Conference Plenary Schedule and Abstracts (Oral and Poster ...brainmaster.com/software/pubs/brain/loreta... · Conference Plenary Schedule and Abstracts (Oral and Poster Sessions)

Conference Plenary Schedule and Abstracts (Oral and Poster Sessions)

Time Wednesday September 14, 2011 Conference Schedule

8:00 AM-12:15 PM & 1:15-5:30 PM

Pre-Conference 8 Hour Workshops - See Workshop Schedule

7:30 AM-5:00 PM ISNR Golf Tournament (Off Site Golf course, Scramble format). Pre-registration required, please contact the ISNR office to register. A portion of proceeds go to the ISNR Research Foundation. Prizes awarded at banquet dinner.

2:00-5:00 PM Vendor Setup in Vendor Area

4:30- 6:30 PM ISNR Board of Directors Meeting

6:00-7:30 PM Joe Kamiya, PhD & Tom Collura, PhD; Neurofeedback, Biofeedback and First Person Science

7:30-9:30 PM Outdoor Welcome Cocktail Reception (Inclement Weather provision in Vendor Area)

Time Thursday September 15, 2011 Conference Schedule

8:00-8:15 AM Presidentʼs Welcome - Leslie Sherlin, PhD

8:15-8:45 AM Validation of a Global Live Z-Score Protocol

in a Randomized, Sham-Controlled Study of Cognitive Decline in Aging; Tom Collura, Elena Festa & William Heindel

Reliability of Quantitative EEG (qEEG): Power, Phase, Coherence and LORETA Current Source Density; Rex Cannon & Debora Baldwin

Beta Reset: Capitalizing on Novelty, Optimizing Neuroplasticity; Jaclyn Gisburne & Jana Harr

8:45-9:15 AM Neurofeedback for Adult Attention-deficit / Hyperactivity Disorder (ADHD): Preliminary Findings of Slow Cortical Potential Feedback; Kerstin Mayer, Sarah Wyckoff & Ute Strehl

9:15-9:30 AM Break

9:30-9:45 AM Student Presentation- EEG Source Localization of Emotional Closeness in Relationships and Personality Mechanisms in Transition to College: Pilot Data Investigating the Emotional Closeness in Relationships and the Transition to College Inventory Assessment Instruments; Ann Marie Scruggs, Danielle Gerhard, Rex Cannon, Nancy Foster, John Lounsbury, Brent Mallinckrodt, Debora Baldwin & Sarah Sprague

Student Presentation- Long-Term Effectiveness of Neurofeedback combined with Metacognitive Training for Children with ADHD: A Pilot Study; Wing Sze Wence Leung

9:45-10:35 AM Invited Speaker- Adam Clarke, PhD; EEG Abnormalities in Children with Attention-Deficit/Hyperactivity Disorder: Linking Brain and Behaviour

10:35-10:45 AM Break

10:45-11:35 AM Invited Speaker- Richard Gevirtz, PhD; Communications Between the Heart and Brain: Beyond Heart Rate Variability

11:35-11:45 AM Break - Visit our Vendor Area

11:45 AM-12:45 PM KEYNOTE Speaker- Scott Makeig, PhD; Toward Applications of Functional Brain/Body Imaging

12:45-1:30 PM Lunch Available for Purchase

Time Thursday September 15, 2011 Conference Schedule

1:00-2:00 PM Small Group Discussion – Dan Williams Adjunct/Complimentary Therapies with Neurofeedback

Small Group Discussion- Leslie Sherlin Sports Performance with Neurofeedback

Small Group Discussion – Anne Stevens Learning Disorders and Educational Applications for Neurofeedback

Small Group Discussion – Richard E. Davis & Genie Bodenhamer-Davis Treating Addictions with Neurofeedback

2:15-5:30 PM Workshops - See Workshop Schedule

6:00-8:00 PM Poster Session & Cocktail Reception- See Program for Poster Listings

8:30-10:00 PM By invitation only- International Attendee Reception (hosted by ISNR President)

Time Friday September 16, 2011 Conference Schedule

8:00-8:30 AM A Look at Your Brain on Joy; Sarah Fischer, Debora Baldwin & Rex Cannon

Behavioral and EEG Effects of Lateralized EEG Biofeedback on Lateralized Attention Network Task (LANT) and Lateralized Continuous Performance Task (LCPT); Andrew Hill & Eran Zaidel

What is Common and Unique in ADHD and Schizophrenia: Studies of Event Related Potentials?; Juri Kropotov 8:30-9:00 AM Construct and predictive validity of the

Comprehensive Neurodiagnostic Checklist 10/20 (CNC); Willem Fonteijn, Nicolle Helgers, Thomas Brownback & Derk Mulder

9:00-9:10 AM Break

9:10-9:40 AM Neurofeedback Treatment of Restless Legs Syndrome and Periodic Limb Movement Disorder; Cory Hammond

The Usefulness of QEEG and ERPs in Predicting Treatment Outcome in ADHD and Depression; Martijn Arns

Planning for a Collaborative Multi-Site, Double-Blind, Sham-Controlled Randomized Clinical Trial of Neurofeedback for ADHD; Nick Lofthouse, Eugene Arnold, Martijn Arns, Keith Conners, Roger deBeus, Henry Harbin, Laurence Hirshberg, Cynthia Kerson, Helena Kraemer, Joel Lubar, Keith McBurnnett & Vince Monastra

9:40-10:10 AM A Study Comparing the Brain Function of Healthy and ADHD Adults During Rest and Stroop Task in EEG/ERP and fMRI; Cynthia Kerson, Estate Sokhadze, Rex Cannon, Leslie Sherlin & David Hubbard

10:10-10:20 AM Break

10:20-11:10 AM Invited Speaker- Yann Renard, PhD; OpenViBE Tutorial: A Novel Open-Source Software to Design, Test, and Use Brain-Computer Interfaces and Realtime Neurosciences

11:10-11:25 PM Student Presentation- LORETA Neurofeedback and the Morphology of Working Memory and Processing Speed; Dominic Joseph Di Loreto

Student Presentation- Modulatory Effects of Ambient Prism Lenses on Spatial Attention in Autism: An Event-Related Potential Study; Guela Sokhadze, Melvin Kaplan, Stephen Edelson, Estato Sokhadze, Joshua Baruth, Ayman El-Baz, Marie Hensley & Manual Casanova

11:25 AM-12:15 PM Invited Speaker- Efthymios Angelakis, PhD;

12:15-12:25 PM Break

12:25-1:25 PM Keynote Speaker- Roberto Pascual-Marqui, PhD; The Human Brain Resting State Networks Based on High Time Resolution EEG: Comparison to Metabolism-Based Networks


1:25-5:00 PM Break - Visit our Vendor Area

Time Friday September 16, 2011 Conference Schedule

2:15-3:15 PM Small Group Discussion – Mary Rondeau Nutritional Therapies to Enhance Neurofeedback Practice

Small Group Discussion- Michael & Lynda Thompson Combining Biofeedback and Neurofeedback

Small Group Discussion – Sarah Wyckoff Slow Cortical Potential Training for Neurofeedback

Small Group Discussion – James Evans QEEG Findings and Neurofeedback Implications Regarding Men Convicted of Murder


3:15-6:15 PM BCIA Exams - Must be pre-registered with BCIA to sit for exam

7:00-8:00 PM ISNR Committees meet independently (Scheduled by Committee Chairs)

9:00-11:30 PM By invitation only- Student Reception hosted by the ISNR Student Committee

Time Saturday September 17, 2011 Conference Schedule

8:00-8:30 AM How Reliable Is the Resonance Frequency?; Fredric Shaffer

The NIMH-Funded OSU Randomized, Double-Blind, Sham-Controlled Pilot Feasibility Trial of Neurofeedback for Pediatric ADHD - Complete Results; Nick Lofthouse, Eugene Arnold, Sarah Hersch, Elizabeth Hurt & Xueliang Pan

Why We Make Ourselves Sick and How To Make Ourselves Healthy: The Importance of Nutrition, Exercise and Sunlight; Dave Siever & Ron Swatzyna

8:30-9:00 AM The Effects of Heart Rate Variability on Sensorimotor Rhythm: A Pilot Study; Andrea Reid & Stephanie Nihon

9:00-9:15 AM Break

9:15-9:45 AM Exact Low-Resolution Electromagnetic Brain Tomography (eLORETA) of Adult ADHD: Pre/Post Findings Following Neurofeedback Therapy; Sarah Wyckoff, Kersin Mayer, Leslie Sherlin & Ute Strehl

Potential Clinical Applications for 19 Channel Live Zscore Training Using Percent ZOK and ZPlus Protocols; Penijean Rutter

Setting up for success with Asperger's and Autistic Spectrum Disorders; Michael Thompson & Lynda Thompson

9:45-10:15 AM LORETA Neurofeedback and the Precuneus; Rex Cannon, Debora Baldwin, Dominic Di Loreto, & Alexander Khaddouma

10:15-10:30 AM Break

10:30-10:45 AM Student Presentation- Deep Brain Modulations Guided by EEG-Feedback can be Probed by Simultaneous fMRI; Sivan Kinreich, Nathan Intrator, Illana Klovatch & Talma Hendler

10:45-11:45 AM Invited Speaker- Daniel Johnston, MD, MPH; The Role of Omega-3 EPA/DHA in Mood and Cognition: Can Fish Oil Improve Neurofeedback Outcomes?

11:45 AM-12:00 PM Break

12:00-1:00 PM KEYNOTE Speaker- Paola Arlotta, PhD; Molecular Development of Projection Neuron Types and Building of Local Microcircuitry in the Cerebral Cortex


1:00-5:00 PM Break- Visit the Vendor Area

Time Saturday September 17, 2011 Conference Schedule

1:30-2:30 PM Small Group Discussion – Jacqueline de Vries An Integrative Perspective for Autism: EEG Biofeedback Approaches

Small Group Discussion – Cory Hammond Neurofeedback Application with Obsessive Compulsive Disorders

Small Group Discussion – Stu Donaldson Pain & Neurofeedback: How We Use qEEGs to Direct Our Treatments and What We Look for Within Treatments to Make Changes to Our Protocols

Small Group Discussion – Dave Siever, Richard Soutar, Tom Collura Boosting the Effectiveness of Neurofeedback and Audio-Visual Entrainment By Combining the Two Modalities


7:00-7:30 PM Membersʼ Meeting

7:30-8:45 PM Banquet Dinner & Recognitions

8:45-11:00 PM After Dinner Entertainment

Time Sunday September 18, 2011 Conference Schedule

8:15-8:30 AM Closing Remarks - Incoming President - Richard E. Davis, MS

8:30-9:00 AM The Effect of Neurofeedback and Cranial Electrotherapy on Immune Function Within a Group of HIV+ Subjects: A Randomized Controlled Study; Gary Schummer & Sharon Noe

EEG Theta/Beta Ratio, EEG Vigilance, and Arousal in Adult Attention-Deficit/Hyperactivity Disorder: Reevaluation of Current Methods; Marie Gonzales, Sarah Wyckoff, Christian Sander & Ute Strehl

QEEG-Guided Neurofeedback for the Remediation of Dysgraphia - An Outcome Study; Jonathan Walker

9:00-10:00 AM Invited Speaker- Paul Hamilton, PhD; Neuromodulatory Approaches to the Treatment of Major Depressive Disorder

10:00-10:10 AM Break

10:10-11:10 AM Invited Speaker- William Bosl, PhD;

9:00 AM-2:00 PM Last Chance to Visit the Vendor Area *Note Closing hour

11:30 AM-2:30 PM & 3:00-6:00 PM

Vendor Seminar/Workshops - See Vendor Seminar Schedule

11:30 AM-2:30 PM ISNR Board Meeting

ISNR 2011 Conference Oral Presentation Abstracts

Please note: Authors are responsible for their submissions. The Student Scholarship abstracts are included in this section. The category of presentations is indicated by “C” for Clinical Application or Clinical Experience, “R” for Research, and “T” for Theoretical. The abstracts, learning objective and agenda are presented in order according to the conference schedule. Where possible, the oral presentations have been grouped by theme to facilitate the Continuing Education process. Note the number given to the presentation(s). Full information for obtaining CMEs, American Psychological Association (APA), National Board of Certified Counselors (NBCC), American Social Work Board (ASWB), and California Board of Behavioral Sciences credits and Biofeedback Certification Institute of America (BCIA) recertification credits is in your conference packet.

Wednesday, September 14, 2011

Plenary Room 1 - Opera House

Panel: Dimensions of Experience and First-Person Science(R,C,T) Joe Kamiya, PhD, University of California, [email protected]

Thomas Collura, PhD, BrainMaster Technologies, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1.5 Abstract This presentation proposes a long-range program of research on the relationship of human subjective experience to its physiological and environmental concomitants. Subjective experience has been a long debated topic, and attempts to rule it out of bounds of scientific inquiry have not been totally successful. The history of modern psychology started in the late 19th century as the study of consciousness, with trained introspection as the method of observation and the verbal reports of the results being the data. Among the reasons for the failure of the approach was disagreement among different observers in the verbal reports of their introspections, presumably of the same object of observation. However the process of introspection itself, apart from the reporting thereof, is the observing of events internal to the observer. Dreams, imagery, pains, hopes, thoughts and feelings are still present, for all their privacy, waiting to be comprehended more adequately in a framework of all scientific knowledge. Since the activity being observed is private to the observer, we refer to this as first person observation, and when reported verbally is commonly termed subjective report, and sometimes non-falsifiable. This contrasts with the third person reporting of observation of events external to the observer, where the reports of the observations can be publicly verified. Such reports are called objective, sometimes falsifiable. Stoyva and I (Psychological Review, 1968, 75, 192-205) have pointed out that the temporal correlates of such private events with publicly observable events (both physiological processes of the observer himself as well as events of his external environment) are an important tool for consciousness studies. The logic is that when there is covariation over time between the occurrence of a private event (such as dreaming as indicated by verbal report upon being awakened) and the presence of eye movements and EEG stage changes prior to the awakening), the convergence of the two observables provides increased confidence that dreaming did occur as suggested by the report. That physiological events can thus be at least partial indicators of subjective experience underlies several avenues of research. One, exemplified by current work of Richard Davidson and associates, shows that the magnetic resonance images of the brains of

meditators is related to their activity of meditation. Thus the ancient human activity of deploying attention in specific ways as reported by the meditators is at least partially indexed by a physiological marker, thus making possible studies of the specific brain processes underlying the first person experiences of these subjects. Another approach is one I reported on in 1962, 1968 to train subjects by operant discrimination procedures to identify moments, each time I rang a single ding of a bell, when occipital EEG alpha activity was dominant, vs. moments when it was absent, by a simple dichotomous verbal response ("A" for alpha dominance, "B" for its absence). Successful discrimination was achieved by most subjects, permitting inquiry of the subjects as to the subjective differences between the two EEG states. Despite considerable individual differences in some of the verbal reports there was a tendency toward common verbal characterizations of the differences, suggesting that the subjective experiences themselves of most persons may have common correlates in brain activity. To reduce the noise to be found in everyday language reports of subjective experience, it is proposed first that extensive discrimination training and feedback training be used on a selected variety of physiological measures, so as to increase the sensitivity of the individual to the "feel" of discriminating and/or controlling each of, say, 20 measures. Then, from each subject are obtained paired comparison ratings of the degree of subjective similarity (on a 5 point scale, say) of each measure to every other measure. This will result in a matrix to which principal components analysis can be done to specify the independent dimensions of the total subjective space associated with the measures. Thus, for example, the "feel" of EEG alpha at the central leads might be specified as occupying a specific spot in the derived multidimensional space. Verbal labels can be applied to the dimensions later, with the risk of introducing cultural biases in word usage. But to the extent that the maps derived from all subjects are similar to each other, there will be a basis for improved verbal agreements about the subjective qualities. The method would significantly increase the precision of mapping the subjective judgments of physiological measures. Of course, the representativeness to real life of the measures selected for training will be crucial, and this will emerge only after very extensive multi-measure research in basic psychophysiology of everyday life, particularly that involving interpersonal interactions. PowerPoint slides will be used to illustrate the power of dimensional analysis of a matrix of subjective ratings of paired comparisons. As an example, a map will be shown of different food tastes, dimensionalized along sweet, sour, bitter and salty (which rather well account for all food tastes). It would show where, for example, where apples, sweet pickles, raw cucumbers and beef would likely appear as projections along the axes of the multidimensional space. References Kamiya, J. (1962) Conditioned discrimination of the EEG alpha rhythm in humans, Abstract of a paper presented at the Western Psychological Association. Kamiya, J. (1968) Conscious control of brain waves Psychology Today 1(11): 55-60. Kamiya, J. (1969) A fourth dimension of consciousness, Experimental Medicine and Surgery, 27:13-18. Kamiya, J. (1969) Operant control of the EEG alpha rhythm and some of its reported effects on consciousness in: C.Tart (ed) Altered States of Consciousness New York: John Wiley and Sons, 489-501. Kamiya, J. (1972) Training of the left-right alpha amplitude ratio, Proceedings of the Biofeedback Research Society, Fourth Annual Meeting, Boston, 28. Kamiya, J. (1974) Autoregulation of the EEG alpha rhythm: A program for the study of consciousness, in: M.H. Chase(ed) Operant Control of Brain Activity, Los Angeles: UCLS Brain Research Institute. Kamiya, J., Barber, T.X., DiCara, L.V., Miller, N.E., Shapiro, D., and Stoyva, J. (1971) (eds) Biofeedback and self-control: An Aldine Reader on the Regulation of Bodily Processes and Consciousness, Chicago: Aldine. Kamiya, J. (2003) Evolution, consciousness and biofeedback. Invited address at the Annual Meeting of the Biofeedback Foundation of Europe, Italy. Hardt, J.V. and J. Kamiya . (1978) Anxiety change through EEG alpha feedback seen only in high anxiety subjects. Science, 201, 79-81. Naifeh, K. and J. Kamiya (1980). Changes in alveolar carbon dioxide tension during meditation. Biofeedback and Self Regulation, 5, 378. Learning Objective Describe issues related to EEG and personal experience. Discuss the relationship between consciousness and the brain. Describe how evolution and consciousness may be related. Outline Physiological monitoring and personal experience (30 minutes) The scientific study of consciousness (30 minutes) Needed research in social psychophysiology (30 minutes)

Financial Interest: No commercial product or service is discussed as part of this presentation.

Thursday, September 15, 2011 Plenary Room 1 - Opera House

Validation of a Global Live Z-Score Protocol in a Randomized, Sham-Controlled Study of Cognitive Decline in Aging (R)

Thomas Collura, PhD, BrainMaster Technologies, [email protected] Elena Festa, PhD, Brown University

William Heindel, PhD, Brown University

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract The objective of this report is to present and interpret objective data which validate a global live z-score training (LZT) neurofeedback protocol. We present data for three levels of validation of a global live z-score protocol used for operant training. These are single-subject within-session (N=1), single-subject across-sessions (N=3), and a blind, multiple-subject randomized sham-controlled study (RCT, N=79). The validation consists of (a) specific within-session z-score changes (40 minutes), (b) specific across-session z-score changes (10 sessions each), and (c) specific enhancement of cognitive processing (8 sessions each). The RCT outcomes were measured by a battery of neuropsychological tests in the controlled study. The sham feedback consisted of “yoked” recorded EEG derived from a matched subject, who was undergoing similar neurofeedback training. The results confirm the proposed mechanism of action, which is operant learning (self-regulation) of a complex set of QEEG-derived parameters, in a conventional operant learning biofeedback paradigm. Both healthy elderly, and mild Alzheimer’s (AD) patients were included, and both were present in the experimental as well as the control groups. The measures that improved with Real NFT were different across the groups. The most compelling data was from a visual search task with the healthy elderly real NFT group. They showed enhanced selective attention across two different sensory binding conditions in an integrated visual-motor task. Real NFT improved attentional disengagement and alerting measures in the mild AD NFT group. Mock NFT either had no effect or elicited generalized slowing for both groups. Results confirmed that the global live z-score protocol leads to the expected specific EEG changes, and that EEG changes were associated with expected cognitive improvement over time. Cognitive improvements were not seen in the sham-treated subjects in the RCT. These results verify that the mechanisms of LZT training operate as described, and that they can produce measurable benefits in improved brain activation and connectivity, and associated cognitive function. Clinical application, including relationships to observed phenotypes, will described. These results provide a basis for clinical application, continued studies, and further development of protocol designs. Possible weaknesses in the study will be described, including the sham feedback, possible repeated measures effects and the need for analysis of additional behavioral measures which were taken, including methods proposed for refining these results. References Collura, T.F., Guan, J., Tarrant, J., Bailey, J., & Starr, F. (2010). EEG biofeedback case studies using live z-score training and a normative database. Journal of Neurotherapy, 14: 1, 22-46. Collura, T.F., Thatcher, R.W., Smith, M.L., Lambos, W.A., and C.R. Stark (2009). EEG Biofeedback training using Z-scores and a normative database, in: (Evans, W., Budzynski, T., Budzynski, H., and A. Arbanal, eds) Introduction to QEEG and Neurofeedback : Advanced Theory and Applications, Second Edition. New York: Elsevier. Collura, T.F. (2009). Neuronal Dynamics in Relation to Normative Electroencephalography Assessment and Training, Biofeedback, Volume 36, Issue 4, pp. 134-139. Festa, E.K., Heindel, W.C., Connors, N.C., Hirschberg, L., & Ott, B.R. (2009). Neurofeedback training enhances the efficiency of cortical processing in normal aging. Cognitive Neuroscience Society Annual Meeting Program, A11, p. 41. supplement of the Journal of Cognitive Neuroscience. Festa, E.K., Insler, R.Z., Salmon, D.P., Paxton, J., Hamilton, J.M., & Heindel, W.C. (2005). Neocortical disconnectivity disrupts sensory integration in Alzheimer's disease, Neuropsychology, 19(6), 728-738. Gunkelman, J., (2006). Transcend the DSM using phenotypes. Biofeedback, 34(3), 95- 98. Johnstone, J., Gunkelman, J., & Lunt, J. (2005). Clinical database development: Characterization of EEG phenotypes. Clinical EEG and Neuroscience, 36(2), 99-107.

Kerson, C., Gunkelman, J., and Collura, T.F. (2008) Neurofeedback Using the Phenotype and Z-Score Modalities, NeuroConnections April, 24-28. Martino, E.F. (2008). Efficacy of qEEG Neurocognitive training in early-stage Alzheimer’s disease. Scientific Progress Report, Alzheimer’s Association. Whiston, S.C. (2009). Principles and Applications of Assessment in Counseling. Belmont, CA: Brooks/Cole. Learning Objective Describe changes in cognitive function, as well as EEG, in the aging population. Identify specific cognitive changes associated with mild Alzheimer’s, and their EEG correlates. Describe how neurofeedback has been shown to help reverse degradation in brain connectivity associated with aging and cognitive decline. Outline Use of neurofeedback using live z-scores to normalize brain connectivity (10 minutes) Single-subject results validating live z-scores in normalizing brain function (10 minutes) Controlled study results (10 minutes) Financial Interest: Dr. Collura has a financial interest in BrainMaster Technologies, Inc.

Neurofeedback for Adult Attention-Deficit/Hyperactivity Disorder (ADHD): Preliminary Findings of Slow Cortical Potential Feedback (R,C)

Kerstin Mayer, MSc, University of Tübingen, [email protected] Sarah Wyckoff, University of Tübingen

Ute Strehl, University of Tübingen Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Objectives: Attention deficit hyperactivity disorder (ADHD) is characterized by symptoms of inattention, impulsivity, and hyperactivity, which persist into adulthood for 4-5% of patients (Goodman & Thase, 2009). Hitherto, only a few EEG studies have investigated ADHD in an adult population (Bresnahan, Anderson, & Barry, 1999; Bresnahan & Barry, 2002; Clarke et al., 2008a; Clarke et al., 2008b, Hale et al, 2009; Koehler et al., 2009; Loo et al., 2009; Thompson & Thompson, 2005; White, 2001, 2003) and to our knowledge no studies have assessed the efficacy of neurofeedback training on symptom reduction. Neurofeedback training has been applied effectively in various areas, especially in the treatment of childhood ADHD (Arns, de Ridder, Strehl, Breteler, & Coenen, 2009). This study is designed to investigate the effect of slow cortical potentials (SCP) neurofeedback training on symptomatology and neurophysiological parameters in an adult ADHD population following 30 training sessions and after a six-month follow-up period. Methods: Continuous 19-channel EEG was acquired from 10 adult participants that met DSM-IV criteria for ADHD (combined, inattentive, or hyperactive type), without additional serious physical, neurological, or psychiatric disorders, and a full scale IQ > 80. EEG recordings were collected at pre/mid/post/follow-up treatment intervals and included EO, EC, P300, and CNV tasks, as well as ADHD behavioral questionnaires. Participants underwent 30 sessions of SCP neurofeedback training at CZ, referenced to A1, ground A2, with vertical and horizontal ocular correction (Strehl et al., 2006). Results: This investigation is in progress. The changes in behavioral and neurophysiologic parameters following 15 sessions of SCP feedback will be presented at the time of the conference. Conclusion: SCP neurofeedback therapy has not previously been investigated in an adult population and may yield valuable findings related to alternative treatments for adult ADHD. Treatment implications, study limitations, and future directions in research will be addressed. References

Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clin EEG Neuroscience, 40(3), 180-9. Bresnahan, S. M., Anderson, J. W., & Barry, R. J. (1999). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry, 46(12), 1690-1697. Bresnahan, S. M., & Barry, R. J. (2002). Specificity of Quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res, 112(2), 133-144. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Selikowitz, M., & Bryne, M.K. (2008a). EEG coherence in adults with attention-deficit/hyperactivity disorder. International Journal of Psychophysiology, 76(1), 35-40. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Seilkowitz, M., & Bryne, M.K. (2008b). EEG in adults with attention-deficit/hyperactivity disorder. Int J Psychophysiology, 70(3), 176-183. Goodman, D. W., & Thase, M. E. (2009). Recognizing ADHD in adults with comorbid mood disorders: Implications for identification and management. Postgrad Med, 121(5), 20-30. Hale, T. S., Smalley, S. L., Hanada, G., Macion, J., McCracken, J. T., McGough, J. J., & Loo, S. K. (2009). Atypical alpha asymmetry in adults with ADHD. Neuropsychologia, 47(10), 2082-2088. Koehler, S., Lauer, P., Schreppel, T., Jacob, C., Heine, M., Boreatti-Hummer, A., et al. (2009). Increased EEG power density in alpha and theta bands in adult ADHD patients. Journal of Neural Transmission, 116(1), 97-104. Loo, S. K., Hale, T. S., Macion, J., Hanada, G., McGough, J. J., McCracken, J. T., & Smalley, S. L. (2009). Cortical activity patterns in ADHD during arousal, activation, and sustained attention. Neuropsychologia, 47(10), 2114-2119. Strehl, U., Leins, U., Goth, G., Klinger, C., & Birbaumer, N. (2006). Physiological regulation of slow cortical potentials-a new treatment for children with ADHD. Pediatrics, 118(5), 1530-1540. Thompson, L., & Thompson, M. (2005). Neurofeedback Intervention for Adults with ADHD. Journal of Adult Development, 12(2 - 3), 123-130. White, J. N., Jr. (2001). Neuropsychological and electrophysiological assessment of adults with attention deficit hyperactivity disorder. Unpublished doctoral dissertation, The University of Tennessee, Knoxville. White, J. N., Jr. (2003). Comparison of QEEG Reference Databases in Basic Signal Analysis and in the Evaluation of Adult ADHD. Journal of Neurotherapy, 7(3/4), 123-169. Learning Objective Understand the prospects of slow cortical potential neurofeedback in the treatment of adult ADHD. Outline Background and result presentation, (20 minutes) Discussion of treatment implications, study limitations, and future directions (10 minutes) Financial Interest: The authors of this presentation have no significant financial interest or relationship with commercial supporter(s) or manufacturer(s) of any commercial product or service that is discussed as part of the presentation.

STUDENT PRESENTATION

Pilot Data Investigating the EEG Sources of Personality and Attachment (R)

Ann Marie Scruggs, BA, University of Tennessee, [email protected] Danielle Gerhard, BS, Rex L Cannon, PhD, Nancy Foster, MS, John Lounsbury, PhD

Brent S Mallinckrodt, PhD, Debora R Baldwin, PhD, Sarah Sprague, BS Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .25 Abstract Introduction: In recent years there has been a surge of data investigating the neural assemblies implicated in attachment and personality, specifically the Big Five (Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness). The current study presents pilot data investigating the EEG current source density associated with the Experiences in Close Relationships Scale (CRS) and the 34-item Transition to College inventory (TTC). Methods: We collected data from 16 non-clinical undergraduate students (8 male) while EEG was continuously recorded during the completion of each of the assessment measures. Response items were marked within the EEG record. The segments prior to the response were extrapolated and compared to eyes-opened baseline, as well as to each other.

Results: Data indicate the maximal increases in CSD for the Emotional Closeness in Relationships (ECR) as compared to baseline occurs in BA 21 at right middle temporal gyrus, BA 44 at left inferior frontal gyrus, BA 13 at left insular cortex, BA 18 at left cuneus and BA 7 left parietal lobe /precuneus. TTC compared to baseline shows maximal increases in CSD in right BA 21 middle temporal gyrus, BA 31 left posterior cingulate, left BA 18/19 cuneus and left BA 7 precuneus. ECR compared to TTC showed maximal increased CSD in BA 32 medial and right anterior cingulate, BA 10 left middle frontal gyrus, left BA 4 precentral gyrus and BA 20 right fusiform gyrus. Conclusions: The data obtained in this early study are very similar to studies that have employed fMRI to measure personality and other attachment measures. The ECR and TTC show similarities as contrasted with EOB and significant differences between the two measures. The regions shown increased in current source density (CSD) include regions known to be associated with self-relevant information processing as well as self-regulation and social and language processes. Future research aims are to increase sample size to investigate both of these measures in more depth. Potential network properties and implications for neurofeedback paradigms will be discussed. References Cannon, R., Lubar, J., & Baldwin, D. (2008). Self-perception and experiential schemata in the addicted brain. Appl Psychophysiol Biofeedback, 33(4), 223-238. Cannon, R., Lubar, J., Clements, J. G., Harvey, E., & Baldwin, D. (2008). Practical Joking and Cingulate Cortex: A Standardized Low-Resolution Electromagnetic Tomography (sLORETA) Investigation of Practical Joking in the Cerebral Volume. Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 11(4), 51 - 63. Lounsbury, J.W. & Gibson, L.W. (2009). Personal Style Inventory: A personality measurement system for work and school settings. Knoxville, TN: Resource Associates, Inc. Lounsbury, J. W., Tatum, H., Gibson, L. W., Park, S. H., Sundstrom, E. D., Hamrick, F. L., & Wilburn, D. (2003). The development of a Big Five adolescent personality scale. Psychoeducational Assessment, 21, 111-133. Lounsbury, J. W., Welsh, D. P., Gibson, L. W., & Sundstrom, E. (2005). Broad and narrow personality traits in relation to cognitive ability in adolescents. Personality and Individual Differences, 38, 1009-1019. Lounsbury, J. W., Hutchens, T., & Loveland, J. (2005). An investigation of Big Five personality traits and career decidedness among early and middle adolescents. Journal of Career Assessment, 13, 25-39. Lounsbury J. W., Saudargas, R. A., Gibson, L. W., & Leong, F. T. (2005). An investigation of broad and narrow personality traits in relation to general and domain-specific life satisfaction of college students. Research in Higher Education, 46(6), 707-729. Lounsbury, J. W., Huffstetler, B. C., Leong, F. T., & Gibson, L. W. (2005). Sense of identity and collegiate academic achievement. Journal of College Student Development, 46, 501-514. Wei, M., Russell, D.W., Mallinckrodt, B., Vogel, D.L. (2007). The Experiences in Close Relationships Scale (ECR) - Short Form: Reliability, Validity and Factor Structure. Journal of Personality Assessment, 88(2), 187 - 204. Learning Objective Apply an integrative understanding of EEG current source density in 11 major BA regions as it relates to the Experiences in Close Relationships Scale (ECR) and the 34-item Transition to College inventory (TTC). Discuss current source density (CSD) of 11 major BA regions as they relate to personality, attachment, and self-relevant information processing in terms of self-regulation, social and language processes. Outline Introduction (1 minute) Methods and Presentation of Results (7 minutes) Conclusion and Discussion (7 minutes) Financial Interest: None.

INVITED PRESENTATION

EEG Abnormalities in Children with Attention-Deficit/Hyperactivity Disorder: Linking

Brain and Behaviour (R,C) Adam Clarke, PhD, University of Wollongong, [email protected]

Robert Barry, Rory McCarthy, Mark Selikowitz Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract

AD/HD is one of the most common psychiatric disorders of childhood, affecting approximately 5% of primary school children. Almost all models of the disorder accept that the behavioural cluster which is AD/HD results from an underlying central nervous system (CNS) dysfunction. However, the exact nature of this dysfunction is poorly understood. Several electrophysiological based models of AD/HD have been proposed and recent research has suggested that most are too simplistic in nature, and the underlying CNS dysfunctions are inaccurately labeled. Part of the problem results from the use of multiple bands in the analysis of the EEG, as this approach does not allow an understanding of the role of any discrete band on functioning. In a different approach, our group has been decomposing the EEG into single bands and relating anomalies in these bands to specific brain states (such as arousal), and to behavior. Results from a number of studies, and their implications for understanding the link between brain and behaviour, will be discussed. References Barry, R., Clarke, A., Johnstone, S. & Rushby, J. (2008). Timing of Caffeine’s Impact on the Autonomic and Central Nervous System. Biological Psychology. 77, 304-16. Barry, R., Clarke, A., Johnstone, S., McCarthy, R. & Selikowitz, M. (2009). Electroencephalogram theta/beta ratio and arousal in AD/HD: evidence of independent processes. Biological Psychiatry. 66, 398-401. Barry, R., Clarke, A., Johnstone, S. & Brown, C. (2009). EEG Differences in Children between Eyes-Closed and Eyes-Open Resting Conditions. Clinical Neurophysiology. 120, 1806-1811. Barry R, Clarke A, Johnstone S, Brown C, Bruggemann J, van Rijbroek I. (2009). Caffeine effects on resting-state arousal in children. International Journal of Psychophysiology. 73, 355-61. Barry, R., Clarke, A., Hajos, M., McCarthy, R., Selikowitz, M. & Dupuy, F. (2010). Resting-state EEG gamma activity in children with Attention-Deficit/Hyperactivity Disorder. Clinical Neurophysiology 121, 1871–1877. Clarke, A., Barry R., Irving, A., McCarthy, R., Selikowitz, M. (2011). Children with Attention-Deficit/Hyperactivity Disorder and autistic features: EEG evidence for comorbid disorders? Psychiatry Research. 185, 225–231. Clarke, A., Barry, R., Dupuy, F., Heckel, L., McCarthy, R., Selikowitz, M. & Johnstone, S. (In Press 6/12/2010). Behavioural differences between EEG defined subgroups of children with Attention-Deficit/Hyperactivity Disorder. Clinical Neurophysiology. Barry, R.,Clarke, A., Hajos, M., Dupuy, F., McCarthy, R. & Selikowitz, M. (in press 5/1/2011). EEG coherence and symptom profiles of children with Attention-Deficit/Hyperactivity Disorder. Clinical Neurophysiology. Learning Objective Learn to identify common EEG abnormalities in children with ADHD. Outline Links between specific behaviours and EEG abnormalities in AD/HD (10 minutes) EEG abnormalities in AD/HD (35 minutes) Questions and answers (5 minutes) Financial Interest: No financial conflicts of interest.


Communications Between the Heart and Brain: Beyond Heart Rate Variability (R,C)

Richard Gevirtz, PhD, Alliant International University, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Heart rate variability (HRV) has long been known to reflect vagal traffic from brain centers like the nucleus ambiguous or dorsal motor complex, and presumably tell us information about limbic and pre-frontal brain areas. HRV is a powerful predictor of health outcomes and cognitive processing. Less is known about the afferent pathways, which are actually stronger than the efferent systems. With the advent of HRV biofeedback, a number of researchers (including my group), have postulated that the slow diaphragmatic breathing involved in achieving a resonant frequency, may be affecting important aspects of brain function. Evidence for this proposition will be reviewed and clinical implications discussed. References Armour, J. A. (2007). The little brain on the heart. Cleve Clin J Med, 74 Suppl 1, S48-51. Brown, R. P., & Gerbarg, P. L. (2005). Sudarshan Kriya Yogic breathing in the treatment of stress, anxiety, and depression. Part II--clinical applications and guidelines. J Altern Complement Med, 11(4), 711-717.

Brown, R. P., & Gerbarg, P. L. (2009). Yoga breathing, meditation, and longevity. Ann N Y Acad Sci, 1172, 54-62. Gevirtz, R. (2011). Autonomic nervous system markers for psychophysiological, anxiety, and physical disorders. In E. K. Gordon, S. (Ed.), Integrative neuroscience and personalized medicine (pp. 164-180). Oxford: Oxford Press. Learning Objective Understand the communication patterns between the heart and brain and how this may affect both biofeedback and neurofeedback. Outline Introduction to the autonomic nervous system (10 minutes) The vagal efferent system (15 minutes) The vagal afferent system (15 minutes) Implications for training (10 minutes) Financial Interest: No financial relationships.

KEYNOTE PRESENTATION

Toward Applications of Functional Brain/Body Imaging (R,C)

Scott Makeig, PhD, University of California, San Diego, [email protected] Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Learning Objective More clearly understand the differences between EEG data as generated in the brain and recorded on the scalp, and may become more familiar with current research directions in functional EEG imaging and the many emerging applications this research enables. Outline Origins and modulation of EEG Differences between source and scalp EEG signals Varieties of brain-computer interfaces Mobile brain/body imaging (MoBI) Current and future developments in mobile EEG Financial Interest: No financial interests.

Thursday, September 15, 2011

Plenary Room 2 - Cholla Ballroom I

Reliability of Quantitative EEG (qEEG): Power, Phase, Coherence and LORETA Current Source Density (R,C)

Rex Cannon, PhD, University of Tennessee, [email protected] Debora Baldwin, PhD, University of Tennessee

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Introduction: In recent years the use of quantitative EEG and LORETA methods in clinical and research settings has increased. It has been poorly demonstrated that qEEG or computerized analyses of the EEG is less than reliable across time, despite numerous studies demonstrating the opposite. The current study sought to determine the reliability of qEEG, connectivity measures and LORETA current source density measured across a span of 30 days. We hypothesized that all measures collected would show significant reliability across time.

Methods: We recorded four minute eyes-closed and eyed-opened baseline recordings at two intervals thirty days apart. We analyzed the EEG data using NeuroGuide, version 6.3. We calculated peak frequency, phase, and coherence as well as LORETA z-scored current source density as compared to the Lifespan database. We entered the data into reliability analyses, using SPSS 17 with a two-way mixed model with an absolute agreement definition. We compared all frequency bands at randomly selected electrode sites. Early Results: The reliability analyses for peak frequency(alpha 8 – 12 Hz) shows a Chronbach’s Alpha (CA) of .71 for the ECB in select sites, while the EOB shows an alpha of .95 at the same sites. The Coherence analyses show CA of .79 at select sites for ECB and .99 for the same sites in EOB. Phase shows an alpha of .88 for ECB and .93 for EOB. LORETA CSD from select ROI shows similar effects with ECB showing .88 and EOB showing .92. Discussion: The current data analysis is in progress; however, early results suggest that even in small samples the qEEG and LORETA data are reliable measures across time. The results of this study are further evidence supporting the use of computerized EEG and LORETA in both the clinical and research setting. References Corsi-Cabrera, M., Galindo-Vilchis, L., del-Rio-Portilla, Y., Arce, C., & Ramos-Loyo, J. (2007). Within-subject reliability and inter-session stability of EEG power and coherent activity in women evaluated monthly over nine months. Clin Neurophysiol, 118(1), 9-21. Corsi-Cabrera, M., Guevara, M. A., Arce, C., & Ramos, J. (1996). Inter and intrahemispheric EEG correlation as a function of sleep cycles. Prog Neuropsychopharmacol Biol Psychiatry, 20(3), 387-405. Corsi-Cabrera, M., Solis-Ortiz, S., & Guevara, M. A. (1997). Stability of EEG inter- and intrahemispheric correlation in women. Electroencephalogr Clin Neurophysiol, 102(3), 248-255. Learning Objective Understand reliability of computerized EEG and its clinical importance. Outline Rationale (10 minutes) Methods (10 minutes) Results (10 minutes) Discussion (10 minutes) Clinical implications (10 minutes) Questions (10 minutes) Financial Interest: I have no financial interest in any of the companies that produce the hardware or software. I do have interest in increasing the validity and acceptance of the scientific merit associated with qEEG, LORETA and Neurofeedback.


Long-Term Effectiveness of Neurofeedback Combined with Metacognitive Training for Children with ADHD: A Pilot Study (R,C)

Wing Sze Leung, Med, University of Alberta, [email protected] Jacqueline Pei, University of Alberta, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .25 Abstract Introduction: Neurofeedback is an alternative treatment to alleviate the primary symptoms of Attention Deficit Hyperactivity Disorder (ADHD) including inattention, hyperactivity, and impulsivity. Several studies have shown that neurofeedback training is effective in improving behavioural functioning, but more research is still needed to help us understand how the neurofeedback training benefits students with ADHD especially in Canadian populations. Typically, most ADHD interventions focus on managing the behaviours in students with ADHD and neglecting the intervention in optimizing the academic performance of these students. In this study, the intervention incorporated metacognitive training as a part of the neurofeedback training to address both academic and behavioural difficulties. This paper will cover part of a pilot study. In

this paper, a secondary data analysis approach is used to evaluate the short-term impact of a 40-session neurofeedback training program combined with metacognitive strategies training. The goal of this paper is to determine whether the number of ADHD traits rated by caregivers changed from pre-training to post-training. Methods: In this secondary data analysis, the existing questionnaire and computerized assessment data from the ADD Centre (Mississauga), at the pretreatment and the immediate post-treatment points was collected. The sample size was 318, and the inclusion criteria were: (a) a diagnosis of ADHD/ADD, (b) age six to seventeen years at the time of training, (c) completion of 40-sessions of one-hour neurofeedback training combined with metacognitive strategies training (typically twice a week). The training program focuses on decreasing the theta wave activity (typically 3-7 Hz) and increasing the sensorimotor rhythm (typically 13-15 Hz). Metacognitive training was taught for 5-10 minutes during the session to learn strategies related to academic tasks. The questionnaire data, collected for all participants, was completed by caregivers and included the (1) Conner’s Global Index – Parent Version, (2) DSM symptom list, and (3) ADD-Q. For a subset of 110 participants computerized assessment data was also collected: (1) Test of Variables of Attention (T.O.V.A.) and (2) IVA+Plus – Visual & Auditory Attention Testing. These computerized assessment data were then later correlated with the questionnaire data to determine the reliability of the questionnaire results. Results: In this study, significant behaviour improvements in both hyperactive, F(4,132)=969.200, p<0.0001, and inattentive traits, F(3,123)= 389.440, p<0.0001, were reported on the three questionnaires and two computerized assessments from pre and post 40-sessions of training. Furthermore, there are 5 control variables in this study: gender, age at the time of training, IQ at the time of training, medication used at the time of training, and ADHD subtypes. No significant difference between gender (male, n = 252, and female, n = 66), age at the time of training (age 6-12, n=212, and age 13-17, n=67), IQ at the time of training (Below Average, n= 20; Average, n=97; Above Average, n=42), medication intake at the time of training (Have medication, n=69, and no medication, n=209), and ADHD subtypes (ADHD Combined Type, n=95; ADHD Inattentive Type, n=96; ADD, n=19; ADHD without a labeled subtype, n=69) were found. Conclusions: The results of this study provide evidence supporting the use of neurofeedback combined with metacognitive training as an effective intervention for ADHD. References Banaschewski, T., Coghill, D., Santosh, P., Zaddas, A., Asherson, P., Buitelaar, J., Dankaerts, M., Dopfner, M., Faraone, S.V., Rothenberger, A., Sergeant, J., Steinhausen, H.C., Sonuga-Barke, E.J.S., & Taylor, E. (2006). Long-acting medications for the hyperkinetic disorders: A systemic review and European treatment guideline. European Child and Adolescent Psychiatry, 15, 476-495. Fox, D. J., Tharp, D. F., & Fox, L.C. (2005). Neurofeedback: An Alternative and Efficacious Treatment for Attention Deficit Hyperactivity Disorder. Applied Psychophysiology and Biofeedback, 30(4), 365-373. Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H. and Kaiser, J. (2003). Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate. Applied Psychophysiology Biofeedback, 28 (1), 1-12. Gani, C., Birbaumer, N., Strehl, U. (2008) Long term effects after feedback of slow cortical potentials and of theta-beta-amplitudes in children with attention-deficit/hyperactivity disorder. International Journal of Bioelectromagnetism, 10, 4, 209 - 232. Lubar, J. O., & Lubar, J. F. (1984). Electroencephalographic biofeedback of SMR and beta for treatment of attention deficit disorders in a clinical setting. Biofeedback and Self-Regulation, 9 (1), 1 - 23. Monastra,V. J., Monastra, D.M.,& George, S. (2002). The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder. Applied Psychophysiology and Biofeedback, 27, 231–249. Thompson, L, & Thompson, M. (1998). Neurofeedback combined with training in metacognitive strategies: Effectiveness in students with ADD. Applied Psychophysiological Biofeedback, 23, 243-263. Learning Objective Learn about short-term and long-term impact of the combination of neurofeedback and metacognitive training. Outline Short-Term Impact (7.5 minutes) Long-Term Impact (7.5 minutes) Financial Interest: No financial interests.

Thursday, September 15, 2011

Plenary Room 3 - Cholla Ballroom II

Beta Reset: Capitalizing on Novelty, Optimizing Neuroplasticity (C) Jaclyn Gisburne, PhD, Rocky Mountain NeuroAdvantage, [email protected]

Jana Harr, MS, Rocky Mountain NeuroAdvantage Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract The purpose of this presentation is to summarize the collected research and clinical outcomes to date that reflect the use of the Beta Reset™ protocol and treatment model developed within our practice for clients diagnosed with chronic conditions such as chronic pain (e.g., arthritis, fibromyalgia, and accident-induced nerve pain), Parkinson’s tremors, migraines, PTSD, neurofibromatosis type 1, MS, dementia, and other chronic conditions. The Beta Reset™ protocol was developed initially by Jaclyn Gisburne, PhD in 2005, and subsequently expanded and refined in collaboration with Jana Harr, MS (Cand.). Our hypotheses are based, in part, on brain frequency, deep brain stimulation (DBS) and site specific stimulation, neural development, and other evidence-based brain research, as well as on the collective client outcomes. To that end, we surmise that the protocol capitalizes on the brain’s unique response to novelty, which synchronizes the brain globally, interrupting and rebounding the frequencies into more regulated states. We believe that this is evidenced in both the alleviation of symptoms and the smoothing of the EEG, indicative of a more well-regulated brain. We will also be discussing some of the more current research associated these chronic conditions, which has yielded a wealth of insights, neuroimagery, and evidence as to the structural, genetic, and psychological impacts associated with these conditions. This, in turn, has contributed to the advancement of insights and innovations in the treatment and management of these conditions. Some of areas of research and innovation include: the development of new medications and therapies for the control of symptoms, genetic modifications and adaptation potentials, deep brain and other stimulation therapies to correct neuronal dysregulation, the identification of nutritional and gastrointestinal issues, and the use of exercise (e.g., Tai Chi, yoga, walking, and use of an exercise bike) to increase flexibility, mindfulness, mobility, and a sense of well-being. These are in essence all management strategies. In spite of the billions of dollars invested to date, “research” has yet to identify any actual “causation” (i.e., what precipitated these pathological progressions). Nor has it produced any “cures.” Notwithstanding, in light of our clients’ remission of symptoms along with the emergence of “precipitatory” trauma memories (i.e., trauma memories and the subsequent adoption of core belief that are directly or indirectly related to the chronic condition), we will present how we have come to believe that both the causation and resolution of these conditions may be in the brain’s own neuroplasticity. Because of timing and nature of these “stories” subsequent to the Beta Reset™ process, we now believe that trauma, is pivotal, if not foundational, to the development of chronic and neurodegenerative conditions. We believe that this is especially true of early-life trauma-event(s) that were: unexpected, dramatic, isolating, for which the individual had no strategy to resolve, resulting in an undischarged freeze response. This hypothesis is both consistent with our clients’ stories and that of the research on the trauma-induced distortions in neural development that has been conducted over the three decades by Schore, Seigel, Perry, van der Kolk, and others. Their research, in essence, postulates that trauma-events have the capacity to distort and/or alter neural development and functions, the impacts of which are observable on into adulthood. To what degree might these early life developmental distortions act as “precipitatory agitants” is open for discussion. In any case, in this presentation, we will introduce some new insights, commonalities in story-themes and adopted core beliefs we have found to be associated with specific neurologic and psychophysiological progressions and the development of condition-specific constellations of symptoms. References Bibbig A, Traub RD, and MA Whittington. (2002). Long-range synchronization of gamma and beta oscillation and the plasticity of excitory and inhibitory synapses: A network model. Journal of Neurophysiolo, 1634-1654. Canolty RT, Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE, Berger MS, Barbaro NM, and RT Knight. (2006, September 15). High gamma power is phase-locked to theta oscillation in human neocortex. Science , pp. 1626-1628. Dawson, K & Allenby, S. (2010). Matrix Reimprinting with EFT. Carlsbad, CA: Hay House. Fine, CG. 1991. Treatment stabilization and crisis intervention: Pacing the therapy of the multiple personality patient. Psychiatric Clinics of North America 14:3. Pp661-675.

Medendorp WP, Kramer GFI, Jensen O, OOstenveld R, Schoffelen JM, and P Fries. (2007). Oscillatory activity in human parietal and occipital cortex shows hemispheric lateralization and memory effects in a delayed double-step saccade task. Cerebral cortex , 17, 2364-2374. Ogden P & Minton K. (October 2000). Sensorimotor Psychotherapy: One method for processing traumatic memory, 6(3). Rofe, Y. (2010). The Rational-choice Theory of Neurosis: Unawareness and an integrative approach. Journal of Psychotherapy Integration, 22. Pp. 152-222. Sederberg PB, Kahana MJ, Howard MW, Donner EJ, and JR Madsen. (2003). Theta and gamma oscillations during encoding predict subsequent recall. The Journal of Neuroscience , 23 (34), 10809-10814. Siegel M, Warden MR, and EK Miller. (2009, December 15). Phase-dependent neuronal coding of objects in short-term memory. PNAS , 21341-21346. Van der Hart O, Brown P. (1992). Abreaction Re-evaluated. Dissociation, 5(3): 127-140. van der Kolk, B, Pelcovitz, D, Roth S, Mandel FS, McFarlane A, & Herman JL. (1995) Dissociation, Affect dysregulation: The complex nature of adaptation to trauma. American Journal of Psychiatry, 154(7): 83-93. Van Der Werf J, Jensen O, Fries P, and WP Medendorp. (2008). Gamma-band activity in human posterior parietal cortex encodes the motor goal during delayed prosaccades and antisaccades. The Journal of Neuroscience 28(34), 8397-8405. Learning Objective Articulate the correlation between trauma and neurophysiological pathologies. Outline The role of trauma in the development of neurodegenerative and chronic disorders (30 minutes) The role of Beta Reset™ and sensorimotor modalities for the resolution of chronic conditions (30 minutes) Financial Interest: No part of this presentation was supported by financial support was provided or relationships with service or product providers.

Friday, September 16, 2011


A Look at Your Brain on Joy (R) Sarah Fischer, MS, University of Tennessee, [email protected]

Debora Baldwin, PhD, University of Tennessee Rex Cannon, PhD, University of Tennessee

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Introduction: Why should we take a look at the brain on joy? Besides being a part of what makes life worth living, positive emotions such as joy have been shown to have health benefits. Resisting the common cold and flu are linked to the tendency to experience positive emotion (Cohen et al., 2003; 2006). Positive emotional style is associated with lower rates of stroke (Ostir et al., 2001) and better coronary recovery (Middleton et al., 1996). The lack of joy was found to be one of the most important symptoms linked with risk of depression after age 60 (Hein, et al., 2003). Methods: After providing informed consent, a non-clinical sample of twenty-five university students underwent continuous EEG recording while they envisioned a personal experience that brought them maximal joy. After baseline and task EEG recording, participants also supplied open-ended reports of their experiences during recording, as well as completion of a health symptoms inventory (CHIPS) and optimism/pessimism scale (LOT-R). EEG source localization using sLORETA was performed and comparison of the self in experience of joy condition to baseline was made using all voxel-by-voxel tests. Voxels of significant difference were mapped onto a Montreal Neurological Institute (MNI ) atlas containing 6,329 5mm voxels. Results: Differences between task and eyes-open baselines will be discussed with regard to regions of interest.

References Aftanas, L. I., Reva, N. V., Savotina, L. N., & Makhnev, V. P. (2006). Neurophysiological correlates of induced discrete emotions in humans: an individually oriented analysis. Neurosci Behav Physiol, 36(2), 119-130. Cohen, S., Alper, C. M., Doyle, W. J., Treanor, J. J., & Turner, R. B. (2006). Positive emotional style predicts resistance to illness after experimental exposure to rhinovirus or influenza a virus. Psychosom Med, 68(6), 809-815. Hein, S., Bonsignore, M., Barkow, K., Jessen, F., Ptok, U., & Heun, R. (2003). Lifetime depressive and somatic symptoms as preclinical markers of late-onset depression. European Archives of Psychiatry and Clinical Neuroscience, 253(1), 16-21. Jasper, H. H. (1958). The Ten-Twenty Electrode System of the International Federation. Clinical Neurophysiology. 10: 371-375. Kiebel, S. J. and A. P. Holmes (2004). The general linear model. Human Brain Function, 2nd Edition, Part II - Imaging Neuroscience - Theory and Analysis. K. J. Friston, J. Ashburner and W. D. Penny, Elsevier. Nichols, T.E., Holmes, A.P., 2002.Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Map 15, 1-25. Pascual-Marqui, R. D. (2002). Standardized low resolution electromagnetic tomography (sLORETA): technical details, Methods & Findings in Experimental & Clinical Pharmacology, 24, pp. 5-12. Takahashi, H., Matsuura, M., Koeda, M., Yahata, N., Suhara, T., Kato, M., et al. (2008). Brain Activations during Judgments of Positive Self-conscious Emotion and Positive Basic Emotion: Pride and Joy. Cereb. Cortex, 18(4), 898-903. Learning Objective Describe some neural regions associated with positive affect and corresponding health benefits/symptoms. Outline Joy and Health: So why study joy? (5 minutes) Selected Neurological and Electrophysiological Investigations of Joy (8 minutes) Results of current research (7 minutes) Neural correlates of self experience of joy (10 minutes) Financial Interest: No commercial interests.

Construct and Predictive Validity of the Comprehensive Neurodiagnostic Checklist 10/20 (CNC) (R,C)

Willem Fonteijn, Masters, Catharina Hospital, [email protected] Nicolle Helgers, Master Student

Thomas Brownback, M Ed, EEG Professionals Derk Mulder, PhD, EEG Professionals

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract The Comprehensive Neurodiagnostic Checklist 10/20 (CNC) is a neurodiagnostic questionnaire, specifically developed for neurofeedback practice, which could be of good value to elicit widespread syndrome analysis. The CNC consists of 300 items which are related to 42 different widespread neuropathologies. After the client has completed the checklist, the severity of the 42 neuropathologies is represented in percentages. Exploring the neuropathologies of the client will provide appropriate information on which location of the brain neurofeedback treatment would be helpful. The CNC will also provide information of the frequency that should be used in training during neurofeedback treatment. Furthermore, the CNC enhances neurofeedback treatment even without available EEG data by guiding protocol selection (EEG professionals, 2011). In this study we explored the factor structure of the CNC and we validated the CNC with the SCL-90 (in adults) and with the CBCL (in children). We examined a population of 2000 clients referred to the Neurofeedback Institute in the Netherlands. The age of the clients varies between 5 and 80 years old. We will present the results of a preliminary study. Results indicate that the CNC has a good construct and predictive validity.

References Carlozzi, N. E. and Long, P.J. (2008) Reliability and Validity of the SCL-90-R PTSD Subscale. Journal of Interpersonal Violence, 23,9, 1162-1176. EEG-professionals (17-04-2011) http://www.eegprofessionals.nl/cnc1020.html. Hammond, D. C. (2009). The need for individualization in neurofeedback: heterogeneity in EEG patterns associated with diagnoses and symptoms. Applied Psychophysiology Biofeedback 35:31-6. Leiner, M., Rescorla, L., Medina, I., Blanc, O. and Ortiz, M. (2010) Psychometric comparisons of the Pictorial Child Behavior Checklist with the standard version of the instrument. Psychological Assessment 22(3):618-27. Learning Objective Use the CNC 10/20 to predict and evaluate the outcome of neurofeedback treatment. Financial Interest: Nicolle Helgers is doing the research for her masters and Willem Fonteijn is supervising her. Willem Fonteijn is connected as supervisor to Neurofeedback Institute.

Neurofeedback Treatment of Restless Legs and Periodic Limb Movements (R,C)

D. Corydon Hammond, PhD, University of Utah, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract This paper will review the EEG and neurophysiology associated with restless leg syndrome and PLM, as well as the current medical treatments. Although there can be multiple etiologic factors involved (e.g., uremia, iron depletion) abnormal EEG activity clearly appears to often be associated with this condition and it may carry a genetic vulnerability called EEG alpha activity gate dyscontrols. Two case reports will be presented with their QEEG assessments. Both cases had been diagnosed by the patients, their physicians, and with sleep lab evaluations, and in both cases significant improvements were found on follow-up evaluations. References Akpinar, S. (2002). The primary restless legs syndrome pathogenesis depends on the dysfunction of EEG " activity. Medical Hypotheses, 60(2), 190-198. Akpinar, S., Aydin, H., & Kutukcu, Y. (2007). In restless legs syndrome, during changes in vigilance, the forced EEG shifts from alpha activity to delta or high alpha may lead to the altered states of dopamine receptor function and the symptoms. Medical Hypotheses, 69, 273-281. Ferri, R., Zucconi, M., Rudo, F., Sprut, K., Manconi, M., & Ferini-Strambi, L. (2007). Heart rate and spectral EEG changes accompanying periodic and non-periodic leg movements during sleep. Clinical Neurophysiology, 118, 438-448. Hening, W. (2004). The clinical neurophysiology of the restless legs syndrome and periodic limb movements. Part I: diagnosis, assessment, and characterization. Clinical Neurophysiology, 115, 1965-1974. Hornyak, M., & Trenkwalder, C. (2004). Restless legs syndrome and periodic limb movement disorder in the elderly. Journal of Psychosomatic Research, 56, 543-548. Sforza, E., Nicolas, A., Lavigne, G., Gosselin, A., Petit, D., & Montplaisir, J. (1999). EEG and cardiac activation during periodic leg movements in sleep: support for a hierarchy of arousal responses. Neurology, 52(4), 786-791. Symvoulakis, E., Anyfantakis, D., & Lionis, C. (2010). Restless legs syndrome: literature review. Sao Paulo Medical Journal, 128(3), 167-170. Trankwalder, C., Hening, W. A., Montana, P., Oertel, W. H., Allen, R. P., Walters, A. S., Costa, J., Stiasny-Kolster, K., & Sampaio, C. (2008). Treatment of restless syndrome: An evidence-based review and implications for clinical practice. Movement Disorders, 23(16), 2267-2302. Learning Objective Describe common EEG patterns associated with restless legs syndrome (RLS) and periodic limb movement disorder (PLMD). Outline Review of literature of EEG patterns associated with restless legs syndrome and periodic limb movement disorder (10 minutes) Description of the QEEG’s and subsequent treatment of a case of RLS, and of a case of RLS and PLMD with LENS neurofeedback, and the follow-up evaluations (20 minutes)

Financial Interest: I have no financial interests with any commercial product or manufacturer that will be discussed in my presentation.

A Study Comparing the Brain Function of Healthy and ADHD Adults During Rest and Stroop Task in EEG/ERP and fMRI (R)

Cynthia Kerson, PhD, Main Biofeedback, [email protected] Estate Sokhadze, PhD, University of Louisville, [email protected]

Rex Cannon, PhD, University of Tennessee, [email protected] Leslie Sherlin, PhD, NovaTech EEG, [email protected]

David Hubbard, MD, Applied Functional MRI Institute, LLC, [email protected] Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Introduction: The prevalence of Attention Deficit Hyperactivity Disorder (ADHD) is an estimated 4.1% in adults, second only to depression. Recently, several quantitative electroencephalographic (QEEG), event-related potential (ERP) and functional magnetic resonance imaging (fMRI) studies have been completed to examine electrophysiological and blood flow behaviors in adults with Attention Deficit Hyperactive Disorder (ADHD). This EEG/ERP/fMRI study correlated brain behavior from each neuroimaging method and elucidated functional connectivity patterns in the ADHD group during resting state (eyes open and eyes closed) and an active cognitive task (Stroop). We examined the default mode network (DMN) to ascertain the differences during rest and the Stroop task. The DMN consists of twelve functionally related regions that are consistently shown increased in activity in an eyes-closed resting condition as compared to functionally specific cognitive tasks or eyes-opened resting condition. Given the regional deficits shown in ADHD research we examined them and their specific relationship with the bilateral anterior insular cortices. Numerous regions within the default network, especially left medial prefrontal and anterior medial regions are shown (assuming sources at or near the surface electrodes F3, Fz and F7 contribute to the ERP average) to contribute many of the putative mechanisms found in ERP research (e.g., frontal NoGo-N2 and P3, Error-related Negativity, etc.). Methods: Seven controls (4 F, 3M) and 6 ADHD (3 F, 3 M) adults (mean age 42.9) came to Applied fMRI Institute to complete a fMRI FanTab neuropsychological test battery, eyes open, eyes closed EEG and Stroop task for ERPs. They were age and sex-matched as closely as possible and were counterbalanced for EEG/fMRI recordings. Results: The fMRI data indicate resting state networks (RSN) are less active in ADHD than controls – with the largest effect shown in the right insula (which has also been shown to exact some control over activity in the DMN). These fMRI data identify numerous regions with decrements in activity in task versus resting condition, including the anterior cingulate, left BA 40 and regions in prefrontal cortex. EEG current source density indicate the ADHD subjects show deficits in delta and theta in the default mode network (DMN) during rest and less activity overall as contrasted to controls. Specific regions within the default network show deficits in ADHD as compared to controls, specifically in the delta, alpha and beta frequencies in left BA 10/47, 32, and 8. Conclusion: The effects shown in the data suggest that regions associated with salience, attention and self-regulatory processes are dysfunctional in the ADHD population. At the core of these network deficits are the bilateral insular cortices, the AC and regions known to be associated with affect regulation, monitoring the physical state of the body (e.g., insula and inferior frontal cortex –BA 10/47). This data shows that left BA 40 is a very important area for attentional maintenance and integrative processes. Additionally, it has been shown that the visual N100 (frontal sites) and P300 (parietal sites) ERPs are attenuated in ADHD children, which may be an indication of a deficit in selective attention. Our ERP data are still under review. Recruitment of neural resources involving temporal correlations provides important information about both attentional and self-regulatory processes in ADHD individuals as compared to healthy controls. These data provide important information relating to potential biomarkers for ADHD as well as increases the specificity of methods for neurotherapy treatment of ADHD. The data also confirm that EEG is an adequate methodology to evaluate ADHD. References Barry RJ, Clarke AR, Johnstone SJ, (2003). A review of electrophysiology in attention deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography, Clinical Neurophysiology 114:171-183.

Bregadze, N. & Lavric, A. (2006). ERP differences with vs. without concurrent fMRI. International Journal of Psychophysiology. Vol. 62 54-59. Beauregard, M & Levesque, J (2006). Functional magnetic resonance imaging investigation of the effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention deficit/hyperactivity disorder. Applied Psychophysiology & Biofeedback, 31(1) 3-20. Hughes JR, John ER. (1999). Conventional and quantitative electroencephalography in Psychiatry. Journal of neuropsychiatry & clinical neurosciences. Vol 11: 190. Kropotov, JD, Grin-Yatsenko, VA, Ponomarev, VA, Chutko, LS, Yakovenko, EA, Nildshena, IS. (2005). ERPs correlates of EEG relative beta training in ADHD children. International Journal of Psychophysiology, 55(1), 23-34. Otswald, D, Porcara, C, Bagshaw, AP. (2010). An informative theoretic approach to EEG- fMRI integration of visually evoked responses. NeuroImage: 49, 498-516. Uddin, LQ, Kelly, AM, Bharat, B, Margulies, DS, Shehzad, Z, Shaw, D, Ghaffari, M, Adler, LA, Castellanos, FX, Milham, MP, 2008. Network homogeneity reveals decreased integrity of default-mode network in ADHD. Journal of Neuroscience Methods, (169) 249 – 254. Learning Objective Learn how the default mode network presents and how the event related potentials differ in ADHD compared to control adults during rest and Stroop task. Outline The issues when using multiple neuroimaging modalities (5 minutes) The findings in fMRI and LORETA (15 minutes) The ERP findings (15 minutes) Q&A (5 minutes) Financial Interest Statement: No financial interests.


OpenViBE Tutorial: A Novel Open-Source Software to Design, Test, and Use Brain-Computer Interfaces and Realtime Neurosciences (R,C)

Yann Renard, MS, Independent Consultant (former INRIA research engineer), [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Introduction: OpenViBE is a novel, free and open-source software for Brain-Computer Interfaces and realtime neurosciences, which is meant to accelerate research and applications of BCI. OpenViBE software: OpenViBE is an open-source software devoted to the design, test and use of Brain-Computer Interfaces. The OpenViBE platform consists of a set of software modules that can be integrated easily and efficiently to design BCI applications. Key features of the platform are its modularity, its high-performance, its portability, its multiple-users facilities and its connection with high-end/Virtual Reality displays. The “designer” of the platform enables to build complete scenarios based on existing software modules using a dedicated graphical language and a simple Graphical User Interface (GUI). This software is available under the terms of the LGPL-V2 license (http://openvibe.inria.fr ). Talk: The talk will start with a presentation of the Brain-Computer Interface concept and the origins of the OpenViBE research project. I will then get into a description of the software itself, the different tools available and the different user needs that it addresses. Next, a few “made with OpenViBE” sample applications will be listed and demonstrated. Finally, I will discuss the opportunities of using OpenViBE for Neurofeedback and from a community perspective. Attendees of this talk may also be interested in the associated tutorial workshop. References OpenViBE website = http://openvibe.inria.fr

Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby, V. Delannoy, O. Bertrand, A. Lécuyer, “OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments”, Presence : teleoperators and virtual environments, vol. 19, no 1, 2010. Learning Objective Understand what OpenViBE can do for neuroscience research and for Neurofeedback applications. Outline What is a Brain Computer Interface (10 minutes) Where does OpenViBE come from (10 minutes) What is OpenViBE able to do (10 minutes) What “click and play” applications come with OpenViBE (10 minutes) The OpenViBE community (10 minutes) Financial Interest: The speaker has no financial interest or relationship with commercial supporter(s) or manufacturer(s) of any commercial product or service that is discussed as part of his presentation.


LORETA Neurofeedback and the Morphology of

Working Memory and Processing Speed (R) Joseph Di Loreto, BA, University of Tennessee, [email protected]

Sarah Jane Halford, BA, University of Tennessee Kelli Cox, BS, University of Tennessee

Alexander Khaddouma, BA, University of Tennessee Rex Cannon, PhD, University of Tennessee

Deborah Baldwin, PhD, University of Tennessee Kasey Broyes, BA, University of Tennessee

Jasmine Hewlett, BA, University of Tennessee

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .25 Abstract Introduction: Operant conditioning of the electroencephalogram (EEG) offers the potential to improve attentional and cognitive processing in psychological syndromes, but also has the exciting potential for improving these functions in normal populations. In recent years, there is increasing interest in basic mechanisms of self-regulation and the associated neuronal distributions. Moreover, there is an exciting trend toward uncovering the morphology of psychological constructs. This study sought to describe the morphological neural substrates of working memory and processing speed as influenced by LORETA neurofeedback in the anterior cingulate and bilateral prefrontal cortices. Method: We examine the neural correlates of self-regulation in both normal populations of 16 undergraduate students, each underwent between 20 and 30 sessions of LNFB training in one of six regions of interest: dorsal anterior cingulate, bilateral prefrontal cortex and precuneus. The sessions consisted of 3 minute pre and post baselines and four-five minute training rounds. We measured efficacy of LNFB with pre and post working memory (WMI) and processing speed index (PSI) scores of the Wechsler Adult Intelligence Scale - 3rd edition. Results: The participants were able to produce significant learning curves and increase levels of current source density at the specified region of training. The regions of training exhibit differential effects upon other regions of the cortex as determined by LORETA when trained exclusively. There are also significant comodulation effects within the region of training as well as between regions when examining the correlational structure of the EEG current source density. The training influenced increases in WMI and PSI scores. Conclusions: Operant conditioning of the EEG in spatial specific intracranial regions of interest increases the likelihood of improving our functional knowledge of self regulatory and cognitive mechanisms in the human brain. The correlative structure between the subtest scores of the WMI and PSI appears to influence specific frequency*region associations. The data presented offer an exciting direction for understanding the basic mechanisms of self-regulation and its cognitive correlates.

References Cannon, R., Lubar, J., Thornton, K., Wilson, S., Congedo, M (2005). Limbic Beta Activation and LORETA: Can Hippocampal and Related Limbic Activity Be Recorded And Changes Visualized In An Affective Memory Condition? Journal of Neurotherapy 8(4), 5 - 24. Coutin-Churchman, P., & Moreno, R. (2008). Intracranial current density (LORETA) differences in QEEG frequency bands between depressed and non-depressed alcoholic patients. Clin Neurophysiol, 119(4), 948-958. Esslen, M., Pascual-Marqui, R. D., Hell, D., Kochi, K., & Lehmann, D. (2004). Brain areas and time course of emotional processing. Neuroimage, 21(4), 1189-1203. Greenblatt, D. J., Gan, L., Harmatz, J. S., & Shader, R. I. (2005). Pharmacokinetics and pharmacodynamics of single-dose triazolam: electroencephalography compared with the Digit-Symbol Substitution Test. Br J Clin Pharmacol, 60(3), 244-248. Holmes, M. D., Brown, M., & Tucker, D. M. (2004). Are "generalized" seizures truly generalized? Evidence of localized mesial frontal and frontopolar discharges in absence. Epilepsia, 45(12), 1568-1579. Kim, Y. Y., Roh, A. Y., Namgoong, Y., Jo, H. J., Lee, J. M., & Kwon, J. S. (2009). Cortical network dynamics during source memory retrieval: current density imaging with individual MRI. Hum Brain Mapp, 30(1), 78-91. Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., et al. (2000). Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp, 10(3), 120-131. Lehmann, D., Faber, P. L., Achermann, P., Jeanmonod, D., Gianotti, L. R., & Pizzagalli, D. (2001). Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self. Psychiatry Res, 108(2), 111-121. Lehmann, D., Faber, P. L., Galderisi, S., Herrmann, W. M., Kinoshita, T., Koukkou, M., et al. (2005). EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multicenter study. Psychiatry Res, 138(2), 141-156. Lehmann, D., Faber, P. L., Gianotti, L. R., Kochi, K., & Pascual-Marqui, R. D. (2006). Coherence and phase locking in the scalp EEG and between LORETA model sources, and microstates as putative mechanisms of brain temporo-spatial functional organization. J Physiol Paris, 99(1), 29-36. Mulert, C., Jager, L., Schmitt, R., Bussfeld, P., Pogarell, O., Moller, H. J., et al. (2004). Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. Neuroimage, 22(1), 83-94. Oakes, T. R., Pizzagalli, D. A., Hendrick, A. M., Horras, K. A., Larson, C. L., Abercrombie, H. C., et al. (2004). Functional coupling of simultaneous electrical and metabolic activity in the human brain. Hum Brain Mapp, 21(4), 257-270. Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol, 24 Suppl D, 5-12. Pascual-Marqui, R. D., Esslen, M., Kochi, K., & Lehmann, D. (2002). Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. Methods Find Exp Clin Pharmacol, 24 Suppl C, 91-95. Pascual-Marqui, R. D., Lehmann, D., Koenig, T., Kochi, K., Merlo, M. C., Hell, D., et al. (1999). Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neurolepticnaive, first-episode, productive schizophrenia. Psychiatry Res, 90(3), 169-179. Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol, 18(1), 49-65. Sanei, S. C., J.A. (2007). EEG Signal Processing. West Sussex: John Wiley & Sons, Ltd. Learning Objective Improve functional knowledge of self-regulatory and cognitive mechanisms in the brain, specifically working memory and processing speed. Outline Introduction: Operant conditioning of EEG and psychological constructs of working memory and processing speed (5 minutes) Methods used (3 minutes) How correlative structure of WM and PS appears to influence specific frequency-region associations (7 minutes) Financial Interest: No financial supporters.


EEG, fMRI, and tDCS/tACS Applications for Disorders of Consciousness and Movement Disorders (R,C)

Efthymios Angelakis, University of Athens Medical School, [email protected] Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Part 1: Assessment of consciousness in non-responsive patients: fMRI and EEG data. Is there a way to assess consciousness in apparently non-responsive patients? This presentation attempts to address this question by measuring hemodynamic and electrical brain activity from patients with persistent vegetative state (PVS) or minimally conscious state (MCS). fMRI, EEG, and PET data will be presented, recorded from patients with PVS or MCS, and from healthy controls. Significant findings and methodological drawbacks for each technique will be discussed. Part 2: tDCS and tACS in patients with dystonia and with PVS/MCS. A recently expanding application for the treatment of brain disorders is transcranial electrical stimulation. This non-invasive method has been shown to affect motor and cognitive functions in healthy volunteers and in neurological patients, as well as to reduce symptoms in a number of brain disorders. This presentation will illustrate the potential of transcranial direct current stimulation (tDCS) and of transcranial alternate current stimulation (tACS) through two paradigms: idiopathic intractable dystonia and PVS/MCS. The former is a movement disorder with no other neurological or cognitive deficits, and with absent radiological findings. The later is a totally incapacitating condition with moderate to severe brain lesions of atrophy. References Angelakis E, Liouta E, Andreadis N, Ktonas P, Leonardos A, Stavrinou LC, Sakas DE. Transcranial alternating current stimulation reduces symptoms in intractable idiopathic cervical dystonia (submitted for publication). Angelakis E, Andreadis N, Liouta E, Flaskas Th, Verganelakis D, and Sakas D. Consciousness In Non-Responsive Patients: fMRI And EEG Data. Oral presentation in the 2011 meeting of the Society of Applied Neuroscience, Thessaloniki, Greece. Boggio PS, Ferrucci R, Rigonatti SP, et al. Effects of transcranial direct current stimulation on working memory in patients with Parkinson’s disease. J Neurol Sci (2006) 249:31-38. Ferrucci R, Mameli F, Guidi I, Mrakic-Sposta S, Vergari M, Marceglia S, Cogiamanian F, Barbieri S, Scarpini E, Priori A. Transcranial direct current stimulation improves recognition memory in Alzheimer disease. Neurology. (2008) 12;71(7):493-8. Laureys S, Owen AM, Schiff ND. Brain function in coma, vegetative state, and related disorders. Lancet Neurol (2004) 3: 537–46. Miranda PC, Lomarev M, Hallett M. Modeling the current distribution during transcranial direct current stimulation. Clin Neurophysiol (2006) 117:1623-1629. Nitsche MA, Cohen LG, Wassermann EM, Priori A, Lang N, Antal A, Paulus W, Hummel F, Boggio PS, Fregni F, Pascual-Leone A. Transcranial direct current stimulation: State of the art 2008. Brain Stimulation (2008) 1, 206–23. Sakas DE, Panourias IG, Boviatsis EJ, Themistocleous MS, Stavrinou LC, Stathis P, Gatzonis SD. Treatment of idiopathic head drop (camptocephalia) by deep brain stimulation of the globus pallidus internus. J Neurosurg. (2010) 113(6):1246-50. Learning Objective Appreciate the latest developments of EEG, fMRI, and PET in the assessment of consciousness in patients with PVS or MCS. Be introduced to the potential of non-invasive electrical brain stimulation (tDCS/tACS) in the treatment of neurological disorders either with absent radiological findings (dystonia), or with brain lesions/atrophy (PVS/MCS). Outline Assessment of consciousness in non-responsive patients (PVS/MCS) with EEG, fMRI, and PET (25 minutes) tDCS and tACS in patients with dystonia and with PVS/MCS (25 minutes) Financial Interest: I have no financial or other disclosures with any manufacturers or other commercial parties.


The Human Brain Resting State Networks Based on High Time Resolution EEG: Comparison to Metabolism-Based Networks (R)

Roberto Pascual Marqui, PhD, The KEY Institute for Brain-‐Mind Research, [email protected] Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Intrinsic resting activity is not simply the ground state of an inactive brain. Rather, it constitutes the dynamic substrate of the “present”, momentary state of the brain, and determines the fate of incoming information (Lehmann 1990). Early intrinsic activity studies were due to Hans Berger (1929) with the first human EEG measurements. The introduction of multichannel EEG, with emphasis on scalp imaging (topographic mapping) of the brain electric activity, advanced the field significantly, with the development of the microstate model (Lehmann et al 1987) and the normative databases of brain rhythm properties (John et al 1977). Renewed interest in the resting state comes from the field of brain imaging techniques such as fMRI and PET. A large number of resting state studies converge in producing brain networks that allegedly have functional significance such as attention, executive control, salience, and the default mode, to name but a few. These results are based on an independent components analysis (ICA) of extremely slow time series of metabolic activity. In this study we analyze electric brain activity at very high time resolution, based on resting, awake, eyes closed EEG recordings from six different groups of subjects across five different labs. Scalp electric potential differences were used to compute electric neuronal activity on the cortex using standardized low resolution electromagnetic tomography (sLORETA; Pascual-Marqui 2002). Each group of subjects was analyzed separately, using the most common ICA methodology as in recent fMRI literature (Allen et al 2011; Pascual-Marqui and Biscay-Lirio 2011). Validation of the electric resting state networks was assessed by the reproducibility across different groups, which was remarkably high. Results show that the many of the electric networks consist of fewer core brain areas, e.g. the left and right temporal regions appear in two distinct electric networks, while they typically appear in one single metabolic network. This difference might be due to the slow temporal nature of metabolic changes, which lumps together over time what is actually taking place independently when seen at higher time resolution with electric activity tomography. Another unique interesting difference to metabolic networks is the appearance of electric networks that consist of pairs of brain regions working against each other, i.e. activation in one region is linked to deactivation of the other. These methods and results may help in understanding normal and pathological brain function from a high time resolution network perspective. References Berger, H., 1929. Uber des Elektrenkephalogramm des Menschen. Archiv fur Psychiatrie und Nervenkrankheiten 87, 527-580. Allen Elena A, Erhardt Erik B , Damaraju Eswar, Gruner William, Segall Judith M, Silva Rogers F , Havlicek Martin, Rachakonda Srinivas, Fries Jill, Kalyanam Ravi, Michael Andrew M Caprihan Arvind, Turner Jessica A, Eichele Tom, Adelsheim Steven, Bryan Angela D, Bustillo Juan, Clark Vincent P, Feldstein Ewing Sarah W, Filbey Francesca, Ford Corey C, Hutchison Kent, Jung Rex E, Kiehl Kent A, Kodituwakku Piyadasa, Komesu Yuko M, Mayer Andrew R, Pearlson Godfrey D, Phillips John P, Sadek Joseph R, Stevens Michael, Teuscher Ursina, Thoma Robert J, Calhoun Vince D. A baseline for the multivariate comparison of resting state networks. Frontiers in Systems Neuroscience, Vol. 5, 2011, DOI={10.3389/fnsys.2011.00002}. John ER; Karmel BZ; Corning WC; Easton P; Brown D; Ahn H; John M; Harmony T; Prichep L; Toro A; Gerson I; Bartlett F; Thatcher F; Kaye H; Valdes P; Schwartz E. Neurometrics. 1977 Jun 4;196(4297):1393-410, Science. Lehmann D (1990) Brain electric microstates and cognition: the atoms of thought. In: E.R. John (ed): Machinery of the Mind. Birkhäuser, Boston. pp. 209-224. Lehmann D, Ozaki H & Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroenceph Clin Neurophysiol 67:271-288. Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sloreta): Technical details. Methods Find Exp Clin Pharmacol. 2002; 24 Suppl D: 5-12. Pascual-Marqui RD and Biscay-Lirio RJ. Interaction patterns of brain activity across space, time and frequency. Part I: methods. arXiv:1103.2852v2 [stat.ME], 2011-March-15, http://arxiv.org/abs/1103.2852. Learning Objective Have a basic understanding of the EEG and fMRI resting state networks of brain function, and some of the methodological aspects on how they are computed and analyzed. Outline

Review of resting state networks (10 minutes) EEG-LORETA based analysis of resting state networks (30 minutes) Results and discussion (10 minutes) Questions and answers (10 minutes) Financial Interest: I declare no conflict of interests, i.e., my one and only income is my academic university salary. I have no relation to any company, no stocks, nothing.



Behavioral and EEG Effects of Lateralized EEG Biofeedback on Lateralized Attention Network Task (LANT) and Lateralized Continuous Performance Task (LCPT) (R,C)

Andrew Hill, MA, UCLA, [email protected] Eran Zaidel, PhD, UCLA

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Introduction: We conducted a randomized, double blind, placebo controlled study of a short course of EEG Biofeedback to identify concurrent changes in behavioral and physiological correlates of hemispheric attention under EEG Biofeedback. Physiological changes throughout the biofeedback process were also measured with dense array EEG. Lateralized behavioral measures were created and examined in the context of lateralized EEG measures. Methods: Participants received one of 4 biofeedback protocols (C3-A1 SMR, C4-A2 SMR, C3-A1 Beta or Sham biofeedback) in 5 half-hour training sessions in 5 consecutive days. Veridical feedback was provided by a brief tone and a visual reward (a progressive image display). 64-channel EEG was recorded during biofeedback training and during two lateralized tests of hemispheric attention (LANT; Lateralized Attention Network Test, and LCPT; Lateralized Continuous Performance Test). The LANT measures covert orienting of spatial attention and distinguishes between a measure of Orienting Benefit due to a valid spatial cue and a measure of Orienting Cost due to a spatially invalid cue. The LCPT contains lateralized CPT And Go-No/Go components. The LANT and LCPT were administered before biofeedback training and after 3 and 5 consecutive training sessions over 5 days of biofeedback. Results: Behavioral: The biofeedback training protocols produced different behavioral effects on attention in the two hemispheres. Accuracy to targets proceeded by invalid cues yielded a significant interaction: Protocol (Sham, C3 SMR, C4 SMR, C3 Beta) x Session (1, 3, 5) x Visual Field (LVF, RVF); p < .025. Similarly there was a near significant interaction: Protocol x Session x Cue (Valid, Neutral, Invalid); p < .07. These near significant results become significant when we restrict our analysis to comparing specific protocol groups (e.g. Orienting Benefit of Sham v. C4 SMR; p < .024). Orienting performance to targets using valid and invalid spatial cues showed dissociation in visual fields across groups. Physiological: Reward signals evoked by the training stimulus were characterized by a P50, an N100, both early and late P300 ERP components, and an N400. Training had a selective effect measured at trained scalp region, shown in ERP and ERSP components, including specific peak and latency changes to the first reward event in a series, in P300 Amplitude (Electrode x Group; p < .039), N400 Latency (Session x Electrode x Group; p < .073), and N400 Peak Amplitude (Session x Electrode; p < .005). The second reward events in a series also often exhibited near significant results (second N100 amplitude at Session x Electrode x Group; p < .07, second P300 peak latency at Electrode x Group; p < .022). Pair wise comparisons of protocol groups showed that behavioral and physiological responses also diverged by laterality of training and by veridical versus sham biofeedback. Conclusion:

We introduced a paired battery of lateralized attention tests and showed that measures of transient (LANT) and continuous (LCPT) attention networks are selectively affected by different EEG biofeedback protocols. In particular, lateralized attention 1) can be characterized both behaviorally and electrophysiologically, and 2) responds selectively to different biofeedback protocols. In addition, ERP components evoked by the biofeedback reward signal discriminated biofeedback effects within and across training sessions and protocols. These measures are therefore likely to reflect a monitor engaged by the biofeedback process. References Greene, D.J., Barnea, A., Herzberg, K., Rassis, A., Neta, M., Raz, A., Zaidel, E. (2008). Measuring attention in the two hemispheres: the lateralized attention network test (LANT). Brain & Cognition, 66 (21-31). Hill, A., Barnea, A., Herzberg, K., Rassis-Ariel, A., Rotem, S., Meltzer, Y., Li, Y.H., and Zaidel, E. (2008), Measuring and Modulating Hemispheric Attention. In Aboitiz, F., and Cosmelli, D. (Eds.) From Attention To Goal-Directed Behavior. Riccio, C. A., C. R. Reynolds, et al. (2002). The continuous performance test: a window on the neural substrates for attention? Arch Clin Neuropsychol 17(3): 235-72. Raz, A., Attentional Mechanisms and Networks. In Spielbereger C (ed), Encyclopedia of Applied Psychology. San Diego, CA: Elsevier Science Academic Press, San Diego, CA. Zaidel, E., Clarke, J. M., & Suyenobu, B. (1990). Hemispheric independence: A paradigm case for cognitive neuroscience. In A. B. Scheibel and A. F. Wechsler (Eds.), Neurobiology of Higher Cognitive Function (pp. 297-355). New York: Guilford Press. Learning Objective Gain insight into recent experimental data that shows the clear differences between active and sham EEG biofeedback in the ongoing EEG signal (seen in ERP and ERSP data), as well examine the divergent effects of C3 versus C4 training on two tests of lateralized visual attention. Outline Laterality and Biofeedback (20 minutes) Ipsilateral versus Contralateral Referencing; Laterality in EEG measures – ERPs, ERSPs; The ERP to the biofeedback reward signal; Testing Lateralized attention using the LANT / LCPT battery; Randomized Double-Blind EEG Biofeedback with Placebo control; Methods – protocol selection, placebo/sham biofeedback implementation (10 minutes) EEG and Behavioral results (20 minutes) Additional Questions (10 minutes) Financial Interest: No financial interests.

The Usefulness of QEEG and ERPs in Predicting Treatment Outcome in ADHD and Depression (R,C)

Martijn Arns, MSc, BrainClinics Diagnostics, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract The application of rTMS in major depressive disorder (MDD) has been investigated intensively over the last years with several meta-analyses demonstrating that compared to placebo fast rTMS (>5 Hz) to the left DLPFC (Schutter, 2009) and slow rTMS (> 1 Hz) over the right DLPFC (Schutter 2010) both exert mood enhancing effects. Similarly another neuromodulation technique, the application of neurofeedback in the treatment of ADHD, has also been well investigated and a recent meta-analysis concluded that neurofeedback has demonstrated a large effect size (ES) on impulsivity and inattention in the treatment of ADHD (Arns et al., 2009). This presentation will focus on how these treatments can be improved in clinical practice using QEEG and ERP data, and to what degree treatment outcome can be predicted. Depression study: Ninety patients with a primary diagnosis of depression or dysthymia were included in this study. All subjects underwent neurophysiological testing before treatment (Eyes Open and Eyes Closed EEG, Oddball ERP) and in addition several rating scales were assessed. The BDI was used to assess response to treatment. Thirty-three patients received slow rTMS over the right DLPFC (1 Hz) and 57 patients received fast rTMS over the left DLPFC (10 Hz). On average there was a 77.2% decrease in depressive symptoms after on average 20.56 sessions (BDI). There was no significant difference for response rates between fast rTMS (76,8%) and slow rTMS (81,8%).\ Based on the literature several EEG and ERP predictors of treatment response in MDD were investigated and preliminary analysis demonstrates that non-responders were characterized by 1) a slow frontal alpha peak frequency (p=.010) in agreement with Arns et al

(2010) 2) parietal excess Delta (p=.015) in agreement with Knott et al. (1996; 2000) and 3) an increased P300 amplitude (p=.031) which was opposite to Bruder et al (2001) but may suggest a subgroup characterized by increased anxiety complaints (Bruder et al. 2002). ADHD study: Twenty-one patients with a primary diagnosis of ADHD were included in this study. Diagnosis was confirmed using the MINI. At intake, every 10th session and outtake an ADHD rating scale and a BDI were assessed to monitor treatment progress. For nonresponders and drop-outs a last-observation carried forward procedure was used. One to two protocols were selected from five standard protocols and were personalized based on the individual QEEG. At outtake 76% patients could be considered a responder (> 50% decrease on one or more subscales of the ADHD rating scales), 14% a non-responder and 10% a drop-out. The ES on inattention was 1.78 and for Impulsivity/Hyperactivity was 1.22. The presented results are similar to the results from Monastra et al. (2002) and substantially larger than the ES obtained in the meta-analysis. These results show promise for personalizing well-established neurofeedback protocols (such as central SMR/Theta and Fronto-central theta/beta) based on the individual EEG. Furthermore, a slow-alpha peak frequency (APF) had no relation to treatment outcome on ADHD specific scales, but did demonstrate a clear relationship to comorbid depression symptoms, with subjects with a slow frontal APF responding less well. These results require further replication employing larger sample sizes, randomization and adequate control groups. Discussion: The results from these studies (along with pre- and post-QEEG results) and other studies from the literature will be integrated and recommendations will be made on what the implications of the presented biomarkers could be for current treatments and current practice, but also specifically for designing new treatments. References Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40(3), 180-9. Arns, M., Spronk, D., & Fitzgerald, P. B. (2010). Potential differential effects of 9 Hz rTMS and 10 Hz rTMS in the treatment of depression. Brain Stimulation, 3, 124-126. Bruder, G. E., Stewart, J. W., Tenke, C. E., McGrath, P. J., Leite, P., Bhattacharya, N., et al. (2001). Electroencephalographic and perceptual asymmetry differences between responders and nonresponders to an SSRI antidepressant. Biological Psychiatry, 49(5), 416-25. Bruder, G. E., Kayser, J., Tenke, C. E., Leite, P., Schneier, F. R., Stewart, J. W., et al. (2002). Cognitive erps in depressive and anxiety disorders during tonal and phonetic oddball tasks. Clinical EEG (Electroencephalography), 33(3), 119-24. Knott, V. J., Telner, J. I., Lapierre, Y. D., Browne, M., & Horn, E. R. (1996). Quantitative EEG in the prediction of antidepressant response to imipramine. Journal of Affective Disorders, 39(3), 175-84. Knott, V. J. (2000). Quantitative EEG methods and measures in human psychopharmacological research. Human Psychopharmacology, 15(7), 479-498. Lansbergen, M. M., van Dongen-Boomsma, M., Buitelaar, J. K., & Slaats-Willemse, D. (2010). ADHD and eeg-neurofeedback: A double-blind randomized placebo-controlled feasibility study. Journal of Neural Transmission.DOI: 10.1007/s00702-010-0524-2. Monastra, V. J., Monastra, D. M., & George, S. (2002). The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder. Applied Psychophysiology and Biofeedback, 27(4), 231-49. Perreau-Linck, E., Lessard, N., Levesque, J., & Beauregard, M. (2010). Effects of neurofeedback training on inhibitory capacities in ADHD children: A single-blind, randomized, placebo-controlled study. Journal of Neurotherapy, 14(3), 229-242. Schutter, D. J. (2009). Antidepressant efficacy of high-frequency transcranial magnetic stimulation over the left dorsolateral prefrontal cortex in double-blind sham-controlled designs: A metaanalysis. Psychological Medicine, 39(1), 65-75. Schutter, D. J. (2010).Quantitative review of the efficacy of slow-frequency magnetic brain stimulation in major depressive disorder. Psychological Medicine, DOI: 10.1017/S003329171000005X. Learning Objective Understand what EEG based predictors for depression have been investigated. Understand how specific well investigated neurofeedback protocols can be used along with QEEG findings and understand what is meant with ‘QEEG informed neurofeedback. Understand how the efficacy of neuromodulation treatments can potentially be improved in current practice. Outline Introduction to EEG based Personalized Medicine (15 minutes) Presentation of the results from rTMS in Depression and predictors of treatment outcome (15 minutes) Presentation of the results of QEEG informed neurofeedback in ADHD (15 minutes) Summary and recommendations (15 minutes)

Financial Interest: No financial conflicts of interest.


Modulatory Effects of Ambient Prism Lenses on Spatial Attention in Autism: An Event-Related Potential Study (R) Guela Sokhadze, University of Louisville, [email protected]

Melvin Kaplan, OD, Stephen M Edelson, PhD; Estato M Sokhadze, PhD Joshua Baruth, PhD, Ayman S El-Baz, PhD, Marie K Hensley, Manuel F Casanova, MD

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .25 Abstract Background and Aims: Autism is a developmental disorder marked by deficits in social interaction, communication, and behavior. One of the less studied deficits in autism is the dysfunction of the ambient visual system, which can affect attention, movement, and visual motor coordination (Kaplan et al., 1998). Focal vision, which involves the central visual field, is the visual system that traditional ophthalmologists address with eyeglasses. Ambient vision, which involves the entire visual field, is more dynamic and largely non-conscious, and integrates with other sensory systems (Kaplan, 2006). Most autistic children have a preference for focal vision, which is why many display a fascination for numbers, letters, and objects. Their lack of attention to ambient vision limits their ability to process information regarding their gait, posture, movement, speech, etc. The current study investigates the efficacy of ambient prism lenses used to correct deficits in ambient vision present in autism. In addition, this study aims to understand the abnormal neural and functional mechanisms underlying visual distortion in autism by incorporating neurophysiologic studies, behavioral studies, and event-potential (ERP) measurements of spatial attention. Methods: Potential participants were recruited from a pool of individuals with autism spectrum disorder (ASD) with the assistance of FEAT (Families for Effective Autism Treatment, Louisville Chapter), Home of Innocents, and Weisskopf Child Evaluation Center (Louisville, KY). Pre-screening questionnaires were filled out by parents, while the evaluation of visual abnormalities related to ambient vision deficits was conducted by Dr. Kaplan. Twenty subjects with autism were screened, and ambient correcting lenses were selected to match their visual deficits. Of the 20 individuals, 12 were able to comply with dense-array EEG recording required for ERP analysis. Mean age of subjects was 13.9±3.0 years. The spatial attention task, programmed in E-Prime (Psychology Software Tools, PA), was represented a modification of a cued Posner spatial attention task (Posner; 1982). The experimental procedure consisted of 2 tasks and 4 total blocks, lasting total of 20 minutes. In task A, the subjects were instructed to focus on the fixation cue in the center of the screen, then were given a cue (red square) in either the left of right visual field, followed by a target (a black X) in place of either left or right cue. In task B, the procedure was the same, except the cues and targets appeared diagonally, in either top left and bottom right corners, or top right and bottom left corners. Probability of correctly cued targets was 80% in both blocks. Each subject completed each block with ambient lenses and placebo lenses. The order or prism and placebo lenses were counterbalanced. The ERP of interest included early (N100) and late ( P300) components at the centro-parietal, and parieto-occipital topographic areas reflecting spatial attention processes (Di Russo et al., 2003; Gomez-Gonzalez et al., 1994; Polich & Herbst, 2000). The analysis included comparison of behavioral performance (reaction time, accuracy) and ERP measures during blocks with and without ambient prism lenses. Results and Discussion: Reaction time in prism lenses condition tended to be faster than in placebo lenses condition (356.1 ms vs. 382.2 ms, n.s.). Accuracy of responses in lenses vs. placebo condition also showed trend to lower percentage of errors (5.6 % vs. 14.2%) but did not reach significance level. Amplitude of the parieto-occipital N100 in horizontal congruent trial significantly higher (-3.92 µV vs. -1.37 µV, F1,23=7.79, p=0.012) during prism lenses condition. Similar effect was significant for incongruent (invalid cue) trials (0.21 µV in placebo vs. -3.33 µV in lenses condition, F1,23=6.40, p=0.021). Amplitude of the centro-parietal P300 (P3b) during more difficult diagonal incongruent condition was higher (4.65 µV vs. 2.05 µV, F1,23=4.57 p=0.045) in ambient prism lenses condition, while latency P3b shorter (349.1 ms vs. 380.1 ms, F1,23=7.72 p=0.012). These ERP effects of wearing ambient prism lenses is indicative of more effective special attentional processing, especially in more complex diagonal incongruent trials. Conclusions: Our pilot study provides preliminary support to utility of wearing prism lenses to correct ambient vision in autism and sensitivity of ERP indices to detect visuospatial attention improvement. We have found that using prism lenses and comparing the autistic patient’s

performance and ERPs with and without such lenses can be a very informative approach to understand the mechanisms of visual deficits and spatial attention orienting impairments typical for autism. References Di Russo, F., Martinez, A., & Hillyard, S.A. (2003) Source Analysis of Event-related Cortical Activity during Visuo-spatial Attention. Cerebral Cortex, 13, 486-499. Gomez-Gonzales, C.M., Clark, V.P., Luck, S.J., & Hillyard, S.A. (1994). Sources of Attention-sensitive Visual Event-related Potentials. Brain Topography, 7, 41-51. Kaplan, M. (2006) Seeing Through New Eyes: Changing the Lives of Children with Autism, Asperger Syndrome and Other Developmental Disabilities through Vision Therapy. Jessica Kingsley Publishers: Philadelphia. Kaplan, M., Edelson, S.M., & Seip, J.L. (1998) Behavioral Changes in Autistic Individuals as a Result of Wearing Ambient Transitional Prism Lenses. Child Psychiatry & Human Development, 29(1) 65-76. Polich, J., & Herbst, K.L. (2000) P300 as a Clinical Assay. International Journal of Psychophysiology, 38, 3-19. Posner, M.I., Cohen, Y., & Rafal, R.D. (1982). Neural Systems Control of Spatial Orienting. Transactions of the Royal Society of London. B298:187–198. Learning Objective Learn about ambient vision deficits in autism, application of corrective lenses and assessment of ambient lenses effects on spatial attention in the Posner cued attention test. Outline Introduction (3 minutes) Methods (3 minutes) Results and discussion (6 minutes) Questions and answers (3 minutes) Financial Interest: No financial conflicts.



What is Common and Unique in ADHD and Schizophrenia: Studies of Event Related Potentials (R,C)

Juri Kropotov, PhD, Institute of the Human Brain, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract The paper represents review of research of event related potentials (ERPs) in ADHD and schizophrenia. The most common scientific observation is decrease of the P3b wave both in ADHD and schizophrenia. This wave is usually evoked in the oddball paradigm in response to deviant rare targets when compared with responses to standard frequent non-target stimuli. This observation appears to reflect a common dysfunction in ADHD and schizophrenia in the parietal attention system. Dopamine hypotheses of ADHD and schizophrenia will be discussed. The hypotheses imply involvement of different aspects of information processing within the basal ganglia thalamocortical circuits. A recently emerged independent component analysis (ICA) provides a powerful tool for decomposing ERPs into components of different functional meanings. This paper describes results of application of Independent Component Analysis (ICA) for decomposing a collection of ERPs into independent components associated with different psychological operations (such processing in dorsal and ventral visual streams, orienting response, engagement, motor suppression and conflict monitoring operations). 1000 healthy subjects, 1000 patients with ADHD and 100 patients with schizophrenia participated in this multi-center European study including laboratories from Switzerland (A. Mueller et al.), Norway (S.Hollup et al.), Macedonia (N. Pop-Jordanova et al), Russia (Kropotov et al). The results of application of the ERP ICA for diagnosis (discrimination) ADHD and schizophrenia from healthy subjects will be presented. In the final part of the paper a methodology for constructing protocols of neurofeedback and tDCS on the basis of comparison the individual ERP parameters with the normative data will be presented. Recently emerged methods of neurotherapy such as sLORETA-based, ERP-based neurofeedback and tDCS will be also introduced in relation to neuromodulation in ADHD and schizophrenia.

References Kropotov J.D. 2009. Quantitative EEG, event related potentials and neurotherapy. Academic Press, Elsevier. Learning Objectives Understand electrophysiological markers of ADHD and schizophrenia and how to use them in diagnosis and therapy. Outline Brain systems impaired in ADHD and schizophrenia: brain imaging evidence (30 minutes) ERP correlates of brain dysfunction in ADHD and schizophrenia and how this correlates can be used to treatment protocols (30 minutes) Financial Interest: The NovaTech company and Mitsar company are paying for this trip. I am a co-owner of HBImed AG company (Switzerland).

Planning for a Collaborative Multi-Site, Double-Blind, Sham-Controlled Randomized Clinical Trial of Neurofeedback for ADHD (R)

Nick Lofthouse, PhD, Ohio State University, [email protected] L. Eugene Arnold, MD, Martijn Arns, MSc, Keith Conners, PhD, Roger deBeus, PhD,

Henry Harbin, MD, Laurence Hirshberg, PhD, Cynthia Kerson, PhD, Helena Kraemer, PhD, Joel Lubar, PhD, Keith McBurnnett, PhD, Vincent Monastra, PhD

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Medication and behavior modification, the established treatments for ADHD, are not universally effective or acceptable to all, and have not been shown to have sustained effects beyond 2 years (Jensen et al, 2007). The 8-year outcome paper (Molina et al, 2009) for the NIMH Multimodal Treatment Study of ADHD (the MTA) pointed out the need for new treatments with lasting effects. Among complementary/alternative treatments for ADHD, neurofeedback (NF) is one of the most prominent, despite its expense and technical difficulties. However, despite a meta-analysis of 6 randomized control trials (RCTs) with a large effect size (ES) for inattention and medium ESs for hyperactivity and impulsivity (Arns et al 2009) and 16 RCTs with a mean medium ES for overall ADHD, inattentive, and hyperactive/impulsive symptoms, many of these studies are small and have not used adequate blinding. The results from the recent NIMH funded OSU feasibility pilot study indicated that a well-blinded large RCT of NF utilizing a sham control of equal intensity and duration is feasible and necessary, although questions have been raised about whether the sham placebo was truly inert. As with any treatment, it is difficult to determine how much of the apparent treatment effect is specific to the treatment, and how much is placebo effect. Two small studies (Perreau-Link et al., 2010; [N=8] & Lansbergen et al., 2010 [N=14]) published after the Arns meta-analysis had a blinded sham control and showed no advantage of NF over placebo, raising question about the unblinded studies. These inconclusive scientific results pose a public health dilemma. The treatment involves considerable initial expense and lengthy commitment by the patients and families. However, if NF has lasting specific benefit, the initial cost and time may compare favorably with medication. Therefore it is important to know whether NF has a specific effect beyond placebo response, whether the persistence of benefit can be replicated, and whether a biological endophenotype can be identified who will reliably benefit from it. Without resolution of the effectiveness question, this potentially valuable adjunct to the ADHD treatment armamentarium will not be fully utilized and widely accessible. In November 2010, Drs. Gene Arnold, Roger deBeus, Larry Hirshberg, and Nick Lofthouse presented a symposium on “EEG Neurofeedback for ADHD: Review of the Science and New Findings” at the annual meeting of ChADD. Chaired by Drs. Russell Barkley and Ann Abramowitz, this symposium led to a discussion about the possibility of a large-scale multi-site double-blind, sham-controlled randomized clinical trial (RCT) of neurofeedback (NF) for pediatric ADHD. This discussion continued with weekly telephone conferences involving a group of NF experts (Drs. Joel Lubar, Vincent Monastra, Cynthia Kerson, Henry Harbin, Roger deBeus, Larry Hirshberg & Mr. Martijn Arns) and mainstream ADHD scientists (Drs. Gene Arnold, Keith McBurnett, Keith Conners, Helena Kraemer, & Nick Lofthouse). In April 2011, these discussions led to an agreed-upon pre-application letter of intent to NIMH for multi-million dollar funding of this project. This proposed study is the first to involve planning and execution by both mainstream ADHD scientists (to insure credible scientific rigor) and NF experts/advocates (to insure credible and rigorous treatment). In such a study it is essential that all stakeholders have input so that the results, whatever they are, will be credible to all. This proposal is significant and innovative at the scientific, clinical

and public health level. Scientifically, the lack of a large well-controlled double-blinded examination of NF has been a critical barrier to progress in the field, with disagreement between NF and most mainstream ADHD investigators about interpretation of the available data. On a clinical and public health level, testing of this promising treatment in a way that is rigorous in both clinical method and research design is greatly needed to see whether NF is an effective alternative treatment option for the many youth who do not respond to or refuse current evidence-based treatments and to see if NF holds additional promise as a complimentary treatment option. This 60 minute oral presentation will present the theoretical, scientific, clinical and public health background for the proposed study and discuss the collaborative team’s agreements, disagreements and resolutions in developing the NIMH letter of intent. The study’s main objectives, specific hypotheses, design, participants, instruments and procedures will also be reviewed. Finally, we will report whether our efforts to obtain NIMH-funding were successful or not and the next step in our collaboration. References Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen., A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity & hyperactivity: a meta-analysis. EEG & Clinical Neuroscience; 40, 180-189. Jensen PS, Arnold LE, Swanson J, Vitiello B, Abikoff HB, Greenhill LL, Hechtman L, Hinshaw SP, Pelham WE, Wells KC, Conners CK, Elliott GR, Epstein J, Hoza B, Molina BSG, Newcorn JH, Severe JB, Wigal T, Gibbons RD, Hur K. Follow-up of the NIMH MTA study at 36 months after randomization. Journal of the American Academy of Child and Adolescent Psychiatry 46(8):988-1001, 2000. Lansbergen MM, van Dongen-Boomsma M, Buitelaar JK, Slaats-Willemse D (2011): ADHD and EEG-neurofeedback: a double-blind randomized placebo-controlled feasibility study. Journal of Neural Transmission, 118:275-284. Lofthouse, N, Arnold L.E, Hersch, S, Hurt, E, Barkley, R, DeBeus, R, Hirshberg, L, & Abramowitz, Ann (2010, November). EEG Neurofeedback for ADHD: Review of the Science and New Findings. Part of a symposium presented at the annual meeting of Children and Adults with Attention Deficit/Hyperactivity Disorder (CHADD), Atlanta, GA. Molina, B.S.G., Hinshaw S.P., Swanson J.M., Arnold, L.E., Vitiello, B., Jensen, P.S., Epstein, J.N., Hoza, B., Hechtman, L., Abikoff, H.B., Elliott, G.R., Greenhill, L.L.,Newcorn, J.H., Wells, K.C., Wigal, T.L., Gibbons, R.D., Hur, K., Houck, P.R., and the MTA Cooperative Group. The MTA at 8 Years: Prospective Follow-Up of Children Treated for Combined Type ADHD in a Multisite Study. Journal of the American Academy of Child and Adolescent Psychiatry 48(5):484-500, 2009. Perreau-Linck E, Lessard N, Levesque J, Beauregard M (2010): Effects of Neurofeedback Training on Inhibitory Capacities in ADHD Children: A Single-Blind, Randomized, Placebo-Controlled Study. Journal of Neuropathy, 14:229-242. Learning Objective Understand how a group of NF experts and mainstream ADHD scientists collaborated on a plan (& NIMH application) for a multisite, double-blind, sham-controlled randomized clinical trial of neurofeedback for ADHD. Outline The theoretical, scientific and clinical/public health background, the collaborative team of NF experts and mainstream ADHD scientists and the agreements, disagreements and resolutions in planning this study (30 minutes) The application’s main objectives, specific hypotheses, design, participants, instruments and procedures (30 minutes) Financial Interest: No financial interests.

Saturday, September 17, 2011


How Reliable is the Resonance Frequency? (R,C) Fredric Shaffer, PhD, Truman State University, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Introduction: Lehrer and colleagues (2004) proposed that each client has a unique breathing rate, called the resonance frequency, at around 6 breaths per minute. Heart rate and blood pressure oscillations are 1800 out of phase, the baroreflex is strongest, and RSA is greatest at this respiration rate (DeBoer, Karemaker, & Strackee, 1987; Vaschillo, Lehrer, Rishe, & Konstantinov, 2002).

Since training clients to breathe at their resonance frequency is a crucial component of heart rate variability (HRV) biofeedback, it is important that these measurements are reliable. Why train a client to breathe at 5.5 breaths per minute today if her resonance frequency will be 6.5 breaths per minute next week? The present study examined the 2-week test-retest reliability of resonance frequency measurements using a modified version of a protocol developed by Lehrer and Gevirtz. Method: Participants Nineteen undergraduates (16 males and 3 females), 19-22 years of age, participated in this study. Apparatus A Thought Technology ProComp™ Infiniti system detected the EKG using an Infiniti EKG™ sensor with leads placed on the upper chest and below the sternum, and measured respiration rate using a Resp-Flex/Pro™ sensor placed around the abdomen at the level of the navel. BioGraph Infiniti™ software measured the resonance frequency, three global indices of HRV (HR Max – HR Min, pNN50, and SDNN), and the LF/HF ratio. The resonance frequency is the breathing rate that produces the greatest synchrony between respiration band and instantaneous heart rate signals and that maximizes the most global measures of HRV. HR Max – HR Min is the difference between the highest and lowest heart rates during each respiratory cycle. The pNN50 index calculates the percentage of adjacent N-to-N intervals that differ from each other by more than 50 milliseconds. SDNN is the standard deviation of the interbeat interval for all normal sinus beats measured in milliseconds. The LF/HF ratio represents the percentage of power in the low frequency band divided by the percentage of power in the high frequency band. Procedure Subjects sat upright in a straight-backed chair with eyes open throughout this study. Following a 10-minute resting baseline without feedback, we instructed subjects to follow an animated pacing display designed to guide their breathing from 7.5 to 4.5 breaths per minute in seven descending ½-breath-per-minute steps. Subjects breathed at each target rate for 2 minutes followed by a 1-minute buffer period. We retested all subjects using the same procedure 2 weeks later to assess the reliability of these measurements. They received no HRV training or breathing practice during the intervening period. Results: We measured 2-week test-retest reliabilities measured using a Pearson Product-Moment Correlation Coefficient. We assessed the global HRV indices and HRV frequency components while our subjects breathed at their resonance frequency. Resonance Frequency Resonance frequency measurements were reliable, r(17) = 0.73, p = .000. Global HRV Indices While HR Max – HR Min measurements were unreliable, both pNN50, r(17) = 0.65, p = .002; and SDNN measurements, r(17) = 0.59, p = .008, were also reliable. HRV Frequency Components While the VLF, LF, and HF measurements were unreliable, the LF/HF ratio was reliable, r(17) = 0.58, p = .009. Discussion: Resonance frequency measurements achieved acceptable 2-week test-retest reliability. Among the global HRV indices, SDNN and pNN50 measurements were also reliable, while HR Max – HR Min was not. Among the HRV frequency components, the LF/HF ratio was reliable, but VLF, LF, and HF were not. These findings support protocols that train clients to breathe at their unique resonance frequency to maximize HRV. Since our subjects were primarily healthy male undergraduates, researchers should replicate these findings with a gender-balanced clinical population to ensure external validity. References Agelink, M. W., Boz, C., Ullrich, H., & Andrich, J. (2002). Relationship between major depression and heart rate variability. Clinical consequences and implications for antidepressant treatment. Psychiatry Research, 113, 139-149. Bigger, J. T., Fleiss, J. L., Rolnitzky, L. M., & Steinman, R. C. (1992). The ability of several short-term measures of RR variability to predict mortality after myocardial infarction. Circulation, 88, 927-934.

Cohen, H., Kotler, M., Matar, M., & Kaplan, Z. (1997). Power spectral analysis of heart rate variability in posttraumatic stress disorder patients. Biological Psychiatry, 41, 627–629. Combatalade, D. (2009). Basics of heart rate variability applied to psychophysiology. Montreal, Canada: Thought Technology Ltd. Cowan, M. J., Pike, K. C., & Budzynski, H. K. (2001). Psychosocial nursing therapy following sudden cardiac arrest: Impact on two-year survival. Nursing Research, 50, 68-76. DeBoer, R. W., Karemaker, J. M., & Strackee, J. (1987). Hemodynamic fluctuations and baroreflex sensitivity in humans: A beat-to-beat model. American Journal of Physiology—Heart and Circulatory Physiology, 253(22), H680-H689. Dixhoorn, J. V., & White, A. (2005). Relaxation therapy for rehabilitation and prevention in ischemic heart disease: A systematic review and meta-analysis. European Journal of Cardiovascular Prevention and Rehabilitation, 12, 193-202. Goldberger, A. L. (1991). Is the normal heartbeat chaotic or homeostatic? News in Physiological Science, 6, 87-91. Kleiger, R. E., Miller, J. P., Bigger, J. T., et al., & the Multicenter Post-Infarction Research Group. (1987). Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. American Journal of Cardiology, 59, 256-262. Lehrer, P. M. (2007). Biofeedback training to increase heart rate variability. In P. M. Lehrer, R. L. Woolfolk, & W. E. Sime (Eds.). Principles and practice of stress management (3rd ed.). New York: The Guilford Press. Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S. E., Eckberg, D. L., Edelberg, R., et al.(2003). Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow. Psychosomatic Medicine, 65, 796-805. Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S. E., Scardella, A., Siddique, M., et al. (2004). Biofeedback as a treatment for asthma. Chest, 126, 352-361. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation, 93, 1043-1065. Learning Objective Learn how to measure a client's resonance frequency. Outline Explanation of the resonance frequency and its importance in HRV biofeedback (10 minutes) Description of a standardized protocol to measure the resonance frequency (10 minutes) Discussion of the reliability of the resonance frequency (10 minutes) Financial Interest: No financial interests or relationships.

The Effects of Heart Rate Variability on Sensorimotor Rhythm: A Pilot Study (R)

Andrea Reid, MA, ADD Centre, [email protected] Stephanie Nihon, ADD Centre, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Heart Rate Variability (HRV) Training and EEG Biofeedback are techniques that have been used to improve neurological disorders, such as ADHD, as well as to optimize performance in athletes. HRV is a measurement of the variation in the respiration rate at which the heart is beating. Vaschillo, Lehrer and Rische (2002) call the low-frequency range 0.05-0.15 Hz which generally corresponds to 5-6 breaths per minute and assumes respiratory sinus arrhythmia (RSA). The calculation for 6 breaths per minute is 6/60sec which is equal to 1/10 or .1. RSA describes the relationship between heart rate changes and increased heart rate during inhalation and decreased heart rate during exhalation. Resonance found between these frequencies for breathing and heart rate variations relate to the heart rate closed loop of the baroreflex system, through which blood pressure changes are regulated. This specific signature of breathing and heart rate changes being synchronized may correlate with a more relaxed cognitive clarity. Improvements in cognition (Vaschillo et al, 2002) and emotional stability (Applehans &Luecken, 2006) have been demonstrated as a result of HRV training. A similar mental state is the target of EEG biofeedback training to increase sensorimotor rhythm (SMR 12-15Hz across the sensorimotor strip C3,Cz,C4). SMR has been closely linked to a quieting state of calm relaxed focus (Sterman, 1996). In 2010, Thompson and Thompson developed the Systems Theory of Neural Synergy which outlines a link between Heart Rate Variability and brain function. In this paper, we discuss a link between Heart Rate Variability and SMR. We propose that training for increased HRV can lead to increased levels of SMR.

At the ADD Centre, Dr. Lynda Thompson and Dr. Michael Thompson have been combining EEG biofeedback with physiological biofeedback including respiration and heart rate training. The authors of this paper, while working with clients at the ADD Centre, noticed that many clients, including athletes were showing increased SMR during sessions when they were training to achieve synchrony between respiration and heart rate changes (HRV training). This observation lead to the hypothesis that Heart Rate Variability training may enhance increases in SMR. Method: Preliminary data has been collected for 10 clients (n=10). 5 clients were athletes training to improve performance and 5 clients were from a clinical population aiming to increase SMR as a part of their program. The age range in this study was age 6 to 60. 3 minute EEG assessment data was collected and artifacted at Cz for each client. Statistics were selected to measure mean microvolt values for SMR (12- 15hz). EMG was also measured to ensure that the reflection of EMG on EEG (muscle artifact) did not act to artificially increase SMR. A second 3 minute sample of EEG was collected during 3 minutes of HRV training during which the client demonstrated HRV and synchronous RSA. Clients had to achieve a peak frequency heart rate between .05 and .15 during HRV training to be included in the study. The authors expect to collect data for 20 more clients during the next few months. Results: Mean microvolt values were collected for SMR during the baseline recording and during the HRV training. All clients demonstrated an increase in SMR during HRV training as compared to baseline measures. T-Tests were done on the data which showed p < .01. This demonstrates that the increase in SMR during HRV training is statistically significant. Discussion: The preliminary results suggest that HRV training can lead to increases in SMR. These results have implications in the clinical setting. Clients with neurological disorders such as ADHD, Seizure Disorders, and Asperger’s Syndrome who are working towards increasing SMR may benefit from combining this neurofeedback training with HRV training. This combination of biofeedback and neurofeedback may lead to better clinical outcomes, possibly in less time. HRV training gives the athletes more flexibility in controlling their autonomic nervous systems and thereby allows them to better regulate their emotional states during sports performance, which is a critical tool during a high stress performance. Breathing at one’s resonant frequency can help maintain a calm, relaxed focus in the body and mind. SMR has been closely linked to a quieting state of calm relaxed focus (Thompson & Thompson). This study suggests that, by training HRV and SMR, athletes can obtain synergy between body and mind and thereby reach a more ideal performance state. Based on the preliminary results of this study, practicing heart rate variability training with clinical populations and with athletes is associated with increases in SMR at the central location (CZ), which is associated with a calm and alert mental state. Future research could investigate the combination of SMR training with HRV training as an effective method/intervention for working with both clinical populations and athletes to ameliorate symptoms and optimize performance. References Applehans, B.M., & Luecken, L. J. (2006). Attentional processes, anxiety, and regulation of cortisol reactivity. Anxiety, Stress & Coping,(19),81-92. Sterman, M.B. (1996) Physiological origins and functional correlates of EEG rhythmic activities: Implications for self-regulation. Biofeedback and Self-Regulation. (21),3-33. Thompson, L. & Thompson, M. (1998) Neurofeedback combined with training in metacognitive strategies: Effectiveness in students with ADD. Journal of Applied Psychophysiology and Biofeedback, (23), 4, 243-263. Thompson, M., Thompson, L.,& Reid, A. (2010) Neurofeedback Outcomes in 150 Clients with Asperger’s Syndrome and 9 Clients with Autism. Journal of Applied Psychophysiology and Biofeedback, (35), 1, 63-81. Vaschillo, E.G., Lehrer, P.M., Rishe, N. & Konstantinov, M. (2002). Heart rate variability biofeedback as a method for assessing baroreflex function: A preliminary study of resonance in the cardiovascular system. Applied Psychophysiology and Biofeedback (27)1-27.

Learning Objective Understand how training to achieve synchrony between respiration and heart rate changes (HRV training) may influence increases in sensorimotor rhythm. Outline Description of how HRV training and EEG biofeedback are currently being used to improve performance in athletes and ameliorate symptoms in clinical populations. Including an explanation of HRV training (5 minutes) Hypothesis – description of our hypothesis and how we came to investigate it (5 minutes) Description of our Method (5 minutes) Results (5 minutes) Conclusions that can be drawn from our results (5 minutes) Discussion regarding future research (5 minutes) Financial Interest: None

Exact Low-Resolution Electromagnetic Brain Tomography (eLORETA) of Adult ADHD: Pre/Post Findings Following Neurofeedback Therapy (R,C)

Sarah Wyckoff, MA, University of Tübingen, [email protected] Kerstin Mayer, MSc, University of Tübingen

Leslie Sherlin, PhD, NovaTech EEG Ute Strehl, PhD, University of Tübingen

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Objectives: Attention–deficit/hyperactivity disorder (ADHD) is one of the most common disorders of childhood with a cumulative incidence of 7.5% by 19 years of age (Barbaresi et al., 2004). The primary symptoms of ADHD include inattentiveness, impulsivity, and hyperactivity, which persist into adulthood for 4-5% of patients (Goodman & Thase, 2009). EEG/QEEG analysis of adults with ADHD compared to healthy controls and/or normative database populations have produced a variety of patterns of activity, highlighting the heterogeneity of this population (Bresnahan, Anderson, & Barry, 1999; Bresnahan & Barry, 2002; Clarke et al., 2008a; Clarke et al., 2008b, Hale et al, 2009; Koehler et al., 2009; Loo et al., 2009; Thompson & Thompson, 2005; White, 2001, 2003). The objective of this study was to investigate the specific frequency band-pass regions and spatial locations associated with adult ADHD using exact low-resolution electromagnetic brain tomography (eLORETA) in comparison to healthy controls and following 30 sessions of neurofeedback therapy. Methods: Continuous 19-channel EEG was acquired from 40 adult participants that met DSM-IV criteria for ADHD (combined, inattentive, or hyperactive type), without additional serious physical, neurological, or psychiatric disorders, and a full scale IQ > 80. EEG recordings were collected at pre/mid/post/follow-up treatment intervals and included EO, EC, P300, and CNV tasks, as well as ADHD behavioral questionnaires. eLORETA analysis was computed on 2 min of EC data (Pascual-Marqui, 2002). The eLORETA output data was compared with age-matched individuals in a healthy control database (Nova Tech EEG, Mesa, Arizona, 2005) using a multiple comparison procedure for the following frequency bands: absolute and relative power in delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta1 (13-18 Hz), beta2 (19-21 Hz), beta3 (22-30 Hz); alpha and theta bands adjusted to individual alpha peak frequency (Pascual-Marqui, The KEY Institute for Brain-Mind Research, Zurich, Switzerland, 2002). Pre/post changes in the sources of EEG rhythms were also assessed following 30 sessions of Theta/Beta or Slow Cortical Potential neurofeedback training. Results: This investigation is part of a long-term treatment study currently in progress. The most current results related to eLORETA EEG source localization of adult ADHD patients compared to a control population and following 15 sessions of neurofeedback therapy will be presented at the time of the conference. Conclusion: Analysis of eLORETA current source activities in adult ADHD patients compared to healthy controls and following neurofeedback training has not previously been investigated and may yield valuable insights related to alternative treatments for this population. Specific findings will be discussed and implication in the current treatment study and future research will be explored.

References Barbaresi, W., Katusic, S., Colligan, R., Weaver, A., Pankratz, V., Mrazek, D., et al. (2004). How common is attention-deficit/hyperactivity disorder? Towards resolution of the controversy: Results from a population-based study. Acta Paediatr Suppl, 93(445), 55-59. Bresnahan, S. M., Anderson, J. W., & Barry, R. J. (1999). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry, 46(12), 1690-1697. Bresnahan, S. M., & Barry, R. J. (2002). Specificity of Quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res, 112(2), 133-144. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Selikowitz, M., & Bryne, M.K. (2008a). EEG coherence in adults with attention-deficit/hyperactivity disorder. International Journal of Psychophysiology, 76(1), 35-40. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Seilkowitz, M., & Bryne, M.K. (2008b). EEG in adults with attention-deficit/hyperactivity disorder. Int J Psychophysiology, 70(3), 176-183. Goodman, D. W., & Thase, M. E. (2009). Recognizing ADHD in adults with comorbid mood disorders: Implications for identification and management. Postgrad Med, 121(5), 20-30. Hale, T. S., Smalley, S. L., Hanada, G., Macion, J., McCracken, J. T., McGough, J. J., & Loo, S. K. (2009). Atypical alpha asymmetry in adults with ADHD. Neuropsychologia, 47(10), 2082-2088. Koehler, S., Lauer, P., Schreppel, T., Jacob, C., Heine, M., Boreatti-Hummer, A., et al. (2009). Increased EEG power density in alpha and theta bands in adult ADHD patients. Journal of Neural Transmission, 116(1), 97-104. Loo, S. K., Hale, T. S., Macion, J., Hanada, G., McGough, J. J., McCracken, J. T., & Smalley, S. L. (2009). Cortical activity patterns in ADHD during arousal, activation, and sustained attention. Neuropsychologia, 47(10), 2114-2119. Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details. Methods Find Exp Clin Pharmacol 24(Suppl D), 5–12. Thompson, L., & Thompson, M. (2005). Neurofeedback Intervention for Adults with ADHD. Journal of Adult Development, 12(2 - 3), 123-130. White, J. N., Jr. (2001). Neuropsychological and electrophysiological assessment of adults with attention deficit hyperactivity disorder. Unpublished doctoral dissertation, The University of Tennessee, Knoxville. White, J. N., Jr. (2003). Comparison of QEEG Reference Databases in Basic Signal Analysis and in the Evaluation of Adult ADHD. Journal of Neurotherapy, 7(3/4), 123-169. Learning Objective Understand and identify the specific frequency band-pass regions and spatial locations associated with adult ADHD using exact low-resolution electromagnetic brain tomography (eLORETA). Visualize eLORETA activity changes following 30 sessions of Theta/Beta and SCP neurofeedback. Outline Background on adult ADHD EEG findings and description of eLORETA analysis method (15 minutes) Study population demographics, eLORETA analysis methods, and Results (10 minutes) Discussion of treatment implications, study limitations, and future directions (5 minutes) Financial Interest: The authors of this presentation have no significant financial interest or relationship with commercial supporter(s) or manufacturer(s) of any commercial product or service that is discussed as part of the presentation.

LORETA Neurofeedback and the Precuneus (R,C) Rex Cannon, PhD, University of Tennessee, [email protected]

Debora Baldwin, PhD, University of Tennessee Dominic Di Loreto, BA, University of Tennessee

Alexander Khaddouma, BA, University of Tennessee

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Introduction: The aims of this study were to determine the cortical effects of LORETA Neurofeedback (LNFB) in the precuneus as a potential target region of training for substance abuse and attention deficit disorders. We evaluated pre and post training current source density in the ROT and the network effects of 20 sessions of LNFB in the left precuneus. We assessed training effects using the Personality Assessment Inventory (PAI) and Delis-Kaplan Executive Function System (D-KEFS) pre and post training. Methods:

Four undergraduate students, with a mean age of 24 completed twenty sessions of LNFB training in the left precuneus. The training consisted of five sessions per week with six, five-minute training rounds within each session. The effects of LNFB were assessed with D-KEFS and PAI post training. We utilized a linear mixed model with repeated measures to analyze the effects of LNFB on current source density levels within the default network. Results: The region and frequency of training shows a significant increase in CSD as a result of LNFB. Network analyses show increases in left BA 3, 6, 7 and frontal regions with a corresponding decrease in right amygdaloid regions including BA 34, 28 and 35. Delta CSD is shown decreased in left BA 44, theta shows increased CSD in right BA 46, and alpha shows increased CSD in right BA 45, while beta shows maximal increased CSD in left BA 10. Perceptible changes in executive functions and personality assessment inventory scales are forthcoming. Discussion: The data obtained in this study are part of a larger methodology to employ LNFB in treatment paradigms for SUD and Adult ADHD. These results demonstrate LNFB in the precuneus to produce specific network increases and decreases between regions shown to be important to attention and self-regulation. In effect, decreases between network assemblies may reflect the direct effects of operant learning, such that less power is needed to produce the desired behavior. Numerous studies of neuroplastic changes associated with practice have shown similar effects and will be discussed. Thus, the current data demonstrate this to be a feasible method for larger clinical research trials and randomized control double blind studies using LNFB methodology. Clinical implications are discussed. References Cannon, R., Congedo, M., Lubar, J., & Hutchens, T. (2009). Differentiating a network of executive attention: LORETA neurofeedback in anterior cingulate and dorsolateral prefrontal cortices. Int J Neurosci, 119(3), 404-441. Cannon, R., & Lubar, J. (2008). EEG Spectral Power and Coherence: Differentiating Effects of Spatialâ€“Specific Neuro-Operant Learning (SSNOL) Utilizing LORETA Neurofeedback Training in the Anterior Cingulate and Bilateral Dorsolateral Prefrontal Cortices. Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 11(3), 25 - 44. Cannon, R., Lubar, J., & Baldwin, D. (2008). Self-perception and experiential schemata in the addicted brain. Appl Psychophysiol Biofeedback, 33(4), 223-238. Cannon, R., Lubar, J., Congedo, M., Thornton, K., Towler, K., & Hutchens, T. (2007). The effects of neurofeedback training in the cognitive division of the anterior cingulate gyrus. Int J Neurosci, 117(3), 337-357. Castellanos, F. X., Margulies, D. S., Kelly, C., Uddin, L. Q., Ghaffari, M., Kirsch, A., et al. (2008). Cingulateprecuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biol Psychiatry, 63(3), 332-337. Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: a review of its functional anatomy and behavioural correlates. Brain, 129(Pt 3), 564-583. Learning Objective Understand the role of the precuneus in self-regulation and attention. Outline Rationale (5 minutes) Methods (5 minutes) Results (10 minutes) Discussion (5 minutes) Clinical Implications (5 minutes) Financial Interest: I have no financial interest in any of the companies that produce the hardware or software. I do have interest in increasing the validity and acceptance of the scientific merit associated with qEEG, LORETA and Neurofeedback.


Deep Brain Modulations Guided by EEG Feedback Can be Probed by Simultaneous fMRI (R,C)

Sivan Kinreich, MA, Tel Aviv University, [email protected] Nathan Intrator, PhD; Ilana Klovatch; Talma Hendler, PhD, MD

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .25 Abstract Introduction: Decades of Electroencephalogram (EEG)-feedback practice proved that people can be effectively trained to selectively modulate their brain activity. The premise of such a practice has been based on the idea that people can regulate their brain activity, thus improve its performance1, 2. A common protocol of EEG-feedback aimed to help people in relaxation is based on closed loop guidance by shifts from high-amplitude of alpha (8-14Hz) to low-amplitude of theta (4-7 Hz) oscillations3 through training. The induction of such a shift in EEG oscillations has been useful in reaching a state of deep relaxation in psychiatric conditions of anxiety and mood disorder1,4,5. However, the clinical implication of this practice in psychiatry remained elusive and considered of relatively low therapeutic yield, possibly due to its wide spread cortical representations. The hurdle may lay in the poor spatial resolution of the EEG, thus precluding valid probing of deeper brain structures such as limbic regions which are critical for modulating emotional states. The current project aims to use simultaneous acquisition of Functional Magnetic Resonance Imaging (fMRI) and EEG in order to unfold in high spatial and temporal resolutions the neural modulations induced via EEG feedback on shifts in alpha/theta ratio. Methods: 15 healthy subjects participated in a pre-scanning 15 minutes training with eyes closed to apply EEG-neurofeedback for increasing the ratio of theta to alpha. In the 3T MRI scanner subjects followed a similar EEG neurofeedback protocol which alternates between increasing and decreasing the Alpha/Theta ratio (i.e. inducing neural modulation. BrainVoyager, EEG-Lab and at-home software packages were used for preprocessing and analyzing the raw brain signals in correspondence to induced mental states. Analysis & Results: General linear model for the whole brain using the changing mental states as predictors was calculated. Defined contrast between eyes closed wakefulness and eyes closed relaxation revealed two intriguing brain areas. While the subjects got relaxed the superior frontal gyrus was more active and the subgenual cingulate cortex was deactivated. Conclusions: Simultaneous fMRI during EEG feedback via alpha/theta ratio modulation probed activation variation in deep brain limbic area such as the subgenual cingulate cortex. This limbic area is known to play a role in generation of affective states presumably mediated by parasympathetic autonomic tone6 which might lead decreased focal activation. An extensive work in the last decade points to alterations in this area activity in major depression and to the moderating effect of its focal electrical stimulation. The recruitment of the superior frontal gyrus as relaxation took place fits its proposed role in moderating high arousal7 in emotional self regulation. Altogether our results clearly demonstrated the advantage in combining EEG and fMRI for optimizing neurofeedback procedure at the individual level. Methodological and practical aspects of such approach will be further discussed. References Peniston, E.G., Marrinan, D.A., Deming W.A., Kulkosky P.J. (1993), 'EEG alpha theta brainwave synchronization in Vietnam theater veterans with combat-related post-traumatic stress disorder and alcohol abuse', Advances in Medical Psychotherapy, vol. 6, pp. 37–50. Gruzelier, J. (2009), 'A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration' Cogn Process, vol. 10, pp.101-109. Vogel, G., D. Foulkes, et al. (1966), 'Ego functions and dreaming during sleep onset', Arch Gen Psychiatry , vol. 14, no. 3, pp. 238-248. Gevensleben H., Holl B., Albrecht B., et al. (2009), 'Is neurofeedback an efficacious treatment for ADHD? A randomized controlled clinical trial', J Child Psychol Psychiatry, vol. 50, no 7, pp.780–789. Lantz, D. L. and M. B. Sterman (1988), 'Neuropsychological assessment of subjects with uncontrolled epilepsy: effects of EEG feedback training', Epilepsia, vol. 29, no 2, pp. 163-171. Critchley, H. D. (2005), 'Neural mechanisms of autonomic, affective, and cognitive integration', J Comp Neurol, vol. 493, no 1, pp. 154-156. Beauregard, M., J. Levesque, et al. (2001), 'Neural correlates of conscious self regulation of emotion', J Neurosci, vol. 21, no 18, RC165. Davidson, R. J., K. M. Putnam, et al. (2000), 'Dysfunction in the neural circuitry of emotion regulation--a possible prelude to violence', Science, vol. 289, no 5479, pp. 591-594.

Learning Objective Understand the practice of EEG Neurofeedback. Understand the brain mechanism behind the EEG neurofeedback process. Understand the human emotional relaxation process mental and physiologically. Outline The relaxation process via neurofeedback (7 minutes) The relaxation brain network and the neurofeedback relation (8 minutes) Financial Interest: For this research there are no financial conflicts of interest.


The Role of Omega-3 EPA/DHA in Mood and Cognition: Can Fish Oil Improve Neurofeedback Outcomes? (R,C)

Daniel Johnston, MD, Uniformed Services University of the Health Sciences, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Based on existing fatty acid research around Omega-3 EPA/DHA from fish oil and their known benefits involving both neuronal development in the young, but also in cognition and mood in various populations, one might expect that they might quantifiably improve the desired outcomes in neurofeedback protocols. It is interesting to note that the literature seems to support EPA more for mood benefits and DHA more for cognitive and these differences will be discussed. Since we know DHA is important component in the cell wall in the brain, this is something to be aware of when we discuss plasticity and synaptic transmission as a result of neurofeedback training. This type of intervention would be safe, free of short- or long-term health consequences, easily administered prior to, during and immediately following neurofeedback training across a wide range of protocols and conditions. Omega-3 EPA/DHA has fairly well-defined mechanistic neuroscience principles although more so for DHA than EPA. In the future, this nutritional intervention for soldiers (a type of “nutritional armor”) might be a part of a multi-modal approach to enhance treatment outcomes and even baseline resilience to CNS insults (physical and psychological). References Simopoulos, A.P., Summary of the NATO advanced research workshop on dietary omega 3 and omega 6 fatty acids: biological effects and nutritional essentiality. J Nutr, 1989. 119(4): p. 521-8. Simopoulos, A.P., Human requirement for N-3 polyunsaturated fatty acids. Poult Sci, 2000. 79(7): p. 961- 70. Stoll, A.L., The omega-3 connection: The groundbreaking omega-3 antidepression diet and brain program. 2001. 303. Stoll, A.L., et al., Omega 3 fatty acids in bipolar disorder: A preliminary double-blind, placebo-controlled trial. Archives of General Psychiatry, 1999. 56(5): p. 407-412. Simopoulos, A.P., Commentary on the workshop statement. Essentiality of and recommended dietary intakes for Omega-6 and Omega-3 fatty acids. Prostaglandins Leukot Essent Fatty Acids, 2000. 63(3): p. 123-4. Sinclair, A.J., The good oil: Omega 3 polyunsaturated fatty acids. Today's Life Science, 1991. August: p. 18-27. Singer, B.J. and G.L. Nicholson, The fluid mosaic model of the structure of cell membrane. Science, 1972. 178: p. 720-731. Young, G.S., J.A. Conquer, and R. Thomas, Effect of randomized supplementation with high dose olive, flax or fish oil on serum phospholipid fatty acid levels in adults with attention deficit hyperactivity disorder. Reprod Nutr Dev, 2005. 45(5): p. 549-58. Joshi, K., et al., Supplementation with flax oil and vitamin C improves the outcome of Attention Deficit Hyperactivity Disorder (ADHD). Prostaglandins Leukot Essent Fatty Acids, 2006. 74(1): p. 17-21. Rojas, N.L. and E. Chan, Old and new controversies in the alternative treatment of attention deficit hyperactivity disorder. Ment Retard Dev Disabil Res Rev, 2005. 11(2): p. 116-30. Hallahan, B. and M.R. Garland, Essential fatty acids and their role in the treatment of impulsivity disorders. Prostaglandins Leukot Essent Fatty Acids, 2004. 71(4): p. 211-6. Bryan, J., et al., Nutrients for cognitive development in school-aged children. Nutr Rev, 2004. 62(8): p. 295-306. Richardson, A.J., Clinical trials of fatty acid treatment in ADHD, dyslexia, dyspraxia and the autistic spectrum. Prostaglandins Leukot Essent Fatty Acids, 2004. 70(4): p. 383-90.

Richardson, A.J. and B.K. Puri, A randomized double-blind, placebo-controlled study of the effects of supplementation with highly unsaturated fatty acids on ADHD-related symptoms in children with specific learning difficulties. Prog Neuropsychopharmacol Biol Psychiatry, 2002. 26(2): p. 233-9. Richardson, A.J., et al., Fatty acid deficiency signs predict the severity of reading and related difficulties in dyslexic children. Prostaglandins Leukot Essent Fatty Acids, 2000. 63(1-2): p. 69-74. Richardson, A.J. and M.A. Ross, Fatty acid metabolism in neurodevelopmental disorder: a new perspective on associations between attention-deficit/hyperactivity disorder, dyslexia, dyspraxia and the autistic spectrum. Prostaglandins Leukot Essent Fatty Acids, 2000. 63(1-2): p. 1-9. Kidd, P.M., Attention deficit/hyperactivity disorder (ADHD) in children: rationale for its integrative management. Altern Med Rev, 2000. 5(5): p. 402-28. Jensen, C.L., et al., Effects of maternal docosahexaenoic acid intake on visual function and neurodevelopment in breastfed term infants. Am J Clin Nutr, 2005. 82(1): p. 125-32. Heird, W.C., The role of polyunsaturated fatty acids in term and preterm infants and breastfeeding mothers. Pediatr Clin North Am, 2001. 48(1): p. 173-88. Crawford, M.A., et al., Nutrition and neurodevelopmental disorders. Nutr Health, 1993. 9(2): p. 81-97. Young, G. and J. Conquer, Omega-3 fatty acids and neuropsychiatric disorders. Reprod Nutr Dev, 2005. 45(1): p. 1-28. Learning Objective Understand the role of omega-3 EPA/DHA in neuro-cognitive development, performance and mood. Outline Omega-3 fatty acid biochemistry (20 minutes) Epidemiological and clinical evidence showing the role of fish and fish oil consumption in depression (20 minutes) Clinical studies on fish and fish oil consumption in cognition including IQ, attention, verbal and fine motor skills in a variety of populations (20 Minutes) Financial Interest: At this time, I have no commercial or financial interest in any omega-3 company or supplier.


Molecular Development of Projection Neuron Types and Building of Local Microcircuitry in the Cerebral Cortex (R)

Paola Arlotta, PhD, Harvard Medical School, [email protected] Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract The activity and function of the mammalian cerebral cortex rely on the integration of an extraordinary diversity of excitatory projection neurons and inhibitory interneurons into balanced local circuitry. The developmental events governing the proper interaction between excitatory projection neurons and inhibitory interneurons are poorly understood. Here, we have first investigated the function of the transcription factor Fezf2 in controlling the fate-specification of corticofugal projection neurons (CfuPN) of the neocortex. We find that Fezf2 acts as a powerful master gene that is sufficient to instruct the birth of CFuPN even from progenitors fated to become medium spiny neurons in the striatum. Secondly, we report that different subtypes of projection neurons uniquely and differentially determine the laminar distribution of cortical interneurons into cortical layers. We find that in Fezf2-/-cortex, the exclusive absence of subcerebral projection neurons and their replacement by callosal projection neurons cause distinctly abnormal lamination of interneurons. This results in physiological imbalance of excitation due to altered GABAergic inhibition. In addition, experimental generation of either corticofugal neurons or callosal neurons below the cortex is sufficient to recruit cortical interneurons to these ectopic locations. Strikingly, the identity of the projection neurons generated, rather than strictly their birth date, determines the specific types of interneurons recruited. These data demonstrate that in the neocortex individual populations of projection neurons cell-extrinsically control the laminar fate of interneurons and the assembly of local inhibitory References Arlotta, P., * Molyneaux, B.J., * Chen, J., Inoue, J., Kominami, R. and Macklis, J.D. Neuronal Subtype-Specific Genes that Control Corticospinal Motor Neuron Development in vivo. Neuron. 2005; 45 (2) 207-221. (*equal contribution). PMID:15664173. With cover and preview article. Molyneaux, B.J.*, Arlotta, P.*, Hirata, T., Hibi, M. and Macklis J.D. Fezl is Required for the Birth and Specification of Corticospinal Motor Neurons. PMID:16157277. Neuron 2005; 15;47(6): 817-31. (*equal contribution).

Arlotta, P. and Macklis, J.D. Archeo-Cell Biology: Carbon Dating is not just for Pots and Dinosaurs. Cell 2005; 122(1): 4-6. PMID:16009125. Molyneaux, B.J.*, Arlotta, P.*, Menezes, J. and Macklis J.D. Neuronal Subtype Specification in the Cerebral Cortex Nat. Rev. Neurosci. 2007; 8(6): 427-437. PMID:17514196. (*equal contribution). Gao, X., Arlotta P., Macklis, J.D., Chen, J. Conditional Knockout of b-catenin in Newborn Dentate Gyrus Granule Neurons results in Dendritic Malformation in the Postnatal Hippocampus. The Journal of Neuroscience. 2007; 27(52):14317-25. PMID:18160639. Arlotta, P.*, Molyneaux, B.J.*, Jabaudon, D., Yoshida, Y. and Macklis J.D. CTIP2 Controls the Differentiation of Medium Spiny Neurons and the Establishment of the Cellular Architecture of the Striatum. The Journal of Neuroscience. 2008; 16; 28 (3):622-32. PMID:18199763. (*equal contribution. Lai, T., Jabaudon, D., Molyneaux, B.J., Azim, E, Arlotta, P., Menezes, J. and Macklis, J.D. SOX5 Controls the Sequential Generation of Distinct Corticofugal Neuron Subtypes. Neuron. 2008; 57(2):232-47. PMID:18215621. Molyneaux, B.J. *, Arlotta, P. *, MacQuarrie, K. and Macklis, J.D. Novel Subtype-specific Genes Identify Distinct Subpopulations of Callosal Projection Neurons. The Journal of Neuroscience. 2009; 30; 29(39):12343-54. PMID:19793993 (*equal contribution. Rouaux C. and Arlotta, P. Fezf2 Directs the Differentiation of Corticofugal Neurons from Striatal Progenitors in vivo. Nature Neuroscience. 2010; 13(11):1345-7. PMID:20953195. With “News and Views” article in Nature Neuroscience. Zhang, F., Cong, L, Lodato, S., Kosuri, S. Church, G. and Arlotta, P. Programmable Sequence-Specific Transcriptional Regulation of Mammalian Genome Using Designer TAL Effectors. Nature Biotechnology. 2011; 29(2):149-153. PMID:21248753. With “commentary” in Nature Methods. Lodato, S., Rouaux, C., Quast, K., Jantrachotechatchawan, C., Studer, M., Hensch, T. and Arlotta, P. Excitatory Projection Neurons Control the Distribution of Inhibitory Interneurons in the Cerebral Cortex. Neuron. 2011; 69(4):763-79. PMID:21338885. With “preview” article in Neuron. Learning Objective Gain insights on the cellular and molecular mechanisms that control the establishment of the local excitatory/inhibitory microcircuitry that control balanced cerebral cortical function. Outline Molecular development of excitatory neuron types of the cerebral cortex (30 minutes) Role of excitatory neurons in controlling the integration into circuitry of inhibitory interneurons and relevance to disease (25 minutes) Questions and answers (5 minutes) Financial Interest: No financial interests.



The NIMH-Funded OSU Randomized, Double-Blind, Sham-Controlled Pilot Feasibility Trial of Neurofeedback for Pediatric ADHD –

Complete Results (R) Nick Lofthouse, PhD, Ohio State University, [email protected]

L. Eugene Arnold, MD, MEd, Sarah Hersch, BS, BA, Elizabeth Hurt, PhD, Xueliang Pan, PhD

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Background/Objectives: The established treatments for attention-deficit/hyperactivity disorder (ADHD) are not universally effective or acceptable to all, and have not been shown to have sustained effects beyond 2 years (e.g., Jensen et al, 2007). The 8-year outcome paper (Molina et al, 2009) for the NIMH Multimodal Treatment Study of ADHD (the MTA) pointed out the need for new treatments with lasting effects. Among complementary/alternative treatments for ADHD, neurofeedback (NF) is one of the most prominent, despite its expense and technical difficulties. A meta analysis of 6 randomized clinical trials (RCTs) reported large effects on inattention and medium effects on impulsivity and hyperactivity (Arns et al., 2009). However, the control conditions were not blinded and often not of equal duration

and intensity compared to the NF conditions (Lofthouse et al. in-press). Preparing for a definitive RCT, this pilot study explored feasibility of a double-blind, sham-controlled design and compliance/palatability/relative effect of 2 vs. 3 treatments/week. Method: Unmedicated 6-12 year-olds with DSM-IV ADHD were randomized to active- NF or sham-NF (2:1 ratio) and to 2X vs. 3X/week treatment frequency (1:1) for 40 treatments (Tx). Switch of Tx frequency was allowed after Tx 24. Multi-informant assessments at baseline, treatments 12, 24, & 40, and 2-month follow-up included preferences/satisfaction, caregiver ratings, and objective tests. Results: Of 39 randomized; 3 dropped by Tx 6, 2 after Tx 22; 86% completed all 40 Tx. At 40th Tx, child and parent guesses about assigned Tx were worse than chance. At Tx 24, of 34 families continuing, 13 (38%) chose 2 Tx/wk; 21 (62%) chose 3 Tx/wk. Of 8 experiencing both frequencies, no children and 1 parent preferred 2X/wk. Parent/teacher rated ADHD symptoms were as good with 3X/wk as 2X/wk. In the active Tx, improvement asymptoted by Tx 24. Both active NF and sham yielded large pre-post improvement on parent ratings, but NF no more than sham. Conclusions: Compliance/completion was acceptable. 3X/wk Tx frequency seems preferred over 2X/wk and is as effective. Blinding appears to work. Thirty treatments appeared adequate for maximal benefit. In view of the large placebo effect compared to unblinded positive results in the literature, a large double-blind RCT is necessary to test specific effectiveness. References Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen., A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity & hyperactivity: a meta-analysis. EEG & Clinical Neuroscience; 40, 180-189. Jensen PS, Arnold LE, Swanson J, Vitiello B, Abikoff HB, Greenhill LL, Hechtman L, Hinshaw SP, Pelham WE, Wells KC, Conners CK, Elliott GR, Epstein J, Hoza B, Molina BSG, Newcorn JH, Severe JB, Wigal T, Gibbons RD, Hur K. Follow-up of the NIMH MTA study at 36 months after randomization. Journal of the American Academy of Child and Adolescent Psychiatry 46(8):988-1001, 200. Lofthouse, N, Arnold, E., Hersch, S., Hurt, E., & deBeus, R. (in-press). A Review of Neurofeedback Treatment for Pediatric ADHD. Journal of Attention Disorders. Molina, B.S.G., Hinshaw S.P., Swanson J.M., Arnold, L.E., Vitiello, B., Jensen, P.S., Epstein, J.N., Hoza, B., Hechtman, L., Abikoff, H.B., Elliott, G.R., Greenhill, L.L., Newcorn, J.H., Wells, K.C., Wigal, T.L., Gibbons, R.D., Hur, K., Houck, P.R., and the MTA Cooperative Group. (in press). The MTA at 8 Years: Prospective Follow-Up of Children Treated for Combined Type ADHD in a Multisite Study. Journal of the American Academy of Child and Adolescent Psychiatry 48(5):484-500, 2009. Learning Objective Learn the complete results from the OSU NIMH-Funded Randomized, Double-Blind, Sham-Controlled Pilot Feasibility Trial of Neurofeedback for Pediatric ADHD Outline Describe the study’s background, hypotheses, design, participants, measures and procedures (30 minutes) Report and discuss the study’s results (& their implications) pertaining to the feasibility of a double-blind, sham-controlled design; feasibility of 2 vs. 3 neurofeedback treatments/week; necessary number of treatments and the relative efficacy of real- vs. sham-neurofeedback (30 minutes) Financial Interest: No financial interests.

Potential Clinical Applications for 19 Channel Live Z-Score Training Using Percent ZOK and ZPlus Protocols (C)

Penijean Rutter, MA, Stress Therapy Solutions, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract This presentation will discuss the 19 channel Z-score training software and the underlying theoretical clinical applications of the Percent ZOK and the ZPlus protocols. Before and after QEEG brain maps of individuals who have been trained with these protocols will be reviewed and compared. All brain maps

will be analyzed using the NeuroGuide database for consistency, as that is the database used in creating the live Z-scores utilized for feedback. Case studies presented will track symptoms of anxiety, depression and mixed anxiety/depression through pre-training, during training, and after training stages to see what changes have occurred. Clinician observations, client self-report, and symptom tracking scales will be utilized. A description of the training process and data analysis methods will be included, and implications for future research and possible clinical applications will be explored. While there is a body of literature addressing the use of neurofeedback to ameliorate symptoms of anxiety, depression and mixed anxiety/depression, there are currently no available studies on the specific effects of the 19 channel Percent ZOK protocol on clinical symptoms of anxiety, depression or mixed anxiety/depression. This presentation will set forth some initial data collected with the intent to discuss the possible efficacy of 19 channel Percent ZOK neurofeedback regarding the reduction of symptoms of anxiety, depression, and mixed anxiety depression by increasing self-regulation and systemic relaxation. Attendees will gain a better understanding of the principles behind 19 channel live Z-score training, become familiar with the percent ZOK and ZPlus training approaches, and observe the results of QEEG database guided Neurofeedback using 19 channel live Z-score training methods. This presentation is appropriate for individuals or clinicians who are interested in an introduction to the 19 channel live Zscore software and methodology, and also interested in before and after QEEG brain maps that document changes in the EEG throughout the training process. References Bloomfield, P. (2000). Fourier Analysis of Time Series: An Introduction, John Wiley & Sons, New York. Box, G. E. P. and Cox, D. R. (1964), An Analysis of Transformations, Journal of the Royal Statistical Society, 211-243, discussion 244-252. Collura,TF, Guan, J., Tarrant, J., Bailey, J., and Starr, F. (2010) EEG Biofeedback Case Studies Using Live Z-Score Training (LZT) and a Normative Database , Journal of Neurotherapy 14(2), 22-46. Collura, T.F. (2008) Whole-head Normalization using Live Z-scores for Connectivity Training (part 1), NeuroConnections April, 12-18. Collura, T.F. (2008) Whole-head Normalization using Live Z-scores for Connectivity Training (part 2), NeuroConnections July, 9-12. Collura, T.F. (2009) Neuronal Dynamics in Relation to Normative Electroencephalography Assessment and Training, Biofeedback Volume 36, Issue 4, pp. 134-139. Collura, T.F. (2009) Towards a Coherent View of Brain Connectivity , Journal of Neurotherapy, vol. 12 (2- 3), 99-110). Duffy, F., Hughes, J. R., Miranda, F., Bernad, P. & Cook, P. (1994). Status of quantitative EEG (QEEG) in clinical practice. Clinical. Electroencephalography, 25 (4), VI - XXII. Fehmi, L., and Collura, T.F. (2007) The Effects of Electrode Placement Upon EEG Biofeedback Training: The Monopolar / Bipolar Controversy, Journal of Neurotherapy, Vol. 11 (2) 45-63. Heilman, K.M. and Valenstein, E. (1993). Clinical Neuropsychology (3rded.)., Oxford University Press, New York. Hughes, J. R. & John, E. R. (1999). Conventional and quantitative electroencephalography in psychiatry. Neuropsychiatry, 11, 190-208. John, E. R., Prichep, L. S. & Easton, P. (1987). Normative data banks and neurometrics: Basic concepts, methods and results of norm construction. In A. Remond (Ed.), Handbook of electroencephalography and clinical neurophysiology: Vol. III. Computer analysis of the EEG and other neurophysiological signals (pp. 449-495). Amsterdam: Elsevier. John, E. R., Prichep, L. S., Fridman, J. & Easton, P. (1988). Neurometrics: Computer assisted differential diagnosis of brain dysfunctions. Science, 293, 162-169. John, E.R. (1990). Machinery of the Mind: Data, theory, and speculations about higher brain function. Birkhauser, Boston. 14. Kerson, C., Gunkelman, J., and Collura, T.F. (2008) Neurofeedback Using the Phenotype and ZScore Modalities, NeuroConnections April, 24-28. Matousek, M. & Petersen, I. (1973). Frequency analysis of the EEG background activity by means of age dependent EEG quotients. In P. Kellaway & I. Petersen (Eds.), Automation of clinical electroencephalography (pp. 75-102). New York: Raven Press. Otnes, R.K. and Enochson, L. (1978). Applied Time Series Analysis, John Wiley & Sons, New York. Pikovsky, A., Rosenblum, M. and Kurths, J. (2003). Synchronization: A universal concept in nonlinear

sciences. Cambridge Univ. Press, New York. Robinett, R.W. (1997). “Quantum Mechanics: Classical Results, Modern Systems and Visualized Examples”, Oxford University Press, New York. Thatcher, R.W. EEG normative databases and EEG biofeedback (1998). Journal of Neurotherapy, 2(4): 8-39. Thatcher, R.W. EEG database guided neurotherapy (1999). In: J.R. Evans and A. Abarbanel Editors, Introduction to Quantitative EEG and Neurofeedback, Academic Press, San Diego. Thatcher, R.W. (2000a). EEG Operant Conditioning (Biofeedback) and Traumatic Brain Injury. . Clinical EEG, 31(1): 38-44. Thatcher, R.W. (2000b) "An EEG Least Action Model of Biofeedback" 8th Annual ISNR conference, St. Paul, MN, September. Thatcher, R.W., North, D., and Biver, C. EEG inverse solutions and parametric vs. nonparametric statistics of Low Resolution Electromagnetic Tomography (LORETA). (2005a). Clin. EEG and Neuroscience, Clin. EEG and Neuroscience, 36(1), 1 - 9. Thatcher, R.W., North, D., and Biver, C. (2005b). Evaluation and Validity of a LORETA normative EEG database. Clin. EEG and Neuroscience, 36(2): 116-122 Thatcher, R.W., Walker, R.A., Biver, C., North, D., Curtin, R., (2003). Quantitative EEG Normative databases: Validation and Clinical Correlation, J. Neurotherapy, 7 (No. ¾): 87 – 122. 15 Thatcher, R.W. – 3-Dimensional EEG Biofeedback using LORETA., Society for Neuronal Regulation, Minneapolis, MN, September 23, 2000b. Thatcher, R.W., North, D., and Biver, C. (21007). Self-organized criticality and the development of EEG phase reset. Human Brain Mapping (In press, 2007). Learning Objective Discuss the underlying ideas behind 19 channel live Z-score training and its observable effects on EEG. Outline Underlying principles of 19 channel live Z-score training (20 minutes) Presentation of before and after QEEG brain maps of individuals trained using the discussed methodology (20 minutes) Analysis of data and discussion of potential implications for future research and clinical applications (20 minutes) Financial Interest: I am serving as a clinical consultant and trainer for Stress Therapy Solutions, Inc.



Why We Make Ourselves Sick and How To Make Ourselves Healthy: The Importance of Nutrition, Exercise and Sunlight (R,C)

David Siever, CET, Mind Alive, Inc., [email protected] Ron Swatzyna, PhD, Tarnow Center for Self-Management, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract E. Roy John and Leslie Prichep (2006) proposed an EEG Homeostatic Model for the brain. In this model, neurotransmitters mediate neuroanatomical structures that generate behavior regulated by emotional experience in the moment of the event. They proposed that "psychological and neurological disorders produce and are caused by deviations from homeostasis" (p.135). If an environment or psychological challenge continues for an extended period, the set point for homeostasis adapts to a new point. This short course provides information about the importance of nutrition, exercise and sunlight in the process of reestablishing EEG homeostasis. Dave will review the literature concerning the effects of nutrition, exercise and sunlight on the brain while Ron will present his EEG/QEEG study on the impact of breakfast patterns in children.

Nutrition affects oscillatory rhythms and neuronal functioning. Iodine boosts IQ and helps prevent mental retardation. Omega 3s, selenium and magnesium all improve mental function and reduce depression. Exercise has been shown to be highly effective for improving IQ, math ability and reducing depression. Exercise should be a mandatory part of any academic program. Preliminary findings using QEEG suggest that what is eaten for breakfast has an effect on both mood and mental performance. As for the importance of sunlight, as much as 6% of northern populations are believed to develop winter blues. Depression, anxiety, insomnia, multiple sclerosis, migraine, seizure, fibromyalgia and dementia are well correlated with a deficiency in Vitamin D. Previous research has always assumed that winter blues was the result of Seasonal Affective Disorder, which comes about as a lack of optic stimulation to UV receptors in the eyes, and ultimately, the pineal gland. There are several similarities in the symptoms of SAD and vitamin D deficiency and therefore it’s possible that SAD has been misdiagnosed since its inception. A recent study on vitamin D deficiency has shown that vitamin D supplementation eliminates the symptoms of SAD in “SAD” sufferers. The human brain was never designed to be healthy, it was designed to survive. Despite the robustness of the brain, we are not sharp and productive, nor social, lively and joyful unless we address good nutrition and exercise. We have to work at being mentally healthy. As long as we are ignoring our basic needs, the brain will do what it has to along a predictive pathological course to survive, and we will suffer. Active voluntary participation in good nutritional decisions, exercise routine and outside activities (if possible) are necessary for EEG Homeostasis. References John, E., Roy & Prichep, L. (2006). The Relevance of the QEEG to the Evaluation of Behavioral Disorders and Pharmacological Interventions. Clinical EEG and Neuroscience, 37, 2, 135-143. Berg, K., & Siever, D. (2009) A controlled comparison of audio-visual entrainment for treating SAD. Journal of Neurotherapy, 13(3), 166-175. Siever, D. (2010). The Need For Vitamin D. http://www.mindalive.com/1_0/article%209.pdf Davis, C. (2011). Exercise Helps Overweight Children Think Better, Do Better in Math. http://www.sciencedaily.com/releases/2011/02/110210111309.htm. Learning Objective Understand the role of nutrition in neuronal regulation and performance. Understand the difference between SAD and vitamin D deficiency. Outline Effects of nutrition on mental health (40 minutes) Personal experiences with vitamin D and iodine (including brain maps) (20 minutes) Financial Interest: None.

Setting Up for Success with Asperger’s and Autistic Spectrum Disorder (C)

Michael Thompson, MD, ADD Centre, [email protected] Lynda Thompson, PhD, ADD Centre, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Attendees at this presentation will become familiar with how symptoms differ between Asperger’s and autism. They will be able to outline, on the basis of functional Neuroanatomy (which includes discussion of Brodmann Areas, neural networks and connections, including vagal inputs to the medulla), why a combination of NFB + BFB + Strategies improves social functioning in addition to significantly improved scores on academic, intelligence, and attention measures. The authors’ first talk at an ISNR meeting in 1995, Exceptional Results with Exceptional Children, included a case example of successful NFB training at Cz with a student with severe autism. This presentation gives an overview of how our interventions using Neurofeedback (NFB) + Biofeedback (BFB) in clients with Asperger’s and autistic spectrum disorders (ASDs) has evolved.. Current interventions incorporate an understanding of the functional significance of different areas of the brain and neural networks.

Functional significance of cortical areas is partially elucidated in Brodmann Areas booklet, (Thompson, Thompson, & Wu, 2008). This talk expands on this to include brain networks that depend on cortical-basal ganglia-thalamo-cortical connections. These networks may account for the observation that training these clients over central midline structures (CMS) at Fz or Cz, may have effects on broader functional networks (affect, attention, executive, salience, and default networks). Having an effect on more than a single network is particularly important in this group of patients because they demonstrate symptoms that involve a number of different networks. The symptoms may include, in addition to their difficulties with social interactions, high anxiety, difficulties with attention and impulsivity, and specific learning difficulties. Regarding assessment, we discuss how high tactile sensitivity may mean we begin with only a single channel QEEG assessment and follow-up later with a 19-channel QEEG. EEG interpretations used to illustrate findings range from raw data to quantitative analysis with LORETA and, when possible, event related potentials, (ERPs). Participants will see commonly observed EEG and QEEG patterns including a very common presentation of excess frontal slow wave activity, a dip at Pz in the low alpha (8-10Hz) range, and higher than expected low and high frequency beta. Correlation of findings to symptoms and networks is made and exceptions are noted. The QEEG findings are the basis for setting NFB parameters for training and common initial settings will be described. This is complemented by a discussion of the functional neuroanatomical basis for doing BFB, particularly heart rate variability (HRV) training, with NFB and why we also provide one-to-one coaching in metacognitive strategies related to both cognitive and social skills.. The training addresses the symptoms that interfere with the child being able to interact constructively with caregivers including, in order: anxiety, impulsivity, attention span, executive functions, and finally, understanding and responding to social cues. Statistical Support: The pre-post training results for NFB over CMSs combined with BFB + Metacognitive strategies includes results showing EEG, TOVA, IVA, Wechsler Intelligence Scale (WISC & WAIS), academic measures (WRAT), and questionnaires for 150 patients with Asperger’s and 9 with Autism. References De Ridder, Dirk (2009). An evolutionary approach to brain rhythms and its clinical implications for brain modulation. Journal of Neurotherapy, (13)1, 69-70. Kouijzer, E.J., Jan M.H., de Moor, B., Gerrits, J.L., Congedo, M., & van Schie, H. T. (2009). Neurofeedback improves executive functioning in children with autism spectrum disorders. Research in Autism Spectrum Disorders 3, 145–162. Porges , S. W. (2007). The Polyvagal Perspective . Biological Psychiatry , 74 , 116 – 143. Thompson, M. & Thompson, L. (2007). Neurofeedback for Stress Management. Chapter in Paul M. Lehrer, Robert L. Woolfolk and Wesley E. Sime (Eds.) Principles and Practice of Stress Management, 3rd Edition. New York: Guilford Publications. Thompson, M. & Thompson, L., (2009). Systems Theory of Neural Synergy: Neuroanatomical Underpinnings of Effective Intervention Using Neurofeedback plus Biofeedback. Journal of Neurotherapy,(13)1, 72-74. Thompson, M. & Thompson, L., (2010). Functional Neuroanatomy and the Rationale for Using EEG Biofeedback for Clients with Asperger’s Syndrome. Journal of Applied Psychophysiology and Biofeedback, (35)1, 39-61. Thompson, L., Thompson, M., Reid, A., (2010). Neurofeedback Outcomes in Clients with Asperger’s Syndrome. Journal of Applied Psychophysiology and Biofeedback,(35)1, 63-81. Uddin, L. Q, Iacoboni, M., Lange, C. & Keenan, J.P. (2007). The self and social cognition: the role of cortical midline structures and mirror neurons. Trends in Cognitive Sciences, (11)4, 153-157. Learning Objective List the primary symptoms observed in Asperger’s Syndrome and relate these symptoms to Neural Networks that are most likely to be involved. List reasons why initial NFB training over central midline structures, based on QEEG and LORETA assessment findings, is likely to produce improvement in some of the core symptoms of people with Asperger’s. State why up training of high frequency alpha and or 14 Hz SMR may be contraindicated in some patients. Outline with reference to affect, executive. and distress networks, and the hypothalamic-pituitary-adrenal axis (HPA), why HRV training may have a positive influence on the outcomes of patients with Asperger’s. Outline Outline of major symptoms of autism and of Asperger’s. Case examples will be accompanied by QEEG and LORETA findings, psychophysiological stress assessments and descriptions of how the results of these assessments governed the prescription of NFB + BFB training (25 minutes) How NFB over central midline structures affects an important combination of neural networks (affect, executive, salience, and default networks) and neuroanatomically how these networks may also be affected by HRV training. (30 minutes) Showing pre-post results and answering questions (5 minutes) Financial Interest: Lynda Thompson is co-author of THE A.D.D. BOOK. Michael and Lynda are co-authors of SETTING UP FOR CLINICAL SUCCESS. Michael and Lynda Thompson are co-authors of THE NEUROFEEDBACK BOOK. It is likely that these books may be on sale at the meeting. The authors will state their interest in these books during the workshop.

Sunday, September 18, 2011


The Effect of Neurofeedback and Cranial Electrotherapy on Immune Function Within a Group of HIV+ Subjects:

A Randomized Controlled Study (R) Gary Schummer, PhD, ADD Treatment Center, [email protected]

Sharon Noh, BS, ADD Treatment Center Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Introduction: Modulation of the functional capacity of the immune system utilizing neurofeedback therapy may be expected given the many pathways and dense communication matrix mediating activity within and between the central nervous system and the immune system. Those who promote cranial electrotherapy have stated that immune health is improved due to a decrease in negative mood states when using this device. Method: This study investigated the effects of neurofeedback and cranial electrotherapy on a group (n=40) of HIV+ male subjects between ages 18-55 over a period of 16 weeks. Subjects all had baseline T-4 helper cell (CD4+) counts of 200 to 400/cc (lab normal is 400 to 1770/cc). They were randomly assigned to one of four groups: neurofeedback only (n=10), cranial electrotherapy only (n=10), combined neurofeedback and cranial electrotherapy (n=10), or a waitlist control group (n=10). Subjects in the neurofeedback treatment condition were provided two 20 minute sessions in the office each week. Neurofeedback was performed using linked ears (reference and ground) with the active electrode at the occipital midline, Oz (according to the International 10-20 system). Fast Fourier Transform using a Cooley-Tukey algorithm was applied to each 2.56 second epoch and the square root of the absolute power coefficients were computed for each epoch. Subjects were rewarded by a tone when higher alpha amplitude (8-12 Hz) exceeded their initial testing amplitude (30 seconds with eyes closed). When subjects could sustain alpha amplitude at twice their baseline amplitude for twenty minutes, the reward tone was shifted to a lower theta-alpha frequency (6-8 Hz). Drowsiness was discouraged by inhibiting slower (4-6 Hz) activity as well intervention by a technician who was actively monitoring EEG activity. If the EEG indicated sleepiness, a technician would verbally tell the subject to re-focus on the tone. Subjects selected to use the cranial electrotherapy were provided an Alpha-Stim unit (Model 2000GL) along with detailed information on proper utilization of the device. Subjects agreed to use the unit at home as directed every day for 20 minutes. The combined group had both neurofeedback and cranial electrotherapy. The waitlist control group received neither neurofeedback nor cranial electrotherapy. Each subject remained in their respective condition continuously for 16 weeks. All subjects completed a stress audit questionnaire and symptom check list (SCL-90-R) every week for the duration of the study. Also at the baseline, after 8 weeks, and after 16 weeks, subjects in all four groups had their blood drawn at their individual physician’s office which was then analyzed by independent laboratories. This provided CD4+ measurements that were statistically analyzed. Results: One-way ANOVA was used to test for overall difference among groups for each dependent variable. This was followed by pairwise comparisons between groups using Dunnett’s test. Results indicated that at baseline, basal total lymphocyte counts (CD4+) counts did not differ between groups (p> 0.72). After 8 weeks, CD4+ counts were significantly greater than controls for the combined group (p= .01) only. After 16 weeks, CD4+ counts were significantly greater than controls for the neurofeedback group (p< .01) and combined group (p< .01). There was no significant change in CD4+ count for the control and cranial electrotherapy only groups over the 16-week period. Results of the subjective stress and physical symptoms inventories corroborated the statistically significant changes in the neurofeedback and combined groups. Conclusion: This pilot study suggests neurofeedback may be a promising tool to improve immune function and warrants further investigation. A replication study might better control for placebo effect bias and insure compliance by having both conditions receive treatment in a clinical setting. Newly developed software which provides sham feedback would facilitate a stronger, double-blind placebo design. Lastly, although Oz was effectively trained to produce higher amplitudes within specified frequency ranges, recent literature suggests an even stronger effect may be seen using a Pz sensor placement.

References Auerbach J.E., Oleson T.D., Soloman G.F. (1992). A behavioral medicine intervention as an adjunctive treatment for HIV related illness. Psychology & Health. 6, 325-334. Baldeweg T., Gruzelier J.H. (1997). Alpha EEG activity and subcortical pathology in HIV infection. International Journal of Psychophysiology. 26(1-3), 431-432. Baldeweg T., Catalan J., Pugh K., Gruzelier J., Lovett E., Schurlock H., Burgess A., Riccio M., Hawkins D. (1997). Neurophysiological changes associated with psychiatric symptoms in HIV-infected individuals without AIDS. Biological Psychiatry. 41(4), 474-87. Campbell P.J., Aurelius S., Blowes G., Harvey D. (1997). Decreasing CD4 lymphocyte counts with rest; implications for the monitoring of HIV infection. Int J STD AIDS. 8(7), 423-6. Creswell, J.D., Myers H.F., Cole S.W., Irwin M.R. (2009). Mindfulness meditation training effects on CD4+ T lymphocytes in HIV-1 infected adults: a small randomized control trial. Brain, Behavior, and Immunity. 23, 184-188. Derogatis L.R., Spencer P.M. (1990). The Brief Symptom Inventory: SCL-90 (ver. iv). Baltimore, MD: Clinical Psychometric Research. Dhabar F.S., Miller A.H., McEwen B.S., Spencer R.L. (1995). Effect of stress on immune cell distribution: dynamics and hormonal mechanisms. Journal of Immunology. 154, 5511-5527. McCain N.L., Zeller J.M., Cella D.F., Urbanski P.A., Novak R.M. (1996). The influence of stress management training in HIV disease. Journal of Nursing Research. 45(4), 246-253. Smith R.B., Shiromoto F.N. (1992). The use of cranial electrotherapy stimulation to block fear perception in phobic patients. Journal of Current Therapeutic Research. 51(2), 249-253. Sommershof A., Aichinger H., Engler H., Adenaur H., Catani C., Boneberg E., Elbert, T., Groettrup M., Kolassa I. (2009). Substantial reduction of naïve and regulatory T-cells following traumatic stress. Brain, Behavior, and Immunity. 23, 1117-1124. Learning Objective Appreciate the neurorehabilitative effect of neurofeedback in a group of individuals with a chronic debilitating disease and consider further applications in the many other diseases impacting the immune system. Outline Why immune enhancement is a reasonable expectation with neurofeedback (2 minutes) Why this population was idea to study (1 minutes) T-4 helper cells and their use as a marker for immune system integrity (2 minutes) Introduction to alpha-stim (1 minutes) Explanation of the 3 treatment and 1 control group (5 minutes) How the T-4 levels were tracked (1 minutes) Results of the statistical analysis comparing the 4 groups (6 minutes) Future implication of this research (2 minutes) Q&A (10 minutes) Financial Interest: No financial interests.


Neuromodulatory Approaches to the Treatment of Major Depressive Disorder (R,C)

Paul Hamilton, PhD, Stanford University, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Nearly two decades of functional neuroimaging research on Major Depressive Disorder (MDD) have revealed the functional neural substrates of this debilitating illness. Only recently, however, have we begun to apply this neural-systems-level knowledgebase to the treatment of MDD. In this talk, I will summarize findings from structural and functional neuroimaging investigations of MDD and then review work on exogenous neuromodulation therapies for depression (e.g., deep brain stimulation, transcranial magnetic stimulation). Next, I will turn to endogenous neuromodulation techniques, describing hardware and software configurations for

implementing functional magnetic resonance imaging (fMRI) based neurofeedback training regimens. Finally, I will present work from our laboratory examining the therapeutic efficacy of real-time neurofeedback training in MDD. References Caria A, Veit R, Sitaram R, Lotze M, Welskopf N, Grodd W, Birbaumer N. (2007): Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage 35(3):1238-1246. deCharms RC, Maeda F, Glover GH, Ludlow D, Pauly JM, Soneji D, Gabrieli JDE, Mackey SC. (2005): Control over brain activation and pain learned by using real-time functional MRI. Proceedings of the National Academy of Sciences of the United States of America 102(51):18626-18631. Hamilton JP, Glover GH, Hsu JJ, Johnson RF, Gotlib IH. (2011): Modulation of Subgenual Anterior Cingulate Cortex Activity With Real-Time Neurofeedback. Human Brain Mapping 32(1):22-31. Learning Objective Understand current approaches for exogenous and endogenous neuromodulation therapy for Major Depressive Disorder (MDD). Outline Findings from functional and structural neuroimaging investigations of MDD (25 minutes). Hardware and software configurations for real time functional magnetic resonance imaging neurofeedback protocols (25 minutes) Questions and Answers (10 minutes) Financial Interest: No financial interests.


Measuring Neural Correlates of Early Infant Behavior William Bosl, PhD, Harvard Medical School, [email protected]

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: 1 Abstract Complex mental disorders such as autism exhibit abnormal neural connectivity on many scales that varies between different regions of the brain. In the autistic brain, high local connectivity and low long-range connectivity may develop concurrently due to problems with synapse pruning or formation. Similarly, epilepsy has been described as a heterogeneous spectrum disorder that is also characterized by abnormal neural connectivity in the brain. One might even say that all developmental brain disorders are neural connectivity disorders. Understanding and measuring brain connectivity is essential to finding neural correlates of behavior or psychiatric biomarkers.

The human brain contains on the order of 1011 neurons and more than 1014 synaptic connections. Although sparsely connected, each neuron is within a few synaptic connections of any other neuron. This remarkable connectivity is achieved by a kind of hierarchical organization that is ubiquitous in nature, called scale-free or complex networks. Complex networks are characterized by dense local connectivity and sparser long-range connectivity. Though EEG has long been a useful tool for clinical neuroscience, a great deal of information about the network structure of the nervous system likely remains hidden because linear analysis techniques fail even to detect them. Analysis of signal complexity and transient synchronization using nonlinear analysis and generalized synchronization methods may reveal information about local neural complexity and long-range communication between brain regions that will be clinically useful.

The development of novel EEG sensors with improved resolution, together with new source localization algorithms and methods for computing complexity and synchronization from EEG data promise continued improvement in the ability to measure subtle variations in brain function. Deeper understanding of the relationship between these neurophysiological processes and behavior may yield a new window into the mind, allowing us. Because atypical brain development is likely to precede abnormal behavior by months or even years, this may provide a critical developmental window for early intervention that may be missed if diagnosis is based entirely on a behavioral phenotype. References Barabasi, A.L. (2009). Scale-free networks: a decade and beyond. Science (New York, NY 325, 412-413. Bassett, D.S., and Bullmore, E. (2006). Small-world brain networks. Neuroscientist 12, 512-523. Behne, T., Carpenter, M., Call, J., and Tomasello, M. (2005). Unwilling versus unable: infants' understanding of intentional action. Dev Psychol 41, 328-337.

Belmonte, M.K., Allen, G., Beckel-Mitchener, A., Boulanger, L.M., Carper, R.A., and Webb, S.J. (2004a). Autism and abnormal development of brain connectivity. J Neurosci 24, 9228-9231. Belmonte, M.K., Cook, E.H., Jr., Anderson, G.M., Rubenstein, J.L., Greenough, W.T., Beckel-Mitchener, A., Courchesne, E., Boulanger, L.M., Powell, S.B., Levitt, P.R., et al. (2004b). Autism as a disorder of neural information processing: directions for research and targets for therapy. Molecular psychiatry 9, 646- 663. Bosl, W. J., Tager-Flusberg, H., & Nelson, C. A. (2011). EEG Complexity as a Biomarker for Autism Spectrum Disorder. BMC Medicine, 9(18). Costa, M., Goldberger, A. L., & Peng, C. K. (2005). Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys, 71(2 Pt 1), 021906. Gao, Z., and Jin, N. (2009). Complex network from time series based on phase space reconstruction. Chaos (Woodbury, NY 19, 033137. Gautama, T., Mandic, D.P., and Van Hulle, M.M. (2003). Indications of nonlinear structures in brain electrical activity. Physical review 67, 046204. Gotham, K., Pickles, A., & Lord, C. (2009). Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J Autism Dev Disord, 39(5), 693-705. Johnson, M.H. (1993). Brain development and cognition : a reader (Oxford, UK ; Cambridge, USA, Blackwell). Le Van Quyen, M. (2003). Disentangling the dynamic core: a research program for a neurodynamics at the large-scale. Biol Res 36, 67-88. Mizuhara, H., Wang, L. Q., Kobayashi, K., & Yamaguchi, Y. (2005). Long-range EEG phase synchronization during an arithmetic task indexes a coherent cortical network simultaneously measured by fMRI. Neuroimage, 27(3), 553-563. Ravasz, E., and Barabasi, A.L. (2003). Hierarchical organization in complex networks. Physical review 67, 026112. Sakkalis, V., Tsiaras, V., Michalopoulos, K., and Zervakis, M. (2008). Assessment of neural dynamic coupling and causal interactions between independent EEG components from cognitive tasks using linear and nonlinear methods. Conf Proc IEEE Eng Med Biol Soc 2008, 3767-3770. Sauseng, P., and Klimesch, W. (2008). What does phase information of oscillatory brain activity tell us about cognitive processes? Neurosci Biobehav Rev 32, 1001-1013. Stam, C.J. (2005). Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116, 2266-2301. Supekar, K., Musen, M., and Menon, V. (2009). Development of large-scale functional brain networks in children. PLoS Biol 7, e1000157. Xie, H.-B., He, W.-X., & Liu, H. (2008). Measuring time series regularity using nonlinear similarity-based sample entropy. Physics Letters A, 372(48), 7140-7146. Zhang, D., Ding, H., Liu, Y., Zhou, C., & Ye, D. (2009). Neurodevelopment in newborns: a sample entropy analysis of electroencephalogram. Physiol Meas, 30(5), 491-504. Zwaigenbaum, L., Thurm, A., Stone, W., Baranek, G., Bryson, S., Iverson, J., et al. (2007). Studying the emergence of autism spectrum disorders in high-risk infants: methodological and practical issues. J Autism Dev Disord, 37(3), 466-480. Learning Objective Describe in general terms how the relationships between early brain development and disorders, neural network structure or connectivity, and EEG signals. Complex patterns hidden in EEG signals contain information about neural connectivity in the brain and thus can be expected to contain diagnostic information that is useful for monitoring brain development in infants and children. Outline Introduction (15 minutes) Autism and Epilepsy are connectivity disorders. Early brain development is concerned with building and pruning neural arbors Network science and neurophysiology (25 minutes) Characterizing complex networks; complex networks in nature (10 minutes) Electrophysiology and neural networks (5 minutes) Nonlinear time series analysis for characterizing complex systems behavior. Manifestations of thought and behavior in brain functional activity (10 minutes) Applications to neuropsychiatric medicine (20 minutes)

Recent research results in cognitive development Near-future applications in diagnosis, personalized therapy planning and neurofeedback Financial Interest: No financial interests.



EEG Theta/Beta Ratio, EEG Vigilance, and Arousal in Adult Attention-Deficit/Hyperactivity Disorder: Reevaluation of Current Methods (R,C)

Marie Gonzales, BS, University of Tübingen, [email protected] Christian Sander, PhD, University of Tübingen

Ute Strehl, PhD, University of Tübingen

Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract Objectives: EEG/QEEG analysis of adults with ADHD has produced a variety of activity patterns (Hale et al, 2009; Koehler et al., 2009; Look et al., 2009; Thompson & Thompson, 2005; White, 2001, 2003) as well as the typical increases in theta/beta ratios seen in pediatric populations (Bresnahan, Anderson, & Barry, 1999; Bresnahan & Barry, 2002; Clarke et al., 2008a). The theta/beta ratio has been considered a marker of nervous system arousal and is a cornerstone of current models of ADHD. However, this measure has not been validated and does not correlate with skin conductance level (SCL) in adolescent ADHD populations (Barry, Clarke, Johnstone, McCarthy, & Selikowitz, 2009). Recently the EEG Vigilance (Bente, 1964; Hegerl et al., 2008a, Hegerl et al., 2008b) model has emerged to explain trait and state differences in clinical populations and refers to the pattern of distinct states of global brain activation observable on the continuum from full wakefulness to sleep onset during eye-closed resting state (Olbrich et al., 2009). Analysis of EEG Vigilance in a childhood ADHD population indicated that individuals with ADHD have more frequent vigilance state shifts and tend to spend more time in lower vigilance stages (Sander et al., 2010). EEG Vigilance and heart rate was accessed in a control population and the average heart rate (HR) decreased as participants entered the lower arousal/vigilance stages (Olbrich et al., 2009). This study aims to test the theta/beta ratio, EEG vigilance, SCL, and HR as markers of arousal in order to investigate current “arousal” models of ADHD within an adult population. Methods: Continuous 19-channel EEG, SCL, and HR were acquired from 20 adult participants that met DSM-IV criteria for ADHD (combined, inattentive, or hyperactive type), without additional serious physical, neurological, or psychiatric disorders, and a full scale IQ > 80. EEG recordings included EO, EC, P300, and CNV tasks. EEG vigilance clusters (A1, A2, A3, B1, B2/3 + C) and state changes were assessed in 1- minute blocks over the 15 minutes eyes closed recording in accordance with the latest version of the Vigilance Classification Algorithm originally presented by Hegel and colleagues (2008a). SCL, HR, and theta/beta (calculated as the ratio of theta [4-7 Hz] to beta [13-21 Hz] relative power at Cz) were also calculated for the one minute blocks. For vigilance and theta/beta ratio, individual means were correlated with individual SCL and HR. Results: This investigation is part of a long-term ADHD treatment study currently in progress. The most current results related to theta/beta ratios, EEG vigilance, SCL, and HR states in the adult ADHD populations will be presented at the time of the presentation. Conclusion: Specific findings and study shortcomings will be discussed and current models of arousal in ADHD evaluated. References Bente, D., (1964). Vigilanz, dissoziative Vigilanzverschiebung und Insuffizienz des Vigilitätstonus [Vigilance, dissociative vigilance shifting and insufficiency of vigilance stages]. In: Kranz, H., Heinrich, K. (Eds.), Begleitwirkung und Miβerfolge der psychiatrischen Pharmakotherapie [Accompanying effects and

failures of psychiatric pharmacotherapy]. Thieme, Stuttgart. Bresnahan, S. M., Anderson, J. W., & Barry, R. J. (1999). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry, 46(12), 1690-1697. Bresnahan, S. M., & Barry, R. J. (2002). Specificity of Quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res, 112(2), 133-144. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Selikowitz, M., & Bryne, M.K. (2008a). EEG coherence in adults with attention-deficit/hyperactivity disorder. International Journal of Psychophysiology, 76(1), 35-40. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Seilkowitz, M., & Bryne, M.K. (2008b). EEG in adults with attention-deficit/hyperactivity disorder. Int J Psychophysiology, 70(3), 176-183. Hale, T. S., Smalley, S. L., Hanada, G., Macion, J., McCracken, J. T., McGough, J. J., & Loo, S. K. (2009). Atypical alpha asymmetry in adults with ADHD. Neuropsychologia, 47(10), 2082-2088. Hegerl, U., Olbrich, S., Schönknecht, P., & Sander, C. (2008a). Manic behaviour as an autoregulatory attempt to stabilize vigilance. Nervenarzt, 79(11), 1283–1290. Hegerl U., Stein M., Mulert C., Mergl R., Olbrich S., Dichgans E., Rujescu D., Pogarell O. (2008b). EEG-vigilance differences between patients with borderline personality disorder, patients with obsessive-compulsive disorder and healthy controls. European Archives of Psychiatry and Clinical Neuroscience, 258, 137–143. Koehler, S., Lauer, P., Schreppel, T., Jacob, C., Heine, M., Boreatti-Hummer, A., et al. (2009). Increased EEG power density in alpha and theta bands in adult ADHD patients. Journal of Neural Transmission, 116(1), 97-104. Loo, S. K., Hale, T. S., Macion, J., Hanada, G., McGough, J. J., McCracken, J. T., & Smalley, S. L. (2009). Cortical activity patterns in ADHD during arousal, activation, and sustained attention. Neuropsychologia, 47(10), 2114-2119. Olbrich, S., Mulert, C., Karch, S., Trenner, M., Leicht, G., Pogarell, O., & Hegerl, U. (2009). EEG-vigilance and bold effect during simultaneous EEG/fMRI measurement. Neuroimage, 45, 319–332. Sander, C., Arns, M., Olbrich, S., & Hegerl, U. (2010). EEG-vigilance and response to stimulants in pediatric patients with attention deficit/hyperactivity disorder. Clinical Neurophysiology, 121, 1511-1518. White, J. N., Jr. (2001). Neuropsychological and electrophysiological assessment of adults with attention deficit hyperactivity disorder. Unpublished doctoral dissertation, The University of Tennessee, Knoxville. White, J. N., Jr. (2003). Comparison of QEEG Reference Databases in Basic Signal Analysis and in the Evaluation of Adult ADHD. Journal of Neurotherapy,7(3/4), 123-169. Learning Objective Understand the linkage between the theta/beta ratio, EEG vigilance dominance, and the nervous system (SCL and HR) arousal during resting state in an adult ADHD population. Outline Background and description of nervous system response during resting state EEG. Previous research on theta/beta ratio – arousal theory of ADHD (15 minutes) Study population demographics, methods to assess nervous system arousal, theta/beta ratios, EEG vigilance, and results (10 minutes) Discussion of treatment implications, study limitations, and future directions (5 minutes) Financial Interest: The authors of this presentation have no significant financial interest or relationship with commercial supporter(s) or manufacturer(s) of any commercial product or service that is discussed as part of the presentation.



QEEG-Guided Neurofeedback for the Remediation of Dysgraphia – An Outcome Study (C)

Jonathan Walker, MD, Neurotherapy Center of Dallas, [email protected] Credits: CME, American Psychological Association, NBCC, ASWB and CA Board of Behavioral Sciences Credits and BCIA recertification credits: .5 Abstract

Twenty-five individuals, aged 6 – 51 years, all right-handed, presented at our clinic with a complaint of handwriting difficulty. Each of them had a Quantitative EEG (QEEG). Our attention focused on the areas controlling motor planning (F3) and sensorimotor integration (C3) of the right hand. Eighteen of the individuals had excessive 1-10 Hz activity at C3, and seven of them had excessive 1-10 Hz activity at F3, and two had excessive 21-30 Hz activity at F3. Those who had excessive 1-10 Hz at C3 underwent five sessions of training to decrease 1-10 Hz and increase 15-18 Hz at C3. Those individuals who had only excessive 1-10 Hz at F3 had five sessions to decrease 1-10 Hz and increase 5-18 Hz at F3. The two with excessive 21-30 Hz at F3 had training to decrease 21-30 Hz and increase 10 Hz at F3. Two individuals chose not to do neurofeedback. Handwriting was scored pre and post-neurofeedback with a modification of the Checklist of Written Expression, on a scale from 1/10 –10/10. The results and statistical analysis will be discussed. The results were judged as good to excellent in all but one of the 23 subjects who did the neurofeedback training (p< .01). References Suttler, J. M. “Checklist of Written Expression,” in Assessment of Children, Jerome M. Suttler (publisher), San Diego, 3rd edition, 1992 (table 20-6), Penmanship). Parkinson, Lesley. Neurofeedback for Childhood Disorders: Underlying Neurophysiology and the Implications for Optimizing Neurofeedback Response—WS11, ISNR, August 28, 2008. Case-Smith, J. “Effectiveness of school-based occupational therapy intervention in handwriting (2002), Am. J. Occup. Therapy 57:152-160. Learning Objective Evaluate handwriting difficulties and the QEEG abnormalities associated with them. The participant will then be able to normalize the handwriting using QEEG-guided neurofeedback training. Outline Evaluation of handwriting difficulties (10 minutes) Recognition of QEEG abnormalities associated with dysgraphia (10 minutes) Remediation of dysgraphia with QEEG-guided neurofeedback training (10 minutes) Financial Interest: No disclosures.

ISNR 2011 Poster Abstracts

The category of presentations is indicated by “C” for Clinical Application or Clinical Experience, “R” for Research, and “T” for Theoretical.

Thursday, September 15, 2011 Transcranial Magnetic Stimulation (rTMS) Modulates Selective Attention and Executive

Functioning in Autism (R,C) Joshua Baruth, MS, University of Louisville, [email protected]

Manuel Casanova, MD, University of Louisville Lonnie Sears, PhD, University of Louisville

Estate Sokhadze, PhD, University of Louisville

Abstract BACKGROUND: Autism Spectrum Disorder (ASD) has been previously shown by our group to be associated with abnormalities in later-stage event-related potential (ERP) indices of selective attention. Specifically the attention-orienting frontal P3a and the sustained attention centro-parietal P3b have been found to be atypical in ASD during a visual oddball task; this may be related to reduced inhibitory tone of the dorsolateral prefrontal cortex (DLPFC) in ASD, as the DLPFC has been associated with selective attention and working memory. OBJECTIVES: In this study we wanted to test the effects of bilateral low frequency repetitive transcranial magnetic stimulation (rTMS) applied to the dorsolateral prefrontal cortices on novelty processing in ASD. We hypothesized that rTMS would improve cortical inhibitory tone by selectively activating inhibitory GABAergic double bouquet interneurons, and this would improve task performance. METHODS: We recruited 25 participants with ASD and randomly formed a 15 subject active-TMS group and a 10 subject wait-list group. We assessed task performance before and after twelve sessions of bilateral low frequency rTMS in the active TMS group and before and after a six week waiting period in the waitlist group. RESULTS: Individuals with ASD showed significant improvement following treatment evidenced by improved P3b responses to targets and better stimulus discrimination. There was also a significant improvement in frontal reactivity to novelty as indicated by the P3a component. The wait-list group did not show any significant changes. CONCLUSIONS: We propose that that low-frequency rTMS may have increased cortical inhibitory tone and subsequently improved performance in the novelty processing task. TMS has the potential to become an important therapeutic tool in ASD treatment with few, if any side effects.

References Casanova, M. F., van Kooten, I., Switala, A. E., van England, H., Heinsen, H., Steinbuch, H. W. M., et al. (2006). Abnormalities of cortical minicolumnar organization in the prefrontal lobes of autistic patients. Clinical Neuroscience Research, 6, 127–133. George and Belmaker (2007) Transcranial Magnetic Stimulation in Clinical Psychiatry. Arlington, VA: American Psychiatric Publishing, Inc. Pritchard, W. S. (1981). Psychophysiology of P300. Psychological Bulletin, 89, 506-540. Rubenstein, J.L.R., Merzenich, M.M., (2003). Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes, Brain, and Behavior, 2, 255–267. Matzel, L.D., Kolata, S. (2010). Selective attention, working memory, and animal intelligence. Neuroscience and Biobehavioral Reviews, 34, 23–30.

EEG-Assessed Bandwidth Activity Differences Between Individuals With SCI With and Without Chronic Pain (R,C) Alan Braden, University of Washington

Leslie Sherlin, PhD, NovaTech EEG, [email protected] Jon D Howe, Mark P Jensen, Shahin Hakimian, Maria R Reyes, Amy K Kupper, Ann D Gianas

Abstract Chronic pain is a significant problem for many individuals living with a spinal cord injury (SCI). However, not all people with SCIs experience chronic pain as a direct result of the injury. Electroencephalograph (EEG) technology may be useful to understand possible differences in brain activity in individuals with SCI with and without chronic pain. The purpose of the current study is to measure and compare baseline brain activity between participants who experience daily SCI-related pain to those who do not. Seventy-one participants with SCI and chronic pain (N=42), SCI without pain (N= 13), or with neither SCI nor pain (N=16) underwent an EEG assessment. Participants with SCI and chronic daily pain exhibited slightly more relative fast wave activity (β-wave) and slightly less slow wave (α-wave) activity than participants with and without SCI who did not have pain. The alpha/beta ratio was significantly lower in participants with SCI and pain than participants without pain. The results suggest that the presence of pain is associated with brain activity as measured by EEG, and supports the potential utility of EEG for identifying these differences. The findings also suggest the possibility that interventions that alter brain wave activity in persons with a SCI and pain, such as neurofeedback training, could influence the experience of pain. References Finnerup, N. B., Johannesen, I. L., & Sindrup, S. H. (2001). Pain and dysesthesia in patients with spinal cord injury: a postal survey. Spinal Cord, 39, 256-262. Osaka, M., Osaka, N., Koyama, S., Okusa, T., & Kakigi, R. (1999). Individual differences in working memory and the peak alpha frequency shift on magnetoencephalography. Cognitive Brain Research, 8, 365-368.

Asymmetrical Frontal Gamma Activity During A Telekinesis Demonstrational (R,C)

Thomas Brod, MD, UCLA, [email protected] William Scott, BS, UCLA

Abstract Early this year we had the opportunity to observe a demonstration of “telekinesis” with EEG monitoring for a reality TV show. We utilized the Hilbert-Huang transform (HHT), a new method to construct a sharp and clean time-frequency spectrum of a non-linear and non-stationary signal. Using empirical mode decomposition while retaining intra-wave modulation makes it very suitable for quantitative EEG analysis; also, HHT has excellent potential for clinical EEG neurofeedback, as will also be presented. Using HHT analysis we discovered that coincident with the mentalist apparently moving a pen in a glass without touch (but not under control conditions), there was a sharp rise in left frontal gamma activity with no corresponding rise on the right. These non-blind observations will not convince skeptics (including ourselves), but they do open a path for open-minded rigorous evaluation of the phenomena that were observed. References Bengstron, WF (2007) A method used to train skeptical volunteers to heal in an experimental setting, Journal of Alternative and Complementary medicine, 13, 329-331. Bengstron, WF, & Moga M (2007) Resonance, placebo effects, and Type II errors: some implications from healing research for experimental methods, Journal of Alternative and Complementary medicine, 13, 317-327. Buzsaki G (2006) Rhythms of the Brain. Oxford University Press Chia-Lung Y, Hsiang-Chih C, et al (2010) Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition, BioMedical Engineering OnLine 9-25. Hendricks L, Bengston WF, Gunkelman J (2010) The Healing Connection: EEG harmonics, Entrainment, and Schumann’s Resonances, Journal of Scientific Exploration 24:4 655-66. Leder D (2005),“Spooky Actions at a Distance”: physics, psi, and distant healing. J Altern Complemen Med, Oct;11(5):923-30. Petrantonakis PC & Hadjiileontiadis LJ (2010) Emotion recognition from EEG using higher order crossings, IEEE Trans Inf Techol Biomed 14:2, 186-97. Schlitz M, Radin D, et al (2003) Distant healing intention: definitions and evolving guidelines for laboratory studies, Altern Ther Health Med, May-Jun;9(3 Suppl):A31-43.

Factors Related to Income, Quality of Work Life, and Burnout for Neurofeedback Practitioners (R)

Thomas Patrick Cothran, BA, Illinois Institute of Technology, [email protected] Lauren Drandorff, BS, M. Bill Baerentzen, MS, Catherine Ryan, BA, Ana E. Salvatierra, BA, Charles Morgan, BS, Patricia Murman, BS, Sarah Lemp, MS, Aanchal Taneja, Jonathon Eugene Larson, EdD

Abstract Introduction: Research on neurofeedback therapy (NFT) practitioner variables that influence outcomes is still in a nascent phase. Larson, Ryan, & Baerentzen (2010) investigated NFT practitioner perspectives. They utilized a systematic, qualitative method to analyze survey data from seventy-one practitioners. These authors captured practitioner perspectives on the advantages and disadvantages of NFT and on the knowledge, skills, and personality traits necessary to be a successful NFT practitioner. They reported three major findings. They found that practitioners generally view ongoing NFT as effective in reducing symptoms and improving quality of life. They found that practitioners view commitment to NFT as essential for overcoming the complexity of NFT. They identified 34 personality traits practitioners endorse as essential. This current study sought to build off of these previous findings to add to the research base on NFT practitioner perspectives with the goal of identifying practitioner variables that may influence practitioner outcomes. From these previous findings, we hypothesized commitment, knowledge, inquiry/intake ratio, successful outcomes, supervision, training, drop outs, traits, and caseload are associated with quality of work life, burnout, and income. Method: We utilized an online survey system to collect surveys from 238 NFT practitioners to engage them in the process of identifying factors related to NFT. We contacted practitioners through email and discussion boards and each practitioner was asked to complete an online survey. For each survey completed $10 was donated to a neurofeedback professional organization research fund. The survey included questions about demographics and variables identified in our previous research. Practitioners were also asked to choose ten traits that best described them from the list of 34 traits identified in the previous study (Larson et al., 2010). We utilized SPSS descriptive statistics for our demographic and NFT experience information. We completed independent sample t-tests with Bonferroni correction, crosstab chi square analyses, Cronbach alpha tests, regression analyses, and slope plotting. Results: Preliminary findings indicated burnout scores are negatively associated with quality of work life (QOWL). QOWL scores are positively associated with using NFB in practice. Non NFT methods in practice are negatively associated with QOWL. We also found QOWL scores are associated with commitment to: understanding brain function, learning new NFT techniques, and improving interpersonal skills. We found income rates are positively associated with providing supervision/training, inquiry/intake ratio, sessions per month, and successful outcomes. Moreover, out of the 34 NFB practitioner traits identified in previous research, frequency analyses indicated the highest endorsed traits in rank order were: 1) ethical; 2) attentive; 3) empathic; 4) accepting; 5) calm; 6) observant; 7) sense of humor; 8) analytical; 9) confident; and 10) realistic expectations. Conclusions: These data support the notion that single focus commitment to NFT is a requisite for a socially, emotionally, and financially satisfying experience of neurofeedback practice. An eclectic approach in which neurofeedback is used occasionally may dilute practitioner proficiency; subsequently impacting NFT-related revenue and zest for a personally involved intervention. It may be that the complexity of NFT contraindicates delving into other treatment modalities. Alternatively, NFT may attract individuals with a tendency to temporarily adopt novel approaches. Dedicated commitment to life-long training in physiology, cognition, learning, and statistics may moderate the relationship between emotional satisfaction and financial reward. It seems reasonable to hypothesize that dedicated commitment, emotional satisfaction, and financial reward each affect practitioner competency. References Larson, J. E., Ryan, C. B., & Baerentzen, M. B. (2010). Practitioner perspectives of neurofeedback therapy for mental health and physiological disorders. Journal of Neurotherapy, 14 (4), 280-290.

EEG/LORETA Frequency and Localization Characteristics of Compassion versus Egocentrism versus Universal Mind (R)

Janeen Denny, BA, Northern Arizona University

Larry Stevens, Northern Arizona University, [email protected] Abstract There appears to be an epidemic of human violence in the world today, with over 37 violent conflicts currently underway worldwide. One way of combating violence is the installation of compassionate attitudes and behaviors in human beings. In an effort to better understand and to promote compassion, the present study examines EEG cortical neuroimaging during compassionate versus self-centered versus universal mind meditations among a cohort of 60 young adults. Outcomes indicate, uniquely for the compassion meditators, significant enhancements of slow wave (Theta/Low Alpha EEG frequencies) activity in brain regions involved in emotional inhibition, verbal self memory, emotional processing of sensory experiences, sensorimotor short term memory, and auditory processing, consistent with the scripted compassion constructs contemplated. Such an activation of specialized brain regions during compassionate meditation suggests unique cortical localizations and neuroelectrical frequencies involved in compassion. Implications are explored for the enhancement of compassionate behaviors and attitudes via directed site- and frequency-specific neurotherapy. References Fronsdal, G. (2006). Mindfulness as a Buddhist Practice. Retrieved July 15, 2010 from http:// www.insightmeditationcenter.org/books-articles/articles/mindfulness- Hsieh, Y. (2008). Self-Compassion and Self-construal in the United States, Thailand, and Taiwan. Journal of Cross-Cultural Psychology. 39, 267-285. Wayment, H.A. & Bauer J.J. (2008). Transcending Self-Interest: Psychological Explorations of the Quiet Ego. Washington, DC: American Psychological Association.

A Randomized Trial of Computer Attention Training in Children with Attention-

Deficit/Hyperactivity Disorder (R,C) Elizabeth Frenette, MPH, Tufts Medical Center, [email protected]

Naomi Steiner, MD, Tufts Medical Center Tahnee Sidhu, BA, Tufts Medical Center Katie Mitchell, BA, Tufts Medical Center

Abstract Background: We report preliminary results from a study in 17 schools examining the efficacy of two computer-based attention training systems in teaching children with Attention Deficit/Hyperactivity Disorder (ADHD) to concentrate more effectively. Several studies suggest that attention training using neurofeedback may result in decreased symptoms of ADHD and improved academic performance and behavior at school. In one such study of 100 children on stimulant medication (Monastra, Monastra, & George, 2002), only the participants who received additional neurofeedback sustained the positive gains after the stimulant medication was discontinued. A small randomized controlled trial of neurofeedback with a waitlist control (Linden, Habib, & Radojevic, 1996), demonstrated improvements in behavioral symptoms of ADHD. Unfortunately, there were only 18 participants, so there was insufficient power to demonstrate a statistically significant difference between the groups. We compared a neurofeedback (NFB) computer attention training system that teaches children to alter their brainwave activity with a Standard Computer Format attention training system (SCF). We hypothesize that both treatments will show improvement in ADHD symptoms and academic outcomes compared to a control condition. Methods: 45 children with ADHD in grades 2 and 4 were randomly assigned to receive the NFB, SCF, or a Waitlist-Control condition (WLC) that receives NFB or SCF the following academic year. Children received forty 45-minute sessions three times a week at school for 4 months. As part of a comprehensive assessment, we report data on the T-SKAMP completed by teachers that assesses symptoms of ADHD in the classroom, the PERMP, a math test completed by students that analyzes speed and accuracy, and the BOSS, double-blind classroom observations. Intervention / Program / Practice: The neurofeedback intervention system used is commercially available and was chosen for several reasons: (1) the NFB component is directed at increased theta waves and decreased beta waves in the frontal cortex, which are the most frequently observed cortical deficits in children with ADHD (Lubar, 1991); (2) it uses EEG sensors that are embedded in a bicycle helmet, as opposed to EEG sensors placed directly on the scalp with wires, which significantly eases delivery in children. This system includes different tasks to train attention stamina, visual tracking (as required in the classroom), increased time-on-task, short-term memory and sequencing, and discriminatory processing. As the child advances, s/he progresses to more challenging tasks that include visual and auditory distractions, such as colorful shapes moving around on the screen and background noise. The SCF intervention used is also commercially available and was chosen for several reasons: (1) it includes an extensive array of cognitive exercises that target many

areas of attention as well as working memory; and (2) the SCF system is designed to improve sustained concentration and working memory through a variety of specific interactive exercises manipulated with a standard computer mouse and keyboard. The exercises aim to maximize attention, decrease impulsivity, and train auditory and visual working memory. The tasks become more challenging as the participant progresses. Results: Wave 1 participants include 41 children diagnosed with ADHD in the 2nd and 4th grades. We conducted preliminary analyses of variance of the PERMP, T-SKAMP, and BOSS (see Tables 3 and 4, Appendix B). The NFB intervention group showed improvement on the number of math problems correct on the PERMP math test (p=0.03), indicating an increase in accuracy, as well as in increase in number of problems attempted on the PERMP math test (p=0.02), illustrating an increase in speed. The NFB intervention group also demonstrated a decrease in ADHD symptoms as reported by teachers on the T-SKAMP Attention scale (p=0.01). The SCF group showed improvement on the number of problems correct on the PERMP math test (p=0.01) indicating an increase in accuracy, and a trend towards decreased ADHD symptoms on the T-SKAMP. The WLC showed no significant effects on either the PERMP or the T-SKAMP. The BOSS showed a trend towards ADHD symptom reduction in the classroom setting. Conclusion: Our preliminary data from a four-year study evaluating the efficacy of two computer-based attention training systems in schools shows significant results as well as promising trends. As this is a preliminary analysis of preliminary data we have not addressed all of the study’s aims and have not yet compared data between the three intervention groups. Teacher report of ADHD symptoms, math achievement by students, and objective classroom observations for our first wave were analyzed. Our preliminary data on these outcome measures suggest that computer-based attention training programs offered in an elementary school setting may be effective in reducing symptoms of ADHD and improved math achievement. We hope that analysis of full data collected after the intervention of wave 2 will consolidate our findings and further explore the feasibility and effectiveness of computer attention training as a method to support children with attention issues in schools. References Linden. M., Habib, T., & Radojevic, V. (1996). A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities.[erratum appears in 1996 Sep;21(3):297]. Biofeedback & Self Regulation,21(1),35-49. Lubar, J.F. (1997). Neocortical Dynamics: Implications for Understanding the Role of Neurofeedback and Related Techniques for the Enhancement of Attention. Applied Psychophysiology and Biofeedback,22 (2),111-126. Monastra, V.J., Lubar, J.F., Linden, M., VanDeusen, P., Green, G., Wing, W., et al. (1999). Assessing attention deficit hyperactivity disorder via quantitative electroencephalography: an initial validation study. Neuropsychology,13(3),424-433. Monastra, V.J., Lubar, J.F., & Linden, M. (2001). The development of a quantitative electroencephalographic scanning process for attention deficit-hyperactivity disorder: reliability and validity studies. Neuropsychology,15(1),136-144. Monastra, V.J., Monastra, D.M., & George, S. (2002). The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention deficit/hyperactivity disorder. Applied Psychophysiology & Biofeedback,27(4),231-249.

EEG Theta/Beta Ratio, EEG Vigilance, and Arousal in Adult Attention-Deficit/Hyperactivity Disorder: Reevaluation of Current Methods (R,C)

Marie Gonzales, BS, University of Tübingen, [email protected]

Christian Sander, PhD, University of Tübingen Ute Strehl, PhD, University of Tübingen

Abstract Objectives: EEG/QEEG analysis of adults with ADHD has produced a variety of activity patterns (Hale et al, 2009; Koehler et al., 2009; Look et al., 2009; Thompson & Thompson, 2005; White, 2001, 2003) as well as the typical increases in theta/beta ratios seen in pediatric populations (Bresnahan, Anderson, & Barry, 1999; Bresnahan & Barry, 2002; Clarke et al., 2008a). The theta/beta ratio has been considered a marker of nervous system arousal and is a cornerstone of current models of ADHD. However, this measure has not been validated and does not correlate with skin conductance level (SCL) in adolescent ADHD populations (Barry, Clarke, Johnstone, McCarthy, & Selikowitz, 2009). Recently the EEG Vigilance (Bente, 1964; Hegerl et al., 2008a, Hegerl et al., 2008b) model has emerged to explain trait and state differences in clinical populations and refers to the pattern of distinct states of global brain activation observable on the continuum from full wakefulness to sleep onset during eye-closed resting state (Olbrich et al., 2009). Analysis of EEG Vigilance in a childhood ADHD population indicated that individuals with ADHD have more frequent vigilance state shifts and tend to spend more time in lower vigilance stages (Sander et al., 2010). EEG Vigilance and heart rate was accessed in a

control population and the average heart rate (HR) decreased as participants entered the lower arousal/vigilance stages (Olbrich et al., 2009). This study aims to test the theta/beta ratio, EEG vigilance, SCL, and HR as markers of arousal in order to investigate current “arousal” models of ADHD within an adult population. Methods: Continuous 19-channel EEG, SCL, and HR were acquired from 20 adult participants that met DSM-IV criteria for ADHD (combined, inattentive, or hyperactive type), without additional serious physical, neurological, or psychiatric disorders, and a full scale IQ > 80. EEG recordings included EO, EC, P300, and CNV tasks. EEG vigilance clusters (A1, A2, A3, B1, B2/3 + C) and state changes were assessed in 1- minute blocks over the 15 minutes eyes closed recording in accordance with the latest version of the Vigilance Classification Algorithm originally presented by Hegel and colleagues (2008a). SCL, HR, and theta/beta (calculated as the ratio of theta [4-7 Hz] to beta [13-21 Hz] relative power at Cz) were also calculated for the one minute blocks. For vigilance and theta/beta ratio, individual means were correlated with individual SCL and HR. Results: This investigation is part of a long-term ADHD treatment study currently in progress. The most current results related to theta/beta ratios, EEG vigilance, SCL, and HR states in the adult ADHD populations will be presented at the time of the presentation. Conclusion: Specific findings and study shortcomings will be discussed and current models of arousal in ADHD evaluated. References Bente, D., (1964). Vigilanz, dissoziative Vigilanzverschiebung und Insuffizienz des Vigilitätstonus [Vigilance, dissociative vigilance shifting and insufficiency of vigilance stages]. In: Kranz, H., Heinrich, K. (Eds.), Begleitwirkung und Miβerfolge der psychiatrischen Pharmakotherapie [Accompanying effects and failures of psychiatric pharmacotherapy]. Thieme, Stuttgart. Bresnahan, S. M., Anderson, J. W., & Barry, R. J. (1999). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry, 46(12), 1690-1697. Bresnahan, S. M., & Barry, R. J. (2002). Specificity of Quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res, 112(2), 133-144. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Selikowitz, M., & Bryne, M.K. (2008a). EEG coherence in adults with attention-deficit/hyperactivity disorder. International Journal of Psychophysiology, 76(1), 35-40. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Seilkowitz, M., & Bryne, M.K. (2008b). EEG in adults with attention-deficit/hyperactivity disorder. Int J Psychophysiology, 70(3), 176-183. Hale, T. S., Smalley, S. L., Hanada, G., Macion, J., McCracken, J. T., McGough, J. J., & Loo, S. K. (2009). Atypical alpha asymmetry in adults with ADHD. Neuropsychologia, 47(10), 2082-2088. Hegerl, U., Olbrich, S., Schönknecht, P., & Sander, C. (2008a). Manic behaviour as an autoregulatory attempt to stabilize vigilance. Nervenarzt, 79(11), 1283–1290. Hegerl U., Stein M., Mulert C., Mergl R., Olbrich S., Dichgans E., Rujescu D., Pogarell O. (2008b). EEG-vigilance differences between patients with borderline personality disorder, patients with obsessive-compulsive disorder and healthy controls. European Archives of Psychiatry and ClinicalNeuroscience, 258, 137–143. Koehler, S., Lauer, P., Schreppel, T., Jacob, C., Heine, M., Boreatti-Hummer, A., et al. (2009). Increased EEG power density in alpha and theta bands in adult ADHD patients. Journal of Neural Transmission, 116(1), 97-104. Loo, S. K., Hale, T. S., Macion, J., Hanada, G., McGough, J. J., McCracken, J. T., & Smalley, S. L. (2009). Cortical activity patterns in ADHD during arousal, activation, and sustained attention. Neuropsychologia, 47(10), 2114-2119. Olbrich, S., Mulert, C., Karch, S., Trenner, M., Leicht, G., Pogarell, O., & Hegerl, U. (2009). EEG-vigilance and bold effect during simultaneous EEG/fMRI measurement. Neuroimage, 45, 319–332. Sander, C., Arns, M., Olbrich, S., & Hegerl, U. (2010). EEG-vigilance and response to stimulants in pediatric patients with attention deficit/hyperactivity disorder. Clinical Neurophysiology, 121, 1511-1518. White, J. N., Jr. (2001). Neuropsychological and electrophysiological assessment of adults with attention deficit hyperactivity disorder. Unpublished doctoral dissertation, The University of Tennessee, Knoxville. White, J. N., Jr. (2003). Comparison of QEEG Reference Databases in Basic Signal Analysis and in the Evaluation of Adult ADHD. Journal of Neurotherapy,7(3/4), 123-169.

Application of Quantitative Electroencephalogram (QEEG) and Neurofeedback in General Neurology Practice (R,C)

J. Lucas Koberda, MD, Tallahassee NeuroBalance Center, [email protected]

Abstract Despite major advances in computer technology quantitative electroencephalography (QEEG) has been underutilized in general neurology practice for uncertain reasons. EEG-biofeedback also called neurofeedback has had very limited application in US neurology practice since has been frequently called experimental-therefore not reimbursable by most health insurances. Therefore, this study was conducted in order to evaluate the clinical usefulness of QEEG and neurofeedback in general neurological practice. Over the period of approximately 9 months, 150 consecutive patient’s QEEG recordings were analyzed for potential clinical benefits. QEEG patients were divided in 5 groups based on their initial clinical presentation. The main groups included patients with seizures, headaches, head injury, cognitive problems and behavioral dysfunctions. Subsequently, patient’s cases were reviewed and decision was made if QEEG analysis contributed to the diagnosis and/or further patient’s treatment. Selected and representative cases from each group are presented in more detail including QEEG data with additional low resolution electromagnetic tomography analysis (LORETA) and/or using computerized cognitive testing. Statistical analysis showed that QEEG analysis contributed to most (over 90%) neurological cases which indicates great potential for wider application of this modality in general neurology. Many patients were also started with neurofeedback therapy depending on the patient’s desire to be involved in this treatment modality. Preliminary results of effectiveness of neurofeedback treatment are presented. References Michel, C.M., Koenig, T., Brandeis, D., Gianotti, L.R. and Waxkerman, J. (2009). Electrical Neuroimaging. Cambridge Univ. Press, New York. Newer MR, Hovda DA, Schrader LM, Vespa PM. Routine and quantitative EEG in mild traumatic brain injury. Clin Neurophysiol. 2005 Sep: 116(9):201-25. Aminoff MJ. Electrodiagnosis in clinical neurology. 1999. Churchill Livingstone. Philadelphia. Rizzo M, Eslinger PJ, Behavioral neurology and neuropsychology. 2004. Saunders, Philadelphia. Naunheim RS, Treaster M, English J, Casner T, Chabot R. Use of brain electrical activity to quantify traumatic brain injury in the emergency department. Brain Inj. 2010 Aug 19. Thatcher et al. TBI severity index. J. Neuropsychiatry and Clinical Neuroscience 13, 77, 2001. Thatcher RW et al. EEG discriminant analysis of children with learning disabilities. July, 2002. Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology. 1994, 18:49-65. Pascual-Marqui. R.D., 1999. Review of Methods for Solving the EEG Inverse Problem. International Journal of Bioelectromagnetism, Volume 1, Number 1, pp: 75-86. Walker JE. QEEG-guided neurofeedback for recurrent migraine headaches. J. Clin. EEG. Neuroscience. Jan. 2011. Seagrave RA et al. Individualized alpha activity and frontal asymmetry in major depression. J. Clin. EEG . Neuroscience. Jan. 2011. Coben R. et al. EEG power and coherence in autistic spectrum disorder. Clin. Neurophysiol. May, 119, 2008. Deslandes A. et al. QEEG to discriminate primary degenerative dementia from major depressive disorder. Arq. Neuropsiquatr. March, 62, 2004. Fonseca LC, et al. Epileptiform abnormalities and QEEG in children with attention deficit/hyperactivity disorder. Arq Neuropsiquatr, Sept, 66, 2008. Coburn KL, et al. The value of QEEG in clinical psychiatry. J Neuropsychiatry Clin Neuroscience, 18, 2006. Arns M et al. Efficiency of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin EEG Neuroscience, 40, 3, July 2009. Clark CR et al. Evidence-based medicine evaluation of electrophysiological studies of the anxiety disorders. Clin EEG Neuroscience, 40, 2, 2009.

Memory Deficit and Malingering: An ERP-Based Assessment with a “Dual-Probe” Protocol and Countermeasure Use (R)

Elena Labkovsky, PhD, Northwestern University, [email protected]

Abstract Introduction: Memory deficit is one of the most common symptoms accompanying many psychological/neuropsychological conditions. In traumatic injury cases (like closed head injury) where monetary compensation can be claimed, the potential motivation to exaggerate memory deficit increases. Thus, it often becomes difficult to estimate actual memory deficit. Literature shows that estimates of malingering reach up to 50% for malingered psychological symptoms. The large number of papers published on the topic demonstrates the concern among clinicians that successful malingering does take place. An ERP-based memory deficit tests reveal a high level of resistance to the effects of malingering compared to behavioral tests of memory.

Our previous studies demonstrated effectiveness of the Complex Trial Protocol (CTP) to detect concealed information (Rosenfeld et al., 2004, 2008, 2010). The CTP hit rates range from 84 to 100%. Sometimes results are inconclusive because either the difference in P300 amplitudes between “Probe” and “Irrelevants” does not reach significance or due to excessive artifacts. To address this issue a novel “Dual-Probe” protocol was developed. Methods: The original CTP utilizes only one probe (which is a relevant/familiar to the subject item). Rare “Probe” or frequent “Irrelevant” (irrelevant/unknown to the subject stimulus) appears on screen first and is followed by either Target or Nontarget in the same trial. Subject’s birth date was Probe1(P1) and Irrelevants were four other dates. Four strings of numbers were Nontargets and one string -Target. There was no probe in the second part. In “Dual-Probe” CTP the 1st part is exactly as the original CTP but in the 2nd part target/nontarget numbers were replaced with probe/irrelevant/target city names. Subject’s hometown was Probe2 (P2) and there were three irrelevant city names and a Target. Subjects randomly pressed 1of 5 buttons on one response box to a date, and they pressed 1of 2 buttons on another response box to a city name. We tested 3 groups (N=36). Simple Guilty (SG), N=13(with P1& P2); Innocent (IN), N=12.(no probes); and Countermeasure (CM), 2(of4) “Irrelevants” in1 part were countered. The countermeasures (CMs) were the subject’s silent, mental imaging of his/her first name (CM1) and last name (CM2). After a subject saw a to-be-countered irrelevant in the first part of a trial, he/she had to mentally state first or last name before randomly pressing one of the five buttons – “left hand” response. Subjects were instructed to perform countermeasures so that the experimenter could not detect the silent, mental act. Results: Hit rates: SG-13/13 total. 2 subjects were “caught” with only 1 probe and the rest - with both P1&P2. IN – 1/12 false positive (only P1). CM -11/11 total, 2 subjects with only 1 probe and the rest with both P1& P2). ANOVA on P300 amplitudes (3groupsX4stimulus type) revealed no group difference, F(2,33)=2.233, p=.123, significant stimulus effect F(3,99)=22.749,p<.000, and significant interaction (6,99)=8.661, p<.000.T-test revealed significant difference between P1 and Iall1 amplitudes in SG, t(12)=5.472, p<.000) and CM, t(10)=5.825, p<.000) and no difference in the IN, t(11)=0.733.p= 0.479).Significant differences were found between P2and Iall2 in SG,t(12)=5.37,p<.000) and CM, t(10)=5.793,p<.000) and no difference in the IN,t(11)= -2.146,p=.055) Conclusions: The “Dual-Probe” ERP-based protocol for assessment of memory deficit and malingering shows a high level of accuracy. Even when mental countermeasures are implemented by subjects in order to alter their ERP results, the “Dual-Probe” approach reflects the subject’s ability to recognize familiar/learned stimuli. Thus, the “Dual-Probe” protocol can be used in situations where subjects are unable, or unwilling, to report their recollection for learned material. Further research is required to investigate how introduction of countermeasures to the second part of a trial might affect the “Dual-Probe” protocol accuracy. References Allen, J., Movius, H.L.II. (2000). The objective assessment of amnesia in dissociative identity disorder using event-related potentials International Journal of Psychophysiology 38 Pp. 21-41. Ellwanger J, Rosenfeld JP, Sweet JJ, Bhatt M.(1996). Detecting simulated amnesia for autobiographical and recently learned information using the P300 event-related potential. Int J. Psychophysiology. Aug-Sep; (1-2), Pp.9-23. Labkovsky E., Rosenfeld J.P. (2009). P300-based protocol (with acoustic stimuli) for assessing memory deficit, malingering, and deception in clinical and forensic settings. Psychophysiology. 46, s.1 Pp.141. Rosenfeld JP, Labkovsky E. (2010). New P300-based protocol to detect concealed information: Resistance to mental countermeasures against only half the irrelevant stimuli and a possible ERP indicator of countermeasures. Psychophysiology. 47, Pp.1002–10. Neurofeedback Training for the Enhancement of Attention in ADD/ADHD Children (C)

Jeffry La Marca, MA, University of California, Riverside, [email protected] Abstract The phenotypical expression of Attention Deficit Disorder (ADD)/Attention Deficit Hyperactivity Disorder (ADHD) is complex and includes impairments in executive functioning, impulsive behaviors, and pervasive difficulties with inattention; areas that are critical to successful academic performance. Neurophysiological measures, including electroencephalography (EEG) that records the electrical activity of the brain, provide objective data to distinguish individuals with attention deficits from others (Doehnert, Brandeis, Straub, Steinhausen, & Drechsler, 2008). The most salient feature found in the EEGs of students with ADD/ADHD is that of cortical

slowing or an overabundance of slower brainwave patterns (Doehnert et al., 2008). These patterns are associated with distractibility, inattention, and daydreaming. Neurofeedback is often used to train students to produce faster brainwave patterns, similar to those of typically developing individuals, and has been found to be conducive to learning. \ A best-evidence analysis of existing research was conducted on the efficacy of neurofeedback as an intervention for enhancing attention in students with ADD/ADHD. Initially, 135 studies on neurofeedback were examined with 113 being excluded for insufficient data, leaving 22 to be matched for compliance with pre-established criteria: studies limited to children, subjects matched with diagnostic criteria established by the American Psychiatric Association (DSM-IV), pre- and post-test scores provided on objective behavioral measures, sufficiently large sample sizes (n>15), and data that permitted the calculation of effect sizes. Of the two studies that met all criteria, it was determined that effect sizes exceeded .6 between pre-test and post-test objective measures of attention. Findings on measures of impulsivity and reaction time were inconclusive. One feature of EEG neurofeedback is that it provides real-time data on brain function and can be used as a non-invasive intervention to treat attention deficits and improve academic performance (Monastra, Lubar, Linden, VanDeusen, Green, Wing, et al., 1999). Studies consistently suggest that neurofeedback training enhances cognitive performance (Vernon, Egner, Cooper, Compton, Neilands, Sheri, et al., 2003), increases IQ (Linden, Habib, & Radojevic, 1996), and improves attention (Leins, Goth, Hinterberger, Klinger, Rumpf, & Strehl, 2007). Furthermore, positive changes in these domains remain robust in follow-up studies (Strehl, Leins, Goth, Klinger, Hinterberger, & Birbaumer, 2006). Most research examined in this analysis supports the contention that neurofeedback is an efficacious intervention but given the limited number of fully controlled studies with adequate sample sizes, there remains a need for additional research. References Carmody, D., Radvanski, D., Wadhwani, S., Sabo, M., & Vergara, L. (2000). EEG biofeedback training and attention-deficit/hyperactivity disorder in an elementary school setting. Journal of Neurotherapy, 4(3), 5-27. Doehnert, M., Brandeis, D., Straub, M., Steinhausen, H. C., & Drechsler, R. (2008). Slow cortical potential neurofeedback in attention deficit hyperactivity disorder: is there neurophysiological evidence for specific effects? Journal of Neural Transmission, 115(10), 1445-1456. Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H., & Kaiser, J. (2003). Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate. Applied Psychophysiology and Biofeedback, 28(1), 1-12. Leins, U., Goth, G., Hinterberger, T., Klinger, C., Rumpf, N., & Strehl, U. (2007). Neurofeedback for children with ADHD: a comparison of SCP and Theta/Beta protocols. Applied Psychophysiological Biofeedback, 32(2), 73-88. Linden, M., Habib, T., & Radojevic, V. (1996). A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and Self-regulation, 21(1), 35. Monastra, V. J., Lubar, J. F., Linden, M., VanDeusen, P., Green, G., Wing, W., Phillips, A., & Fenger, T. N. (1999). Assessing attention deficit hyperactivity disorder via quantitative electroencephalography: an initial validation study. Neuropsychology, 13(3), 424-433. Rossiter, T. R., & La Vaque, T. J. (1995). A comparison of EEG biofeedback and psychostimulants in treating attention deficit/hyperactivity disorders. Journal of Neurotherapy, 1(1), 48-59. Strehl, U., Leins, U., Goth, G., Klinger, C., Hinterberger, T., & Birbaumer, N. (2006). Self-regulation of slow cortical potentials: a new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics, 118(5), e1530-1540. Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., & Gruzelier, J. (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. International Journal of Psychophysiology, 47(1), 75-85.

The Frontal Alpha Asymmetry During Luteal Phase and Follicle Phase in Premenstrual Dysphoric Disorder (R)

I-Mei Lin, PhD, Kaohsiung Medical University, [email protected] Yu-Ting Chen, Yu-Che Tsai, Erik Peper, PhD

Abstract Objective:

This study presents the characterization of premenstrual dysphoric disorder (PMDD) as a premenstrual syndrome (PMS) disorder with extreme negative affectivity, such as a depressed mood. Resting frontal alpha asymmetry was a biomarker in this major depressive disorder. The purpose of the present study was to explore the frontal alpha asymmetry in an experimental task during the luteal phase and the follicle phase in PMDD and non-PMDD. Method: This study recruited 10 PMDD college women (mean age was 20.40 ± .97) and 10 non-PMDD control women (mean age was 20.10 ± 1.37). There was no significant difference in age (t =.57, p > .05). The PMDD met the following criteria: Menstrual Discomfort Questionnaire (MDQ >70) and Beck Depression Inventory-II (>19); the non-PMDD included MDQ ≦ 70 and BDI-II ≦19. The frontal electroencephalogram (F3/ F4) of all participants were measured during the luteal phase and the follicle phase of the menstrual cycle in the following sequences: baseline measurement (3 minutes); recall of a depressive event guided by experimenter (5 minutes); recovery measurement (3 minutes); and relaxation measurement (3 minutes). The alpha asymmetry score (A2) was computed by subtracting left alpha amplitude from the right alpha amplitude [A2=(R-L)/(R+L)]. Results: There were significant differences in the A2 score between PMDD and non-PMDD during the luteal phase (t = -2.253, p < .05), but not in the follicle phase (t = -1.254, p > .05). The three-way ANOVA showed there was no interaction effect (F = 1.62, p > .05), but there was significantly group differences between PMDD and non-PMDD (d = 0.33, p < .05). The participants with PMDD tended to have alpha asymmetry during the depressive recall situation. However, the frontal alpha asymmetry was not found in non-PMDD. Conclusions: The present study supports the frontal alpha asymmetry during the depressive recall situation in the luteal phase for PMDD, but not in the follicle phase. The results of this study can apply the alpha training of neurofeedback in the luteal phase for PMDD. References Baehr, E., Rosenfeld, J. P., Miller, L., & Baehr, R. (2004). Premenstrual dysphoric disorder and changes in frontal alpha asymmetry. International Journal of Psychophysiology, 52, 159-167. Baehr, E., Rosenfeld, J. P., Baehr, R., & Earnest, C. (1998). Comparison of two EEG asymmetry indices in depressed patients vs. normal controls. International Journal of Psychophysiology, 31, 89-92. Davidson, R. J. (1998). Anterior electrophysiological asymmetries, emotion, and depression: Conceptual and methodological conundrums. Psychophysiology, 35(5), 607-614. Coan, J. A., & Allen, J. J. B. (2004). Frontal EEG asymmetry as a moderator and mediator of emotion. Biological Psychology, 67(1-2), 7-50.

Results of a Survey of Practices by U.S. Neurofeedback Practitioners (R)

Nick Lofthouse, PhD, Ohio State University, [email protected] Elizabeth Hurt, PhD, L. Eugene Arnold, MD

Abstract Introduction: As approximately 33% of children with ADHD fail to benefit fully from the established treatments of medicine and behavior modification (MTA Cooperative Group, Swanson et al., 2001) and an unknown proportion refuse the most effective treatment (medication), additional complimentary or alternative treatments (CATs) are greatly needed. Recently, there has been considerable academic and consumer interest in youth CATs shown by a dramatic increase in Medline published randomized controlled trials (Chan 2008), 11.8% of US youth utilizing CATs annually (Barnes et al., 2008), annual (1996) expenditures of $127 million on pediatric CAM visits & $22 million on remedies (Yussman et al., 2004), 12-to-68% use in pediatric ADHD (Sinha & Efron 2005) & 93% of pediatricians reporting patients with ADHD asking about & 38% patients using CATs (American Academy of Pediatrics, 1997). One such CAT is neurofeedback (NF, Kamiya, 1968), which has been used to treat several psychiatric problems particularly child and adolescent ADHD (Hirshberg, et al., 2005). Research on the NF treatment of youth with ADHD has recently dramatically increased in quantity and improved in quality (Lofthouse, et al., in-press). Alongside, these developments are indications that the marketing of NF has intensified and parents of youth with ADHD are increasingly seeking-out NF for their children. Despite these changes, unlike the aforementioned research on CAT’s in general, there is no information on patterns of NF usage or treatment practices of practitioners who routinely treat, usually without insurance coverage, youth with ADHD. With this study we aim to identify specific practice patterns of U.S. practitioners who use NF to treat youth with ADHD. Identifying such practice patterns offers great benefits for NF practitioners, consumers, related professional associations and future research grant applications. Method:

On May 1, 2011 an OSU IRB-approved online survey of treatment practices associated with the NF treatment of youth with ADHD will be sent to a sample of U.S. NF practitioners. The sample will be composed of members of the top two U.S. NF practice/research organizations and certificants who have agreed to participate in this study: the International Society for Neurofeedback & Research (ISNR); Association for Applied Psychophysiology and Biofeedback (AAPB) and Biofeedback Certification International Alliance (BCIA). Only members who treat youth (i.e., ≤ 18 years-old) with NF in the U.S. will be asked to participate. ISNR estimates that will include 640 of its members, AAPB 134, and BCIA 500 certificants for a total of 1274 potential participants. The survey’s 40 questions were developed via consultations with the ISNR, AAPB, BCIA and several top researchers in the field of NF. Results and Discussion: Data collection will run from May 1st through May 31st 2011. In June, data will be downloaded, analyzed by basic descriptive statistics to identify response patterns and ready for presentation at the ISNR conference in September. Results will be presented on practitioners’ general background (i.e., location [U.S. state], type of practice, training, certification/license, professional title, & years of training), assessment/treatment outcome practices, clinical samples, & NF practices (i.e., approach, technology, effect/adverse effects, insurance coverage). To examine the representative nature of this sample, we intend to calculate a percentage response rate (# of association members who treat pediatric ADHD/number of Respondents) to quantify the representative nature (in terms of a % of the entire population) of our sample. The interpretation and implications of all these results will also be discussed. References Barnes, P.M., Bloom, B., & Nahin, R.L. (2008). Complementary and alternative medicine use among adults and children: United States, 2007. National health statistics reports; no 12. Hyattsville, MD: National Center for Health Statistics. Chan, E. (2008). Quality of efficacy research in complementary and alternative medicine. JAMA, 299(22), 2685-2686. Hirshberg, L.M., Chiu, S. & Frazier, J.A. (2005). Emerging brain-based interventions for children & adolescents: overview & clinical perspective. Child & Adolescent Psychiatric Clinics of North America, 14, 1-19. Kamiya, J. (1968). Conscious control of brain waves. Psychology Today, 1, 57-60. Lofthouse, N., Arnold, L.E., Hersch, S., Hurt, E. & deBeus, R. (in-press). A review of NF treatment for Pediatric ADHD, Journal of Attentional Disorders. Sinha, D., & Efron, D. (2005). Complementary and alternative medicine use in children with attention deficit hyperactivity disorder. Journal of Pediatrics and Child Health, 41(1-2), 23-26. Swanson, J.M., Kraemer, H.C., Hinshaw, S.P., Arnold, L.E., Conners, C.K., Abikoff, H.B., Wu, M.(2001). Clinical relevance of the primary findings of the MTA: success rates based on severity of ADHD & ODD symptoms at the end of treatment. Journal of the American Academy of Child & Adolescent Psychiatry, 40,168-179. Yussman, S.M., Ryan, S.A., Auinger, P., Weitzman, M. (2004). Visits to complementary and alternative medicine providers by children and adolescents in the United States. Ambulatory Pediatrics, 4(5), 429-435. Neurofeedback for Adult Attention-Deficit/Hyperactivity Disorder (ADHD): Preliminary

Findings of Slow Cortical Potential Feedback (R,C) Kerstin Mayer, MSc, University of Tübingen, [email protected]

Sarah Wyckoff, MA, University of Tübingen Ute Strehl, PhD, University of Tübingen

Abstract Objectives: Attention deficit hyperactivity disorder (ADHD) is characterized by symptoms of inattention, impulsivity, and hyperactivity, which persist into adulthood for 4-5% of patients (Goodman & Thase, 2009). Hitherto, only a few EEG studies have investigated ADHD in an adult population (Bresnahan, Anderson, & Barry, 1999; Bresnahan & Barry, 2002; Clarke et al., 2008a; Clarke et al., 2008b, Hale et al, 2009; Koehler et al., 2009; Loo et al., 2009; Thompson & Thompson, 2005; White, 2001, 2003) and to our knowledge no studies have assessed the efficacy of neurofeedback training on symptom reduction. Neurofeedback training has been applied effectively in various areas, especially in the treatment of childhood ADHD (Arns, de Ridder, Strehl, Breteler, & Coenen, 2009). This study is designed to investigate the effect of slow cortical potentials (SCP) neurofeedback training on symptomatology and neurophysiological parameters in an adult ADHD population following 30 training sessions and after a six-month follow-up period. Methods: Continuous 19-channel EEG was acquired from 10 adult participants that met DSM-IV criteria for ADHD (combined, inattentive, or hyperactive type), without additional serious physical, neurological, or

psychiatric disorders, and a full scale IQ > 80. EEG recordings were collected at pre/mid/post/follow-up treatment intervals and included EO, EC, P300, and CNV tasks, as well as ADHD behavioral questionnaires. Participants underwent 30 sessions of SCP neurofeedback training at CZ, referenced to A1, ground A2, with vertical and horizontal ocular correction (Strehl et al., 2006). Results: This investigation is in progress. The changes in behavioral and neurophysiologic parameters following 15 sessions of SCP feedback will be presented at the time of the conference. Conclusion: SCP neurofeedback therapy has not previously been investigated in an adult population and may yield valuable findings related to alternative treatments for adult ADHD. Treatment implications, study limitations, and future directions in research will be addressed. References Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clin EEG Neuroscience, 40(3), 180-9. Bresnahan, S. M., Anderson, J. W., & Barry, R. J. (1999). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry, 46(12), 1690-1697. Bresnahan, S. M., & Barry, R. J. (2002). Specificity of Quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res, 112(2), 133-144. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Selikowitz, M., & Bryne, M.K. (2008a). EEG coherence in adults with attention-deficit/hyperactivity disorder. International Journal of Psychophysiology, 76(1), 35-40. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Seilkowitz, M., & Bryne, M.K. (2008b). EEG in adults with attention-deficit/hyperactivity disorder. Int J Psychophysiology, 70(3), 176-183. Goodman, D. W., & Thase, M. E. (2009). Recognizing ADHD in adults with comorbid mood disorders: Implications for identification and management. Postgrad Med, 121(5), 20-30. Hale, T. S., Smalley, S. L., Hanada, G., Macion, J., McCracken, J. T., McGough, J. J., & Loo, S. K. (2009). Atypical alpha asymmetry in adults with ADHD. Neuropsychologia, 47(10), 2082-2088. Koehler, S., Lauer, P., Schreppel, T., Jacob, C., Heine, M., Boreatti-Hummer, A., et al. (2009). Increased EEG power density in alpha and theta bands in adult ADHD patients. Journal of Neural Transmission, 116(1), 97-104. Loo, S. K., Hale, T. S., Macion, J., Hanada, G., McGough, J. J., McCracken, J. T., & Smalley, S. L. (2009). Cortical activity patterns in ADHD during arousal, activation, and sustained attention. Neuropsychologia, 47(10), 2114-2119. Strehl, U., Leins, U., Goth, G., Klinger, C., & Birbaumer, N. (2006). Physiological regulation of slow cortical potentials-a new treatment for children with ADHD. Pediatrics, 118(5), 1530-1540. Thompson, L., & Thompson, M. (2005). Neurofeedback Intervention for Adults with ADHD. Journal of Adult Development, 12(2 - 3), 123-130. White, J. N., Jr. (2001). Neuropsychological and electrophysiological assessment of adults with attention deficit hyperactivity disorder. Unpublished doctoral dissertation, The University of Tennessee, Knoxville. White, J. N., Jr. (2003). Comparison of QEEG Reference Databases in Basic Signal Analysis and in the Evaluation of Adult ADHD. Journal of Neurotherapy, 7(3/4), 123-169.

A Longitudinal Study of the Effects of Sexual Reassignment Surgery in the Brain: A

Comparison of qEEG Results (R) J J Miles, PhD, University of Calgary, [email protected]

Stuart Donaldson, PhD

Abstract The aim of the present study was to examine the effects of surgery on the brain functions in a male to female transsexual client. A longitudinal design was utilized in which the male to female transsexual's qEEG results of her brain wave functioning were assessed before, after sexual reassignment surgery and one year follow up. The hypothesis was brain wave functioning would change and become similar to brain wave patterns of genetic women. As identified by the quantitative Electroencephalogram (qEEG), statistically important brain wave patterns related to gender identity might be observed when a transgendered person has undergone SRS (sexual reassignment surgery). The observed differences in brain wave patterns may prove useful in treatment, and or assessment for readiness for post surgery success for a transsexual person. Re-assessment of brain wave patterns immediately after diagnosis, treatment and surgery will perhaps reveal that the previous pattern was not altered, and no longer statistically important. This pilot study might support the concept that brain wave patterns related to gender identity do have a correlated and measurable energetic effect. In addition, this study may objectively identify an immediate energetic change after HRT/surgeries in the direction of normalcy and health. “Your brain is the front door to change. It determines how you think, feel and

behave. It influences how you view your past, create your present and invent your future.” (Howell, 2010). Results of the study, including serendipitous findings, will be available for discussion regarding the above questions and many others. Rationale for Pilot Study According to Dixen et al (1984) longitudinal studies should be undertaken to determine the developmental process and characteristics of transgendered. There have been recent longitudinal studies of the effects on memory in female to male transsexuals (Gomez-Gil, et al, 2008). However, they did not specifically study brain wave activity. The effort to discover the usefulness of the qEEG as a tool for providing empirical data for assessment, treatment and prediction of success for post sexual reassignment surgery in transsexuals would seem appropriate. Does sexual reassignment surgery create or cause new neural networks that literally change the brain, and allow it to operate with greater efficiency, productivity and functionality in the post surgery transsexual compared to genetic males and females? The qEEG is very useful in revealing the underlying abnormal brainwave patterns associated with Attention Deficit Hyperactivity Disorder (ADHD) and many other disorders. The system can discriminate with more than 90% accuracy ADHD from learning difficulties and from normal. Many psychiatrists, pediatricians and psychologists involved in the diagnosis of learning disorders and ADHD are unaware of a significant body of research which supports the use of topometric (visual) quantitative Electroencephalographic (qEEG) analysis as a diagnostic tool for differentiating between organic and functional brain disorders including learning difficulties, ADHD, schizophrenia, epilepsy, and cerebral atrophy associated with alcohol abuse, depression and anxiety. Psychophysiologists have established normative qEEG databases. The differences in brain wave patterns revealed in these comparisons point to subtypes of ADHD that are not documented in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Studies of qEEG patterns of ADHD children and adults are consistent with findings revealed by Positron Emission Topography, functional Magnetic Resonance Imaging, Single Photon Emission Computed Tomography (SPECT) and other neuroimaging studies. More recently, research from SPECT brain studies by Daniel Amen and his colleagues have identified six subtypes of ADHD which correlate to qEEG patterns found in individuals with Attention Deficit Disorder (ADD). While various psychometric measurements are available to clinicians, presently there is no data available which documents the qEEG for use in assessments. Thus, topometric (or Visual) QEEG analysis is a powerful adjunct to psychometric assessment in this area (Duff, 2002). Quantitative Electroencephalogram (qEEG) A quantitative electroencephalogram (qEEG) is a topographic/visual enhancement of a traditional EEG. During the procedure, electrical activity of the brain, at rest and during stimulation, is recorded for analysis. Each area of the brain normally spends a characteristic amount of time in alpha, beta, theta, and delta activity. By comparing a patient’s brain mapping to a control population, it may be possible to localize areas of focal slowing and enhanced areas of electrical activity. qEEG is not an invasive procedure; it can be used on all age groups but requires the interpretation of a specialist trained in quantitative encephalographic analysis. Interpretation of the qEEG involves an assessment of the statistical degree of congruence or lack of congruence between a patient and the normal population, or the degree of similarity between a given patient and a qEEG profile that may be characteristic of some defined clinical group. The quantitative approach can display not only variations in the qEEG profiles but also progressive changes in neurophysiological function over time. qEEGs are presently utilized in the evaluation of a) attention deficit disorder, b) anxiety, c) depression, e) substance abuse disorders, f) psychiatric disorders and g) closed head injuries (Donaldson, 2009). The most common and well-documented use of neurofeedback is in the treatment of attention deficit hyperactivity disorder with multiple studies showing neurofeedback to be useful in the treatment of ADD. Other areas where neurofeedback has been researched include treatment of substance abuse, anxiety, depression, epilepsy, Obsessive Compulsive Disorder, learning disabilities, Bipolar Disorder, Conduct Disorder, anger and rage, cognitive impairment, migraines, headaches, chronic pain, autism spectrum disorders, sleep, Post Traumatic Stress Disorder and Mild Traumatic Brain Injury (Donaldson, 2009). References Miles, J. J. and Donaldson, S. (2010) Does Sexual Reassignment Surgery Rewire the Brain: A comparison of pre/post qEEG results, The International Society for Neurofeedback and Research Conference, Denver, Colorado. Vander Werf, J., Crook, J., Ciccocioppo, A. L., and Miles, J. J., (2010) “Coming Together to Enhance Students’ Effective Learning”, Alberta Services for Students’ Conference 2010, Olds College, May 13, 2010, Refereed. Laverty, A. and Miles, J. J. (2009) “H1N1 Managing your Anxiety and fears, and Maybe Panic“, Article for Counselling Centre Website, University of Calgary. Miles, J. J. and Donaldson, S. (2009) “The Use of the qEEG in Assessment of Gender Identity” World Professional Association for Transsexual Health, Oslo, Norway. Miles, J. J. and Shaw, M. (2009) “The Use of a Journal in a Comprehensive Treatment Paradigm” T World Professional Association for Transsexual Health, Oslo, Norway.

Miles, J. J. (2008), “Therapy for sleep issues and the integration of a comprehensive holistic treatment model”, International Conference on Integrative, Complimentary Alternative Medicine and Mental Health, Toronto, Ontario, Canada. Miles, J. J. (2006), “Alcohol Use Amongst the Transgendered Community”, BACCHUS Western Regional Conference, Olds College, September/06, Refereed. Miles, J. J. (2006), “Working with Clients with Stuttering Disorder”, Alberta Services for Students' Conference, 2006, Northern Lakes College, 05/2006, Refereed. Cairns, Shar; Crozier, Sharon; VanderWerf, Jeff; Mottosky, Richelle and Miles, JJ (2005), “Outcome Measure: One Year Later”, Alberta Services for Students Conference 2005, Mount Royal College, 05/18/2005, Refereed. Miles, J. J. (2005), “Time Management for work and life balance”, Law Clerks' Forum: Advanced Litigation and Corporate Commercial Issues, Calgary, 05/31/2005, Invited, Insight Information conference attended by approximately 40, law clerks, paralegals, legal assistants, Managers and Partners. Very Highly rated. Presentation was on the second day of the conference June 1/05. Participants and Faculty were from Canada and the United States. Miles, J. J. (2004), “The New Sexual Performance Drugs”, Alberta Services for Students Conference, Red Deer, Alberta, 12/05/2004, Refereed.

Binaural Beats Alter Lateralized Attention (R)

Sharon Noh, BS, ADD Treatment Center, [email protected] Jacob Johnston, Andrew Hill, MA, Eran Zaidel, PhD

Abstract Introduction: Little evidence exists that binaural beat entrainment can alter attention and behavioral performance. The anecdotal reports and few experimental studies that do exist tend to reach conflicting conclusions. We conducted a randomized controlled study with two different types of binaural beats to investigate their effects on attention. Methods: Eight participants were exposed to different binaural beat protocols (beta and theta) on two separate days. Pairing entrainment sessions allowed participants to act as their own control for possible entrainment effects. Protocol order was counterbalanced across participants to control for order effects. 64-channel EEG was recorded before, during, and after the presentation of binaural beats. The Lateralized Network Attention Test (LANT) was also administered during the presentation of binaural beats, to measure changes in covert orienting of spatial attention in each hemisphere. Results: There were significantly different effects of the two binaural beat types on conflict resolution and spatial orienting in the two hemispheres. EEG analysis is expected to reveal differences in individual subject spectral measures before and after entrainment sessions. References Greene, D.J., Barnea, A., Herzberg, K., Rassis, A., Neta, M., Raz, A., Zaidel, E. (2008). Measuring attention in the two hemispheres: the lateralized attention network test (LANT). Brain & Cognition. 66, 21-31. Kennel S., Taylor A.G., Lyon D., Bourguignon C. (2010). Pilot feasibility study of binaural auditory beats for reducing symptoms of inattention in children and adolescents with Attention-Deficit/Hyperactivity Disorder. Journal of Pediatric Nursing. 25, 3-11. Lane J.D., Kasian S.J., Owens J.E., Marsh G.R. (1998) Binaural auditory beats affect vigilance performance and mood. Physiology & Behavior. 63(2), 249-52. Logan, B. "What Is Gnaural?" Gnaural: A Binaural-Beat Audio Generator. Sourceforge.net, 2009. Web. 14 Jan. 2010. <http://gnaural.sourceforge.net/>. Wahbeh H., Calabrese C., Zwickey H., Zajdel D. (2007). Binaural beat technology in humans: a pilot study to assess neuropsychologic, physiologic, and electroencephalographic effects. Journal of Alternative and Complementary Medicine. 13(2), 199-206. The Effect of Neurofeedback and Cranial Electrotherapy on Immune Function Within a Group of HIV+ Subjects: A Controlled Study (R,C)

Sharon Noh, BS, ADD Treatment Center, [email protected] Gary Schummer, ADD Treatment Center

Abstract

Immune enhancement utilizing neurofeedback therapy may be expected given the many pathways and dense communication matrix mediating activity within and between the central nervous system and the immune system. Those who promote cranial electrotherapy have stated that immune health is improved due to a decrease in negative mood states when using this device. This study investigated the effects of neurofeedback and cranial electrotherapy on a group (n=40) of HIV+ male subjects between ages 18-55 over a period of 16 weeks. Subjects recruited all had baseline T-4 helper cell (CD4+) counts of 200 to 400/cc (lab normal is 400 to 1770/cc). They were randomly assigned to one of four groups: neurofeedback only (n=10), cranial electrotherapy only (n=10), combined neurofeedback and cranial electrotherapy (n=10), or a waitlist control group (n=10). Those in the neurofeedback treatment condition were given two 20 minute sessions in the office each week. Subjects in the cranial electrotherapy group were provided units for daily home practice for 20 minutes. The combined group had both neurofeedback and cranial electrotherapy. The waitlist control group received neither neurofeedback nor cranial electrotherapy. All 40 subjects completed a stress audit questionnaire and symptom check list (SCL-90-R) every week for the 16 week duration of the study. At the baseline, after 8 weeks, and after 16 weeks, subjects in all four groups had their blood drawn at their individual physician’s office and sent to independent laboratories. This provided CD4+ measurements that were statistically analyzed. Results indicated that at baseline, basal total lymphocyte counts (CD4+) counts did not differ between groups (p> 0.72). After 8 weeks, CD4+ counts were significantly greater than controls for the combined group (p= .01) only. After 16 weeks, CD4+ counts were significantly greater than controls for the neurofeedback group (p< .01) and combined group (p< .01). There was no significant change in CD4+ count for the control and cranial electrotherapy only groups over the 16 week period. Results of the subjective stress and physical symptoms inventories corroborated the statistically significant changes in the neurofeedback and combined groups. This pilot study suggests neurofeedback may be a promising tool to improve immune function and warrants further investigation. References Auerbach J.E., Oleson T.D., Sloman G.F. (1992). A behavioral medicine intervention as an adjunctive treatment for HIV related illness. Psychology & Health. 6, 325-334. Baldeweg T., Gruzelier J.H. (1997). Alpha EEG activity and subcortical pathology in HIV infection. International Journal of Psychophysiology. 26(1-3), 431-432. Baldweg T., Catalan J., Pugh K., Gruzelier J., Lovett E., Schurlock H., Burgess A., Riccio M., Hawkins D. (1997). Neurophysiological changes associated with psychiatric symptoms in HIV-infected individuals without AIDS. Biological Psychiatry. 41(4), 474-87. Campbell P.J., Aurelius S., Blowes G., Harvey D. (1997). Decreasing CD4 lymphocyte counts with rest; implications for the monitoring of HIV infection. Int J STD AIDS. 8(7), 423-6. Creswell, J.D., Myers H.F., Cole S.W., Irwin M.R. (2009). Mindfulness meditation training effects on CD4+ T lymphocyts in HIV-1 infected adults: a small randomized control trial. Brain, Behavior, and Immunity. 23, 184-188. Derogatis L.R., Spencer P.M. (1990). The Brief Symptom Inventory: SCL-90 (ver. iv). Baltimore, MD: Clinical Psychometric Research. Dhabar F.S., Miller A.H., McEwen B.S., Spencer R.L. (1995). Effect of stress on immune cell distribution: dynamics and hormonal mechanisms. Journal of Immunology. 154, 5511- 5527. McCain N.L., Zeller J.M., Cella D.F., Urbanski P.A., Novak R.M. (1996). The influence of stress management training in HIV disease. Journal of Nursing Research. 45(4), 246-253. Smith R.B., Shiromoto F.N. (1992). The use of cranial electrotherapy stimulation to block fear perception in phobic patients. Journal of Current Therapeutic Research. 51(2), 249-253. Sommershof A., Aichinger H., Engler H., Adenaur H., Catani C., Boneberg E., Elbert, T., Groettrup M., Kolassa I. (2009). Substantial reduction of naïve and regulatory T cells following traumatic stress. Brain, Behavior, and Immunity. 23, 1117-1124.

A Book Finally Written: Case Study of Effective Intervention Five Years Post Closed Head Injury (R,C)

Lena Santhirasegaram, BSc, ADD Centre, [email protected] Abstract Introduction: This case study concerns a 42 year old female (JS) who suffered a closed head injury in a car accident in 2000. This client received all the best medical and rehabilitation care but she was still unable to function at work or within her social setting. The goal of this presentation is to demonstrate how this client was able to regain full functioning after 84 sessions that combined Neurofeedback (NFB), Biofeedback (BFB), and learning strategies. JS came to the ADD Centre in August 2005 and started her program in September 2005 for neurofeedback (NFB) and biofeedback (BFB) treatment. Previously she had treatment through the Toronto Rehab Institute and made improvements on most of her functions but she continued to have difficulties with reading, writing, cognitive processing and attention. The loss of writing was particularly devastating for the client because she was a writer before the accident. In addition she had trouble with listening and her emotional response was flattened. She had lost her sense of humor and would know something was supposed to be funny but not quite get it. She often needed people to speak more slowly so that she would be able to process the

information communicated to her. She presented with traits much like those of a client with Asperger’s Syndrome combined with ADHD, LD, and Anxiety Disorder. She communicated in a monotone and very matter of fact manner. While doing her NFB and BFB from September 2005 till August 2007, her speech and communication skills as well as the emotional tone in her voice improved. These improvements were substantial to the extent that she wrote and published a book “Life Liner” which she had started prior to her brain injury. Another major improvement was her ability to read again, as prior to her accident she was an avid reader. She currently is working on another novel. Method: Over a period of 2 years, this client had 84 neurofeedback (NFB) combined with biofeedback (BFB) training sessions. The instrument used was the Biograph Infiniti from Thought Technology and the screens were from the Thompson, Setting-up-for-Clinical Success suite which allows NFB AND BFB to be monitored at the same time. Biofeedback included: skin conduction (SC) and heart rate variability (HRV). Each of the treatment sessions lasted 50 minutes. The training parameters were set by Dr. Thompson after analysis of the 19-Channel EEG recording. Such analysis was carried out using NeuroGuide (NG) and LORETA. Sites and frequencies were selected based on the correlation of her symptoms and the findings of her QEEG compared with the normative database from NG. Examples will be shown in the presentation. The biofeedback parameters where decided upon after a psychophysiological stress assessment. Results: This patient made significant gains as measured on objective testing. The Integrated Visual and Auditory Continuous Performance Test (IVA), hyperactivity went from extreme to none; IVA Full Scale - Response control Quotient went from 93 pre-training to 116 post-training;(more than one standard deviation); auditory and visual response control standard score went from 93 and 95 pre-training to 108 and 119 post-training respectively. Her Full Scale Attention Quotient (FAQ) was more than three standard deviations below the mean pre-training and went from 50 on the FAQ to 111. Auditory and visual attention quotient went from 47 and 64 pre-training to 100 and 119 post-training respectively. On all the standard scores she showed gradual improvements over time. WAIS (Wechsler Adult Intelligence Scale) prior to coming to the ADD Centre but after the accident (in 2003) were: verbal 73rd percentile and performance 95th percentile. In 2007, post 80 sessions training, the WAIS IV was readministered, and her verbal score was improved to the 99.5thpercentile and her performance score was also at the 99th percentile. These updated scores were likely a return to her pre-accident baseline as she had been a very high functioning individual. Her training had included a combination of NFB + BFB + learning strategies. In all sessions the patient had HRV feedback. With 50 minute sessions she had single channel EEG referential feedback. Her training was at F8, CPz, and F3 always referenced to the left ear. At F8, 3-7Hz was decreased and 13-15 Hz increased while she was doing visual games. CPz-left ear reference, training was carried out to decrease 3 to 9 Hz, and 23-35 Hz, and increase 11-12 Hz. At F3 while doing verbal / reading cognitive tasks she was trained to decrease 3-7 Hz and increase 15-18 Hz. This was followed by continuing HRV and SC training. Between baseline and post 80 sessions assessments demonstrated using Dr. Lubar’s A620 ( Autogenics) assessment program, a decrease in theta, 3-7 Hz from 13.21 to 11.98 µv, increase in beta, 15-18 Hz from 4.40 to 6.11 µv and this program does not measure high frequency beta. In our centre pre and post µv ratios at Cz are calculated at baseline and post 40, 60, and 80 sessions for (4-8/16-20), (3-7/15-18). These ratios decreased from 2.16 to 1.61 and 2.45 to 2.05 respectively. Baseline picawatt (pW) (4 to 8Hz)2 / (13 to 21 Hz )2 (Monastra, Lubar 1999) ratio decreased from 2.05 to 1.15. In the 19 channel EEG assessments, Dr. Thatcher’s NG Learning Disability Discriminant Analysis post 40 to post 80 sessions, her NG, LD Probability Index changed from an 85.0 percent probability of having a learning disability to zero probability. This corresponded to this client demonstrating a continuous and marked improvement in learning ability. After amplitude training was done remaining coherence problems were addressed. She was trained to increase theta coherence F8-FZ. Theta hypo-coherence F4-F8 decreased from -2.12 to + 0.31. Although initially she had difficulty with metaphors and her voice was very monotone, as coherence improved in the right frontal area her vocal tone normalized completely and she was fully capable of understanding metaphors. Conclusion: As can be observed from this case study the combination of neurofeedback (NFB) with biofeedback (BFB) and learning strategies is an effective intervention for individuals with closed head injury. Hopefully the results from this case study will encourage further research into combining Neurofeedback with biofeedback and cognitive strategies as a viable treatment for clients even several years after the injury. References Byers, A.,(1995), Neurofeedback Therapy for a Mild Head Injury, Journal of Neurotherapy, 1 (1), 22–37. Hoffman, D., Stockdale, S., Hicks, L., Schwaninger, J., (1995), Diagnosis and Treatment of Head Injury, Journal of Neurotherapy, 1 (1), 14-21. Keller, I., (2001), Neurofeedback Therapy of Attention Deficits in Patients with Traumatic Brain Injury, Journal of Neurotherapy, Vol.5 (1/2).

Monastra, V., Lubar, J., Linden, M., VanDeusen, P., Green, G.,Wing, W., Fenger, N., Phillipis, A. (1999) Assessing attention deficit hyperactivity disorder via quantitative electroencephalography: An initial validation study. Neuropsychology, 13,(3), 424-433. Thornton, K., Carmody, D., (2005), Electroencephalogram biofeedback for reading disability and traumatic brain injury, Child and Adolescent Psychiatric Clinic of North America, Vol.14, 137-162. Thornton, K., (2000), improvement /Rehabilitation of Memory Function with Neurotherapy/QEEG Biofeedback, J head Trauma Rehabil, 15(6): 1-13. Walker, J., Norman, C., Weber, R., (2002), Impact of qEEG-Guided Coherence Training for patients with a Mild Closed Head injury, Journal of Neurotherapy, Vol. 6(2).

Where Fear, Risk, Thrill, and Performance Mastery Meet: Action Sport Athlete Brain States (R)

Leslie Sherlin, PhD, Neurotopia, [email protected] Michael Gervai, PhD, Neurotopia

Andy Walshe, PhD, Red Bull North America

Abstract Action sport athletes spend a lifetime pursuing performance mastery in extremely high-risk environments. Mistakes often are closely linked with risk of injury. While more traditional “stick and ball” sport development creates opportunities to “learn” in a relatively safe environment, the action sport athlete is not permitted such luxury. Additionally, the path to become an action sport athlete is not clearly defined. The action-athlete has very few options to join sport teams, developmental programs, or receive explicit and continual skill instruction. They are often required to develop their technical, physical, and mental skills in a self determined, high-risk manner. To become a world-class action sport athlete, it is reasonable to assert that the psychological, emotional, physical, environmental, and possibly genetic factors are different than their traditional sport athlete counterparts. The search for performance mastery in a highly charged, high-risk environment fundamentally would require a different set of performance mental capabilities and characteristics. In order to test these theoretical differences a pilot analysis of quantitative electroencephalography measurements (scalp electrode analysis and eLORETA) of 10 professional action sport athletes were compared to a control group of professional athletes in the sports of baseball, basketball, golf, running, and tennis. The control group of professional players was chosen because they are similar in the time commitment, training and high performance demands however have a lower incidence of previous mild traumatic brain injury than other non-action professional sports (e.g. soccer, North American football, hockey, etc).

Event-related Potential Study of Attention Regulation in ADHD, Autism Spectrum Disorder, and Typical Children (R)

Estato Sokhadze, PhD, University of Louisville, [email protected] Joshua Baruth, PhD, Lonnie Sears, PhD, Guela E Sokhadze,

Ayman El-Baz, PhD, Marie Hensley, Allan Tasman, MD, Manuel F Casanova, MD Abstract Background: Autism Spectrum Disorders (ASD) and Attention- Deficit/Hyperactivity Disorder (ADHD) are very common developmental disorders which share some similar symptoms of social, emotional, and attention deficits. This study is aimed to help understand the differences and similarities of these deficits using analysis of dense array event-related potentials (ERP) during Kanizsa illusory figure (Kanizsa, 1976) recognition task. Although ADHD and ASD seem very distinct, they have been shown to share some similarities in their symptoms. According to diagnostic criteria enunciated in the DSM-IV-TR both ASD and ADHD are mutually exclusionary diagnoses. There is a growing consensus from clinicians, however, that behavioral characteristics of ADHD are observed in 14-78% of autism spectrum disorder (ASD) patients (Holtman et al., 2007; Keen et al., 2004; Lee & Ousley, 2006; Leyfer et al, 2006; Reiersen et al., 2007; Ruggieri, 2006; Sinzig et al., 2009; Yoshida & Uchiyama, 2004). These studies question the validity of comorbidity as an exclusionary criterion within current DSM-IV-TR guidelines and argue in favor of its revision for the upcoming DSM-V (Ruggieri, 2006). Objectives and aims: Although behavioral characteristics of autism and ADHD may coexist the more poignant question is whether both conditions share the same underlying pathophysiology. Without the presence of biomarkers diagnosis based on observed behaviors is fraught with difficulties. The aim of this study involved comparing the ERP profiles of ADHD, ASD, and typical control subjects in a shape recognition task in order to investigate effectiveness of differentiation of target and non-target stimuli. Our hypothesis was that children with ASD will show less pronounced differences in ERP response to target and non-target stimuli as compared to typical

children and children with ADHD. The latter group was predicted to have less reactivity to non-target cues. We expected to find other ERP manifestations of attention regulation and other executive function differences between ASD and ADHD. Methods: Participants with ASD (N=16) and ADHD (N=16) were referred by the Department of Pediatrics. Typical children (N=16) were recruited through advertisements in the local media and schools. There was no significant difference in age (mean 13.6 years, SD=2.5), gender, or IQ between the three groups. EEG was collected using 128 channel EGI EEG system. The task involves the recognition of a specific illusory shape, in this case a square or triangle, created by three or four inducer disks. Subjects were instructed to press button only in response to an illusory square figure. Results: There were no between group differences in reaction time (RT) to target stimuli, but both ASD and ADHD committed more errors, specifically the ASD group had statistically higher commission error rate than controls. Post-error RT in this group was exhibited in a post-error speeding rather than corrective RT slowing typical for the controls. The ASD group also demonstrated an attenuated error-related negativity (ERN) as compared to ADHD and controls. The fronto-central P200, N200, and P300 were enhanced and less differentiated in response to target and non-target figures in the ASD group. The same ERP components were featured by more prolonged latencies in the ADHD group as compared to both ASD and typical controls. Conclusions: Our results show significant differences both in behavioral and electrocortical responses between ASD, ADHD, and typical controls during performance on illusory figure test. The findings are interpreted according to the “minicolumnar” hypothesis proposing existence of neuropathological differences in ASD and ADHD, in particular minicolumnar number/width morphometry spectrum differences. In autism, a model of local hyperconnectivity and long-range hypoconnectivity explains many of the behavioral and cognitive deficits present in the condition, while the inverse arrangement of local hypoconnectivity and long-range hyperconnectivity in ADHD explains some deficits typical for this disorder (Casanova et al., 2009; Williams & Casanova, 2010). Current ERP study supports the proposed suggestion that some between group differences (ASD vs. ADHD) could be manifested in the frontal ERP indices of executive functions during performance on illusory figure categorization task. References Casanova MF, El-Baz A, Mott M, Mannheim G, Hassan H, Fahimi R, Giedd J, Rumsey JM, Switala AE, Farag A. Reduced gyral window and corpus callosum size in autism: possible macroscopic correlated of a minicolumnopathy. J Autism Dev Disord 39(5): 751-764, 2009. Holtman M, Bolte S, Poustka F. Attention deficit hyperactivity disorder symptoms in pervasive developmental disorders: association with autistic behavior domains and coexisting psychopathology. Psychopathology 40: 172-177, 2007. Kanizsa G. Subjective contours. Sci Am. 234(4):48-52, 1976. Keen D, Ward S. Autistic spectrum disorder: a child population profile. Autism 8:39-48, 2004. Lee DO, Ousley OY. Attention-deficit hyperactivity disorder symptoms in a clinic sample of children and adolescents with pervasive developmental disorders. J Child Adolesc Psychopharmacol 16: 737-746, 2006. Leyfer OT, Folstein SE, Bacalman S, Davis No, Dinh E, Morgan J, Tager-Flusberg H, Lainhart JE. Comorbid psychiatric disorders in children with autism: interview development and rates of disorders. J Autism Dev Disord 36: 849-861, 2006. Reiersen AM, Constantino JN, Volk HE, Todd RD. Autistic traits in a population-based ADHD twin sample. J Child Psycho Psychiatry 48: 464-472. Ruggieri VL. Attentional processes and attention deficit disorders in autism. Rev Neurol 42(Suppl 3): S51-S56, 2006. Sinzig J, Walter D, Doepfner M, Sinzig J, Walter D, Doepfner M. Attention deficit/hyperactivity disorder in children and adolescents with autism spectrum disorder: symptom or syndrome? J Atte Disor 13: 117-126, 2009. Williams E, Casanova, M.F. Autism and dyslexia: a spectrum of cognitive styles as defined by minicolumnar morphometry. Med Hypotheses, 74(1):59-62, 2010. Yoshida Y, Uchiyama T. The clinical necessity for assessing attention deficit/hyperactivity disorder (ADHD) symptoms in children with high functioning pervasive developmental disorder (PDD). Eur Child Adolesc Psychiatry 13: 307-314, 2004.

Neuromodulation Using rTMS Improves Error Monitoring and Correction Function in Autism Spectrum Disorders (R,C)

Estato Sokhadze, PhD, University of Louisville, [email protected] Joshua Baruth, PhD, Lonnie Sears, PhD, Guela E Sokhadze, Ayman S El-Baz, PhD,

Marie Hensley, Eric Gross, B.Eng, Allan Tasman, MD, Manuel F Casanova, MD Abstract Background:

One important executive function known to be compromised in Autism Spectrum Disorders (ASD) is related to response error monitoring and post-error response correction. Current theory and research suggests that these prefrontal deficits may contribute to social-emotional and social-cognitive impairments in autism (Henderson et al., 2006). Several reports (Bogte et al., 2007; Sokhadze et al., 2010a; Thakkar et al., 2008; Vlamings et al., 2008) indicate that children with ASD show reduced error processing and deficient behavioral correction after an error is committed. This finding could be explained as a reflection of ASD patients' decreased sensitivity to behavioral errors or a reduction in behavior correction ability. Error sensitivity can be readily examined by measuring event related potential (ERP) components associated with responses to errors: the fronto-central error-related negativity (ERN) and the error-related positivity (Pe). The ERN is a response locked negative ERP deflection, emerging between 40-150 ms after the onset of a commission error. Usually this ERN is followed by a positive wave referred to as the Pe potential. It is suggested that the ERN reflects an initial automatic brain response as a result of an error, and the Pe indicates the conscious reflection and comprehension of the error (Overbeek et al., 2005). ERN and Pe are generally accepted as neural indices of response monitoring processes in psychophysiological research and clinical neurophysiology. Objectives and aims: The goal of our study was to investigate whether behavioral response error rate, post-error RT change, ERN, and Pe will show positive changes following 12-week long slow frequency repetitive TMS (rTMS) in group of high functioning children with ASD. Considering that in our prior studies we showed reduction in error rate in ASD group post 6 sessions of the left dorsolateral prefrontal cortex (DLPFC) rTMS, we hypothesized that 12 sessions of rTMS bilaterally applied over the DLPFC will result in improvements reflected in RT, ERN and Pe measures. Methods: High-functioning participants with ASD (N=30) were referred by the Wisskopf Child Evaluation Center. Diagnosis was made according to DSM-IV (APA, 2000) and further ascertained with ADI-R (Le Couteur et al., 2003). Then participants were randomly assigned to either active rTMS treatment (N=15) or wait-list (WTL) groups. There were no significant differences in age (mean 13.5± 2.6 years), gender, or IQ between groups. Baseline and post-TMS/or WTL EEG was collected using 128 channel EEG system. The task involved the recognition of a specific illusory shape, in this case a square or triangle, created by three or four inducer disks. Subjects were instructed to press button only in response to an illusory square figure. Treatment group received 12 weekly 1 Hz rTMS sessions (150 pulses, 90% of motor threshold), while the WTL subjects were tested twice after 8-12 weeks of waiting period. Results: There were no between group differences in reaction time (RT) to target stimuli nor in rate of commission and omission errors. ERN in TMS treatment group became significantly more negative (by 4.99± 4.35 µV, F=5.07, p=0.03), while Pe increased (from 5.96±5.02 to 9.72±5.28 µV, F=5.55, p=0.019). No latency differences were detected. The number of omission errors decreased (t=2.26, p=0.034). The RT did not change, but posterior RT became slower (from -22.3 ms to 10.6 ms post-TMS). There were no changes in RT, error rate, post-error RT slowing, or in ERN/Pe measures in the wait-list group. Conclusions: Our results show significant post-TMS differences in the response-locked ERP such as ERN and Pe, as well as behavioral response monitoring measures (omission errors, post-error slowing) indicative of improved error monitoring and correction function. This executive function is important for ability to correctly evaluate committed error and adjust behavior to prevent from rigid and repetitive actions. Elucidating the neurobiological basis and clinical significance of response monitoring and correction deficits in ASD represents a promising direction for further quantitative EEG-based research. The ERN and Pe along with behavioral performance measures can be used as functional outcome measures to assess the effectiveness of neurotherapy (e.g., rTMS or neurofeedback) in children with autism spectrum disorders and thus may have important practical implications. References American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders (DSM-IVTR). 4th ed. Text Revised, Washington, D.C. Baruth, J., Casanova, M., El-Baz, A., Horrell, T., Mathai, G., Sears, L., & Sokhadze, E. (2010) Low-frequency repetitive Transcranial Magnetic Stimulation modulates evoked-gamma frequency oscillations in autism spectrum disorders. Journal of Neurotherapy, 14 (3), 179-194. Bogte, H., Flamma, B., van der Meere, J., & van Engeland, H. (2007) Post-error adaptation in adults with high functioning autism. Neuropsychologia, 45, 1707–1714. Henderson, H., Schwartz, C., Mundy, P., Burnette, C., Sutton, S., Zahka, N., & Pradella, A. (2006) Response monitoring, the error-related negativity, and differences in social behavior in autism. Brain and Cognition, 61, 96-109. Le Couteur, A., Lord, C., & Rutter, M. (2003). The Autism Diagnostic Interview – Revised (ADI-R). Los Angeles, CA: Western Psychological Services.

Overbeek, T.J.M., Nieuwenhuis, S., & Ridderinkhof, K.R. (2005) Dissociable components of error processing, Journal of Psychophysiology, 19, 319–329. Sokhadze E, Singh S, El-Baz A, Baruth J, Mathai G, Sears L, & Casanova M. (2009) Effect of a low-frequency repetitive transcranial magnetic stimulation (rTMS) on induced gamma frequency oscillations and event-related potentials during processing of illusory figures in autism spectrum disorders. Journal of Autism and Developmental Disorders, 39, 619-634. Sokhadze, E., Baruth, J., Tasman, A., El-Baz, A., Mansoor, M., Ramaswamy, R., Mathai, G., Sears, L., & Casanova, M. (2010a) Low-frequency repetitive transcranial magnetic stimulation (rTMS) affects event-related potential measures of novelty processing in autism. Applied Psychophysiology & Biofeedback 35, 147-161. Sokhadze, E., Baruth, J., El-Baz, A., Horrell, T., Sokhadze, G., Carroll, T., Tasman, A., Sears, L. & Casanova, M. (2010b) Impaired error monitoring and correction function in autism. Journal of Neurotherapy, 14, 79-95. Thakkar, K.N., Polli, F.E., Joseph, R.M., Tuch, D.S., Hadjikhani, N., Barton, J.J., & Manoach, D.S. (2008) Response monitoring, repetitive behaviour and anterior cingulate abnormalities in autism spectrum disorders (ASD). Brain. 131, 2464-2478. Vlamings, P.H., Jonkman, L.M., Hoeksma, M.R., van Engeland, H., & Kemner, C. (2008) Reduced error monitoring in children with autism spectrum disorder: an ERP study. European Journal of Neurosciences, 28, 399-406.

Neural Networks: An Exploration of Functions Influenced by Neurofeedback (T,C)

Michael Thompson, MD, ADD Centre, [email protected] Lynda Thompson, PhD, ADD Centre, [email protected]

Andrea Reid, MA, ADD Centre, [email protected] Lena Santhirasegaram, BSc, ADD Centre, [email protected]

Abstract This talk will give a brief overview of neural networks, with an emphasis on neural connectivity loops that underlie these networks. The underlying loop that goes cortex to basal ganglia to thalamus and back to functionally related areas of the cortex is central to the discussion. There will also be an explication of the relationship between common symptom pictures and how those difficulties relate to dysfunction in various networks. The origins of this presentation go back to 1995 at one of the early meetings of the newly formed Society for Neuronal Regulation where we presented a paper entitled, “Exceptional Results with Exceptional Children”. The cases presented were children with severe behavioral disorders who had not responded to traditional treatments such as medications, behaviour modification, and extensive psychotherapy. With parental consent we had tried a new, purely experimental approach for which there was little research support. The parents were desperate and hopeful, even though we were honest about having no way of explaining why Neurofeedback might work, except to cite outcomes of increased attention span in children with ADD who were treated using neurofeedback. Despite our limited knowledge and equipment that did only single channel EEG recordings and basic biofeedback (Autogenics A620 and Focused Technologies F1000), the cases we worked with made remarkable improvements regarding both behaviour and being weaned off medications. A dramatic example was a 13-year-old boy with Autism who initially just screeched and flailed his arms when seated in front of the computer. By the time he finished 85 sessions he had been demitted from the MID class (for children with mental retardation) and had moved on to high school where he was enrolled in regular classes except for Mathematics, in which he took an advance class. Additionally, he was being invited by his peer group to parties. Eight years later when we called to invite him back for follow-up his father declined, explaining that his son was doing well in college and did not want anyone to know that anything had ever been wrong with him. We reviewed our early work with the goal of better understanding what might have been the underlying mechanisms that produced these rather unexpected, positive results. We had done single channel assessments and training, placing the active electrode over Cz, referenced to the left ear in most cases or, in some cases (especially when EMG artifact was a problem), we had used a sequential placement: FCz and PCz. Both referential and sequential training were successful with very difficult cases. With today’s perspective and the knowledge gleaned from sixteen additional years in the field, we suggest that a possible explanation of the good results might be based on a theoretical framework that derives from both other people’s research and our own evidence based practice. QEEG data (either single channel or 19-channel) continues to provide the basis for planning all interventions. The 19-channel data, when available, is combined with LORETA analysis for source localization and then we apply knowledge of Brodmann Areas, functional networks, and the client’s symptoms. Our observation is that there are both symptoms in common, and functional neural networks in common, among many of the disorders we worked with, both then and now.

Additionally, many of the disorders we successfully treated using Neurofeedback (NFB) + BFB may have had underlying mechanisms in common. For the most part, there appears to be dysfunction, to different degrees, in only a few basic networks. Many of the disorders, for example, have in common difficulties in attention (executive network) and/or anxiety (affect network). The autistic spectrum disorders (ASDs) have major difficulties in at least three major networks: executive, affect, and default. The majority of our patients, regardless of diagnosis, appear to have difficulties related to these networks with just a different “balance” of involvement across clients and diagnostic categories. These three networks can be influenced by Neurofeedback at various sites over the central midline structures (CMS). They are also altered by means of biofeedback and, in particular, by Heart Rate Variability (HRV) training, which will influence the same CMSs through afferents to the brain stem medulla and from these nuclei to the basal ganglia and the cortex (Thompson & Thompson, 2009). For all of the NFB training neural loops exist that involve connections from cortex – basal ganglia – thalamus to cortex. These may affect many functionally related areas of the cortex. This will be discussed in terms of how we now think that our results with these early clients were achieved. We were initially doing feedback, for the most part, over the central midline structures (CMS) but we will note that there are a number of exceptions to using CMS feedback as a starting point. We do not, for example, begin our training over CMSs if the main problem is a reading disorder or a seizure disorder. With dyslexia we usually follow the QEEG, which typically shows inactivity over Wernicke’s area near the angular gyrus in the dominant hemisphere. With seizure disorders we may alternate SMR enhancement at C3 and C4 while decreasing slow wave near the focus of the epileptiform activity. Traumatic brain injury (TBI) is another clear exception and there are others. Nevertheless, beginning training over the CMSs is a reasonable starting point with many of our patients due to the network properties of the structures that underlie the CMS midline sites. We will note that we have always combined NFB and BFB and this talk will briefly explain how a CMS, the AC, connects with the medial and orbital prefrontal cortex and the entire limbic system. In addition it receives input from the brain stem which importantly for our work, includes output from vagal afferents from the heart to the nucleus solitarus in the medulla which connects to the locus coeruleus (noradrenaline production) and then to the limbic system including the AC. The AC has direct links to the hypothalamic-pituitary-adrenal (HPA) axis. The audience should immediately see the role of these connections in the human stress response and the importance for treatment that combines HRV training with NFB to control stress (Thompson & Thompson, 2007). We will briefly describe how this relates to the distress network and supports our decision then and now to almost always combine NFB with BFB. References Benededetti, F., Mayberg, H. S., Wager, T. D., Stohler, C. S., & Zubieta, J-K. (2005). Neurobiological Mechanisms of the Placebo Effect. Journal of Neuroscience, (25)45, 10390-10402. Bisley, J. W. & Godberg, M. E. (2006). Neural Correlates of Attention in the Lateral Intraparietal Area. Journal of Neurophysiology, ( 95), 1696-1717. De Ridder, Dirk (2009). An evolutionary approach to brain rhythms and its clinical implications for brain modulation. Journal of Neurotherapy, (13)1, 69-70. Koob, G. F. & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, (35)4, 217-238. Kropotov, Juri (2009). Quantitative EEG, Event Related Potentials and Neurotherapy. San Diego, CA: Academic Press/Elsevier. Langguth, B. & De Ridder, D. (2011). Transcranial Magnetic Stimulation for Tinnitus. In Robert Coben & James R. Evans (Eds) Neurofeedback and Neuromodulation Techniques and Applications, Elsvier/Academic Press 293-318. Lubar, J. F. (1991). Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback and Self Regulation, 16(3), 201-225. Uddin, L. Q, Iacoboni, M., Lange, C. & Keenan, J.P. (2007). The self and social cognition: the role of cortical midline structures and mirror neurons. Trends in Cognitive Sciences, (11)4, 153-157. Northoff, Georg, Heinzel, Alexander, de Greck, Moritz, Bermpohl, Felix, Dobrowolny, Henrik, & Panksepp, Jaak (2006). Self-referential processing in our brain - A meta-analysis of imaging studies on the self. NeuroImage, 31, 440 – 457. Polich, John (2007). Updating P300: An Integrative Theory of P3a and P3b. Clinical Neurophysiology, 118(10), 2128–2148. Thompson, M. & Thompson, L., (2010). Functional Neuroanatomy and the Rationale for Using EEG Biofeedback for Clients with Asperger’s Syndrome. Journal of Applied Psychophysiology and Biofeedback, (35)1, 39-61.

Treatment Analysis of SMR-theta Neurofeedback Session Data After Control for EMG: Changes in Power and Ratios (R)

Martin van Beek, MSc, EEG Resource Institute, Rien Breteler, PhD, EEG Resource Institute,

Abstract Introduction:

In everyday practice of neurofeedback (NF) treatment evaluation often is focused on questionnaires or self-reports. However, EEG parameters (e.g., amplitudes and ratios) within and over sessions also are valuable measures for treatment effect. Often these two approaches do not concur. Treatment evaluation research often addresses questionnaires only. If EEG is involved it mostly concerns pre and post treatment QEEG only. There is considerable debate whether one is able to see any changes after treatment, and as such it questions the processes due to NF, if any. Research Questions: This study investigates the trends in EEG data within and over sessions of 24 patients treated with either a discrete or continuous SMR/theta feedback protocol for ADHD. The research questions are: 1) which changes can be found in the designated bandwidth areas of these protocols? 2) What are the differences between these two protocols? 3) What factors affect EEG parameters? Primary outcome measures are: absolute and relative power/percent power of SMR and theta, standard deviations, and the SMR/theta power ratio. Secondary outcome measures are: associations with time of day, EMG power, duration of training trial, type of feedback, time between sessions and seasonal effects. Method: All session data will be reviewed for EMG and any other artifacts. Trend analyses will be used to plot any learning curves. Results: to be published at the conference. Discussion: the results have major implications for everyday practice. If no changes are found, and no associations with the secondary outcome measures, the use of EEG power in order to monitor change processes become questionable. The study design does not allow for the assessment of placebo effects, but this suggestion may then gain support. For research purposes FMRI validation of the training effects may then be an option for further insight into the processes involved in neurofeedback. References Vernon, D, Dempster, T, Bazanova, O., Rutterford, N, Pasqualini, M & Anderson, S. (2009). Alpha neurofeedback training for performance enhancement: reviewing the methodology, Journal of Neurotherapy, 13(4): 214-227.

QEEG Guided Neurofeedback to Treat Schizophrenia: A Case Study (C)

Jason Von Stietz, ADD Treatment Centers, [email protected] Gary Schummer, PhD, ADD Treatment Centers

Abstract The subject of this case study was a 21-year-old male college student diagnosed with adult onset Schizophrenia, undifferentiated type. Due to the advancement of this disorder he was unable to complete mandatory coursework and forced to take a leave of absence prior to his senior year at the University of Southern California. He was given a very poor prognosis by his psychiatrist and placed on aripiprazole (Abilify) with the dosage varying between 5 mg and 20 mg. His neurofeedback therapy was directed mainly by the results of five serial quantitative EEG’s (QEEG’s) administered over an 18 month period of time. With each QEEG new areas of statistically significant hypocoherence were targeted for treatment. The subject engaged in intensive neurofeedback having four to six 30-minute sessions per week totaling 530 in-office sessions utilizing EEG Spectrum’s EEGer software (amplitude, sum, and coherence modules). The subject engaged in no other therapy or interventions. Initially neurofeedback therapy was conducted predominantly at C3 and C4 (according to the International 10-20 System) with the goal being to obtain cortical stabilization. This phase of treatment lasted for 66 sessions. In the second phase of neurofeedback the subject was given 287 sessions intended specifically to remediate statistically significant QEEG derived coherence abnormalities. Interspersed throughout this second phase of treatment were 117 sessions of cortical stabilization. Stabilization was done before and after each QEEG was administered as well as when clinically indicated. Neurofeedback therapy involves application of certain protocols which determines proper electrode placement and identifies which frequencies or coherence pairs are trained. In both phases of training the reward provided the subject positive feedback (visual and auditory) when identified criteria were met. Degree of cortical stabilization was determined by the subject successfully reaching previously attained optimal levels of amplitude readings in critical frequency ranges as well an ability to maintain low coefficients of variation. Coherence reward was provided when immediate computer analysis indicated that the areas identified showed improved coherence readings. The choice of protocol was optimized based upon either abnormal frequency characteristics or statistically significant Z-Scored FFT hypocoherence abnormalities identified by NeuroGuide (Thatcher, et.al, 2003). Convergence

between QEEG abnormal findings with neurophysiological correlates of functional impairments were the main factors considered in determining the treatment plan. The number of sessions trained for each coherence abnormality was estimated using a calibrated multiplier positively correlated with standard deviation abnormalities. The main factors used to determine the point of maximum benefit for any hypocoherent pair identified for training were improved functional capacity and real-time percent coherent data provided by the EEGer software. Over the course of this subject’s neurofeedback the QEEG identified a total of 36 coherence abnormalities. Results of neurofeedback therapy and subsequent QEEG analysis showed that the neurofeedback was effective at normalizing previously abnormal coherence readings with the exception of two which then normalized after being treated a second time. In spite of the fact that targeted coherence abnormalities normalized, with each new QEEG there appeared new coherence abnormalities presumably caused by the encroaching schizophrenia. During the time we were identifying and intensely treating QEEG identified coherence abnormalities the subject experienced a significant reduction in symptoms as well as a reduced need for aripiprazole (20mg was reduced to 7.5 mg). This window of improved functioning allowed him to return to school where he successfully completed his senior year in spite of a challenging course load and ultimately graduated from USC. Unfortunately, against the advice of his doctors and his family, the subject discontinued neurofeedback treatment. Within six months after stopping neurofeedback therapy and in spite of increased pharmacological intervention the subject became markedly psychotic exhibiting delusions of grandeur, hearing voices, and paranoid ideation. Adult onset Schizophrenia beginning at the age of this subject typically has a very poor prognosis. However, for this subject, neurofeedback treatment was a powerful intervention enabling him to reach a difficult scholastic milestone on reduced levels of medication. This case study indicates that further research is warranted given the profoundly positive effect demonstrated by the neurofeedback intervention. References Bolea, A. S. (2010). Neurofeedback treatment of chronic inpatient schizophrenia. Journal of Neurotherapy, 14(1), 47-54. Cook, P. (2007). White matter and connectivity: Brain volume abnormality in schizophrenia patients. Psychiatry Weekly, 2(37). Donaldson, M., Moran, D., & Donaldson, S. (2010), Schizophrenia in retreat. NeuroConnections Newsletter, 19-23. Fernandez, T., Harmony, T., Rodriguez, M., Reyes, A., Marosi, E., & Bernal, J. (1993). Test-retest reliability of EEG spectral parameters during cognitive tasks: I. Absolute and relative power. The International Journal of Neuroscience, 68(3- 4), 255-61. Gruzelier, J. (2000). Self regulation of electrocortical activity in schizophrenia and schizotypy: A review. Clinical Electroencephalography, 31(1), 23-29. Gruzelier, J., Hardmann, E., Wild, J., Zaman, R., Nagy, A., & Hirsch, S. (1999). Learned control of interhemispheric slow potential negativity in schizophrenia. International Journal of Psychophysiology, 34, 341-348. Harmony, T., Fernandez, T., Rodriguez, M., Reyes, A., Marosi, A., Bernal, J. (1993). Test-rest reliability of EEG spectral parameters during cognitive tasks: II. Coherence. The International Journal of Neuroscience, 68(3-4) 263-271. McEvoy, L.K., Smith, M.E., Gevins, A. (2000). A test-retest reliability of cognitive EEG. Clinical Neurophysiology, 111(3), 457-463. Schneider, F., Rockstroh, B., Heimann, H. et al. (1992). Self-regulation of slow cortical potentials in psychiatric patients: Schizophrenia. Biofeedback & Self-Regulation, 17, 277-292, 17, 277-292. Exact Low-Resolution Electromagnetic Brain Tomography (eLORETA) of Adult ADHD:

Pre/Post Findings Following Neurofeedback Therapy (R,C)

Sarah Wyckoff, MA, University of Tübingen, [email protected] Kerstin Mayer, MSc, University of Tübingen

Leslie Sherlin, PhD, NovaTech EEG Ute Strehl, PhD, University of Tübingen

Abstract Objectives: Attention–deficit/hyperactivity disorder (ADHD) is one of the most common disorders of childhood with a cumulative incidence of 7.5% by 19 years of age (Barbaresi et al., 2004). The primary symptoms of ADHD include inattentiveness, impulsivity, and hyperactivity, which persist into adulthood for 4-5% of patients (Goodman & Thase, 2009). EEG/QEEG analysis of adults with ADHD compared to healthy controls and/or normative database populations have produced a variety of patterns of activity, highlighting the heterogeneity of this population (Bresnahan, Anderson, & Barry, 1999; Bresnahan & Barry, 2002; Clarke et al., 2008a; Clarke et al., 2008b, Hale et al, 2009; Koehler et al., 2009; Loo et al., 2009; Thompson & Thompson, 2005; White, 2001, 2003). The objective of this study was to investigate the specific

frequency band-pass regions and spatial locations associated with adult ADHD using exact low-resolution electromagnetic brain tomography (eLORETA) in comparison to healthy controls and following 30 sessions of neurofeedback therapy. Methods: Continuous 19-channel EEG was acquired from 40 adult participants that met DSM-IV criteria for ADHD (combined, inattentive, or hyperactive type), without additional serious physical, neurological, or psychiatric disorders, and a full scale IQ > 80. EEG recordings were collected at pre/mid/post/follow-up treatment intervals and included EO, EC, P300, and CNV tasks, as well as ADHD behavioral questionnaires. eLORETA analysis was computed on 2 min of EC data (Pascual-Marqui, 2002). The eLORETA output data was compared with age-matched individuals in a healthy control database (Nova Tech EEG, Mesa, Arizona, 2005) using a multiple comparison procedure for the following frequency bands: absolute and relative power in delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta1 (13-18 Hz), beta2 (19-21 Hz), beta3 (22-30 Hz); alpha and theta bands adjusted to individual alpha peak frequency (Pascual-Marqui, The KEY Institute for Brain-Mind Research, Zurich, Switzerland, 2002). Pre/post changes in the sources of EEG rhythms were also assessed following 30 sessions of Theta/Beta or Slow Cortical Potential neurofeedback training. Results: This investigation is part of a long-term treatment study currently in progress. The most current results related to eLORETA EEG source localization of adult ADHD patients compared to a control population and following 15 sessions of neurofeedback therapy will be presented at the time of the conference. Conclusion: Analysis of eLORETA current source activities in adult ADHD patients compared to healthy controls and following neurofeedback training has not previously been investigated and may yield valuable insights related to alternative treatments for this population. Specific findings will be discussed and implication in the current treatment study and future research will be explored. References Barbaresi, W., Katusic, S., Colligan, R., Weaver, A., Pankratz, V., Mrazek, D., et al. (2004). How common is attention-deficit/hyperactivity disorder? Towards resolution of the controversy: Results from a population-based study. Acta Paediatr Suppl, 93(445), 55-59. Bresnahan, S. M., Anderson, J. W., & Barry, R. J. (1999). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry, 46(12), 1690-1697. Bresnahan, S. M., & Barry, R. J. (2002). Specificity of Quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res, 112(2), 133-144. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Selikowitz, M., & Bryne, M.K. (2008a). EEG coherence in adults with attention-deficit/hyperactivity disorder. International Journal of Psychophysiology, 76(1), 35-40. Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Seilkowitz, M., & Bryne, M.K. (2008b). EEG in adults with attention-deficit/hyperactivity disorder. Int J Psychophysiology, 70(3), 176-183. Goodman, D. W., & Thase, M. E. (2009). Recognizing ADHD in adults with comorbid mood disorders: Implications for identification and management. Postgrad Med, 121(5), 20-30. Hale, T. S., Smalley, S. L., Hanada, G., Macion, J., McCracken, J. T., McGough, J. J., & Loo, S. K. (2009). Atypical alpha asymmetry in adults with ADHD. Neuropsychologia, 47(10), 2082-2088. Koehler, S., Lauer, P., Schreppel, T., Jacob, C., Heine, M., Boreatti-Hummer, A., et al. (2009). Increased EEG power density in alpha and theta bands in adult ADHD patients. Journal of Neural Transmission, 116(1), 97-104. Loo, S. K., Hale, T. S., Macion, J., Hanada, G., McGough, J. J., McCracken, J. T., & Smalley, S. L. (2009). Cortical activity patterns in ADHD during arousal, activation, and sustained attention. Neuropsychologia, 47(10), 2114-2119. Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details. Methods Find Exp Clin Pharmacol 24(Suppl D), 5–12. Thompson, L., & Thompson, M. (2005). Neurofeedback Intervention for Adults with ADHD. Journal of Adult Development, 12(2 - 3), 123-130. White, J. N., Jr. (2001). Neuropsychological and electrophysiological assessment of adults with attention deficit hyperactivity disorder. Unpublished doctoral dissertation, The University of Tennessee, Knoxville. White, J. N., Jr. (2003). Comparison of QEEG Reference Databases in Basic Signal Analysis and in the Evaluation of Adult ADHD. Journal of Neurotherapy, 7(3/4), 123-169.

Enhanced EEG Coherence During Bilateral Eye Movements While Recalling an Unpleasant Memory: Implications for EMDR (R,C)

Matthew Yaggie, MA, Northern Arizona University, [email protected] Larry Charles Stevens, PhD, Seth Miller, BA, Angela Abbott, BA,

Chad Woodruff, PhD, Mike Getchis, MA, Sean Stevens

Abstract A number of therapeutic techniques to relieve traumatic stress have been developed in the past decades. One of the most frequently used clinical therapies is Eye Movement Desensitization and Reprocessing (EMDR), which uses bilateral eye movements to facilitate a reduction in the vividness of memories and associated affect. Although this technique has an extensive research history, EMDR has been challenged because the underlying mechanisms are not fully understood. A particular target of this criticism has been the role of and mechanisms underlying the bilateral eye movement component of EMDR. The goal of this study was to examine the bilateral eye movements used in EMDR therapy and to test an interhemispheric integration model for EMDR by exploring EEG coherence. Participants were not diagnosed with PTSD, but they did recall a moderately unpleasant event during the bilateral eye movement process. The procedure followed previous research using moderately unpleasant memory recall to approximate PTSD characteristics. The bilateral eye movement procedure also followed the EMDR protocol and conditions from a previous study examining bilateral eye movements and EEG coherence. A sample of 55 undergraduate and graduate females was used in this study. Each participant experienced a 5’ eyes opened baseline condition followed by one of three treatment conditions: (a) an eye fixation condition, (b) an eye fixation with background bilateral light movement condition, and (c) a bilateral eye movement condition. During the treatment condition, the participant recalled a moderately unpleasant episodic memory. Each of 5 eyes opened treatment conditions lasted for 1’ followed by an eyes opened 1’ EEG recording period, for a total of 5’ of EEG recordings. EEG data were noise artifacted, power spectral analyzed, and statistically analyzed for interhemispheric coherence differences between conditions for clusters of frontal pole (Fp), frontal (F), central (C), parietal (P), and occipital (O) electrodes. An ANCOVA, with baseline values as the covariate, was used to compare EEG coherence values following the three treatment conditions. The results revealed significantly higher EEG coherence for Beta and Gamma frequencies in the frontal region following bilateral eye movements compared to the other two conditions. LORETA virtual MRI neuroimages of these effects are presented. These results supported the hypothesis of increased interhemispheric coherence following bilateral eyes movements during recall of an unpleasant memory, consistent with the hypothesized effects of EMDR. Furthermore, these effects were found in frontal brain regions involved in planning, reasoning, decision making, and verbal episodic memory retrieval, also consistent with the interhemispheric integration model. The lack of significantly increased signal coherence at the Fp region and the removal of eye movement artifacts prior to data analysis reduce the likelihood of this obtained effect being a result of eye movement artifacts. References American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revisio). Washington DC; American Psychiatric Association. Andres, F.G., Mima, T., Schulman, A.E., Dichgan, J., Hallet, M., & Gerloff, C. (1999). Functional coupling of human cortical sensorimotor areas during bimanual skill acquisition. Brain, 122, 855-870. Propper, R.E. & Chrisman, S.D (2008). Interhemispheric interaction and saccadic horizontal eye movements: Implications for Episodic Memory, EMDR, and PTSD. Journal of EMDR Practice and Research (2)4, 269-281. Propper, R.E., Pierce, J., Geisler, M. W., Christman, S.D., & Bellorado, N. (2007). Effect of bilateral eye movements on frontal interhemispheric gamma EEG coherence: Implications for EMDR therapy. The Journal of Nervous and Mental Disease, 195, 785-788. Tull, M. (2008). Exposure therapy for PTSD. About.com. Retrieved April 7, 2010 United States Department of Veterans Affairs (2010).

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