springer, Berlin Heidelberg New York

20. Richardson CA, Mitchell RA (1982) Power spectral analysis of inspiratory nerve activity in the decerebrate cat. Brain Res 233: 317 - 336

21. Ruelle D (1980) Strange attractors. Math Intell 2: 126-137 22. Ruelle D (1987) Diagnosis of dynamical systems with fluctuating parameters. Proc R Soc

Lond [A] 413:5-8 23. Skarda CA, Freeman WJ (1987) How brains make chaos in order to make sense of the

world. Behav Brain Res 10:161-195 24. Webber CL Jr (1989) High-frequency oscillations within early and late phases of the

phrenic neurogram. J Appl Physiol 66:886-893 25. Winfree AT (1987) When time breaks down: the three-dimensional dynamics of electro­

chemical waves and cardiac arrhythmias. Princeton University Press, Princeton

Discussion on the Theoretical and Neuronal Basis of Rhythm Coordination C. L. WEBBER, JR.

In the history of science it is not unusual for mathematical theories derived for the physical and engineering sciences to be applied to the biological and physiological sciences. It is important to recognize the contributions and limitations of various approaches without insisting upon anyone universal theory to encompass all known experimental observations [6]. In the past decade the catastrophe theory of Thom [7,8] was very fashionable because it described how continuously changing causes (inputs) can induce dramatic discontinuities in results (outputs). Presently, however, the catastrophe model has fallen into disrepute since it is a theory based on very restricted mathematical equations that provide no new information, but only classification of known equations of motion. Application of the theory out­side of the physical sciences is seriously questioned.

In contradistinction to the catastrophe theory, which merely promotes a new way of calculation, the theories of synergetics [5] and chaos [3] have much broader mathematical bases (numerous classes of equations) and are applicable to systems characterized by fluctuations or oscillations. These subsequent theories are more powerful and accurate in modeling two nonlinear dynamical characteristics com­monly occurring in physical and biological systems: hysteresis and turbulence.

The physiological utility of any model system is proportionate to its ability to accurately predict some dynamical behavior of living systems. The Van der Pol relaxation oscillator, for example, displays the characteristics of type 1 weak resetting, type 0 strong resetting, and phase singularity for certain stimulus parameters. Since the respiratory system mimics the Van der Pol equations and displays similar topological responses to phase resetting of the phrenic discharge upon stimulation of the mesencephalon [4], it can be argued that the model predicts the physiological responses. Alternately, it can be asserted that such responses were fully anticipated within the boundary conditions of the model before being tested experimentally. The important question, however, is whether the model system when pushed beyond its boundary conditions can predict unan­ticipated physiological results that are actually observable in living systems.

The theory of synergetics has an excellent reputation for establishing a healthy give-and-take relationship between experiment and theory. For example, the the­ory has predicted hysteresis and the onset of critical fluctuations in specific motor movements. By varying an experimental control parameter and then SUbjecting the system to perturbations, as a phase transition is approached (but not crossed), the system requires longer and longer to return to its original pattern (critical slowing down). By examining the switching time from one pattern to another (across a

Discussion on the Theoretical and Neuronal Basis of Rhythm Coordination 249

phase transition such as a gait change) it is possible to learn about the differentials that are inherent in the patterns. Thus, these are understood to be new and unexpected results which are confirmed by theory.

The synchronization of the respiratory and sympathetic rhythms is usually attributed to one of two mechanisms. One hypothesis is that each system possesses its own unique oscillator, but that the two oscillators are in some manner coupled together [2]. Another hypothesis is that the respiratory rhythm dominates over a passive sympathetic network and imposes a respiration-related discharge on the sympathetic outflow [1]. Phase-resetting experiments should not only look for strong type 0 resetting which would tightly couple the sympathetics to the respira­tory cycle, but stimulus parameters should be adjusted in the direction of weak type 1 resetting to look for phase singularities. These types of experiments might better explain the presence or absence of respiratory rhythms in sympathetic nerves.

References

1. Bachoo M, Polosa C (1987) Properties of the inspiration-related activity of sympathetic preganglionic neurones of the cervical trunk in the cat. J Physiol (Lond) 385: 545- 564

2. Barman SM, Gebber GL (1976) Basis for synchronization of sympathetic and phrenic nerve discharges. Am J PhysioI231:1601-1607

3. Crutchfield JP, Farmer JD, Packard NH, Shaw RS (1986) Chaos. Sci Am 255:46-57 4. Eldridge FL, Paydarfar D, Wagner PG, Dowell RT (1989) Phase resetting of respiratory

rhythm: effect of changing respiratory "drive." Am J Physiol 257: R271- R277 5. Haken H (1977) Synergetics, an introduction: nonequilibrium phase transitions and

self-organization in physics, chemistry and biology. Springer, Berlin Heidelberg New York

6. Rolf L (1981) Nonlinearity, multistability, and fluctuations: reviewing the reviewers. Am J Physiol 241: R1 07 - R113

7. Thorn R (1975) Structural stability and morphogenesis: an outline of a general theory of models. Translated from the French, as updated by the author, by D. H. Fowler, W A. Benjamin, Reading

8. Thorn R (1977) Structural stability, catastrophe theory, and applied mathematics. Soc Ind Appl Math Rev 19:189-201

Analysis of Cardiorespiratory Variability and Rhythmicity in Humans: Physiological Basis and Clinical Application

Human Respiratory-Cardiovascular Interactions in Health and Disease D. L. ECKBERG

During quiet breathing, healthy humans have strong respiration-related waxing and waning of cardiac-vagal and muscle sympathetic nerve activities (Eckberg et al. 1985). This chapter reviews respiratory modulation of human autonomic cardiovascular outflow and the distortion of this physiologic mechanism that occurs in heart failure patients.

Respiratory Fluctuations of Vagal-Cardiac Nerve Activity

At rest, R-R intervals shorten (that is, the heart rate speeds) during inspiration and lengthen during expiration (Eckberg 1983). Although earlier studies pointed to­ward inspiratory suppression of vagal-cardiac nerve activity (Davies and Neilson 1967), it is now clear that the relation between R-R interval shortening and inspiration is simply a function of respiratory rate. At very slow breathing rates, R-R interval shortening begins in the expiratory phase of respiration, and at rapid breathing rates R-R interval shortening begins well after the onset of inspiration (Eckberg 1983).

During quiet breathing, muscle sympathetic bursts are followed by transient increases of arterial pressure (Wallin and Nerhed 1982). These bursts tend to follow reductions of arterial pressure and presumably result from transiently reduced baroreceptor input (SundlOf and Wallin 1977). In healthy human subjects, brief pressure elevations triggered by sympathetic bursts are associated with in­creases of R-R intervals, which are thought to result from transiently increased baroreceptor input (Fritsch et al. 1986). The relation between R-R intervals and preceding systolic pressures is described well by linear functions whose slopes are similar to those derived from R-R interval responses to bolus injections of phenylephrine (Smyth et al. 1969). These R-R interval changes are probably medi­ated by respiration-related surges of vagal-cardiac activity because they occur after very short latency [usually one heart beat (Fritsch et al. 1986)] and disappear after large doses of atropine.

Respiratory fluctuations of vagal-cardiac nerve traffic are associated with fluc­tuations of the susceptibility of vagal nuclei to baroreceptor stimulation. Vagal responsiveness to baroreceptor input varies sinusoidally during the respiratory cycle; at usual breathing rates, R-R interval prolongation caused by brief neck

254 D. L. ECKBERG

suction are minimal during early inspiration and are maximal during late inspira­tion and early expiration (Eckberg and Orshan 1977).

The study of Katona and Jih (1975) suggests that in anesthetized dogs, respira­tion-related fluctuations of R-R interval are related linearly to total vagal outflow and therefore can be used as surrogates for vagal traffic. In alert human subjects, respiratory peak-valley R-R interval changes decline during pharmacologic arteri­al pressure reductions but do not increase during pressure elevations (Eckberg et al. 1988). This suggests that phasic, respiration-related suppression of barore­ceptor influences is finite and can be overridden. This conclusion was drawn originally by Anrep et al. (1935-1936) who reported on respiratory R-R interval changes during very low and very high arterial pressures in dogs. The upper panel of Fig. 1 is redrawn from their study, and the lower panel depicts calculated differences between the longest and shortest R-R intervals during the respiratory cycle. Measurements of respiratory sinus arrhythmia with such large acute pres­sure elevations have not been made in humans; however, large increases of carotid baroreceptor input provoked by intense neck suction appear to override respirato­ry influences; such stimuli are equally effective during inspiration and expiration (Eckberg and Or shan 1977).

An elegant study by Gilbey and coworkers (1984) suggests that respiratory sinus arrhythmia results from fluctuations of vagal-cardiac motoneuron excitability mediated by muscarinic cholinergic receptors. They found that firing rates of vagal motoneurons treated with excitant amino acids were decreased by iontophoresis of acetylcholine and increased, especially during inspiration, by iontophoresis of atropine. A human study bore similar results, which are shown in Fig. 2. For this study, R-R intervals during controlled breathing were measured before and after a small dose (0.73 !!g/kg) of intravenous atropine. [Atropine sulfate crosses the blood-brain barrier freely and appears to augment vagal-cardiac motoneuron activity through a central, rather than a peripheral mechanism (Epstein et al. 1990).] R-R intervals during expiration were similar before and after atropine, but

2.0

U

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0

c: .8 1.0

c

0:: 0.5 I

0::

0.0

1.5 U

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0 c: 1.0

.8 c

0:: 0.5

I 0::

<l 0.0

Expiration

o

Inspiration

•

50 100 150

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200

Fig. 1. Upper panel, respiratory R-R inter­val changes in anesthetized dogs during pharmacologic changes of arterial pres­sure, redrawn from the study of Anrep et al. (1935-1936); lower panel, calculated differences between the expiratory and in­spiratory R-R intervals shown in upper panel. These data indicate that at extremes of arterial pressure, respiratory sinus arrhythmia is absent

Human Respiratory-Cardiovascular Interactions in Health and DiSease 255

u

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1.2

0.50

u

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Fig. 2. P-P interval changes during the respiratory cycle measured in healthy young adults before and after intravenous injection of atropine sulfate, 0.73 ~g/kg. Bars (right), the integral of P-P interval changes during inspiration and expiration. These .data suggest that low dose atropine increases vagal-cardiac activity primarily in inspiration

R-R intervals during inspiration were augmented after atropine. These results complement those of Gilbey et al. (1984) and suggest that in humans as well as experimental animals, phasic, inspiratory depression of vagal-cardiac nuclei is mediated by inhibitory muscarinic cholinergic receptors.

Respiratory Fluctuations of Muscle Sympathetic Nerve Activity

Early published records of human muscle sympathetic nerve activity show clear respiratory periodicity (Hagbarth and Vallbo 1968); Hagbarth and Vallbo found that although muscle sympathetic bursts occur at any time during the respiratory cycle, they tend to be most prominent during inspiration. Eckbergetal. (1985) studied respiratory periodicity of muscle sympathetic nerve activity prospectively and evaluated the influence of the phase of respiration upon sympathetic responses to brief neck pressure [which reduces carotid dimensions (Kober and Arndt 1970) and is interpreted as falling arterial pressure]. During rate and tidal volume con­trolled breathing, muscle sympathetic nerve activity fluctuated sinusoidally; sym­pathetic activity was least at the beginning of expiration and reached a peak of activity at the beginning of inspiration. The biochemical mechanism responsible for respiratory modulation of sympathetic activity is not known; as shown in Fig. 3, low dose atropine, which appears to increase vagal-cardiac motoneuron activity (see above), does not alter muscle sympathetic nerve activity.

We applied brief pulses of neck pressure at different times during the respirato­ry cycle to determine whether respiration gates sympathetic, as well as vagal responsiveness to baroreceptor input. The results show that neck pressure applied during inspiration does not alter sympathetic activity, but that neck pressure applied during early and middle expiration triggers large sympathetic bursts. These

256 D. L. ECKBERG

c

1.5 1050

0 Fig. 3. Mean R-R intervals (closed circles) ill Ul and muscle sympathetic nerve activity • E

1.0 R-Ri/ 1000 . (open circles) measured in eight healthy -

0 human subjects during controlled breath-e • ill ing before (0) and after two low doses of

0/-0

~

0.5 950 c atropine sulfate. These data point to a role ° Sympathetic

0:: for muscarinic cholinergic receptors in cen-I • 0:: tral modulation of cardiac-vagal but not

0.0 900 muscle sympathetic nerve activity 0

Atropine dose, vg/kg

results complement those of Seller and coworkers (1968) who found that the duration of sympathetic silence is greater when electric carotid sinus nerve stimuli are delivered in expiration than inspiration. A subsequent human study evaluated the role of steady-state arterial pressure in determining respiratory modulation of muscle sympathetic activity (Eckberg et al. 1988). In this study, although the quantity of muscle sympathetic nerve activity varied inversely with arterial pres­sure, the clustering of bursts during late expiration and early inspiration tended to remain constant. With moderate pressure elevation, the very few bursts that were present occurred only during early inspiration and with moderate pressure reduc­tion, the large number of sympathetic bursts that were present occurred at the end of expiration and the beginning of inspiration.

Sympathetic and Vagal Mechanisms in Congestive Heart Failure

Patients with heart failure have high levels of plasma norepinephrine (Thomas and Marks 1978) and muscle sympathetic nerve activity (Leimbach et al. 1986; Porter et al. 1990) and low levels vagal-cardiac activity, as gauged by heart rate power at respiratory frequencies (Saul et al. 1988) or standard deviations of R-R intervals (Porter et al. 1990). Whereas in healthy subjects, there is no significant relationship between sympathetic and vagal outflows, in heart failure patients there is a signif­icant, inverse relation, such that patients with the highest levels of plasma nore­pinephrine or muscle sympathetic nerve activity have the lowest levels of estimated vagal-cardiac output (Porter et al. 1990). Since heart failure patients have strong respiratory grouping of sympathetic activity, near absence of respiratory sinus arrhythmia probably cannot be ascribed to absence of respiratory rhythmicity within the central nervous system. Also, since sympathetic activity correlates closely with cardiac activity (as judged by coherence analysis), excessive levels of sympathetic activity probably cannot be attributed to absence of baroreceptor influences. Rather, close coherence between sympathetic and cardiac rhythms points toward sympathetic entrainment by baroreceptors (Barman and Gebber 1976).

Human Respiratory-Cardiovascular Interactions in Health and Disease 257

Summary

There is strong respiratory periodicity in muscle sympathetic nerve and electrocar­diogram recordings obtained from healthy humans at rest. There is substantial evidence that this periodicity is due in part to respiration-related variation of responsiveness of vagal and sympathetic motonuclei to arterial baroreceptor influ­ences. Sympathetic and vagal activities tend to move in parallel during breathing and responses of both groups of nuclei to baroreceptor influences are greatest in expiration. Heart failure patients have high levels of sympathetic activity and low levels of vagal traffic. Since in patients, sympathetic activity varies normally during the respiratory cycle, near absence of vagally mediated respiratory sinus arrhyth­mia cannot be ascribed to absence of respiratory rhythmicity in the central nervous system. Since sympathetic activity seems to be entrained to cardiac activity, exces­sive levels of sympathetic activity probably cannot be ascribed to absence of baroreceptor inputs.

References

Anrep GV, Pascual W, Rossler R (1935-1936) Respiratory variations of the heart rate. I-The reflex mechanism of the respiratory arrhythmia. Proc Roy Soc Lond B 119: 191-230

Barman SM, Gebber GL (1976) Basis for synchronization of sympathetic and phrenic nerve discharges. Am J Physiol 231: 1601-1607

Davies CTM, Neilson JMM (1967) Sinus arrhythmia in man at rest. J Appl Physiol22: 947-955

Eckberg DL (1983) Human sinus arrhythmia as an index of vagal cardiac outflow. J Appl PhysioI54:961-966

Eckberg DL, Nerhed C, Wallin BG (1985) Respiratory modulation of muscle sympathetic and vagal cardiac outflow in man. J Physiol (Lond) 365: 181-196

Eckberg DL, Orshan CR (1977) Respiratory and baroreceptor reflex interactions in man. J Clin Invest 59: 780-785

Eckberg DL, Rea RF, Andersson OK, Redner T, Pernow J, Lundberg JM, Wallin BG (1988) Baroreflex modulation of sympathetic activity and sympathetic neurotransmitters in humans. Acta Physiol Scand 133:221-231

Epstein AE, Hirschowitz BI, Kirklin JK, Kirk KA, Kay GN, Plumb VJ (1990) Evidence for a central site of action to explain the negative chronotropic effect of atropine: studies on the human transplanted heart. J Am Col CardioI15:1610-1617

Fritsch JM, Eckberg DL, Graves LD, Wallin BG (1986) Arterial pressure ramps provoke linear increases of heart period in humans. Am J Physiol 251: R1086- Rl090

Gilbey MP, Jordan D, Richter DW, Spyer KM (1984) Synaptic mechanisms involved in the inspiratory modulation of vagal cardio-inhibitory neurones in the cat. J Physiol (Lond) 356: 65-78

Hagbarth K-E, Vallbo AB (1968) Pulse and respiratory grouping of sympathetic impulses in human muscle nerves. Acta Physiol Scand 74:96-108

Katona PG, Jih F (1975) Respiratory sinus arrhythmia: noninvasive measure of parasym­pathetic cardiac control. J Appl Physiol 39: 801-805

Kober G, Arndt JO (1970) Die Druck-Durchmesser-Beziehung der A. carotis communis des wachen Menschen. Pflugers Arch 314: 27 - 39

Leimbach WN Jr, Wallin BG, Victor RG, Aylward PE, Sundlof G, Mark AL (1986) Direct evidence from intraneural recordings for increased central sympathetic outflow in pa­tients with heart failure. Circulation 73:913-919

258 D. L. ECKBERG: Human Respiratory-Cardiovascular Interactions in Health

Porter TR, Eckberg DL, Fritsch JM, Rea RF, Beightol LA, Schmedtje JF Jr, Mohanty PK (1990) Autonomic pathophysiology in heart failure patients. J Clin Invest 85: 1362-1371

Saul JP, Arai Y, Berger RD, Lilly LS, Colucci WS, Cohen RJ (1988) Assessment of auto­nomic regulation in chronic congestive heart failure by heart rate spectral analysis. Am J Cardiol 61: 1292-1299

Seller H, Langhorst P, Richter D, Koepchen HP (1968) Uber die Abhilngigkeit der pressore­ceptorischen Hemmung des Sympathicus von der Atemphase und ihre Auswirkung in der Vasomotorik. Pflugers Arch 302: 300-314

Smyth HS, Sleight P, Pickering GW (1969) Reflex regulation of arterial pressure during sleep in man. A quantitative method of assessing baroreflex sensitivity. Circ Res 24: 109-121

Sundli.if G, Wallin BG (1977) The variability of muscle nerve sympathetic activity in resting recumbent man. J Physiol (Lond) 272: 383 - 397

Thomas JA, Marks BH (1978) Plasma norepinephrine in congestive heart failure. Am J CardioI41:233-243

Wallin BG, Nerhed C (1982) Relationship between spontaneous variations of muscle sympa­thetic activity and succeeding changes of blood pressure in man. J Auton Nerv Syst 6:293-302

Respiratory Heart Rate Variability in Fetal and Neonatal Lambs *

T. METSALA, 1. GRONLUND, A. SIIMES, and I. VALIMAKI

Introduction

Respiratory heart rate variability (respiratory sinus arrhythmia, RSA) is a well­known phenomenon in adults, but its contribution to total heart rate variability (HRV) is small in newborn babies [1]. Fetal HRV is known to increase during fetal breathing movements (FBM) [2, 3], which is thought to be due to RSA. At present there is fairly little frequency-specific information on the relationship between fetal HRV and FBM. In this study we investigated the magnitude of respiratory HRV in chronic fetal and neonatal lamb models using power spectral analysis of heart rate and respirograms.

Material and Methods

Experimental procedures. Six pregnant ewes during the last trimester of gestation and six newborn lambs at the age of 3 days were operated on. Catheters were inserted into one carotid artery and jugular vein and into fetal trachea and amni­otic cavity. Electrodes were implanted bilaterally in the chest wall to record ECG [4]. A recovery period of at least 3 days was allowed before starting the monitoring stage of the study. The age of the fetuses on the study day was 120-139 days of gestation (term in the Finnish breed is approximately 143 days). Regularly repeat­ed weighing confirmed normal growth of the newborn lambs. The blood gases of both the fetuses and the newborn lambs were examined daily and found to be normal. Intratracheal pressure was used as a source respirogram for FBM respiro­gram and transthoracic impedance for neonatal respirogram. The signals were stored on a magnetic tape.

Spectral Analysis. The records were analyzed off-line. A trigger pulse was generat­ed for each R-wave in ECG. R-R intervals were fed into a minicomputer, and the reciprocal of each R - R interval was computed to obtain the respective instan­taneous heart rate (IHR). This signal was linearly interpolated. Respirogram and

* This research was supported by the Finnish Heart Foundation, Lydia Maria Fund, and Sigrid luselius Foundation.

260 T. METSALA et al.

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Fig. 1. Left (top to bottom), the instantaneous heart rate, respirogram, and intrauterine pres­sure of a fetal lamb during fetal breathing movements. Right, the respective autospectra, the cross-spectrum and squared coherence

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intra-amniotic pressure were low-pass filtered at 3 Hz and digitized. The intra-am­niotic pressure was subtracted from the tracheal pressure. Two-minute noise-free and stationary segments of ECG and respirogram were selected for further analy­sis (Fig. 1). The signals were band-pass filtered (0.05-2.0 Hz) and the fast Fourier transform was computed for the auto covariance functions of IHR and respiro­gram to obtain the auto spectra to 1 Hz. The cross-spectra were computed for the cross-covariance functions [5] between these signals to assess simultaneous fre­quency-specific variation of both signals. The cross-spectrum displays the magni­tude of similar periodicities in two signals in the frequency domain but does not show their timing. To combine the information of the timing and the similarity of periodicities the respective squared coherence was computed [5] (Fig. 1).

The maximum cross-spectral peak at 0.3 -1.0 Hz (widthz) was detected to show the frequency ofRSA. The width (widthl) of the cross-spectral peak was measured from the half point of its height. The auto spectral density of IHR or respirogram was integrated over the respective width l (areal) and also over 0.3 -1.0 Hz (areaz).

Respiratory Heart Rate Variability in Fetal and Neonatal Lambs 261

The relative auto spectral height (heightre1 ) displays the amount of variability at a precise frequency in relation to the overall variability [6]:

. area1/width1 helghtre1 = .

area2/wldth2

By the use of the band-integrated spectral density and relative spectral height the information of several 2-minute periods could be combined and applied for inter­group compansons.

In addition to spectral analysis, HRV was also estimated by statistical indices coefficient of variation of intervals (CV, overall variation) and coefficient of vari­ation of interval differences (CVS, beat-to-beat variation):

CVS=

N-l

L: (~-~+1)2 i= 1 ------:T%, CV=

N-l

~ = R - R interval value, T = mean value of ~.

_i=_l ____ : T% . N

On the basis of amplitude changes in the respirogram the fetal data were divided into two groups: FBM present (FBM group) and no FBM present (no-FBM group). The data of the neonatal lambs was divided into two groups on the basis of age: four lambs were studied at an age of under 30 days and two thereafter (Table 1).

Statistical Methods. The Mann-Whitney U-test was used for the statistical testing of HR and HR V indices. Statistical analysis of the spectral parameters was done using a two-way nested analysis of variance. Analyses were computed using BMDP statistical software (program 3V) [7]. Before statistical test a suitable transformation (square root, logarithm, 1/X) was done to the data to achieve a nearly normal distribution. The statistical significance (p-values) is expressed after the Bonferroni correction.

Results

The mean HR of the fetuses was 149/min in both no-FBM and FBM groups. The average amplitude of the tracheal pressure respirogram during FBM varied be­tween 1.5 and 10 mmHg. The range of the mean rate of FBM was 0.5-1.1 Hz (30-66/min). During FBM overall HR variation (CV) and beat-to-beat HR vari­ation (CVS) of the fetuses increased significantly (p = 0.002 and p = 0.008, respec­tively; Table 1).

The mean HR in the group of neonatal lambs aged under 30 days was 197/min and in that with ages over 30 days 122/min. The range of the mean respiratory rate was 0.45-1.0 Hz (27-60/min) in the younger and 0.3-1.9 Hz (19-113/min) in the older neonates. CV was significantly greater in the younger group of neonates (p<0.0001; Table 1).

262 T. METSALA et al.

HEART RATE HEART RATE

RESPIROGRAM RESPIROGRAM

CROSS- SPECTRUM CROSS-SPECTRUM

_ no-FBM _ FBM Il!lliillI <30 days CJ >30 days

Fig. 2. Left , the spectral densities of heart rate, respirogram and their cross-spectrum inte­grated over the width of the maximum joint variability peak. Right, the relative height of the spectral peak (the magnitude of variability indicated by a spectral peak in relation to the total variability)

Table 1. Data on fetuses and neonates

Fetuses Neonates

no-FBM FBM <30 days >30 days

Number of lambs 6 6 4 2 Number of segments 36 33 55 46 HR (bpm) 149 149 197 122 Resp. rate (Hz) 0.5 - 1.1 0.45- 1.0 0.3- 1.9 CV 1.1 2.2 6.5 3.5 CVS 004 1.1 3.3 2.3

Respiratory Heart Rate Variability in Fetal and Neonatal Lambs 263

The program first detected the maximum cross-spectral joint variability of HR and respirogram over the frequency range of 0.3-1.0 Hz to display the peak corresponding to RSA. In the no-FBM group the frequency of the RSA joint-vari­ability peak was lower (p = 0.008) than in the other groups (Fig. 2).

The magnitude of HR V at the frequency of maximum joint variability (areal) was over ten times greater in the young neonatal lambs than in the other groups (p<O.OOOl). It was extremely small in the no-FBM group. However, the relative height of the auto spectral HR peak did not differ significantly between the groups (Fig. 2).

The magnitude of variability in the respirogram at the rate of breathing was similar in the FBM group and in the neonates. In the no-FBM group the variabil­ity was about 50 times less in magnitude (p<0.0001). The relative height of the peak in respirogram was greatest in the younger neonatal group (for the difference between the neonatal groups p = 0.01; Fig. 2)

The density of the cross-spectral peak in the younger neonatal lambs was five times greater than in the older lambs (p=0.005) or FBM group (p=0.3) and over 100 times greater than the cross-spectral peak in the no-FBM group (p<0.0001). However, the relative height of the cross-spectral peak was similar in the two groups of fetuses and in the groups of neonates (Fig. 2).

There was no significant difference in squared coherence between the two groups of fetuses (0.23 and 0.46 in no-FBM and FBM groups, respectively), but in neonatal lambs it was greatest in the younger group (0.54 and 0.28 in < 30 days and> 30 days groups, respectively: p = 0.019).

Discussion

RSA is the largest component of periodic variation of HR in adults [8]. Timor­Tritsch et al. [9] showed in 1977 that, as in adults, fetal HR increases during the inspiratory phase of FBM and decreases during the expiratory phase. Although in earlier studies our group has found that the major variability in HR is under 0.2 Hz in fetal lambs (unpublished observations), human neonates [1], and in neonatal lambs [10], in this study we were particularly interested in the frequency area around the breathing rate. We investigated the magnitude of variation in the heart rate and respirogram of neonatal and fetal lambs using spectral estimator. By the use of the relative height we wanted to standardize the magnitUde of variability indicated by a spectral peak in relation to the total variability [6]. Squared coher­ence was used to indicate the synchronization of two signals oscillating in a same frequency. The respiratory rate was very irregular in each group (Table 1). Because the respiratory rate was occasionally very near to HR, the "sampling frequency" of the HR in comparison to the "signal," the respirogram, may be too low to allow the frequency of breathing to be reproduced consistently in the HR (Nyqvist theorem).

The results show that in the younger neonatal group respiratory sinus arrythmia was clearest. Indeed, regular large-amplitude breathing is typical at the postnatal

264 T. METSALA et al.: Respiratory Heart Rate Variability in Fetal and Neonatal Lambs

adaptation period. In the auto spectrum of HR and in the cross-spectrum of the FBM group a peak corresponding to RSA was found. In the fetus the volume changes of the lungs during FBM are very small [1], and therefore no modulation originating from lung stretch reflexes seems likely. However, the pressure changes during respiratory movements are notable (the largest amplitude in FBM in our study was about 10 mmHg). In the no-FBM group the variability of HR and respirogram was extremely small. It was interesting to find the similarity in relative spectral parameters of the no-FBM group with the other groups. This suggests that peripheral mechanisms alone do not explain RSA. In some studies RSA has been observed when thoracic movements have been eliminated [12-14]. We speculate that there is a central respiratory drive even when no pressure changes in the fetal trachea are seen, and this central modulation is expressed in heart rate.

References

1. Aiirimaa T, Oja R, Antila K, Viilimiiki I (1988) Interaction of heart rate and respiration in newborn babies. Pediatr Res 24:745-750

2. Wheeler T, Gennser G, Lindvall R, Murrills AJ (1980) Changes in the fetal heart rate associated with fetal breathing and fetal movement. Br J Obstet Gynaecol87: 1068-1079

3. Divon MY, Zimmer EZ, Platt LD, Paldi E (1985) Human fetal breathing: associated changes in heart rate and beat-to-beat variability. Am J Obstet GynecoI151:403-406

4. Siimes ASI (1977) Maternal and fetal cardiovascular and metabolic responses to beta adrenergic stimulation. An experimental study in pregnant sheep (Thesis). University of Helsinki, Helsinki

5. Jenkins GM, Watts DG (1969) Spectral analysis and its applications. Holden-Day, San Francisco

6. Viilimiiki I, Gronlund J, Rolfe P (1990) Compromised cerebral circulation in preterm neonates after intraventricular haemorrhage - cerebral electrical impedance approach. In: Hawkins G, DiRenzo E (eds.) Progress in perinatal medicine, Harwood Academic Publishers, Singapore, pp 141-153.

7. Dixon W (1985) BMDP statistical software manual. University of California Press, Berkeley, pp 413-426

8. Hyndman BW, Kitney RI, Sayers BM (1971) Spontaneous rhythms in physiological control systems. Nature 233: 339-341

9. Timor-Tritsch IE, Zador I, Hertz RH, Rosen MG (1977) Human fetal respiratory arrhythmia. Am J Obstet GynecoI127:662-666

10. Siimes ASI, Viilimiiki IAT, Oja RT, Antila KJ (1986) Detection of components of autonomic cardiac control by time series analysis of heart rate in lambs: technical report. In: Rolfe P (ed) Fetal physiological measurements. Butterworths, London, pp 259-266

11. McLain CR (1964) Amniography, a versatile diagnostic procedure in obstetrics. Obstet Gynecol 23:45-50

12. Joels N, SamueloffN (1971) The activity of the medullary centers in diffusion registra­tion. J Physiol (Lond) 133:360-372

13. Levy MN, De Geest H, Zieske H (1966) Effects of respiratory center activity on the heart. Circ Res 18:67-78

14. Hirsch J, Bishop B (1981) Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. Am J PhysioI241:H620-H629

Disturbed Brainstem Interaction and Forebrain Influences in Cardiorespiratory Coordination: Experimental and Clinical Results U. ZWIENER, R. BAUER, M. ROTHER, G. SCHWARZ, H. WITTE, G. LITSCHER, and M. WOHLFARRT

Introduction

The mediating role of the brainstem is commonly accepted in short-term heart rate rhythms (HR), including peripheral feedbacks, efferent transmission, and modifi­cations by forebrain activities. This rough conception presents an initial causal background for the diagnostic use of altered HR, such as in neonatal car­diorespirography and intensive care monitoring including brain death diagnosis, etc., which to date has been completely empirical.

Regarding the forebrain influences, there is a frequent modulation of the contin­uous cardiorespiratory rhythmic coordination by movements or their intention, sudden emotions, and mental load. This is especially pronounced in young awake subjects and in patients with signs of neurovegetative hyperactivity (Zwiener 1976; Zwiener et al. 1982; Witte et al. 1988). Very similar patterns exist in mammals (Zwiener et al. 1987). The most frequent pattern provoked by these stimuli is the decrease in amplitudes of respiratory sinus arrhythmia (RSA) during concomitant heart rate increase and often also an increase in respiratory rate and the appear­ance of 10-s blood pressure and corresponding heart rate waves. There is then also a decrease in the statistical coupling between respiratory and heart rate rhythms, as quantified by coherence (Zwiener et al. 1982). This is a neurovegetative arousal pattern with an increase in sympathetic activity mainly mediating the 10-s waves in contrast to vagally mediated RSA (Pagani et al. 1986). The amount of this arousal measured by changes of spectral power density of HR corresponds with the degree of mental load. Therefore it can be used as an indicator for the latter and is more sensitive than the change in heart rate level (Zwiener et al. 1982; Abel et al., this volume).

The mediation by the brainstem is the background for the diagnostic methods mentioned. We completely distinguished healthy and newborns at risk by spectral parameters of RSA. This is possible presumably because of the effect of metabolic disorders upon the brain stem (Rother et al. 1987). However the decisive mecha­nisms for the change in these rhythms or their coordination as diagnostically used, such as reduction or abolishment of RSA, is unknown.

Regarding efferent transmission, the reduction in RSA and the 10-s HR, by damages of the vagus nerve as functional diagnosis in diabetic autonomic neuropa­thy confirmed the mainly vagal HR control (Aisch et al. 1984).

266 U. ZWIENER et al.

Possible Causal Relations Between Brainstem Disorders or Damages and Change in Rhythms

To elucidate initial relationships between disturbed brainstem regions and changed rhythmic pattern, we measured the latter after brain-stem sections in ten dogs, a species showing the same components of short-term HR as man (Axelrod et al. 1981; Pagani et al. 1986). Under general anesthesia [etornidate infusion 2 mg/kg per hour and NzO-O z inhalation (3:1) or a-chloralose 70 mg/kg showed the weakest effect to the rhythms] the RSA (Fig. 1) is the main component of HR as in man during the same state, whether ventilated or not (unpublished results). After intramesencephalic or mesencephalopontine transsection (Fig. 2) an increase of HR was always observed if the transsection was complete and without side effects (Table 1). The same pattern was observed by Evans et al. (1979) in apallic

Table 1. Changes in HR amplitudes and level of brainstem transsection (number of dogs showing the respective changes)

Level of transsection

Intramesencephalic Pontomedullar Intramedullar or mesencephalopontine

Augmentation 5/5 No change 0 Decrease in HR (;:::20%) 0

4/6 1/6 1/6

o o 7/7

Only sufficient and autoptically correct transsections are included

mmHg

200

100

o

Pa

, 30

, 60

, 90 t[s]

Fig. 1. Instantaneous heart rate rhythms (HR; calculated from R-R in­tervals), respiratory movements (R) and arte­rial pressure (p a; femoral artery) of a slightly anes­thetized dog (suxametho­nium myorelaxation and IPPV)

Cardiorespiratory Coordination: Brainstem Interaction and Forebrain 267

syndromes with good outcome, i.e., without lower brainstem disorders. Pre­sumably, this pattern is provoked by an abolishment of suprapontine attenuation of the vagally mediated RSA. In any case, damages or disorders at this cerebral level cannot abolish the RSA. Cessation of ventilation (Fig. 2) does not abolish the HR, mainly RSA, but the following pontomedullar transsection does, confirming

mmHg

150 Po

100

50 i

o 50 100 t (s ]

Fig. 2. The same parameters of the dog in Fig. 1 after mesencephalopontine transsection. Between the arrows, cessation of artificial ventilation

Fig. 3. The same parameters of the dog in Fig. 1 after pon­tomedullar transsection

l ' min-1

200j HR

150

100 R

mmHg

:::l ... PQ---... j

o i

30 60 90 t( s J

268 U. ZWIENER et al.

l ' min- 1

HR 150 ]

100/

50 R

mmHg

P.

100

1 50

II. \ 1(1" j ,t l

"i' -

o , o 30

, 60

Fig. 4. The same parameters of the dog of Fig. 1 after in­tramedutlar trans section with strong reduced short-term HR

g'O I [s] and slow sinusoidal HR change

also a strong supramedullar part in the respiratory and the thus effected RSA rhythmogenesis. Ponto medullar transsection reduced the HR in most cases but with larger or the same amplitudes in comparison to the anesthetized controls (Fig. 3). The decisive reduction of HR was observed after intramedullar trans sec­tions associated in two cases with the clinically well-known slow sinusoidal heart rate change (Fig. 4). This pattern was often seen in neonates and adults with severe brain damage and frequently having lethal outcome. Although we cannot explain the pathomechanism, our results show the origin of this pattern by severe medullar disorders. Because of similar results that we have obtained after intramedullar sections in nine neonatal rabbits with systematical decrease in HR, the results are not species specific and suggest a reduction in HR, especially in RSA, by a direct disorder of the medullar areas, but provided peripheral mechanisms of efferent transmission are intact, and there is no attenuation by high sympathetic activity. Here, the latter was excluded by normal heart rate levels.

Conclusions and Approaches for Clinical Diagnostics

Because of the topic relationships found between trans sections and HR change, the known correlation between worsening of apallic syndromes and HR depres­sion can be assumed as the autonomic effect of "descending" disorders in the sense of midbrain to medullar syndromes. By this working hypothesis we tried to quan­tify the disturbed and reduced cardiorespiratory coordination in eight patients with apallic syndromes (Table 2).

Cardiorespiratory Coordination: Brainstem Interaction and Forebrain 269

Table 2. Apallic syndromes investigated, etiology, mean coherence of R/HR in the main peak of respiratory movement (calculated from five 128-s intervals) and clinical outcome

Patient Clinical reasons Coherence Outcome

PH Brain trauma O.66±O.OS + Kat Intestinal bleeding O.S3±O.13 + Pri Laparotomy/thoracotomy O.S2±O.OS + Rei Arterial bypass/complications O.57±O.lS + Mun Brain trauma O.39±O.lS (+) Mat Brain trauma O.13±O.11 Leo Brain trauma O.13±O.OS Ret Arterial bypass/complications O.lHO.07

In our animal experiments, the respiratory movements were relatively constant; this was also the case in instances without artificial ventilation, i.e., the main input of RSA generation is also regular. But in nonanesthetized man, i.e., also in apallic syndromes, there are other, here unspecific influences upon HR mediation, espe­cially that of RSA mediation, resulting in a more stochastic pattern. The main modifying influences upon the amplitudes ofRSA besides the actual cardiorespira­tory coupling are amplitudes and rate of respiratory movements, changing sympa­thetic activity roughly quantified by heart rate levels, motoric movements, and other influences from the forebrain with mainly statistical effects on the HR. In addition, there is a decrease in this nervously mediated cardiorespiratory coupling with age (Rabending et al. 1982).

To exclude such inputs as respiratory variations and nonspecific influences upon quantitative RSA parameters, we measured parameters of statistical coupling between respiratory movements and related RSA by their mean peak related coherence rather than by RSA parameters themselves. Indeed, the direct physio­logical or pathophysiological background is the coupling performance between respiratorily and cardiovascularly active brainstem neurones (Langhorst et al. 1986) rather than the resulting modulation of heart rate sequence itself.

We calculated the mean coherence only within the main peak area of the au­to spectra of R exceeding the 1 % confidence level (Fig. 5). Because of the men­tioned stochastic and other influences, a single 128-s interval is statistically too small (Fig. 6), and only the averaging of four or five intervals yields statistically relevant results (Fig. 5). From 18 spectral and coherence parameters, patients with poor or good outcome were best separated by this mean coherence (Table 2): mean coherence levels below 0.3 are correlated with a poor outcome (Fig. 7) and those above 0.3 with a good outcome. In the subsequent days of measurement, the mean coherence changed at most only 0.1 if the clinical state did not change drastically.

Because of these limited results we can see only tendencies and suggest this possible indicator for the assessment of disordered vegetative functions of the lower brainstem, excluding other reasons mentioned for reduced coupling. (A cardiac disorder was excluded by ECG control.) This parameter is probably not suited as a general indicator of prognosis in severe brain disorders because the outcome depends upon the specific pathogenesis. Nevertheless, it is often correlat-

270 U. ZWIENER et al.

Coh

0 .5

0

Power spectral densities and Coherence : l: 5 l: Coh : 1.S

Coh : 0 .66

Power spectral densities : --- Heart role voriobility (HRY) - - - - - - Respiratory movements (R M)

Coherence (Conf.9S~ 0 .231 ) .. .. ........... RM f HRY \ ''"''~' \~\ \ \

\ I \r--'"--,, Pil. 20/9

. : ---, . I "-\

,---.I t ./ ' .... ,,-',

t ./", :'""\. " t •. · ....... ~.. , ., '_'. ,

,::~~ .. .... '-- .'

0 0 .5 1.0 f t Hz]

SlOH. SU).

t min'1] [y'l]

10 1 10 3

10 0 102

10-1 10 1

10.2 100

Fig. 5. Averaged autospectra (5 x 128 s) of HR and R and coherence spectra of HR/R in a 20·year·old woman with good outcome. Hatched area, the part of the coherence spectra corresponding to the main peak of R auto spectra. (Confidence levels of coherence ;::0.231 (5%), ;::0.410 (1 %)

Stf}HA S (OR

Coh [min-1 ) [v'2 J

Power speclral densities : 10 2 10~

--- Heart rate variabi tily t HRY) - - - - - - Respiratory movements t R M )

Coherence ~Conf. g51. - 0 .231) 10 1 10 3 ............. RM/HRY

Pil. 20/9

10° 102

10'1 101

o o 0 .5 1.0 f[HzJ

Fig. 6. Single auto spectra of 128-s intervals averaged in Fig. 5

Coh

\

0.5

o

Cardiorespiratory Coordination: Brainstem Interaction and Forebrain 271

\ \ \ \ \ ,..-",,(

... _"

.......

Power spectral densities and Coherence:); 5 E Coh : 1.29

Coh: 0.17

Power spectral densities: --- Heart rate variability (HRV) ------- Respiratory movements tRM)

\ Coherence (Conf. 951.=0.231) \ ....... ··RM/HRV

\ \ \

Leo 1 st day

S(flHR S(f)R

[min-1] [v- 2 )

Fig. 7. Averaged auto spectra and coherence analogue to Fig. 5 in a 31-year-old man with poor outcome

ed with outcome because of the key functional position of neurovegetative brain­stem functions.

Concerning this coupling parameter as a possible one for diagnosis of brain death, we must take into account the mediation of a small part of RSA by spinal or other structures outside the brain. After caudal medullar transsection, we observed in half of the dogs a residual RSA ( < 20%) which could not be removed by atropine injection or vagotomy. In spinal dogs, this residual disappeared after destruction of the spinal columnae laterales (Klossek et aL 1988), showing the sympathetic mediation of this residual RSA outside the brain. Conci et aL (1986) confirmed the existence of such reflexes outside the brain in human brain death. Thus, a complete disappearance of coupling within cardiorespiratory rhythms and RSA or other HR cannot be a prerequisite for the diagnosis of brain death. Indeed, we observed in four out of six patients with clinically confirmed brain death a mean coherence of under 0.2, i.e., outside the 5% confidence range, confirming the absence of statistically significant coupling. But in two cases we measured a mean coherence of over 0.3, connected with RSA amplitudes above 4/min. The RSA immediately disappeared during apnea, suggesting an absent or only small contri­bution by brain activity. Thus, a significant mean coherence between respiratory movements and HR or RSA does not exclude brain death, but an absent one can confirm it.

272 U. ZWIENER et al.

Complex Interaction Between Forebrain and Brainstem

Especially in awake animals and man, the forebrain initiates, modifies, and also attenuates rhythmic and nonrhythmic HR in a complex manner. As we have seen, the general modification by the mentioned arousal mechanisms, often with con­comitant arousal effects and/or increasing sympathetic activity, reduces nervously mediated cardiorespiratory coupling and favors 10-s blood pressnre waves and heart rate waves. In addition, there are fnrther special patterns elicited by the forebrain in behavioral states: dnring arrest behavior in rabbits, in some neurotic patients, dnring apperception phases of mental load in healthy volunteers, and in defence reactions the mirror pattern (Zwiener et al. 1987; Richter et al. 1988, 1989). The pathways between forebrain and lower brainstem regarding such com­plex patterns, including especially limbic structures such as amygdalar and hypo­thalamic, have been partly evaluated (Stock et al. 1981; Kapp et al. 1982). During such heart rate diagnosis in apallic syndromes, especially during improvement intervals (Fig. 8), these emotional and movement-related patterns must also be taken into account. Such influences change the cardiorespiratory coupling dis­cussed above and are excluded in our power spectral analysis by visual control of original data. On the other hand, they can help in monitoring improvements in the clinical state as signs of interaction between the increasingly active forebrain and the brain stem (unpublished observations).

Forebrain influences cannot change only the level of RSA and other HR but also their main coupling structures and the directions of mutual influence (within cardiorespiratory coordination). In earlier extensive studies (Zwiener 1976, 1978;

150~ HR ~'"'-c.._--.r

120 -,JVV'"

90 R

I, min- 1

150 HR b

120~~~~.~~ 90 R

Fig. 8 a, b. Respiratory movements and HR be­fore and after a short movement during an improvement phase in an apallic syndrome.

o 50 b Follows immediately

100 t [5] upon a

Cardiorespiratory Coordination: Brainstem Interaction and Forebrain 273

Fig. 9. a Autospectra of R, blood pressure waves (BP), and HR. b Coherence spectra of R/HR at 17 -min intervals without and with mental load (bars, 5% confidence range)

without load

Coh 1.0 ,----,--------,---+----,

0.81----+;c----

) 0.6 I-----j·

.... ~ j

0.2-11'\---+-

! ~ : l.

! \j \-;' \

0+-~-4------+----\~'~·~' 0.05

b 0.1 0.2 0.3

rCHz]

Zwiener et al. 1982) healthy volunteers showed more stable auto spectral, coher­ence, and phase spectra parameters than patients suffering from neurovegetative disorders. During mental load (chain arithmetic tasks) 4 out of 182 patients showed changes in coupling and its direction in the following manner (Fig. 9 a). The main autospectral peak of R disappears, and several smaller peaks of lower frequency appear, including one that in terms of frequency and position is exactly analogous to the lO-s blood pressure and heart rate waves. The coherence between Rand HR changes from the respiratory preference frequency to those of 10-s waves (Fig. 9 b). Because the latter are nourespiratory in origin, this is a change in direction of cardiorespiratory coherence from respiratory to cardiovascular origin of the main wave components. This result confirms the general validity of Koepchen's conception of centrally mediated cardiorespiratory interaction, derived from animal experiments (1981). Altogether this coupling change suggests a dynamic functional state of brainstem in man modulated by forebrain activities. A change in the direction of cardiorespiratory rhythmic coupling was also ob­served in atropinized rabbits (Zwiener et al. 1987) showing a 1: 1 relationship of heart beat to R. This can be explained only by a transmission of cardiac intrinsic

274 U. ZWIENER et al.

a Coh' 0..8

0..6

0..4

0..2

0.

b Coh' 0..8

0..6

0..4

0.2

0.

C

Coh: 0..8

0..6

0..4

0..2

0. C"l (") N N N ..... N '" <i) 00 0 N 0 0 0 0 ..... ..... ci ci ci ci ci 0 I I I I I I

0> 00 00 r-- r-- <i) 0 N '" <i) 00 0 0 0 0 0 0

51 ci ci ci ci ci N

:5 -

..... '" ..... ci I

<i) N

ci

000>0>0000['-['­'-'>OOC1'r(V)U')t---CJ'I '~,NNNNN

cicicicicicicici I I I I I I I I

<i)<i)II)"''''C''lC''l('oj

~~~2~~~~ ci ci ci ci ci ci ci ci

Fig. lOa-c. Mean coherences of blood pressure waves/HR (open circles), R/HR (crosses), and R/blood pressure waves (filled circles) of healthy volunteers with main autospectral peak of R. a 0.22 Hz (40 subjects). b 0.17-0.22 Hz (9 subjects). c <0.17 Hz (6 subjects)

rhythms upon central respiratory rhythmicity mediated by baroreceptor afferents, as shown by Gebber (1980). The properties of the functional organization of neuronal aggregates within the lower brain stem shown by Langhorst et al. (1986) can explain such rhythm dynamics. Summarizing, we can presume not only changes in the actual functional determination of subgroups of neurones, but a "tuner" effect of sympathetic arousals or vagal block to a dominance of 10-s rhythmic generator or the pulse-elicited brainstem discharges in the whole lower brainstem system of cardiorespiratory rhythmic coordination.

The mutual influence of these two rhythmic structures regarding the coupling degree also depends on their own frequency distance (Fig. 10). In the nearest position of the main peak of R and that of the 10-s cardiovascular waves are the highest autospectral densities of eliciting rhythms (blood pressure and heart rate

Cardiorespiratory Coordination: Brainstem Interaction and Forebrain 275

rhythms at 0.1 Hz and respiratory rhythm at 0.18 Hz) and coherences (Fig. 10c). However, in these healthy volunteers, there is no coupling change (as shown in Fig. 8) and no fusion of the two different rhythm generators but a mutual strength­ening of the coherences among the three rhythms. This confirmed a general inde­pendence of the generator of these cardiovascularrhythms but a mutual influence of cardiorespiratory coupling and that of blood pressure and HR depending on their frequency distance. Here also the reason can be found within the functional organization of neuronal groups of the lower brainstem (Langhorst et ai. 1986; Langhorst et aI., this volume). Quantitative diagnostic approaches must take into account further properties within the complex brain and signal interactions besides those mentioned. If the respiratory rate is higher than half of the heart rate, the respiratory cycles are no longer reflected in the HR in the sense of known RSA, but slower HR according to the laws of signal aliasing are elicited, called by us cardiac aliasing. We have observed this phenomenon in human newborns (as a reason of misinterpretation of the cardiorespirograms),adult and newborn rab­bits, and dogs (Zwiener et ai. 1987; Witte et ai. 1988 a). Summarizing the main influences upon HR, we developed an analytic model in human newborns and are preparing another one for adults (Rother et ai. 1988; Witte and Rother 1989).

References

Aisch W, Zwiener U, Tiedt N, Kaiser WD, Panzram G (1984) Spontaneous rhythms of the cardiovascular system.indiabetic autonomic neuropathy. Psychiatr Neurol Med Psychol (Leipz) 36:26-31

Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC, Cohen RJ (1981) Power spectral analysis of heart rate fluctuations: a quantitative probe of beat-to-beat cardiovascular control. Science 213:220-223

Conci F, Procaccio F, Arosio M, Boselli L (1986) Viscerosomatic and viscero-visceral reflexes in brain death. J Neurol Neurosurg Psychiatry 49:695-702

Evans BM (1979) Heart rate studies in association with electroencephalography (EEG) as a mean of assessing the progress of head injuries. Acta Neurochir [Suppl] (Wien) 28:52-57

Gebber GL (1980) Central oscillators responsible for sympathetic nerve discharge. Am J PhysioI239:H143-H155

Kapp BS, Gallagher M, Underwood MD, McNall CL, Whitehorn D (1982) Cardiovascular responses elicited by electrical stimulation of the amygdala central nucleus in the rabbit. Brain Res 234:251-262

Klossek H, Konkel J, Gehrig W (1988) Tiermodell der operativen externen kardialen Denervierungsuntersuchungen zur neurovegetativen Herzfrequenzsteuerung. Zentralbl Chir 113:138-150

Koepchen HP (1980) The respiratory-cardiovascular brain stem oscillator in the context of afferent and central excitatory and inhibitory systems. In: Koepchen HP, Hilton SM, Trzebski A (eds) Central interaction between respiratory and cardiovascular control systems. Springer, Berlin Heidelberg New York, p 197

Langhorst P, Schulz G, Lambertz M (1986) Integrative control mechanisms for cardiores­piratory and somatomotor functions in the reticular formation of the lower brain stem. In: Grossmann P, Jansson KH, Veitl D (eds) Cardiorespiratory and cardiosomatic psychophysiology. Plenum, New York, p 9

276 U. ZWIENER et al.: Cardiorespiratory Coordination: Brainstem Interaction

Pagani M, Lombardi F, Guzzetti S, Rimoldi 0, Furlan R, Pizzinelli P, Sondrone G, Malfotto G, Dell'Orto S, Piccaluga E, Tusiel M, Baselli G, Cerutti S, Malliani A (1986) Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho­vagal interaction in man and conscious dog. Circ Res 59: 178-193

Rabending G, Kl6ckner H, Reichel G (1982) Quantitative Bestimmung der respiratorischen Herzarrhythmie fUr die neurovegetative Funktionsdiagnostik. In: Tiedt N, Vogel J, Zwiener U (eds) Methoden und Ergebnisse der Funktionsdiagnostik. Ergeb Exp Med, Vol 41. Volk und Gesundheit, Berlin, p 391

Richter A, Zwiener U, Schumann NP, Glaser S (1988) Neurovegetative pattern of rabbit arrest behaviour considering individual type of behaviour. Act Nerv Super (Praha) 30:226-228

Richter A, Schumann NP, Glaser S, NeiBe U, Zwiener U (1989) Neurovegetative and neuromuscular correlates of active and passive coping with different reaction types in neurotics. Int J Psychophysioll0: 75-83

Rother M, Zwiener U, Eiselt M, Witte H, Zwacka G, Frenzel J (1987) Differentiation of healthy newborns and newborn-at-risk by spectral analysis of heart rate fluctuations and respiratory movements. Early Hum Dev 5:349-363

Rother M, Witte H, Koschel U, Eiselt M, Zwiener U (1988) Respiratory sinus arrhythmia in neonates - quantification based on a new model. In: Willems JL, van Bemmel JH, Michel E (eds) Progress in computer-assisted function analysis. Elsevier, Amsterdam, pp 369-374

Stock G, Rupprecht U, StumpfH, Schl6r KH (1981) Cardiovascular changes during arousal elicited by stimulation of amygdala, hypothalamus und locus coeruleus. J Auton Nerv Syst 3:503-510

Witte H, Liese F, Glaser S, Hoyer D (1988) Results of modelling and physiological examina­tion of movement-related heart rate reactions in neonates. Med Bioi Eng Comput 26:599-604

Witte H, Zwiener U, Rother M, Glaser S (1988 b) Evidence of a previously undescribed form of respiratory sinus arrhythmia (RSA) - the physiological manifestation of "cardiac aliasing". Pfliigers Arch 412: 442-444

Witte H, Rother M (1989) Better quantification of neonatal respiratory sinus arrhythmia -progress by modelling and model-related physiological examinations. Med Bioi Eng Comput 27:298-306

Zwiener U (1976) Pathophysiologie neurovegetativer Regelungen und Rhythmen. Fischer, Jena

Zwiener U (1978) Physiological interpretation of autospectra, coherence and phase spectra of blood pressure, heart rate, and respiratory waves in man. Automedica 2: 161-169

Zwiener U, Bauer R, Scholle HC (1982) The influence of menta! arithmetic upon autospec­tra, coherence, and phase spectra of autonomic rhythms in men. Automedica 4: 113 -121

Zwiener U, Richter A, Schumann NP, Glaser S, Witte H (1987) Neurovegetative organiza­tion of heart rate rhythms and patterns in rabbits. Fiziol Zh SSSR 73: 1650-1656

Low-Frequency Rhythms in the Respiratory and Cardiovascular Systems (With a Reference to Obstructive Sleep Apnea Syndrome) J.M. KAREMAKER and J.G. VAN DEN AARDWEG

In recent years we have witnessed a renewal of interest in the spontaneous oscilla­tions of blood pressure and heart rate that are observed in registrations of humans under steady state conditions. The present study is intended to investigate more deeply the role of the respiratory system as "organizer" or "co-organizer" of the rhythms that are observed in the cardiovascular system. There are a number of reasons for the revival of the old physiological theme of blood pressure instability: (a) the techniques to measure heart rate and blood pressure in humans noninva­sively and continuously are well-developed and readily available; (b) the computa­tional techniques to treat these signals in large amounts are widespread, especially since the development of the personal computer; and (c) the conviction has grown upon investigators that the different oscillations, as quantitized by Fourier analy­sis, give insight into the momentary condition of the autonomic nervous system.

Specifically, the oscillations around the respiratory frequency and those around a frequency of 0.1 Hz are considered of importance in this respect. Earlier publi­cations from our group [2-4] have tried to support the development of the phys­iological theory that gives an explanation for these phenomena. Briefly summa­rized the model that we have is as follows:

1. The baroreflex, through its neural efferents to the heart and vasculature, coun­teracts any change in blood pressure by changes in heart period and total peripheral resistance (TPR).

2. The changing heart periods tend to stabilize diastolic pressures and induce changes in stroke volume (the former by changing the diastolic cutoff point; the latter due to Starling's law and the restitution phenomenon).

3. The changes in TPR cause changes in diastolic runoff, thereby stabilizing the level of mean blood pressure.

This set of rules, transformed into a set of beat-to-beat difference equations [3], constitutes a simple feedback model for the stabilization of blood pressure by the baroreflex. When this model is used to generate blood pressure values, it gives a stable set-point without any auto-oscillation. In order to make the system oscillate we had to add a respiration induced oscillation and some random (noiselike) perturbations that act on the beat-to-beat values of heart period and stroke vol­ume.

Respiratory oscillation could have been injected into the model in many differ­ent ways, considering the complete body of knowledge in the experimental phys­iology literature. The most important choice here was whether to view the respira-

278 1. M. KAREMAKER, 1. G. VAN DEN AARDWEG

tory effect on the circulation as a central nervous phenomenon (vagal suppression during the phase of inspiration) or as a peripheral, mechanical effect of the respira­tory act on the end-diastolic filling of the ventricles.

The central nervous system having a prime role was ruled out in our research by analysis of the phase relations between blood pressure and heart rate variability at the respiratory frequency [3]; if the heart rate effect were first, it should precede the variations in blood pressure. This is not the case, however. Blood pressure changes and heart rate changes occur more or less in phase in the same heart beat, a rise in systolic pressure inducing a proportional increase in heart period and the same for decreases in pressure. Such changes in pressure may be caused by the effect of the respiratory movement on the filling of the ventricles: an inspiratory movement induces an increased right ventricular filling, leaving less space in the pericardium for the left ventricle [9], moreover, the return to the left heart is decreased when the pulmonary vascular bed expands. Combination of these effects means that the inspiration causes a decrease in pressure and, by way of the baroreflex, a rise in heart rate.

Both reasonings explain a combined decrease in blood pressure and increase in heart rate at the start of inspiration. However, in the case of a centrally induced heart rate change, the relation between those two changes carries no information on the sensitivity of the baroreflex, but in the other (pressure first) explanation it does.

This forms the physiological basis for the analysis of the respiratory oscillations in blood pressure and heart rate: the heart rate changes may be considered as a measure of the vagal nervous activity; the ratio between blood pressure and heart rate variability at that frequency is a measure for the sensitivity of the vagal branch of the baroreflex.

The oscillation around 0.1 Hz has a different explanation: it is caused, in the model described above, by the feedback properties of the sympathetic.system. The time course of the circulatory response to a large brief burst of sympathetic activity shows a considerable delay time (1-2 s) and a delayed peak (in the order of 5 s after the actual stimulus). If we feed a system with this response characteristic a random noise as input signal, it behaves as a band-pass filter and shows a waxing and waning oscillation around 0.1 Hz at its output. (Remember that we attributed to the cardiovascular actuators some inherent; "noisy" properties, which are dampened by the baroreflex, through both the vagus and the sympathetic nerves.)

Viewed in this way, the oscillations around 0.1 Hz reflect a modulation of ongoing sympathetic activity. It is still undecided whether this bears a direct relation to the steady-state level of sympathetic tone.

If, with this knowledge, we turn to some actual registration of blood pressure and heart rate of a human subject at rest (Fig. 1), we find the oscillations as we have described them, but there is more than this: even after careful correction of trends that may have existed during the period of the registration we observe a considerable amount of low-frequency variability, below this frequency of 0.1 Hz. In the literature [11] this has been explained as a sign of the activity of the temperature regulating system, which can show a low-frequency auto-oscillation. It is the intention of this chapter to point to another physiological system that might explain this phenomenon.

~ :J: E .§.

OJ 100 L ::J (/) (/)

OJ L a. "0 o o

......

.c

Low-Frequency Rhythms in Respiratory and Cardiovascular Systems 279

time (5)

1250 UJ .§.

L OJ 3: o a.

Fig. 1. Left panel, registration of systolic (8) and diastolic (D) pressures and RR intervals (1) from a normal subject. Right panel, power spectrum of the same registration (in arbitrary units, a. u.). Note the absence of respiratory variability in the spectrum of diastolic pressures and the large amount of power below 0.1 Hz. (From [4] by kind permission)

Figure 2 describes the control loops for the blood pressure and respiratory system, viewed from the medulla oblongata. Moreover the drawing shows a num­ber of interactions between the two systems: at the level of the medulla, in the nervous outflow and in the intrathoracic pressure changes. Many (and even more) of these interactions are the subject of detailed reports in this volume and will not be explained here. In this paper we focus on a few of those that are of direct relevance to our subject.

At the level of the medulla we find mutual influences: strong changes in barore­ceptor afference also have respiratory effects, chemoreceptor stimulation activates vagal and sympathetic activity. The central "state of respiration" modulates the processing of baroreceptor afferent information. The nervous outflow, therefore, is not determined purely by the "needs" of one control system but carries a mixture of influences from both systems.

Inside the thorax we find the mechanical coupling between respiratory pressures and its effects on the filling and output of the heart. Moreover, this changing intrathoracic pressure leads to changes in stretch of the vascular walls on the low-pressure side of the heart, thereby exciting low-pressure baroreceptors. A possibly confusing arrow is left out of the diagram, pointing from systemic blood pressure to chemoreceptor perfusion. However, this coupling has been mentioned as the main cause of the slow Mayer waves by Heymans and Neil (p. 181 in [10]).

This tight coupling between the two systems inevitably leads to intermodulation effects. Above we explained the O.l-Hz cardiovascular oscillation; this same fre­quency could be present in respiratory variability. Since the respiratory rate is generally low in comparison to the O.l-Hz oscillation, we do not find much of it in most measurements. On the other hand, however, the respiratory control loop is not a perfect system, either. It has been shown to have a control delay in the order of 5 s, mainly caused by transport time between lungs and chemoreceptors. The time constant for the control via peripheral arterial chemoreceptors is estimat­ed at around 5-10 s [1, 5, 7]. The central chemoreceptors (not shown in Fig. 2) have time constants of around 2 min [1, 5].

280 J. M. KAREMAKER, J. G. VAN DEN AARDWEG

Pie u a 0 onga a I

CARDIO- I RESPI-I

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I I

I

I I

lov- I r.:: pressure I

=:= PC02 P0 2 I rec.p1:ors I :.:

I :::

I :.:

I .:.:.:.;.:.:.:.:.:.:. :':':':':':1

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i syst ... ic h.~r1: vessels blood r4t1:. & 1no-pressure 1:ropy

.:.:.:.:.:.:.;.:.:.;., :.:.:.:.:.:. I 1

h.4tr1: 1:horax

Fig. 2. Block diagram of the neural controls of the cardiovascular and respiratory systems with some of their interactions. NX, vagus nerve; resp. mov., respiratory movements; symp, sympathetic outflow. The "regulated" parameters (systemic blood pressure, Peo, and Po,) have been shadowed for clarity of the diagram

A system such as this is not necessarily unstable; considering the available experimental evidence it even seems that the chemoreceptor control loop is too heavily dampened to cause any oscillations at all. However, most of these exper­iments have been made in animals under anesthesia or after decerebration. In the literature on constancy of respiration in humans under resting conditions we find evidence that the phenomenon of Cheyne-Stokes breathing or periodic breathing is not restricted to pathological circumstances. Not only under acute hypoxia or in congestive heart failure does the breathing pattern oscillate with a loose period­icity of 30-60 s [6]; this has also been observed (although much less clearly) in perfectly healthy persons [8, 12].

In our laboratory we have started a new line of research into this inconstancy of breathing in humans, especially during sleep. We focus our attention on the interrelations between periodic changes in respiratory parameters and cardiovas­cular measures, among which noninvasive continuous (finger) blood pressure.

Low-Frequency Rhythms in Respiratory and Cardiovascular Systems 281

1 minute

FBP (nun Hg)

100

[ 0

125

HR (bpm)

25

Pes -20

0

(cm H2O)

Nose

Therm.

expiration

• t inspiration

Fig. 3. Sleep registration in a patient suffering from obstructive sleep apnea syndrome. FBP, Finger blood pressure, measured by Finapres - the periodic interruptions of the signal are due to automatic internal set-point checks of the device; HR, heart rate as measured from the ECG; Pes, esophageal pressure (negative pressures give upward deflection); Nose Therm., a thermistor in the nose signals temperature of the passing air. Note the recurring periods of interrupted air flow with increasing inspiratory effort as evidenced by the esophageal pressure

Figure 3 shows an example of a measurement during sleep in a patient suffering from the obstructive sleep apnea syndrome, as it is frequently observed in heavy snorers. They undergo periodic interruptions of inspiratory airflow, as evidenced by the ongoing pattern of activity in the esophageal pressure tracing but absence of temperature changes at the nose and mouth, indicating loss of airflow.

These apneic periods cause changes in P O 2 and P co2 , but possibly the most life-threatening consequences are to be found in the cardiovascular system: at the resumption of breathing, periods of hypertension combined with bradycardia occur. Patients suffering from this condition, even if they are (still) normotensive during the daytime, may be hypertensive for the better part of the night.

282 1. M. KAREMAKER, 1. G. VAN DEN AARDWEG: Low-Frequency Rhythms

Again, in the periodicity of respiration and blood pressure during sleep we expect to show that the observations in sleep apnea are but the extremes of the total range of instability in blood pressure that is linked to, or even caused by, instability of the respiratory control systems.

References

1. Daubenspeck JA (1973) Frequency analysis of CO2 regulation: afferent influences on tidal volume control. J Appl Physiol 35:662-672

2. DeBoer RW, Karemaker JM, Strackee J (1985) Relations between short term blood pressure fluctuations and heart rate variability in resting subjects. II. A simple model. Med BioI Eng Comput 23:359-364

3. DeBoer RW, Karemaker JM, Strackee J (1987) Hemodynamic fluctuations and barore­flex sensitivity in humans: a beat-to-beat model. Am J PhysioI253:H680-H689

4. DeBoer RW, Karemaker JM, Wieling W (1985) Suppression of respiratory variability as evidence of a functioning baroreflex in man (Abstract). J Physiol (Lond) 366: 55P

5. DeGoede J, Berkenbosch A, Ward DS, Bellville JW, Olievier CN (1985) Comparison of chemoreflex gains obtained with two different methods in cats. J Appl Physiol 59: 170-179

6. Dowell AR, Buckley CE, Cohen R, Whalen RE, Sieker HO (1971) Cheyne-Stokes respiration: a review of manifestations and critique of physiological mechanisms. Arch Int Med 127: 712-726

7. Drysdale DB, Strange Petersen E (1977) Arterial chemoreceptors, ventilation and heart rate in man. J Physiol (Lond) 273: 109-120

8. Goodman L (1964) Oscillatory behaviour of ventilation in resting man. IEEE Trans Biomed Eng 11:82-93

9. Guz A, Innes JA, Murphy K (1987) Respiratory modulation of left ventricular stroke volume in man measured during Doppler ultrasound. J Physiol (Lond) 393:449-512

10. Heymans C, Neil E (1958) Reflexogenic areas of the circulation. Churchill, London 11. Kitney RI, RompeIman 0 (1980) An analysis of the thermoregulatory influences on

heart rate variability. In: Kitney RI, Rompelman 0 (eds) The study of heart rate variability. Clarendon, Oxford

12. Lenfant C (1967) Time-dependent variations of pulmonary gas exchange in normal man at rest. J Appl Physiol 22:675-684

Thermal and Postural Influences on Cutaneous Microvascular Blood Cell Flux in Young Men * A. LINDQVIST, 1. VALIMAKI

Introduction

Sympathetic vasoconstrictor nerve fibers to the smooth muscle of the peripheral vasculature are the efferent arm of both the thermoregulatory reflexes originating from cutaneous thermoreceptors and the baroreflexes originating from arterial and cardiopulmonary baroreceptors. Electrical stimulation of the hypothalamus modifies the baroreceptor reflex in experimental animals (Gebber and Snyder 1970). In man, stimulation of peripheral cutaneous thermoreceptors with afferent fibers terminating in the hypothalamus modify the baroreceptor reflex (Ebert et al. 1982). A theory of the spontaneously oscillating thermoregulatory and blood pressure regulatory control system has been presented (Hyndman et al. 1971; Kitney 1974). The controlled variables are the thermal gradient of the skin and the arterial blood pressure, respectively.

Physiological and pathological processes in the cardiovascular control system can be examined quantitatively by measuring synchronization of cardiovascular oscillations to externally applied stimulation (Hyndman et al. 1971; Kitney 1974; Lindqvist 1990; Lindqvist et al. 1989a, b, 1990). This paper reviews our current clinical experience of a stimulator containing an automatic water bath and rocking tilt table in the investigation of the effect of repeated thermal and constant postural stimulation on the oscillations of the cutaneous microvascular blood cell flux in young men.

Subjects and Methods

Spontaneous supine and thermally or posturally stimulated cardiovascular activity was recorded from 18 healthy men aged 19-24 years (21 ± 1). Cardiovascular thermal and postural stimulations were performed using a special-purpose water bath/tilt table stimulator (Fig. 1; Lindqvist 1989). A standardized thermal stimula­tion was performed by immersing the left hand and forearm or both feet and calves in the water baths of 18°C and 38 °C periodically at the frequencies of 0.01-0.10 Hz. A standard postural stimulation was performed by lifting the tilt table

* This research was supported by the Finnish Defense Forces and the Turku University Foundation.

284 A. LINDQVIST, 1. VALIMAKI

Fig. 1. Water bath and rocking tilt table stimulator equipment

from 2.5" to 76°, and active sitting and standing were used in the present studies (Lindqvist 1990; Lindqvist et al. 1989 a, b, 1990).

Spontaneous and thermally stimulated oscillations of cutaneous blood cell flux were recorded from the right forearm skin with a laser Doppler flowmeter (Periflux PF2B, Perimed KB, Stockholm, Sweden). The forearm was held relaxed beside the trunk 5 cm above the bed level in the supine subject and at the level of the xiphisternum in the upright subject. The patterns of the thermal stimulus signal were verified by measuring the temperature changes produced in the water bath stimulator (Lindqvist 1989).

The signals were recorded on magnetic tape for 18 min during each test. The skin blood flux and thermal stimulus signals were low-pass filtered from 0.7 Hz and sampled at 5.0 Hz. A detrending procedure was carried out using a digital Hamming high-pass filter with a cutoff at 0.006 Hz. The fast Fourier transformed power spectral density functions were computed for the signals. The magnitude of synchronization was studied by integrating the power spectral density functions over preselected frequencies and calculating the respective bandwidth entrainment factors (BWEF; de Trafford et al. 1982; Lindqvist 1990; Lindqvist et al. 1989 a, b, 1990). The integral of the power spectral density function of skin blood flux over the 0.05 Hz bandwidth centered at the stimulus frequency was divided by the integral over the 0.01 to 0.12-Hz bandwidth by the formula:

BWEF(%)

Integral over 0.05 Hz bandwidth centered at the stimulus frequency --~------------------------------------~--~*100

Integral over 0.01 to 0.l2-Hz bandwidth

Cutaneous Microvascular Blood Cell Flux in Young Men 285

Analysis of variance for repeated measures with one within-subjects factor (various stimulations) or two within-subjects factors (various stimulations and stimulus frequencies) was done separately for each experiment. When the analysis of variance showed statistically significant stimulation or stimulus frequency ef­fects, the pairwise comparisons were made by means of the Newman-Keuls multi­ple range tests. Results were expressed as mean ± SEM, and p values under 0.05 were considered statistically significant (Lindqvist 1990; Lindqvist etal. 1989a, b, 1990).

Experiences of the System

Study i: Skin Blood Cell Flux During Rest and During Periodic Thermal Stimula­tion. It was found in repeated experiments in nine subjects that the regular period­icity of the thermal stimulus signal appeared in skin blood flux signal during ther­mal stimulation. However, the same periodicity was not present during sponta­neous activity (Fig. 2). Spectral analysis using BWEF showed that the thermal synchronization of skin blood flux took place at the stimulus frequencies between 0.01 and 0.10 Hz (Fig. 3). The power spectral density increased at the stimulus frequency and decreased at frequencies close to the stimulus (Lindqvist 1990). This condition fulfills the criteria of metastable entrainment (Kitney 1974; see Lindqvist 1990).

Study 2: Sham Stimulation. Sham stimulations were done to five subjects at the frequencies of 0.013, 0.033, and 0.096 Hz by the operation of the empty water

Fig. 2A, B. Oscillations of a subject's forearm skin blood flux in 2.5 0 recumbency above and thermal stimulus signal below. A Thermal stimulation at 0.05 Hz. B Spontaneous conditions

A

B

::~I ::~ -I ::~I il~1

o 200 400 600 800 1000 TIME (5)

286 A. LINDQVIST, 1. VALlMAKI

BANDWIDTH

ENTRAINMENT 80 r FACTOR (%) N TIMU:LATED

50 j

I 20 I I SPDNTAN~DUS

o 0.05 0.1

FREOUENCY (Hz)

SPONT ANEOUS ACTIVITY

200

100

N 0 I

N THERMAL STIMULATION 2 mOL 0 >

>- 50 I-Vi Z W 0 0

0:: SHAM STIMULATION w s: 0 Q..

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Fig. 3. Bandwidth entrainment factors of skin blood flux measured at 12 frequencies between 0.01 and 0.10 Hz. Thermally stim­ulated activity exceeded spontaneous activ­ity at all frequencies

o~--=""='= ..... 0.5

FREQUENCY 1Hz)

Fig. 4. Power spectral density functions of skin blood flux during two spontaneous, 12 thermally and 3 sham-stimulated records of the same subject

baths. The sham stimulations caused the same vibrations and noise as the real water bath stimulations, but the thermal stimulus effect was absent, and no effect on forearm skin blood flux was found (p>0.10, Newman-Keuls; Fig. 4; Lindqvist 1990).

Cutaneous Microvascular Blood Cell Flux in Young Men 287

Study 3: The Influence of Stimulus Amplitude. For five other subjects the sponta­neous activity was recorded, and thermal stimulations were applied with ampli­tudes of 0 °e, 10 °e, and 20 °e by changing the thermal gradient between the warm and cool water baths. The average temperature of the water during a stimulus cycle was 28°e and the stimulus frequency was 0.03 Hz. At the stimulus amplitude of o °e, the oscillations of skin blood flux did not differ from the corresponding spontaneous activity (p>0.10, Newman-Keuls). The synchronization of the oscil­lations of skin blood flux occurred when the thermal stimulus amplitude rose from ooe to 20 0 e (p<0.05, Newman-Keuls; Fig. 5; Lindqvist 1990).

Study 4: The Effect of Venous and Arterial Occlusion. The synchronization of the 0.03-Hz oscillation of skin blood flux of the right forearm did not halt despite the venous or arterial occlusion of the feet and calves which were thermally stimulated at 0.03 Hz (Fig. 6; Lindqvist et al. 1989 a).

Study 5: The Effect of Head-up Tilting. The oscillations of five subjects' skin blood flux followed the rhythmicity of the thermal stimulus signal both at 2.5 ° supine rest and during 76° head-up tilting. The head-up tilting decreased mean skin blood flux by 40% and decreased the integral of the power spectral density function over 0.01-0.42 Hz by 25% but did not desynchronize the thermally stimulated oscilla­tions of skin blood flux as compared to 2S recumbency (Fig. 7; Lindqvist et al. 1989a).

Study 6: The Effect of Active Standing Up. In five subjects, the 0.01 to 0.12-Hz low-frequency and the 0.07 to 0.12-Hz and 0.15 to 0.42-Hz high-frequency oscilla-

Fig. 5. Power spectral density functions of skin blood flux and thermal stimulus signals of five subjects during O.03-Hz thermal stimulation with am­plitudes of 0 DC, 10 DC, and 20 DC

N I

N

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>-I-Vi Z W 0

0:: W 3:: 0 Q.

THERMAL STIMULUS AMPLITUDE

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THERMAL STIMULATION

50[ ~! L ! ~IL..L.o.-..-A ------'

0 0.50 0.50 0.5 FREQUENCY (Hz)

288 A. LINDQVIST, 1. VALIMAKI

SKIN BLOOO 2

0

0[ FLUX (%) [

TEMPERATURE 40E ("C) a

tvenous occlusion

~r ° 348

t g~~~~~n t after occlusion

Fig. 6. Skin blood flux signal of the right forearm and thermal stimulus signal during water bath stimulation of both feet and calves at 0.03 Hz. The venous (60 mmHg) and arterial (250 mmHg) blood flow of the lower legs was successively occluded (arrows)

N :c

N

2 0 >

>-f-iJi z w 0

C:: w 3: 0 0..

200

100 1

0 0.5

FREQUENCY (Hz)

Fig. 7. The power spectral density functions of forearm skin blood flux during 0.03 Hz thermal stimulation at 2S recumbency and 76.0° head-up tilting in a control subject. The oscillations of skin blood flux attenuated after head­up tilting (shaded area) as compared to supine rest. The peak corresponding to the stimulus frequency (double arrow) dominated the power spectral density function in both postures

tions of skin blood flux decreased by 20% from the sitting posture to the active standing posture (p < 0.05). This response was different during the 0.10-Hz thermal stimulation: the low-frequency, 0.07 to 0.12 Hz and high-frequency oscillations of the forearm skin blood flux increased by 7% when the subjects rose from a sitting to a standing posture (for thermal stimulation, p;;::: 0.30, but for interaction be­tween posture and thermal stimulation, p~0.04). No corresponding influence of the thermal stimulation on the oscillations of skin blood flux during the postural change occurred at frequencies of 0.05 Hz or below (for thermal stimulation and for interaction between posture and thermal stimulation, p;;::: 0.17). The ratio of the low-frequency to the high-frequency oscillation of skin blood flux did not change significantly (Fig. 8; Lindqvist et al. 1990).

Cutaneous Microvascular Blood Cell Flux in Young Men 289

Fig. 8. Spontaneous and thermally stimulated 0.07- to 0.12-Hz and 0.01- to 0.12-Hz low-frequency and 0.15- to 0.42-Hz high-frequency oscil­lations and the ratio of low­frequency to high-frequency oscillations of skin blood flux in the sitting and stand­ing postures. Spontaneous activity at 0 Hz on the fre­quency scale

0.07- 0.12 Hz OSCILLATIONS

0.Q1 - 0.12 Hz LOW-FREQUENCY OSCILLATIONS

0.15 - 0.42 Hz HIGH-FREQUENCY OSCILLATIONS

RATIO OF LOW-FREQUENCY TO HIGH-FREQUENCY OSCILLATIONS

Comments and Conclusions

SPONTANEOUS THERMAL ACTIVITY STIMULATION

8];.a[1 j 3:J!) 'j: 1t i I:2===~I=-~==-J~sTANDING 1 ~SITTING

l'~r 1 ~~ r>M-4STANDING 0 0 SITTING 0..2-

o

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0.._ 0 f'l I ~STANDING .............. SITTING

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o

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STIMULUS FREQUENCY (Hz)

1. Thermal synchronization of skin blood flux could be induced in both supine and standing subjects.

2. Sham stimulation or water bath stimulation of the feet and calves with a zero thermal gradient did not synchronize the forearm skin blood flux. Venous or arterial occlusion of the stimulated legs did not prevent the synchronization. The synchronization of skin blood flux depended on the neural afferentiation from the stimulated skin.

3. Postural stimulation attenuated oscillations of skin blood flux on a large fre­quency range both in the presence of and in the absence of thermal stimulation of the skin at 0.01-0.05 Hz.

4. Thermal stimulation at 0.10 Hz amplified the oscillations of the forearm skin blood flux more in the standing position than in the sitting position. This response suggested interaction between thermally and posturally induced vascu­lar activity.

290 A. LINDQVIST, I. VALIMAKI: Cutaneous Microvascular Blood Cell Flux in Young Men

References

Ebert TJ, Stowe DF, Barney JA, Kalbfleisch JH, Smith JJ (1982) Summated circulatory responses of thermal and baroreflexes in humans. J Appl Physiol 52: 184-189

Gebber GL, Snyder DW (1970) Hypothalamic control of baroreceptor reflexes. Am J Physiol 218:124-131

Hyndman BW, Kitney RI, Sayers BM (1971) Spontaneous rhythms in physiological control systems. Nature 233:339-341

Kitney RI (1974) The analysis and simulation of the human thermoregulatory control system. Med BioI Eng Comput 12:57-64

Lindqvist A (1989) Combined water bath and rocking tilt table stimulator to test autonomic function by a thermal and postural entrainment method. Med BioI Eng Comput 27:435-439

Lindqvist A, Jalonen J, Parviainen P, Halkola L, Antila K, Laitinen LA (1989a) Testing of heat exchanging capacity and effect of the subject's position on thermal entrainment in a water bath stimulator. Med BioI Eng Comput 27:429-434

Lindqvist A, Parviainen P, Kolari P, Tuominen J, Viilimiiki I, Antila K, Laitinen LA (1989b) A non-invasive method for testing neural circulatory control in man. Cardiovasc Res 23:262-272

Lindqvist A, Jalonen J, Parviainen P, Antila K, Laitinen LA (1990) Effect of posture on spontaneous and thermally stimulated cardiovascular oscillations. Cardiovasc Res 24:373-380

Lindqvist A (1990) Noninvasive methods to study autonomic nervous control of circulation. Acta Physiol Scand 138 (suppl 588): 1-108

de Trafford JC, Lafferty K, Kitney RI, Cotton LT, Roberts VC (1982) Modelling of the human vasomotor control system and its application to the investigation of arterial disease. lEE Proc 129:646-650

Power Spectral Analysis of Heart Rate and Arterial Pressure Variabilities as an Experimental and Clinical Tool A. MALLIANI, M. PAGANI, F. LOMBARDI, G. BASELLI, and S. CERUTTI

Introduction

The possibility recently offered by computer techniques for quantifying the small spontaneous beat-by-beat oscillations characterizing the cardiovascular variables and in particular the electrocardiographic R -R intervals aroused a growing interest in view of the hypothesis that these rhythmical oscillations could provide some insight into the neural regulatory mechanisms operating in the intact organism under various real-life conditions.

Indeed, even somewhat crude analyses of the variability phenomena, such as those offered by the use of standard deviation, frequency histograms, or simple filtering techniques provide important information on the course of pathophysio­logical processes such as diabetes (Ewing et al. 1984) and myocardial infarction (Kleiger et al. 1987). However, the application of computationally efficient spec­tral techniques (Cooley and Tukey 1965) offered the opportunity of assessing specifically the nonrandom components of heart rate variability, thus quantifying the possible different rhythmicities hidden in the signal.

Sayers (1973), for instance, employing the fast Fourier transform (FFT) tech­nique reported the existence in humans of three major components in R-R vari­ability, observed in specific bands of predetermined frequencies of 0.25, 0.1 0, and 0.03 Hz, respectively: a respiration-linked component (0.25 Hz) and two others at lower frequencies. Following this pioneering work, several other investigators applied this technique, and in spite of the fact that the heart rate variability signal is not strictly periodical, as required by the deterministic nature of the FFT algorithm, it became clear that it could be used as a quantitative probe to assess heart rate fluctuations (Akselrod et al. 1981).

As to the neural mechanisms underlying these fluctuations, vagal efferent activ­ity appeared responsible for the higher frequency, i.e., the respiration-linked com­ponent of heart rate variability. This conclusion was based on the disappearance of this component after vagotomy performed in experiments on decerebrate cats (Chess et al. 1975) and after muscarinic receptor blockade in conscious dogs (Akselrod et al. 1981) and man (Pomeranz et al. 1985). Both vagal and sympathet­ic outflows were considered to determine the lower frequency components, togeth­er with the hypothetical participation of other regulatory mechanisms such as the renin-angiotensin system (Akselrod et al. 1981).

292 A. MALLIANI et al.

Methods

The methodology used in our laboratory, simply stated, aims at quantifying the nonrandom components present in either heart rate or arterial pressure beat-by­beat variabilities. Usually two major components are recognized, one synchro­nized with respiration and referred to as high frequency (HF) and the other referred to as low frequency (LF), at about 0.1 Hz. The ECG is obtained with a standard AC amplifier, and care is taken to record an analog signal with a promi­nent and positive R-wave which decreases the likelihood of errors in the following steps. After suitable amplification, the ECG is fed through an analog-to-digital (A/D) converter to the mass memory of a computer. Then the individual R-waves, with the peak as the fiducial point, are sequentially determined, and the series of R-R intervals (T 1, T 2, T 3' ... Tn) is stored in the memory as a function of the beat number. This series constitutes the tachogram.

From sections of tachogram including 200-512 interval values, simple statistics (mean and variance) of the data are computed. The length of the tachogram is selected as the best compromise between the need for a large time series to achieve greater accuracy in the computation and the need to obtain stationary recordings, which is easier for short time periods. The computer program automatically calcu­lates the model, i.e., the autoregressive coefficients, that provides the best statisti­cal estimate of the spectral distribution (see the "Appendix" in Pagani et al. 1986) and prints out the power and frequency of every spectral component. Each spectral component is presented in absolute units as well as in normalized form. In order to facilitate comparisons between spectra with large differences in total power (i.e., variance), as frequently occurs in the case of heart rate variability, a normalization procedure is useful. Normalized data (Pagani et al. 1986) are calculated by dividing the power of a given component by total power (i.e., variance) minus the DC component, when present, this ratio multiplied by 100.

It should be pointed out that in order properly to apply spectral analysis techniques to a tachogram, the series must be stationary (Brown et al. 1989); this can be assessed with the appropriate procedures. As a corollary, the very slow oscillations - those with a frequency of less than 0.02-0.03 Hz and which may contain significant physiological information and appear on short time recordings as slow trends - cannot be assessed adequately with this methodology but require different algorithms (Saul et al. 1987).

Another important aspect is the fact that with this methodology, which does not require any filtering or windowing of the data, the duration of the periodical phenomena in the variability signal is measured as a function of cardiac beats, rather than seconds. As an example, a four-beat periodical component is repre­sented with a frequency of 1:4, i.e., 0.25 cycles per beat. However, this frequency is easily converted into hertz equivalents (Hz Eq) by dividing it by the average R-R interval length. For instance, an average R-R length of 1000 ms corresponds to 0.25 Hz Eq.

This technique, in principle, can be applied to any variable. In the case, for instance, of simultaneous measurements of heart rate and systemic arterial pres­sure variabilities, as schematically depicted in Fig. 1, after synchronous acquisition

Heart Rate and Arterial Pressure Variabilities 293

1.30.----------,

RR

0.005.-------------.

[sec] PSD

2 [sec'

Hz .,]

0.75'---_________ ----' OL--____ ......;:,, ___ ~_' o 512 beats 0 0.50Hz

120L--______________ ~ OL-______ ~== ____ ~

0.55

CROSS [sec. mmHg]

o 512beats 0 0.50Hz

3.14

¢[rad]

OL-______ ~=======d '---' _____________ ---' 3.14

o 0.50Hz 0.50Hz

Fig. 1. Example of the simultaneous computer analysis of the RR interval and systolic arterial pressure (SAP) variabilities. As explained in the text, after appropriate analog-to­digital conversion of the EeG and arterial pressure signals, the RR interval and SAP series are obtained (top, middle left panels). Then, the corresponding spectra (power spectral density, PSD) are computed (top, middle right panels). Bottom left panel, the cross-spectrum (CROSS); bottom right panel, the phase relationship (<:]i) and the squared coherence (K2, curve with two major peaks). (From Pagani et al. 1988b)

and appropriate calibration, a similar procedure is used to compute the spectrum of the arterial pressure variability data for both systolic and diastolic values.

In the case of simultaneous analysis of R-R and systolic arterial pressure vari­abilities, the computer program first calculates the interval tachogram (the series of consecutive R-R intervals) and the systogram (the series of maximal systolic values synchronized to the beat at the beginning of each R-R interval).

Further computations can be performed on the data, such as cross-spectral analysis, which can provide quantitative information on the coherence function, i.e., a measure of the statistical link between the two signals at any given frequency, and on the phase relationship (Pagani et al. 1986; Cerutti et al. 1987; Pagani et al. 1988b). As to the latter, with the conventions used in our laboratory, the negative sign indicates that pressure oscillations lead heart period oscillations, particularly in correspondence to the 0.1 Hz components.

294 A. MALLIANI et al.

A recent development of the methodology refers to the possibility of analyzing long periods of analog recordings, by using a recursive version of the program of spectral analysis. For instance, Holter tapes, digitized at appropriate speed, can furnish a quantitative assessment of R-R and arterial pressure variabilities throughout the 24-h period of the recording (Furlan et al. 1990). To minimize the time necessary for the A/D conversion, a tape play-back unit running at 64 times real time and using 19200 samples/s per channel provides the entire 24-h signal of ECG, digitized at 300 samples per second, in about 22 min. From this signal a single tachogram approximately 100000 intervals long is computed, from which approximately 250 spectra are derived, describing the entire 24-h period.

Experimental and Clinical Studies

In a first-generation approach it has been possible to quantify numerous physio­logical and pathophysiological conditions characterized by various levels of sym­patho-vagal interaction. The methodological soundness of this simple attributive stage has been checked through a variety of experimental and clinical states: the collected data always fitted the basic hypothesis that the respiration-linked HF rhythm present in heart rate variability was a marker of vagal tone, while the LF third-order rhythm was a marker of sympathetic tone.

In human studies, the LF component of both heart rate and arterial pressure variability was increased with tilt (Pagani et al. 1986), mental stress (Koepchen et al. 1986; Pagani et al. 1989), and moderate physical exercise (Cerutti et al. 1987; Pagani et al. 1988 b). In experimental studies, the LF component was increased by moderate hypotension (Pagani et al. 1986), coronary artery occlusion (Rimoldi et al. 1990a), carotid artery occlusion, and physical exercise (Rimoldi et al. 1990 b). An LF component was also detectable in the spectral analysis of discharge of cardiac sympathetic preganglionic fibers in decerebrate cats, thus supporting the ,hypothesis that the OJ-Hz rhythm characterizing heart rate variability is mediated by the sympathetic nerves (Lombardi et al. 1990).

Conversely, the HF component of heart rate variability was increased by con­trolled respiration (Pagani et al. 1986b) and by diving reflex (Pizzinelli et al. to be published).

In pathophysiological conditions, the LF component of heart rate variability was increased in subjects with essential arterial hypertension (Guzzetti et al. 1988) and in patients 2 weeks after myocardial infarction (Lombardi et al. 1987). In patients with diabetic neuropathy, not only the known reduction in variance (Kitney et al. 1982) was confirmed, but a reduced response during tilt of the spectral indices of sympathetic activation and of vagal withdrawal was also ob­served (Pagani et al. 1988 a) suggestive of a complex modification in the neural control activities. A similarly altered response of spectral indices during standing was described in patients with Chagas' disease (Guzzetti et al. 1990).

295

Simultaneous Analysis of Heart Rate and Arterial Pressure Variabilities and the Gain in Their Relationship

We can only mention a sort of second-generation approach based on continuous assessment, with spectral techniques, of the relationship between heart period and arterial pressure (Akselrod et al. 1985; Cerutti et al. 1987; Robbe et al. 1987; Pa­gani et al. 1988b).

Usually the slope of the linear regression of the heart period, as a function of systolic arterial pressure plotted during the pressor rise produced by the intra­venous injection of a pressor agent such as phenylephrine, is taken as a measure of the gain in baroreflex control of heart rate (Smyth et al. 1969). This approach, modeled as an open loop has provided a very useful clinical tool in spite of its simplifications, and it has recently been extended to furnish a more continuous estimate of the gain in baroreflex mechanisms over periods of 24 h (Bertinieri et al. 1985). This model, however, does not take into account that changes in heart period can also induce changes in systolic arterial pressure, such as those related to variations in stroke volume and hence in systolic arterial pressure, which accom­pany beat-to-beat changes in heart period.

For these reasons we used (Pagani et al. 1988 b) a closed-loop model for analysis of the relationship between the beat-to-beat variability of systolic arterial pressure (s) and heart period (t; Akselrod et al. 1985; Cerutti et al. 1987; Robbe et al. 1987). An important consequence of this model is that it provides a way of computing by spectral and cross-spectral analysis of spontaneous variabilities an index (called (X) of the overall gain of this neural interaction. This index ((X) is usually computed both in correspondence of LF and HF components, provided the coherence func­tion between R-R and systolic arterial pressure variabilities is high (> 0.50; for more details see Pagani et al. 1988 b). We have observed that this approach furnish­es, both at rest and during physical exercise, clinical results comparable to those obtained with the phenylephrine method (pagani et al. 1988b).

This approach has been used in a group of mildly hypertensive patients, and it was observed that after physical training there was an increase in the gain. In a second group of normotensive (n = 9; systolic arterial pressure 133 ± 3 mmHg) and hypertensive (n = 9; systolic arterial pressure 177 ± 9 mmHg) subjects undergoing 24-h diagnostic continuous recording of ECG and high fidelity arterial pressure monitoring, the index (X was significantly reduced in the hypertensive group at rest ((XLF=4± 1 versus 10±2 msjmmHg).

Furthermore, this methodology allowed us to describe the behavior of the gain in the heart period -arterial pressure relationship throughout the 24 h. The gain in the relationship (i.e., the index (X) underwent large changes during the 24-h record­ing period; in particular, a higher gain was present during the night and a lower gain during the day. Furthermore, large minute-to-minute variations were also present, indicating (whatever technique is used) the unrealistic goal of searching for a characteristic resting value for each individual of the gain of the heart period-arterial pressure relationship.

296 A. MALLIANI et al.

It is important to reiterate that with this technique the computation is performed directly by using the spontaneous variability of heart period and systolic arterial pressure, without the need of inducing a disturbance from the outside or of filtering the data.

In the previous approach, respiration, which is an important modulatory com­ponent of both heart period and arterial pressure, has been disregarded to simplify the analysis.

Recently in an attempt to provide a more correct interpretation of the spectral analysis of heart period and arterial pressure variabilities, we modified the math­ematical and physiological model, in order to consider simultaneously both these variabilities together with respiration.

As shown in Fig. 2, this approach (Baselli et al. 1988) allows the separate anal­ysis of the contribution of the various factors involved in the generation of cardio­vascular variabilities. The core of the model considers the closed-loop interaction between the systogram (s) and the tachogram (t), where the neural link between them is represented by the block Hts, while the block Hst represents the mechanical coupling. It should also be pointed out that in the neural block, Hts , both negative and positive feedback mechanisms are still lumped together (pagani et al. 1988). The influence of respiration is represented by the input (r), which is divided into two separate pathways, Rs and Rt. Rs represents that fraction of the respiratory influence which directly affects s, while ~ represents that respiratory fraction acting by way of changes in t. Additionally, the model allows analysis of the effects of inputs coming from outside the s-t loop, which are considered white noises CWs and Wt) acting through the filters Ms and Mt, respectively onto sand t. An additional component of this complex model accounts for the effects of s on s itself, by way of the block Hss.

Us

ns

Fig. 2. Block diagram of the model of in­teractions of t, s, and r signals. The physi­ological mechanisms taken into account in the suggested model are explained in the text. wS' w,' White noise; US, u" inputs of external or central origin to the s-t-s regu­lating loop; M s ' M" spectral factors of Us and U,; Rs ' direct effect of ron s (changes in chest pressure, in venous return, etc.); R" direct effect of ron t (changes induced by lung receptors, atrial pressure receptors, central effects, etc.); H", nonneural effects of t on s; H,s' baroreceptor and neural mechanisms; HsS' vascular control, periph­eral resistances and arterial compliances, myocardial contractility, venous return, etc.); ns , disturbance on s independent from t and from the past of s; n" distur­bance on t independent from s. (From Baselli et al. 1988)

Heart Rate and Arterial Pressure Variabilities 297

The validity of this model has been assessed in preliminary studies aimed at clarifying relationships between the various oscillatory signals in terms of gain and phase, in the two main frequency band (i.e., second- and third-order oscillations; Baselli et al. 1988). The simultaneous analysis of s, t, and r allowed quantification of the respiratory influence on the s-t loop; it was observed that in man the importance of respiration is minimal, so that the computations performed consid­ering only the simple interaction between heart period and arterial pressure provide results similar to those obtained with the more complex model (Baselli et al. to be published); however, in the dog the influence of respiration on both s and t is such that only the complex trivariate model of Fig. 2 is capable of furnishing meaningful results (Baselli et al. to be published). The validity of the assumption that the gain in the Hts block represents an estimate of the gain in baroreceptive mechanisms in the conscious dog has been verified with denervation experiments which showed that in animals deprived of baroreceptor influences the gain in the block is virtually nil.

Conclusions

The push-pull of sympatho-vagal interaction is of paramount importance not only for physiological life but for disturbances of regulation such as arterial hyperten­sion. In the nervous structures a link seems to exist between sympathetic activation and increases in the LF rhythm and between vagal activation and increases in the HF rhythm. The hypothesis of a functional reciprocal organization of the two outflows is in this way linked to a reciprocal organization of two major cardiovas­cular rhythms. To have a crude measure of this interaction in different conditions, up to the 24-h period in humans (Pagani et al. 1988b; Furlan et al. 1990) corre­sponds, in our opinion, to a new tool which might open the door to important novelties.

References

Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC, Cohen RJ (1981) Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardio­vascular control. Science 213:220-222

Akselrod S, Gordon D, Madwed JB, Snidman NC, Shannon DC, Cohen RJ (1985) Hemo­dynamic regulation: investigation by spectral analysis. Am J Physiol249:H867-H875

Baselli G, Cerutti S, Civardi S, Malliani A, Pagani M (1988) Cardiovascular variability signals: towards the identification of a closed-loop model of the neural control mecha­nisms. IEEE Trans Biomed Eng 35:1033-1046

Bertinieri G, Di Rienzo M, Cavallazzi A, Ferrari AO, Pedotti A, Mancia G (1985) A new approach to analysis of the arterial baroreflex. J Hypertens 3 (suppl3):s79-s81

Brown DR, Randall DC, Knapp CF, Lee KC, Yingling JD (1989) Stability of the heart rate power spectrum over time in the conscious dog. FASEB J 3:1644-1650

Cerutti S, Baselli G, Civardi S, Furlan R, Lombardi F, Malliani A, Merri M, Pagani M (1987)

298 A. MALLIANI et al.

Spectral analysis of heart rate and arterial blood pressure variability signals for physio­logical and clinical purposes. Comput Cardiology. IEEE Computer Society, Washington D.C., pp 435~438

Chess GF, Tam RMK, Calaresu FR (1975) Influence of cardiac neural inputs on rhythmic variations of heart period in the cat. Am J Physiol 228:775~ 780

Cooley JW, Tukey JW (1965) An algorithm for the machine calculation of complex Fourier series. Math Comput 19: 297 ~ 301

Ewing DJ, Neilson JMM, Travis P (1984) New method for assessing cardiac parasympathetic activity using 24 hour electrocardiograms. Br Heart J 52:396~402

Furlan R, Guzzetti S, Crivellaro W, Dassi S, Tinelli M, Baselli G, Cerutti S, Lombardi F, Pagani M, Malliani A (1990) Continuous 24-hour assessment of the neural regulation of systemic arterial pressure and RR variabilities in ambulant subjects. Circulation 81: 537 ~ 547

Guzzetti S, Piccaluga E, Casati R, Cerutti S, Lombardi F, Pagani M, Malliani A (1988) Sympathetic predominance in essential hypertension: a study employing spectral analysis of heart rate variability. J Hypertens 6:711 ~ 717

Guzzetti S, Iosa D, Pecis M, Bonura L, Prosdocimi M, Malliani A (1990) Effects of sympa­thetic activation on heart rate variability in Chagas' patients. J Auton Nerv Syst 30 (suppl): S79~S81

Kitney RI, Byrne S, Edmonds ME, Watkins PJ, Roberts YC (1982) Heart rate variability in the assessment of autonomic diabetic neuropathy. Automedica 4: 155~ 167

Kleiger RE, Miller JP, Bigger JT, Moss AJ and the multicenter post-infarction research group (1987) Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 59:256~262

Koepchen HP, Abel HH, Klussendorf D (1986) Central cardiorespiratory organisation. In: Lown B, Malliani A, Prosdocimi M (eds) Neural mechanisms and cardiovascular dis­ease. Fidia Res Series, vol 5. Liviana, Pad ova and Springer, Berlin, Heidelberg New York, pp 119~ 131

Lombardi F, Sandrone G, Pernpruner S, Sala R, Garimoldi M, Cerutti S, Baselli G, Pagani M, Malliani A (1987) Heart rate variability as an index of sympathovagal interaction after acute myocardial infarction. Am J Cardiol 60: 1239~ 1245

Lombardi F, Montano N, Finocchiaro ML, Gnecchi Ruscone T, Baselli G, Cerutti S, Malliani A (1990) Spectral analysis of sympathetic discharge in decerebrate cats. J Auton Nerv Syst 30 (suppl):S97~S99

Pagani M, Lombardi F, Guzzetti S, Rimoldi 0, Furlan R, Pizzinelli P, Sandrone G, Malfatto G, Dell'Orto S, Piccaluga E, Turiel M, Baselli G, Cerutti S, Malliani A (1986) Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho­vagal interaction in man and conscious dog. Circ Res 59: 178 ~ 193

Pagani M, Malfatto G, Pierini S, Casati R, Masu AM, Poli M, Guzzetti S, Lombardi F, Cerutti S, Malliani A (1988 a) Spectral analysis of heart rate variability in the assessment of autonomic diabetic neuropathy. J Auton Nerv Sys 23:143~153

Pagani M, Somers Y, Furlan R, Dell'Orto S, Conway Y, Baselli G, Cerutti S, Sleight P, Malliani A (1988 b) Changes in autonomic regulation induced by physical training in mild hypertension. Hypertension 12: 600~61O

Pagani M, Furlan R, Pizzinelli P, Crivellaro W, Cerutti S, Malliani A (1989) Spectral analysis of R-R and arterial pressure variabilities to assess sympatho-vagal interaction during mental stress in humans. J Hypertens 7 (suppl 6): S14~S15

Pomeranz P, Macaulay RJB, Caudil MA, Kutz I, Adam D, Gordon D, Kilborn KM, Barger AC, Shannon DC, Cohen RJ, Benson H (1985) Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 248:H151~H153

Rimoldi 0, Pierini S, Ferrari A, Cerutti S, Pagani M, Malliani A (1990a) Analysis of the short term oscillations of R-R and arterial pressure in conscious dogs. Am J Physiol 258: H967 ~ H976

Heart Rate and Arterial Pressure Variabilities 299

Rimoldi 0, Pagani M, Pagani MR, Baselli G, Malliani A (1990b) Sympathetic activation during treadmill exercise in the conscious dog: assessment with spectral analysis of heart period and systolic pressure variabilities. J Auton Nerv Syst 30 (suppl): S129-S132

Robbe HWJ, Mulder LJM, Riiddel H, Langewitz WA, Veldman JBP, Mulder G (1987) Assessment of baroreceptor reflex sensitivity by means of spectral analysis. Hypertension 10:538-543

Saul JP, Albrecht P, Berger RD, Cohen RJ (1987) Analysis oflong term heart rate variability: methods, 1fF scaling and implications. Computers in Cardiology. IEEE Computer Society, Silver Spring, pp 419-422

Sayers B (1973) Analysis of heart rate variability. Ergonomics 16: 17 -32 Smyth HS, Sleight P, Pickering GW (1969) Reflex regulation of arterial pressure during sleep

in man. Circ Res 24: 109-121

Heart Rate Control and Metabolic Parameters After Fatiguing Exercise E. SCHUBERT, W DINTER and W RIELKE

Introduction: Heart Rate and Metabolic Control in Exercise

Heart rate and its variability are frequently used parameters for estimating cardiac regulatory effects after exercise in man [1-5]. Variability in this context is generally measured as sinus arrhythmia [6]. The levels of their setting and the dynamics of the reactions to diverse kinds of load depend on the intensity of the strain [7]. Various conditions of the investigated persons influence these reactions in different ways; these include age, physical fitness, respiratory patterns, pharmacological interventions, and several pathologies. In many loading situations heart rate and sinus arrhythmia demonstrate differing responses. This suggests varied ways or mechanisms of the reactions, which can be corroborated by investigations under blocking of the parasympathetic or sympathetic drive to the heart. Such experi­ments elucidate the various efferent pathways for regulatory reactions to the heart concerning either heart rate or sinus arrhythmia. Sinus arrhythmia can be demon­strated to depend principally on the parasympathetic drive. Heart rate, however, follows chiefly the sympathetic activity and is influenced to a minor extent by vagal effects (e.g., [1, 2, 4]).

From these results the role of the medullary cardiorespiratory centers in con­trolling reactions of cardiac frequency becomes evident [8]. Nevertheless, the ques­tion of influence from other control mechanisms such as the motoric system or metabolic feedback information remains open and is worthy of consideration [9, 10]. To find an approach to the assessment of such influences especially the resetting of reactions after effort should be successful [11, 12]. Attempts at separat­ing the possibly multiple influences of exercise reactions resulting from the motoric "central command" or the metabolic situation may help to provide further insight into the regulatory network.

Pursuing the conception of the metabolic influences after ruling out effects of a central command, an investigation was undertaken to follow up the recovery of heart rate and sinus arrhythmia after the ending of exhausting exercise simulta­neously with that of the metabolic state, estimated from the resetting of the acidosis and concomitant parameters. As an intervention for discriminating be­tween different metabolic objectives, compensation of the acidosis immediately after ending the exercise can be used.

301

Methods and Procedures: Fatiguing Exercise and Metabolic Compensation

Two groups of healthy, untrained men served as test persons. Each group con­tained 20 men who underwent the whole program. All persons were informed about the procedure and the aim of the study, and they expressed their consent to participate. Each person was investigated before the test in terms of medical history, physical investigation, resting ECG, and blood pressure measurement to rule out the possibility of pathological interferences.

The physical data of the test groups are given in Table 1. A few of the members of the groups participated in both programs within a time interval of at least 2 weeks.

Table 1. Test groups

Group n

A 20 B 20

Age (years)

25.2 (22-32) 23.6 (22-33)

Mean body mass (kg)

69.9 (55-87) 67.9 (63-87)

Body height (em)

177.6 (170-187) 176.7 (170-187)

As exercise, an exhausting bicycle ergometer load was used. Both groups had to cycle with a frequency of 90 revolutions per minute beginning at a load of 2 W /kg body mass. The protocol contained a warming phase of 10 min, then an increase in load by a step of 50 W, which was repeated after every 150 s until complete subjective fatigue. The maximal values reached were: duration of the test in group A, 21 min with a maximal load of 380 W; in group B: 17 min and 300 W. Immedi­ately after reaching fatigue each test person was placed in a comfortable arm chair near the bicycle ergometer for recovery.

During exercise the test person was observed carefully, and all criteria of the World Health Organization for breaking off exercise tests were observed; in no case were any of these criteria met. The maximal heart rate observed in one exercise phase reached 208 beats/min, the mean values were 197 beats/min in group A and 189 in B. No extrasystols and no ST suppression occurred. The maximal blood pressure values were 190/55 mmHg (25.3/7.3 kPa).

The members of group A went through the protocol with the exercise phase until fatigue, and a subsequent recovery time of 60 min. As observations during the load we measured heart rate from the monitored ECG - from chest leads according to NEHB A and blood pressure after Riva-Rocci in 5-min intervals. All data were stored. These measurements were continued throughout the recovery phase imme­diately after ending the load (i.e., at 30 s after ending the exercise) and 6, 12,24, 40, and 60 min later. The lobe of one ear of each test person was hyperemized by using Finalgon-ointment® and blood samples were taken at the same time points after exercise for estimation of the capillary acid-base status. The measurements were carried out with the acid-base laboratory unit ABL-2, Radiometer Kopen-

302 E. SCHUBERT et al.

hagen (Copenhagen, Denmark). Furthermore, the concentration of lactate was measured using the conventional Boehringer (Mannheim, FRG) UV test.

All members of group B underwent the same protocol and the same observation ofECG and blood pressure. For the infusion ofa compensating NaHC03 solution a permanent plastic cannula was inserted into one superficial vein of the right forearm. From the first capillary blood sample 30 s after ending the exercise, the acidosis was measured, and by using the base excess value (BE) a 1 mol NaHC03

solution was taken as necessary for buffering the acidosis in terms of the formula:

ml NaHC0 3 = neg. BE x 0.3 x body mass (kg)

according to Mellemgaard and Astrup [13]. This amount was infused by perma­nent cannula immediately for compensation of the acidosis.

The values measured in both groups during the experiment were: (a) heart rate, taken as heart period duration (HPD) from the RR distance in the ECG; (b) sinus arrhythmia, taken as variation of heart period duration (dHPD), evaluated as the mean difference of the absolute values of 11 consecutive RR-distances [6]; (c) blood pressure for supervision of the exercise reactions; (d) PH and pCOz, pOz, and hemoglobin content, as measured and BE and standard bicarbonate as evaluated values from the blood; and (e) lactate, as measured out of the blood samples. For both experimental procedures the correlations between HPD and dHPD (heart rate and sinus arrhythmia) became calculated.

Results: Reactions of Heart Rhythm and Metabolic Parameters

The results of the measurements are given for HPD and dHPD in Fig. 1 and for PH and lactate in Fig. 2. All the other acid-base parameters behaved as PH'

Figure 1 demonstrates the reactions of the rhythm parameters. HPD showed the same reactions with or without PH compensation. Fatiguing effort diminished the values from the pretest resting level of about 700 ms to about 300 ms immediately after the test. Then a first rapid resetting to more than 500 ms was reached within about 10 min with a half reaction time (t 1/ Z) of 3.5 min. Thereafter a slower resetting continued, during which nearly resting values were achieved within 60 min. From separate regression calculations for the fast and slow parts of the reaction the passing between these was established in A at 10.34 min and in B at 10.22 min after ending of the load. The heart rate values for these moments were 110 and 114 beats/min.

dHPD also demonstrated no significant differences between the two experimen­tal groups. Starting from resting values of about 25 ms a level of about 3 ms was reached after load. From this a slow monophasic resetting occurred in the direc­tion of the resting level without arriving within 60 min. The tl/Z was 42.5 min. The exponents of the regression equation over time were similar in both groups (0.008 and 0.006).

For PH different dynamics of recovery were observed between groups A and B (Fig. 2). From a resting level of about 7.40, all test persons showed decreased

Heart Rate Control and Metabolic Parameters After Fatiguing Exercise 303

Fig. 1. Dynamics of resetting of HPD and dHPD after exhausting exercise.

700

[msJ

500t---+-1I'--l1--------1C------~

300

25

[msJ

• not compensated

o cOmptnsofed

IIPO

dHPO

t1f2 : ~Z.5min

15~-+~------------~~--~

Left, the preexercise resting values. 5 Time 0, the end of the exercise. Values as mean±SD

Rest o 10 30

Recovery SO [min]

values by more than 0.1 to about 7.25 after fatigue. The noncompensated group attained the resting values entirely within the 60-min recovery period in a slow course with a t1/2 of 19.6 min. The compensated group reacted rapidly and reached normal PH-levels within 6 min. The tl/2 was 4.5 min. After this the PH-values reacted by overshooting by about 0.5 units and remained, raised almost until the end of the measuring period. Similar reactions were found for BE and standard bicarbonate with different reaction courses for A and B.

In contrast to this, the measured courses of the lactate values did not differ significantly between groups A and B. Starting at resting values of about 2 mmol/l after fatigue, an increase to values between 10.3 and 16.2 mmol/l (NS) was reached. The maximal values were measured 6 min after the end of exercise. From this point an exponential decrease occurred, attaining a level near the resting values after 60 min. The t1/2 values of the resetting were 19.4 and 16.3 min, respectively. The small differences between the groups, the parallel courses of the dynamics, and the identical moments of the maximal values demonstrate the lactate curves in A and B to be identical.

730

304 E. SCHUBERT et al.

• not compensated t 1f2 : 19.6 min

o compensated t 1f2 : 4.Smin

18r---.-~~---------------'

[m70'] 14

10

6

2 ! f Loctat

0 10 Rest

o t Y2 16.3 min

• t 1,12 : 19.4 min

3D SO [min] Recovery

Fig. 2. Recovery dynamics of pH and lactate after exhausting exercise. Left, the preexercise resting levels. Values as mean±SD

Regression of PH over time and BE over time showed statistically significant differences between groups A and B. For the lactate measurements no differences were significant.

Discussion: Connection Between Heart Rate Control and Metabolism during Recovery?

The data from these investigations demonstrate a diphasic recovery of heart rate after fatigue. The course of this did not depend on metabolic deviations measured

Heart Rate Control and Metabolic Parameters After Fatiguing Exercise 305

as acidosis or venous lactate level. Even the different moments of maximal meta­bolic deviations, for PH' etc. immediately after ending of exercise and for lactate 6 min after the ending and the changed time course of recovery of PH after compen­sation, did not find a reflection in the heart rate behavior. Its diphasic course conformed to data of Linnarson [14], whereas Perini [11] and Allsop [12] report monoexponential decrements of heart rate. This may be attributed to the different intensities of load applied in the experiments. Nevertheless the similarly high values for the time constants of the initial heart rate reactions in recovery hint at an affiliation of this effect to a beginning restoration of vagal tone immediately after the end of exercise [11]. The slower second component may depend on the slower return of the sympathetic nervous activity. Interestingly, the passing be­tween these two phases was situated at the same frequency region of 110 beats/min at which the vagal control of heart rate became exhausted after imposition of heavy loads. This was demonstrated by the disappearance of sinus arrhythmia at this heart rate region after the start of exercise [1].

In contrast, heart rate variability needed more time for restoration and was not yet finished 60 min after the end of the exercise. The parallelism of dHPD to the behavior of lactate and the independence from PH and its compensation indicate other means of control. If no dependence upon metabolism is suspected, the effect of a long-lasting vagal inhibition after intense sympathetic stimulation which may occur in fatiguing exercise may be responsible for the slow restoration of dHPD. The connection between vagal activity and dHPD is well investigated (e.g. [1, 4, 15]). On the other hand, a long-lasting vagal inhibition after intense work caused by the neuropeptide Y has been reported by Potter [16]. This peptide is known to generate chronotropic effects at the heart; it is released during exercise and has a relatively long life time (t 1/2 of about 20 min) [17]. In newer investigations also the dynamics of catecholamines or p-endorphins after exercise are reported to show comparable courses [11, 18].

Other possible explanations may be sought in the influences of metaboreceptors [9]. These are supported by observations of the effects of exercise on ventilatory changes, not investigated in our experiments, and of acid-base changes as well as lactate dynamics after maximal exercise [18, 19]. The values in our experiments show corresponding values and courses for lactate.

Summarizing the results, there seem to be different relationships between heart rate control parameters and metabolism. Heart rate itself resets byphasic with no relationship to either PH or lactate. No influence of compensation of PH appears. Thus the main reaction of heart rate may reflect central command effects with contributions of vagal and sympathetic activity at different times. Only the late slow reaction of heart rate could be attributed to long-lasting effects of lactate return, possibly transmitted by metaboreceptors. Heart rate variability, on the other hand, demonstrates a much closer relationship to metabolic recovery after exercise. Even when there is a direct parallel with PH' the simultaneous reaction of lactate and dHPD suggests influences of lactate, possibly via metaboreceptors, on the resetting of dHPD. Furthermore, the metabolism of exercise-dependent hor­mones such as neuropeptide Y seems to be correlated with the exercise-coupled behavior of heart rate variability.

306 E. SCHUBERT et al.: Heart Rate Control and Metabolic Parameters

References

1. Schubert E (1981) La determination quantitative de l'activite du nerfvague et sa valeur pour la regulation du fonctionnement du ca:ur. Bull Acad Med Bel 136: 195 - 206

2. Eckberg DL, Abboud FM, Marx AL (1976) Modulation of carotid baroreflex respon­siveness in man: effects of posture and propranolol. J Appl Physiol 41:383-387

3. Eckberg DL (1983) Human sinus arrhythmia as an index of vagal cardiac outflow. J Appl Physiol 54:961-966

4. Katona PG, Jih F (1975) Respiratory sinus arrhythmia: noninvasive measure of parasympathetic cardiac control. J Appl Physiol 39: 801-805

5. Hirsch JA, Bishop B (1981) Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. Am J PhysioI241:H620-H629

6. Eckoldt K, Cammann H, Pfeifer B, Schubert E (1973) The continuous acquisition of the sinus arrhythmia of the human heart at rest and work, Digest 10th. ICMBE, Dresden, p 360

7. Schubert E (1990) Heart rate and heart rate variability deliver different information about chronotropic regulation. In: Antaloczy Z (ed) Advances in electro cardiology. Excerpta Medica, Amsterdam, pp 55-58

8. Koepchen HP (1982) Zentralnerv6se und reflektorische Steuerung der Herzfrequenz. In: Brisse B, Bender F (eds) Autonome Innervation des Herzens. Steinkopff, Darmstadt, pp 66-86

9. Jeyaranjan R, Goode R, Duffin J (1989) The effect of metabolic acid-base changes on the ventilatory changes at the end of heavy exercise. Eur J Appl Physiol 58:405-410

10. Freund H, Oyono-Enguelle S, Heitz A, Marbach J, Ott C, Gartner M (1989) Effect of exercise duration on lactate kinetics after short muscular exercise. Eur J Appl Physiol 58:534-542

11. Perini R, Orizio C, Comande A, Castellano M, Beschi M, Veicsteinas A (1989) Plasma norepinephrine and heart rate dynamics during recovery from submaximal exercise in man. Eur J Appl Physiol 58:879-883

12. Allsop P, Cheetham M, Brooks S, Hall GM, Williams C (1990) Continuous intramuscu­lar pH measurement during the recovery from brief, maximal exercise in man. Eur J Appl PhysioI59:465-470

13. Mellemgaard K, Astrup P (1960) The quantitative determination of surplus amounts of acid or base in the human body. Scand J Clin Lab Invest 12:187-199

14. Linnarson D (1974) Dynamics of pulmary gas exchange and heart rate changes at start and end of exercise. Acta Physiol Scand [Suppl] 415:1-68

15. Penaz J (1962) Frequency response of the cardiac chronotropic action of the vagus in the rabbit. Arch Int Physiol Biochem 70: 636-650

16. Potter EK (1985) Prolonged non-adrenergic inhibition of cardiac vagal action following sympathetic stimulation: neuromodulation by neuropeptide Y? Neurosci Lett 54: 117-121

17. Potter EK (1988) Neuropeptide Y as an autonomic neurotransmitter. Pharmacol Ther 37:251-273

18. Brooks S, Burrin J, Cheetham ME, Hall GM, Yeo T, Williams C (1988) The response of the catecholamines and j3-endorphin to brief maximal exercise in man. Eur J Appl Physiol 57: 230-234

19. Jeyaranjan R, Goode R, Duffin J (1988) Changes in respiration in the transition from heavy exercise to rest. Eur J Appl Physiol 57:606-610

Cardiorespiratory Relations in Human Heart Rate Pattern * H.-H. ABEL, D. KLii"SSENDORF, R. DROH, and H.P. KOEPCHEN

Introduction Modern noninvasive techniques and computer-assisted methods of data process­ing can give a certain amount of insight into neurovegetative regulatory functional states in humans. These approaches are increasing in importance for clinical application.

An important step forward is the development of dynamic analyses, as opposed to conventional approaches based on the concept of separate feedback reflex control systems with set points and constancy of parameters as the main goal of physiological regulation. This development means that the conventional computa­tion of mean values and standard deviations is being replaced or extended by describing the time structures of regulatory processes.

The analysis of variations in heart rate and their relationship to respiration has attracted a growing interest in recent years and has already been introduced for clinical diagnostics. Using heart rate analysis, several attempts have been made to determine the classical clinical parameters of vagal tone and sympathetic tone, corresponding to the physiological terms of ergo tropic and trophotropic state (Hess 1949). It has been suggested that these functional states are a vegetative expression of the central excitatory state or "central drive" which is governed by behavioral factors.

Cardiorespiratory control represents an interplay between behavioral drives and homeostatic autoregulatory mechanisms. The clinical application of heart rate analysis, for example, must be based on the analysis and interpretation of car­diorespiratory parameter dynamics in healthy subjects in various functional states.

Some components of heart rate rhythmicity are closely related to respiration and neurophysiological research has presented evidence that there is an interrelation­ship between brainstem reticular substrates for basic behavioral drive and those for common cardiorespiratory control. Therefore, our investigations of heart rate dynamics include the analysis of respiratory pattern. One advantage to conducting investigations in humans is the possibility of experimentally changing the degree of general activation even at physical rest.

It was the aim of our study to reveal the changes in heart rate dynamics during different states of activity and their interdependence with simultaneous changes in respiratory parameters.

* This research was supported by a grant from Sporthilfe e.Y., Federal RepUblic of Ger­many.

308 H.-H. ABEL et al.

Mean Values of Heart Rate and Respiratory Parameters as Indicators of the General State of Activity

The most easily accessible indicator of autonomic cardiac control is mean heart rate. In respiratory parameters the basic central drive is expressed by the mean inspiratory flow velocity, corresponding to the slope of the inspiratory neuronal "ramp" recorded in animal experiments (Richter 1982; von Euler 1983).

Physical Rest. A typical time course of relaxation could be observed during a 44-min period registration. In 17 healthy volunteers (23.3 ± 0.4 years old) the electrocardiogram was recorded together with the thoracic and abdominal circum­ferences during quiet sitting. Mean values of R-R intervals and mean inspiratory flows were calculated from consecutive 2-min periods of the experimental runs and plotted as relative changes in the experiment mean value (Fig. 1, left). Mean heart beat interval was determined from the R -R interval of the electrocardiogram. The mean inspiratory flow was computed as relative tidal volume divided by inspirato­ry time from the sum of the thoracic and abdominal signals.

In the course of the resting session, the R-R interval significantly lengthened (p < 0.0001), and mean inspiratory flow decreased (p = 0.0005), as shown by trend analysis (F test, cx=0.05). The inverse time course of these parameters was con­firmed by correlation analysis (Fig. 1, right).

Mental Stress. To elicit autonomic behavioral activation without physical work, 16 healthy volunteers (22.2 ± 0.6 years old) were exposed to three experimental condi­tions. After a pretest period they had to solve tasks during the Advanced Progres­sive Matrices (APM) test followed by a posttest period. Each experimental phase lasted 14 min. The APM test is a nonverbal intelligence test and does not depend

1,2 - R·R --0-- MIF ~

1,2,-----------,0-,.8:-:9""::"15

.0005 r==-p<O

0 (f) -= (J) Ol 1,1 >-c o~ <tl -.l!l .r:: <tl .-() ..... c

1,1 .... f-<

.- ::J (J) a. . > .~~ .~ 1,0 Qi c

1,0

T

I" a: <tl

(J) "I"'T ~

0,9 0,9 +---.,..--~--,--_.,.--_1 0 10 20 30 40 50 0,90 0,95 1,00 1,05 1,10

Time (min) R-R interval (rel.units)

Fig. 1. Left, relative time courses of the R-R interval (R-R) and mean inspiratory flow (MIF) during 44-min physical rest. The mean values of 2-min recording sections were related to the total experimental mean. R-R interval lengthened, and mean inspiratory flow decreased as an expression of a declining cardiorespiratory drive. Right, correlation between concomitant 2-min mean values of mean inspiratory flow and R-R interval

Cardiorespiratory Relations in Human Heart Rate Pattern 309

1,6 APM-test

1,4 r -0.771 - R-R p<0.0005 ---0-- MIF ;:

0 1,3 en 1,4 ;;:: Q) ';::::;; ~. OJ >. c: o~ 1,2 .... d oJ -!!l .c: pre-test oJ .-u 1,2 ..... c: 1 .- :::J Q) 0.. > .~~ 1,1 ~

~ Qi 1,0 c: a: oJ 1,0 Q)

:!:

0,8 0,9 0 10 20 30 40 50 0,8 0,9 1,0 1,1

Time (min) R-R interval (rel.units)

Fig. 2. Left, relative time courses of the R-R interval (R-R) and mean inspiratory flow (M/F) during cognitive performance (14-min pretest, 14-min APM test, and 14-min posttest). The mean values of 2-min recording sections were related to the total experimental mean value. R-R interval shortened, and mean inspiratory flow increased at the beginning of the APM test. Both cardiorespiratory parameters returned toward their pretest values in the course of the ongoing APM test, indicating habituation of the initially enhanced cardiorespiratory drive. Right, corelation between concomitant 2-min mean values of mean inspiratory flow and R-R interval

on education. The volunteers were instructed to select, as quickly as possible, the right segments to complete the graphic pattern of the tasks. Correct results were rewarded by payment. At the beginning of the APM test, the mean inspiratory flow increased, and the R-R interval shortened, as shown by the relative time courses in Fig. 2 (left). During the test period both parameters returned towards pretest levels indicating a gradual release of the initially elevated common car­diorespiratory drive. Thus, under both resting and test conditions, there was a distinct negative correlation between mean inspiratory flow and mean R-R inter­val (Fig. 1; Fig. 2, right). This confirms that both parameters do represent parallel indicators of the central drive.

Central Drive and Heart Rate Pattern

Heart rate control is characterized not only by mean heart rate but also by rhythmic fluctuations in different frequency ranges (Peniz et al. 1968; Sayers 1973). One approach for assessing heart rate rhythmicity is the application of power spectral analysis (Rompelman 1980).

Physical Rest. A three-dimensional diagram of heart rate power spectra derived from one individual during rest was plotted as an example in Fig. 3. The power spectra were computed by fast Fourier transformation from 22 consecutive 122.9-s periods of the R-R interval signal. The spectra exhibited two preferential chains of prominent peaks which change their amplitude and frequency over time: mid-

310 H.-H. ABEL et al.

8 N --en .§. ~ ·iii c:: 4 Q) -0

iii ~ 0- 0

"'

Fig. 3. Intrasubject three-dimensional plot of serial power spectra of the heart rate during physical rest; 22 spectra were calculated from consecutive 122.9-s recording sections of the experimental run. Prominent peaks at about 0.1 Hz and 0.25 Hz waxing and waning over the course of time

frequency (0.05-0.15 Hz) and high-frequency (0.16 - 0.37 Hz). The high-frequency band usually corresponds to the frequency range of the resting respiratory rhythm, the mid-frequency band to a range in which oscillations of other cardiovascular parameters are also found. For quantification of heart rate rhythmicity, the area under the power spectrum in the respective frequency bands was calculated. The mean time courses of the heart rate parameters are depicted in Fig. 4 (left). With decreasing chronotropic drive, as indicated by R-R interval lengthening, the rela­tive time course of the area in the mid-frequency band (p=0.0024) and the area in the high-frequency band (p = 0.0092) rose significantly during 44-min rest (Ftest, a=0.05). The similarity between the time courses of the R-R interval and its rhythmic fluctuations was confirmed by correlation analysis (Fig. 4, right).

Mental Stress. The volunteers which participated in the APM experiment were examined during physical rest 1 week before the start of the APM test to obtain a more valid "resting" control value than during the pretest period immediately before mental stress. The mean value and standard deviation of the R-R interval.

1,3

C/) 1,2

OJ Cl c ('(j 1,1

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Qi a: 0,9

0,8 0

Cardiorespiratory Relations in Human Heart Rate Pattern 311

R-R interval area in the MFB area in the HFB

10 20 30

Time (min)

40 50

>. ()

1,6 r~0.601 c ~ ~ 1,4 CTC/) Q) ."!:: .;: C -a.3 1,2

·E ~ ~ -g 1,0 - ('(j cD

0,8

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>- .....

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0,6 +----.----.----r------I 0,90 0.95 1,00 1,05 1,10

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OJ "0 -5 ~ 0,9 I cD

0,7 +----.----.----.------1 0,90 0,95 1,00 1,05 1,10

R-R interval (rel.units)

Fig. 4. Left, relative time courses of the R-R interval, area in the mid-frequency band (MFB, 0.05-0.15 Hz) and area in the high-frequency band (HFB, 0.16-0.37 Hz) during 44-min physical rest. The mean values of 2-min recording sections were related to the total experi­mental mean. All heart rate parameters increased significantly (F test, p < 0.01). In compar­ison with the area in the high-frequency band, the area in the mid-frequency band showed a more pronounced scattering in the course of the recording session. Right, correlation between concomitant 2-min mean values of R-R interval and those of both frequency band rhythmicities

area in the mid-frequency band, and area in the high-frequency band for the experimental situations physical rest, pretest, APM test, and posttest are graphi­cally presented in Fig. 5. The R-R interval and area in the high-frequency band differed significantly between the 4 experimental situations (Friedman test, 1X=0.05). The changes in the mean values of the mid-frequency band showed a similar tendency, but here the changes were not statistically significant. As expect­ed, the comparison of physical rest with "pretest" rest established significant differences in the R-R interval (p=0.0084). In comparison with the pretest phase, the R-R interval (p=0.0005) and area in the high-frequency band (p=0.0004) decreased significantly during the APM test (Wilcoxon test, IX = 0.05). Also, in this case, the slight decrease in area in the mid-frequency band did not reach the significance level. The lack of significant differences in the area in the mid-frequen­cy band is probably caused by the pronounced scattering. The basic rule of

312 H.-H. ABEL et al.

7 >. 0

p . 0.0007 c: 6 P • 0.1664 Q) :::I

~ r:r 5 Q) ..... ~ m - en -oE 4 ~ ·E :; Q)

E Q) c: 3 .r::. nI

a: _D

ci: c: 2

nI Q)

< 0 rest pre APM post rest pre APM post

4~----------------------~

p • 0.0003

rest pre APM post

Fig. 5. Mean values with standard deviation of the R-R interval, area in the mid-frequency band, and area in the high-frequency band calculated for 44 min rest, 14 min immediately before the APM test (pre) , 14 min performance on cognitive tasks (APM) , and 14 min after the test period ended (post) . Significant differences between the four situations existed in the mean R-R interval and the mean area in the high-frequency band (Friedman test, 1)(=0.05). The diminution in the mean area in the mid-frequency band during the pretest phase and the APM test were not significant

increasing rhythmicity with increasing mean R-R interval could be observed under all experimental conditions: rest, pretest, APM test, and posttest.

Interrelation Between Respiratory and Heart Rate Patterns

There are well-known close interrelations between respiratory and cardiovascular control, manifested in the parallel increase of respiration and cardiac output during physical exercise (e.g. , Astrand and Rodahl 1977). Hirsch and Bishop (1981) demonstrated a strong dependency of "respiratory sinus arrhythmia" on respiratory frequency and tidal volume. Analysis of respiratory rhythm ogene sis in animal experiments has disclosed several different components of the respiratory cycle. Indirect conclusions on the behavior of these components in man can be

Cardiorespiratory Relations in Human Heart Rate Pattern 313

derived from a closer analysis of respiratory movements (Koepchen et al. 1987). Thus, we have studied the relation between components of the respiratory cycle and R-R interval parameters.

Physical Rest. All cardiorespiratory parameters were transformed by dividing the successive 2-min values of each experimental run by its mean value. Then the variables R-R interval, area in the mid-frequency band, and area in the high-fre­quency band were sorted according to five respiratory parameters, each of them subdivided into three classes (Table 1). With the exception of mean inspiratory flow, the respiratory parameters related well to the variable area in the high-fre­quency band (Table 2). However, the area in the mid-frequency band correlated significantly only with respiratory cycle time. With regard to the relationship between the mean R-R interval and its rhythmicity in both frequency ranges, as shown above, the general question arises whether changes in heart rate rhythmicity result primarily from changes in mean R-R interval or from changes in certain respiratory parameters. It is noteworthy, however, that differences in the area in the high-frequency band coincided with simultaneous differences in the variable R-R interval only in the tidal volume classes.

Mental Stress. Table 2 shows that during rest heart rate rhythmicity in both frequency ranges depended primarily on the respiratory frequency because the increase in rhythmicity with a lengthening respiratory cycle time was not accompa-

Table 1. Definition of three classes of the R-R interval and respiratory parameters trans­formed relatively

Class 1 Class 2 Class 3

R-R interval classes

0.80sR-R<0.98 0.98sR-R<1.02 1.02sR-Rs1.20

Respiratory parameter classes

0.70sTI, TE, TT, VT, MIF <0.98 0.98 s TI, TE, TT, VT, MIF < 1.02 1.02 s n, TE, TT, VT, MIF s 1.30

n, Inspiratory time; TE, expiratory time; TT, respiratory cycle time; VT, tidal volume; MIF, mean inspiratory flow

Table 2. R-R interval, area in the mid-frequency band, and area in the high-frequency band, divided into three classes according to each of the five respiratory parameters

Inspiratory time Expiratory time Respiratory cycle time Tidal volume Mean inspiratory flow

R-R interval Area in the mid-frequency band

p=0.1185 p=0.2348 p=0.1000 p<O.OOOl a

p<O.OOOl a

p=0.0527 p=0.1512 p=O.0394 a

p=0.3134 p=0.5230

a 1X=0.05, differences assessed by the Kruskal-Wallis test

Area in the high-frequency band

p=0.0309 a

p=0.0131 a

p=0.0004 a

p=0.0322 a

p=0.5608

314 H.-H. ABEL et al.

1,1 -0-- rest (p:O.OOO5) - pre (p<O.OOOl) - APM (p:O.5295)

1,0 --0-- post (p:O.0838)

0,9

a: d: 0,8

0,7 +---+---+---=--+---='---1 2 3 4 6 20 s

Respiratory cycle time classes

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8,---------,-~__, -0----6 --0--

rest (p<O.OOOl) pre (p<O.OOOl) APM (p:O.0144) post (p<O.OOOl)

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c

C1l C1l

~

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2

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4 -0-- rest(p:O.1218) - pre (p:O.OO17) - APM (p:O.OO55)

3 --0-- post (p:O.0212)

2

n=26 n=12

n=174 n:=:6 n=12

n=41 n:14 n=35 n=34

0+---~~-4---+--~ 2 3 4 6 20 s

Respiratory cycle time classes

Fig. 6. The 2-minmean values of the R-R interval, area in the mid-frequency band, and area in the high-frequency band were sorted into four classes according the 2-min mean value of the respiratory cycle time. Class mean values were calculated for the situations rest, pretest, APM test, and posttest. In each situation, the hypothesis that the class mean values belong to the same population was tested (Kruskal-Wallis test, 1X=0.05). For further explanation see text

nied by corresponding changes in the mean R-R interval. The question arises whether central excitation influences this relationship between the respiratory cycle time and the heart rate rhythmicity. The variables R-R interval, area in the mid-frequency band, and area in the high-frequency band were sorted into four classes according to the 2-min mean values of the respiratory cycle times (2~TT<3 s, 3~TT<4 s, 4~TT<6 s, 6~TT~20 s). Mean values of the respec­tive R-R interval parameters were calculated for each respiratory cycle time class for the four different experimental situations (rest, pretest, APM test, posttest). Figure 6 shows that generally heart rate rhythmicity increases with increasing respiratory cycle time under all of the experimental conditions. During the APM test and the posttest period these significant relationships are independent of the mean R-R interval. The most prominent heart rate rhythmicity in both frequency bands occurred at the longest spontaneous respiratory cycle times when the mean R-R interval was shortened. It should be noted that heart rate rhythmicity in the high-frequency band can be high even when respiratory frequency has left the high-frequency range.

315

Discussion

The well-known dependency of both cardiovascular and respiratory activity on the general state of vigilance (e.g., BUlow 1963) can be seen in the correlated sponta­neous variations in mean R-R interval and mean inspiratory flow during a resting session. The mean inspiratory flow as an expression of the activity of the inspira­tory "ramp generator" proves to be a useful indicator of the actual central respira­tory drive in man.

The common behavioral drive acting on both cardiorespiratory parameters becomes more effective during preparation for the mental test and even more so during the mental test itself. These are two gradual steps towards neurovegetative behavioral adaptation to enhanced activity. This process is also observed for increasing physical performance.

Whether a truly reproducible resting control state exists at all under any exper­imental conditions is questionable since even the expectancy of mental/physical stress produces a reaction in the cardiorespiratory parameters investigated. In man, emotional factors can never be totally excluded; they are accompanied by small subthreshold "preadaptations to exercise." The lowest cardiorespiratory parameter values were found after a resting period of about 30 min. For practical purposes this could represent a sufficient approximation of a resting state.

However, this approach takes into account only the mean value of parameters. A characteristic feature of all cardiorespiratory parameters is their distinct vari­ability, which comprises certain time structures. For example, the ongoing heart rate is modulated by a multitude of superimposed rhythms ranging from periods of a few seconds up to periods of 24 h. Under several of the conditions described, these modulations represent an even more sensitive indication of the central drive than the mean values. In comparison with the calculation of heart rate variability, power spectral analysis is a more differentiated tool to obtain information about the time structure of heart rate fluctuations. Two physiologically significant fre­quency ranges which reflect the short-term chronotropic control are of interest (Akselrod et al. 1981). There is general agreement about the origin of heart rate fluctuations at about 0.25 Hz which normally, but not always, coincide with the frequency range of resting respiration. The 0.25-Hz rhythmicity depends largely on the parasympathetic innervation of the heart: autonomic blockade with atropine extinguishes this kind of heart rate fluctuation (Katona and Jih 1975). It is inter­esting that a special kind of heart rate variability analysis, the computation of mean beat-to-beat differences, correlates highly with the high-frequency band (Eckoldt 1984; Abel et al. 1989). Nevertheless, there is still much controversy about the causal mechanisms of heart rate fluctuations at about 0.1 Hz. It is assumed that sympathetic and parasympathetic mechanisms are involved in heart rate fluctuations in the mid-frequency band, as indicated by the results of mus­carinergic and beta-adrenergic blockades of the heart (pomeranz et al. 1985).

It is well known that heart rate variability correlates positively with the mean R-R interval (Schlomka 1937). The differentiated examination of heart rate vari­ability revealed the result that, with a lengthening mean R-R interval, not only heart rate rhythmicity within the 0.25-Hz range but also in the O.1-Hz range

316 H.-H. ABEL et al.

increased significantly. Thus, a diminution in central nervous activity during rest is accompanied not only by a low mean heart rate but also by pronounced heart rate fluctuations. The enhanced rhythmicity could be interpreted as a continuous interaction between spontaneous endogeneous autonomic events and homeostatic feedback regulation. The autonomic excitations could be assumed to be vegetative components of repeated organized abortive behavior of which the motor compo­nents remained subthreshold.

Because of the close relationship between heart rate and its rhythmicity, it is necessary to relate the rhythmicity to the mean R-R interval when comparing OJ-Hz or 0.25-Hz rhythmiticy between individuals.

The interpretation of the significance of OJ-Hz heart rate rhythmicity is still controversial (Malliani et al. 1986; Abel et al. 1989). When relating the spectral density of the OJ-Hz band to the spectral density of the whole power spectrum, the decrease in mean R -R interval was accompanied by a rise in OJ-Hz rhythmicity during sympathetic activation in humans such as transition from the supine to the erect position. Consequently, the OJ-Hz heart rate rhythmicity was proposed as an indicator of the sympathetic tone (Pagani et al. 1986). In our study it was shown that using absolute values of frequency band spectral density, both the mean R-R interval and the 0.1-Hz rhythmicity decreased during mental strain which enhances sympathetic activity. Similar results were obtained from other cognitive tasks (Mulder and Mulder 1981) and during the performance of physical work. These contradictory results can be attributed to the fact that a moderate rise in heart rate can be caused primarily by a withdrawal of parasympathetic cardiac innervation which leads especially to a diminution in the 0.25-Hz rhythmicity. The increase in the relative 0.1-Hz rhythmicity is caused, to a certain degree, by the decreased absolute 0.25-Hi rhythmicity which reduces the total spectral density. Thus, the OJ-Hz rhythmicity cannot be used as a direct measure of sympathetic tone. One may, however, draw some conclusions using this approach because in most condi­tions decrease in vagal activity is accompanied by an increase in sympathetic activity. In this case, the stronger relative decrease in vagally mediated heart rate fluctuations coincides with an increase in antagonistic sympathetic activity. More­over, the power density in a frequency band of the power spectrum describes the specific time structure of the parameter analyzed in the respective frequency range and not the mean value as suggested by the term sympathetic tone.

In conclusion, during elevated central nervous activity, the increased sympathet­ic and decreased parasympathetic chronotropic innervation of the heart are ac­companied by a diminution in the rhythmic fluctuations in both frequency ranges. These results are in agreement with the general experience that in systems contain­ing several coupled oscillators as the cardiovascular system (Koepchen 1984) regular oscillations usually occur in an indisturbed state in a medium range of excitation. One example of this general rule are the rhythmic contractions of blood vessels in a medium range of tension (Seller et al. 1967; Siegel et al. 1989). Thus, it is not surprising that R-R interval rhythmicity in the OJ-Hz and 0.25-Hz range is a function of the mean R-R interval over a wide range of physiological activity.

The physiological research on cardiovascular-respiratory interrelations has a long history (Koepchen 1962, 1984). Many central nervous interconnections, me-

Cardiorespiratory Relations in Human Heart Rate Pattern 317

chanical factors, reflex feedback mechanisms are involved. The interrelations ob­served in man are caused by a complex interaction of many factors whose individ­ual contributions can only be studied in animal experiments. Such studies have led to and will continue to lead to a greater understanding of phenomena found in the human being. Man, however, must be regarded as a whole and studies in the human subject should embrace the physiological and pathophysiological meaning of the continuous signals given by the entire neurovegetative system. This requires the development of new methods for the quantitative treatment of complex sys­tems. The analysis of the time structure of cardiorespiratory parameters by means of power spectra represents one step in this direction.

Acknowledgement. We thank Ms. C. Mahoney-Heidelmeyer for assisting with the preparation of the English mansucript.

References

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Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC, Cohen RJ (1981) Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardio­vascular control. Science 213:220-222

Astrand P-O, Rodahl K (1977) Textbook of work physiology. McGraw-Hill, New York BUlow K (1963) Respiration and wakefulness in man. Acta Physiol Scand 59: (suppl) 209 Eckoldt K (1984) Verfahren und Ergebnisse der quantitativen automatischen Analyse der

Herzfrequenz und deren Spontanvariabilitat. Dtsch Gesundh-Wesen 39:856-863 Euler C von (1983) On the central pattern generator for the basic breathing rhythmicity. J

Appl Physiol 55: 1647 -1659 Hess WR (1949) Das Zwischenhirn. Syndrome, Lokalisationen, Funktionen. Schwabe, Basel Hirsch JA, Bishop B (1981) Respiratory sinus arrhythmia in humans: how breathing pattern

modulates heart rate. Am J PhysioI241:H620-H629 Katona PG, Jih F (1975) Respiratory sinus arrhythmia: noninvasive measure of parasympa­

thetic cardiac control. J Appl Physiol 39: 801- 805 Koepchen HP (1962) Die Blutdruckrhythmik. Steinkopff, Darmstadt Koepchen HP (1984) History of studies and concepts of blood pressure waves. In: Miyakawa

K, Koepchen HP, Polosa C (eds) Mechanisms of blood pressure waves. Japan Sci Soc, Tokyo; Springer, Berlin Heidelberg New York Tokyo, pp 3-23

Koepchen HP, Abel H-H, KliiBendorf D, Lazar H, Semmler H (1987) Applicability of concepts of respiratory rhythmogenesis deduced from animal experiments to respiratory control in humans. In: Sieck GC, Gandevia SC, Cameron WE (eds) Respiratory muscles and their neuromotor control. Liss, New York, pp 103-107

Malliani A, Lombardi F, Pagani M, Cerutti S (1986) The problem of approaching the sympathetic and vagal "tone". J Auton Nerv Syst (suppl) 191-196

Mulder G, Mulder LJM (1981) Information processing and cardiovascular control. Psycho­physiology 18: 392-402

Pagani M, Lombardi F, Guzzetti S, Rimoldi 0, Furlan R, Pizzinelli P, Sandrone G, Malfatto G, Dell'Orto S, Piccaluga E, Turiel M, Baselli G, Cerutti S, Malliani A (1986) Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho­vagal interaction in man and conscious dog. Circ Res 59: 178 -193

Penaz J, Roukens J, Waal HJ van der (1968) Spectral analysis of some spontaneous rhythms in the circulation. Biokybernetic I: 233 - 236

318 H.-H. ABEL et al.: Cardiorespiratory Relations in Human Heart Rate Pattern

Pomeranz B, Macaulay RJ, Caudill MA, Kutz I, Adam D, Gordon D, Kilborn KM, Barger AC, Shannon DC, Cohen RJ, Benson H (1985) Assessment of autonomic function in humans by heart rate spectral analysis. Am J PhysioI17:H151-H153

Richter DW (1982) Generation and maintenance of the respiratory rhythm. J Exp BioI 100:93-107

Rompelman 0 (1980) The assessment of fluctuations in heart-rate. In: Kitney RI, Rompel­man 0 (eds) The study of heart-rate variability. Clarendon, Oxford, pp 59-77

Sayers BM (1973) Analysis of heart rate variability. Ergonomics 16:17-32 Schlomka G (1937) Untersuchungen tiber die physiologische UnregelmiiBigkeit des

Herzschlages. III. Mitteilung. Uber die Abhiingigkeit der respiratorischen (Ruhe-)Ar­rhythmie von der Schlagfrequenz und vom Lebensalter. Z Kreislaufforschg 29: 510-524

Seller H, Langhorst P, Polster J, Koepchen HP (1967) Zeitliche Eigenschaften der Vasomo-torik. II. Erscheinungsformen und Entstehung spontaner und nervos induzierter GefiiBrhythmen. Pflugers Arch 296:110-132

Siegel G, Hofer HW, Schnalke F, Adler A, Walter A, Koepchen HP (1989) Membrane physiological basis of vascular autorhythmicity. Prog Appl Microcirculation 15:10-31

Concluding Lecture

Thoughts Concerning the Essence of Life: Integrative Power and the Governance of Function C. McC. BROOKS

Preliminary Remarks

Although this symposium has dealt chiefly with cardiorespiratory and motor coordination, its major objective has been integrative thought. The emphasis of the recent era has been on analysis and reductionist thought. While this has been very rewarding, its assumption has seemingly been that the secret of our existence is to be found in relative minutia. We should not denegrate these efforts, but recently it has seemed to many that the secret oflife is to be found rather in its totality. The new need is for consideration of integrative power and the essence of the totality. Our theme here has been a consideration of the operation of integration as essen­tial to the totality.

I am sure that I speak for all when I acknowledge and thank Profs. Hans Peter Koepchen and Timo Huopaniemi for providing, organizing, and managing this meeting of 1989 in Espoo, Finland. It has been pleasant, intellectually stimulating, and informative. The only need now is to put it in perspective relative to the future; questions worthy of further consideration have been raised.

Introduction

Small arctic animals called lemmings periodically congregate and rush into the sea, either in search of a land that no longer exists or perhaps so that future lemmings may continue to exist in a land of meager resources. Periodically, physiologists congregate and migrate often to far-away lands but not to effect self-extermina­tion. What are physiologists and what do they seek to accomplish by their migra­tions?

A migration of physiologists occurred 54 years ago in which many came through Helsinki bound for a meeting in Leningrad and Moscow. For me the ghosts of many of those who attended that meeting have accompanied me here: Abder­halden, Lord Adrian, Detlev Bronk, A. V. Hill, Walter B. Cannon, Kato, with his white-gloved assistants to demonstrate dissection of single nerve fibers, and many, many others now gone but still our companions. They came to see and to honor the great Pavlov. They came as we do now to meet friends, often those known to us only as names, to present our ideas and findings, and to learn the thoughts of

322 C. M. BROOKS

others who travel by our way of life. By coming here we have, at least for the moment, identified ourselves as physiologists.

A physiologist is one who studies life because life is a flow of functions, integrat­ed and controlled to attain certain purposes, the major one of which is the contin­uation of life. But what is life? We recognize life from observing its primary characteristics. The study and description of these characteristics has provided the major channels of thought for both biology and medicine. What are they?

The Major Channels of Physiological Thought

There are at least five major channels of thought oriented toward the study oflife's major characteristics.

First, life is characterized by irritability, defined as the ability to detect imping­ing forces (excitability) and to respond to them in an appropriate fashion (respon­siveness). This is the major focus of neuroscientists, those who study cells, neurons, and the brain. They supposedly deal with control systems and behavior or compo­nent fields.

A second and possibly a more fundamental characteristic is that of metabolism. Metabolism means change, but usually we use the word to indicate energy trans­formation and consumption: anabolism and catabolism. Metabolism involves the conversion of inorganic and organic materials into living matter: protoplasm. It powers life's functional activities which depend on energy conversions. In a sense, the study of metabolism is what we are engaged in here at this symposium because the body's metabolism involves three major systems: the digestive, responsible for procurement and handling of material essential to the metabolic need; the respira­tory system, which acquires the oxygen essential to the metabolic action, and which eliminates CO2; and the cardiovascular system, essential to the delivery of needed materials to body tissues. It also transports unusable residues to sites of elimination. In this symposium we have dealt with the relationship of only two of the three: the cardiovascular and respiratory systems, their rhythms, and their integrative control. Incidentially, why do we seem to think that the occurrence of rhythms is so remarkable? We operate by pumps, and this requires rhythms; if there are no rhythms, there is no life.

A third channel of thought is that of reproduction. By reproduction I refer not only to sexual reproduction and the endocrine functions involved but also to other functions. The endocrine system is a complex that promotes or regulates both metabolism and reproduction. Reproduction really involves differentiation, devel­opment, production of tissue, as well as production of other individuals. Certainly duplication is an ultimate achievement, but reproduction is involved in most functions of and in the maintenance of the totality. Endocrinology, popUlation control, obstetrics, and pediatrics are a few of the components of this major channel of thought.

A fourth channel of thought can be termed the study of adaptability. Certainly adaptability is a requirement for excistence in our ever-changing environment.

Thoughts Concerning the Essence of Life 323

Adaptation must meet two requirements: adaptation to maintain essential con­stancies and adaptation to meet new requirements. It is obvious that genetics and evolution are components of this concept of adaptation. Of course, examples of adaptation occur in the other major channels of thought already mentioned. The neurophysiologist studies accommodation; others study the development of the body to meet special requirements; sports, combat, economic gain. Adaptation involves the final channel of thought, one that concerns us at the present time: integrative control.

This fifth concept of integrative power is certainly a major, if not the major, secret of life. A cell is a highly integrated unit that can manufacture and sustain the membrane which confers its excitability and triggers its characteristic response. A cell alone does nothing - only integrated masses of cells are of functional significance as tissues. Tissues of various properties constitute organs; organs are components of systems which are integrated to produce individuals and to convey life and potentials for behavior.

All of us, as physiologists, know this. However, do we really realize that in this symposium, although we have discussed integration, we have considered only a small proportion of what occurs in life? To deal fully with this channel of thought we would have to consider digestion, transport, and transfers of materials as well as the integration of the respiratory and cardiovascular pumping systems. Integra­tive physiology requires the study of totalities, individuals, and their interaction with societies and cultures.

Those who engage in integrative, behavioral thought must select some species for their study. Most of us deal with invertebrates or "lower" vertebrate orders. Ultimately, since we are man, our goal is the nature of the life and the control of the behavior of man. What are the essences of man's life and his performances? For that matter, what is the essence of physiology?

The Nature of Man, the Mind, and the Soul of Man

Man is probably the most sophisticated complex, creatively evolved by the life force. Man is an animal that for some reason began to stand erect and, although tottering in age and extreme youth, can move quickly and accurately. In many ways he is physically inferior to other species; he cannot run as fast as a horse, he lacks the olfactory keeness of a dog, he does not have the ability of the hawk or owl to see; yet by standing he frees his forelimbs for uses other than locomotion and developed hands. With hands and a brain man has empowered himself to control or destroy all other living things and possibly the biosphere and even mankind itself. Today we are actually confronted by this latter possibility. What we will eventually do seems to depend upon what we call the mind and soul of man.

The totality of the individual constitutes the mind. To be sure, we think of the brain as the essential seat of the mind, but the brain is not an isolated unit. The brain contributes perception and reason, the ability to think and decide. But is the powers of memory, our fund of knowledge, and our power of reason enough? Is behavior determined by these?

324 C. M. BROOKS

Certainly the major diseases of our day are social diseases, neither controlled nor avoided by powers of reason, although reason is involved. These diseases are addiction, crime, perversion, greed, AIDS, stress - all interrelated in cause and ultimate effect. The prevention and cure is or could be an ethic which is heeded.

We scientists normally do not accept responsibility for dealing with two of the most unique features of man: his ability to define ethical behavior and his concept of the existence of a power greater than his own. The latter as defined provides the major basis for ethical decision. Adherence to the two together seems to determine the quality of life, and we can not escape involvement. In the United States, committees are determining whether or not men of science are adequately govern­ing themselves. On the basis of statistics which I have seen, we can, because there are only 77 cases of dishonesty on record, and this is insignificant in the context of so many thousands of us. Do you accept the verdict? Is there a quality or essence of man which is identified by the word "soul?" This is a question foreign to our ears, but what is our essence, our fundamental objective, and obligation?

The Essence and Objectives of Physiology

Is it enough to say that our objective is to know, to understand, and to convey to others what we have learned? Our technical skill, our reductionist thought have produced factual information about the world, animate and inanimate, never dreamed of before. Having sought to know, we have produced an avalanche of information which is so vast that we must stress our powers to engage in the integrative thought that converts information into knowledge, knowledge of a life and knowledge for man.

Despite the fact that the Greeks of ancient days knew little of what we know today they had an understanding of mankind. In their stories, half myth and half truth, they personalized the essences oflife. Thus, long ago they described one who illustrated the essence of what I think we are. I speak of Ulysses as described by Tennyson. Ulysses in speaking to his comrades in life said:

I am become a name, For always roaming with a hungry heart.

And this gray spirit yearning in desire To follow knowledge, like a sinking star, Beyond the utmost bound of human thought.

Come, my friends, 'Tis not too late to seek a newer world. Push off, and sitting well in order smite The sounding furrows: for my purpose holds To sail beyond the sunset, and the baths of all the western stars, until I die. It may be the gulfs will wash us down:

Thoughts Concerning the Essence of Life 325

It may be that we shall touch the Happy Isles, And see the great Achilles, whom we knew.

Though much is taken, much abides; and though We are not now that strength which in old days Moved earth and heaven, that which we are, we are, One equal temper of heroic hearts, Made weak by time and fate, but strong in will To strive, to seek, to find, and not to yield.

This is our essence - to seek and to find. It is not so much the finding as it is the search which determines the nature of our life, our soul.

Conclusion

In conclusion, I would say: we are the men and women of this day, but the ghosts of those who have gone before us are with us now. Our destiny is as was theirs: to search beyond the bounds of human thought and not to yield.

Subject Index

IX-blockade 183, 184 IX-motoneurones 185 Al-area 90, 94, 95, 97 A5-area 93 abdominal sympathetic nerves 121 abdominal vagus 69 abnormal cardiovascular function 111 acetylcholine 92, 115, 254 acid-base parameters 303-305 acidosis 53, 302, 305 active expiration 46, 120 acupuncture 111, 116 adiabatic parameters 244 adrenal denervation 185 - medulla 44, 46 - sympathetic nerves 40, 44, 121 adrenaline 44, 47, 182 adrenergic innervation 181, 182 - neurones 89, 91, 92, 95-97, 198 adrenomedullary function 39 afterhyperpolarization 54, 55, 57 age 300 amphibians 119, 126 analgesia 115 antidromic activation 106-109,206 - latency 106-108, 113 aortic depressor nerve 60-63, 65, 114 apallic syndrome 266, 268, 269, 272 aperiodic dynamics 240 apnea 124, 125, 203, 271, 281 Arnold tongues 232,234,240,241 arousal reaction 89, 92, 116, 270, 274 arterial baroreceptors 7, 91, 103, 123, 124 - blood pressure 26, 29, 31, 33, 37, 61,

62, 66, 68, 69, 90, 91, 96, 97, 103, 113, 124, 125, 131, 132, 135, 138, 149, 152, 159, 168, 176, 188-190, 192, 221, 253-256, 280, 283, 301

- occlusion 287, 289 ascending reticular activating system

(ARAS) 158 asphyxia 16, 17, 19, 20, 23, 124 atropine 113, 114, 116, 253-255, 269,

273, 315

attractor 227, 228, 230, 231, 241-243 auto-correlation, auto-covariance 10, 12,

13,23,27,31,139,144,147-149,151, 154, 165, 244, 260, 261, 263, 264, 269, 273, 292

P-blockade 183, 184, 291, 315 p-endorphins 305 baroreceptor( s) - afferents 9, 26, 27, 29, 31, 41, 60, 63,

65, 95, 114, 116, 121, 123, 124, 159, 162, 196, 198, 253, 257, 274, 279

- arterial 7, 91, 103, 123, 124 - carotid 254 - low-pressure 279, 283 - reflex 46, 61, 62, 64, 66, 67, 77, 89, 93,

104, 106, 107, 111, 114, 115, 124, 131, 138, 160, 195, 253-257, 277, 278, 283, 295

- reflex gain 295, 297 - reflex responsiveness 257 - sensitive neurones 114 basal forebrain 92 basal ganglia 147 basins of attraction 227, 241, 242 beat-to-beat variation 261, 315 behavioral adaptation 39, 138, 158, 162,

167-169,172,175,225,272,307,315 - components 108 - organization 228, 229, 231, 235, 236 bifurcation 224,228-230,241 biological noise 226 birds 119 bistability 232 blood flow - - coronary 209 - - cutaneous 283-286, 288, 289 - - femoral 112 - - muscular 188-191 - - oscillations 287 - - peripheral 126, 185, 209 - - renal 111,209 blood pressure - arterial 26,29,31,33,37,61, 62, 66,

328 Subject Index

68, 69, 90, 91, 96, 97, 103, 113, 124, 125, 131,132,135,138,149,152,159,168, 17~ 188-190, 19~ 221, 253-256, 280, 283, 301

- rhythms 153, 155, 159, 168,265, 272-275,277-279,282,283,291,292

- variability 278, 282, 291, 293-296 body temperature 72 Botzinger complex 92 bony fish 119 bradycardia 46,61-63,114,116 brain death 265, 269 brain disorders 266, 269 brainstem 71, 72, 74, 75, 97, 103, 108,

109,118,126,136,144,172,175,176, 193, 195, 208, 265, 26~ 272-274, 307

- common system 158-162,164,167, 168,171,172,175

- neurones 120, 131, 138, 186, 241, 269

- networks 138, 139, 141, 142 - pontine 201, 202, 206 - transection 147, 152, 266, 268 - - caudal medullary 271 - - midbrain 176 - - midcollicular 93, 103, 138, 266 - - midpontine 150, 151 - - pontomedullary 267, 268 - - postcollicular 114 - - precollicular 200, 201, 205, 266 breathing - gill 118, 119, 121, 126 - fetal 259,261,263 - Cheyne-Stokes 280 - laryngeal 119, 121, 126 - lung 118, 119, 121, 126, 127 - pattern 119,239 - rapid shallow 120, 128 brown adipose tissue 72 bulbospinal neurones 91, 96, 97, 121,

126, 127, 142, 175 bulbospinal projections 131

Cl-area 90,91,93,95-98, 198 calamus scriptorius 95 calcitonin gene-related peptide

(CGRP) 77, 78, 80-82, 84 capsaicin 195 cardiac - aliasing 275 - arrhythmia 111, 116 - modulation of sympathetic activity 33,

144, 257 - output 3, 111, 126, 192, 279, 312 - receptors 37 - related neuronal activity 131, 138

- rhythm in sympathetic discharge 9, 33, 34,36, 37, 93, 95, 104, 124,256

- sympathetic nerves 33, 40, 43, 121, 125, 138, 294

- vagal activity, cardiac vagal con-trol 256, 278, 279, 291, 294, 297, 305, 315, 316

- vagal blockade 300, 315 - vagal nerves 43, 46, 121 cardioacceleration 46 cardioaccelerator sympathetic fibers 34,

35, 37 cardiopulmonary - afferents 3, 33, 89, 95 - integration 89 - reflexes 93, 96, 98 cardiorespiratory - activity 53 - center 300 - coordination, coupling 221, 253, 265,

268, 269, 271, 272, 274, 275, 279, 280, 296, 297, 307, 312, 316

- control 53, 58,65, 123, 126, 131, 136, 155, 176, 181, 193,202,209,213,279, 307-309,312

- function 118,119,126 - network 118,119,126,131,176 - neurones 131, 132, 149 - response 209, 211, 213 cardiovascular - control 135,138,153,158,171,188-

191, 208, 211, 27~ 283, 291, 300 - network 118 - neurones 154, 155, 159,269 - responses 93, 116, 138, 190, 191, 193-

195, 198 - rhythms 147,148,153,155,239,277,

297, 310, 316 - system 118, 121, 128 - tone 138 carotid - baroreceptors 254 - body 60, 66, 84, 98 - sinus 77, 93 - sinus nerve 41,43, 60-63, 65-69, 256 catastrophe theory 248 catecholamines 115, 182, 184, 305 - synthesis 83 cat 60,98, 103, 104, 116, 121, 123, 126,

147, 291, 293 central - chemoreceptors 43, 53, 57, 89, 279 - chemosensitivity 54, 57, 58, 96, 206 - cholinergic system 111, 113, 114 - command 181, 190-193, 208, 209, 300,

305

- coupling 33 - drive, state of activity 172,307-309,

315 - organization 141, 142, 147, 150, 155,

158, 161, 163, 164, 166, 169, 171, 172, 175-177,195,274,275

- pattern generator (CPG), pattern gener-ation 224-230, 234, 235

- respiratory activity 121 - sleep apnea 206 - sympathetic nerve-related activity 107-

110, 142, 144, 209, 211 cerebral - cortex 71,92,147,153,175 - ischemia 16, 23, 24, 89 - vasodilatation 89 cervical sympathetic nerve 153, 183, 184 chaos 244 chaos theory 239, 241 chaotic dynamics 240 chaotic mode 243 chemical oscillations 217 chemodenervation 44, 47 chemoreceptor( s) - afferents 60, 65, 95, 124, 159, 163 - central 43, 53, 57, 89, 279 - peripheral 41, 43, 44, 46, 65, 77, 89,

96, 98, 123, 279 - reflex 61,63,67, 96, 111, 115, 279 chemosensitive neurones 53, 54, 55, 57,

58,96. Cheyne-Stokes breathing 280 cholecystokinin 77, 78, 82, 84 cholinergic receptors 255 circaannual rhythm 246 circadian rhythm 246 circle map 231 CO2 3, 16, 26, 33, 40, 43, 44, 49, 53-57,

111, 118, 119, 139, 149, 150, 152, 153, 206,281

CO2-sensitive neurones 58 coherence 13, 19-23, 49, 176, 256, 260,

263, 265, 269, 271, 273, 275, 293, 295 cold defense reaction 71, 73, 74 cold receptors 71 cold responsive neurones 72, 75 cold shivering 71, 73, 74 colon temperature 72 colored noise 243 common brainstem system (CBS) 158-

162, 164, 167, 168, 171, 172, 175 complex systems 217, 218, 221, 222, 227,

235,317 commissural - region 65 - nuleus 95

Subject Index 329

contour map 244 control parameter 218,220,226-228,

230, 232, 248 coronary - blood flow 209 - heart disease 111, 116 - perfusion 7 correlation dimension 221 coupled oscillators 141,176,177,230-

232, 240, 249, 316 coupling function 228, 269 coupling term 221 cranial nerves 60 crayfish 119 critical fluctuations 221 cross-correlation, cross-spectrum, cross-

covariance 27, 132, 135, 139, 147, 150, 154, 164-168, 260, 263, 264, 293, 295 .

current clamp 54 Cushing reaction 16, 17, 21, 24 cutaneous - blood flow 283-286,288,289 - thermoreceptors 283 cycle triggered histogram 132 cycle-triggered pump 26, 30, 31

decerebrate rigidity 200 decerebration 280, 291, 294 decortication 114 deep peroneal nerve (DPN) 111-113,115 defence - areas 111, 112 - reaction 89, 108, 111, 115, 116 - - related neurones 113, 115 degrees of freedom 217, 221 depressor response 93, 108, 111, 113,

116 desynchronized sleep 16, 116 deterministic 24 deterministic chaos 218, 229, 232, 234,

239,248 diabetic autonomic neuropathia 265, 291,

294 diaphragm 119,120,201-205 dissipative systems 234 diving reflex 294 DL-homocysteic acid 114 dorsal tegmental field 92,200-206 dog 111, 123, 147, 159,253,271,275,

291,297 dopamine 125 dp/dtmax 112 dynamical diseases 246

E2 phase 120 eigenfrequency 228

330 Subject Index

electrocardiogram (ECG) 159,221,257, 259,270, 291, 292,294,295, 301, 308

electrocorticogram (ECoG) 147 -153 electroencephalogram (EEG) 147, 158,

159,164,175,176,221 electromyogram (EMG) 159, 190, 200-

203 emergency reaction 159, 163, 164, 167,

172 emotional reactions 186, 265, 272, 315 enddiastolic filling 278, 279 enkephalin 92,96, 115 entrainment 31,93, 153, 155, 229,231,

256, 257, 284, 285 entropy 244 ergo tropic state 307 evolution 39 evolution equation 218 excitatory amino acids 195,211,213,254 exercise 116,172,181,184,190,192,193,

208,211,213,294,295, 300, 301, 303, 305,312, 315, 316

- pressor reflex 193 expiration 119, 120, 126 expiratory muscles 120 - neurones 41, 92 - phases 120

fast Fourier transform (FFT) 9, 243, 260, 284,291, 309

fetal breathing movements 259, 261, 263

fetal lamb 259, 263 field potential 201 fish 126 flicker noise 243 flight 225 fluctuating forces, fluctuations 218, 221,

224, 248 fluorescence microscopy 78 florebrain 265, 269, 272, 273 Fourier analysis 277 fractal dimension 244 free running oscillator 23 frequency histogram 291

GABA 90,92,208,209,213 gaits 219, 249 ganglion - petrose 77, 78, 80-84 - nodose 77, 78, 80-82, 84 - stellate 20,22, 33-35, 104, 108, 139,

141, 142, 144 gas exchange 118, 119 gastrointestinal afferents 95 gene expression 77

gestation 259 gill breathing 118, 119, 121, 126 gill/laryngeal rhythm generator 126 gill rhythm 126 glia cells 57, 98 glossopharyngeal motoneurones 121 glossopharyngeal nerve 65, 69, 83 glutamate 40, 41, 43, 46, 67 -69, 90-92,

98, 104, 116 - antagonists 69 glutamic acid decarboxylase 90 glutaminase 92 glutaminergic neurones 95 glycine 96, 97 gravitational clustering analysis 132 group II afferents 186 group III afferents 111,115,181,194,

196 group IV afferents 111,116,141,194,

196 guinea pig 71- 73, 75

halothane 125 heart - cells 230 - failure 253, 256, 257, 280 heart period duration 253-256, 259 277,291-293,302,305,308-316 - - -arterial pressure relation 295, 297 heart rate 3, 4, 7, 34, 47, 61, 97, 118,

138, 144, 192, 209, 211, 259, 261, 264, 265,268,272,278,295, 300-302, 304, 305, 308, 315, 316

- - instantaneous 259 - - pattern 307, 309, 312 - - rhythmicity 265,266,271-275,277,

278, 291, 29~ 30~ 30~ 311-316 - - variability 259, 261, 264, 278, 292,

294-296, 300, 302, 305, 307 heat defense reaction 74 high-frequency-oscillation (HFO) 11, 13,

26, 175, 241, 245 H+ -ions, pH 53, 54, 57, 58 hippocampal neurones 67 Holter tape 294 homeostasis, autoregulation 118, 158,

200, 307 horseradish peroxidase 60, 65, 91, 95, 98 hybridization 77, 78, 82 hypercapnia 23,24,39-41,43,44,46,

53, 54, 56, 57, 139, 141, 150, 159 hypertension 39,46, 67, 89, 93, 111, 116,

281,294,295 hyperthermia 74 hyperventilation 203 hypocapnia 150

hypotension 67,111,113,294 hypothalamus 71,92,103-111,147,208,

209, 213, 272, 283 hypoxia 23,24, 39, 40, 41, 43, 44, 46,47,

61-63,65,66,69,89,124,159 hysteresis 220, 230, 232, 240, 248

immunocytochemistry 91,97 immunoreactivity 77 information 217, 231 information compression 229 inhibitory postsynaptic potentials

(IPSP) 123 input resistance 54, 56 insomnia 116 inspiration 119, 120 inspiratory intercostal muscles 120, 201,

203-205 - motor nerves 13 - muscles 119 - neurones 13, 92, 175, 120-128 - off-switch 128 - phase 119 instability 235 integer frequency relationship 124, 229,

231, 232, 234 intermediate area 89, 90, 96-98 intermittency 246 interspike interval histogram 144, 164,

165, 166, 167 interval tachogram 292-294,296 intracellular recordings 53, 54, 57, 121,

123 intracentral stimulation 104, 106-110,

112,203-206,208,211,213,248 intracranial pressure 16, 20, 21 intrafusal fibres 182 intrathoracic pressure 241, 279 irradiation 121 irregular dynamics 241

jaw closing muscles 181, 185 - opening reflex 183 kainic acid 65-67, 69, 93 K + -conductance, K + -channels 56, 57 Kolliker-Fuse nucleus 11, 92 kynurenic acid 195

lactate 302 - 305 laryngeal abductor motoneurones 126 - adductor muscles 120 - adductor neurones 123 - breathing 119, 121, 126 - motoneurones 119, 121 - muscles 119, 120 - nerves 119

- network 124 - receptors 124 - rhythm 125, 126 larynx 120, 125 laser 217, 218

Subject Index 331

lateral tegmental field 90, 92, 103 -11 0, 142, 144

learning 231 left ventricular pressure 112, 113 lidocaine 114 limbic system 147, 158, 186 limit cycle 227, 231, 241 Ljapunow exponent 244 lobster 226 locomotion 200, 217 locomotor control 208, 209, 213 - rhythm 200, 209, 225 locus coeruleus 71, 185 low-pressure baroreceptors 279, 283 lung breathing 118, 119, 121, 126, 127 - inflation 7, 11, 27, 29-31, 49, 239,

240

mammals 119, 121, 126 many neuron recordings 131, 158, 159,

163,168,176 mathematical approaches 217, 218 Mayer waves 279 mean inspiratory flow 308, 309, 313, 315 medium-frequency oscillation (MFO)

241 medulla oblongata 53, 60, 77, 90, 91,

106, 107, 109, 113, 124, 131, 142, 144, 159, 164, 175, 195,209,213,268,279

- - dorsal 55, 57, 91 - - rostral ventrolateral (RVLM) 40,41,

46,89,90-98,103-110,112-116,131, 132, 142, 176, 195, 196

-.~ ventral surface 40, 53, 89-91, 93, 96-98, 112

- - ventrolateral 49, 57, 58, 90-96, 131, 13~ 13~ 19~ 19~ 198, 213

medullary neurones 49, 132, 136, 209, 211,213

- slices 53, 54 membrane conductance 54, 56, 176, 177,

185 - oscillation 232 memory 231 mental load 265, 272, 273, 294, 308, 310,

313, 315, 316 mesencephalic periaqueductal gray 109 - trigeminal nucleus 183, 184 mesencephalon 82, 84, 248 messenger RNA (mRNA) 77, 78, 80-83

332 Subject Index

metabolic responses 71, 74, 75 metabolites 196 metabolic control, metabolism 300-302,

304, 305 metaboreceptors 305 micro iontophoresis 75, 98, 104, 113,

254 microscopic chaos 218 midbrain 111,113 minipig 124 mode-locking, frequency locking 221,

229-232,234 monaminergic pathways 185 mono stability 234 monosynaptic reflexes 186 morphine 112, 113, 115 morphogenesis 217 ()(-motoneurones 185 motoneurones - glossopharyngeal 121 - laryngeal abductor 126 - laryngeal 119, 121 - pharyngeal 119 - phrenic 95, 121, 126 - respiratory 89, 92, 98, 126 - spinal 119, 185, 186 - spinal respiratory 127, 147 - vagal cardiac 252 motor movements 219,221,269,248 - responses 185 - rhythms 224 multifrequency pattern 234 multifunctional neurones 175 multifunctionality 224 - 226, 228 multistability 224, 227 - 229 muscarinic receptors 254,255, 281, 315 muscle(s) - abdominal 119 - caudofemoralis 188, 189 - digastric 182-184 - expiratory 120 - gastrocnemius 188, 189 - genioglossus 205 - inspiratory intercostal 120, 201, 203-

205 - inspiratory 119 - internal intercostal 41 - jaw closing 181 - laryngeal adductor 120 - laryngeal 119, 120 - masticatory 182, 185 - respiratory 120, 131 - sceletal 181, 182 - soleus 188, 189,200, 202-205 - thoracic 119 - triceps surae 209

muscle spindle 181,182,185 - sympathetic nerves 253, 255, 256, 257 - tone 172, 200, 203 muscular afferents 211 - blood flow 188-191 - contraction 182-185,188-196,208,

209,211, 213 - - volontary 190 - fatigue 189, 300-303 muskrat 46 myocardial infarction 294

naloxone 112, 113 Na nitroprusside 26, 27 neck pressure 255 neck suction 253, 255 neonatal lamb 259, 261, 263 neonates 26, 32, 268 neostigmine 113 nerve(s) - aortic depressor 60-63, 65, 114 - carotid sinus 41, 43, 60-63, 65-69,

256 - cardiac vagal 43, 46, 121 - cranial 60 - deep peroneal 111-113,115 - glossopharyngeal 65, 69, 83 - hypoglossal 201, 204 - inferior cardiac 16,17,19-22,34,35,

104, 108 - intercostal 119 - laryngeal 119 - phrenic 9, 11,26,27,29, 31, 32, 34,

37,40,41,43,96, 119, 120, 124, 1-25, 131, 13~ 148, 150-15~ 15~ 159, 17~ 240, 241, 248

- recurrent laryngeal 26,27,29, 31, 120, 125

- sciatic 111 - splanchnic 26, 27, 31, 91, 121 - superficial peroneal 113, 114, 116 - superior laryngeal 62 - vagoaortic 33, 37 - vagus 33-35, 37, 38, 93 network bifurcation 241 network oscillator 144, 176, 240 neural assemblies 131, 135 - circuits 23, 24 - networks 22,24, 31, 58, 75, 135, 147,

153,155,161,162,167-169,176,177, 224, 226, 229, 235, 300

neurobiological dynamical systems 224-231,234, 235

neurokinin A (NKA) 77, 78, 80, 82 neuromodulator 84, 226 neuromuscular blockade 184,209

neurones - adrenergic 89, 91, 92, 95-97, 198 - baroreceptor sensitive 114 - brainstem 120, 131, 138, 186,241,269 - bulbospinal 91, 96, 97, 121, 126, 127,

142, 175 - cardiorespiratory 131, 132, 149 - cardiovascular 154, 155, 159,269 - chemosensitive 53, 54, 55, 57, 58, 96 - CO z-sensitive 58 - cold responsive 72, 75 - defence reaction related 113, 115 - expiratory 41, 92 - glutaminergic 95 - hippocampal 67 - hypothalamic 211 - inspiratory 92, 175 - laryngeal abductor 126 - laryngeal adductor 123 - medullary 49, 132, 136, 209, 211, 213 - multifunctional 175 - noradrenergic 185, 186 - pacemaker 92 - preganglionic sympathetic 49, 91, 92,

95,98, 123, 126, 138, 147, 177 - propriobulbar 121, 123 - respiratory 67, 120, 121, 123, 124,

126-128,136,152,154,155,159,176, 241

- reticular 49, 159, 167 - reticulospinal 91, 92 - sympathoexcitatory 45,49, 91,

103-108,110,136,176 - sympathoinhibitory 106, 108, 136 - vagal cardioinhibitory 123, 126, 128,

147, 254 - vasomotor 93 - warm responsive 72, 75 neuronal connectivity 138, 145 - cooperativity 132 - rhythms 164, 165-168, 175, 176, 198,

229, 239 neuropeptides 77, 84, 92 neuropeptide Y (NPY) 78, 80-84, 305 neurotic patients 272 neurotransmitters 75, 77, 84, 91, 92, 96,

136, 226, 227 neurovegetative disorders 273 newborns 265, 275 nocireceptor afferents 161, 165 nodose ganglion 77, 78, 80-82, 84 no-inflation test 11, 12, 27, 29, 31 nonequilibrium systems 225 - 227 nonlinear dynamical systems 225, 226,

229, 231, 248 - dynamics 224, 228, 235

Subject Index 333

- feedback 239 - oscillator 229, 246 non-shivering thermogenesis 73 noradrenaline 44,47, 111, 185, 186, 256 normocapnia 17, 21 nucleotides 78 nucleus ambiguous 43, 90, 195 - amygdalae 168, 272 - arcuatus (ARC) 113, 115, 116 - caudoventrolateralis (CVL) 89, 90, 93,

96 - facialis 90, 91 - Kiilliker-Fuse 11,92 - parabrachialis (medialis) 11, 185 - paragigantocellularis lateralis

(NPGCL) 89,90, 113 - paraolivaris 90 - mesencephalicus trigemini 183, 184 - phrenic motor 94, 241 - pontis oralis (PoO) 206 - raphe magnus 71, 72, 74, 75, 90 - - obscurus (NRO) 113,115,116,131,

132 - reticularis dorsalis 92 - - gigantocellularis 211 - - lateralis 89, 93 - - parvocellularis 90 - - rostroventrolateralis (RVLM) 40,41,

46,89,90-98,103-110,112-116,131, 132, 142, 176, 195, 196

- retroambigualis (nRA) 90, 91, 93-96 - retrofacialis 131, 132 - subcoeruleus 71, 74, 75, 185 - tractus solitarii (NTS) 53, 54, 56, 57,

60,61, 63-69, 77, 84, 89, 90, 92-96, 114,131

Oz 3, 16, 33,40, 54, 63, 118, 119, 152, 153, 281

obex 60, 64-67, 69, 92, 132, 195 obstructive sleep apnea 205, 206, 277,

281, 282 octopamine 226 opiate receptor 113, 115 opioids 111,113,116 oral-gustatory afferents 60 order parameter 218, 222, 227 - 232, 234,

235 oxygen consumption 72

pacemaker neurones 92 pain 111-117, 120, 194 panting 120, 128 paraambigual area 43, 46 parabrachial complex 92, 93 parameter space 224

334 Subject Index

parasympathetic network 118 pattern generation 224 perception 217 periaqueductal gray (PAG) 92, 103, 104,

108,109,112,113,115 peripheral resistance (TPR) 6, 277 period doubling 229, 232, 241, 246 periodic respiration 282 periodic stimulation 239 pharyngeal motoneurones 119 phase angle 139 - attraction 232, 234 - attractive circle map 229, 232 - dynamics 231 - locking 228, 229, 240 - - diagram 240 - singularity 240, 248, 249 - space 242 - spectrum 139, 141, 176, 273, 297 - transition 219, 224, 226-230, 232, 234,

235, 246, 248, 249 Phaseolus vulgaris leukoagglutinin

(PHA-L) 93, 95, 96, 98 phentolamine 183, 184 phenylephrine 26, 27, 61, 62, 198,253,

295 phenylethanolamine N-methyltransferase

(PNMT) 90,91, 95, 96, 98 phrenic motoneurones 95, 121, 126 - motor nucleus 94, 241 - nerve 119 - - activity 9, 11,26,27,29, 31, 32, 34,

37,40,41,43,96,120,124,125,131, 132,148,150-152,154,159,175,240, 241,248

phylogeny 119, 126, 127 physical fitness 300 physostigmine 116 piglet 26, 30 pneumothorax 37 pontine brainstem 201, 202, 206 - respiratory related neurones 11 positive end-expiratory pressure

(PEEP) 3,7 post event time histogram 114, 160 postinspiration 119, 120 postinspiratory depression 124 poststimulus time histogram 34, 104, 105 postural atonia 205, 206 - effects 283, 288, 289, 294, 316 - tone 200-202,205,206 potential function 220, 221 power spectrum 10,16,17,19-21,23,

27,49,138, .142, 152, 165,241,259,265, 272, 284, 285, 287, 291, 292, 309, 310, 315-317

precollicular transection 200,201,205, 266

preprotachykinin A (ppTA) 78, 80 pressor response 46, 91, 93, 98, 106, 110-

113,116,213,295 probabilistic relationship 144, 146 propanolol 183, 184 propriobulbar neurones 121, 123 Prussian blue 72, 73 psychosocial stress 116 pulmonaryafferents 9,26,27,29-31, 37,

41, 69, 95 - circulation 3 - stretch reflex 264

quasi periodicity 234, 246

rabbit 111, 147, 153, 181,268,272, 273, 275

raphe 142 rapid shallow breathing 120, 128 rat 39, 40, 43, 45, 60, 63, 65, 66, 69, 90,

98, 121, 239 recovery 303-305 recruitment 49 recurrence plot 244 relaxation 308 relaxation time 227, 228 REM sleep 205, 206 renin-angiotensin system 291 reptiles 119 R-R interval 253-256,259,277,291-

293, 30~ 305, 308-316 resetting 141,239,240, 300, 302, 303,

305, 248, 249 respiration 119 respiratory activity 119 - control 119,126,158,171,172,200,

205, 206, 208, 241, 264, 279, 280, 282 - cycle 9, 13, 118, 135,249, 313, 314 - evaporative heat loss 71, 74, 75 - frequency 41, 203, 209, 265, 269, 275,

277,278, 312-315 - mechanics 6 - modulation of cardiac vagal activ-

ity 121, 123,253,257,265, 277, 278 - modulation of sympathetic activity 26, 2~ 2~ 31, 33, 36-41, 43, 45, 4~ 49, 95, 121,124-126,175,176,253,255-257, 249

- motoneurones 89, 92, 98, 126 - -motor coordination 200, 209, 229 - movements 119,200, 203, 264, 269,

271, 313 - muscles 120, 131

- neurones 67, 120, 121, 123, 124, 126-128,136,152,154,155,159,176,241

- network 118, 124, 127, 147 - pattern 307, 312 - phases 121, 123, 124, 127, 128, 132,

133, 135 -pump 118 - related neurones 45, 46, 75 - related neuronal activity 131-133, 135,

136, 154, 155, 269 - related rhythms 147, 149, 150, 153,

291, 294 - responses 60-63,65-67,69,96, 131,

132, 159, 193,201,203,206,211,279 - rhythm 9, 132, 135, 147, 200, 239, 245,

256, 257, 274, 275, 277, 249, 310 - rhythm generator, rhythmogenesis 26,

27, 29, 31, 32, 119, 124, 126-128, 135, 153, 154, 225, 268, 312

- sinus arrhythmia (RSA) 254, 256, 257, 259,260, 263, 265-269, 271, 272, 275, 277,292, 300, 302, 312, 315

- system 118, 158, 240, 248 - units 40 resting state 315 reticular formation 95, 149, 154, 155,

158, 15~ 168, 17~ 17~ 201, 307 - neurones 49, 159, 167 reticulospinal neurones 91, 92 - projections 90, 97 retroambigual area 40 rhodamine B 66 rhythm(s) - blood pressure 153, 155, 159, 168,265,

272-275,277-279,282,283,291,292 - cardiac in sympathetic discharge 33,

34, 36, 37, 93, 95, 104, 124 - cardiovascular 147, 148, 153, 155,239,

277, 297, 310, 316 - circaannual 246 - circadian 246 - coordination 217, 219, 222, 228, 231,

265,271,275, 248 - generation, generator - - common 32, 124, 139 - - gill/laryngeal 126 - - respiratory 26,27,29,31, 32, 119, 124,

126-128, 135, 153, 154, 225, 268, 312 - gill 126 - heart rate 265,266,271-275,277,278,

291, 29~ 307, 309, 311-316 - laryngeal 125, 126 - locomotor 200, 209, 225 - motor 224 - respiratory related 147, 149, 150, 153,

291,294

Subject Index 335

- respiratory 9, 132, 135, 147, 200, 239, 245, 249, 256, 257, 274, 275, 277, 310

- 1O-s-,0.IHz 265,272-274,277-279, 292-294, 315, 316

- smooth muscular 226 - stability 153,224,227-231,234-236,

243 - sympathetic 26,31, 32,49, 124, 138,

144, 145, 154, 175-177, 198 - vascular 316 rostral ventrolateral medulla

(RVLM) 40,41,46,89,90,103-110, 112-116,131,132,142,176,195,196

sacral spinal cord 91 sceletal muscle 181, 182 self-organization 217, 224, 225, 227, 229,

230 self-organized criticality 243 serotonin 75,92, 111, 115,226 shivering threshold 74, 75 shock 113 sigmoid gyrus 147, 149 sine circle map 234 single fiber analysis 33, 35, 121 single unit studies 57, 72, 75, 142, 144, 14~ 14~ 158-16~ 172, 195, 201, 208, 209, 229

sino aortic denervation 7, 16,23,43, 114, 123, 124, 138, 139, 142, 149, 151, 153, 198

skin temperature 72, 75 slaving principle 218 sleep 239,241,243-245,280, 282 - central apnea 206 - desynchronized 16,116 - REM 205, 206 - dyspnea 206 - obstructive apnea 205,206,277, 281,

282 sleep-wake cycle 92 smooth muscle tone 118 snail 119 somatic nerve afferents 111 somatomotor control 158,171,176,181,

186, 191, 193, 300 - system 158 somatosensoryafferents 159, 160, 161,

163, 165, 166 somatostatin (SOM) 77, 78, 80-82, 92,

96 somatosympathetic reflexes 89, 93, 193,

195 somato-vegetative interrelations 181 spatial summation 136

336 Subject Index

species differences 39, 45-47, 60, 121, 126

specificity 22, 24, 124, 175, 176, 231 spectral analysis 10-12,13,16,17,19,

20-24, 26, 27, 31, 49, 103, 138, 139, 141, 142, 144-147, 149, 176, 259, 261, 263-265,269,272,273,284,285,287, 291-295, 309-311, 314-316

speech 200 spike triggered averaging (STA) 103, 104,

133, 136, 142, 144 spinal cord 94, 96, 109, 118, 136, 269 - - thoracic 91, 92, 113, 142 - - lumbar 91 - - sacral 91 - dorsal horn 71, 92, 93 - intermediolateral column (IML) 89, 91,

94, 97, 103, 104, 107, 142, 175, 198 - intermediomedial column (IMM) 89,

91 - lateral columns 271 - lateral funiculus 91, 94 - motoneurones 119, 185, 186 - respiratory motoneurones 127, 147 - transection 114, 138, 195 spindle afferents 185 splanchnic nerve 26, 27, 31, 91, 121 spontaneously hypertensive rats 46, 111 Sprague-Dawley rats 46, 54, 78 squid axon 229 Starling's law 277 state vector 217,218,226 stochastic events 23,141,176,177,224,

239,269 stress 116 stroke volume 118, 277, 295 subambigual region 93 substance P (SP) 75, 77, 78, 80, 82, 84,

92, 115, 198 superposition of rhythms 147 supraspinal brain 119 - networks 23 swine 26 switching 226 - time 227,228,248 sympathetic activity 7, 9, 11, 13, 14, 16,

21,27,29-31,93,96, 103, 104, 108-111,116,124-126,128,131,138,141, 142, 144, 146, 149, 159, 181, 182, 185, 186, 198, 209, 268, 269, 272, 278, 279, 291, 294, 297, 305

sympathetic nerves/neurons 109 - - abdominal 121 - - adrenal 40, 44, 121 - - cardiac 33, 40, 43, 121, 125, 138, 294 - - cervical 153, 183, 184

- - lumbar 40 - - muscle 253, 255, 256, 257 - - preganglionic 46, 91, 92, 95, 98, 123,

126, 138, 147, 177 - - postganglionic 33-38, 138, 139, 141,

177 - - renal 16,17,19-22,40,113,121,

139,142,148,149,151,152,154,176 - - thoracic 33-35, 121 - - vertebral 16, 17, 19-22, 141 - network 118, 144, 145, 146, 249 - stimulation 181-185 - tone 92, 96, 135, 138, 278, 294, 307,

316 - vasoconstrictor fibres 283 sympathoexcitatory area 95 - neurones 45,49,91, 103-108, 110,

136, 176 - response 109, 116 sympatho-vagal interaction 294, 297 synaptic blockade 54, 55, 57, 67, 75 synergetics 217,219,221,222,224-226,

230, 235, 246, 248 synchronization 11, 136, 229, 263, 287 systogram 293, 296

tachycardia 62, 112, 116 tetrodotoxin (TTX) 54, 56, 57 thalamus 71, 147 thermal stimulation 73,75,285-289 thermo afferent pathways 71 thermoregulation 71-76, 120, 283 tilting 284, 287, 294 time series analysis 221, 244, 292 tracheobronchial afferents 95 transfer function 141 trophotropic state 307 tuning 241, 246, 274 turbulence 241, 248 tyrosine hydroxylase (TH) 83, 94, 96, 97

vagal afferents 7 - cardiac motoneurones 254 - cardioinhibitory neurones 123, 126,

128, 147, 254 - tone 294, 305, 307 vagoaortic nerves 33, 37 vagotomy 40, 41, 271, 291 vagus nerve 33-35, 37, 38, 93 van der Pol oscillator 248 vascular rhythm 316 vasoactive intestinal polypeptide (VIP) 77 vasoconstriction 24, 46, 47, 61, 71, 74,

118 vasodilatation 46, 47, 118

vasomotor area 91, 92, 94, 95 - center 90, 118 - neurones 93 - tone 115 vector field 231 venous occlusion 287, 289 ventilation 126 ventral medullary surface 40, 53, 89-91,

93, 96-98, 112 ventral respiratory group (VRG) 92 ventral root stimulation 193, 209 ventricular contractility 4 - hemodynamics 3

Subject Index 337

- performance 6 - work 6,7 vigilance 172, 307 - 309, 315 viscerotopic organization 60 visceral afferents 89 visual cortex 229

wakefulness 162, 164, 167, 168,239, 241-245

warm responsive neurones 72, 75 white noise 296 wi star rats 46 WKY rats 46