Inhibition of Gamma Oscillations as a Neurophysiological ...€¦ · Natasha Radhu and Mawahib Semeralul completed the recruitment for the study. Mawahib Semeralul screened all research

Inhibition of Gamma Oscillations as a Neurophysiological

Endophenotype of Schizophrenia

by

Natasha Radhu

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Graduate Department of Institute of Medical Science

University of Toronto

© Copyright by Natasha Radhu 2015

ii

Inhibition of Gamma Oscillations as a

Neurophysiological Endophenotype of Schizophrenia

Natasha Radhu

Doctor of Philosophy

Graduate Department of Institute of Medical Science

University of Toronto

2015

Abstract

Background

The pathophysiology of schizophrenia (SCZ) has not been fully elucidated. Studies have

demonstrated that SCZ patients have impairments in the dorsolateral prefrontal cortex

(DLPFC). Two findings have been shown in the DLPFC: deficits in GABAergic inhibitiory

neurotransmission and abnormal inhibition of cortical oscillations. Thus, we aimed to assess

frontal inhibition as a potential endophenotype of SCZ.

Objectives

The first objective was to quantitatively assess transcranial magnetic stimulation (TMS)

motor cortex measures of inhibition and excitation in obsessive-compulsive disorder (OCD),

major depressive disorder (MDD) and SCZ, as a meta-analysis. The second objective was to

evaluate the inhibition of overall and gamma oscillations in the DLPFC and motor cortex

iii

using TMS and EEG in SCZ and OCD. The final objective was to evaluate the inhibition of

overall and gamma oscillations in SCZ patients, OCD patients, and their unaffected first-

degree relatives with TMS and EEG.

Hypotheses

First, we hypothesized that motor cortex inhibitory deficits would be a ubiquitous finding

across OCD, MDD and SCZ patients. Second, we hypothesized that patients with SCZ would

show deficits in overall and gamma inhibition in the DLPFC compared to healthy subjects

and patients with OCD. Lastly, it was hypothesized that frontal inhibition in first-degree

relatives of SCZ would be intermediate of healthy subjects and their related probands.

Results

The first study showed that motor inhibitory deficits were a ubiquitous finding across OCD,

MDD, and SCZ. The second paper found that SCZ patients demonstrated inhibitory deficits

in the DLPFC (overall and gamma inhibition), not observed in OCD patients. This was found

to be independent of illness severity and medication. The final study demonstrated deficits in

frontal inhibition in SCZ patients, which were significantly less than their unaffected first-

degree relatives. No differences were found between first-degree relatives of SCZ and

healthy controls. First-degree relatives were intermediate of their related probands and

healthy controls. We did not show inhibition deficits in OCD patients and their first-degree

relatives.

iv

Conclusions

Frontal inhibition (measured via TMS and EEG) may be an essential neurophysiological

process that is impaired in SCZ. Multi-site trials are needed to investigate inhibition as a

potential endophenotype for SCZ.

v

Acknowledgements

I am extremely thankful to everyone who has been for me through every step of the way

during my PhD and would like to express my acknowledgements to each of you.

First and foremost, I would like to thank my PhD supervisor, Dr. Zafiris Jeffrey Daskalakis. I

am so fortunate to have Dr. Daskalakis as my mentor as he is a very well-rounded and

brilliant scientist, physician, and teacher. He has been a strong influence in my life not only

academically, while also demonstrating strong commendable family values. I am very

grateful for his guidance throughout my graduate education and his continuous admiration

for my abilities in pursuing a career in neuroscience. I look forward to future collaborations

with Dr. Daskalakis.

I would like to thank two esteemed members of my program advisory committee

Dr. Robert Chen and Dr. James Kennedy who have always provided their extensive feedback

and have continually set aside time to provide their expertise regarding my PhD project.

Also, I am very thankful to my examiners, Dr. Richard Staines, Dr. Sean Kidd, and

Dr. Daniel Mueller for their valued suggestions and comments to help strengthen my PhD

dissertation. Thank you to our collaborators on this work: Dr. Daniel Blumberger,

Dr. Faranak Farzan, Dr. Danilo De Jesus, Dr. Margaret Richter, Dr. Lakshmi Ravindran, Dr.

Paul Fitzgerald and Dr. Tiffany Greenwood. Also, thank you to Anosha Zanjani for applying

her creative artistic talent to this work. All of the figures in this dissertation are beautiful due

to Anosha’s dedicated efforts and endless perfection.

vi

To the entire team at the Temerty Centre for Therapeutic Intervention, thank you for your

hard work and assistance during my doctoral work. The lab environment has always been

both productive and fun. In particular, many thanks to Dr. Luis Garcia Dominguez for his

superb Matlab abilities and always thinking critically about the data. I am very appreciative

to Mawahib Semeralul for all of her prompt help and for making the heritability project a true

victory. I am very enthusiastic to be a co-CEO with Mawahib one day.

My family’s support has made this work exceptionally possible and would be impossible

without them. I would like to thank my mother Usha Radhu and my father Prem Radhu for

instilling in me the value of education, for teaching me their morals and always allowing for

me to put my education before everything else. My parents have given me unconditional love

and extreme generosity throughout my graduate career; words can’t express how thankful I

am to both my mother and father for giving me the opportunity to pursue my dreams. I would

also like to thank Anita Idrees (my sister), Adnan Idrees (my brother-in-law), Jasmine Idrees

(my niece), Isha Idrees (my niece), Reena Radhu (my sister), Mark Snyder (my brother-in-

law), and Anya Snyder (my niece) as their infinite support and love has been so important to

me throughout my PhD journey, I am forever appreciative.

Last but definitely not the least; I would like to thank my husband, Manoj Gandhi. His

passion for mathematics, his drive for education, his zest for life and his endless love to me

have all been significant motivators to my success. I am very excited to begin a new chapter

in my life with Manoj Gandhi right by my side.

vii

Contributions

Literature Review Chapters

Chapter 1, Inhibition of the Cortex Using Transcranial Magnetic Stimulation in Psychiatric

Populations: Current and Future Directions

Authors: Radhu N, Ravindran LN, Levinson AJ, Daskalakis ZJ.

All authors of this paper published in the Journal of Psychiatry and Neuroscience (Natasha

Radhu, Lakshmi Ravindran, Andrea Levinson and Zafiris Daskalakis) reviewed the article

critically, approved the final version for publication and assisted with writing the paper.

Natasha Radhu and Lakshmi Ravindran conducted the systematic literature search. Natasha

Radhu, Lakshmi Ravindran and Zafiris Daskalakis designed the study, analyzed the papers

and organized the structure of the paper. Anosha Zanjani created and designed the figures for

this paper.

Chapter 2, Neurophysiological Measurements Associated with Transcranial Magnetic

Stimulation

Authors: Radhu N, Blumberger DM, Zanjani A, Daskalakis ZJ.

All authors of this book chapter published by Oxford University Press (Natasha Radhu,

Daniel Blumberger, Anosha Zanjani and Zafiris Daskalakis) reviewed the chapter critically,

approved the final version for publication and assisted with writing the chapter. Natasha

Radhu conducted the systematic literature review search. Natasha Radhu and Zafiris

Daskalakis designed the study, analyzed the papers and organized the structure of the

chapter. Anosha Zanjani created and designed the figures for this book chapter.

Chapter 3, Schizophrenia and Their First-Degree Relatives

Author: Radhu N

This chapter was written solely by Natasha Radhu to serve as part of the literature review for

the dissertation.

viii

Chapter 4, Research Aims and Hypotheses of the Experiments

Author: Radhu N

This chapter was written solely by Natasha Radhu to state the objectives and hypotheses for

the three original research articles.

Original Research Articles

Chapter 5, A Meta-Analysis of Cortical Inhibition and Excitability Using Transcranial

Magnetic Stimulation in Psychiatric Disorders

Authors: Radhu N, de Jesus DR, Ravindran LN, Zanjani A, Fitzgerald PB, Daskalakis ZJ.

All authors of the meta-analysis published in the Journal of Clinical Neurophysiology

(Natasha Radhu, Danilo de Jesus, Lakshmi Ravindran, Anosha Zanjani, Paul Fitzgerald,

Zafiris Daskalakis) reviewed the paper critically, approved the final version for publication

and contributed with writing the paper. Natasha Radhu and Zafiris Daskalakis designed the

study. Natasha Radhu acquired the data from published studies, analyzed the data, interpreted

the results and created the forest plots for illustrating the effect size data. Anosha Zanjani

provided expertise with the effect size (Hedge’s G) analysis and assisted with all figures for

this publication.

Chapter 6, Evidence for Inhibitory Deficits in the Prefrontal Cortex in Schizophrenia

Authors: Radhu N, Garcia Dominguez L, Farzan F, Richter MA, Semeralul MO, Chen R,

Fitzgerald PB, Daskalakis ZJ.

All authors of this paper published in Brain (Natasha Radhu, Luis Garcia Dominguez,

Faranak Farzan, Margaret Richter, Mawahib Semeralul, Robert Chen, Paul Fitzgerald, Zafiris

Daskalakis) reviewed the paper critically, approved the final version for publication and

contributed with writing the paper. Natasha Radhu and Zafiris Daskalakis designed the study

and interpreted the findings. Natasha Radhu and Mawahib Semeralul completed the

recruitment. Mawahib Semeralul screened all research participants using a structured clinical

interview and TMS safety screener questionnaire. Natasha Radhu acquired and pre-processed

the data. Natasha Radhu and Luis Garcia Dominguez completed the final analyses of the

ix

data. Luis Garcia Dominguez created the figures for this publication. Faranak Farzan

provided consultation and expertise with the EEG analysis.

Chapter 7, Investigating the Heritability of Cortical Inhibition in First-Degree Relatives and

Probands in Schizophrenia

Authors: Radhu N, Garcia Dominguez L, Greenwood TA, Farzan F, Semeralul MO, Richter

MA, Kennedy JL, Blumberger DM, Chen R, Fitzgerald PB, Daskalakis ZJ.

Manuscript Submitted

All authors of the manuscript (Natasha Radhu, Luis Garcia Dominguez, Tiffany Greenwood,

Faranak Farzan, Mawahib Semeralul, Margaret Richter, James Kennedy, Daniel Blumberger,

Robert Chen, Paul Fitzgerald, and Zafiris Daskalakis) reviewed the paper critically, approved

the final version for publication and contributed with writing the paper. Natasha Radhu and

Zafiris Daskalakis designed the study as well as completed the interpretation of the results.

Natasha Radhu and Mawahib Semeralul completed the recruitment for the study. Mawahib

Semeralul screened all research participants using a structured clinical interview and TMS

safety screener questionnaire. Natasha Radhu acquired and pre-processed the data. Natasha

Radhu and Luis Garcia Dominguez designed and completed the final analyses of the data.

Tiffany Greenwood completed the heritability analyses and provided genetic expertise.

James Kennedy provided genetic expertise with the data. Luis Garcia Dominguez created the

figures for this publication. Faranak Farzan provided consultation and expertise with the

EEG analysis.

Chapter 8, General Discussion, Future Directions and Conclusions

Author: Radhu N

This chapter was written solely by Natasha Radhu to serve as the discussion for the

dissertation.

x

Table of Contents

Abstract…………………………………………………………………………………….ii-iv

Acknowledgments………………………………………………………………………….v-vi

Contributions……………………………………………………...………………………vii-ix

List of Abbreviations………………………………………………………………..…xviii-xix

List of Tables……………………………………………………………………...…………xx

List of Figures…………………………………………………………………………xxi-xxiv

Chapter 1 Inhibition of the Cortex using Transcranial Magnetic Stimulation in

Psychiatric Populations: Current and Future Directions………………...…………..1

1.1 Abstract……………………………………………………………………………………2

1.2 Introduction………………………………………………………………………………..3

1.3 Evaluating Cortical Inhibition with Transcranial Magnetic Stimulation………………….4

1.3.1 Cortical Silent Period……………………………………………………………………6

1.3.2 Short Interval Cortical Inhibition……………………………………………………..…6

1.3.3 Long Interval Cortical Inhibition………………………………………………………..7

1.3.4 Interhemispheric Inhibition……………………………………………………………...7

1.4 Transcranial Magnetic Stimulation as a Method to Measure Excitability………………...8

1.4.1 Resting Motor Threshold………………………………………………………………..8

1.4.2 Active Motor Threshold…………………………………………………………………9

1.4.3 Intracortical Facilitation…………………………………………………………………9

1.5 Motor Cortex Inhibition in Psychiatric Disorders……………………………………….11

1.5.1 Schizophrenia…………………………………………………………………………..11

1.5.2 Bipolar Disorder………………………………………………………………………..13

xi

1.5.3 Major Depressive Disorder………….....………………………………………………14

1.5.4 Obsessive-Compulsive Disorder……………………………….………………………15

1.5.5 What Are the Implications of These Findings?…..……………………………………16

1.5.6 Applications beyond the Motor Cortex…………………………...……………………17

1.6 TMS-EEG Studies of Inhibition………………………………………………………....18

1.6.1 Assessing Connectivity with TMS and EEG…………………………………………..18

1.7 Functional Consequences of Disordered Inhibition……………………………….……..19

1.8 Limitations……………………………………………………………………………….21

1.9 Summary of Findings…………………………………………………………………….22

Chapter 2 Neurophysiological Measurements Associated with Transcranial

Magnetic Stimulation …………………………………………………………..…..........24

2.1 Abstract…………………………………………………………………………………..25

2.2 Introduction………………………………………………………………………………26

2.3 Overview of TMS Technology…………………………………………………………..26

2.3.1 Motor Cortex TMS Studies in Psychiatric Illnesses and Clinical Utility……………...26

2.4 Applications of TMS and EEG……………………………………………………..……27

2.4.1 Overview of EEG………………………………………………………………………27

2.4.2 Overview of TMS and EEG…………………………………………………………....28

2.4.3 Advantages of TMS and EEG………………………………………………………….30

2.4.4 Rhythms of the Brain as Measured by EEG…………………………………………...30

2.4.5 Single-Pulse TMS Combined With EEG …………………………………………...…31

2.4.6 Paired-Pulse TMS Combined With EEG………………………………………………32

2.4.7 Application of TMS-EEG in Sleep Studies……………………………………………33

2.4.8 Application of TMS-EEG in Loss of Consciousness Studies……………………….…34

2.5 How Can We Apply Combined TMS and EEG in Psychiatric Disorders…………….....34

2.6 Limitations...............................................................................................................…......37

Chapter 3 Schizophrenia and Their First-Degree Relatives…………………….....38

3.1 Abstract…………………………………………………………………………………..39

xii

3.2 Introduction………………………………………………………………………………40

3.3 The Relationship between GABA-Mediated Inhibition and Gamma Oscillations……....40

3.3.1 Gamma Oscillations, GABAergic Inhibition and Working Memory………………….42

3.4 Inhibitory Deficits in Schizophrenia……………………………………………………..43

3.4.1 NMDA-receptor Hypofunction Hypothesis……………………………………………43

3.4.2 Neuroanatomic Findings ………………………………………………………………44

3.4.3 Magnetic Resonance Spectroscopy Studies…………………………………………....45

3.4.4 The N100 Response Evoked by TMS……………..…………………………………...45

3.4.5 Main Findings…………………………………...……………………………………..46

3.5 Endophenotypes in Schizophrenia……………………………………………………….46

3.5.1 Heritability of Schizophrenia…………………………………………………………..47

3.5.2 Applications of Endophenotypes………………………………………………………47

3.5.3 P50 Suppression……………………………………………………………………......48

3.5.4 Anti Saccade Paradigm………………………………………………………………...49

3.5.5 Prepulse Inhibition……………………………………………………………………..49

3.5.6 Auditory Event-Related Potentials…………………………………………………….50

3.5.7 Auditory-Related N100 Findings…………………………………………..........……..51

3.5.8 Mismatch Negativity…………………………………………………………………...51

3.6 The Bipolar-Schizophrenia Network on Intermediate Phenotypes Research……………52

3.6.1 Neuroimaging and EEG B-SNIP Studies……………………………………………...53

3.7 Transcranial Magnetic Stimulation Studies with First-Degree Relatives of Schizophrenia

Patients ………………………………………………………………………………………54

3.8 Outline of the Dissertation…………………………………………………………..…...55

Chapter 4 Research Aims and Hypotheses of the Experiments………………......56

4.1 Introduction………………………………………………………………………………57

4.2 A Meta-Analysis of Cortical Inhibition and Excitability Using Transcranial Magnetic

Stimulation in Psychiatric Disorders (Chapter 5) Objectives and Hypotheses………………58

4.2.1 Objective 1, Inhibitory Deficits Present in Severe Psychiatric Disorders……………..58

4.2.2 Hypothesis 1, Inhibitory Deficits Present in Severe Psychiatric Disorders……………58

xiii

4.3 Evidence for Inhibitory Deficits in the Prefrontal Cortex in Schizophrenia (Chapter 6)

Objectives and Hypotheses………………………………………………………………..…58

4.3.1 Objective 1, Replication of Frontal Inhibitory Deficits………………………………..59

4.3.2 Hypothesis 1, Replication of Frontal Inhibitory Deficits………………………………59

4.3.3 Objective 2, Diagnostic Specificity of Frontal Inhibitory Deficits…………………….59

4.3.4 Hypothesis 2, Diagnostic Specificity of Frontal Inhibitory Deficits…………………..59

4.3.5 Objective 3, Large Effect Size Differences via Cohen’s D……………………………60

4.3.6 Hypothesis 3, Large Effect Size Differences via Cohen’s D…………………………..60

4.3.7 Objective 4, Trait Stability of Frontal Inhibition………………………………………60

4.3.8 Hypothesis 4, Trait Stability of Frontal Inhibition……………………………………..60

4.4 Investigating the Heritability of Cortical Inhibition in First-Degree Relatives and

Probands in Schizophrenia (Chapter 7) Objectives and Hypotheses………….…………..…60

4.4.1 Objective 1, Assessing Inhibition in Unaffected First-Degree Relatives…………...…60

4.4.2 Hypothesis 1, Frontal Inhibition in Unaffected First-Degree Relatives……………….61

Chapter 5 A Meta-Analysis of Cortical Inhibition and Excitability Using

Transcranial Magnetic Stimulation in Psychiatric Disorders…………………...62

5.1 Abstract…………………………………………………………………………………..63

5.2 Introduction………………………………………………………………………………64

5.3 Inhibitory TMS Paradigms…………………………………………...………………….64

5.3.1 Excitatory TMS Paradigms………………………………………………………….…65

5.4 Applications within Psychiatric Disorders…………………………………………….…65

5.5 Methods……………………………………………………………………………….….66

5.5.1 Data Sources…………………………………………………………………………...66

5.5.2 Study Selection……………………………………………………………………...…66

5.5.3 Data Extraction…………………………………………………………………….…..67

5.5.4 Hedge's g Calculation for the Meta-Analysis………………………………………….67

5.5.5 Test of Heterogeneity………………………………………………………………..…67

5.5.6 N Fail-Safe …………………………………………………………………………….68

5.6 Results……………………………………………………………………………………68

xiv

5.6.1 Patients with OCD……………………………………………………………………..69

5.6.2 OCD - Resting Motor Threshold………………………………………………………69

5.6.3 OCD – SICI………………….………………………………………………………...69

5.6.4 OCD - Intracortical Facilitation…………………………………………………..……70

5.6.5 OCD - CSP …………………………………………………………………………….70

5.7 Patients with MDD………………………………………………………………………72

5.7.1 MDD - Resting Motor Threshold……………………………………………………...72

5.7.2 MDD – SICI……………………………………………………………………………72

5.7.3 MDD - Intracortical Facilitation……………………………………………………….73

5.7.4 MDD - CSP………………………………………………………………………….…73

5.7.5 MDD - Motor Evoked Potential Amplitude………………………………...…………73

5.8 Patients with SCZ……………………………………………………………………..…75

5.8.1 SCZ - Resting Motor Threshold…………………………………………………….....75

5.8.2 SCZ – SICI……………………………………………………………………………..76

5.8.3 SCZ - Intracortical Facilitation……………………………………………......……….78

5.8.4 SCZ – CSP……………………………………………………………………………..79

5.8.5 SCZ - Motor Evoked Potential Amplitude………………………………………….…80

5.9 Discussion……………………………………………………………………………..…80

5.9.1 Clinical Implications…………………………………………………………………...82

5.9.2 Limitations……………………………………………………………………………..83

5.9.3 Main Findings Summarized….………………………………………………….……..84

Chapter 6 Evidence for Inhibitory Deficits in the Prefrontal Cortex in

Schizophrenia.......................................................................................................................85

6.1 Abstract…………………………………………………………………………………..86

6.2 Introduction ……………………………………………………………………………...87

6.3 Materials and Methods….………………………………………………………………..90

6.3.1 Clinical Severity………………………………………………………………………..96

6.3.2 Transcranial Magnetic Stimulation Data Recording…………………………………...96

6.3.3 Localization of the Motor Cortex……………………………………………………...96

xv

6.3.4 Localization of the DLPFC………………………………………………………..…...96

6.3.5 Electromyography Recording……………………………………………………….…97

6.3.6 EEG Recording and Pre-Processing………………………………………………...…97

6.4 Results………………………………………………………………………………..…103

6.4.1 Comparing Single and Paired-Pulse Conditions (Within-Group Analysis)……...…..103

6.4.2 Between-Group Results for DLPFC Stimulation…………………………………….104

6.4.3 Local Grid of Electrodes Analysis for DLPFC Stimulation………………………….104

6.4.4 Effect Size for DLPFC Stimulation…………………………………………………..105

6.4.5 Motor Cortex Stimulation………………………………………………………….....105

6.4.6 Local Grid of Electrodes Analysis for Motor Cortex Stimulation…………………....105

6.4.7 Sum of T-Score Topology………...……………………………………………….…105

6.4.8 Effect of Medication Treatments…………………………………………………..…106

6.4.9 Clinical Severity Correlation Analysis..................................................................…...106

6.5 Discussion ………………………………………………………………………..…….113

6.6 Frontal LICI Deficits in SCZ…………………………………………………………...113

6.6.1 Frontal Gamma LICI Deficits in SCZ……………………………………………..…114

6.6.2 Neurophysiology of OCD………………………………………………………….....115

6.7 Advancements in Analyses ………………………………………………………….....116

6.8 Limitations …………………………………………………………….……………….117

6.9 Clinical Implications…………………………………………………….…………...…117

Chapter 7 Investigating the Heritability of Cortical Inhibition in First-Degree

Relatives and Probands in Schizophrenia……………………………..…………….118

7.1 Abstract………………………………………………………...……………………….119

7.2 Introduction……………………………………………………………………………..120

7.3 Materials and Methods……………………………………………………………….…123

7.3.1 Clinical Severity………………………………………………………………………127

7.3.2 Data Recording……………………………………………………………………….128

7.3.3 Transcranial Magnetic Stimulation…………………………………………………...128

7.3.4 Localization of the Motor Cortex…………………………………………………….128

xvi

7.3.5 Localization of the DLPFC…………………………………………………..……….128

7.3.6 Electromyography Recording………………………………………………..……….129

7.3.7 EEG Recording and Pre-Processing……………………………………………….…129

7.4 Post-Processing Analyses …………………………………………………...…………130

7.4.1 Calculating Inhibition by Subject…………………..…………………...……………130

7.4.2 Between-Group Analyses………………………………….....………………………131

7.4.3 Assessing Clinical Severity and Effects of Medication Analyses in Schizophrenia

Patients............................................................................................................................…...132

7.4.4 Evaluating Clinical Severity in First-Degree Relatives of Schizophrenia…………....132

7.4.5 Stratification of Age in First-Degree Relatives of Schizophrenia Patients………...…132

7.4.6 The Heritability of Inhibition in Schizophrenia………………………...…………….133

7.5 Results………………………………………………………………..…………………133

7.5.1 Frontal Overall (2-50 Hz) Inhibition………………………………………………….133

7.5.2 Frontal Gamma (30-50 Hz) Inhibition………………………………………………..134

7.5.3 Motor Cortex Overall (2-50 Hz) Inhibition ……………………………………..…...136

7.5.4 Motor Cortex Gamma (30-50 Hz) Inhibition………………………… ……………...137

7.5.5 Stratification of Age in First-Degree Relatives of Schizophrenia Patients…………...137

7.5.6 Clinical Severity Analysis in First-Degree Relatives of Schizophrenia Patients….....137

7.5.7 Effect of Antipsychotic Medications and Anti-Deprssant Medications……….......…137

7.5.8 Clinical Severity in Schizophrenia Patients……………………………………..……138

7.6 Discussion…………………………………………………………….......…………….141

7.6.1 Frontal Inhibition in First-Degree Relatives of Schizophrenia Probands…..……..….141

7.6.2 Endophenotypes (Intermediate Phenotypes) in Schizophrenia……………………….142

7.6.3 Frontal Inhibitory Deficits in Schizophrenia……………………………………...….143

7.7 Limitations…………………………………………………………………………..….144

7.8 Summarizing the Main Findings …….……………………..……………………..……145

Chapter 8 General Discussion, Future Directions and Conclusions…..………..146

8.1 Summary of the Literature Review……………………………………………………..147

8.1.1 Summary of the Original Research…………………………………………….……..147

8.2 How Can Neuroscience Revolutionize Psychiatry?........................................................148

xvii

8.2.1 Combined TMS and EEG ……………………………………………………………149

8.2.2 Why Implement TMS-EEG?........................................................................................149

8.3 Advancements in Analyses ……………………………………………….……………149

8.3.1 Detecting and Removing Artifacts in EEG ………………………………….……….150

8.3.2 Cluster-Based Analyses………………………………………………………..……..150

8.3.3 LICI Analyses…………………………………………………………….…………..151

8.4 Pathophysiology of Schizophrenia………………………..……………………………152

8.4.1 GABAergic Deficits in Schizophrenia………………...…...…………………………152

8.5 Pathophysiology of OCD…………………………………………………………….…154

8.6 Neurophysiological Biomarkers in Clinical Practice…………………….……………..156

8.6.1 Assessing Biological First-Degree Relatives of Patients………………….………….157

8.7 Limitations………………………………………………………………………...……158

8.8 Future Directions…………………………………………………………...…………..160

8.9 Conclusions……………………………………………………………………………..161

References or Bibliography……………………………………………………………..….162

xviii

List of Abbreviations

APB abductor pollicis brevis

BD bipolar disorder

CI cortical inhibition

CS conditioning stimulus

CSP cortical silent period

DF degrees of freedom

DLPFC dorsolateral prefrontal cortex

DSM diagnostic and statistical manual of mental disorders

ECT electroconvulsive therapy

EEG electroencephalography

EMG electromyography

EPSP excitatory post synaptic potential

fMRI functional magnetic resonance imaging

GABA gamma-aminobutyric acid

GAD glutamic acid decarboxylase

ICA independent component analysis

ICF intracortical facilitation

IHI interhemispheric inhibition

IPSP inhibitory post synaptic potential

ISI interstimulus interval

ISP interhemispheric signal propagation

LICI long interval cortical inhibition

LTP long-term potentiation

MDD major depressive disorder

MEP motor evoked potential

MRI magnetic resonance image

MRS magnetic resonance spectroscopy

mV millivolt

xix

NMDA N-methyl D-aspartate

OCD obsessive-compulsive disorder

PAS paired associative stimulation

PPI prepulse inhibition

RMT resting motor threshold

S1 stimulus one

S2 stimulus two

SCZ schizophrenia

SICF short interval cortical facilitation

SICI short interval cortical inhibition

SSRI Selective serotonin reuptake inhibitor

tDCs transcranial direct current stimulation

TS test stimulus

TRD treatment-resistant depression

TMS transcranial magnetic stimulation

YBOCS Yale-Brown obsessive compulsive scale

WM working memory

5-HT serotonin

xx

List of Tables

Table 1. Summary of TMS Paradigms……………………………...………………………...4

Table 2. Summary of Significant TMS Findings in Psychiatric Populations………………..23

Table 3. Number of Included and Excluded Studies………………………………………...68

Table 4. Summary of Significant Hedge's g Results in Psychiatric Populations….…………84

Table 5. Description of the Psychotropic Medications Displayed as Number of

Subjects/Dose(s)………………………………………………………………………...…...91

5A. Patients with Schizophrenia Medication Details………………………………………..91

5B. Patients with Obsessive-Compulsive Disorder Medication Details. ……………………93

Table 6. Diagnostic Information for Schizophrenia and Obsessive-Compulsive Disorder

patients……………………………………………………………………………………….95

Table 7. All p-values of the between-group comparisons by site of stimulation, frequency

band, and electrode grids. The primary analysis is the first line; the second line displays the

pooled variance analysis…………………………………………..…………………..……101


Subjects/Dose(s)…………………………………………………………………………....125

Table 8A. Patients with Schizophrenia Medication Details…………………………......…125

Table 8B. Patients with Obsessive-Compulsive Disorder Medication Details …………….126


patients………………………………………………………………………………….......127

xxi

List of Figures Figure 1. Surface electromyography recordings from a right hand muscle…………….........10

(1A) A single test stimulus (TS) applied to the left motor cortex producing a motor evoked

potential (MEP).

(1B) The cortical silent period (CSP) is induced following a 40% suprathreshold TS applied

to the left motor cortex while the right hand muscle is tonically activated. The CSP starts at

the onset of the MEP and ends with the return of motor activity.

(1C) Short-interval cortical inhibition (SICI) occurs when a conditioning stimulus (CS)

precedes the TS by 2 ms to and inhibits the MEP produced by the TS.

(1D) Long-interval cortical inhibition (LICI) occurs when the CS precedes the TS by 100 ms

and inhibits the MEP produced by the TS.

(1E) Intracortical facilitation (ICF) occurs when the CS precedes the TS by 20 ms,

facilitating the MEP produced by the TS.

Figure 2. A single TMS pulse is applied to the motor cortex activating cortical tissues

associated with the abductor pollicis brevis muscle, eliciting a motor evoked potential at the

periphery captured through electromyography……………………………………………....22

Figure 3. This illustration demonstrates that patients with SCZ have selective deficits in the

inhibition of gamma (30 - 50 Hz) oscillations in the dorsolateral prefrontal cortex compared

to healthy controls using interleaved TMS and EEG……………………………………...…35

Figure 4. Forest Plot of the Hedge's g Analysis For All Studies That Included Patients With

Obsessive-Compulsive Disorder Compared to Healthy Controls…………………….……..71

(A) Resting Motor Threshold

(B) Short-interval Cortical Inhibition

(C) Intracortical Facilitation

(D) Cortical Silent Period

Figure 5. Forest Plot of the Hedge's g Analysis For All studies That Included Patients With

Major Depressive Disorder Compared to Healthy Controls. ………………………………..74




xxii


Figure 6. Forest Plot of Resting Motor Threshold Hedge's g Analysis For All Studies That

Included Patients With Schizophrenia Compared to Healthy Controls……………………...76

Figure 7. Forest Plot of Short-Interval Cortical Inhibition Hedge's g Analysis For All Studies

That Included Patients With Schizophrenia Compared to Healthy Controls. ………………77

Figure 8. Forest Plot of Intracortical Facilitation Hedge's g Analysis For All Studies That


Figure 9. Forest Plot of Cortical Silent Period Hedge's g Analysis For All Studies That


Figure 10. Statistical significance of each voxel (single pulse vs. paired pulse) in the time-

frequency domain corresponding to each specific electrode in healthy subjects when

stimulating the dorsolateral prefrontal cortex.……………………………….......…………107

Figure 11. Statistical Significance of each voxel (single pulse vs. paired pulse) in the time-

frequency domain corresponding to each specific electrode in schizophrenia patients when

stimulating the dorsolateral prefrontal cortex......…………………………………………..107


frequency domain corresponding to each specific electrode in obsessive-compulsive disorder

patients when stimulating the dorsolateral prefrontal cortex……………………………….108

Figure 13. Strength of inhibition by electrode. Each value consists of the sum of all t-scores

of inhibition in the major cluster of inhibition in the time-frequency maps for each electrode.

These plots show the three groups by frequency bands in the dorsolateral prefrontal cortex.

Values have been normalized within each frequency band. The color bar is omitted since

only the pattern matters, as the actual sum is dependent on the resolution of the time-

frequency-spatial domain………………………………………………..………………….108



These plots show the three groups by frequency bands in the motor cortex. Values have been

normalized within each frequency band. The color bar is omitted since only the pattern

matters, as the actual sum is dependent on the resolution of the time-frequency-spatial

domain.……………………………………………………………………………………...109

xxiii



stimulating the motor cortex. ……………………………………………………………...110



stimulating the motor cortex………………………………………………...…...…………110



patients when stimulating the motor cortex.………………………………………………..111

Figure 18. Effect size (Cohen’s d) of each voxel (single pulse vs. paired pulse) in the time-

frequency domain corresponding to each specific electrode when comparing schizophrenia

and healthy subjects in the dorsolateral prefrontal cortex. Red corresponds to when healthy

subjects show greater inhibition, blue corresponds to when SCZ show greater inhibition...112

Figure 19. The index of frontal inhibition from a cluster analysis at different thresholds of p-

values in healthy controls, first-degree relatives of schizophrenia patients and their related

probands. A cluster analysis was performed for each group by sampling a subset 19 of

subjects with replacement. The procedure was repeated 2000 times for each threshold. Error

bars indicate one standard deviation………………………………….……………..…..….135

Figure 20.The index of frontal inhibition from a cluster analysis at different thresholds of p-

values in healthy controls, first-degree relatives of obsessive-compulsive disorder patients

and their related probands. A cluster analysis was performed for each group by sampling a

subset 13 of subjects with replacement. The procedure was repeated 2000 times for each

threshold. Error bars indicate one standard deviation………………………………………136

Figure 21. The frequency of significant values for each group, summarized from subject data,

on each voxel for all the nine central electrodes (F1, Fz, F2, FC1, FCz, FC2, C1, Cz, C2).

The threshold for significance was chosen to be p < 0.01. Each graph corresponds to healthy

subjects, first-degree relatives of SCZ patients, and their related probands. The stimulation

area was the dorsolateral prefrontal cortex. Values are masked over the left bottom area (dark

navy blue) indicating that those windows of the wavelet analysis, which contains points from

the pre-stimulus interval……………………………...…………………………………….139

xxiv




subjects, first-degree relatives of obsessive-compulsive disorder patients, and their related

probands. The stimulation area was the dorsolateral prefrontal cortex. Values are masked

over the left bottom area (dark navy blue) indicating that those windows of the wavelet

analysis, which contains points from the pre-stimulus interval.............................................140

1

Chapter 1

Inhibition of the Cortex using Transcranial Magnetic Stimulation in

Psychiatric Populations: Current and Future Directions

Contents of this chapter have been reprinted by permission from the Journal of Psychiatry

and Neuroscience

“Inhibition of the Cortex using Transcranial Magnetic Stimulation in Psychiatric Populations:

Current and Future Directions” Reprinted from the Journal of Psychiatry and Neuroscience

November 2012; (37) (6), Pages 369-378 by permission of the publisher. © 2014 Canadian

Medical Association.

Radhu N, Ravindran LN, Levinson AJ, Daskalakis ZJ. (2012). Inhibition of the cortex using

transcranial magnetic stimulation in psychiatric populations: current and future directions.

Journal of Psychiatry & Neuroscience. 37(6): 369-378.

A link to the published paper can be found at:

http://www.jpn.ca/vol37-issue6/37-6-369/

http://www.jpn.ca/vol37-issue6/37-6-369/

2

1.1 Abstract Several lines of evidence suggest that deficits in gamma-aminobutyric acid (GABA)

inhibitory neurotransmission are implicated in the pathophysiology of schizophrenia (SCZ),

bipolar disorder (BD), major depressive disorder (MDD), and obsessive-compulsive disorder

(OCD). Cortical inhibition (CI) refers to a neurophysiologic process, whereby GABA

inhibitory interneurons selectively attenuate pyramidal neurons. Transcranial magnetic

stimulation (TMS) represents a non-invasive technique to measure CI, excitability, and

plasticity in the cortex. These measures were traditionally limited to the motor cortex which

is a significant limitation when non-motor neurophysiological processes are of primary

interest. Recently, TMS has been combined with electroencephalography (EEG) to derive

such measurements directly from the cortex. This review will focus on neurophysiological

studies related to inhibitory and excitatory paradigms linking dysfunctional GABAergic

neurotransmission to disease states. We review evidence that suggests CI deficits among

psychiatric populations and conclude by discussing the future directions of TMS,

demonstrating the potential to identify biological markers of neuropsychiatric disorders.

3

1.2 Introduction TMS is an important neurophysiological tool that allows researchers to non-invasively study

the cortex of healthy individuals and in patients with neuropsychiatric disorders [Barker et

al., 1985]. TMS is used to understand the neurobiology of cognitive function, behaviour and

emotional processing [McClintock et al., 2011] by assessing neurophysiological markers of

inhibition, excitation and plasticity [Classen et al., 1998; Kujirai et al., 1993]. In 1985,

Barker et al. introduced TMS as a tool for investigating the functional state of the motor

pathways in patients with neurological disorders and healthy participants [Barker et al.,

1985]. It involves the generation of a magnetic field through the use of an electromagnetic

coil connected to a TMS device which induces an electrical current in the brain [Wagner et

al., 2007]. They demonstrated that a single TMS pulse applied to the motor cortex could

activate cortical tissues associated with the hand or leg muscles and this activation could

elicit motor evoked potentials (MEP) at the periphery captured through electromyography

(EMG) [Barker et al., 1985] (figure 1A). Recently, TMS has been combined with EEG to

evaluate the effects of electromagnetic induction on cortical oscillations (figure 3) [Fuggetta

et al., 2005; Paus et al., 2001; Rosanova et al., 2009]. This review will emphasize the

neurophysiological evidence underlying psychiatric disorders through the application of

TMS and demonstrate the functional consequences of disordered inhibition. A literature

search was performed using PubMed from 1990 through December 2011, Ovid Medline

from 1990 through December 2011, Embase Psychiatry from 1990 through December 2011,

and PsycINFO from 1990 through December 2011. The following search terms were

used: transcranial magnetic stimulation, TMS, TMS-EEG, psychiatry, psychiatric

disorder, neuropsychiatric disorder, schizophrenia, bipolar

disorder, mania, depression, major depressive disorder, obsessive-compulsive disorder,

cortical inhibition, cortical silent period, short interval cortical inhibition, long interval

cortical inhibition, interhemispheric inhibition, cortical excitability, resting motor threshold,

active motor threshold, intracortical facilitation, motor evoked potential amplitude,

interhemispheric signal propagation, plasticity, paired-associative stimulation, and use-

dependent plasticity.

4

1.3 Evaluating Cortical Inhibition with Transcranial Magnetic

Stimulation CI refers to a neurophysiological process, whereby gamma-aminobutyric acid (GABA)

inhibitory interneurons selectively attenuate the activity of other neurons (e.g., pyramidal

neurons) in the cortex [Daskalakis et al., 2007]. Pyramidal cell activity is coordinated

through a balance of inhibitory postsynaptic potentials (IPSPs) and excitatory postsynaptic

potentials (EPSPs) [Krnjevic, 1997]. IPSPs are generated by GABAergic interneurons

terminating on the pyramidal cell [Krnjevic, 1997]. GABA is the main inhibitory

neurotransmitter in the brain regulating the modulation of cortical excitability and neural

plasticity [DeFelipe et al., 1986; Schieber and Hibbard, 1993]. The following TMS

paradigms will be described implicating GABAergic inhibitory neurotransmission: cortical

silent period (CSP), short interval cortical inhibition (SICI), long interval cortical inhibition

(LICI), and interhemispheric inhibition (IHI). Table 1 provides a summary of these TMS

paradigms.

Paradigm Definition Neurotransmitter

System Involved

Cortical

Silent Period

(CSP)

CSP is measured by stimulating the

contralateral motor cortex with a single

suprathreshold pulse in a moderately tonically

active muscle (i.e., 20% of maximum

contraction), resulting in the interruption of

voluntary muscle contraction [Cantello et al.,

1992; Kujirai et al., 1993].

GABAB

Short-Interval

Cortical

Inhibition

(SICI)

SICI is a paired pulse paradigm, whereby, a

subthreshold conditioning stimulus (CS) is

applied to the motor cortex before a

suprathreshold test stimulus (TS) at

interstimulus intervals (ISIs) between 1

millisecond (ms) to 4 ms. The subthreshold

CS suppresses the motor-evoked potential

GABAA

5

(MEP) produced by the TS [Cantello et al.,

1992; Kujirai et al., 1993].

Long-Interval

Cortical

Inhibition

(LICI)

LICI refers to the pairing of a suprathreshold

CS followed by a suprathreshold TS at long

ISIs (e.g. 50 - 100 ms), resulting in inhibition

of the MEP produced by the TS [Claus et al.,

1992; Valls-Sole et al., 1992].

GABAB

Interhemisph

eric Inhibtion

(IHI)

IHI is measured using two magnetic

stimulating coils, whereby, a suprathreshold

TMS pulse delivered to one hemisphere can

inhibit the MEP response to a suprathreshold

TMS pulse delivered within 6 to 50 ms to the

opposite hemisphere [Ferbert et al., 1992;

Gerloff et al., 1998].

GABAB

Resting

Motor

Threshold

(RMT)

RMT is defined as the minimal intensity

(single pulse) that produces a MEP > 50 μV in

5 of 10 trials in a relaxed muscle [Kujirai et

al., 1993].

Glutamate

Active Motor

Threshold

(AMT)

AMT is defined as the first intensity (single

pulse) that produces an MEP of > 100 μV in 5

of 10 trials in an isometrically moderately

active muscle [Chen et al., 1998].

Glutamate

Intracortical

Facilitation

(ICF)

ICF is a paired pulse paradigm that can be

used to index excitability of the excitatory

circuits in the motor cortex, whereby,

conditioning stimuli are applied to the motor

cortex before the TS usually at ISIs between

7ms – 20ms [Nakamura et al., 1997].

Glutamatergic

NMDA receptors

Table 1. Summary of TMS Paradigms.

6

1.3.1 Cortical Silent Period CSP is measured by stimulating the contralateral motor cortex in a moderately tonically

active muscle (i.e., 20% of maximum contraction) with TMS intensities of 110% - 160% of

the resting motor threshold (RMT), resulting in the interruption of voluntary muscle

contraction [Cantello et al., 1992; Kujirai et al., 1993] (figure 1B). The duration of CSP is

measured from MEP onset to the return of any voluntary EMG activity, ending with a

deflection in the EMG waveform [Tergau et al., 1999]. Studies have demonstrated that CSP

is related to GABAB receptor-mediated inhibitory neurotransmission as it displays a similar

time course to the GABAB receptor-induced IPSP, approximately 150 to 200 ms post

stimulus [McCormick, 1989; Roick et al., 1993; Siebner et al., 1998; Werhahn et al., 1999b].

For instance, administration of tiagabine, a GABA reuptake inhibitor, leads to an increased

concentration of GABA in the synaptic cleft and predominately activates GABAB receptors

[Thompson and Gahwiler, 1992], which has resulted in a dose-dependent prolongation of the

CSP [Werhahn et al., 1999b]. Furthermore, baclofen, a potentiator of GABAB receptor-

mediated inhibitory neurotransmission, was also found to lengthen the CSP [Siebner et al.,

1998].

1.3.2 Short Interval Cortical Inhibition The SICI paradigm was first reported by Kujirai et al. [Kujirai et al., 1993], involves a

subthreshold conditioning stimulus (CS) set at 80% of RMT that precedes a suprathreshold

test stimulus (TS), adjusted to produce an average MEP of 0.5–1.5 millivolt (mV) peak-to-

peak amplitude in the contralateral muscle (figure 1C) [Cantello et al., 1992; Kujirai et al.,

1993]. To measure SICI conditioning stimuli are applied to the motor cortex before the TS at

interstimulus intervals (ISIs) between 1 millisecond (ms) to 4 ms as the subthreshold CS

suppresses the MEP produced by the TS (figure 1C). Research suggests that SICI is related to

GABAA receptor-mediated inhibitory neurotransmission [Ziemann et al., 1996b] as it has

been demonstrated that SICI is increased by medications that facilitate GABAA

neurotransmission such as lorazepam [Ziemann et al., 1996a]. Baclofen (GABAB agonist) has

also been shown to decrease SICI [McDonnell et al., 2006], possibly related to presynaptic

GABAB autoreceptors [Daskalakis et al., 2002b]. Moreover, SICI is related to GABAA

7

receptor-mediated inhibitory neurotransmission as it displays a similar time course to the

GABAA receptor-induced IPSP. For example, Wang and Buzsaki [Wang and Buzsaki, 1996]

showed through computer simulations that the synaptic time constant for GABAA receptors

approximately ranges from 10 to 25 ms, confirming that SICI is associated with activity of

GABAA receptor-mediated inhibitory neurotransmission.

1.3.3 Long Interval Cortical Inhibition LICI refers to the pairing of a suprathreshold CS followed by a suprathreshold TS at long

ISIs (e.g. 50 - 100 ms), resulting in inhibition of the MEP produced by the TS (figure 1D)

[Claus et al., 1992; Valls-Sole et al., 1992]. Studies strongly suggest that LICI is mediated by

slow IPSPs via activation of GABAB receptors [McDonnell et al., 2006; Sanger et al., 2001;

Werhahn et al., 1999a]. For example, 50 mg of baclofen orally administered to 9 healthy

participants, resulted in enhanced LICI implying that the increase in LICI is likely a result of

increased GABAB receptor- mediated IPSPs [McDonnell et al., 2006]. Also, LICI is optimal

when the CS precedes the TS by 100 to 150 ms [Sanger et al., 2001] comparable to the time

course of the GABAB receptor activation, shown to typically peak around 150 to 200 ms post

stimulus [McCormick, 1989]. More recently, a significant positive relationship has been

shown between the suppression of MEP amplitudes in LICI (with an ISI of 100 ms), and the

duration of the silent period in the CSP paradigm in healthy individuals [Farzan et al.,

2010b], providing evidence for the mediation of the GABA B receptor in both LICI and CSP.

1.3.4 Interhemispheric Inhibition IHI is measured using two magnetic stimulating coils, whereby a suprathreshold TMS pulse

delivered to one hemisphere can inhibit the MEP response to a suprathreshold TMS pulse

delivered within 6 to 50 ms to the opposite hemisphere [Ferbert et al., 1992; Gerloff et al.,

1998]. Inhibitory GABAergic neurons mainly serve local circuits [Somogyi et al., 1998]; IHI

may be mediated through excitatory axons that cross the corpus callosum to act on local

inhibitory neurons in the contralateral motor cortex [Berlucci, 1990]. Daskalakis et al.

[Daskalakis et al., 2002b] demonstrated that SICI is reduced in the presence of IHI.

Furthermore, IHI is reduced in the presence of LICI when matched for test MEP amplitude

8

but no significant change is seen when matched for TS intensity. These results demonstrate

that IHI may be related to GABAB activity. This is consistent with Ziemann et al. who

showed that lorazepam increased SICI but did not change IHI, suggesting that IHI is not

related to GABAA activity [Ziemann et al., 1996a].

1.4 Transcranial Magnetic Stimulation as a Method to

Measure Excitability Glutamate and aspartate are the main excitatory neurotransmitters within the central nervous

system [Monaghan et al., 1989]. EPSPs in neurons of rat sensorimotor cortex are mediated

by α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), N-methyl-D-aspartate

(NMDA) and kainate receptors [Hwa and Avoli, 1992]. Voltage-gated sodium channels are

vital in regulating axon excitability [Hodgkin and Huxley, 1952], while non-ionotropic non-

NMDA glutamate receptors are responsible for fast excitatory synaptic neurotransmission

within the neocortex [Douglas and Martin, 1998]. The following excitatory paradigms will be

discussed which include: RMT, active motor threshold (AMT), and intracortical facilitation

(ICF).

1.4.1 Resting Motor Threshold TMS permits assessment of the RMT, defined as the minimal intensity that produces a MEP

> 50 μV in 5 of 10 trials in a relaxed muscle [Kujirai et al., 1993]. The RMT is a global

measure of corticospinal excitability and depends on the excitability of axons activated by

the TMS pulse, as well as the excitability of synaptic connections at both the cortical and

spinal level [Paulus et al., 2008a]. The RMT depends on glutamatergic synaptic excitability

[Paulus et al., 2008a]. It has been shown that drugs which block voltage-gated sodium

channels, in particular anticonvulsants such as carbamazepine, lamotrigine and losigamone,

increase RMT [Ziemann et al., 1996b]. By contrast, NMDA antagonists such as ketamine

reduce RMT [Di Lazzaro et al., 2003]. Lastly, drugs with GABAergic properties such as

vigabatrin (GABA analogue), baclofen (GABAB receptor agonist) and gabapentin (GABA

analogue) do not affect motor threshold [Ziemann et al., 1996b].

9

1.4.2 Active Motor Threshold The AMT is defined as the first intensity that produces an MEP of > 100 μV in 5 of 10 trials

in an isometrically moderately active muscle [Chen et al., 1998]. The AMT is measured

during muscle contraction, where corticospinal neurons and spinal motor-neurons are very

close to firing threshold [Paulus et al., 2008a].

1.4.3 Intracortical Facilitation ICF is a paired pulse paradigm that can be used to index excitability of the excitatory circuits

in the motor cortex, whereby, conditioning stimuli are applied to the motor cortex before the

TS at ISIs usually between 7ms – 20ms (figure 1E). It has been shown that ICF originates

from EPSPs transmitted by glutamatergic NMDA receptors [Nakamura et al., 1997]. In fact,

the latency of onset of the EPSP mediated by the NMDA receptor is approximately 10 ms,

which is consistent with the time course of ICF [Kujirai et al., 1993; Ziemann et al., 1996c].

This is supported by the majority of pharmacological studies which demonstrated that

NMDA receptor antagonists such as dextromethorphan and memantine decrease ICF

[Schwenkreis et al., 1999; Ziemann et al., 1998]. Benzodiazepines such as lorazepam

(GABAA agonist) decreases ICF [Ziemann et al., 1996a] and baclofen (GABAB agonist)

increases ICF [Ziemann et al., 1996b]. Lastly, it has also been suggested that ICF is not

exclusively mediated by excitatory interneurons, but rather by a net balance between

inhibition and excitability [Daskalakis et al., 2004].

10

Figure 1. Surface electromyography recordings from a right hand muscle.

Figure 1A. A single test stimulus (TS) applied to the left motor cortex producing a motor

evoked potential (MEP).

Figure 1B. The cortical silent period (CSP) is induced following a 40% suprathreshold TS

applied to the left motor cortex while the right hand muscle is tonically activated. The CSP

starts at the onset of the MEP and ends with the return of motor activity.

Figure 1C. Short-interval cortical inhibition (SICI) occurs when a conditioning stimulus (CS)

precedes the TS by 2 ms to and inhibits the MEP produced by the TS.

Figure 1D. Long-interval cortical inhibition (LICI) occurs when the CS precedes the TS by

100 ms and inhibits the MEP produced by the TS.

11

Figure 1E. Intracortical facilitation (ICF) occurs when the CS precedes the TS by 20 ms,

facilitating the MEP produced by the TS.

1.5 Motor Cortex Inhibition in Psychiatric Disorders Several lines of evidence suggest that deficits in GABA functioning are implicated in the

pathophysiology of SCZ, BD, MDD, and OCD. The integration of TMS with EMG has

offered a valuable tool for the assessment of the pathological processes associated with

psychiatric disorders.

1.5.1 Schizophrenia SCZ is a severe psychiatric illness characterized by delusions, hallucinations, disorganized

thinking and often life-long disability [van Os and Kapur, 2009]. SCZ is a debilitating

disorder that exacts enormous personal, social and economic costs [van Os and Kapur, 2009].

CI may represent an important mechanism responsible for the symptoms observed in patients

with SCZ. Several lines of evidence suggest that abnormalities in CI are an important

neurophysiological mechanism in SCZ and these impairments have been shown to be related

to GABAergic deficits. Benes et al. [Benes et al., 1991] first reported that patients with SCZ

have morphologic changes in cortical GABA interneurons by demonstrating a decreased

density of non-pyramidal cells (i.e., interneurons) in anterior cingulate layers II-VI and in

prefrontal cortex layer II.

Research has shown CI abnormalities in SCZ using TMS. For example, Daskalakis et al.

[Daskalakis et al., 2002a] measured MT, SICI, ICF, CSP and IHI in 15 unmedicated patients

with SCZ (14 medication-naïve and 1 medication-free for over 1 year), 15 medicated SCZ

patients and 15 healthy controls. They found that unmedicated SCZ patients had significantly

lower CI compared with healthy controls in measures of SICI, CSP and IHI providing TMS

evidence for deficient GABAergic neurotransmission in SCZ. Similarly, Fitzgerald et al.

[Fitzgerald et al., 2002b] found comparable results in 22 medicated patients with SCZ

compared with 21 healthy controls. They demonstrated significantly lower SICI and CSP

within SCZ group compared with healthy controls. Fitzgerald et al. [Fitzgerald et al., 2002a]

12

also evaluated IHI in 25 patients with SCZ and 20 healthy controls. They similarly

demonstrated a significant decrease in IHI in patients independent of medication dose. More

recent studies have also demonstrated deficits in CI using TMS in patients with SCZ. For

example, Daskalakis et al. [Daskalakis et al., 2008b] reported that 10 clozapine-treated

patients with SCZ had significantly longer CSPs compared with 10 healthy participants and 6

unmedicated SCZ patients. A subsequent study by Liu et al. [Liu et al., 2009] with a large

sample of 78 SCZ patients and 38 healthy controls confirmed that clozapine-treated SCZ

patients demonstrated a longer CSP and reduced SICI compared with healthy control

participants. However, patients treated with other antipsychotics and unmedicated patients

demonstrated a significantly shorter CSP duration. These findings suggest that CI is involved

in the pathophysiology of SCZ and that clozapine may potentiate GABAB receptor-mediated

inhibitory neurotransmission. Additionally, across all SCZ patients in this study, CSP was

inversely related to negative symptoms, while SICI was inversely associated with positive

symptoms, highlighting the role of both GABAB and GABAA receptor-mediated inhibitory

neurotransmission in SCZ. This finding is consistent with recent neurochemical evidence

demonstrating that there is a direct link between clozapine and the GABAB receptor [Wu et

al., 2011]. Wu et al. showed through autoradiography, synaptic membrane binding, and

HEK293 expression of the GABAB receptor, they showed that clozapine facilitates the

binding of CGP54626A (a specific high-affinity antagonist) to the GABAB receptor.

Furthermore, Wobrock et al. [Wobrock et al., 2010] examined 12 first-episode SCZ patients

with a history of comorbid cannabis use and 17 without. They found that patients with a

history of comorbid cannabis use had lower SICI and increased ICF but no significant

differences were found in RMT and CSP. Comorbid cannabis abuse was suggested to

potentiate the reduced SICI and enhanced ICF observed in first episode SCZ patients. This

finding is consistent with a previous study, in which Fitzgerald et al. [Fitzgerald et al.,

2009b] found that heavy and light users of cannabis demonstrated significantly decreased

SICI compared to healthy controls. Taken together, these studies provide evidence to confirm

that SCZ is associated with CI deficits in the motor cortex. Future studies are necessary to

advance current knowledge by identifying biological markers of both illness and treatment

response to developing a deeper understanding of the neurophysiological mechanisms

underlying SCZ.

13

1.5.2 Bipolar Disorder

BD is a serious psychiatric illness with prevalence estimates of 2.4% worldwide [Merikangas

et al., 2011]. It is characterized by periods of mania or hypomania alternating with phases of

depression [Benazzi, 2007] and is associated with an early age at onset, usually between 16

to 26 years [Javaid et al., 2011; Manchia et al., 2008]. Suicide and suicide attempts are

significant contributors to premature mortality and disability [Goodwin et al., 2003].

Relatively little work has been done to understand the neurophysiological underpinnings of

this disease. Limited neuroanatomical evidence demonstrates that BD patients have impaired

cortical inhibitory neurotransmission [Benes et al., 1998]. Benes and Berretta found that the

density of cortical GABA interneurons, which mediate CI, is reduced in the anterior

cingulate cortex among patients with BD [Benes and Berretta, 2001] and also found a 30%

decrease in cortical inhibitory GABAergic interneurons in BD, compared with a 16%

decrease in patients with SCZ [Benes and Berretta, 2001]. The data suggests a loss of

GABAergic interneurons in both BD and SCZ. However, there is little in vivo

neurophysiological evidence supporting such impairments in BD. Levinson et al. [Levinson

et al., 2007] used TMS to evaluate SICI, CSP and IHI in 15 BD patients (13 medicated with a

single mood stabilizer and two unmedicated) compared to 15 healthy control participants.

They found that BD patients demonstrated significant deficits in SICI, CSP and IHI

compared with healthy individuals. The authors concluded that GABAergic inhibitory

neurotransmission is deficient in the motor cortex of patients with BD. Furthermore, the

majority of patients were medicated and the evidence suggested that these inhibitory deficits

were attenuated with treatment. Nevertheless, additional studies are needed with large

unmedicated samples, and more severely ill patient populations. It would be hypothesized

that any inhibitory deficits would be magnified under these conditions.

14

1.5.3 Major Depressive Disorder MDD is one of the most prevalent psychiatric disorders, and is estimated to affect 16.6% of

individuals in their lifetime [Kessler et al., 2005]. It not only affects physical and cognitive

functions but also has a profound impact on psychosocial well-being [Kessler et al., 2005].

Preclinical work has demonstrated that chronic stress induces compensatory changes in the

GABAergic system in animal models [Acosta and Rubio, 1994]. Evidence suggests that

MDD may be associated with abnormalities in cortical excitability, and more specifically

deficits in CI. For example, Fitzgerald et al. [Fitzgerald et al., 2004a] assessed cortical

excitability prior to a trial of repetitive TMS (rTMS) treatment in MDD patients. This study

included 60 patients with treatment-resistant depression (TRD), of which, 46 were medicated

during the trial (antidepressants, mood stabilizers, and antipsychotics). The authors found a

decreased SICI of the right motor cortex (1 ms ISI) and reported that an increased CSP in the

left motor cortex predicted a poorer response to rTMS treatment. Bajbouj et al. [Bajbouj et

al., 2006b] assessed 20 patients with MDD who had been washed off of medication for at

least 4 weeks compared with 20 healthy participants. They found reduced SICI and CSP in

patients with MDD, consistent with the hypothesis of deficient GABAergic tone in

depression. Similarly, Lefacheur [Lefaucheur et al., 2008] demonstrated that MDD patients

showed a reduced excitability of both excitatory (RMT, ICF) and inhibitory (CSP, SICI)

processes in the left hemisphere when compared to healthy controls. More recently, Levinson

et al. [Levinson et al., 2010] examined CI in 25 medicated individuals with TRD, 19

medicated euthymic partcipants, 16 unmedicated depressed patients and 25 healthy controls

and found that all patients with MDD, regardless of symptom or medication state,

demonstrated significant CSP deficits compared with healthy participants. Patients with TRD

also demonstrated significant deficits in SICI compared with healthy participants. The

findings above all held true after controlling for benzodiazepine use which has been shown to

affect TMS parameters [Levinson et al., 2010]. Since all MDD patients showed CSP

abnormalities but only TRD subjects additionally demonstrated SICI reductions, the authors

concluded that the depressed state may be overall associated with GABAB deficits, but severe

symptomatology, as seen in TRD, may be associated with greater deficits in both GABAA

and GABAB neurotransmission. Taken together, the above findings suggest that MDD is

15

associated with deficits in GABAergic inhibitory neurotransmission and abnormalities in

inhibitory functions of the motor cortex. Future studies are needed to explore these findings

further in cortical regions that are more closely associated with the pathophysiology of this

disorder (e.g., the dorsolateral prefrontal cortex (DLPFC)).

1.5.4 Obsessive-Compulsive Disorder OCD is a serious psychiatric illness characterized by the presence of recurrent, intrusive and

thoughts, impulses or images (obsessions) that are often also accompanied by repetitive

rituals or behaviours (compulsions) designed to counteract the associated anxiety. As

obsessive thoughts and/or rituals may cause great distress and take up significant time during

the day, OCD often leads to pronounced psychosocial impairment [Eisen et al., 2006]. It is

estimated to affect up to 2.5% of the world’s population [Karno et al., 1988; Kessler et al.,

2005]. Although, its pathophysiology remains to be fully elucidated, research suggests that

OCD may involve inhibitory deficits in orbitofrontal striatal circuits [Menzies et al., 2008].

One preliminary study found decreased SICI in 12 patients with OCD without a history of

comorbid tics or Tourette’s syndrome compared to 12 healthy control participants

[Greenberg et al., 1998], implicating a role for GABAA inhibitory neurotransmission in

OCD. These results were expanded with 9 medicated and 7 unmedicated OCD patients

compared to 11 healthy control participants [Greenberg et al., 2000]. In this case, both RMT

and AMT were found to be significantly lower in OCD patients compared to controls.

Similarly, SICI was significantly lower in OCD patients relative to healthy individuals and

this difference remained significant even when the same comparison was made with only

unmedicated OCD patients; no differences were found for SICI between unmedicated and

medicated OCD patients. By contrast, there were no differences in ICF or CSP detected

between patient and control groups. Furthermore, OCD patients with tics had significantly

less SICI than those without tics. The authors concluded that OCD, in the presence or

absence of comorbid tics, was characterized by deficient SICI.

16

More recently, Richter et al. [Richter et al., 2012] assessed a larger sample of OCD patients,

as they compared 34 OCD patients (23 medicated and 11 unmedicated) to 34 healthy

individuals. In contrast to the previous study, no overall difference was found in RMT

between OCD and healthy groups; however, RMT was significantly lower in the medicated

compared to the unmedicated OCD population. Additionally, CSP duration was also found to

be significantly shorter in OCD but no further differences were detected between the OCD

subgroups. Finally, although this study failed to detect differences in SICI between OCD and

healthy individuals, OCD subjects were found to have a significantly greater ICF, regardless

of medication status. No correlations were found between illness severity and TMS

parameters in either medicated or unmedicated OCD populations. In this case, the results

suggest that OCD is associated with deficient CSP and excessive ICF, regardless of

medication state, reflecting abnormalities in GABAB and NMDA-mediated

neurotransmission, consistent with several genetic studies that have been reported in this

disorder [Arnold et al., 2006; Dickel et al., 2006; Samuels et al., 2011; Stewart et al., 2007;

Voyiaziakis et al., 2011; Zai et al., 2005]. The authors suggested that differences between

their results and those previously published could be due to the greater number of

unmedicated OCD patients and elevated symptom severity included in their data or to the

different stimulation intensities used to elicit measures. The discrepant findings in the limited

number of studies highlight the need for further research to better characterize the potential

abnormalities seen in OCD.

1.5.5 What Are the Implications of These Findings? These findings provide compelling evidence to suggest that GABAergic inhibitory deficits

are closely involved in the pathophysiology of SCZ, MDD, OCD and BD. Taken together,

research has suggested that patients with SCZ have demonstrated impairments in SICI, CSP

and IHI. Moreover, two studies have showed that clozapine-treated SCZ patients

demonstrated significantly longer CSP durations, implicating the role of the GABAB receptor

in clozapine and demonstrating a specific response profile of treatment in this disorder

[Daskalakis et al., 2008b; Liu et al., 2009]. Similarly, previous research has demonstrated

that selective serotonin reuptake antidepressants normalize GABAergic deficits in depression

17

as assessed by the TMS paradigms through enhanced SICI and decreased ICF [Manganotti et

al., 2001; Minelli et al., 2010]. Along the same lines, OCD patients have demonstrated

decreased SICI, CSP and enhanced ICF, independent of medication status [Greenberg et al.,

2000; Greenberg et al., 1998; Richter et al., 2012]. However, BD patients have showed

impairments in SICI, CSP and IHI, not ameliorated with treatment [Levinson et al., 2007].

These studies suggest an overall lack of GABAergic inhibitory neurotransmission in these

psychiatric disorders, however, each may have a distinct profile of response to treatment.

Research in this direction is needed for more objectively based diagnostic methods and novel

treatment.

1.5.6 Applications beyond the Motor Cortex The neurophysiological studies mentioned above demonstrate the conventional approaches to

measuring CI and excitability of the motor cortex. Such approaches have been used to

demonstrate important neurophysiological findings in both healthy and diseased states.

However, the restriction of such recordings over the motor cortex is of limited interest since

the pathophysiology of many psychiatric disorders is associated with non-motor brain

regions. As a result, it is important to evaluate the neurophysiology of brain regions that are

more proximal to the underlying phenotype (e.g., the DLPFC). Recently, TMS has been

combined with EEG to derive inhibitory measurements directly from the DLPFC and the

motor cortex in healthy subjects [Daskalakis et al., 2008c; Farzan et al., 2010b; Fitzgerald et

al., 2008]. LICI can be measured using a combination of paired-pulse TMS and EEG to study

how GABAB receptors modulate oscillations in the brain in both the motor cortex and

DLPFC with high test-retest reliability [Daskalakis et al., 2008c; Farzan et al., 2010b;

Fitzgerald et al., 2008]. LICI using TMS-EEG is defined using the area under rectified

unconditioned and conditioned waveforms for averaged EEG recordings between 50-150 ms

post-TS. This interval was chosen as it represents the earliest artifact free data (i.e., 50 ms

post TS) and reflects the duration of GABAB receptor-mediated IPSPs (i.e., 250 ms post CS)

[Deisz, 1999b]. Gamma oscillations (30 to 50 Hz) in the cortex are generated as a result of

rapid firing of output pyramidal neurons. Inhibitory interneurons exert fine control over the

firing of pyramidal neuronal networks, which translates into high frequency gamma

18

oscillatory activity on EEG [Sohal et al., 2009]. Several reports also suggest that different

GABA receptor subtypes are active during different phases of gamma oscillations. It has

been shown that GABAA IPSPs contribute to generation of gamma oscillations and GABAB

IPSPs contribute to the modulation of gamma oscillations [Bartos et al., 2007; Whittington et

al., 1995].

1.6 TMS-EEG Studies of Inhibition Two studies have used combined TMS and EEG to examine the pathophysiology of SCZ.

Farzan et al. [Farzan et al., 2010a] demonstrated that overall LICI using TMS-EEG in SCZ

patients did not differ significantly in any region compared with BD patients and healthy

controls. However, when the evoked EEG response was filtered into different frequency

bands, they found a significant deficit in the inhibition of gamma oscillations in the DLPFC

of SCZ patients relative to BD patients and healthy controls, but no inhibitory deficit was

found within the motor cortex. The authors concluded that this selective deficit in the

inhibition of gamma oscillations demonstrates that the DLPFC is a region in the brain closely

related to the pathophysiology of SCZ. Deficits in frontal gamma inhibition of the DLPFC

are consistent with neurophysiological evidence of frontal impairments implicated in SCZ as

deficits in cognitive functions, such as working memory (WM), a major feature of SCZ

[Weinberger et al., 1986]. An earlier study provided additional support for this finding as

Ferrarelli et al. [Ferrarelli et al., 2008] also demonstrated a decrease in EEG-evoked

responses in the gamma band when TMS was applied directly to the frontal cortex,

suggesting frontal gamma deficits in SCZ patients. Taken together, these studies point to

important new directions in which TMS-EEG can provide new insights into the

neurophysiological underpinnings of SCZ.

1.6.1 Assessing Connectivity with TMS and EEG Current pathophysiological theories of SCZ emphasize the role of altered brain connectivity

[Friston, 1998; Stephan et al., 2006]. This disconnectivity may manifest anatomically,

through structural changes of association fibers at the cellular level, or functionally through

aberrant control of synaptic plasticity[Stephan et al., 2006]. TMS combined with EEG can be

19

used to evaluate the connectivity between and within hemispheres [Voineskos et al., 2010],

providing potential to ascertain functional connectivity between cortical regions

[Bloom and Hynd, 2005; Gazzaniga, 2000]. Voineskos et al. [Voineskos et al., 2010]

examined the relationship between microstructural integrity of subdivisions of the corpus

callosum with TMS-induced interhemispheric signal propagation (ISP) using a single-pulse

paradigm. They found a significant inverse relationship between microstructural integrity of

genu fibers of the corpus callosum and TMS-induced ISP from left to right DLPFC. Further,

they found a significant inverse relationship between microstructural integrity of callosal

motor fibers with TMS-induced ISP from left to right motor cortex. The authors concluded

that the examination of corpus callosum microstructure in relation to TMS-induced ISP may

provide novel insight into the neurobiological mechanisms of severe psychiatric disorders,

such as SCZ. Research has shown that during early cortical development reelin plays an

important role in lamination of the cortex. Reelin is a protein that regulates cortical

pyramidal neurons, interneurons, and Purkinje cell positioning [Curran and D'Arcangelo,

1998; Rice and Curran, 2001]. In SCZ, reelin was found to be decreased in layers I and II of

the prefrontal cortex [Impagnatiello et al., 1998]. Furthermore, Costa et al.[Costa et al., 2001]

found in patients with SCZ a downregulation of reelin expression and attenuated dendritic

spine expression that in turn reduce cortico-cortical connectivity and glutamic acid

decarboxylase 67 expression. These findings may explain the deficits in GABAergic

inhibitory neurotransmission and the subtle disruptions in connectivity found in SCZ. Future

research may consider evaluating the relationship between LICI and ISP, hypothesizing a

strong relationship between deficits frontal gamma inhibition and a lack of TMS-induced ISP

in patients with SCZ.

1.7 Functional Consequences of Disordered Inhibition Plasticity in the human cortex involves the functional reorganization of synaptic connections

in an effort to change or to adapt throughout life is characterized by processes involved in

learning, memory and neural repair [Hallett, 2000]. Evidence suggests that neural plasticity

may also be a corollary of CI as mechanisms mediating plasticity include unmasking existing

cortico-cortical connections [Schieber and Hibbard, 1993] by removing cortical inhibitory

20

neurotransmission [Jacobs and Donoghue, 1991]. For example, in humans administration of a

GABAergic agonist disrupts plasticity [Butefisch et al., 2000]. Abnormalities in brain

plasticity, possibly related to abnormal CI, have been proposed to underlie the

pathophysiology of SCZ [Fitzgerald et al., 2004b; Oxley et al., 2004]. We will discuss paired

associative stimulation (PAS) and use-dependent plasticity as a way of measuring plasticity

in the cortex. The evidence suggests that decreased neural plasticity is even more pronounced

in SCZ patients with impaired CI.

PAS represents a neurophysiologic paradigm that involves peripheral nerve stimulation of

the median nerve, followed by TMS of the contralateral motor cortex. PAS has been shown

to result in long-term potentiation-like activity (PAS-LTP) if peripheral nerve stimulation

precedes TMS by 25 ms (PAS-25) [Stefan et al., 2000]. Rajji et al. [Rajji et al., 2011]

demonstrated MEP potentiation after PAS-25 which was associated with enhanced motor

learning at 1-week post-PAS in healthy participants. Moreover, Frantseva et al. [Frantseva et

al., 2008] demonstrated that SCZ patients showed deficits in MEP facilitation indicating

disrupted LTP-like plasticity associated with impaired motor skill learning compared to

healthy participants. This study highlighted the role of PAS-TMS in the motor regions to

assess synaptic plasticity in SCZ patients. The authors concluded that SCZ patients

demonstrated impaired LTP-like plasticity which may be associated with deficits in learning

and memory.

Use-dependent plasticity involves the use of a TMS paradigm which can measure neural

plasticity in the cortex [Classen et al., 1998]. The spontaneous direction of TMS-induced

thumb movements is measured in two axes (x and y). As a result, use-dependent plasticity is

assessed using a task in which individuals are trained to perform a simple motor task

opposite to the direction of TMS-induced thumb movement. TMS is then reapplied to the

cortex while evaluating the direction of the induced thumb movement over time. Classen et

al. [Classen et al., 1998] found that immediately after training, the direction of TMS-induced

movements followed the direction of training. Both GABA and NMDA receptor-mediated

neurotransmission play an important role in use-dependent plasticity [Butefisch et al., 2000].

Daskalakis et al. [Daskalakis et al., 2008a] evaluated use-dependent plasticity in 14

21

medicated and six unmedicated patients with SCZ compared with 12 healthy participants. A

significant reduction of use-dependent plasticity was demonstrated in SCZ compared with

healthy participants. That is, SCZ patients demonstrated significantly small angular

deviations in the 5-10 minutes of post-training period versus pre-training compared with

controls. The authors concluded that such abnormalities may be related to dysfunctional

neurophysiological brain processes, including LTP, that exist as a result of disturbances of

GABA, NMDA, and dopamine neurotransmission. These findings potentially account for the

aberrant motor performance demonstrated in SCZ. Taken together, these studies provide

preliminary evidence for a diminution of the neurophysiological process that mediate neural

plasticity in SCZ.

1.8 Limitations The aforementioned studies relating to deficits in CI in SCZ, BD, MDD and OCD are limited

in several ways. Numerous studies are limited to measuring the motor cortex as the exact

mechanism underlying the generation and modulation of the TMS-evoked MEPs remains

unclear. Additional limitations include small samples, differences in the TMS methodologies

between research groups, heterogeneous populations studied and an overall lack of diagnostic

specificity. Furthermore, it has been shown that medication may affect outcomes of TMS

measures. As such, the inclusion of medicated individuals on various classes of psychotropic

agents in these studies may be a significant confounder of results. Addressing these issues

systematically in future research by assessing a large sample of unmedicated psychiatric

populations will allow for a greater confidence in results and provide a more stable evidence

base for elucidating biological markers involved in psychiatric illnesses.

There are several methodological limitations of using combined TMS and EEG paradigms to

measure the cortex in human participants. TMS-evoked EEG responses may be contaminated

by muscular activity, indirect cranial reflexes, and somatosensory evoked potentials [Pope et

al., 2009; Whitham et al., 2008; Whitham et al., 2007] producing artifact in the recordings.

22

1.9 Summary of Findings TMS has provided us with the ability to evaluate cortical processes such as inhibition,

excitation and plasticity in healthy participants, which has led to invaluable evidence in

elucidating the pathophysiology of neuropsychiatric disorders (figure 2).

Figure 2. A single TMS pulse is applied to the motor cortex activating cortical tissues

associated with the abductor pollicis brevis muscle, eliciting a motor evoked potential at the

periphery captured through electromyography.

Taken together, the literature has demonstrated that disorders such as SCZ, BD, MDD and

OCD are characterized by abnormalities in CI, highlighting the lack of GABAergic

inhibitory neurotransmission (summary of findings in table 2). It is important to assess the

neurophysiology of brain regions that are more proximal to the underlying phenotype (e.g.,

the DLPFC). Additionally, the ability to evaluate the response profiles of different oscillatory

frequency bands via EEG in response to TMS may ultimately serve as a key method to

identify endophenotypes of psychiatric illness. Endophenotypes are valuable as they are

presumably upstream in the pathophysiology of the illness and closer to the genetic variation

underlying complex psychiatric disorders [Gottesman and Gould, 2003]. Compared to

23

current subjective clinical diagnoses, endophenotypes are objective, quantifiable, and

heritable. They also allow for measurement of aberrant neural circuitry [Braff et al., 2008;

Gottesman and Gould, 2003]. In conclusion, there is a great need to better understand the

neurobiological underpinnings of psychiatric disorders for more objective diagnosis and for

the potential of treatment discovery.

Disorder Main Findings

SCZ Impairments in SICI, CSP, IHI [Daskalakis et al., 2002a; Fitzgerald et

al., 2002a; Fitzgerald et al., 2002b].

Two studies have shown that clozapine-treated SCZ patients

demonstrated potentiated CSP durations [Daskalakis et al., 2008b; Liu

et al., 2009].

SCZ patients with history of comorbid cannabis show decreased SICI

and increased ICF [Wobrock et al., 2010].

Deficits in gamma inhibition of the DLPFC using LICI with combined

TMS and EEG [Farzan et al., 2010a].

BD Impairments in SICI, CSP, IHI [Levinson et al., 2007].

MDD Deficits in SICI and CSP [Bajbouj et al., 2006b; Fitzgerald et al., 2004a;

Lefaucheur et al., 2008; Levinson et al., 2010].

Impairments in ICF and RMT [Lefaucheur et al., 2008].

OCD Decreased SICI, CSP and enhanced ICF [Greenberg et al., 2000;

Greenberg et al., 1998; Richter et al.].

Table 2. Summary of Significant TMS Findings in Psychiatric Populations.

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Chapter 2

Neurophysiological Measurements Associated with Transcranial

Magnetic Stimulation.

Contents of this chapter have been reprinted by permission from Oxford University Press

Radhu N, Blumberger DM, Zanjani A, Daskalakis ZJ. (2014). Neurophysiological

Measurements Associated with Transcranial Magnetic Stimulation. Clinical Guide to the

Administration of Transcranial Magnetic Stimulation for Neuropsychiatric Disorders. (1):

98-116.

A link to the published chapter can be found at:

http://oxfordmedicine.com/view/10.1093/med/9780199926480.001.0001/med-

9780199926480-chapter-8

http://oxfordmedicine.com/view/10.1093/med/9780199926480.001.0001/med-9780199926480-chapter-8

http://oxfordmedicine.com/view/10.1093/med/9780199926480.001.0001/med-9780199926480-chapter-8

25

2.1 Abstract Previous studies have used TMS as a way to evaluate severe psychiatric disorders. However,

these measures were conventionally limited to the motor cortex. This past decade has seen

significant improvements in the concurrent use of TMS and EEG to assess cortical network

properties such as CI, cortical excitability, plasticity and connectivity in non-motor regions.

New hardware solutions and advanced data processing techniques have allowed substantial

reduction of the TMS-induced artifact on EEG recordings. In this chapter, the past, present

and future status of TMS-EEG research is discussed. First, a description of the working

principle of TMS is provided. Second, commonly used TMS paradigms are defined while

providing evidence for the cortical mechanisms underlying each method. Finally, this chapter

highlights the application of these novel cutting-edge techniques vis à vis healthy and disease

states to provide a clear platform from which diagnostic procedures can be developed.

26

2.2 Introduction TMS is a cutting-edge non-invasive neurophysiological tool used to investigate the cortex in

healthy and disease states [Barker et al., 1985]. TMS is a useful method to further understand

the neurobiology of cognitive function, behavior and emotional processing [McClintock et

al., 2011]. It involves the generation of a magnetic field through the use of an

electromagnetic coil connected to a TMS device which induces an electrical current in the

brain [Wagner et al., 2007]. TMS is used as an investigational tool as it assesses a variety of

cortical phenomena including CI, excitation and plasticity [Classen et al., 1998; Kujirai et al.,

1993]. Assessing the cortical phenomena using TMS provides valuable insights into the

neurophysiological substrates underlying psychiatric and neurological disorders. However,

the restriction of such recordings to the motor cortex is of limited interest as the

pathophysiology of many neuropsychiatric disorders is associated with the frontal cortex.

Thus, evaluating the neurophysiology of brain regions that are more proximal to the

underlying phenotype is essential.

2.3 Overview of TMS Technology TMS capitalizes on the ability of time-varying magnetic fields to induce eddy currents in

biological tissue via Faraday’s principle of electromagnetic induction. TMS fields pass

through the scalp unimpeded and non-invasively stimulates brain areas compared to more

invasive transcranial electrical stimulation [Hallett, 2000]. Conventional approaches to

measure cortical neurophysiology involve stimulation of the motor cortex while using MEPs

as the primary dependant variable of interest, which is measured in the periphery through

EMG. Such approaches have been used to demonstrate important neurophysiological

findings in both healthy and disease states, which will be discussed in this chapter.

2.3.1 Motor Cortex TMS Studies in Psychiatric Illnesses and Clinical

Utility A series of studies have reported that TMS paradigms that generate a functional index of

GABA inhibitory neurotransmission from the cortex of healthy human subjects have

27

demonstrated a distinct and consistent pattern of deficiency in severe psychiatric disorders.

These paradigms show high test-retest reliability and large effect size differences between

healthy subjects and patient populations. These tests are relatively easy to perform,

inexpensive, and easy to interpret. Several lines of evidence suggest that CI is impaired in

these disorders. For example, previous TMS studies have demonstrated deficits in CI

assessed from the motor cortex in patients with OCD [Greenberg et al., 2000; Greenberg et

al., 1998; Richter et al., 2012], MDD [Bajbouj et al., 2006b; Fitzgerald et al., 2004a;

Lefaucheur et al., 2008; Levinson et al., 2010], SCZ [Daskalakis et al., 2002a; Daskalakis et

al., 2008b; Fitzgerald et al., 2002a; Fitzgerald et al., 2002b; Fitzgerald et al., 2003; Liu et al.,

2009; Wobrock et al., 2010; Wobrock et al., 2009; Wobrock et al., 2008] and BD [Levinson

et al., 2007]. Collectively, these studies provide evidence to suggest that impairments in

GABA inhibitory neurotransmission are a ubiquitous finding in severe psychiatric illnesses.

TMS paradigms hold potential as biomarkers of psychiatric disorders and treatment response.

Biomarker development will lead to strategies that prevent manifestation of the illness and

increase our understanding of the underlying neurobiological mechanisms. However, further

replication of findings is required. The use of TMS to establish molecular engagement of

novel psychopharmacological and somatic treatments (i.e., electroconvulsive therapy (ECT),

repetitive TMS, magnetic seizure therapy, transcranial direct current stimulation or cognitive

behaviour therapy), particularly within the GABA and glutamate circuits, are other potential

biomarker roles for these tests. Conceivably TMS measures of GABAergic and glutamatergic

functioning could be used as biological markers of novel treatments that are aimed at

enhancing inhibition or decreasing facilitation in the cortex.

2.4 Applications of TMS and EEG 2.4.1 Overview of EEG In the 1920s, the psychiatrist Hans Berger recorded brain waves from the surface of the

human scalp and coined the technique as EEG [Buzsaki, 2006; Swartz and Goldensohn,

1998]. Electrical activity of the cortex is measured by placing multiple electrodes along the

scalp, these electrodes record electrical signals that are primarily generated by coordinated

28

output of neurons from the scalp’s surface [Nunez and Srinivasan, 2006]. Cortical potentials

recorded through EEG represent the oscillatory activity of underlying neuronal activity

[Nunez and Srinivasan, 2006]. Such recordings at rest can be used clinically to diagnose

tumours, seizures, encephalopathies, and brain death and can be potentially used as

biological markers of neuropsychiatric illnesses [Babiloni et al., 2011; Sponheim et al., 2000;

Tot et al., 2002; Venables et al., 2009]. By contrast, when sensory stimuli are presented to

patients, evoked activity that is of greater electrical power is produced and recorded at the

scalp surface when compared to resting EEG recordings. Such activity can be used to

evaluate the neurophysiological mechanisms involved in the processing of emotional or

cognitive stimuli.

2.4.2 Overview of TMS and EEG The past decade has seen significant developments in the concurrent use of TMS and EEG to

directly assess cortical network properties such as CI, excitability and connectivity.

Simultaneous EEG recording during TMS stimulation was previously unattainable because

of the technological shortcomings of EEG amplifiers that would saturate for a long duration

due to the large artifact produced by the magnetic stimulation. For example, application of a

single TMS pulse would result in artifact lasting for several seconds after the pulse. Such

long lasting artifact blocks the window of time during which neurophysiological processes

such as CI occur. Through advances in EEG amplifier technology, researchers have

conducted a series of studies to examine TMS paradigms in the motor cortex through

simultaneous EEG and EMG as well as in frontal brain regions through EEG recordings.

A significant electromagnetic artifact field is generated by the TMS (at the site of

stimulation) and is several-fold larger than that produced by sensory evoked potentials on

EEG recordings [Ilmoniemi et al., 1997]. Several developments in EEG amplifier technology

have led to a reduction of this artifact. First, Ilmoniemi and colleagues reported that

decoupling of the electrode from the amplifier at or immediately before TMS can markedly

reduce the impact of the TMS stimulus artifact on EEG recordings [Ilmoniemi et al., 1997].

This was achieved through a sample-and-hold circuit that maintains amplifier output at a

29

constant level during stimulus delivery [Ilmoniemi et al., 1997]. They showed that this

modification permitted amplifier recovery within 100 μsec after the TMS [Ilmoniemi et al.,

1997]. Second, in a traditional alternating-current (AC) coupled EEG amplifier, the typical

500 mV and 50 μsec TMS pulse prevent the signal from returning to zero immediately after

the pulse. Rather, the signal that is recorded is followed by a negative deflection that can take

seconds to return to its initial state. With the introduction of direct-current coupled EEG

amplifiers, this prolonged negative swing is eliminated and immediately returns to its linear

range after the stimulus stops. Direct-current coupling has become available only in recent

years with the introduction of fast 24-bit analogue digital converter resolution (i.e., 24nV/bit)

that is superior to the older 16-bit analogue digital converter resolution that was limited to 6.1

μV/bit, a resolution that fails to limit the TMS stimulus artifact. The third modification is to

record EEG at very high sampling rates (e.g., 20 kHz) to permit full characterization of the

TMS pulse and limit the stimulus artifact that is produced on the recordings. By using any of

these strategies, EEG recording can become TMS compatible (for a review, please refer to

Ilmoniemi and Kicic [Ilmoniemi and Kicic, 2010]). Furthermore, the EEG electrodes used

during TMS-EEG should satisfy the physical requirements to operate within the harsh TMS

environment. The electrodes must be designed with a small enough diameter to avoid

overheating or to be affected by the forces that result from the induced TMS currents. Also,

the electrodes must be coated with suitable surface material to ensure a proper interface with

skin contact [Ilmoniemi and Kicic, 2010]. It is suggested that the optimal electrodes to record

TMS-EEG are small silver/silver chloride pellet electrodes (e.g., to allow the measurement of

the electrical potential on the skin [Ives et al., 2006; Roth et al., 1992; Virtanen et al., 1999]).

There are several post-processing procedures for removal of TMS-induced artifact from the

EEG recording as extensively reviewed by Ilmoniemi and Kicic [Ilmoniemi and Kicic,

2010]. EEG amplitudes greater than 100 µV, and containing large artifacts from

electromagnetic residuals, eye blinks, eye movement or muscle activity should be rejected.

Alternatively, there are more superior methods that enable the separation of brain signals

from artifacts such as signal-space projection [Ilmoniemi and Kicic, 2010], independent

component analysis (ICA) [Hamidi et al., 2010; Korhonen et al., 2011], modeling of sources

and artifacts [Ilmoniemi and Kicic, 2010] and principal component analysis [Levit-Binnun et

30

al., 2010; Litvak et al., 2007]. Offline procedures such as the use of filters to eliminate TMS-

related artifact from EEG have also been proposed; these procedures require further

investigation [Morbidi et al., 2007]. A major limitation in using these post-processing

techniques is the fact that there is no way of verifying that neuronal activity is not being

removed along with the artifact components.

2.4.3 Advantages of TMS and EEG There are four main advantages of using TMS combined with EEG in research studies. First,

by using TMS-EEG, investigators can study the mechanisms through which MEPs are

generated and modulated. Second, online EEG recording allows for the possibility to

evaluate the effects of electromagnetic induction on cortical oscillatory activity to

appropriately identify the cortical oscillations that are closely associated with the TMS-

induced MEP generation and modulation. A third major advantage of combined TMS-EMG

and EEG is the possibility to evaluate the cortico-cortical connectivity between motor

cortices. Functional connectivity between cortical regions (e.g. left and right motor cortices)

is easily probed by measuring the propagation of TMS-induced cortical responses. TMS-

EEG methodologies permit the investigation of the frontal brain areas that are more proximal

to the underlying phenotype (non-motor regions of the cortex). For example, examining LICI

in the DLPFC enhances our understanding of the inhibitory mechanisms that underlie a

cortical area that is more closely associated with the pathophysiology of psychiatric

disorders.

2.4.4 Rhythms of the Brain as Measured by EEG Network oscillations are generated from the rhythmic and synchronized firing of output

neurons in the cortex. Oscillations can be recorded from the surface of the cortex through

EEG and are represented as five frequency bands. These bands include: delta (1 to 3.5 Hz);

theta (4 to 7 Hz); alpha (8 to 12 Hz); beta (12.5 to 28 Hz); and gamma (30 to 50 Hz). Each

frequency band is related to different states. For example, the delta and theta bands are

greatest during deep sleep and are demonstrated during wakefulness in various pathological

conditions (e.g., tumors, Alzheimer’s disease) [Babiloni et al., 2004; Babiloni et al., 2006;

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Huang et al., 2000; Montez et al., 2009]. Alpha bands are greatest in the incipient stages of

sleep, during low arousal periods and when individuals close their eyes. Beta oscillations

show greatest activity during resting wakefulness. Gamma oscillations are associated with

the most complex cognitive demands, including information encoding, feature binding as

well as information storage and recall [Meltzer et al., 2008; Tallon-Baudry et al., 1998].

Several studies have suggested that frontal cortical gamma oscillations are necessary for WM

[Barr et al., 2011; Barr et al., 2009; Barr et al., 2010; Basar-Eroglu et al., 2007; Cho et al.,

2006; Howard et al., 2003]. Functionally, gamma oscillatory activity has been suggested to

provide the temporal dimension in information encoding [Fries et al., 2007], whereby the

successful encoding of information depends on its arrival time relative to the gamma cycle

[Fries et al., 2007]. Interneuron activity mediated by GABA then shapes the time course for

prefrontal pyramidal activation [Constantinidis et al., 2002] that is maximally activated when

the fast-spiking interneurons are not firing [Wilson et al., 1994]. GABA receptor activity is

also responsible for the generation (GABAA) and modulation (GABAB) of gamma

oscillations [Bartos et al., 2007; Brown et al., 2007; Leung and Shen, 2007; Traub et al.,

1996; Wang and Buzsaki, 1996; Whittington et al., 1995]. Thus, GABA plays a critical role

in the generation and modulation of gamma oscillations, which are vital in cognitive tasks.

2.4.5 Single-Pulse TMS Combined With EEG Ilmoniemi and colleagues were one of the first research groups to use interleaved TMS and

EEG to investigate the effect of TMS on cortical excitability [Ilmoniemi et al., 1997]. It was

demonstrated that TMS applied to the hand representation area of the human motor cortex

elicited a cortical response that spread to the adjacent ipsilateral area and to the homologous

regions in the opposite hemisphere. It was further shown that the application of TMS to the

visual cortex resulted in a similar pattern of signal propagation to the contralateral areas,

therefore providing evidence that the cortical potentials following motor cortex stimulation

were less likely to be a result of peripheral sensory activation. This original experiment

resulted in a series of studies that further characterized the EEG substrate of cortical

excitability, inhibition, plasticity, and connectivity in healthy participants [Esser et al., 2006;

32

Kahkonen et al., 2001; Kahkonen et al., 2003; Komssi et al., 2002; Komssi and Kahkonen,

2006; Nikulin et al., 2003; Paus et al., 2001; Thut et al., 2003].

2.4.6 Paired-Pulse TMS Combined With EEG Daskalakis et al. and Fitzgerald et al. were the first to demonstrate that recording LICI

(paired- pulse technique) through interleaved TMS-EEG was feasible [Daskalakis et al.,

2008c; Fitzgerald et al., 2008] in both the motor cortex and DLPFC in healthy subjects. In

the motor cortex, EEG measures of LICI were represented by the reduction of cortical

evoked activity in the electrode C3, which best represents evoked activity in the hand area of

motor cortex closest to the optimal site of abductor pollicis brevis activation through TMS

[Cui et al., 1999]. LICI was defined using the area under rectified unconditioned and

conditioned waveforms for averaged EEG recordings between 50 and 150 msec post-TS.

This interval was chosen because it represents the earliest artifact-free data (i.e., 50 msec

post-TS) and reflects the duration of GABAB receptor-mediated IPSPs (i.e., 250 msec post

conditioning stimulus) [Deisz, 1999a]. There was a significant inhibition in mean cortical

evoked activity through LICI compared to the TS alone in both the motor cortex and DLPFC

(targeted through cortical co-registration methods [Rusjan et al., 2010]. Farzan et al. has

demonstrated the validity, replicability, and test re-test reliability (Cronbach’s alpha > 0.7) of

LICI using the TMS-EEG method in both the motor cortex and DLPFC [Farzan et al.,

2010b]. In this study, a significant correlation was found between MEP suppression and

suppression of cortical evoked EEG activity [Farzan et al., 2010b]. These results provide

compelling evidence to suggest that TMS-induced EEG suppression is related to GABAergic

processes (i.e., GABAB inhibition), which mediate EMG measures of LICI [Sanger et al.,

2001; Siebner et al., 1998]. Similar research was also developed through experiments by

Fitzgerald and colleagues who used equivalent methods of assessing LICI and reported

maximal inhibition from 50 to 250 msec in the DLPFC, and from 50 to 175 msec in the

parietal lobe. They concluded that LICI can be recorded from several cortical regions with a

time course similar to GABAB receptor-mediated inhibition [Fitzgerald et al., 2009a].

33

More recently, Ferreri et al. also investigated the ability to record SICI and ICF using TMS-

EEG [Ferreri et al., 2011]. In these experiments, SICI was recorded using an ISI of 3 msec

while ICF was recorded using an ISI of 11 msec. These authors demonstrated that significant

inhibition could be reliably recorded in the motor cortex through both EMG and EEG

(recorded from the Cz electrode) and that these recordings were correlated suggesting that

such measures are mechanistically related to those recorded from peripheral hand muscles

through EMG.

2.4.7 Application of TMS-EEG in Sleep Studies Massimini and colleagues investigated cortical effective connectivity during wakefulness and

sleep using TMS with high-density EEG, evaluating the premotor cortex [Massimini et al.,

2005]. They found that during wakefulness, TMS-induced a sustained response made of

recurrent waves of activity; time-locked high frequency (20 to 35 Hz) oscillations followed

by a few slower (8 to 12 Hz) components that persisted until 300 msec. During stage 1 sleep,

this TMS-evoked response grew stronger and became shorter in duration. With the onset of

non rapid-eye movement (NREM), the TMS-induced brain response changed markedly. The

initial wave doubled in amplitude and lasted longer; however, no further TMS-locked

activity could be detected following this large wave. Based on these findings, they concluded

a breakdown of long-range effective connectivity during NREM sleep. Recently, Massimini

et al. used TMS with high-density EEG over the premotor cortex and found that during REM

sleep, the TMS-evoked brain response consisted of a sequence of fast oscillations during the

first 150 msec similar to wakefulness [Massimini et al., 2010]. They also found that activity

during stage 1 sleep replicated previous findings [Massimini et al., 2005]. Using TMS-EEG

in sleeping participants, Massimini et al. demonstrated that TMS evoked a high-amplitude

slow wave that originated under the coil and spread over the cortex; this triggered slow

waves during sleep that were state-dependent [Massimini et al., 2007]. Regardless of

stimulation site and intensity, TMS pulses that evoked slow waves during NREM and could

not do so during wakefulness. Taken together, these findings suggest that the effects of TMS-

EEG are strongly dependent on the state of the activated brain region (i.e. initial level of

34

underlying cortical activity) [Silvanto et al., 2008a; Silvanto et al., 2008b; Silvanto and

Muggleton, 2008; Silvanto et al., 2007; Silvanto and Pascual-Leone, 2008].

2.4.8 Application of TMS-EEG in Loss of Consciousness Studies Similarly, Ferrarelli and colleagues indexed TMS-evoked EEG responses in wakefulness

compared to induced loss of consciousness using midazolam [Ferrarelli et al., 2010]. Before

the injection of the anesthetic, TMS pulses to the premotor cortex evoked a complex

spatiotemporal pattern of low-amplitude high frequency activity. Conversely, following

midazolam-induced loss of consciousness, TMS pulses gave rise to high amplitude low-

frequency EEG potentials that faded shortly after the stimulation. They concluded that a

breakdown of effective cortical connectivity was a key mechanism mediating midazolam-

induced loss of consciousness. More recently, Rosonova et al. evaluated cortical effective

connectivity in patients emerging from a coma after a severe brain injury using TMS-EEG

[Rosanova et al., 2012]. They found that patients in a vegetative state who were behaviorally

awake (open-eyed) but unresponsive. TMS triggered a simple, local response, indicating a

breakdown of effective connectivity similar to unconscious, sleeping or anaesthetized

participants. In contrast, in minimally conscious patients (who were non-reflexive), TMS

triggered complex long-range activation in distant cortical areas. Taken together, the

literature indicates that TMS-EEG can evaluate effective connectivity in sleep, wakefulness,

anethetheized and vegetative states.

2.5 How Can We Apply Combined TMS and EEG in

Psychiatric Disorders? Several studies have used combined TMS and EEG to examine the pathophysiology of

psychiatric disorders. For example, Ferrarelli et al. stimulated the premotor cortex using

combined TMS-EEG and reported that reduced TMS-evoked gamma oscillations within the

first 100 msec post-stimulus in patients with SCZ. Gamma oscillations were significantly

attenuated in amplitude and demonstrated less synchrony in the fronto-central regions

[Ferrarelli et al., 2008]. The authors concluded that there was an intrinsic dysfunction in

35

frontal thalamocortical circuits in SCZ. Similarly, Farzan et al. demonstrated assessed

patients with SCZ, BD and healthy controls using the TMS-EEG paired-pulse technique (i.e.,

LICI) in both the motor cortex and DLPFC [Farzan et al., 2010a]. They found that overall

LICI (1 to 50 Hz) in SCZ patients did not differ significantly in any region when compared

with BD patients and healthy controls. However, when the evoked EEG response was filtered

into different frequency bands, they found a significant deficit in the inhibition of gamma (30

to 50 Hz) oscillations in the DLPFC of SCZ patients relative to patients with BD and healthy

controls (figure 3).

Figure 3. This illustration demonstrates that patients with SCZ have selective deficits in the

inhibition of gamma (30 - 50 Hz) oscillations in the dorsolateral prefrontal cortex compared

to healthy controls using interleaved TMS and EEG.

They also found no differences in the inhibition of other oscillatory frequencies in the

DLPFC or in the motor cortex between the three groups. The authors concluded that this

selective deficit in the inhibition of gamma oscillations demonstrates that the DLPFC is a

region in the brain that is closely related to the pathophysiology of SCZ. Furthermore,

36

Frantseva and colleagues demonstrated an increased TMS-induced cortical activation (in the

gamma frequency range) that spread across the cortex as measured by TMS-EEG in SCZ,

however, in healthy controls this activation faded away soon after stimulation [Frantseva et

al., 2012]. Recently, Hoppenbrouwers et al. showed that psychopathic offenders suffer from

dysfunctional inhibitory neurotransmission in the DLPFC as measured through combined

TMS and EEG assessing LICI [Hoppenbrouwers et al., 2013]. The authors concluded that the

impairments demonstrated in the study might render the psychopath unable to regulate

impulses, in turn, subjecting them to a disinhibited, antisocial life. Casarotto et al.

investigated frontal cortex excitability in healthy young and elderly individuals compared to

patients with Alzheimer's disease [Casarotto et al., 2011]. They found that TMS-evoked

potentials were not affected by physiological aging, unless an abnormal cognitive decline

(Alzheimer's disease) was associated. They demonstrated that frontal cortex excitability

identified as early and local cortical response to TMS was reduced in elderly patients with

Alzheimer's disease; however, this was not significantly different between healthy young and

elderly individuals. Lastly, Casarotto et al. were the first to evaluate MDD patients using

TMS-EEG in order to assess neuroplastic responses before and after their last administration

of ECT [Casarotto et al., 2013]. They demonstrated that there was a significant increase of

frontal cortical excitability (in every patient) after a course of ECT when compared to

baseline, suggesting that ECT produces synaptic potentiation. These above mentioned studies

illustrate that TMS-EEG can be used as a clinical tool to characterize the underlying

neurobiological dysfunction and to evaluate the neurophysiological effects of treatments over

time.

37

2.6 Limitations Advances in cortical stimulation and cortical recording techniques over the past few decades

have allowed for the systematic and non-invasive investigation of the neurophysiological

processes from the cortex in humans. TMS is a cutting-edge technique that allows for the

investigation of the cortical phenomena in both motor and non-motor regions to further

elucidate the pathophysiology of psychiatric disorders. Among such advancements,

concurrent TMS and EMG recordings have been instrumental in identifying and probing

cortical processes that underlie the generation and modulation of MEPs. Although the

evidence is still limited, research to date suggests that disorders such as SCZ, MDD, OCD

and BD are characterized by specific deficits in CI and abnormalities in cortical excitability.

However, the published studies are not entirely consistent. Factors that may play a role in the

discrepant results include small sample sizes, differences in TMS parameters used, the use of

heterogeneous populations, and presence of comorbid illness. Further, medications may

affect outcomes of TMS measures and it is likely that different classes of psychotropic

medications may do this in unique ways. As such, the inclusion of medicated individuals on

various classes of psychotropic agents in these studies is a significant confounder of results.

Addressing these issues systematically in future research would allow greater confidence in

results and provide a more stable evidence base for elucidating biological markers and

mechanisms involved in psychiatric illnesses. The ability to evaluate physiological response

profiles of different oscillatory frequencies in response to TMS combined with EEG may

ultimately serve as a key technique for evaluating biological markers in psychiatric illnesses.

Combined TMS and EEG will continue to provide a deeper insight into the neurobiological

underpinnings of psychiatric disorders.

38

Chapter 3

Schizophrenia and Their First-Degree Relatives

39

3.1 Abstract Several lines of evidence suggest that deficits in inhibitory neurotransmission are implicated

in the pathophysiology of SCZ. Despite more than 100 years of research in psychiatry, an

objective laboratory-based biological marker has not been identified. Biomarkers facilitate

the development of etiologic rather than symptom-based diagnostic methods, foster early

identification and treatment, and advance our understanding of the complex genetic and

neurobiological mechanisms. True biomarkers should also facilitate advanced treatment of a

population at high risk for the disorder, which may ultimately translate into prevention of

developing an illness in a subset of individuals. Endophenotypes can be used in psychiatry to

provide a way to dissect the underlying neural circuit abnormalities of complex disorders.

This chapter will first discuss the mechanisms underlying oscillatory activity in the brain

with application to the neurobiology of SCZ. The concluding portion will proceed to discuss

potential candidate neurophysiological endophenotypes currently investigated in SCZ and the

challenges involved in identifying an adequate biological marker for this disorder.

40

3.2 Introduction SCZ is a debilitating disorder that exacts enormous personal, social and economic costs [van

Os and Kapur, 2009]. SCZ affects 0.3-0.7% of the population, whose pathophysiology

remains poorly understood [McGrath et al., 2008]. SCZ is characterized by a constellation of

clinical symptoms such as delusions, hallucinations, disorganized thinking, social

withdrawal, motivational impairments and poor cognitive functioning [van Os and Kapur,

2009]. It is estimated that patients with SCZ occupy 10% of all hospital beds and despite

treatment efforts, as many as 15% of those diagnosed with SCZ eventually commit suicide

[Kaplan et al., 1994]. Despite some treatment successes, up to 45% of patients remain

treatment resistant. After 100 years of research, the neural framework of the underlying risk

of developing SCZ remains unclear and a biological marker of SCZ has not been effectively

identified. Thus, there is a great need to identify markers of disease in order to facilitate early

identification and treatment to better understand the neurobiological underpinnings of this

devastating disorder.

3.3 The Relationship between GABA-Mediated Inhibition

and Gamma Oscillations Gamma oscillations in the cortex are generated as a result of rapid firing of output pyramidal

neurons. Several lines of evidence suggest that pyramidal neuron firing is governed by

GABA inhibitory interneurons (i.e., basket and chandelier cells). GABA interneurons are

located throughout the uppermost layers of the cortex and form extensive synaptic networks

of connectivity, though limited in number (i.e., GABA interneurons only represent 20-30

percent of neurons in the cortex), one GABA interneuron typically connects extensively with

several pyramidal neurons [Bartos et al., 2007] forming neuronal networks that fire

contemporaneously. IPSPs are generated by GABAergic interneurons terminating on

pyramidal cells. As a result of this pattern of connectivity, inhibitory interneurons exert fine

control over the firing of pyramidal neuron networks, which translates into high frequency

gamma oscillatory activity on EEG [Sohal et al., 2009]. This is a crucial property of the

GABAergic system because pyramidal neurons are roughly three times as numerous in the

41

central nervous system [Bartos et al., 2007]. It has been shown that certain forms of

electrical or chemical stimulation can produce highly synchronous rhythmic IPSPs across

multiple pyramidal neurons suggesting that synchronized IPSP waves propagate throughout

cellular networks. If this synchronized activity is sufficiently large, then the amplitude of

these signals will rise above the electrophysiological noise and result in observable

oscillatory rhythms [Buzsaki, 2006]. In this way, GABA-mediated synaptic inhibition plays a

critical role in the production of neuronal synchronization in cortical circuits.

In the cortex, GABAergic interneurons have several important physiological functions, such

as the down-regulation of excessive cortical excitability (e.g. seizures) and neuroplastic

generativity, as well as serving discriminative (e.g. top down modulation) and cognitive

processes (e.g. memory). GABA and gamma oscillations mediate several cognitive functions,

including WM and attention [Daskalakis et al., 2008d]. Previous evidence has demonstrated

an association between GABAergic inhibitory neurotransmission and gamma oscillations

[Bartos et al., 2007; Bragin et al., 1995; Brown et al., 2007; Jefferys et al., 1996; Marrosu et

al., 2006; Scanziani, 2000; Traub et al., 1997; Traub et al., 1996; Wang and Buzsaki, 1996;

Whittington et al., 1995]. GABAA receptor-mediated IPSPs contribute to the generation of

gamma oscillations [Bartos et al., 2007; Wang and Buzsaki, 1996; Whittington et al., 1995].

GABAA receptors typically discharge at 30 to 50 Hz, resulting in a high-frequency on-off

oscillatory pattern of pyramidal cell discharge, recorded as gamma oscillations via EEG.

Thus, GABAA receptor-mediated IPSPs act as a switch which allows pyramidal neurons to

fire at high-frequencies. By contrast, GABAB receptor-mediated IPSPs play a critical role in

the modulation of gamma oscillations. These effects are essential to higher order cognitive

processes including information processing and WM [Benes and Berretta, 2001; Deco and

Rolls, 2003]. Furthermore, the modulation of gamma oscillations represent an important

neurophysiological process that may, in part, be responsible for optimal cognitive

functioning in the DLPFC.

42

3.3.1 Gamma Oscillations, GABAergic Inhibition and Working

Memory WM is a higher order cognitive process governed by the prefrontal cortex, which acts to

retain information (short-term) while other cortical regions organize the continuous

perceptual information processed [Baddeley, 1986; Deco and Rolls, 2003]. WM refers to the

ability to encode, manipulate, and retrieve ordered information online and over a limited

period of time, shown to be critical to language comprehension, learning and reasoning

[Baddeley, 1986; Baddeley, 1992].

GABAergic inhibitory interneurons of the prefrontal cortex are the major subtype subserving

WM [Deco and Rolls, 2003; Lewis et al., 2005]. In particular, gamma band synchrony

appears to be the neurophysiological analogue of inhibitory functioning in the prefrontal

cortex, a process critical to neurologic regulation and thus often referred to as a ‘building

block’ of brain function [Basar-Eroglu et al., 1996]. Oscillations in the gamma frequency

range are associated with WM and cognitive tasks involving prefrontal cortical function

[Palva et al., 2005]. For example, Palva and colleagues [Palva et al., 2005] demonstrated that

in healthy subjects, gamma band oscillations can be enhanced through engaging subjects in a

progressively increasing WM task load. The gamma band oscillation has been observed to be

synchronized in functionally and spatially distinct neural networks, as observed by EEG.

Synchronization of the gamma frequency spectra is seen as primarily dependent on

horizontally oriented GABAergic interneurons in the DLPFC using EEG. As shown above,

Chen et al., [Chen et al., 2014] demonstrated a positive relationship between WM-induced

gamma oscillatory activity (via EEG) and left DLPFC GABA levels. The orientation of these

neurons generates the electrical current that forms the basis of this frequency band [Tallon-

Baudry and Bertrand, 1999]. Taken together, GABAergic inhibitory interneurons may

synchronize pyramidal cell discharge from multiple cortical regions while suppressing

irrelevant or extraneous cortical activation, allowing for the integration of cortical activity

and optimized execution of cognitive information (e.g. WM).

43

3.4 Inhibitory Deficits in Schizophrenia The widespread theory in SCZ has focused on abnormal dopamine signaling due to the

primary form of treatment for SCZ (dopamine antagonists) in treatment of the positive

symptoms. However, these medications are not successful in targeting and relieving the

cognitive and negative symptoms. Since these are also core symptoms of the illness, one such

hypothesis may be due to the elevation in the ratio of cortical cellular excitation to inhibition

[Yizhar et al., 2011]. As a result, other neurotransmitter systems such as the glutamate and

the GABA systems have been implicated in the disease. For example, the increased activity

in excitatory neurons or reduction in inhibitory neuron function, may contribute to the social

and cognitive deficits observed in SCZ [Yizhar et al., 2011]. Several lines of evidence

suggest that SCZ is associated with the dysfunction of GABAergic inhibitory interneurons

and may be a mechanism through which to develop a biomarker for this disorder. The

evidence is not clear in how these GABAergic abnormalities relate to symptoms of the

illness, this section will highlight these findings.

3.4.1 NMDA-receptor Hypofunction Hypothesis The NMDA-receptor (NMDAR) hypofunction hypothesis has been one mechanism

underlying the dysfunctional GABAergic system in SCZ [Moreau and Kullmann, 2013]. It

has been shown that a blockade of the glutamate-mediated excitatory neurotransmission by

NMDAR antagonists mimics positive and negative symptoms as well as cognitive deficits in

SCZ [Krystal et al., 1994]. This hypothesis proposes a specific deficit in NMDAR signaling,

leading to a decrease in parvalbumin (PV) positive GABAergic interneuron activity and

consequent pyramidal cell disinhibition, diminishing GABA synthesis and release [Gonzalez-

Burgos and Lewis; Moreau and Kullmann, 2013; Olney et al., 1999]. The decreased

glutamatergic transmission owing to the lack of NMDAR signalling in pyramidal neurons

could decrease PV and may widely contribute to neuronal circuit hypoactivity. This has been

shown to cause a decrease in glutamic acid decarboxylase (GAD) 67 levels and pruning of

perisomatic inhibition mediated by PV basket cells [Jiao et al., 2006]. At the pyramidal cell

level a reduction in NMDAR activity results in decreased spine density, associated with

44

impairments in cognition. Lastly, disinhibition decreases the power of gamma oscillations

and may contribute to negative and cognitive symptoms in SCZ [Lisman et al., 2008].

3.4.2 Neuroanatomic Findings A potential physiological substrate for abnormal and inefficient DLPFC function is the

deficit in GABAergic neurons. Abnormalities of GABAergic interneurons are some of the

most consistent findings in SCZ [Lewis et al., 2004]. The major determinant of GABA in the

neocortex, glutamic acid decarboxylase, is consistently downregulated in postmortem studies

of SCZ patients [Torrey et al., 2005]. Benes et al. first reported that patients with SCZ have

morphologic changes in cortical GABA interneuron’s, by demonstrating a decreased density

of non-pyramidal cells (i.e., interneuron’s) in anterior cingulate layers II-VI and in prefrontal

cortex layer II [Benes et al., 1991]. Akbarian et al. also reported that in SCZ messenger

RNA, which encodes the 67-kilo-Dalton isoform of GAD, a key enzyme in the synthesis of

GABA was reduced in the DLPFC [Akbarian et al., 1995]. The decrease is specific to layers

III – V [Akbarian et al., 1995; Volk et al., 2000] and is accompanied by reduced expression

of the GABA membrane transporter 1 (also known as SLC6A1) [Volk et al., 2001]. This

reduction has also been localized to PV positive GABAergic interneurons [Hashimoto et al.,

2003] in the DLPFC [Akbarian et al., 1995], anterior cingulate cortex [Woo et al., 2004],

motor cortex [Hashimoto et al., 2008], visual cortex [Hashimoto et al., 2008] and

hippocampus [Benes et al., 2007; Knable et al., 2004]. Taken together, these studies imply

that there is impaired density, synthesis and reuptake of GABA in SCZ. It was also reported

that patients with schizoaffective disorder show a 30 percent reduction in non-pyramidal cells

whereas patients with SCZ demonstrate a 16 percent decrease. A related study of tyrosine-

hydroxylase immunoreactive cells [Benes et al., 1997] indicated a reduction of 18 percent in

the density of non-pyramidal neurons in layer II of the anterior cingulate cortex in patients

with SCZ. Together, these anatomical data suggest that both the structure and function of

GABA inhibitory interneurons are impaired in post-mortem studies of SCZ.

45

3.4.3 Magnetic Resonance Spectroscopy Studies Magnetic resonance spectroscopy (MRS) is an imaging technique, which can assay GABA

concentrations in vivo. Measurement of GABA via MRS is relatively new for SCZ research,

with the first study published in 2009 [Goto et al., 2009]. The MRS results to date are mixed,

a few studies suggest reduced GABA in SCZ [Goto et al., 2009; Rowland et al., 2013; Yoon

et al., 2010], some have shown increased GABA [Kegeles et al., 2012; Ongur et al., 2010]

and no differences between healthy subjects and SCZ have also been found [Tayoshi et al.,

2010]. An approach to exploring these mixed findings may be to link MRS and

neurophysiology. For example, Chen et al. [Chen et al., 2014] recently assessed WM

performance, baseline GABA levels (using MRS) in the left DLPFC and gamma oscillations

at baseline and during a WM task in SCZ and healthy subjects. They showed that, as a whole

(patients and healthy subjects combined), gamma amplitudes during both rest and a working

memory task were positively correlated with left DLPFC GABA levels. Despite gamma band

amplitude deficits in patients across working memory stages, both baseline and working

memory-induced gamma oscillations showed strong dependence on baseline GABA levels.

These findings suggest a critical role for GABA function in gamma band oscillations, even

under conditions of impairment. In summary, more studies are needed to confirm reduced

GABA levels in patients with SCZ via MRS as the power of this technique has yet to be fully

explored.

3.4.4 The N100 Response Evoked by TMS A prominent long-latency negative peak has been commonly observed when TMS is

delivered over the motor cortex in many studies, the N100 is the most pronounced and

reproducible TMS-evoked potential (TEP) component and has been related to the slow IPSP

[Bonato et al., 2006; Bonnard et al., 2009; Ferreri et al., 2011; Kicic et al., 2008; Komssi et

al., 2004; Lioumis et al., 2009; Nikulin et al., 2003; Paus et al., 2001; Rogasch et al., 2013].

This reproducible large negative peak occurs at about 100 ms after the TMS pulse is named

the N100 response [Yamanaka et al., 2013]. Previous studies have suggested that the N100 of

TEPs in the motor cortex may be associated with cortical inhibitory processes and very

sensitive to cortical excitability. Farzan et al., [Farzan et al., 2013] found that the amplitude

46

of the N100 component is strongly associated with the CSP duration, demonstrating that the

N100 component evoked by TMS-EEG may be related to inhibitory mechanisms. The N100

component may reflect GABAB inhibitory mechanisms due to the time course and its

relationship with CSP. Two studies have also shown that the N100 amplitude measured by

TEPs has been enhanced after administration of Baclofen (a GABAB receptor agonist),

suggesting that the N100 reflects activity of the GABAB receptor [Premoli et al., 2014a;

Premoli et al., 2014b]. More work needs to be done with the N100 response and SCZ due to

the similar GABAergic inhibitory properties comparable to LICI.

3.4.5 Main Findings The current use of methodologies available implicate a wide range of functions and brain

regions associated with GABAergic abnormalities in SCZ, providing a platform for future

work and theoretical models to build biomarkers. More enhanced research is needed,

however, combinations of existing methodologies, such as pharmacologic challenges with

neuroimaging and neurophysiology studies, have the potential to yield new information that

may refine our understanding of treatment targets and, ultimately, benefit those patients

suffering from this devastating disorder

3.5 Endophenotypes in Schizophrenia Endophenotypes are laboratory measures that are heritable quantitative traits found in

patients with a disease, and in their unaffected first-degree relatives. Endophenotypes may be

useful because they are presumably upstream in the pathophysiology of the illness and closer

to the genetic variation which underlies complex disorders such as SCZ [Braff et al., 2008].

Endophenotypes are useful for genetic studies in identifying a potentially more homogenous

subgroup that shares a common genetic etiology for both the endophenotype and the disease.

Recently, specific guidelines have been published which have identified criteria that must be

met before a biological marker can be considered an endophenotype of a neuropsychiatric

disorder [Braff et al., 2008]. Endophenotype criteria include: (1) heritability (i.e., the

proportion of genotypic variance that contributes to endophenotypic variance); (2) trait

stability (e.g., an endophenotype that is unrelated to illness duration or pharmacological

47

treatments), this implies relatively insensitive to modest fluctuations in clinical symptoms;

(3) test-retest reliability, suitable for use as repeated measures; (4) diagnostic specificity (i.e.,

an endophenotype is present in the disease of interest but not present in other disorders); and

(5) moderate to large effect size differences between patients and controls [Gottesman and

Gould, 2003]. This last condition suggests that the measure is associated with the illness and

exhibits significant deficits in patients. Future research studies need to identify measures that

demonstrate sensitive, robust, reliable, state-independent, heritable and specific biomarkers

to predict disease, track disease progression and monitor treatment. The following sections

will discuss the heritability of SCZ; the challenges involved in identifying an adequate

endophenotype for this disorder and provide evidence to suggest that frontal inhibitory

deficits may represent a candidate endophenotype.

3.5.1 Heritability of Schizophrenia Heritability is the extent to which a trait is genetically determined. Heritability is calculated

by measuring how much of the variance in a particular trait is accounted for by genetic

variance. The heritability of SCZ has been estimated to be as high as 80 percent [Cardno et

al., 1999a; Cardno et al., 1999b]. However, it is now clear that the genetics of SCZ are

complex: with many susceptibility genes and epigenetic, epistatic, stochastic and non-genetic

(i.e., environmental) influences. In this context, endophenotypes may help to group subjects

into genetic and physiological subtypes to increase the power of genetic association and

linkage analyses, as well as focus investigations into specific neurobiological pathways.

3.5.2 Applications of Endophenotypes Several lines of evidence have demonstrated that SCZ involves neurophysiological, cognitive

and genetic abnormalities. The Consortium of Genetics in Schizophrenia (COGS), a large

multi-site, and government sponsored-collaboration has investigated several

neurophysiological and cognitive markers as potential endophenotypes [Calkins et al., 2007].

Their strategy has been to acquire neurophysiological measures such as P50 suppression,

antisaccade task for eye movements and prepulse inhibition and apply the main

endophenotype criteria to each tool. Cognitive markers evaluated include the Continuous

48

Performance Test as a test of attention, the California Verbal Learning Test, as a test of

verbal declarative memory, and the Letter-Number Sequencing test as a test of WM. Results

to date suggest modest rates of heritability for the above mentioned neurophysiological and

cognitive tests [Greenwood et al., 2007]. For example, the antisaccade task for eye

movements showed a relatively moderate neurophysiological heritability of 0.42 in SCZ. The

authors suggest that factors which may account for such modest heritability include poor trait

stability (e.g., change in relation to antipsychotic treatments), low test-retest reliability and

poor diagnostic specificity [Greenwood et al., 2007]. Consequently, through this work, it has

been shown that identification of better endophenotypes for SCZ is needed, which will be

outlined in this section.

3.5.3 P50 Suppression Accumulating evidence suggests that patients with SCZ have impaired ability to filter

extraneous sensory information, predisposing them to misperceiving environmental stimuli.

These neurophysiological deficits in sensory gating can be formally evaluated through event-

related potential paradigms and indexed as P50 suppression. In P50 suppression an auditory

click generates an evoked potential within 40-80 ms of the click that is attenuated in healthy

subjects by another click which precedes it by 500 ms. Patients with SCZ consistently

demonstrate deficits in P50 suppression compared to healthy subjects. Freedman et al.

[Freedman et al., 2000] have extensively investigated the biological basis for these P50

suppression abnormalities and have concluded that P50 suppression occurs through

activation of GABAB inhibitory interneurons which, in turn, attenuates pyramidal neuron

firing. Furthermore, it has been demonstrated that P50 suppression deficits in SCZ are not

normalized with the addition of selective dopamine D2 antagonists (e.g., haloperidol) but

potentiated with clozapine [Olincy and Martin, 2005]. P50 suppression is currently being

investigated as an endophenotype in SCZ. Results to date demonstrate that P50 suppression

demonstrates low heritability of 0.10 that was not significant in a sample of 183 nuclear

families [Greenwood et al., 2007]. This may be due to the low test-retest reliability of P50

suppression (e.g., intraclass correlation coefficient <0.5)[Boutros et al., 1991], lack of

diagnostic specificity (i.e., P50 suppression abnormalities demonstrated in BD [Olincy and

49

Martin, 2005] and Alzheimer’s disease [Thomas et al., 2010]) and lack of trait stability (i.e.,

P50 suppression is altered with medications).

3.5.4 Anti Saccade Paradigm The antisaccade task requires the participant to make a saccade to an unmarked location

opposite to a flashed stimulus. Interest for this paradigm surged after the discovery that

frontal lobe lesions specifically and severely affect human performance of antisaccades while

prosaccades (i.e., saccades directed to the visual stimulus) are facilitated [Amador et al.,

1998; Campanella and Guerit, 2009]. For example, the antisaccade task for eye movements

showed a moderate to strong neurophysiological heritability of 0.42 in SCZ. The authors

suggest that factors which may account for the moderate heritability include poor trait

stability (e.g., change in relation to antipsychotic treatments), low test-retest reliability and

poor diagnostic specificity [Greenwood et al., 2007].

3.5.5 Prepulse Inhibition Gating deficits, as assessed by prepulse inhibition (PPI) of the acoustic startle has been

shown to be a candidate for an endophenotype in SCZ, reflecting the brains’ ability to filter

or gate sensory information [Braff et al., 1992]. In PPI, a nonstartling stimulus (prepulse

tone) is presented shortly before a startling stimulus [Campanella and Guerit, 2009; Graham,

1975]. When the interval between the prepulse tone and the startle stimulus is 250 ms, the

magnitude of the startle eye blink response is reduced compared with the one evoked in

response to the startle stimulus alone. However, if the interval is longer (e.g. 2000 ms), the

startle eye blink reflex is enhanced, this ‘‘prepulse facilitation’’ reflects a combination of

arousal and sustained attention elicited by the prepulse. Many published studies to date have

demonstrated PPI deficits in SCZ [Swerdlow et al., 2014]. PPI deficits have also been

demonstrated in their relatives [Braff et al., 2001; Kumari et al., 2005]. However, these

findings are not consistent as recently Ivleva et al. [Ivleva et al., 2014] found no differences

between SCZ patients, BD patients, healthy controls and their first degree relatives similar to

other studies which showed no deficits in SCZ compared to healthy controls [Ford et al.,

1999]. The inconsistency in results and modest heritability of PPI demonstrated between

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0.32-0.45 [Hasenkamp et al., 2010] [Greenwood et al., 2007] in SCZ may deem it as an

undesirable endophenotype candidate.

3.5.6 Auditory Event-Related Potentials In this paradigm identical auditory “clicks” are presented in close succession (500 ms), with

stimulus pairs separated by long intervals (6 to 10 s) [Hamm et al., 2013; Hamm et al., 2012].

Evoked brain responses to the first stimulus (S1) and second stimulus (S2) are measured with

simultaneous EEG recording. There is a larger difference between S1 and S2 responses for

healthy subjects than for those with SCZ [Brockhaus-Dumke et al., 2008], caused by either

larger ERPs to S2 [Sanchez-Morla et al., 2009] and/or an attenuated response to S1

[Blumenfeld and Clementz, 2001] among those with SCZ. Ivleva et al., found [Ivleva et al.,

2014] an attenuated response to S1 in patients with SCZ, BD and their first-degree relatives

with a significantly lower magnitude in the theta, alpha and beta frequency bands. The lack

of diagnostic specificity in the results does not fit the criteria of a desirable endophenotype.

Furthermore, oscillatory abnormalities have been identified among those with SCZ in the

gamma band [Clementz et al., 1997; Johannesen et al., 2005], low frequency oscillations to

S1 [Brockhaus-Dumke et al., 2008; Clementz and Blumenfeld, 2001; Johannesen et al.,

2005] and late (200 to 300 ms) beta band oscillations to S1 [Brenner et al., 2009; Hong et al.,

2004].

Growing evidence has shown that abnormalities of high-frequency oscillations in the

gamma-range (30 to 100 Hz) via EEG are heritable, as demonstrated by assessing unaffected

first-degree relatives of SCZ patients. For example, Hall et al. [Hall et al., 2011] examined

the early auditory gamma-band response in SCZ and their unaffected co-twins during an

auditory oddball target detection task in 194 individuals. They found that both evoked power

and phase-locking phenotypes were reduced in unaffected co-twins of patients with SCZ and

both were shown to be heritable traits. The heritability estimates were high and found to be

0.65 for evoked power and 0.63 for phase-locking. As a follow-up to this work, Leicht et al.,

[Leicht et al., 2010] investigated the early auditory gamma band response in first-degree

relatives of SCZ patients. Both patients and unaffected siblings showed a significant

51

reduction of evoked power and phase locking in the early auditory gamma band response

compared to healthy subjects. These findings suggest that the gamma band response may also

have a heritable component in SCZ.

3.5.7 Auditory-Related N100 Findings The N100 is also defined as a response arising from the supratemporal auditory cortex

approximately 100 ms after presentation of an auditory stimulus [Hari et al., 1982; Hari et al.,

1987]. Reductions in the auditory N100 amplitude have been consistently reported in SCZ

and may reflect specific elements of the pathophysiology of SCZ [Rosburg et al., 2008].

Although this has been an observation repeatedly shown in SCZ, the N100 amplitude has

received little attention as a potential endophenotype. Evidence suggests that reduced N100

amplitude is a stable deficit found in both recent-onset [Sumich et al., 2006] and medication-

free patients [Ogura et al., 1991]. However, heritability of the N100 in unaffected first-degree

relatives is understudied. Four studies failed to find any significant differences in N100

amplitude between first degree-relatives of SCZ patients and healthy subjects [Frangou et al.,

1997; Karoumi et al., 2000; Waldo et al., 1988; Winterer et al., 2001]. Based on a twin study,

the N100 appears to be moderately heritable [Anokhin et al., 2007]. As part of the COGS,

Turetsky et al., [Turetsky et al., 2008] found reduced N100 amplitude in SCZ patients and

their unaffected first-degree relatives using EEG via an auditory paired-click paradigm. The

N100 amplitude was found to be heritable measure, as heritability estimates were for click 1

amplitude (heritability of 0.40) and click 2 amplitude (heritability of 0.29) and for the ratio

(heritability of 0.22). Taken together, the N100 is abnormal in patients and their relatives,

however, the inconsistency in these results and low heritability may deem it as an undesirable

endophenotype candidate. It is important to emphasize that, beyond its utility as a

quantitative endophenotype; the auditory N100 may provide a method to investigate the

neural substrates of SCZ.

3.5.8 Mismatch Negativity There is compelling evidence that sensory processing impairments contribute to the cognitive

and psychosocial dysfunction affecting the majority of SCZ patients [Braff and Light, 2004;

52

Kirihara et al., 2012; Light et al., 2006; Light et al.]}. Mismatch negativity is an event-related

potential of the negative component of a waveform obtained by subtracting event-related

responses to a frequency stimulus (standard) from those to a rare stimulus (deviant) with an

ISI of 500 to 1000 ms [Light et al.]. This response has been shown to reflect the function of

the auditory sensory memory system and to reflect a predominantly automatic or pre-

conscious process of detecting a “mismatch” between the deviant stimulus and a sensory–

memory trace [Naatanen et al., 1989]. Mismatch negativity has been shown to be reduced in

patients with SCZ, reduced in their first-degree relatives, heritable, reliability, trait-like

stability. These qualities suggest that the measure may be a potential endophenotype

candidate requiring further investigation [Light et al.; Michie et al., 2002].

3.6 The Bipolar-Schizophrenia Network on Intermediate

Phenotypes Research Multi-site studies increase the likelihood that findings will be generalizable by testing larger

and more heterogeneous samples. Another consortium study specializing in multi-sensor

recordings is the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP)

which provides a platform for investigating markers of disease. The B-SNIP trial has focused

on a single clinical phenotype which is psychosis within the SCZ-BD spectrum. The main

aim was to characterize an intermediate endophenotype with probands and their first-degree

relatives. Similar clinical characteristics observed across SCZ and psychotic BD including

overlapping diagnoses and shared risk genes highlight the importance of evaluating

endophenotypes across these diagnoses. Within the B-SNIP, Tamminga et al. [Tamminga et

al., 2013] found that SCZ and schizoaffective probands had a lower proportion of

Caucasians, were less educated, had higher positive and negative syndrome scale scores and

scored lower on social functioning scales when compared to patients with BD. This study

also found that among relatives of the psychosis probands, only 33% - 38% were free of any

axis I or II diagnosis, suggesting a high burden of psychiatric morbidity in families with

psychosis. Data from biological relatives of probands are important tools for biomarker work.

This section will summarize the B-SNIP imaging and EEG studies that included first-degree

relatives of SCZ and BD probands.

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3.6.1 Neuroimaging and EEG B-SNIP Studies

Studies of biological relatives of psychosis probands have found variable gray matter

alterations in the basal ganglia, parahippocampal gyrus, and prefrontal cortex in the relatives

of SCZ probands [Palaniyappan et al., 2012] and in the frontotemporal regions in relatives of

bipolar probands [Hajek et al., 2013]. The B-SNIP trial aimed to further understand and

expand upon these imaging results with a larger sample. For example, Ivleva et al. [Ivleva et

al., 2013] found gray matter volume reductions in psychosis probands and in relatives with

psychosis spectrum disorders in overlapping cortical regions in the frontotemporal, anterior

cinguate and parietal regions compared with nonpsychotic relatives. Furthermore, pairwise

comparisons reveal substantial overlapping gray matter reduction in SCZ and schizoaffective

probands in the frontal, anterior and posterior cingular, insular, temporal, parietal and

occipital cortices as well as in the basal ganglia, thalamus and cerebellum relative to healthy

controls. Probands with BD showed gray matter reduction in the frontal, anterior and

posterior cingulate, insular, temporal, and parietal cortices relative to healthy controls,

regionally overlapping with those in the SCZ and schizoaffective proband. No increases in

gray matter volume were found in any proband group relative to healthy controls. Many

overlapping brain regions showing structural gray matter reductions in both relatives and

probands of SCZ and BD, may demonstrate this measure to be an undesirable endophenotype

candidate. An additional B-SNIP imaging study evaluated the integrity of the white matter

connections and integrity by diffusion tensor imaging through fractional anisotropy

[Skudlarski et al., 2013]. They found decreases in functional anisotropy in the genu and body

of the corpus collosum in both SCZ and BD patients and no significant differences between

the two proband groups. They also showed that SCZ and BD relatives showed fractional

anisotropy deviations similar to their probands, however, most pronounced in the relatives of

SCZ. Lastly, Arnold et al. [Arnold et al., 2015] examined hippocampal volume as putative

biomarker and found that SCZ patients showed specific hippocampal volume reductions.

Relatives of SCZ patients were not significantly different than healthy controls, however, had

significantly higher volumes than probands, suggesting an intermediate deficit. The findings

from this large B-SNIP trial support further exploration of functional anisotropy in the genu

and body of the corpus collosum and hippocampal volumes in SCZ and their relatives.

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Ethbridge et al. [Ethridge et al., 2015] recently examined an auditory oddball task, whereby

EEG responses were measured to deviant auditory (“oddball”) stimuli interleaved in a train

of standard tones assessing SCZ, BD and their first-degree relatives. Spatial principal

components analysis derived data-driven frequency waveforms for each subject. Some of the

biomarkers of familial risk (N100, P3b) were more specific to SCZ, whereas another

response abnormality (P2) was specific to BD, and another (N2) was common to both

psychoses. The novel methods presented in this work quantify neural oscillatory information

based on spatial and time-frequency components may provide new targets for investigating

the alterations unique to SCZ and BD or shared across the psychosis spectrum.

3.7 Transcranial Magnetic Stimulation Studies with First-

Degree Relatives of Schizophrenia Patients To date, limited studies have evaluated first-degree relatives of SCZ patients using TMS

paradigms. Saka et al., [Saka et al., 2005] evaluated TMS measures of inhibition in

unaffected first-degree relatives of SCZ patients compared to healthy subjects (no proband

group was assessed). They found that 25% of first-degree relatives lacked transcallosal

inhibition and showed psychosis-proneness relative to healthy controls. No differences were

found in MEP amplitude (excitability measure) or the cortical silent period (inhibitory

measure). Furthermore, Hasan et al. evaluated MEP amplitude in patients with SCZ, their

unaffected first-degree relatives and healthy controls after cathodal transcranial direct current

stimulation (tDCS). It was hypothesized that cathodal tDCS would induce long-term

depression of the MEP in healthy subjects. They found that cathodal tDCS reduced the MEP

of the stimulated hemisphere in healthy subjects as predicted, however, had no effect on first-

degree relatives and SCZ patients. Within the non-stimulated hemisphere, MEPs were

facilitated in first-degree relatives and SCZ patients. This study provides preliminary

evidence for impaired plasticity in SCZ patients and their first-degree relatives. Taken

together, more research needs to be done using TMS in biological relatives of SCZ patients.

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3.8 Outline of the Dissertation In summary, evaluating tools using the endophenotype approach will continue to provide a

deeper insight into the neurobiological underpinnings of SCZ. Future studies investigating

brain structure and function characteristics in individuals with severe mental illness using

multimodal investigative approaches such as neuroimaging coupled with genetic, molecular

and TMS measures may help to elucidate disease-related mechanisms and biomarkers. As a

summary of the dissertation, chapters 1, 2 and 3 have provided the background for this

current project. Chapter 4 provides the objectives and hypotheses for each of the three

original research studies. Chapters 5, 6 and 7 represent studies one, two, and three,

respectively. The dissertation will conclude with chapter 8, devoted to the general discussion,

conclusions and future aims of this doctoral project.

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Chapter 4

Research Aims and Hypotheses of the Experiments

57

4.1 Introduction In the introduction chapters we reviewed that the severity of inhibitory dysfunction has been

shown to be linked to functional outcome in SCZ [van Os and Kapur, 2009]. As previously

mentioned, the primary brain region responsible for processing of executive functions is the

DLPFC, implicated in the pathophysiology of SCZ [Chen et al., 2014]. In this regard, two

major DLPFC abnormalities have been shown in SCZ patients: deficits in GABAergic

inhibitory neurotransmission and impairments in the inhibition of cortical oscillations.

In vivo human studies examining the modulatory effect of GABAB receptors on cortical

gamma oscillations in DLPFC are lacking. LICI is a paired-pulse TMS paradigm that is

suggested to reflect the modulatory effect of GABAB receptors in the cortex. Several

additional experiments were designed to confirm the possibility that deficits in frontal LICI

may represent a candidate endophenotype for SCZ. In this regard, several questions were

raised, (1) would inhibitory deficits be a ubiquitous finding in psychiatric disorders as

measured via TMS; (2) would patients with SCZ have specific impairments of frontal

inhibition (previously demonstrated in this patient population); (3) if patients with SCZ have

inhibitory impairments, whether or not such impairments could represent a candidate

endophenotype for this illness; (4) are frontal inhibition deficits a replicable finding in SCZ;

(5) are frontal inhibitory deficits in SCZ independent of antipsychotic medication; (6) are

frontal inhibition deficits disease-specific to SCZ; (7) would inhibitory deficits be

demonstrated in unaffected first-degree relatives of SCZ.

These seven questions raised above gave rise to a series of three studies that were conducted

during the course of my PhD program. This study was designed by my supervisor Dr.

Daskalakis. Upon joining the Temerty Centre for Therapeutic Brain Intervention, I was given

the great opportunity of undertaking this large PhD project, as I contributed extensively to

recruitment of participants, study design, data collection, development of analytical methods,

data analysis, conference presentations as well as publication of manuscripts.

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4.2 A Meta-Analysis of Cortical Inhibition and Excitability

Using Transcranial Magnetic Stimulation in Psychiatric

Disorders (Chapter 5) Objectives and Hypotheses

4.2.1 Objective 1, Inhibitory Deficits Present in Severe Psychiatric

Disorders The objective of the meta-analysis was to quantitatively assess all studies that used TMS

motor cortex measures of inhibition and excitation in OCD, MDD and SCZ. The effect size

statistic we used was a Hedge’s G to calculate mean differences in each measure per group.

The Hedge’s G analysis was used as it had the benefit to control for sample size. The main

aim of the study was to determine the exact pattern of deficit with inhibition and excitation

measures in each psychiatric disorder based on the statistically significant (p < 0.05) Hedge’s

G value.

4.2.2 Hypothesis 1, Inhibitory Deficits Present in Severe Psychiatric

Disorders We hypothesized that motor cortex GABAergic inhibitory deficits would be a ubiquitous

finding across OCD, MDD and SCZ patients. Specifically, OCD and MDD would have

similar significant profiles of inhibitory deficit in both GABAB and and GABAA receptor-

mediated inhibition. SCZ patients would show a pattern of deficit specific to GABAA

receptor-mediated inhibition.

4.3 Evidence for Inhibitory Deficits in the Prefrontal Cortex

in Schizophrenia (Chapter 6) Objectives and Hypotheses

59

4.3.1 Objective 1, Replication of Frontal Inhibitory Deficits The first objective was to evaluate GABAB inhibition via LICI in both the DLPFC and motor

cortex using TMS combined with EEG. The main aim was to conduct neurophysiological

assessments with a large sample size of SCZ, OCD and healthy controls. The main premise

was to demonstrate frontal inhibitory deficits in a large sample size of SCZ patients, based on

previous findings.

4.3.2 Hypothesis 1, Replication of Frontal Inhibitory Deficits The first hypothesis was that SCZ patients would show frontal LICI deficits compared to

healthy subjects. The core study hypothesis was that frontal LICI deficits would be

significantly greater in patients with SCZ than OCD patients. No significant differences

would be found in the motor cortex between all three groups.

4.3.3 Objective 2, Diagnostic Specificity of Frontal Inhibitory

Deficits The second objective was to evaluate the diagnostic specificity of frontal cortex LICI in SCZ

and OCD, due to the comparable levels of psychopathology severity and similar

pharmacology that are used to treat these two disorders.

4.3.4 Hypothesis 2, Diagnostic Specificity of Frontal Inhibitory

Deficits This study hypothesized that frontal inhibition deficits would be specific to patients with

SCZ, while not shown in OCD and healthy participants. The absence of such abnormalities in

OCD would confirm that frontal LICI deficits are not a generalized pattern of severe

psychopathology and may be specific to patients with SCZ. This would be demonstrated by

showing a significant negative relationship between clinical severity scores in SCZ and LICI

deficits.

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4.3.5 Objective 3, Large Effect Size Differences via Cohen’s D The third objective was to demonstrate effect sizes differences in frontal LICI between SCZ

patients and healthy controls using the Cohen’s D measure.

4.3.6 Hypothesis 3, Large Effect Size Differences via Cohen’s D It was hypothesized that large effect size differences would be shown in frontal LICI when

comparing SCZ patients to healthy subjects using the Cohen’s D measure.

4.3.7 Objective 4, Trait Stability of Frontal Inhibition The final objective was to demonstrate that frontal inhibition deficits found in SCZ patients

were independent of antipsychotic treatment.

4.3.8 Hypothesis 4, Trait Stability of Frontal Inhibition We hypothesized that there would be no effect of antipsychotic treatment on frontal LICI in

SCZ patients. This would be demonstrated with two main approaches. First, medicated SCZ

patients would show inhibitory deficits when compared to similarly treated OCD patients (on

antipsychotic medications) who are age and sex matched. Second, this study hypothesized no

significant relationship between LICI and chlorpromazine equivalent dosages in SCZ

patients, thereby, demonstrating no effect of medication.

4.4 Investigating the Heritability of Cortical Inhibition in

First-Degree Relatives and Probands in Schizophrenia

(Chapter 7) Objectives and Hypotheses

4.4.1 Objective 1, Assessing Inhibition in Unaffected First-Degree

Relatives

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The main objective of this study was to evaluate inhibition of the DLPFC and motor cortex

using the LICI paradigm (via TMS-EEG) in SCZ patients, OCD patients and both of their

unaffected first-degree relatives compared to healthy controls. We aimed to assess unaffected

first-degree relatives of SCZ patients as they share degrees of genetic vulnerability with the

proband; however, they are free from confounds related to medication and symptomatology.

4.4.2 Hypothesis 1, Frontal Inhibition in Unaffected First-Degree

Relatives This study hypothesized that frontal inhibition deficits would be demonstrated in SCZ and

would also show the greatest LICI impairment. Furthermore, we hypothesized that frontal

inhibition in first-degree relatives of SCZ would be intermediate of their related probands

and healthy subjects. We hypothesized a moderate heritability for frontal inhibition in SCZ.

Lastly, we hypothesized no significant frontal inhibition differences would be found in OCD

patients when compared to their unaffected first-degree relatives and healthy subjects.

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Chapter 5

A Meta-Analysis of Cortical Inhibition and Excitability Using

Transcranial Magnetic Stimulation in Psychiatric Disorders.

Contents of this chapter have been reprinted by permission from Elsevier Ireland Ltd

Radhu N, de Jesus DR, Ravindran LN, Zanjani A, Fitzgerald PB, Daskalakis ZJ. (2013). A

Meta-Analysis of Cortical Inhibition and Excitability Using Transcranial Magnetic

Stimulation in Psychiatric Disorders. Clinical Neurophysiology. 124 (7): 1309-1320.

A link of this paper can be found at:

http://www.clinph-journal.com/article/S1388-2457(13)00056-4/abstract

63

5.1 Abstract Objective

To evaluate TMS measures of inhibition and excitation in OCD, MDD and SCZ.

Methods

Paradigms included: SICI, CSP, RMT, ICF, and MEP amplitude. A literature search was

performed using PubMed, Ovid Medline, Embase Psychiatry and PsycINFO 1990 through

April 2012. Motor cortex LICI was not included as there were no studies found.

Results

A significant Hedge's g was found for decreased SICI (g = 0.572, 95% confidence interval

[0.179, 0.966], p = 0.004), enhanced intracortical facilitation (g = 0.446, 95% confidence

interval [0.042, 0.849], p = 0.030) and decreased CSP (g = -0.466, 95% confidence interval [-

0.881,-0.052], p = 0.027) within the OCD population. For MDD, significant effect sizes were

demonstrated for decreased SICI (g = 0.641, 95% confidence interval [0.384, 0.898],

p=0.000) and shortened CSP (g = -1.232, 95% confidence interval [-1.530, -0.933], p =

0.000). In SCZ, a significant Hedge's g was shown for decreased SICI (g = 0.476, 95%

confidence interval [0.331, 0.620], p = 0.000).

Conclusions

Inhibitory deficits are a ubiquitous finding across OCD, MDD, SCZ and enhancement of

intracortical facilitation is specific to OCD.

Significance: Provides a clear platform from which diagnostic procedures can be developed.

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5.2 Introduction GABA is the main inhibitory neurotransmitter in the brain, critical for the modulation of

cortical excitability and neuroplasticity [DeFelipe et al., 1986; Schieber and Hibbard, 1993].

GABAergic neurons constitute 25% to 30% of the neuronal population in the motor cortex

and their horizontal connections can extend up to 6 mm or more [Gilbert and Wiesel, 1992;

Jones, 1993]. Pyramidal cell activity is synchronized through a balance of inhibitory

postsynaptic potentials and excitatory postsynaptic potentials [Krnjevic, 1997]. IPSPs are

generated by GABAergic interneurons terminating on the pyramidal cell [Krnjevic, 1997].

Cortical inhibition is a neurophysiological mechanism whereby GABA inhibitory

interneurons attenuate the activity of other neurons (e.g. pyramidal neurons) in the cortex

[Daskalakis et al., 2007].

TMS is a non-invasive method used to assess inhibitory and excitatory mechanisms. TMS

was first introduced in 1985 by Barker et al. for investigating the state of motor pathways in

patients with neurological disorders and in healthy participants [Barker et al., 1985]. They

showed that a single TMS pulse applied to the motor cortex could activate cortical tissues

associated with the hand or leg muscles and elicit motor evoked potentials.

5.3 Inhibitory TMS Paradigms TMS has been used to assess inhibitory processes, these paradigms are referred to as the CSP

[Cantello et al., 1992], LICI [Valls-Sole et al., 1992], and SICI [Kujirai et al., 1993]. The

CSP duration is measured from the motor evoked potential onset to the return of

electromyography activity [Cantello et al., 1992]. LICI involves the pairing of a

suprathreshold CS followed by a suprathreshold TS at long ISIs, resulting in inhibition of the

motor evoked potential [Valls-Sole et al., 1992]. CSP and LICI appear to be assessing

GABAB receptor-mediated inhibitory neurotransmission as evidenced by pharmacological

studies [McDonnell et al., 2006; Siebner et al., 1998], the time course of the GABAB

inhibitory postsynaptic potential [McCormick, 1989; Siebner et al., 1998; Werhahn et al.,

1999b] and the high intensity suprathreshold CS [Sanger et al., 2001]. By contrast, SICI is

measured by applying a subthreshold CS before the suprathreshold TS at short ISIs, resulting

65

in inhibition of the motor evoked potential response by 50% to 90% [Kujirai et al., 1993].

SICI has been associated with the GABAA receptor-mediated inhibitory neurotransmission as

demonstrated by the pharmacological effects on this measure [Ziemann et al., 1996a], the

time course of the GABAA inhibitory postsynaptic potential [Wang and Buzsaki, 1996] and

the low intensity subthreshold CS [Sanger et al., 2001].

5.3.1 Excitatory TMS Paradigms TMS has also been used to examine cortical excitability, these paradigms include: MEP

amplitude, RMT, and ICF. The MEP amplitude is measured as the average response to a

series of pulses applied at a consistent TMS intensity [Zaaroor et al., 2003]. The RMTis

defined as the minimal intensity that produces a motor evoked potential > 50 μV in 5 of 10

trials in a relaxed muscle [Rossini et al., 1994]. Finally, ICF is a paired-pulse paradigm

whereby a CS is applied to the motor cortex before the TS, resulting in an enhanced motor

evoked potential [Kujirai et al., 1993; Nakamura et al., 1997]. ICF originates from excitatory

postsynaptic potentials transmitted by N-methyl-D-aspartate glutamate receptors [Nakamura

et al., 1997]. For a review of the pharmacological effects on inhibitory and excitatory TMS

paradigms, please see [Paulus et al., 2008b].

5.4 Applications within Psychiatric Disorders Numerous studies have implicated GABA in the pathophysiology of neuropsychiatric

disorders, notably OCD, MDD, SCZ, and BD. Several lines of evidence suggest that cortical

inhibition is impaired in these disorders. For example, previous TMS studies have

demonstrated deficits in cortical inhibition assessed from the motor cortex in patients with

OCD [Greenberg et al., 2000; Greenberg et al., 1998; Richter et al., 2012], MDD [Bajbouj et

al., 2006b; Fitzgerald et al., 2004a; Lefaucheur et al., 2008; Levinson et al., 2010], SCZ

[Daskalakis et al., 2002a; Daskalakis et al., 2008b; Fitzgerald et al., 2002a; Fitzgerald et al.,

2002b; Fitzgerald et al., 2003; Liu et al., 2009; Wobrock et al., 2010; Wobrock et al., 2009;

Wobrock et al., 2008] and bipolar disorder [Levinson et al., 2007]. An overall deficit of

GABAergic inhibition has been associated with these psychiatric disorders; however, each

may have a distinct illness profile and response to treatment. This meta-analysis aims to

66

quantitatively assess TMS evoked measures of inhibitory and excitatory paradigms in OCD,

MDD and SCZ.

5.5 Methods 5.5.1 Data Sources A literature search was performed using PubMed, Ovid Medline, Embase Psychiatry and

PsycINFO 1990 through April 2012.

A description of the exact search terms used:

motor cortex tms and psychiatry, motor cortex tms and mental disorder, motor cortex tms

and psychiatric disorder, motor cortex tms and anxiety disorder, motor cortex tms and

bipolar disorder, motor cortex tms and mania, motor cortex tms and depression, motor

cortex tms and obsessive-compulsive disorder, motor cortex tms and posttraumatic stress

disorder, motor cortex tms and schizophrenia, motor cortex tms and major depressive

disorder, short-interval cortical inhibition and schizophrenia, short-interval cortical

inhibition and depression, short-interval cortical inhibition and ocd, intracortical faciliation

and schizophrenia, intracortical facilitation and depression, intracortical facilitation and

ocd, cortical silent period and schizophrenia, cortical silent period and depression, cortical

silent period and ocd, resting motor threshold and schizophrenia, resting motor threshold

and depression, resting motor threshold and ocd, motor evoked potential amplitude and

schizophrenia, motor evoked potential amplitude and depression, motor evoked potential

amplitude and ocd.

5.5.2 Study Selection Studies were included if the following criteria were fulfilled:

1. Cortical inhibition or cortical excitability motor cortex measurements were assessed using

TMS.

2. Psychiatric disorders were diagnosed in accordance with DSM criteria.

3. The study had no specific “narrow” diagnosis or subgroup, such as depression after stroke

or vascular depression.

4. The study included a healthy unaffected comparison group.

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5. The data were sufficient to compute Hedges’ G (sample size, means, and standard

deviations).

6. At least 2 studies per psychiatric disorder/symptom cluster.

7. More than 3 participants per study.

8. Articles written in English.

9. In the case of articles with overlapping samples, the article with the largest sample size

was included.

5.5.3 Data Extraction The following data were acquired: number patients, number of healthy controls, mean and

standard deviation of the outcome measure at baseline. When publications contained

insufficient or incomplete data, the authors in question were contacted and invited to send

additional data so that their study could be included in the meta-analysis.

5.5.4 Hedge's g Calculation for the Meta-Analysis We employed standardized meta-analytic techniques used in the literature. A Hedge's g, 95%

confidence interval and p-value were calculated (patients versus healthy controls) for each

psychiatric disorder for measures of cortical inhibition (SICI, CSP) or excitability (resting

motor threshold, intracortical facilitation) and the motor evoked potential amplitude for

MDD and SCZ. This was analyzed using Comprehensive Meta Analysis Version 2.0

(Biostat, Englewood, New Jersey) in a fixed effects model. The means and standard

deviations of separate studies were weighted according to sample size.

5.5.5 Test of Heterogeneity We evaluated heterogeneity among studies by calculating a Cochran Q, p-value and I2.

Heterogeneity in a meta-analysis refers to the variation in study outcomes between studies

[Higgins and Thompson, 2002]. The Q statistic is a value that demonstrates how the

independent studies varied in terms of their findings. The I² statistic is a percentage of

variation across studies that is due to heterogeneity rather than chance [Higgins and

Thompson, 2002; Higgins et al., 2003]. The I2 ranges from 0% to 100%, a value of 0%

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means no heterogeneity and 100% means a high level of heterogeneity. A meta-regression

was implemented to control for variables such as age and medication status; this allowed for

the comparison of multiple sources of heterogeneity. Three or more studies were needed for

each variable to complete a meta-regression.

5.5.6 N Fail-Safe To examine publication bias, an N fail-safe value was calculated. This value is defined as the

number of non-significant unpublished studies needed to make the obtained effect size

calculations non-significant. Three or more studies were needed to complete this analysis.

We adopted a significance level of p = 0.05, 2-tailed for all of the analyses.

5.6 Results Table 3 provides the total number of studies that fulfilled the 9 stated criteria for inclusion

(described in the methods) and the total number of studies excluded based upon specified

reasons. The search was completed by N.R. and the studies were checked for reliability by

D.R.J. Studies met the checklist for assessing the methodological quality of studies using

TMS [Chipchase et al., 2012].

Psychiatric Disorder Number of Studies Included in

Meta-Analysis

Reasons for Exclusion and

Number of Studies Excluded

OCD Resting Motor Threshold (2)

Short Interval Cortical Inhibition (3)

Intracortical Facilitation (2)

Cortical Silent Period (2)

Motor Evoked Potential Amplitude

(0)

Insufficient Data (1)

No Healthy Comparison Group

(1)

MDD Resting Motor Threshold (8)

Short Interval Cortical Inhibition (3)




(3)



(9)

Epileptic Patients with Major

Depression (1)

69

Table 3. Number of Included and Excluded Studies.

5.6.1 Patients with OCD

5.6.2 OCD - Resting Motor Threshold Figure 4A illustrates the summary of the Hedge's g analysis as a forest plot based on 2

studies [Greenberg et al., 2000; Richter et al., 2012] that met inclusion criteria. The analysis

comprised a total of 50 patients with OCD compared to 45 healthy controls. No significant

differences were found in resting motor threshold in OCD. The Hedge's g was g = -0.251,

95% confidence interval [-0.658, 0.156], p = 0.227. The test of heterogeneity was found to be

significant (Q= 7.822, df(q) = 1, p = 0.005, I2 = 87.216). Meta-regression and publication bias

analyses were not possible due to the fact that only 2 published studies were available.

5.6.3 OCD - SICI Figure 4B displays the summary of the Hedge's g analysis as a forest plot based on 3 studies

[Greenberg et al., 2000; Greenberg et al., 1998; Richter et al., 2012] that met inclusion

criteria. This analysis consisted of 62 OCD patients compared to 57 healthy controls. SICI

was significantly reduced in OCD. The Hedge's g was found to be g = 0.572, 95% confidence

interval [0.179,0.966], p = 0.004. The test of heterogeneity was found to be significant (Q =

SCZ Resting Motor Threshold (21)

Short Interval Cortical Inhibition

(12)




(4)



(2)

Not in English (1)

BD 0 Insufficient Data (2)

Post-Traumatic Stress

Disorder

0 Less than 2 studies for this

disorder (1)

Social Anxiety

Disorder

0 Less than 2 studies for this

disorder (1)

70

36.366, df(q) = 2, p = 0.000, I2 = 94.5). The n-failsafe value was found to be 3 unpublished

studies. A meta-regression was not possible due to 2 studies publishing the values for age.

5.6.4 OCD - Intracortical Facilitation Figure 4C illustrates the summary of the Hedge's g analysis as a forest plot based on 2 studies

[Greenberg et al., 2000; Richter et al., 2012] that fit the inclusion criteria. The analysis

included 50 patients with OCD compared to 45 healthy controls. Intracortical facilitation was

significantly enhanced in OCD. The Hedge's g was found to be g = 0.446, 95% confidence

interval [0.042, 0.849], p = 0.030. The test of heterogeneity was not significant (Q = 1.162,

df(q) = 1, p = 0.281, I2 = 13.912). A meta-regression and publication bias analyses were not

possible due to only 2 published studies available.

5.6.5 OCD - CSP Figure 4D illustrates the summary of the Hedge's g analysis as a forest plot based on 2

studies [Greenberg et al., 2000; Richter et al., 2012] that fit the inclusion criteria. This

analysis contained 50 patients with OCD compared to 45 healthy controls. CSP was

significantly reduced in OCD. The Hedge's g was found to be g = -0.466, 95% confidence

interval [-0.881,-0.052], p = 0.027. The test of heterogeneity was significant (Q = 10.435,

df(q) = 1, p = 0.001, I2 = 90.417). A meta-regression and publication bias analyses were not

possible due to only 2 published studies available.

71

72

Figure 4. Forest Plot of the Hedge's g Analysis For All Studies That Included Patients With

Obsessive-Compulsive Disorder Compared to Healthy Controls.





5.7 Patients with MDD 5.7.1 MDD - Resting Motor Threshold Figure 5A illustrates the summary of the Hedge's g analysis as a forest plot based on 8

studies [Abarbanel et al., 1996; Bajbouj et al., 2006b; Chroni et al., 2002; Grunhaus et al.,

2003; Lefaucheur et al., 2008; Levinson et al., 2010; Maeda et al., 2000; Reid et al., 2002]

that fit the inclusion criteria. This analysis comprised of 176 patients with MDD compared

to 188 healthy controls. No significant differences were found in resting motor threshold in

MDD. The Hedge's g was g = -0.043, 95% confidence interval [-0.248, 0.161], p = 0.677.

The test of heterogeneity was not significant (Q =16.034, df(q) = 9, p = 0.066, I2 = 43.87).

The n-failsafe value was found to be 10 unpublished studies. Controlling for age, the meta

regression yielded a correlation of r = 0.04891 and p = 0.01399. Controlling for medications,

the meta regression yielded a correlation of r = 0.40970, p = 0.08217.

5.7.2 MDD - SICI Figure 5B illustrates the summary of the Hedge's g analysis as a forest plot based on 3 studies

[Bajbouj et al., 2006b; Lefaucheur et al., 2008; Levinson et al., 2010] that fit the inclusion

criteria. The analysis included 115 patients with MDD compared to 130 healthy controls.

SICI was significantly reduced in MDD. The Hedge's g was found to be g = 0.641, 95%

confidence interval [0.384, 0.898], p = 0.000. The test of heterogeneity was significant (Q =

10.362, df(q) = 4 , p = 0.035, I2 =61.398) and the n-failsafe value was found to be 5

unpublished studies. Controlling for age, the meta regression yielded a correlation of r =

0.03221 and p = 0.23094. Controlling for medications, the meta regression was found to be r

= 0.21356, p = 0.44282.

73

5.7.3 MDD - Intracortical Facilitation Figure 5C illustrates the summary of the Hedge's g analysis as a forest plot based on 3 studies

that fit the inclusion criteria [Bajbouj et al., 2006b; Lefaucheur et al., 2008; Levinson et al.,

2010]. The analysis consisted of 115 patients with MDD compared to 130 healthy controls.

No significant differences were found in intracortical facilitation in MDD. The Hedge's g

was g = -0.062, 95% confidence interval [-0.311, 0.188], p = 0.628. The test of heterogeneity

was not significant (Q = 7.465, df(q) = 4, p = 0.113, I2 = 46.413). The n-failsafe value was 5

unpublished studies. Controlling for age, the meta regression yielded a correlation of r = -

0.06835 and p = 0.00855. Controlling for medications, the meta regression was found to be r

= -0.16181 , p = 0.54986 .

5.7.4 MDD - CSP Figure 5D illustrates the summary of the Hedge's g analysis as a forest plot based on 4

studies that fit the inclusion criteria [Bajbouj et al., 2006b; Lefaucheur et al., 2008; Levinson

et al., 2010; Steele et al., 2000]. The analysis comprised of 131 patients with MDD compared

to 149 healthy controls. CSP was significantly reduced in MDD. The Hedge's g was found to

be g = -1.232, 95% confidence interval [-1.530, -0.933], p = 0.000. The test of heterogeneity

was significant (Q = 158.857, df(q) = 5, p = 0.000, I2 = 96.853). The n-failsafe value was 6

unpublished studies. Controlling for age, the meta regression correlation was r = 0.01035 and

p = 0.68408. Controlling for medications, the meta regression was found to be r = 0.69466, p

= 0.04121.

5.7.5 MDD - Motor Evoked Potential Amplitude Three studies [Chroni et al., 2002; Reid et al., 2002; Shajahan et al., 1999] that fit the

inclusion criteria yielded a Hedge's g of g = 0.162, 95% confidence interval [-0.300, 0.623], p

= 0.492. No significant differences were found in the motor evoked potential amplitude in

MDD. The test of heterogeneity was significant (Q = 6.586, df(q) = 2, p = 0.037, I2 =

69.633). This analysis included 34 patients with MDD compared to 37 healthy controls. The

n-failsafe value was 3 unpublished studies. Controlling for age, the meta regression yielded a

74

correlation of r = 0.09093 and p = 0.21375. All studies included medicated patients and a

meta-regression for medication was not possible.

75

Figure 5. Forest Plot of the Hedge's g Analysis For All studies That Included Patients With

Major Depressive Disorder Compared to Healthy Controls.





5.8 Patients with SCZ 5.8.1 SCZ - Resting Motor Threshold Figure 6 displays the Hedge's g as a forest plot based on 21 studies [Abarbanel et al., 1996;

Bajbouj et al., 2004; Boroojerdi et al., 1999; Chroni et al., 2002; Daskalakis et al., 2002a;

Daskalakis et al., 2008b; Eichhammer et al., 2004; Fitzgerald et al., 2002a; Fitzgerald et al.,

2002b; c; Fitzgerald et al., 2004b; Fitzgerald et al., 2003; Herbsman et al., 2009; Hoy et al.,

2007; Liu et al., 2009; Oxley et al., 2004; Pascual-Leone et al., 2002; Reid et al., 2002;

Soubasi et al., 2010; Wobrock et al., 2009; Wobrock et al., 2008] that met inclusion criteria.

No significant differences were found in resting motor threshold in SCZ. The Hedges G was

g = 0.067, 95% confidence interval [-0.053, 0.186], p = 0.274. The test of heterogeneity was

significant (Q = 83.977, df(q) = 30, p = 0.000, I2 = 64.276). This analysis included 500 SCZ

patients and 617 healthy controls. The n-failsafe value was 31 unpublished studies. After

controlling for age, the meta regression yielded a correlation of r = 0.02696, p = 0.05239.

After controlling for medications, the meta regression demonstrated a correlation of r =

0.20309 and p = 0.19117.

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Figure 6. Forest Plot of Resting Motor Threshold Hedge's g Analysis For All Studies That

Included Patients With Schizophrenia Compared to Healthy Controls.

5.8.2 SCZ - SICI Figure 7 displays the Hedge's g as a forest plot based on 12 studies [Daskalakis et al., 2002a;

Daskalakis et al., 2008b; Eichhammer et al., 2004; Fitzgerald et al., 2002b; c; Fitzgerald et

al., 2004b; Hasan et al., 2012; Liu et al., 2009; Oxley et al., 2004; Pascual-Leone et al., 2002;

Wobrock et al., 2009; Wobrock et al., 2008] that met inclusion criteria. SICI was

significantly reduced in SCZ. The Hedge's g was found to be g = 0.476, 95% confidence

77

interval [0.331, 0.620], p = 0.000. The test of heterogeneity was not significant (Q = 19.170,

df(q) = 19, p = 0.446, I2 = 0.887). The analysis included 335 SCZ compared to 440 healthy

controls. The n-failsafe was 20 unpublished studies. After controlling for age, the meta

regression was found to be r = 0.01029, p = 0.56518 . After controlling for medications, the

meta regression demonstrated a correlation of r = -0.08425, p = 0.6429.

Figure 7. Forest Plot of Short-Interval Cortical Inhibition Hedge's g Analysis For All Studies

That Included Patients With Schizophrenia Compared to Healthy Controls.

78

5.8.3 SCZ - Intracortical Facilitation Figure 8 displays the Hedge's g as a forest plot based on 11 studies [Daskalakis et al., 2002a;

Daskalakis et al., 2008b; Eichhammer et al., 2004; Fitzgerald et al., 2002b; c; Fitzgerald et

al., 2004b; Hasan et al., 2012; Liu et al., 2009; Pascual-Leone et al., 2002; Wobrock et al.,

2009; Wobrock et al., 2008] that met inclusion criteria. No significant differences were found

in intracortical facilitation in SCZ. The Hedge's g was g = 0.015 , 95% confidence interval [-

0.130,0.160], p = 0.841. The test of heterogeneity was not significant (Q = 17.236, df(q) =

18, p = 0.507, I2 = 0). The analysis incorporated 323 patients with SCZ compared to 428

healthy controls. The n-failsafe value was 19 unpublished studies. After controlling for age,

the meta regression correlation was r = 0.00200, p = 0.91120. After controlling for

medications, the meta regression was found to be r = -0.11468, p = 0.52264.

79

Figure 8. Forest Plot of Intracortical Facilitation Hedge's g Analysis For All Studies That


5.8.4 SCZ - CSP Eleven studies yielded a Hedge's g of g = -0.093, 95% confidence interval [-0.241, 0.055], p

= 0.218 [Bajbouj et al., 2004; Daskalakis et al., 2002a; Daskalakis et al., 2008b; Fitzgerald et

al., 2002b; c; Fitzgerald et al., 2004b; Hasan et al., 2012; Herbsman et al., 2009; Liu et al.,

2009; Soubasi et al., 2010; Wobrock et al., 2009] (figure 9). No significant differences were

found in CSP in SCZ. The test of heterogeneity was significant (Q = 161.499, df(q) = 18, p =

0.000, I2 = 88.854). The analysis consisted of 334 SCZ patients compared to 457 healthy

controls. The n-failsafe was 19 unpublished studies. After controlling for age, the meta

regression yielded a correlation of r = 0.01088, p = 0.58550. After controlling for

medications, the meta regression was found to be r = 0.53667, p = 0.00855.

80

Figure 9. Forest Plot of Cortical Silent Period Hedge's g Analysis For All Studies That


5.8.5 SCZ - Motor Evoked Potential Amplitude Four studies yielded a Hedge's g of g = -0.102, 95% confidence interval [-0.391, 0.187], p =

0.489 [Chroni et al., 2002; Enticott et al., 2008; Reid et al., 2002; Soubasi et al., 2010]. No

significant differences were found in the motor evoked potential amplitude in SCZ. The test

of heterogeneity was significant (Q = 12.134, df(q) = 3, p = 0.007, I2 = 75.276). The analysis

included 91 SCZ patients compared to 93 healthy controls. The n-failsafe was 4 unpublished

studies. After controlling for age, the meta regression yielded a correlation of r = -0.07118, p

= 0.04532. It was not possible to conduct an analysis to control for medication status (meta-

regression) as all patients were medicated.

5.9 Discussion To our knowledge, this is the first study to provide a quantitative summary of TMS studies

evaluating inhibition and excitatory paradigms in severe psychiatric disorders. The literature

included ample high-quality studies with effect sizes in the low to moderate and moderate to

high range. We found decreased SICI, enhanced intracortical facilitation and reduced CSP

within the OCD population. For MDD, decreases in CSP and SICI were demonstrated.

Lastly, reductions in SICI were shown in SCZ. These findings suggest that impairments in

GABAergic inhibition are a ubiquitous finding in severe psychiatric illnesses.

The greatest significant effect size was found in patients with OCD for decreased SICI.

Furthermore, enhanced intracortical facilitation and shortened CSP were also significant.

This finding held strong in spite of the small number of studies. This is in line with the

literature which has shown decreased SICI [Greenberg et al., 2000; Greenberg et al., 1998],

shortened CSP [Richter et al., 2012] and enhanced intracortical facilitation [Richter et al.,

2012], independent of medication status [Richter et al., 2012]. OCD may be associated with a

dysregulation of both GABAA and GABAB receptor-mediated inhibitory neurotransmission

81

and N-methyl-D-aspartate receptor-mediated excitatory neurotransmission, consistent with

genetic findings [Arnold et al., 2006; Dickel et al., 2006; Samuels et al., 2011; Stewart et al.,

2007; Voyiaziakis et al., 2011; Zai et al., 2005]. Compared to MDD and SCZ, these results

provide further evidence to demonstrate that inhibitory deficits in combination with enhanced

intracortical facilitation may be specific to OCD.

The greatest significant effect size found in patients with MDD was for shortened CSP. Also,

SICI was significantly reduced in patients with MDD. These findings show that a decrease in

SICI and shortened CSP may be unique to MDD. For example, Levinson et al. [Levinson et

al., 2010] demonstrated that all patients with MDD, regardless of symptom or medication

state, demonstrated significant CSP deficits compared with healthy participants. By contrast,

only treatment resistant MDD patients demonstrated SICI deficits. Taken together, this data

suggests that MDD is associated with deficits in neurophysiological indexes of GABAB

receptor-mediated inhibitory neurotransmission; whereas treatment resistant MDD patients

demonstrated deficits in neurophysiological indexes of both GABAB and GABAA receptor-

mediated inhibition. Previous evidence has suggested that the altered function of the

GABAergic system may contribute significantly to the pathophysiology and potential

successful treatment of this disorder [Sanacora and Saricicek, 2007].

With regards to patients with SCZ, studies showed significant deficits in SICI, after

controlling for age and medications using a meta-regression. This finding shows specificity

of decreased SICI as a characteristic of SCZ. Previous research suggests that dysfunctional

cortical inhibition may be a mechanism through which symptoms of SCZ are mediated.

Altered markers of cortical GABAergic neurotransmission are consistently observed

abnormalities in postmortem studies of SCZ [Benes and Berretta, 2001; Lewis et al., 1999;

Stan and Lewis, 2012]. Similarly, several neurophysiological studies have found a reduction

in SICI and CSP duration in both medicated [Daskalakis et al., 2002a; Daskalakis et al.,

2008b; Liu et al., 2009] and unmedicated patients with SCZ [Daskalakis et al., 2002a;

Daskalakis et al., 2008b; Liu et al., 2009] suggesting deficits in cortical inhibition of the

motor cortex. Taken together, SICI may be a specific attribute when characterizing SCZ.

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5.9.1 Clinical Implications This study provides compelling evidence to suggest that impairments in GABAergic

inhibition are involved in the pathophysiology of OCD, MDD and SCZ, nevertheless, the

overall pattern of these deficits differs. For example, in OCD, research has found inhibitory

deficits and enhanced intracortical facilitation, independent of medication status [Greenberg

et al., 2000; Greenberg et al., 1998; Richter et al., 2012]. By contrast, Levinson et al.

[Levinson et al., 2010] found that all MDD patients showed CSP abnormalities but only

treatment-resistant depressed patients demonstrated SICI reductions. MDD is associated with

deficits in neurophysiological indexes of GABAB receptor-mediated inhibitory

neurotransmission, whereas treatment-resistant patients demonstrated deficits in both

GABAB and and GABAA receptor-mediated inhibition. Treatment with antidepressants had

no apparent effects on either measure though other research has shown that selective

serotonin reuptake antidepressants normalize GABAergic deficits in depression through

enhanced SICI and decreased intracortical facilitation [Manganotti et al., 2001; Minelli et al.,

2010]. Serotonin is able to modulate excitatory and inhibitory effects, respectively mediated

by glutamate and GABA [Ciranna, 2006]. The serotonin receptor (5-HT) induces a decrease

of glutamate transmission and a parallel increase in GABA transmission evident in the

hippocoampus, frontal cortex and the cerebellum [Ciranna, 2006]. Previous studies have

shown that selective serotonin reuptake inhibitors (SSRIs) increase GABA by magnetic

resonance spectroscopy [Bhagwagar et al., 2004] and TMS [Robol et al., 2004]. The

modulatory action of the serotonin receptor (5-HT) may serve as a "brake" on neuronal

excitability. Given this inconsistency, replication is warranted to disentangle the effects of

medication. Finally, unmedicated SCZ patients have demonstrated impairments in SICI and

CSP [Daskalakis et al., 2002a]. Two studies have showed that clozapine-treated SCZ patients

demonstrated significantly longer CSP durations, implicating the role of the GABAB receptor

in clozapine [Daskalakis et al., 2008b; Liu et al., 2009]. Enhancing inhibition or decreasing

facilitation in the cortex through pharmacological or non-pharmacological means (i.e.,

electroconvulsive therapy, repetitive TMS, magnetic seizure therapy, cognitive behavioural

therapy) represent an important approach to targeted treatment. Further investigation is

83

needed to develop these TMS measures as neurophysiological markers of both diagnosis and

treatment.

5.9.2 Limitations This study is limited in several ways. First, studies assessing patients with OCD compared to

healthy controls had small sample sizes with limited amount of studies published in this field,

more work needs to be done in this population. Also, there is an overall lack of diagnostic

specificity of these neurophysiological deficits due to the overlap in results. It has been

shown that pharmacological treatment can have an effect on cortical inhibition in healthy

participants, [Langguth et al., 2008; Robol et al., 2004; Ziemann et al., 1998; Ziemann et al.,

1996a; b; Ziemann et al., 1997b] SCZ [Daskalakis et al., 2008b; Liu et al., 2009] and MDD

[Manganotti et al., 2001; Minelli et al., 2010]. No such medication effect has been reported in

OCD as inhibitory deficits in OCD have been found independent of medication status,

suggesting that these neurophysiological abnormalities may be trait related. However, more

studies are needed to investigate the impact of medications on cortical inhibition in

psychiatric disorders. Furthermore, these measures are traditionally limited to the motor

cortex which is a significant limitation since non-motor neurophysiological processes are of

primary interest. Other brain areas such as the dorsolateral prefrontal cortex may be more

proximal to the pathophysiology of these illnesses and can be measured by combining TMS

with electroencephalography [Daskalakis et al., 2008c; Farzan et al., 2010a; b; Fitzgerald et

al., 2008]. Lastly, there are differences in the TMS methodologies between studies. The

following approaches need to be implemented to have consistent measurements, for example,

CSP should be measured by stimulating an active contralateral muscle (i.e., 20% of

maximum contraction) at 140% of the resting motor threshold [Cantello et al., 1992] (Figure

1B). LICI should be evaluated by using a suprathreshold conditioning stimulus that precedes

a suprathreshold test stimulus at a 100 ms interstimulus interval [Valls-Sole et al., 1992]

(Figure 1C). SICI and intracortical facilitation should be assessed by using a subthreshold

conditioning stimulus set at 80% of the resting motor threshold that precedes a

suprathreshold test stimulus [Kujirai et al., 1993]. SICI is measured at interstimulus intervals

of 2 ms and 4 ms and intracortical facilitation is evaluated at interstimulus intervals of 10 ms,

84

15 ms and 20 ms [Kujirai et al., 1993; Nakamura et al., 1997] (Figures 1D and 1E).

Following these exact TMS guidelines can ensure rigorous methods across research groups.

5.9.3 Main Findings Summarized This meta-analytic review of motor cortex TMS paradigms in OCD, MDD and SCZ

(summarized in table 4) has revealed promising findings for objective clinical applications.

This study provides a meaningful summary of research in this field demonstrating a clear

platform from which further studies and diagnostic procedures can be developed.

Table 4. Summary of Significant Hedge's g Results in Psychiatric Populations.

Psychiatric

Disorder

Summary of Significant Hedge's g Results of TMS Paradigms

OCD Decreased cortical silent period (2 studies)

(g = -0.466, 95% confidence interval [-0.881,-0.052], p =

0.027)

Deficits in short-interval cortical inhibition (3 studies)

(g = 0.572, 95% confidence interval [0.179, 0.966], p = 0.004)

Enhanced intracortical facilitation (2 studies)


MDD Shortened cortical silent period (4 studies)

(g = -1.232, 95% confidence interval [-1.530, -0.933], p =

0.000)

Deficits in short-interval cortical inhibition (3 studies)

(g = 0.641, 95% confidence interval [0.384, 0.898], p=0.000)

SCZ Impairments in short-interval cortical inhibition (12 studies)


85

Chapter 6

Evidence for Inhibitory Deficits in the Prefrontal Cortex in

Schizophrenia

Contents of this chapter have been reprinted by permission from Oxford University Press

Radhu N, Garcia Dominguez L, Farzan F, Richter MA, Semeralul MO, Chen R, Fitzgerald

PB, Daskalakis ZJ. (2015). Evidence for Inhibitory Deficits in the Prefrontal Cortex in

Schizophrenia. Brain. 138 (Pt 2): 483-497.

A link to the published paper can be found at:

http://brain.oxfordjournals.org/content/138/2/483

http://brain.oxfordjournals.org/content/138/2/483

86

6.1 Abstract Abnormal gamma-aminobutyric acid (GABA) inhibitory neurotransmission is a key

pathophysiological mechanism underlying SCZ. Transcranial magnetic stimulation (TMS)

can be combined with electroencephalography (EEG) to index long-interval cortical

inhibition (LICI), a measure of GABAergic receptor-mediated inhibitory neurotransmission

from the frontal and motor cortex. In previous studies we have reported that SCZ is

associated with inhibitory deficits in the DLPFC compared to healthy subjects and patients

with bipolar disorder. The main objective of the current study was to replicate and extend

these initial findings by evaluating LICI from the DLPFC in patients with SCZ compared to

obsessive-compulsive disorder (OCD). A total of 111 participants were assessed: 38 patients

with SCZ (average age: 35.71, 25 males, 13 females), 27 patients with OCD (average age:

36.15, 11 males, 16 females) and 46 healthy subjects (average age: 33.63, 23 females, 23

males)]. LICI was measured from the DLPFC and motor cortex through TMS-EEG. In the

DLPFC, LICI was significantly reduced in SCZ patients compared to healthy subjects (p =

0.004) and not significantly different between patients with OCD and healthy subjects (p =

0.5445). LICI deficits in the DLPFC were also significantly greater in patients with SCZ

compared to patients with OCD (p = 0.0465). There were no significant differences in LICI

across all three groups in the motor cortex. These results demonstrate that LICI deficits in the

DLPFC are specific to patients with SCZ and are not a generalized deficit that is shared by

disorders of severe psychopathology.

87

6.2 Introduction SCZ and OCD are psychiatric disorders associated with significant psychopathology and

personal suffering. SCZ is characterized by hallucinations, delusions, cognitive deficits and

negative symptoms [American Psychiatric Association, 2000]. OCD is associated with the

occurrence of unwanted and disturbing intrusive thoughts, images or impulses (obsessions),

followed by repetitive ritualistic behaviors (compulsions) completed in stereotyped

succession [American Psychiatric Association, 2000]. There are substantial areas of overlap

between SCZ and OCD [Lee et al., 2009]. Both are lifelong chronic conditions sharing a

similar distribution for age at onset and affect both men and women equally [Lee et al.,

2009]. Studies show that the rate of co-morbidity between SCZ and OCD is approximately

7% to 26% [Eisen et al., 1997; Fabisch et al., 1997; Porto et al., 1997; Poyurovsky et al.,

1999; Tibbo et al., 2000]. Abnormalities of the prefrontal cortex, anterior cingulate, caudate

nucleus, the basal ganglia, the thalamus, and the cerebellum have been implicated in both

SCZ and OCD [Gross-Isseroff et al., 2003; Tibbo and Warneke, 1999; Venkatasubramanian

et al., 2009]. Both disorders have been shown to have poor global functional performance

[Cavedini et al., 2002; Light and Braff, 2005a; b; van den Heuvel et al., 2005]. Finally, both

disorders also respond to dopaminergic antagonists and serotonin reuptake inhibitors,

suggesting pathophysiological overlap [Poyurovsky and Koran, 2005].

Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the brain,

critical in modulating cortical excitability and neuroplasticity [DeFelipe et al., 1986; Schieber

and Hibbard, 1993]. Cortical inhibition (CI) represents a neurophysiological index of

GABAergic inhibitory neurotransmission in the human cortex [Daskalakis et al., 2007;

Krnjevic, 1997]. Several lines of evidence suggest that CI is impaired in severe psychiatric

disorders. For example, previous TMS studies have demonstrated deficits in CI in patients

with SCZ [Daskalakis et al., 2002a; Daskalakis et al., 2008b; Fitzgerald et al., 2002a;

Fitzgerald et al., 2002b; Fitzgerald et al., 2003; Liu et al., 2009; Wobrock et al., 2010;

Wobrock et al., 2009; Wobrock et al., 2008], OCD [Greenberg et al., 2000; Greenberg et al.,

1998; Richter et al., 2012], major depressive disorder [Bajbouj et al., 2006b; Fitzgerald et al.,

88

2004a; Lefaucheur et al., 2008; Levinson et al., 2010], and bipolar disorder [Levinson et al.,

2007]. These findings suggest that many severe psychiatric disorders are associated with

deficits in GABAergic receptor-mediated inhibitory neurotransmission. However, these

findings were demonstrated in the motor cortex, a cortical region of limited interest in the

pathophysiology of psychiatric disorders.

This past decade has seen significant developments in the concurrent use of TMS with

electroencephalography (EEG) to directly assess the neurophysiology of the motor cortex

and DLPFC [Daskalakis et al., 2008c; Farzan et al., 2010b; Fitzgerald et al., 2008; Miniussi

and Thut, 2010; Taylor et al., 2008]. TMS-EEG allows for the measurement of a paired-pulse

paradigm known as LICI, whereby a suprathreshold CS is followed by a suprathreshold TS at

long ISIs (e.g. 50 ms - 200 ms), associated with the suppression of neuronal activity [Claus et

al., 1992; Valls-Sole et al., 1992]. A relationship between EMG and EEG measures of LICI

has been found in the motor cortex [Daskalakis et al., 2008c; Farzan et al., 2010b].

Additionally, LICI has demonstrated high test-retest reliability in the motor cortex and

DLPFC [Farzan et al., 2010b]. Evidence suggests that LICI is mediated by slow inhibitory

post-synaptic potentials via activation of GABAB receptors. For example, LICI is potentiated

by baclofen (GABAB receptor agonist) [McDonnell et al., 2006]. Furthermore, LICI is

optimal at 100 - 150 ms [Sanger et al., 2001] comparable to the time course of the GABAB

inhibitory post-synaptic potential, peaking at 150-200 ms post-stimulus [McCormick, 1989].

Lastly, LICI is evoked by a suprathreshold CS [Valls-Sole et al., 1992] as GABAB receptor-

mediated responses have higher activation thresholds and their inhibitory influence is longer

[Deisz, 1999a; Sanger et al., 2001].

GABA plays a pivotal role in the generation and inhibition of gamma oscillations in the

cortex [Bartos et al., 2007; Brown et al., 2007; Leung and Shen, 2007; Traub et al., 1996;

Wang and Buzsaki, 1996; Whittington et al., 1995; Whittington et al., 2000]. Research has

shown that GABAA receptor-mediated inhibitory post-synaptic potentials contribute to the

generation of gamma oscillations [Bartos et al., 2007; Wang and Buzsaki, 1996; Whittington

et al., 1995] and GABAB receptor-mediated inhibitory post-synaptic potentials are associated

with the inhibition of gamma oscillations [Brown et al., 2007; Leung and Shen, 2007;

89

Whittington et al., 1995]. Many studies involving neuropsychiatric disorders have focused on

abnormalities in gamma oscillations within the DLPFC due to its association with higher

order cognitive processes including sensory processing, attention, working memory, and

executive functioning, all domains in which SCZ patients are impaired [Uhlhaas et al., 2008].

For example, patients with SCZ have impairments in gamma oscillatory activity in response

to 40 Hz auditory stimulation [Light et al., 2006], during perception of gestalt objects

[Spencer et al., 2003] and during working memory performance [Barr et al., 2010; Cho et al.,

2006]. Disrupted gamma oscillatory activity has also been demonstrated in SCZ using TMS

and EEG [Farzan et al., 2010a; Ferrarelli et al., 2008; Frantseva et al., 2012]. One of the aims

of the study was to replicate findings by Farzan et al. [Farzan et al., 2010a] in which TMS-

EEG was used to assess LICI in the motor cortex and DLPFC in patients with SCZ compared

with bipolar patients and healthy subjects. Bipolar patients were included as these patients

are often treated with dopamine antagonists and can have comparable levels of

psychopathology. It was demonstrated by Farzan et al. that only patients with SCZ had

significant deficits in the inhibition of gamma oscillations that was specific to the DLPFC.

These results suggest that impairments in the inhibition of gamma oscillations in SCZ may be

closely associated with the pathophysiology of this disorder.

Thus, this study had two main objectives. The first was to assess LICI in patients with SCZ

and OCD in both the DLPFC and motor cortex using TMS-EEG. The second aim was to

evaluate the diagnostic specificity of LICI in SCZ and OCD, due to the comparable levels of

psychopathology severity and the similar pharmacology that are used to treat these two

disorders. The core study hypotheses were that both patients with SCZ and OCD would show

LICI deficits in the DLPFC relative to healthy subjects and deficits would be significantly

greater in patients with SCZ.

90

6.3 Materials and Methods A total of 111 participants were included [38 patients with SCZ (average age: 35.71, 25

males, 13 females), 27 patients with OCD (average age: 36.15, 11 males, 16 females) and 46

healthy subjects (average age: 33.63, 23 females, 23 males)] at the Centre for Addiction and

Mental Health in Toronto, Canada. The handedness of the participants were: SCZ patients

(33 right-handed, 3 left-handed, 2 ambidextrous), OCD patients (25 right-handed, 2 left-

handed), healthy subjects (39 right-handed, 4 left-handed, 3 ambidextrous). All subjects gave

their written informed consent and the protocol was approved by the Centre for Addiction

and Mental Health in accordance with the Declaration of Helsinki. The Structured Clinical

Interview for the Diagnostic and Statistical Manual for Mental Disorders (DSM)-IV

confirmed the diagnosis of SCZ or OCD. The exact medication dosage of SCZ and OCD

patients are included in table 5. Diagnostic information of the SCZ and OCD patients are

included in table 6. In healthy subjects, psychopathology was ruled out by the Structured

Clinical Interview for DSM-IV and subjects were only included in the study if they had no

first-degree relative diagnosed with a psychiatric disorder. Exclusion criteria for both patients

and healthy subjects included: (1) individuals meeting DSM-IV criteria for substance abuse

or dependence in the last 6 months, with the exception of nicotine; (2) concomitant major and

unstable medical or neurological illness; (3) experiencing suicidal ideation; (4) pregnant; (5)

positive urine toxicology screen for drugs of abuse; (6) any magnetic material or any other

conditions that would preclude the magnetic resonance image (MRI) scan or TMS-EEG

measures; (7) clinically significant claustrophobia. The exclusion criteria established by

international safety standards for TMS were followed [Rossi et al., 2009]. The TMS Adult

Safety Screen [Keel et al., 2001] was administered to all subjects.

91

Table 5A. Patients with Schizophrenia Medication Details

CLASS MEDICATIONS # OF SUBJECTS/DOSE(S) in

mg

ANTIPSYCHOTICS

Second Generation

Clozapine

n=11: 200 (2), 250 (2), 300 (5),

350 (1), 475 (1)

Olanzapine n=4: 7.5, 12.5, 15, 22.5

Paliperidone n=1: 150/4weeks

Quetiapine n=3: 300 (2), 400

Quetiapine Fumarate n=3: 200, 800, 900

Risperidone n=7: 2 (2), 3 (2), 5, 6, 8

Risperidone Injection n=2: 37.5/2 weeks, 75/4weeks

Ziprasidone n=2: 60, 120

Thioxanthenes Flupenthixol injection n=1: 60/3 weeks

Flupenthixol tabs n=1: 1

Zuclopenthixol injection n=2: 100/2 weeks, 280/2 weeks

Phenothiazines Fluphenazine Decanoate n=1: 37.5/2 weeks

Perphenazine n=1: 8

Dibenzoxazepines Loxapine n=2: 25, 30

Diphenylbutylpiperidines Pimozide n=1: 6

Third Generation Aripiprazole n=2: 15, 20

ANTIDEPRESSANTS

Selective serotonin re-uptake

inhibitors (SSRIs)

Citalopram n=3: 10, 40(2)

Escitalopram n=1: 20

92

Fluoxetine n=2: 10, 60

Paroxetine n=1: 20

Sertraline n=4: 75, 150, 200(2)

Serotonin–norepinephrine

reuptake inhibitors (SNRIs) Desvenlafaxine n=1: 30

norepinephrine-dopamine

reuptake inhibitor (NDRIs) Bupropion SR n=1: 200

MOOD STABILIZERS

Carbamazepine n=1: 400

Divalproex Sodium n=1: 1250

Lamotrigine n=1: 100

Lithium n=4: 900(2), 1050, 1200

Topiramate n=1: 200

BENZODIAZEPINES

Clonazepam n=3: 0.5 (2), 1

Clonazepam prn n=1: 0.25

Lorazepam n=1: 2

Lorazepam prn n=4: 1(2), 2(2)

OTHERS

Benzatropine n=3: 1(2), 4

93

Table 5B. Patients with Obsessive-Compulsive Disorder Medication Details

CLASS Medication # OF SUBJECTS/DOSE(S) in mg

ANTIDEPRESSANTS


inhibitors (SSRIs)

Citalopram n=2: 20, 220

Escitalopram n=8: 15, 20(3), 30, 40(2), 50

Paroxetine n=3: 10, 25, 60

Fluoxetine n=2: 80(2)

Sertraline n=2: 100, 250


reuptake inhibitors (SNRIs)

Duloxetine n=1: 120

Venlafaxine n=2: 100, 187.5

Tricyclic antidepressants

(TCAs) Clomipramine n=5: 50(2), 175, 250(2)

norepinephrine-dopamine

reuptake inhibitor (NDRIs) Bupropion n=1: 300

ANTIPSYCHOTICS

Loxapine n=1: 25

Olanzapine n=1: 20

Quetiapine n=4: 100, 150, 200, 600

Risperidone n=2: 0.125, 0.25

MOOD STABILIZERS


Lithium n=1: 1200

BENZODIAZEPINES

Clonazepam n=2: 0.5, 2

94


Subjects/Dose(s).

Table 5A. Patients with Schizophrenia Medication Details.

Table 5B. Patients with Obsessive-Compulsive Disorder Medication Details.


Diazepam prn n=2: 2.5, 4

Lorazepam prn n=4: 0.5 (2), 1, 2

Oxazepam n=1: 60

Temazepam n=1: 30

Temazepam prn n=1: 15

OTHERS

Buspar n=1: 10

L-Tryptophan n=1: 1500

Trazodone n=1: 25

Trazodone prn n=1: 25

Zopiclone n=2: 7.5, 15

Zopiclone prn n=1: 7.5

95

Schizophrenia (N = 38) Number of

Subjects %

Schizophrenia (paranoid type) 22 57.89 Schizoaffective (bipolar type) 13 34.21

Schizoaffective (depressive type) 3 7.89

Current Comorbidities Number of Subjects

%

Major Depressive Disorder 2 5.26 Obsessive-Compulsive Disorder 3 7.89

Panic Disorder without Agoraphobia

2 5.26

Panic Disorder with Agoraphobia

2 5.26

Social Phobia 2 5.26 Post-Traumatic Stress Disorder 1 2.63 Generalized Anxiety Disorder 1 2.63

Obsessive-Compulsive Disorder (N = 27)


%

Bipolar I Disorder 1 3.70 Major Depressive Disorder 4 14.81

Psychotic Disorder Not Otherwise Specified

1 3.70


3 11.11

Panic Disorder with Agoraphobia

2 7.41

Agoraphobia without Panic Disorder

1 3.70

Social Phobia 8 29.63 Specific Phobia 2 7.41

Generalized Anxiety Disorder 10 37.04 Body Dysmorphic Disorder 2 7.41


patients.

96

6.3.1 Clinical Severity The 24-construct Brief Psychiatric Rating Scale (BPRS) was used for evaluating

psychopathology in patients with SCZ [Overall and Gorham, 1962].

6.3.2 Transcranial Magnetic Stimulation Data Recording Monophasic TMS pulses were administered using a 7 cm figure-of-eight coil, and two

Magstim 200 stimulators (Magstim Company Ltd, UK) connected via a Bistim module. TMS

was administered over the left motor cortex and DLPFC. Inhibition was measured through

LICI and indexed through electromyography and EEG at the optimal 100 ms ISI [Sanger et

al., 2001]. One hundred TMS stimuli were delivered per-condition (paired and single-pulse)

every 5 seconds. The intensity of TMS pulses was determined at the beginning of each

experiment and it was set such that it elicited an average motor evoked potential of 1mV

peak-to-peak upon delivery of 20 pulses over the motor cortex. Both TMS pulses were

delivered at the same suprathreshold intensity. No significant between-group differences

were found for the 1mV peak-to-peak TMS intensity (F (2,108) = 0.794, p = 0.455) (healthy

controls = 69.46% ± 13.23%, SCZ = 73.03% ± 13.49%, OCD = 69.85% ± 14.53%).

6.3.3 Localization of the Motor Cortex The TMS coil was placed at the optimal position for eliciting motor evoked potentials from

the right abductor pollicis brevis muscle, which corresponded to a region between the

electrodes FC3 and C3.

6.3.4 Localization of the DLPFC Localization of the DLPFC was achieved through neuronavigation techniques using the

MINIBIRD system (Ascension Technologies) and MRIcro/registration software using a T1-

weighted MRI scan obtained for each subject with seven fiducial markers in place

[Daskalakis et al., 2008c; Farzan et al., 2010a]. Stimulation was directed at the junction of

the middle and anterior one-third of the middle frontal gyrus (Talairach coordinates (x, y, z)

97

= -50, 30, 36) corresponding with posterior regions of Brodmann area 9, which overlap with

the superior section of Brodmann area 46.

6.3.5 Electromyography Recording Electromyography was captured by placing two disposable disc electrodes over the right

abductor pollicis brevis muscle in a tendon-belly arrangement and motor evoked potentials

were filtered (band-pass 2 Hz to 5 kHz), digitized at 5 kHz (Micro 1401, Cambridge

Electronics Design, Cambridge, UK).

6.3.6 EEG Recording and Pre-Processing To evaluate TMS-induced cortical evoked potentials, EEG was recorded concurrently with

electromyography. EEG was acquired through a 64-channel Synamps 2 EEG system. A 64-

channel EEG cap was used to record the cortical signals, and four electrodes were placed on

the outer side of each eye, and above and below the left eye to closely monitor eye movement

artifacts. All electrodes were referenced to an electrode positioned posterior to Cz electrode.

EEG signals were recorded DC and with a low pass filter of 100 Hz at a 20 kHz sampling

rate, shown to avoid saturation of the amplifiers and minimize the TMS-related artifact

[Daskalakis et al., 2008c; Daskalakis et al., 2012].

EEG recordings were down-sampled to 1000 Hz and epoched from -1000 ms to 2000 ms

after the test TMS pulse. In both, the single and paired-pulse conditions, the segment from -

100 ms to 10 ms was removed (where 0 correspond to the test TMS pulse). This step

removes not only the test-pulse TMS in the single-pulse and paired-pulse conditions but also

the conditioning TMS pulse in the paired-pulse condition. Traces were visually inspected for

artifacts in order to eliminate trials and channels highly contaminated by noise (muscle

activity, 60Hz noise, movement-related activity as well as electrode artifacts). Two rounds of

ICA were subsequently applied. The first found was to minimize and remove the typical

TMS-related decay artifact that appears in some subjects at specific locations. In each

subject, the number of components that needed to be removed to eliminate this kind of

artifact varied from 0 to 6. Following this, a bandpass FIR filter was applied from 1 to 55 Hz

98

and a second round of ICA was computed to remove eye-related artifacts (blinks and

movements) and remaining muscle components. During this analysis, the subject identity and

group was hidden to the researcher using randomly generated files. No actual association of

each recording was made to a particular group (healthy, SCZ or OCD) when completing the

processing steps.

The time frequency decomposition was obtained using the Event-Related Spectral

Perturbation (ERSP) analysis in EEGLab. Specifically the analysis was wavelet based, using

a cycle of the complex Morlet wavelet across all frequencies. The ERSP was computed

independently for the single-pulse and paired-pulse conditions. The analysis is expressed in

decibels of spectral power (µV2/Hz) after subtracting the log baseline to the whole trial. For

comparison, a similar analysis was carried on, in a reduced sample of healthy subjects but

using the Discrete Fourier Transform instead of the wavelet [Garcia Dominguez et al., 2014].

The resulting time-frequency decompositions, for single-pulse and paired-pulse conditions,

were then subtracted (single-pulse minus paired-pulse), to obtain an index of inhibition.

Thus, a value of inhibition was obtained for each subject (46 healthy subjects, 38 SCZ and 26

OCD, 111 in total), site of stimulation (DLPFC, Motor), electrode (1…60), time (400 values,

-442 to 1442 ms) and frequency (50 values, 1…50Hz), that is 266,400,000 data points.

Based on [Garcia Dominguez et al., 2014], LICI was assessed with all electrodes and all

frequencies from 1-50 Hz. We proceed with a cluster-based analysis that is a development of

the one proposed in [Maris and Oostenveld, 2007], which provides corrections for multiple

comparisons due to the large amount of multidimensional data. In this paper, the clusters are

defined in the time-frequency space and also include the two extra spatial dimensions of the

electrode grid. Using this approach, we effectively reduced the number of total comparisons

to only group comparisons by site of stimulation. Since, in the original proposal of the cluster

test, the relative volume of the cluster depends on the threshold used to define the cluster; we

carried out, a fairly intuitive correction which consists of calculating the volumes for a set of

many discrete thresholds exhausting all the volumes to produce a single statistic: the average

volume t-score across all thresholds.

99

Before proceeding to the group comparisons, we assessed CI following the same

methodology within each group independently. To achieve this we applied a one-tailed,

paired t-test to every voxel or 3-tuple (i.e. time, frequency and channel) in the single-pulse

vs. the paired-pulse protocol. In this case the null hypothesis was that the single-pulse

condition is not larger than the paired-pulse, suggesting no inhibition. The method proceeds

as follows:

Step 1: Select all samples whose t-scores are larger than 1.6 and identify all the

clusters to which these voxels belong. Clusters are connected sets on the basis of

time, frequency and spatial adjacency.

Step 2: In each cluster sum all the t-scores and select the maximum of the obtained

values. This value corresponds to the size (or volume) of the major cluster of

inhibition and will be denoted by S.

Step 3: Repeat steps 2 and 3 with a new threshold that is 0.2 larger than the previous

one, obtaining successively new values for the maximum cluster indexed by the

threshold. Until a threshold is reached that does not contain any t-score.

Step 4: Repeat steps 2 to 4 with random reallocation of conditions across all subjects.

The number of random reallocations was chosen to be 10,000 for this specific

analysis.

Step 5: Proceed to calculate the proportion of the permuted values that are larger than

the original one. This proportion is the p-value.

In step 3 multiple cluster sizes are calculated, each for a specific threshold. This is

represented by a vector. In step 5 the original vector has to be compared to all the others

resulting from the randomization. This calculation proceeds as follows:

100

Let us denote the values of S as where k refers to the threshold index and the

superscript i to the randomization (where 0 is the original un-randomized data). One solution

is to compute the following ranks for each i and k:

Also, H counts the number of

times a positive difference arises.

That is, for each particular threshold, sum all the randomizations that have a score S higher

than randomization a, including the original case a = 0. After obtaining this matrix R, a sum

can be performed across the thresholds to obtain a single rank per randomization:

The actual p-value can be expressed as the proportion of R-values larger than the original R-

values.

The results of these analyses are shown in figures 1-3 for three different levels of alpha.

The comparison between groups proceeds the same way except that a) in step 4 the

randomization proceeds not by reallocating conditions but subjects between the two

populations compared preserving the number of subjects in each and b) the S-values are

calculated as the difference between the S values for each population at the same threshold.

The hypothesis is that if sample A is larger than B, then S=SA-SB, otherwise the order of the

factors does not matter and the p-values are computed from the population of absolute S

values is step 5.

Since t-scores are compared across different populations, differences in S values (i.e. SA-SB)

may be confounded by differences in the standard deviation between these two groups. We

101

consider that this effect is not detrimental to the analysis, as it may indicate intrinsic

population differences that should be taken into account. However, in order to observe

inhibitory differences attributed to the average (and not standard deviation), a secondary

analysis was performed in which the t-scores results as a pooled standard deviation from the

two groups compared. The p-values resulting from this analysis are displayed as the second

line of table 7.

Brain

Region

Group Channels 1-50 Hz Delta Theta Alpha Beta Gamma

DLPFC HCL>

SCZ

All 0.0040

0.0390

0.0555

0.1860

0.0175

0.0630

0.0025

0.0175

0.0005

0.0375

0.0405

0.3415

Local 0.0015

0.0140

0.0205

0.2210

0.0065

0.0305

0.0010

0.0100

0.0000

0.0230

0.0080

0.0805

HCL>

OCD

All 0.5445

0.8035

0.6585

0.7205

0.4290

0.6965

0.3295

0.8925

0.4445

0.7000

0.5950

0.7040

Local 0.4910

0.7070

0.6955

0.7165

0.5720

0.7185

0.4280

0.9415

0.2970

0.6140

0.1495

0.5075

SCZ<>OCD All 0.0465

0.0105

0.0710

0.1965

0.0295

0.0250

0.0170

0.0035

0.0565

0.0155

0.0565

0.5930

Local 0.0345

0.0110

0.0550

0.0835

0.0275

0.0175

0.0180

0.0010

0.0320

0.0280

0.1900

0.4895

Motor

HCL>

SCZ

All 0.9220

0.7380

0.7425

0.6965

0.6870

0.5630

0.6170

0.4550

0.9660

0.8545

0.6695

0.9590

Local 0.6870

0.9010

0.6845

0.8650

0.4980

0.4610

0.1785

0.2665

0.8535

0.9540

0.5370

0.4200

HCL>

OCD

All 0.8240

0.6845

0.5765

0.5950

0.5200

0.4180

102

0.7010 0.7020 0.5050 0.5290 0.5220 0.3255

Local 0.9870

0.8945

0.8295

0.8720

0.6850

0.5295

0.4970

0.3525

0.7855

0.5065

0.5565

0.2420

SCZ<>OCD All 0.4805

0.3755

0.4605

0.3000

0.3720

0.2590

0.4100

0.1940

0.5835

0.5665

0.4165

0.6685

Local 0.5700

0.3100

0.3095

0.2600

0.3770

0.2205

0.5150

0.2165

0.7540

0.5400

0.5325

0.6430

Table 7. All p-values of the between-group comparisons by site of stimulation, frequency

band, and electrode grids. The primary analysis is the first line; the second line displays the

pooled variance analysis.

This method was also applied to subsets of the original 4-dimensional space in order to

obtain additional information about the contribution of specific frequencies and electrodes to

the overall group differences. This time only 2000 random permutations were used. For these

purposes the time-frequency space was divided into the five common frequency bands [delta,

theta, alpha, beta, and gamma]. The local grid of electrodes for the DLPFC stimulation used

the following frontal electrodes: FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6,

F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, while for the motor stimulation the

electrodes were: T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4,

CP6, TP8.

In addition to the cluster-based analysis described, a Spearman's rho correlation analysis was

performed between the BPRS (total score) and the size of the larger cluster of DLPFC

inhibition for each SCZ subject. This correlation was also conducted with chlorpromazine

equivalents [American Psychiatric Association, 1997; Bezchlibnyk-Butler et al., 2014; Chue

et al., 2005; Woods, 2003]. The size was determined over the same 4D-space previously

illustrated by counting significant values of the larger cluster using a single threshold of

alpha level: 0.05. The size of the larger cluster of significant values is a way to capture the

103

degree of inhibition at the subject level, since a larger inhibition correlates with the extent of

significant voxels of inhibition in the time-frequency-spatial domain.

The methodological approach we follow in this study is a development of the one presented

in [Garcia Dominguez et al., 2014]. We have now added two dimensions to the

characterization of the cluster, the electrode grid. In this sense, we are better able to capture

and describe all the related responses into a unifying variable. The analysis presented here

also overcomes the dependence of the result on the threshold chosen to define the cluster by

applying the same analysis over a multiple of different thresholds and expressing the results

as a normalized average. This procedure may be considered as virtually threshold free, as

long as a sufficiently small step in the sequence of thresholds is considered.

6.4 Results 6.4.1 Comparing Single and Paired-Pulse Conditions (Within-Group

Analysis) A within-group cluster-based test was conducted to compare single-pulse and paired-pulse

TMS paradigms in order to assess LICI in the DLPFC and motor cortex for each 3-tuple:

electrode, time and frequency. These tests were single-tailed since the null hypothesis (no

inhibition) is when the paired-pulse amplitude is larger or equal to the amplitude of the

single-pulse. Figures 10 to 12 show the time-frequency map of LICI in the DLPFC (the

corresponding for motor cortex are shown in figures 15-17). Significant values imply that the

single-pulse induced response is higher than the paired-pulse condition. The areas of

significant inhibition across the time-frequency plots are designated as three shades of blue

corresponding to three different alpha levels: 0.05, 0.01 and 0.001. Figures 10-12 showed

that there was significant inhibition in most channels across many samples of the time-

frequency domain in all 3 groups. Lower frequencies tend to show extended inhibition up to

around 400ms after the test-pulse stimulation, while higher frequencies show inhibition over

narrower or specific temporal regions. Inhibition is particularly strong over the central,

midline channels.

104

In the DLPFC, all groups demonstrated significant within-group inhibition, healthy subjects

(p < 0.0001), SCZ (p = 0.0018), OCD, (p = 0.0002). That is, the single-pulse condition was

significantly greater than the paired-pulse condition. For the within-group analysis, the SCZ

group (figure 11) showed a reduced, but not absent, degree of inhibition in relation to healthy

subjects (figure 10). In fact, in all three groups, the pattern of inhibition in the time-frequency

space was similar (figures 10-12), as well as the topology (figures 13 and 14).

In the motor cortex (see figures 15-17), all groups demonstrated significant within-group

inhibition, healthy subjects (p < 0.0001), SCZ (p = 0.0002) and OCD (p = 0.01).

6.4.2 Between-Group Results for DLPFC Stimulation The between-group results were single-tailed to match the core study hypotheses

where healthy controls are expected to show more inhibition than SCZ and OCD. In addition,

the comparisons between the two patient groups were two-tailed, as we had no a priori

hypothesis in this case. Overall inhibition (1-50 Hz), assessed through the cluster mass test,

was significantly larger in healthy subjects than in patients with SCZ (p=0.004, i.e. only 40

random permutations, out of 10,000, showed a value for the difference in inhibition larger

than the one from the original samples). No significant differences were found between OCD

and healthy subjects. Significant differences were found between SCZ and OCD in overall

inhibition (p = 0.0465). Using the same approach, we investigated the contribution of

different frequency bands by partitioning the time-frequency space into 5 frequency bands

corresponding to delta, theta, alpha, beta and gamma. LICI was significantly different

between healthy subjects and SCZ in theta (p = 0.0175), alpha (p = 0.0025), beta (p =

0.0005) and gamma frequency bands (p=0.0405). Significant differences were found between

SCZ and OCD in the theta (p = 0.0295), and alpha frequency bands (p = 0.017).

6.4.3 Local Grid of Electrodes Analysis for DLPFC Stimulation Overall inhibition (1-50 Hz) was significantly larger in healthy subjects than in patients with

SCZ (p=0.0015) as well as in the: delta (p = 0.0205), theta (p = 0.0065), alpha (p = 0.001),

beta (p < 0.0001), and gamma frequency bands (p = 0.008). No significant differences were

105

found between OCD and healthy subjects. Significant differences were found between SCZ

and OCD in overall inhibition (p = 0.0345), theta (p = 0.0275), alpha (p = 0.018) and beta

frequency bands (p = 0.032).

6.4.4 Effect Size for DLPFC Stimulation Figure 18 shows the Cohen's d for each channel in the time-frequency space (DLPFC). Large

effect sizes are displayed between patients with SCZ and healthy subjects as demonstrated in

the frontal and midline regions of the time-frequency plot.

6.4.5 Motor Cortex Stimulation No significant differences were found between patients with SCZ and healthy subjects in

LICI across all frequency bands. No significant differences were found between OCD

patients and healthy subjects in LICI across all frequency bands. No significant differences

were found between the two patient groups.

6.4.6 Local Grid of Electrodes Analysis for Motor Cortex Stimulation No significant differences were found between patients with SCZ and healthy subjects in

LICI across all frequency bands. No significant differences were found between OCD

patients and healthy subjects in LICI across all frequency bands. No significant differences

were found between patients with SCZ and OCD.

6.4.7 Sum of T-Score Topology The sum of t-scores over the largest cluster is an index of the strength of inhibition at each

channel by band and by region as presented in figure 13 (DLPFC) and figure 14 (motor

cortex). In this case, red indicates greater inhibition over the specific electrodes in terms of a

wider and/or stronger magnitude of effect of inhibition over the time-frequency space.

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6.4.8 Effect of Medication Treatments A between-group cluster-based analysis was conducted for the subset of antipsychotic-treated

OCD patients (n = 8) compared to SCZ patients (n = 8) (age and sex matched) on LICI

(DLPFC). SCZ patients showed deficits in LICI (p = 0.0298) with 10,000 randomizations.

Additionally, we evaluated the relationship between LICI (DLPFC) and antipsychotic

medications (converted chlorpromazine equivalents) [American Psychiatric Association,

1997; Bezchlibnyk-Butler et al., 2014; Chue et al., 2005; Woods, 2003] in patients with SCZ

treated with antipsychotic medications (n = 38). This analysis revealed no significant

correlation between LICI and chlorpromazine equivalents (Spearman’s rho = -0.0096, p =

0.9542). These results demonstrate that LICI deficits were not related to antipsychotic

treatment. Lastly, in the DLPFC, no significant differences were found between SSRI-

treated OCD patients (n = 17) and SSRI-treated SCZ patients (n = 11) in overall inhibition (p

= 0.52) and gamma inhibition (p = 0.49).

6.4.9 Clinical Severity Correlation Analysis We found a trending correlation between the BPRS and the largest cluster of inhibition in 38

SCZ patients (Spearman's rho = -0.2464, p = 0.0680). The greater the BPRS score is

indicative of increased severity of SCZ symptoms. This negative correlation signifies that the

higher BPRS score is related to a lower degree of inhibition. We ran an outlier detection

algorithm using criteria based on Cook’s distance and removed data points whose distance

were larger than 4/n (n = number of data points) [Bollen and Jackman, 1990]. Two outliers

were identified, after their removal the correlation was statistically significant (Spearman's

rho = -0.2855, p = 0.0457).

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stimulating the dorsolateral prefrontal cortex.



stimulating the dorsolateral prefrontal cortex.

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patients when stimulating the dorsolateral prefrontal cortex.

109



These plots show the three groups by frequency bands in the dorsolateral prefrontal cortex.

Values have been normalized within each frequency band. The color bar is omitted since

only the pattern matters, as the actual sum is dependent on the resolution of the time-

frequency-spatial domain.



These plots show the three groups by frequency bands in the motor cortex. Values have been

normalized within each frequency band. The color bar is omitted since only the pattern

matters, as the actual sum is dependent on the resolution of the time-frequency-spatial

domain.

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stimulating the motor cortex.

111



stimulating the motor cortex.



patients when stimulating the motor cortex.

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Figure 18. Effect size (Cohen’s d) of each voxel (single pulse vs. paired pulse) in the time-

frequency domain corresponding to each specific electrode when comparing schizophrenia

and healthy subjects in the dorsolateral prefrontal cortex. Red corresponds to when healthy

subjects show greater inhibition, blue corresponds to when SCZ show greater inhibition.

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6.5 Discussion To summarize, in the DLPFC we found significant deficits in LICI in patients with SCZ

compared to healthy subjects but there were no significant LICI deficits in patients with

OCD. LICI deficits in the DLPFC were also significantly greater in patients with SCZ

compared to patients with OCD. Finally, there were no significant LICI differences across all

three groups in the motor cortex.

6.6 Frontal LICI Deficits in SCZ Two key lines of evidence support our findings of frontal LICI deficits in patients with SCZ.

First, previous studies have demonstrated reduced GABA inhibitory interneurons in the

DLPFC in SCZ [Akbarian et al., 1995; Benes et al., 1991]. For example, in SCZ, Benes et al.

reported morphologic changes in cortical GABA interneurons, by demonstrating a decreased

density of non-pyramidal cells in anterior cingulate layers II-VI and in prefrontal cortex layer

II [Benes et al., 1991]. Akbarian et al. found reduced messenger RNA in the DLPFC of SCZ

patients (a key enzyme involved in the synthesis of GABA) [Akbarian et al., 1995]. Impaired

GABAergic inhibitory neurotransmission in SCZ may be responsible for several of its key

phenotypic features. Dysfunctional GABAergic inhibitory neurotransmission may be related

to an imbalance between cortical excitation and inhibition [Yizhar et al., 2011]. Excessive

excitability in the cortex may result in discoordinated neuronal activation that may lead to the

disorganized behaviour and impulsivity that is commonly found in SCZ [Uhlhaas et al.,

2006; Uhlhaas and Singer, 2010]. Abnormal GABAergic inhibitory neurotransmission may

also lead to altered neural plasticity and aberrant neuronal wiring [Gaiarsa et al., 2002]. The

phenotypic manifestation of such dysfunction includes cognitive dysfunction, behavioural

disorganization, delusions and hallucinations [Constantinidis et al., 2002; Kapur, 2003;

Lewis et al., 2005].

Additional support for our findings of frontal LICI deficits in patients with SCZ relates to the

fact that LICI also plays a key role in modulating plasticity and in working memory

performance [Akerman and Cline, 2007; Butefisch et al., 2000; Deisz, 1999c;

Hoppenbrouwers et al., 2013]. For example, our group has previously shown a strong

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positive correlation between frontal LICI and working memory [Daskalakis et al., 2008d;

Hoppenbrouwers et al., 2013]. Working memory impairment is considered a core cognitive

deficit in SCZ [Barrantes-Vidal et al., 2007; de Leeuw et al.]. The DLPFC is a functional

brain region critical for higher-order cognitive tasks such as executive functioning, attention,

and working memory performance [Barbey et al., 2012]. In SCZ, dysfunctional activation of

the DLPFC may underlie the working memory deficits present in this disorder [Weinberger

et al., 1986]. This has been demonstrated in both functional MRI (fMRI) [Jansma et al.,

2004; Karlsgodt et al., 2007; Karlsgodt et al., 2009; Potkin et al., 2009] and

neurophysiological studies [Barr et al., 2010; Cho et al., 2006]. Lastly, we demonstrated a

significant negative correlation between LICI and the clinical severity scores of SCZ. Thus,

significant impairments in frontal inhibitory neurotransmission may represent an important

mechanism underlying some of the key phenotypic features of SCZ.

6.6.1 Frontal Gamma LICI Deficits in SCZ Our findings replicated a previous study demonstrating impaired frontal gamma LICI in

SCZ. These results are a larger replication of a previous TMS-EEG study [Farzan et al.,

2010a], emphasizing a deficit in the inhibition of frontal gamma oscillations in SCZ. In this

current study, the findings are extended by showing overall inhibitory deficits in the DLPFC

is selective to patients with SCZ. Frontal gamma inhibitory deficits in SCZ may be due to the

hypofunction of the N-methyl-D-aspartate-receptor (NMDAR). It has been shown that a

blockade of the glutamate-mediated excitatory neurotransmission by NMDAR antagonists

mimics positive and negative symptoms as well as cognitive deficits in SCZ [Krystal et al.,

1994]. This hypothesis proposes a specific deficit in NMDAR signaling, leading to a

decrease in parvalbumin-positive GABAergic interneuron activity and consequent pyramidal

cell disinhibition, diminishing GABA synthesis and release [Gonzalez-Burgos and Lewis;

Moreau and Kullmann, 2013; Olney et al., 1999]. As reviewed above, there have been

several reports suggesting a relationship between GABAergic inhibitory neurotransmission

and gamma oscillations in the cortex [Bartos et al., 2007; Bragin et al., 1995; Brown et al.,

2007; Jefferys et al., 1996; Marrosu et al., 2006; Scanziani, 2000; Traub et al., 1997; Traub et

al., 1996; Wang and Buzsaki, 1996; Whittington et al., 1995]. Gamma oscillations appear to

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be dependent on inhibitory neurotransmission from parvalbumin-containing GABA

interneurons. The lack of GABAergic neurotransmission in SCZ may translate into excessive

gamma oscillations leading to a pathophysiological plasticity (long-term potentiation) and

ultimately translate into aberrant learning and inflexible thinking that over time may lead to

delusions – a manifestation of erroneous information that is learned and reinforced.

6.6.2 Neurophysiology of OCD We found no significant LICI deficits in patients with OCD relative to healthy subjects in the

DLPFC and motor cortex. Previous research has shown that OCD has been associated with

motor cortex impairments in GABAA receptor-mediated inhibition [Greenberg et al., 2000;

Greenberg et al., 1998], GABAB receptor-medicated inhibition [Richter et al., 2012] and

NMDAR-mediated excitation [Richter et al., 2012]. Our findings could be accounted for by

the large medicated OCD sample, as the recent Richter et al., study mentioned above

included a majority of unmedicated patients with OCD (68%). SSRIs are the established

pharmacologic first-line treatment for OCD [Decloedt and Stein, 2010; Kellner, 2010]. In the

current study, 63% of the OCD patients (17/27) were medicated with SSRI's. Serotonin is

able to modulate excitatory and inhibitory effects, respectively mediated by glutamate and

GABA [Ciranna, 2006]. The serotonin receptor (5-HT) induces a decrease of glutamate

transmission and a parallel increase in GABA transmission evident in the hippocoampus,

frontal cortex and the cerebellum [Ciranna, 2006]. Previous studies have shown that SSRI’s

increase GABA by magnetic resonance spectroscopy [Bhagwagar et al., 2004] and TMS

[Robol et al., 2004], thus concealing any potential LICI deficits in the present study. The

modulatory action of the serotonin receptor (5-HT) may serve as a "brake" on neuronal

excitability. Given this inconsistency, replication is warranted to disentangle the effects of

medication. Future directions of this work may be to evaluate LICI (TMS-EEG) before and

after SSRI treatment for OCD to establish a relationship between inhibition and therapeutic

response.

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6.7 Advancements in Analyses In this paper, we have improved our methodology over previous LICI analyses. Our

advanced analyses provide two main benefits: a concise characterization of LICI, including

all relevant dimensions in the data (time, frequency, space) that may have been omitted in

previous studies and addresses the issue of multiple comparisons. Specifically, the analysis

allows for the assessment of LICI over the whole brain cortical network by means of an

extended time and spatial domain [Garcia Dominguez et al., 2014]. The analysis can also be

applied to a subset of the original grid to assess the contribution of specific electrodes.

Inhibition is characterized as a continuous response over a 4-dimensional space and is not

linked to a particular fixed pre-conceived window in this space. Past results have attempted a

variety of methodologies because there is not an immediate, easy to recognize, feature that

indexes LICI. Previous analyses have revealed evidence for inhibition over a restricted

domain, by sacrificing either, the temporal, the spatial or the frequency component.

Examples are: the fixed window analysis [Daskalakis et al., 2008c; Farzan et al., 2010b], the

measure of peak amplitude [Rogasch et al., 2013] and the analysis over a 25ms sliding

window [Fitzgerald et al., 2009a] at a single 1-40Hz band-pass. A second major advantage of

the analysis is that it tackles the problem of multiple comparisons. We applied a cluster-

based permutation analysis, allowing for the use of a single statistic from the whole

multidimensional space [Maris and Oostenveld, 2007; Premoli et al., 2014a]. This global

analysis contains the adequate correction for all of the sub-analyses between the

corresponding groups. In the context of TMS-EEG, limited studies have examined the entire

time-frequency and spatial domain since presenting a multidimensional analysis increases the

likelihood of committing a Type I error due to the problem of multiple comparisons [Maris

and Oostenveld, 2007]. Taken together, the presented methodology is parameter free, while

at the same time, avoids the multiple comparison issue without the need to discard vital

information as done in previous TMS-EEG analyses.

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6.8 Limitations This study is limited in several ways. First, SCZ patients were treated with a variety of

antipsychotic medications and other psychotropic medications and were chronically ill,

which may have effects on neural oscillations and may explain the lack of motor inhibitory

deficits found in this disorder. Future studies should recruit unmedicated SCZ and OCD

patients. Second, while pharmacological findings suggest that LICI is mediated by slow

inhibitory post-synaptic potentials via activation of GABAB receptors [McDonnell et al.,

2006], the effect of other neurotransmitter systems cannot be completely ruled out (dopamine

and serotonin). Third, when interpreting these results, inferences have been made for the role

of LICI vis à vis cognition. We did not measure cognition in this study; therefore, there is

currently no direct evidence to support these implications. In order to better link

neurophysiological findings to the symptoms of SCZ and OCD, future studies should

establish better relationships between LICI, cognition and behavior. More research should be

done to demonstrate the behavioural impacts of decreased inhibition in SCZ. Lastly, the TMS

evoked-potential after 40 ms contains the afferent component in the motor cortex which may

affect inhibition; however, any potential artifact is expected to be similar between groups and

should not account for group differences.

6.9 Clinical Implications This study shows that impairments in frontal GABAB receptor-mediated inhibitory

neurotransmission are associated with pathophysiology specific to SCZ. Conceivably TMS

measures of GABAergic and NMDAR functioning could be used as biological markers of

novel treatments that are aimed at enhancing inhibition or decreasing excitation in the cortex.

Several lines of evidence have suggested that the potentiation in GABAB receptor-mediated

inhibition are associated with clinical improvements as demonstrated by clinical

interventions such as meditation [Guglietti et al., 2013], cognitive behavioral therapy [Radhu

et al., 2012], repetitive TMS [Daskalakis et al., 2006], electroconvulsive therapy [Bajbouj et

al., 2006a] and clozapine treatment for SCZ [Daskalakis et al., 2008b; Liu et al., 2009; Wu et

al., 2011]. These results are promising and suggest the potential of using TMS-EEG in

neurophysiological research and in clinical settings.

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Chapter 7

Investigating the Heritability of Cortical Inhibition in First-Degree

Relatives and Probands in Schizophrenia Radhu N, Garcia Dominguez L, Greenwood TA, Farzan F, Semeralul MO, Richter MA,

Kennedy JL, Blumberger DM, Chen R, Fitzgerald PB, Daskalakis ZJ.

Manuscript Submitted

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7.1 Abstract Deficits in GABAergic inhibitory neurotransmission are a reliable finding in patients with

SCZ as demonstrated through multiple investigational approaches. Previous studies have also

reported that unaffected first-degree relatives of patients with SCZ demonstrate

neurophysiological abnormalities that are intermediate between probands and healthy

controls. In this study, first-degree relatives of patients with SCZ and their related probands

were investigated to assess frontal cortical inhibition – a neurophysiological index of

GABAergic inhibitory neurotransmission - as a potential endophenotype of SCZ. LICI was

measured from the DLPFC using combined TMS and EEG. The study presents an extended

sample of 129 subjects (66 subjects have been previously reported): 19 patients with SCZ (9

females, 10 males, average age = 30.2 years), 30 unaffected first-degree relatives of these

SCZ patients (17 females, 13 males, average age = 53.8 years), 13 obsessive-compulsive

disorder (OCD) patients (9 females, 4 males, average age = 28.9 years), 18 unaffected first-

degree relatives of these OCD patients (12 females, 6 males, average age = 41.9 years), and

49 healthy subjects (25 females, 24 males, average age = 33.4 years). In the DLPFC, cortical

inhibition was significantly less in patients with SCZ (t = 1.76, df = 66, p = 0.041) compared

to healthy subjects. First-degree relatives of patients with SCZ showed significantly more

cortical inhibition than their SCZ probands (t = 2.14, df = 47, p = 0.038). No differences

were demonstrated between first-degree relatives of SCZ patients and healthy subjects (t = -

0.44, df = 77, p = 0.66). While not significant, family members of SCZ were shown to be

intermediate between their related probands and healthy controls. No differences were found

between healthy subjects compared to OCD patients (t = -1.08, df = 60, p = 0.29) and their

first-degree relatives (t = -0.33, df = 65, p = 0.75). Lastly, no frontal inhibition differences

were shown between OCD patients and their first-degree relatives (t = -0.64, df = 29, p =

0.52). Altered frontal inhibition was specific to SCZ and their first-degree relatives, as no

inhibitory differences were found in OCD and their first-degree relatives, demonstrating

further insight into the biological mechanisms of SCZ. Larger family-based studies are

needed to establish frontal inhibition as an endophenotype of SCZ.

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7.2 Introduction SCZ is a severe psychotic disorder characterized by positive symptoms (i.e. hallucinations

and delusions), negative symptoms (i.e. affective flattening and motivational deficits), and

cognitive impairments [American Psychiatric Association, 2000]. OCD typically manifests in

compulsive urges to perform irrational behaviors associated with the occurrence of

obsessions (disturbing intrusive thoughts or impulses) [Abramowitz et al., 2009; American

Psychiatric Association, 2000; Heyman et al., 2006; Stein, 2002]. It has been shown that

there is considerable overlap between SCZ and OCD in their pathophysiology, clinical

symptom profile, and treatment [Poyurovsky and Koran, 2005].

In psychiatry, there are no objective laboratory tests to inform diagnoses and monitor

response to interventions. Biomarkers facilitate the development of etiologic rather than

symptom-based diagnostic methods and also help to advance our understanding of the

genetic mechanisms underlying psychiatric disorders [Daskalakis, 2012; Turetsky et al.,

2007]. Genetic load is known to be one of the strongest determinants for the development of

SCZ with heritability estimates as high as 80% [Cardno et al., 1999a; Cardno et al., 1999b].

Endophenotypes are a way to biologically assess psychiatric illnesses. In order to validate an

endophenotype, specific criteria of the biomarker must be fulfilled, e.g., high test-retest

reliability of the biomarker, trait stability and large effect size differences between patients

and healthy subjects. Lastly, it is important to determine the genetic component in the

marker; a true biomarker shows that unaffected first-degree relatives have an intermediate

deficit in the trait when compared to their related probands and healthy subjects [Gottesman

and Gould, 2003]. Unaffected first-degree relatives of SCZ patients are ideal candidates for

biomarker development as they share degrees of genetic vulnerability with probands,

however, are free from confounding variables such as antipsychotic treatment and

psychopathology, helpful in evaluating the neurobiological mechanisms of the disease [Hall

et al., 2011].

Deficits in GABAergic inhibitory neurotransmission have been a reliable finding in SCZ

across multi-modal approaches. These deficits may be due to an imbalance between cortical

121

excitation and inhibition of the cortex [Yizhar et al., 2011]. For example, Benes et al.

reported a decreased density of non-pyramidal cells in anterior cingulate layers II-VI and in

prefrontal cortex layer II in SCZ [Benes et al., 1991]. Akbarian et al. found reduced

messenger RNA (involved in the synthesis of GABA) in the DLPFC of SCZ patients

[Akbarian et al., 1995]. Additional studies have shown that SCZ patients exhibit deficits in

GABAergic inhibition using transcranial magnetic stimulation (TMS) [Daskalakis et al.,

2002a; Daskalakis et al., 2008b; Fitzgerald et al., 2002a; Fitzgerald et al., 2002b; Fitzgerald

et al., 2003; Liu et al., 2009; Wobrock et al., 2010; Wobrock et al., 2009; Wobrock et al.,

2008], limited to the motor cortex.

TMS combined with EEG is a powerful tool for investigating cortical mechanisms and

networks of frontal brain areas. Using this approach, GABAB receptor-mediated inhibitory

neurophysiological mechanisms can be measured through a paired-pulse paradigm, LICI. We

have demonstrated using TMS-EEG that LICI of gamma oscillations were selectively

impaired in the DLPFC of patients with SCZ compared to both healthy subjects and similarly

treated patients with bipolar disorder [Farzan et al., 2010a]. Patients with bipolar disorder

were similar to patients with SCZ in relation to severity of symptoms, illness duration, and

history of psychosis. In a recent study, we found that frontal LICI was significantly reduced

in SCZ patients, compared to OCD patients and healthy subjects, also showing no effect of

antipsychotic medication [Radhu et al., 2015]. These findings suggest that LICI

abnormalities may be specific to SCZ and are not part of a generalized deficit associated with

severe psychopathology.

Limited studies have investigated first-degree relatives of SCZ patients using TMS

paradigms. Saka et al., [Saka et al., 2005] evaluated TMS measures of inhibition in

unaffected first-degree relatives of SCZ patients compared to healthy subjects (no proband

group was assessed). They found that 25% of first-degree relatives lacked transcallosal

inhibition and showed psychosis-proneness relative to healthy controls. No differences were

found in MEP amplitude (excitability measure) or the cortical silent period (inhibitory

measure). The above findings provide evidence for the genetic liability of these TMS

markers, highlighting the need for further research in this area. Several lines of evidence have

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found a relationship between GABAergic inhibitory neurotransmission and gamma

oscillations [Bartos et al., 2007; Brown et al., 2007; Leung and Shen, 2007; Traub et al.,

1996; Wang and Buzsaki, 1996; Whittington et al., 1995; Whittington et al., 2000].

Specifically, GABAA receptor-mediated inhibitory post-synaptic potentials contribute to the

generation of gamma oscillations [Bartos et al., 2007; Wang and Buzsaki, 1996; Whittington

et al., 1995], whereas, GABAB receptor-mediated inhibitory post-synaptic potentials have

been associated with the modulation of gamma oscillations (Whittington et al., 1995; Brown

et al., 2007b; Leung and Shen, 2007). Growing evidence has shown that abnormalities of

high-frequency oscillations in the gamma-range (30-50 Hz) via EEG are heritable. For

example, Hall et al. [Hall et al., 2011] examined the early auditory gamma-band response in

SCZ and their unaffected co-twins during an auditory oddball target detection task in 194

individuals. They found that both evoked power and phase-locking phenotypes were reduced

in unaffected co-twins of patients with SCZ, with heritability of 0.65 for evoked power and

0.63 for phase-locking. This work suggests that the gamma band response may have a

heritable component in SCZ.

The main objective of this study was to evaluate DLPFC and motor cortex overall and

gamma inhibition using the LICI paradigm in patients with SCZ, patients with OCD, their

unaffected first-degree relatives and compared these groups to healthy subjects. We

hypothesized that frontal inhibition deficits would be demonstrated in SCZ and that this

group would also show the greatest LICI impairment. Furthermore, we hypothesized that

frontal inhibition in first-degree relatives of SCZ would be intermediate of healthy subjects

and their related probands. Lastly, inhibitory deficits would not be shown in OCD patients

and their unaffected first-degree relatives.

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7.3 Materials and Methods The study assessed 129 subjects: 19 patients with SCZ (9 females, 10 males, average age =

30.2 years, age range: 22 - 42 years); 30 first-degree relatives of SCZ patients, including 23

parents (13 mothers, 10 fathers) and 7 full siblings (4 sisters, 3 brothers), with a total of: 17

females, 13 males, average age = 53.8 years, age range: 23 - 69 years. There were a total of

18 families (related to SCZ patients) with an average of 1.61 members (range: 1 - 4

members). 13 OCD patients (9 females, 4 males, average age = 28.9 years, age range: 19 - 40

years); 18 first-degree relatives of OCD patients, including 9 parents (5 mothers, 4 fathers)

and 9 full siblings (7 sisters, 2 brothers), with a total of: 12 females, 6 males, average age =

41.9 years, age range: 20 – 71 years and 49 healthy subjects (25 females, 24 males, average

age = 33.4 years, age range: 20 – 56 years). There were a total of 13 families (related to OCD

patients) with an average of 1.38 members (range: 1-3 members). The results from a subset

of the subjects studies has been published (13 SCZ patients, 7 OCD patients and 43 healthy

subjects) [Radhu et al., 2015].

All subjects gave their written informed consent and the protocol was approved by the Centre

for Addiction and Mental Health in accordance with the Declaration of Helsinki. The

Structured Clinical Interview for the Diagnostic and Statistical Manual for Mental Disorders

(DSM)-IV confirmed the diagnosis of SCZ or OCD. Medications of SCZ and OCD patients

are shown in table 1. Diagnostic information of the SCZ and OCD patients are included in

table 2. In healthy subjects, psychopathology was ruled out by the Structured Clinical

Interview for DSM-IV and healthy subjects were only included in the study if they had no

first-degree relative diagnosed with a psychiatric disorder. Healthy subjects and all first-

degree relatives of probands were administered the Family Interview for Genetic Studies

[Calkins et al., 2007]. Relatives of probands had no psychopathology in the last 2 years as

ruled out through the Structured Clinical Interview for DSM-IV. First-degree relatives were

recruited through advertisements as well as from referrals from their related probands that

were enrolled in the study. Additionally, recruitment methods included advertisements on

public transportation (Toronto subway cars) for a period of one month. At least one first-

degree relative of a proband was a requirement for this study; either one biological parent of

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a proband or full sibling of a proband and the proband had to be available for

neurophysiological assessments. Exclusion criteria for all research subjects included: (1)

individuals meeting DSM-IV criteria for substance abuse or dependence in the last 6 months,

with the exception of nicotine; (2) concomitant major and unstable medical or neurological

illness; (3) experiencing suicidal ideation; (4) pregnant; (5) positive urine toxicology screen

for drugs of abuse; (6) any magnetic material or any other conditions that would preclude the

magnetic resonance image (MRI) scan or TMS-EEG measures; (7) clinically significant

claustrophobia. The exclusion criteria established by international safety standards for TMS

were followed [Rossi et al., 2009]. The TMS Adult Safety Screen [Keel et al., 2001] was

administered to all subjects.

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Table 8A. Patients with Schizophrenia Medication Details

CLASS MEDICATION # OF SUBJECTS/DOSE(S) in mg

ANTIPSYCHOTICS

Second Generation Clozapine n=9: 150 (1), 200 (1), 250 (2), 300 (2), 350 (1), 400 (1) 475 (1)

Olanzapine n=2: 7.5, 22.5

Paliperidone n=1: 150/4 weeks

Quetiapine n=1: 300

Risperidone n=3: 2 (2), 3

Risperidone

Injection n=2: 50/2 weeks, 75/4 weeks

Ziprasidone n=1: 60

Dibenzoxazepines Loxapine n=1: 30

Third Generation Aripiprazole n=2: 20, 30

ANTIDEPRESSANTS

Selective serotonin re-

uptake inhibitors (SSRIs) Citalopram n=2: 40 (2)


reuptake inhibitors (SNRIs) Desvenlafaxine n=1: 50

Norepinephrine-dopamine

reuptake inhibitor (NDRIs) Bupropion SR n=1: 150

MOOD STABILIZERS


Lamotrigine n=1: 100

Topiramate n=1: 200

BENZODIAZEPINES

Clonazepam n=2: 0.5, 1


Lorazepam prn n=1: 2

OTHERS

Benzatropine n=1: 2

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Table 8B. Patients with Obsessive-Compulsive Disorder Medication Details

CLASS MEDICATION # OF SUBJECTS/DOSE(S) in mg

ANTIDEPRESSANTS


inhibitors (SSRIs)

Escitalopram n=1: 50

Fluoxetine n=2: 20, 80

Sertraline n=1: 250


reuptake inhibitors (SNRIs) Duloxetine n=1: 60

Tricyclic antidepressants

(TCAs) Clomipramine n=4: 50, 250 (3)

Norepinephrine reuptake

inhibitor (NRIs) Atomoxetine n=1: 80

ANTIPSYCHOTICS

Loxapine n=1: 25

Olanzapine n=1: 20

Aripiprazole n=1: 2

MOOD STABILIZERS


BENZODIAZEPINES

Clonazepam n=1: 0.5


Diazepam prn n=1: 4

Temazepam n=1: 30


Subjects/Dose(s).

Table 8A. Patients with Schizophrenia Medication Details.

Table 8B. Patients with Obsessive-Compulsive Disorder Medication Details.

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Schizophrenia (N = 19) Number of

Subjects %

Schizophrenia (paranoid type) 14 73.68 Schizoaffective (bipolar type) 5 26.32

Current Comorbidities Number of

Subjects %

Major Depressive Disorder 1 5.26 Post-Traumatic Stress Disorder 1 5.26


1 5.26

Obsessive-Compulsive Disorder (N = 13)


%

Social Phobia 4 30.77 Panic Disorder without

Agoraphobia 2 15.38

Generalized Anxiety Disorder 4 30.77 Major Depressive Disorder 1 7.69

Attention Deficit Hyperactivity Disorder

1 7.69


patients.

7.3.1 Clinical Severity The Schizotypal Personality Questionnaire (SPQ) [Raine, 1991] was used for evaluating

psychopathology in first-degree relatives of SCZ patients. The 24-construct Brief Psychiatric

Rating Scale (BPRS) was used for evaluating psychopathology in SCZ patients [Overall and

Gorham, 1962].

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7.3.2 Data Recording

7.3.3 Transcranial Magnetic Stimulation Monophasic TMS pulses were administered using a 7 cm figure-of-eight coil, and two

Magstim 200 stimulators (Magstim Company Ltd, UK) connected via a Bistim module. TMS

was administered over the left motor cortex and DLPFC. Inhibition was measured through

LICI and indexed through electromyography and EEG at the optimal 100 ms interstimulus

interval [Sanger et al., 2001]. One hundred TMS stimuli were delivered per-condition (paired

and single-pulse) every 5 seconds. The intensity of TMS pulses was determined at the

beginning of each experiment and it was set such that it elicited an average motor evoked

potential of 1mV peak-to-peak upon delivery of 20 pulses over the motor cortex. Both the

conditioning stimulus and test stimulus were delivered at the same suprathreshold intensity.

7.3.4 Localization of the Motor Cortex The TMS coil was placed at the optimal position for eliciting motor evoked potentials from

the right abductor pollicis brevis muscle, which corresponded to a region between the

electrodes FC3 and C3.

7.3.5 Localization of the DLPFC Localization of DLPFC was achieved through neuronavigation techniques using the

MINIBIRD system (Ascension Technologies) and MRIcro/registration software using a T1-

weighted MRI scan obtained for each subject with seven fiducial markers in place

[Daskalakis et al., 2008c; Farzan et al., 2010a]. Stimulation was directed at the junction of

the middle and anterior one-third of the middle frontal gyrus (Talairach coordinates (x, y, z)

= -50, 30, 36) corresponding with posterior regions of Brodmann area 9, which overlap with

the superior section of Brodmann area 46.

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7.3.6 Electromyography Recording Electromyography was captured by placing two disposable disc electrodes over the right

abductor pollicis brevis muscle in a tendon-belly arrangement and motor evoked potentials

were filtered (band-pass 2 to 5 kHz), digitized at 5 kHz (Micro 1401, Cambridge Electronics

Design, Cambridge, UK).

7.3.7 EEG Recording and Pre-Processing To evaluate TMS-induced cortical evoked potentials, EEG was recorded concurrently with

electromyography. EEG was acquired through a 64-channel Synamps 2 EEG system. A 64-

channel EEG cap was used to record the cortical signals, and four electrodes were placed on

the outer side of each eye, and above and below the left eye to closely monitor eye movement

artifacts. All electrodes were referenced to an electrode positioned posterior to Cz electrode.

EEG signals were recorded DC and with a low pass filter of 100 Hz at a 20 kHz sampling

rate, shown to avoid saturation of the amplifiers and minimize the TMS-related artifact

[Daskalakis et al., 2008c; Daskalakis et al., 2012].

EEG recordings were down-sampled to 1000 Hz and epoched from -1000 ms to 2000 ms

after the test TMS pulse. In both, the single and paired-pulse conditions, the segment from -

100 ms to 10 ms was removed (where 0 correspond to the test TMS pulse). This step

removes not only the test-pulse TMS in the single-pulse and paired-pulse conditions but also

the conditioning TMS pulse in the paired-pulse condition. Traces were visually inspected for

artifacts in order to eliminate trials and channels highly contaminated by noise (muscle

activity, 60Hz noise, and movement-related activity as well as electrode artifacts). Two

rounds of ICA were subsequently applied. The first round was to minimize and remove the

typical TMS-related decay artifact that appears in some subjects at specific locations. In each

subject, the number of components that needed to be removed to eliminate this kind of

artifact varied from 0 to 6. Following this, a bandpass FIR filter was applied from 1 to 55 Hz

and a second round of ICA was computed to remove eye movement-related artifacts (blinks

and movements) and remaining muscle components.

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7.4 Post-Processing Analyses A time frequency decomposition was obtained using the Event-Related Spectral Perturbation

(ERSP) analysis in EEGLab. Specifically the analysis was wavelet based, using a cycle of the

complex Morlet wavelet across frequencies 2 - 50Hz. The ERSP was computed

independently for the single-pulse and paired-pulse conditions. The analysis is expressed in

decibels of spectral power (µV2/Hz) after subtracting the log baseline to the whole trial. In

previous studies, we have shown inhibition can be evaluated as the difference, of some

suitable measure of amplitude, of the evoked activity between the single and paired-pulse

stimulation. In this study, the measure of amplitude is the power of a wavelet decomposition.

Nine electrodes were retained for the analysis of inhibition for both the DLPFC and motor

cortex stimulation. This subset of electrodes (F1, Fz, F2, FC1, FCz, FC2, C1, Cz, C2) was

located midline over the frontal-central regions and were chosen for two main reasons. First,

these nine electrodes were the least influenced by muscle activity and TMS-related artifacts,

thus, these electrodes were not excluded due to artifacts. Second, these electrodes show the

greatest and most consistent inhibitory response for both the motor cortex and DLPFC site of

stimulation [Garcia Dominguez et al., 2014; Radhu et al., 2015]. These two reasons are

related since the large signal-to-noise-ratio is mainly due to low noise from muscle combined

with a relatively good proximity and connectivity to the site of stimulation. This was a region

of interest of analysis; the artificat removal was completed soley on these nine electrodes.

7.4.1 Calculating Inhibition by Subject In this study we computed the difference in the evoked power of the two conditions, we also

computed a number of paired surrogate conditions made of sets of randomly selected trials,

without replacement from the pool of trials of the two conditions. The surrogate conditions

serves as a baseline or null hypothesis for the case of no inhibition, i.e. no difference between

the powers of conditions. The power differences extracted from the original conditions as

well as the surrogate ones consist of a set of values over voxels, in the time-frequency-

electrode space for each subject. From this “landscape” of values over the time-frequency-

electrode space, a threshold (p-value) was chosen to label each voxel as inhibited=1 or not-

inhibited=0. A voxel received a value of “1” if its value is greater than 99% of the values in

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the same voxel in the null distribution, otherwise is “0”. The number of randomization in the

null distribution was set to 500. Inhibition was then evaluated by counting the 1’s that forms

a cluster in this time-frequency-electrode domain. A voxel belongs to a cluster if it has a

value of 1, and has at least a neighbour in time, frequency and electrode that is also in the

cluster [Garcia Dominguez et al., 2014]. Thus, for each subject an index of inhibition is

defined as the total sum of significant values in the largest cluster, if more than one cluster is

found. The cluster is only considered in a time domain from the moment of the test pulse to

500 ms after, and frequencies from 2-50Hz. If the analysis is restricted to the gamma band

we sum only over the range: 30-50Hz. The size of the larger cluster of significant values (or

index of inhibition) is a way to capture the degree of inhibition at the subject level, since a

larger inhibition correlates with the extent of significant voxels in the time-frequency-spatial

domain.

7.4.2 Between-Group Analyses After calculating inhibition by subject, we compared groups by a t-test analysis with pooled

variance. In all comparisons, the two-tailed analyses are reported. However, based on our

previous finding [Radhu et al., 2015], one analysis was single-tailed when we compared

healthy subjects to SCZ patients in overall (2-50Hz) and gamma (30-50Hz) inhibition. The

null hypothesis being that the control group is not more inhibited than the SCZ group, in both

the overall frequency band and the gamma frequency band.

Figure 1 depicts differences in inhibition evaluated non- parametrically for patients with

SCZ, their first-degree relatives and healthy subjects, this is how it was calculated:

For each group 19 subjects that were chosen with replacement and a value of inhibition was

obtained from this subset as the largest size of the cluster with significant values resulting

from voxel-by-voxel paired t-test between the single and paired-pulse stimulation. Since

inhibition corresponds to the fact that the power of single pulse is larger than that of the

paired, the analysis was single-tailed [Garcia Dominguez et al., 2014; Radhu et al., 2015].

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This analysis was repeated 2000 times, selecting a different pool of 19 subjects from the

same group.

Nineteen subjects were chosen to avoid differences in sample size between the groups that

could potentially affect the voxel-by-voxel t-test favoring inhibition in the larger group

because of increased statistical power. Nineteen represents the lowest number of subjects

within the SCZ group. We replicated the same analysis for figure 2 for healthy controls,

OCD patients and their unaffected first-degree relatives. Figures 3 and 4 depict group

inhibition over the time-frequency domain evaluated non- parametrically. In this case,

subsets of subjects were also chosen with replacement for each group with the same

cardinality to maintain comparable statistical power. Figure 3 illustrates healthy subjects,

first-degree relatives of SCZ patients, and SCZ patients. Figure 4 shows healthy subjects,

first-degree relatives of OCD patients and OCD patients.

7.4.3 Assessing Clinical Severity and Effects of Medication Analyses

in Schizophrenia Patients A Spearman's rho correlation analysis was performed between the BPRS (total score) and the

index of frontal inhibition (overall and gamma frequency bands) for each SCZ subject. This

correlation was also conducted with chlorpromazine equivalents [American Psychiatric

Association, 1997; Bezchlibnyk-Butler et al., 2014; Chue et al., 2005; Woods, 2003].

7.4.4 Evaluating Clinical Severity in First-Degree Relatives of

Schizophrenia A Spearman's rho correlation analysis was performed between the SPQ (total score) and the

index of frontal inhibition (overall and gamma frequency bands) for the first-degree relatives

of SCZ group.

7.4.5 Stratification of Age in First-Degree Relatives of Schizophrenia

Patients

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DLPFC inhibition (overall and gamma frequency bands) was compared between young (<50

years) first-degree relatives of SCZ patients and older (>50 years) first-degree relatives of

SCZ patients to determine whether there were effects of age. An independent samples t-test

was used to compare the two groups.

7.4.6 The Heritability of Inhibition in Schizophrenia Variance components methods in SOLAR v.4.3.1 were used to estimate the narrow sense

heritability for overall inhibition of the DLPFC, defined as the phenotypic variance explained

by additive genetic factors [Almasy and Blangero, 1998]. Normalized trait values were used

for all analyses, and age and sex were screened as covariates and found to be not significant

(p > 0.05). In first-degree relatives of SCZ patients, there were no differences in ethnicity

between Caucasians and non-Caucasians in frontal overall inhibition (p = 0.39) and frontal

gamma inhibition (p = 0.85). In SCZ patients, there were no difference in ethnicity between

Caucasians and non-Caucasians in frontal overall inhibition (p = 0.26) and frontal gamma

inhibition (p = 0.61), thus, ethnicity was not used a covariate. Corrections were made for

ascertainment bias, since the families were recruited through the identification of a proband

with SCZ and are thus not representative of the general population [Beaty and Liang, 1987].

The significance of the heritability estimate was determined by comparing the full polygenic

model with significant covariates included to a sporadic model in which the genetic

component had been removed.

7.5 Results 7.5.1 Frontal Overall (2-50 Hz) Inhibition Frontal inhibition was significantly greater in healthy subjects compared to subjects with

SCZ (t = 1.76, df = 66, p = 0.041). First-degree relatives of SCZ patients showed

significantly more inhibition than their SCZ probands (t = 2.14, df = 47, p = 0.038). No

differences were demonstrated between first-degree relatives of SCZ and healthy subjects

(t = -0.44, df = 77, p = 0.66). Figure 1 shows that the pattern of frontal inhibition. This

analysis showed that the pattern of inhibition was: healthy subjects > first-degree relatives of

SCZ > SCZ probands, over a wide range of p-value thresholds and was independent of the

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specific threshold chosen. A trending heritability estimate of 0.65 was found with a standard

error of 0.58 and p = 0.107 in overall inhibition. No differences were found between healthy

subjects compared to OCD patients (t = -1.08, df = 60, p = 0.29) and their first-degree

relatives (t = -0.33, df = 65, p = 0.75). Lastly, no inhibition differences were found between

OCD patients and their first-degree relatives (t = -0.64, df = 29, p = 0.52) (figure 2).

7.5.2 Frontal Gamma (30-50 Hz) Inhibition Gamma inhibition was significantly lower in SCZ compared to healthy subjects (t = 2.22, df

= 66, p = 0.015). No significant differences were found between healthy subjects and first-

degree relatives of SCZ (t = 0.16, df = 77, p = 0.87). When comparing first-degree relatives

of SCZ to their probands, no differences in gamma inhibition (t = 1.77, df = 47, p = 0.083)

were found. No significant differences were found between healthy controls compared to

OCD (t = -1.31, df = 60, p = 0.19) and when compared to first-degree relatives of OCD (t = -

0.40, df = 65, p = 0.69). Lastly, no significant differences were found between first-degree

relatives of OCD compared to their OCD probands (t = -0.72, df = 29, p = 0.48) (figures 3

and 4).

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Figure 19. The index of frontal inhibition from a cluster analysis at different thresholds of p-

values in healthy controls, first-degree relatives of schizophrenia patients and their related

probands. A cluster analysis was performed for each group by sampling a subset 19 of

subjects with replacement. The procedure was repeated 2000 times for each threshold. Error

bars indicate one standard error of the mean.

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Figure 20.The index of frontal inhibition from a cluster analysis at different thresholds of p-

values in healthy controls, first-degree relatives of obsessive-compulsive disorder patients

and their related probands. A cluster analysis was performed for each group by sampling a

subset 13 of subjects with replacement. The procedure was repeated 2000 times for each

threshold. Error bars indicate one standard error of the mean.

7.5.3 Motor Cortex Overall (2-50 Hz) Inhibition No significant inhibitory differences were found between patients with healthy subjects and

SCZ patients (t = 0.22, df = 66, p = 0.41) and when compared to their first-degree relatives

(t = -0.44, df = 77, p = 0.66). No significant differences were found between SCZ patients

and their first-degree relatives (t = 0.57, df = 47, p = 0.57). No differences were found

between healthy subjects compared to OCD patients (t = -0.29, df = 60, p = 0.78) and when

compared to their first-degree relatives (t = 0.24, df = 65, p = 0.81). No significant

differences were found between OCD patients and their first-degree relatives (t = -0.58, df =

29, p = 0.57).

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7.5.4 Motor Cortex Gamma (30-50 Hz) Inhibition No significant inhibitory differences were found between patients with healthy subjects and

SCZ patients (t = 0.04, df = 66, p = 0.49) and when compared to their first-degree relatives

(t = 0.22, df = 77, p =0.83). No significant differences were found between SCZ patients and

their first-degree relatives (t = -0.16, df = 47, p = 0.87). No differences were found between

healthy subjects compared to OCD patients (t = 0.15, df = 60, p = 0.88) and when compared

to their first-degree relatives (t = 0.63, df = 65, p = 0.53). No significant differences were

found between OCD patients and their first-degree relatives (t = -0.53, df = 29, p = 0.60).

7.5.5 Stratification of Age in First-Degree Relatives of Schizophrenia

Patients No significant inhibition differences were found when comparing younger (n = 9) (< 50

years) to older (n = 21) (> 50 years) first-degree relatives of SCZ patients in frontal overall

inhibition (p = 0.48) and frontal gamma inhibition (p = 0.66).

7.5.6 Clinical Severity Analysis in First-Degree Relatives of

Schizophrenia Patients In first-degree relatives of SCZ patients, no significant relationship was found between the

SPQ (total score) and frontal overall inhibition (Spearman’s rho = 0.27, p = 0.92). In first-

degree relatives of SCZ, no significant relationship was found between the SPQ (total score)

and frontal gamma inhibition (Spearman’s rho = 0.14, p = 0.76).

7.5.7 Effect of Antipsychotic Medications and Anti-depressant

Medications No significant correlation between overall inhibition and chlorpromazine equivalents was

shown (Spearman’s rho = 0.012, p = 0.52) and no relationship was found between frontal

gamma inhibition and chlorpromazine equivalents (Spearman’s rho = -0.25, p = 0.15).

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In the DLPFC, no significant differences were found between antidepressant-treated OCD

patients (n = 7) and unmedicated OCD patients (n = 6) in overall inhibition (p = 0.15) and

gamma inhibition (p = 0.32). In the DLPFC, no significant differences were found between

antidepressant-treated SCZ patients (n = 4) and SCZ patients who were not treated with

antidepressants (n = 15) in overall frontal inhibition (p = 0.85) and frontal gamma inhibition

(p = 0.98). Lastly, in the DLPFC, no significant differences were found between OCD

patients who were treated with SSRIs (n = 4) and SCZ patients who were treated with SSRIs

(n = 2) in overall frontal inhibition (p = 0.40) and gamma inhibition (p = 0.31).

7.5.8 Clinical Severity in Schizophrenia Patients No significant relationship was found between the BPRS and overall frontal inhibition,

(Spearman’s rho = -0.28, p = 0.12). A trending negative correlation was found between the

BPRS and frontal gamma inhibition (Spearman’s rho = -0.36, p = 0.068). The greater the

BPRS score is indicative of increased severity of SCZ symptoms. This negative correlation

shows that the higher BPRS score is related to a lower degree of frontal inhibition in 19 SCZ

patients (at trending significance).

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subjects, first-degree relatives of SCZ patients, and their related probands. The stimulation

area was the dorsolateral prefrontal cortex. Values are masked over the left bottom area (dark

navy blue) indicating that those windows of the wavelet analysis, which contains points from

the pre-stimulus interval.

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subjects, first-degree relatives of obsessive-compulsive disorder patients, and their related

probands. The stimulation area was the dorsolateral prefrontal cortex. Values are masked

over the left bottom area (dark navy blue) indicating that those windows of the wavelet

analysis, which contains points from the pre-stimulus interval.

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7.6 Discussion We found that first-degree relatives of SCZ patients showed an intermediate pattern of

frontal inhibition compared to their related probands and healthy controls. Significant

differences were found in frontal overall inhibition between SCZ patients and their

unaffected first-degree relatives. We showed a heritability estimate of 0.65 for frontal

inhibition that was trending towards significance. No differences were demonstrated in

frontal inhibition between unaffected first-degree relatives of SCZ and healthy subjects.

Significant deficits were shown in frontal overall and gamma inhibition in patients with SCZ

compared to healthy subjects. No significant frontal inhibition differences were found in

OCD patients when compared to their unaffected first-degree relatives and healthy subjects.

7.6.1 Frontal Inhibition in First-Degree Relatives of Schizophrenia

Probands We demonstrated that first-degree relatives of SCZ had significant differences in overall

frontal inhibition when compared to their related probands. First-degree relatives of SCZ

were not significantly different from healthy subjects. The pattern of frontal inhibition in

first-degree relatives of SCZ was intermediate between their related probands and healthy

controls (figure 1). We showed preliminary evidence suggesting a heritability estimate of

0.65 that was trending and may not have reached signifance due to the relatively small

sample size in our study. Epidemiological studies indicate a heritability of up to 80% for

SCZ, reflecting a strong genetic influence [Cardno et al., 1999b; Sullivan, 2005]. Data from

biological relatives of probands are important for assessing disease-related effects in a

complex disorder like SCZ. The Consortium of Genetics in Schizophrenia (COGS) has

investigated several neurophysiological measures as potential endophenotypes as a means for

understanding the genetic determinants of SCZ [Calkins et al., 2007]. Measures include P50

suppression, antisaccade task for eye movements and prepulse inhibition. Greenwood et al.

demonstrated that P50 suppression shows a low heritability of 0.10 that was not significant in

a sample of 183 nuclear families [Greenwood et al., 2007]. Furthermore, for the antisaccade

task for eye movements, moderate to strong heritability of 0.42 was found [Greenwood et al.,

2007] and a modest heritability of 0.32 for prepulse inhibition was shown [Greenwood et al.,

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2007]. Hasenkamp et al. demonstrated moderate heritability of 0.45 for prepulse inhibition at

the 60 ms interstimulus interval and a trending heritability of 0.33 at the 120 ms interstimulus

interval [Hasenkamp et al., 2010]. These above mentioned studies suggest low heritability for

P50 suppression, moderate to strong for the antisaccade task for eye movements and modest

for pre-pulse inhibition. Identification of better endophenotypes are needed to facilitate the

future of diagnosis and may also facilitate the identification of genes contributing to SCZ

susceptibility.

7.6.2 Endophenotypes (Intermediate Phenotypes) in Schizophrenia Currently, no objective measures exist to inform psychiatric diagnoses. The clinical interview

dominates the methodological approach with respect to diagnosis, with little evolvement over

the last 100 years. Compared to current subjective clinical diagnoses, endophenotypes (i.e.

intermediate phenotypes) are discrete, genetically determined disease-related intermediate

phenotypes that are detected in the laboratory with demonstrated replicability, diagnostic

specificity, trait stability, test-retest reliability and heritability (first-degree relatives of

probands show intermediate values) [Braff et al., 2007; Braff, 2015; Braff et al., 2008;

Gottesman and Gould, 2003]. Psychiatric disorders are genetically complex, no specific

constellation of genes or environmental conditions characterize a large subset of ill

individuals, thus, requiring the study of intermediate phenotypes [Meyer-Lindenberg and

Weinberger, 2006]. Intermediate phenotypes refer to pathophysiological phenomena,

whereby, susceptability-related phenotypes are between genetics and the neurobiological

systems underlying behavioral disturbance [Tan et al., 2008]; they allow for a target to find

disease-associated genetic variants and elucidation of disease-related mechanisms [Flint et

al., 2014]. For example, patients with SCZ have changes in frontal brain function, cognition

and in brain structure that are found more frequently in their unaffected siblings, including

unaffected monozygotic co-twins, significantly more than in healthy control subjects,

suggesting that these intermediate deviations represent biological expressions of increased

genetic risk [Callicott et al., 2003; Cannon et al., 2000; Ettinger et al., 2007; Toulopoulou et

al., 2007]. In a family study of the association of a SCZ susceptibility gene, DISC1 and the

P300 waveform, almost every subject with a structural abnormality in the DISC1 gene had a

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significant reduction in the amplitude of the P300 event-related potential, even if they had no

psychiatric diagnosis [Blackwood et al., 2001]. Individuals in the same family lacking the

DISC1 abnormality also lacked a P300 abnormality. This suggests that a gene linked with

behavioral abnormalities is more strongly associated with a measure of brain function related

to SCZ and genetic risk for SCZ even in the absence of clinical presentation. However, most

intermediate phenotypes related to brain physiology are polygenic and heterogeneous [Tan et

al., 2008]. Our findings demonstrate potential for an intermediate phenotype as we found that

first-degree relatives of SCZ were intermediate of probands and healthy controls in frontal

inhibition. These biological changes are found in at-risk individuals who do not manifest a

psychiatric diagnosis suggesting evidence for susceptibility-related phenotypes, intermediate

between genetics and psychopathology [Preston and Weinberger, 2005; Tan et al., 2008].

7.6.3 Frontal Inhibitory Deficits in Schizophrenia The SCZ patients in this study included the results of 13 of the 19 patients that were

published previously [Radhu et al., 2015]. For these current analyses, the probands were

included if their first-degree relatives had been neurophysiologically assessed. Several lines

of evidence support our findings of frontal LICI deficits in patients with SCZ. Previous

studies have showed a reduced density of GABA interneurons in superficial layers of the

prefrontal cortex has been demonstrated which may account for the inhibitory deficits in SCZ

patients. For example, GABAergic deficits have been found in SCZ based on post-mortem

and animal studies showing reduced expression of pre- and postsynaptic markers of

GABAergic neurotransmission in subpopulations of GABAergic interneurons in the

prefrontal cortex [Benes and Berretta, 2001; Coyle, 2004; Lewis et al., 2004]. Recent work

[Marsman et al., 2014] using magnetic resonance spectroscopy (MRS) has found that SCZ

patients had significantly lower GABA/creatine ratios specific to the medial prefrontal

cortex. These findings are consistent with postmortem SCZ studies demonstrating diminished

GABA production based on decreased levels of mRNA encoding for glutamate

decarboxylase67 (GAD67), an enzyme that facilitates GABA synthesis from glutamate

[Kondziella et al., 2007; Lisman et al., 2008; Olney et al., 1999; Stone et al., 2009]. As a

result of the above mentioned findings, excessive excitability in the cortex may result in

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aberrant neuronal activation that may lead to the disorganized behavior and impulsivity that

is commonly found in SCZ [Uhlhaas et al., 2006; Uhlhaas and Singer, 2010]. Two studies

have demonstrated impaired frontal gamma inhibition specific to SCZ using combined TMS

and EEG [Farzan et al., 2010a; Radhu et al., 2015]. Gamma oscillations appear to be

dependent on inhibitory neurotransmission from parvalbumin-containing GABA

interneurons. The lack of GABAergic neurotransmission in SCZ may translate into excessive

gamma oscillations leading to a pathological plasticity (long-term potentiation) and

ultimately translate into abnormal learning and inflexible thinking that over time may lead to

delusions, a manifestation of erroneous information that is learned and reinforced.

We found no significant frontal inhibitory deficits in patients with OCD compared to their

first-degree relatives and compared to healthy subjects. The results of seven of the OCD

patients had been reported [Radhu et al., 2015] and 6 additional OCD patients were included

in these current analyses. Similar to previous results [Radhu et al., 2015], we found no

differences between OCD patients and healthy controls. Furthermore, the findings that first-

degree relatives of SCZ were intermediate of their probands and healthy controls were

specific to SCZ and their first-degree relatives as this was not demonstrated in OCD and their

first-degree relatives. Future studies are needed to determine the specificity and reliability of

our findings, since there are substantial areas of pathophysiological overlap between SCZ

and OCD [Poyurovsky and Koran, 2005].

7.7 Limitations The results of the present study should be interpreted in light of several limitations. First,

clear neurophysiological and molecular findings in first-degree relatives of SCZ are lacking,

further research needs to be done to disentangle the role of GABA in this population. Second,

when interpreting these results, inferences have been made for the role of LICI vis à vis

cognition, we did not directly measure cognition in this study. Going forward, studies should

establish relationships between LICI, cognition and behavior. Another shortcoming is that

TMS-EEG data is heavily contaminated by artifact. Artifact selection can be very subjective,

making it difficult to decide which ICA component to accurately remove without heavily

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impacting neuronal activity. Further refinement of removing noise from the EEG data is

necessary. Lastly, this study included a small sample of first-degree relatives. Larger samples

of first-degree relatives are needed including more complete nuclear families such as:

unaffected biological parents, a full sibling and the proband to compute heritability analyses.

With family-based approaches, challenges include recruitment of large samples; further

investigations with larger multi-center research trials are needed to develop TMS-EEG as a

neurophysiological method for use in diagnosis.

7.8 Summarizing the Main Findings

The present results offer preliminary evidence to suggest that first-degree relatives of SCZ,

showed inhibition intermediate of probands and healthy controls. First-degree relatives of

SCZ were significantly different than probands. We showed impairment in frontal inhibition

specific to SCZ patients compared to healthy controls. The ability to evaluate the response

profiles of different oscillatory frequency bands via EEG in response to TMS may be a

potential measure for diagnosis. An advantage of using endophenotypes is that they relate to

specific objective biological functions and substrates associated with the disease. The search

for liability genes for complex disorders such as SCZ may be aided by identifying

endophenotypes and relating these genes to cortical inhibition. Such efforts may ultimately

help to enhance our understanding of the complex neurobiological mechanisms underlying

SCZ to help with diagnosis in the future.

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Chapter 8

General Discussion, Future Directions and Conclusions

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8.1 Summary of the Literature Review The introduction of this dissertation is comprised of three literature review chapters. These

chapters summarized TMS and EEG studies assessing inhibition, excitability, plasticity and

connectivity in motor and frontal regions of the brain. Applications of such methods were

applied to psychiatry, neurology, sleep and loss of consciousness research. The first chapter

was a review which summarized motor cortex inhibitory and excitatory findings in patients

with OCD, MDD and SCZ. The literature review focused on neurophysiological studies

which linked dysfunctional GABAergic inhibitory neurotransmission and psychiatric

disorders. The second chapter demonstrated how TMS-EEG is used to directly assess the

DLPFC in psychiatric disorders without being substantially affected by TMS-stimulus

artifact, due to advancements in technology. The third chapter of the introduction assessed

the neurobiology of SCZ and potential candidate endophenotypes for this disorder.

8.1.1 Summary of the Original Research The three main original research studies presented within this dissertation have evaluated

inhibitory measures in severe psychiatric disorders. The first study (chapter 5) provided the

motivation of this current work, hypothesizing an overall inhibitory deficit in severe

psychopathology. This publication quantified all motor cortex inhibitory and excitatory

paradigms with OCD, MDD and SCZ. Our analysis showed that inhibitory deficits were a

ubiquitous finding across OCD, MDD and SCZ and enhancement of excitability (ICF) was

only found in OCD [Radhu et al., 2013]. Specifically, we found significant effect sizes

(Hedge’s G) for decreased SICI, enhanced ICF and reduced CSP within the OCD population.

For MDD, significant effect sizes (Hedge’s G) were found for decreased CSP and SICI.

Lastly, significant deficits in SICI were shown in SCZ. These findings are in line with

previous literature that suggests motor inhibitory deficits among psychiatric disorders;

however, this study suggests that each disease may have a distinct illness profile and

response to treatment.

The second study (chapter 6) evaluated GABAB inhibition in both the DLPFC and motor

cortex using TMS-EEG in SCZ, OCD and healthy controls [Radhu et al., 2015]. The main

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objective was to evaluate the specificity of frontal inhibition deficits in large sample of SCZ

patients with SCZ. We found deficits in frontal inhibition (overall inhibition and gamma

inhibition) in SCZ patients. No differences were found in the motor cortex between the three

groups. We did not find inhibition abnormalities in OCD patients suggesting frontal

inhibitory deficits may be specific to SCZ, allowing us to differentiate SCZ from OCD.

The last study (chapter 7) assessed both frontal and motor LICI (via TMS-EEG) in first

degree-relatives of SCZ and OCD, their probands and healthy subjects. We found that first-

degree relatives of SCZ were significantly different than their probands; however, they were

similar to healthy controls (no significant differences) in both frontal gamma and overall

inhibition. We showed that first-degree relatives had a pattern of frontal LICI at the

intermediate point between their related SCZ probands and healthy subjects, SCZ were the

most impaired in frontal LICI. Furthermore, no differences were found between OCD, their

relatives and healthy subjects. The heritability of 0.65 (trending) in overall inhibition in SCZ

provides pilot data to suggest that frontal inhibition may represent a candidate

endophenotype for this illness, warranting further investigation. Future multi-site trials are

needed to assess larger samples of first-degree relatives.

8.2 How Can Neuroscience Revolutionize Psychiatry? Neuroscience studies rely on disease definitions that are solely based on the DSM, a

subjective tool used to diagnose psychiatric illnesses. To provide an alternative framework

for research into psychiatric disorders, the United States National Institute of Mental Health

introduced its Research Domain Criteria project to provide a complementary way of

classifying mental illness [Casey et al., 2013]. The main focus of the strategy is to develop

new scientific ways of classifying psychiatric diseases based on behavioural dimensions and

neurobiological measures that is not intended to replace the DSM [Casey et al., 2013]. This

discussion will focus on the need for the biomarker approach, as more research needs to be

conducted with psychiatric populations emphasizing the use of laboratory measures in order

to accelerate both diagnosis and treatment monitoring.

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8.2.1 Combined TMS and EEG TMS is a non-invasive brain stimulation technique that allows for in vivo examination of

cortical processes and to test both excitatory and inhibitory circuits [Barker et al., 1985]. In

1985, Barker et al. introduced TMS as a tool for investigating the functional state of the

motor pathways in patients with neurological disorders as well as in healthy human subjects

[Barker et al., 1985]. TMS has been used as an investigational tool to measure

neurophysiological processes [McClintock et al., 2011]. TMS has been combined with EEG

to evaluate the effects of electromagnetic induction on cortical oscillations, a methodological

combination that has generated important neurophysiological leads in both healthy and

disease states [Garcia Dominguez et al., 2014; Radhu et al., 2015].

8.2.2 Why Implement TMS-EEG? Many of the studies highlighted in this dissertation demonstrate the tremendous potential for

the recording of TMS-evoked potentials in both motor and non-motor regions of the brain.

As illustrated in the introduction, TMS-EMG studies have shown to be invaluable in

assessing the pathophysiology of neuropsychiatric disorders [Fitzgerald et al., 2002c;

Levinson et al., 2010; Levinson et al., 2007] and the effects of various medications on

different neurotransmitter pathways in the cortex [Ziemann et al., 1998; Ziemann et al.,

1996a; b; Ziemann et al., 1997a; Ziemann et al., 1996c; Ziemann et al., 1997b]. However,

combined TMS and EEG has the potential to extend such findings to frontal brain regions

[Daskalakis et al., 2008c] and to provide evidence about important physiological mechanisms

that are unique to individual brain regions [Paus et al., 2001].

8.3 Advancements in Analyses Recent advancements in post-processing analyses and methods have rendered EEG as a

powerful, cost-effective and easy-to-use technique in clinical and experimental settings. In

agreement with previous studies, we have identified differences between the conditioned and

unconditioned response in the LICI paradigm. However, this time we have analyzed a larger

temporal window. Our findings have shown large differences over roughly 300 ms between

the activities generated by the two conditions. Actual inhibition mediated by the release of

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GABAB and subsequent generation of inhibitory post-synaptic potentials, can still occur over

a much shorter time scales (i.e., the first 100–150 ms after the test pulse). However, the effect

of such transient inhibition can also resonate over a much larger time scale. The particular

spatial signature of LICI and its evolution in time has been shown to be related to GABAB-

mediated inhibitory neurotransmission.

8.3.1 Detecting and Removing Artifacts in EEG EEG data is dominated by non-physiologic noise (e.g., line noise, electrode, movement, TMS

artifacts) and also contributed by physiological processes (e.g., muscle activity, eye blinks

and saccades) [Rogasch et al., 2014]. Dealing with non-brain artifacts is challenging in

clinical samples. Two rounds of ICA were implemented in the studies presented in this

dissertation to address the noise in the data; the first round was mainly to remove the TMS-

related decay artifact and the second round was to remove eye-blink and muscle-related

artefacts. The second round of ICA was done on the mixed signal after the TMS-related

decay was removed, components were mixed again after the first round of ICA and a new

round of ICA was applied. The ICA does not presume independence of signals. It is a model

that takes dependent signals and produces independent components that explain the same

data. It may happen that the number of components that explain the data is less than the

number of channels. This is the case when the second ICA is applied after the removal of a

few components from the first one. Ideally, one ICA would be enough. The reason we

applied two rounds of ICA was mainly that we wanted to filter the data before ICA was

applied to obtain cleaner components, however this was impossible in some cases because

the large amplitude artifact resulting from the TMS-related decay distorts the output of the

filter. Thus, the final solution was two rounds of ICA [Garcia Dominguez et al., 2014].

8.3.2 Cluster-Based Analyses The statistical analysis described in this dissertation was based on the specific applications of

the cluster-based permutation test, which have produced particularly sensitive results without

assumptions over the null distribution. This non-parametric test is based on random

permutations of conditions across subjects and groups. It corrects for multiple comparisons

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by reducing the whole time-frequency domain to a single number that accounts for the size of

the main cluster of inhibition. In the context of TMS-EEG, limited studies have examined the

entire time-frequency and spatial domain since presenting a multidimensional analysis

increases the likelihood of committing a Type I error due to the problem of multiple

comparisons [Maris and Oostenveld, 2007]. Taken together, the presented methodology is

parameter-free, while at the same time, avoids the multiple comparison issue without the

need to discard vital information as done in previous TMS-EEG analyses [Garcia Dominguez

et al., 2014].

8.3.3 LICI Analyses Our analysis presents a wider picture of LICI, in which the extent of the estimated inhibition

in the magnitude and time span is larger than previously documented. We noticed that the

size of the period of inhibition is strongly associated to the frequency, which was particularly

broad at lower frequencies. The location and strength of the peak of inhibition not only

depends on the frequency but also the stimulation site. Our results show that inhibition can be

indexed, with high sensitivity, from areas distant to the stimulation site, mainly over central

and contralateral channels. While GABAB-mediated inhibition can be local in nature, its

effect can potentially amplify as we move away from the stimulation site in both time and

space. Thus, indexing inhibition from distant sites can offer a more sensitive characterization

of the phenomenon. The electrodes closer to the site of stimulation are more affected by coil-

induced artifacts, and thus the least reliable. This could conceivably help to explain why

inhibition was not particularly strong over the area of stimulation. Additionally, distant

inhibition has been reported as interhemispheric inhibition and is mechanistically similar to

LICI [Daskalakis et al., 2002b]. In summary, the advancements in pre- and post-processing

EEG techniques applied in these neurophysiological experiments may serve as a road map

for investigators to model their data processing pipelines.

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8.4 Pathophysiology of Schizophrenia SCZ is one of the most severe neuropsychiatric disorders, the diagnosis remains symptom-

based, not etiologically based. Studies have previously reported that patients with SCZ show

gamma inhibition deficits in the DLPFC [Farzan et al., 2010a; Radhu et al., 2015]. This

imbalanced circuitry is evident from previous studies in the context of SCZ pathophysiology.

We have shown the specificity of a novel combined TMS-EEG index of GABAB inhibitory

neurotransmission [Radhu et al., 2015]. SCZ patients exhibited LICI deficits in the DLPFC,

and no differences were shown between OCD and healthy subjects. Interestingly, no

differences were observed among the 3 groups in motor cortex, indicating that LICI deficits

in DLPFC were specific to SCZ. This work may represent critical biomarker validation that

can be used to inform diagnostic decisions.

8.4.1 GABAergic Deficits in Schizophrenia Impairments of several neurotransmission systems such as dopamine, glutamate, serotonin,

and GABAergic systems are separately linked to the pathophysiology of SCZ.

Pharmacological studies have examined the neurochemical basis of several TMS paradigms

by examining the effect of dopamine, glutamate and GABA agents on the extent of inhibition

or excitation following the application of a specific TMS paradigm to the motor cortex of

healthy subjects. The results of such studies, as reviewed by Paulus et al. [Paulus et al.,

2008a] indicate that inhibition observed through LICI is likely associated with activation of

GABAB receptors. Previous evidence has demonstrated an association between GABAergic

inhibitory neurotransmission and gamma oscillations [Bartos et al., 2007; Bragin et al., 1995;

Brown et al., 2007; Jefferys et al., 1996; Marrosu et al., 2006; Scanziani, 2000; Traub et al.,

1997; Traub et al., 1996; Wang and Buzsaki, 1996; Whittington et al., 1995]. Furthermore,

the modulation of gamma oscillations represent an important neurophysiological process that

may, in part, be responsible for optimal cognitive functioning in the DLPFC. It has been

postulated that the functional role of gamma modulation may be to provide a temporal frame

for information processing and filtering out information. For example, it has been shown that

the successful encoding of information may depend on its arrival time relative to the gamma

cycle. Information arriving at the fading phase of inhibition would be potentiated, whereas

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information arriving at the beginning of the inhibitory period will be blocked from further

propagation, likely through LTP-like mechanisms (i.e., NMDA receptor-mediated

neurotransmission). Gamma band abnormalities may correspond to a down-regulation of

gamma due to the aberration in the ability of the neural circuits to support this critical

frequency range. Disturbances in chandelier cell functioning could impair the ability of

cortical circuits to engage in high frequency synchronous oscillations [Lewis et al., 2005], as

a result, disrupted LICI may result from disordered synaptic wiring in key cognitive

networks. CI aids suppression of neural noise by filtering irrelevant sensory information

imperative for attention and cognitive performance Thus, gamma oscillations represent an

important neurophysiological process that may, in part, be responsible for optimal cognitive

function and may explain why their functioning (i.e., generation and modulation) is largely

localized to the DLPFC [Farzan et al., 2009], shown to be dysfunctional in SCZ.

Disrupted gamma oscillations in the DLPFC as indexed by EEG have been consistently

demonstrated in SCZ patients [Basar-Eroglu et al., 2007; Cho et al., 2006; Uhlhaas and

Singer, 2010]. For example, it has been shown that patients with SCZ have deficits in gamma

oscillatory activity in response to 40 Hz auditory stimulation [Light et al., 2006] or during

perception of gestalt objects [Spencer et al., 2003] as compared to healthy individuals, while

other studies have shown an impairment in modulation of gamma oscillations in the DLPFC

during working memory tasks [Cho et al., 2006]. Also, both increase and reduction of gamma

oscillations have been reported in patients with SCZ [Ferrarelli et al., 2008; Lee et al., 2003].

We have specifically demonstrated that patients with SCZ exhibit excessive power of gamma

oscillations during working memory performance [Barr et al., 2010]. Barr et al. examined

frontal gamma oscillations using EEG in SCZ and healthy subjects during working memory

performance at three different working memory loads. It was shown that SCZ patients

performed worse than healthy subjects, and generated excessive power of gamma oscillations

from the frontal local electrodes only most notably during the task with the highest working

memory load, the 3-back task condition [Barr et al., 2010]. It has been suggested that the 3-

back may involve attentional components and/or short term memory aspects of working

memory. In this view, working memory is regarded as controlled attention and been shown to

modulate with gamma oscillatory activity. Thus, it is possible that the excessive power of

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gamma oscillations was associated with the impairment in the modulation of gamma

oscillations. To ascertain these findings further, future studies should examine the correlation

between frontal inhibitory deficits, attentional processing and working memory performance

in SCZ patients.

8.5 Pathophysiology of OCD OCD is a debilitating psychiatric illness that has been reported to affect 1-3% of the world’s

population [Horwath and Weissman, 2000; Torres et al., 2006]. OCD consists mainly of

obsessions (i.e., persistent, intrusive thoughts or impulses that potentiate anxiety) and

compulsions (i.e., repetitive ritualistic physical or mental actions performed to reduce

obsession-provoked anxiety) [Abramowitz et al., 2009; Heyman et al., 2006; Stein, 2002].

Studies show that the rate of co-morbidity between SCZ and OCD is approximately 7% to

26% [Eisen et al., 1997; Fabisch et al., 1997; Porto et al., 1997; Poyurovsky et al., 1999;

Tibbo et al., 2000].

Several genetic studies have reported associations between OCD and dysfunctional

GABAergic and glutamatergic genes [Arnold et al., 2006; Dickel et al., 2006; Samuels et al.,

2011; Stewart et al., 2007; Voyiaziakis et al., 2011; Zai et al., 2005]. Arnold and colleagues

[Arnold et al., 2004] found a positive association between variants in the 3′ untranslated

region of the GRIN2B gene— the gene encoding the NR2 subunit of the N-methyl-D-

aspartate (NMDA) glutamate receptor and OCD in 178 affected individuals from 130

families. Similarly, Whiteside et al., demonstrated increased levels of a combined measure of

glutamate and glutamine relative to creatine were found in orbitofrontal white matter in

patients with OCD [Whiteside et al., 2006]. Furthermore, Chakrabarty et al., showed

significantly higher levels of glutamate in OCD [Chakrabarty et al., 2005]. Animal models

confirm the role of corticolimbic glutamatergic hyperactivation in patients with OCD

[Nordstrom and Burton, 2002]. Zai et al., found a positive association between OCD and the

GABAB receptor gene (GABR1) [Zai et al., 2005], implicating a relationship between

dysfunctional GABAB and the pathophysiology of OCD. TMS studies with OCD patients

have demonstrated decreased inhibition [Greenberg et al., 2000; Greenberg et al., 1998;

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Richter et al., 2012] and enhanced cortical excitability [Richter et al., 2012]. Richter et al.

reported [Richter et al., 2012] that patients with OCD have abnormalities in both GABAB and

NMDA receptor-mediated neurotransmission. Deficits were found in inhibition and

excessive intracortical facilitation of the motor cortex, a paradigm reflecting excessive

NMDA-receptor-mediated excitatory neurotransmission, independent of medication status.

Collectively these findings are consistent with genetic findings reporting GABA and NMDA-

related genes involved in the pathophysiology of OCD [Arnold et al., 2006; Dickel et al.,

2006; Samuels et al., 2011; Stewart et al., 2007; Voyiaziakis et al., 2011; Zai et al., 2005].

However, motor cortex TMS studies are of limited interest as the pathophysiology of many

psychiatric disorders are more closely associated with frontal brain abnormalities. Therefore,

it is essential to evaluate the neurophysiology in brain regions that are more proximal to the

underlying phenotype such as the DLPFC.

As demonstrated in this dissertation, we did not find frontal inhibitory deficits in OCD

patients and their unaffected first-degree relatives. Our findings could be accounted for by

the large medicated OCD sample, as the recent Richter et al., study included a majority of

unmedicated patients with OCD (68%) [Richter et al., 2012]. SSRIs inhibitors are the

established pharmacologic first-line treatment for OCD [Decloedt and Stein, 2010; Kellner,

2010]. Serotonin is able to modulate excitatory and inhibitory effects, respectively mediated

by glutamate and GABA [Ciranna, 2006]. The serotonin receptor (5-HT) induces a decrease

of glutamate transmission and a parallel increase in GABA transmission evident in the

hippocoampus, frontal cortex and the cerebellum [Ciranna, 2006]. Previous studies have

shown that SSRI’s increase GABA measured by magnetic resonance spectroscopy

[Bhagwagar et al., 2004] and TMS [Robol et al., 2004], thus concealing any potential LICI

deficits in the present study. The modulatory action of the serotonin receptor (5-HT) may

serve as a "brake" on neuronal excitability. Given this inconsistency, further studies are

warranted to disentangle the effects of medication. Future directions of this work may be to

evaluate LICI (TMS-EEG) before and after SSRI treatment for OCD to establish a

relationship between inhibition and therapeutic response with larger samples of patients.

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8.6 Neurophysiological Biomarkers in Clinical Practice The identification of biological markers for heterogeneous disorders such as SCZ has several

potential benefits. First, there is a great need for developing a biological marker in psychiatry

as no laboratory tests have entered the clinic to inform diagnoses as well as to guide and

monitor treatments. Clinicians still rely on behavioural observation and careful interview

techniques to make inferences about patients’ inner experiences and make assumptions about

the impacted neural systems. Although we have refined indirect clinical assessments for

diagnosis and treatment, these methods have only slightly evolved over the past 100 years

[Light and Makeig, 2015]. Biological identification may also be used for diagnosing

individuals who are at high risk for developing SCZ, allowing for an early intervention. Since

SCZ is a very heterogeneous disorder across a variety of domains including: symptoms,

functional outcome, genetic architecture, and pathophysiology, mixed research data arise as a

result of this heterogeneity, making biomarker work extremely difficult [Light and Makeig,

2015].

The translation from neuroscience research to clinical practice has been challenging,

endophenotypes provide a way for that to occur. Endophenotypes are heritable biomarkers of

psychiatric disorders. To be considered as an endophenotype, a biomarker must fulfill criteria

such as: 1) heritability (i.e., measure of the strength of genetic effects on a trait) 2) trait

stability (e.g., a biomarker that is unrelated to illness duration or pharmacological

treatments); 3) test–retest reliability; 4) diagnostic specificity (i.e., a biomarker is present in

the disease of interest but not present is other disorders); and 5) large effect size differences

between healthy controls and patients [Gottesman and Gould, 2003]. Neurophysiological

tools have potential for providing information on diagnoses, treatment predictors and

treatment monitoring. More research is required to ensure their effective application in

clinical settings. The ability to evaluate physiological response profiles of different

oscillatory frequencies in response to TMS-induced cortical evoked potentials may ultimately

serve to identify endophenotypes or biomarkers for the identification of a variety of

neurological and psychiatric disorders. Much more work is needed to effectively use TMS-

EEG biomarkers as a diagnostic or treatment tool.

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8.6.1 Assessing Biological First-Degree Relatives of Patients

As discussed and emphasized in chapter 7, endophenotypes are a subtype of biological

markers that are independent of illness state and occur with a higher probability in biological

relatives of patients with SCZ. As such, endophenotypes (also known as intermediate

phenotypes) may be used to distinguish biological relatives who are at risk of developing

SCZ prior to illness onset. It has been proposed that an efficient preventive strategy may

involve using laboratory tests to assess children of parents affected with SCZ, and then

provide preventive strategies for children whereby impairments are observed and are at

greater risk for developing the illness later in life compared to their siblings

Estimation of genetic influence on neurophysiological responses necessitates measures of

heritability, which is ideally achieved with twin studies or large samples of family cohorts.

The true test for a biomarker is that the genetic association should be seen within the

intermediate phenotype in individuals who do not have the clinical diagnosis [Tan et al.,

2008]. Our sample utilized nuclear families and families had to consist of at least one relative

per proband (at minimum) and two parents and one sibling (at maximum). Data from

biological relatives of probands are important tools for assessing the etiology of psychiatric

illnesses. First-degree relatives allow for assessment of intermediate phenotypes which refer

to pathophysiological phenomena, whereby, susceptability-related phenotypes are between

genetics and the neurobiological systems underlying behavioral disturbance [Tan et al.,

2008]. Specifically, they allow for a target to find disease-associated genetic variants and

elucidation of disease-related mechanisms [Flint et al., 2014]. Biological markers and in

particular endophenotypes could be used for early identification, etiological diagnosis, and

selection and discovery of optimal treatment strategies for each patient. Intermediate

phenotypes integrate basic neurobiology with specific human phenotypes that are potentially

tractable genetically [Tan et al., 2008].

In this dissertation, we have showed a reliable approach for indexing the inhibition of cortical

oscillations in vivo in humans. Our technique in combination with neuroimaging and

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neurocognitive measures may provide a valuable means for in vivo identification of

biological markers for heterogeneous and complex psychiatric disorders such as SCZ.

8.7 Limitations The experiments outlined in this PhD dissertation are a new beginning for a series of future

experiments. However, there are several limitations to this work, discussed in greater detail

within this section.

First, the functional and regional specificity of modulation of cortical oscillations remains

unsolved. The modulatory pattern of cortical oscillations in other cortices, such as in

occipital, temporal, and parietal cortex should be examined in future experiments. Therefore,

without further evidence, it is not possible to infer whether or not gamma inhibition or

overall inhibition is specific to DLPFC. Similarly, to examine the functional significance of

gamma and overall inhibition, the relationship between LICI and performance on various

domains of cognitive function such as working memory should be investigated. In order to

better link neurophysiological findings to the symptoms of SCZ and OCD, future studies

should establish better relationships between LICI, cognition and behaviour.

Second, this study included a pilot sample of unaffected first-degree relatives and their

probands. Replication studies using a multi-site trial approach assessing larger samples of

probands and their relatives are needed. For example, the COGS is a multi-site National

Institute of Mental Health sponsored collaboration investigating the genetic basis of

candidate endophenotypes in SCZ patients and their relatives. Findings from the COGS

family-based study confirmed the presence of robust deficits in SCZ probands and their first-

degree relatives in several different neurophysiological measures, including P50 suppression

[Olincy et al., 2010], N100 evoked amplitude [Turetsky et al., 2008] and antisaccade

performance [Radant et al., 2010]. The COGS strategy has been used to acquire

endophenotype measures across multiple geographically distributed sites to maximize sample

ascertainment [Calkins et al., 2007]. Larger samples of first-degree relatives are needed in

future studies to further assess the heritability of frontal inhibitory deficits in SCZ. Larger

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sample sizes which include both biological parents and one full sibling are required to

compute heritability analyses, however, with family-based approaches; challenges include

recruitment of large samples. A sample size calculation was completed, to achieve a

heritability estimate of 0.60 with 80% power, 44 families would be required (1 affected

proband and two biological parents) or 17 families would be required (1 affected proband, 1

full sibling and two biological parents) [Schork and Schork, 1993]. To achieve a heritability

estimate of 0.60 with 90% power, 58 families would be required (1 affected proband and two

biological parents) or 23 families would be required (1 affected proband, 1 full sibling and

two biological parents) [Schork and Schork, 1993]. Further investigation with larger multi-

center research trials are needed to develop TMS as a neurophysiological marker for use in

diagnosis and for monitoring treatment outcomes.

Third, frontal inhibition was assessed in SCZ and OCD using a cross-sectional design. Future

studies can consider evaluating patients over time (i.e. before and after starting treatment) to

assess the effects of psychotropic medications, to establish a relationship between inhibition

and therapeutic response.

Lastly, a main shortcoming of TMS-EEG experiments is the contamination of brain signals

by various sources of artifact. As indiciated in the introduction, combined TMS and EEG

measures are susceptible to unwanted physiological and non-physiological artifacts,

particularly over regions such as the DLPFC. Somatosensory-evoked potentials resulting

from either scalp or peripheral TMS-evoked muscle activity or from stimulation of the

trigeminal nerve may also contribute to the noise in the EEG signal. Muscle activity, eye

movement, eye blink and TMS-related decay artifacts can be removed with minimal impact

on neural activity using ICA, allowing the study of TMS-evoked cortical network properties.

However, artifact selection can be very subjective and time consuming, making it difficult to

decide which component to accurately remove without removing TMS-evoked neuronal

activity [Rogasch et al., 2014].

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8.8 Future Directions There is significant potential in the future to evaluate a variety of other neurophysiological

processes in the cortex. Future studies may also permit the recording of plasticity in non-

motor brain regions. For example, 30 min of repeated stimulation of the median nerve

applied simultaneously with TMS to the motor cortex results in LTP in the motor cortex

through a paradigm known as PAS [Stefan et al., 2000]. Such plasticity is LTP-like as

repeated and cotemporaneous excitation of sensory afferents and motor interneurons

translates into increased motor excitability. These and other plasticity measures have been

previously shown to be impaired in SCZ [Daskalakis et al., 2008a; Frantseva et al., 2008].

Thus, combining TMS and EEG with PAS can be used to index LTP in the DLPFC, and it

can provide critical advantages when attempting to understand key brain mechanisms

underlying learning and working memory. Future studies may also be used to examine

potential regional pharmacological effects that may be of particular importance to illnesses

whose pathophysiology may be more regionally specific (e.g., major depressive disorder).

Future studies may include twin studies, assessing both monozygotic and dizygotic twins to

further disentangle the heritability of inhibition. Furthermore, assessing genetic mutations

and correlating liability genes with LICI would be very important in future trials. In

summary, further studies are warranted to validate our findings with larger multi-site trials.

This dissertation expands and develops further the initial reports of LICI by our group using

TMS and EEG. TMS can be combined with EEG to assess neurophysiological profiles of

response; current studies have demonstrated several neurophysiological processes including

excitability, inhibition, and interhemispheric signal propagation. We consider the pre-

processing tools and our statistical approach to quantify LICI to be accepted as a future

guideline in this field. Within this line of research, the characterization of LICI that we have

presented here can be used to enhance the pool of parameters that characterize inhibition.

The novel analyses presented in this dissertation have great potential to detect network

differences between healthy and disease states and can be applied in future biomarker work.

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8.9 Conclusions The ability to evaluate cortical processes such as inhibition, excitation and plasticity in

healthy subjects has further led to the discovery of pathophysiological processes in various

neuropsychiatric disorders. This doctoral work consisted of a series of experiments that

applied TMS to examine the modulation of cortical oscillations whose impairments may be

involved in pathophysiology SCZ. Intermediate phenotypes are vital as they link genetic

variation to complex disease mechanisms. Due to the heterogeneity of SCZ and the above

mentioned limitations, it may be too early to conclude that frontal inhibition may be a central

endophenotype for this disease.

Overall, this dissertation has demonstrated inhibitory deficits in the motor cortex in SCZ in

the meta-analysis. We then showed specific frontal inhibitory deficits in SCZ, differentiating

SCZ from OCD. Lastly, we demonstrated first-degree relatives were intermediate of their

related SCZ probands and healthy controls in frontal inhibition with a trending heritability of

0.65. The use of combined TMS and EEG is a tool that needs to be further explored to apply

to clinical practice and may help to explain the variance in disease mechanisms. Our findings

provide rationale for future biomarker research to further ascertain the role of frontal

inhibitory impairments in SCZ.

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Inhibition of Gamma Oscillations as a Neurophysiological ...€¦ · Natasha Radhu and Mawahib Semeralul completed the recruitment for the study. Mawahib Semeralul screened all research