Using Magnetic Resonance Imaging to Study Neurometabolic and Neuroanatomical Alterations in Patients with Schizophrenia

Eric Plitman

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto

Eric Plitman

Doctor of Philosophy

Institute of Medical Science

University of Toronto

2018

Abstract

Schizophrenia is a debilitating mental illness that places a large burden on patients, their families, and society-at-large. The glutamate hypothesis of schizophrenia puts forth a compelling mechanism to characterize features of the illness. To this end, we explored the neurometabolic and neuroanatomical profiles of patients with schizophrenia using magnetic resonance imaging, with a particular focus on the glutamatergic system. First, we explored associative striatum neurometabolite levels using proton magnetic resonance spectroscopy (1H-MRS) within a sample composed of antipsychotic-naïve patients experiencing their first-episode of psychosis (FEP) and age- and sex-matched healthy controls. The FEP group had elevated myo-inositol, choline, and glutamate levels compared to the healthy control group. Second, again within a sample of antipsychotic-naïve patients with FEP and age- and sex-matched healthy controls, we investigated whether elevated levels of glutamatergic neurometabolites within the precommissural dorsal caudate (PDC), as assessed by 1H-MRS, were related to measures of brain structure. In addition to widespread cortical thinning and suggestions of possible precommissural caudate volume (PCV) deficits within the patient group, a negative association

between PDC glutamate+glutamine levels and PCV was found in the FEP group. Third, striatal neurometabolite levels were examined using 1H-MRS within a sample of patients with schizophrenia who had undergone long-term antipsychotic treatment and healthy controls. No group differences in neurometabolite levels were identified. Multiple study visits permitted a 1H-

MRS reliability assessment. Taken together, the results from this body of work suggest elevated levels of striatal glutamatergic neurometabolites within the early, antipsychotic-naïve stages of schizophrenia, which are contrastingly shown to be comparable to those of healthy controls in the later, medicated stages of illness. Findings also provide evidence for glial activation that may resultantly disrupt glutamatergic tone, as well as a striatal excitotoxic mechanism that may account for some of the vast structural compromise that exists in patients with schizophrenia.

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Acknowledgments

I dedicate this work to my parents, Anastassia and Borya Plitman, my brother, Max Plitman, my grandparents, Elena and Garik Plitman, and Maria and Zorik Shlyonsky, my uncle, Dimitry Shlyonsky, my cousins, Marina Proskurovsky, Gaby Shlyonsky, and Ronen Shlyonsky, and my aunt and uncle, Inna and Sasha Proskurovsky, without whom none of the following pages would have been possible. I am extremely fortunate to have each of you in my life and love you all very much. Thank you for all of your unconditional love and support, and for all that you have sacrificed for me to have the possibility to pursue my studies.

I would also like to especially thank Jane Kobylianskii, who has sacrificed an incredible amount over the last five years for my success. Thank you for being my partner in every aspect of life and for sharing the ups and downs with me.

I sincerely appreciate the supervision that I have received in my work. I wish to extend the largest possible thank you to Dr. Ariel Graff-Guerrero. Thank you for your kindness and inspiration, as well as your exceptional mentorship, teaching, guidance, and support. I will forever be grateful for the opportunities that I have been afforded as your student, and for my extensive professional and personal growth over this time.

I am extremely thankful for the mentorship that I have received from Dr. Philip Gerretsen. Thank you for your constant teaching and support. I am very grateful for everything I have learned from you over the last five years, and for the positive and open environment that you and Dr. Graff- Guerrero have created within the research group.

An immense and special thank you goes to my committee members, Dr. M. Mallar Chakravarty and Dr. Gary Remington, for their supervision, mentorship, teaching, support, and guidance. I am tremendously grateful for your kindness, generosity, and expertise. I have been extremely lucky to have such incredible mentors, teachers, and role models over the course of my graduate degree.

I would also like to especially thank Dr. Sofia Chavez, without whom most of the original work within this thesis would not be possible. I am extremely appreciative of your assistance, mentorship, teaching, and support.

I am also thankful for my research group, the Multimodal Imaging Group, including Jun Ku Chung, Wanna Mar, Shinichiro Nakajima, Yusuke Iwata, Fernando Caravaggio, and Julia Kim. Thank you for all of your assistance and teaching over the years, but most importantly, thank you for the laughs and memories that we shared.

Thank you to my collaborators in Mexico and Japan. In particular, thank you very much to Dr. Camilo de la Fuente-Sandoval for trusting an inexperienced graduate student with the gift of an incredible dataset and for your guidance throughout.

Thank you to the Research Imaging Centre at the Centre for Addiction and Mental Health and the Institute of Medical Science at the University of Toronto for your support.

Thank you to the Canadian Institutes of Health Research and the Ontario Graduate Scholarship.

Finally, thank you to my friends outside of research for all of your support over these years.

Contributions

Eric Plitman (author) solely prepared this thesis. All aspects of this body of work, including the planning, execution, analysis, and writing of all original research and publications was performed in whole or in part by the author. The following contributors are acknowledged:

Dr. Ariel Graff-Guerrero (Supervisor) – mentorship; laboratory resources; guidance and assistance in planning, execution, and analysis of experiments as well as manuscript/thesis preparation.

Dr. Philip Gerretsen (Thesis Committee Member) – mentorship; laboratory resources; guidance and assistance in planning, execution, and analysis of experiments as well as manuscript/thesis preparation.

Dr. M. Mallar Chakravarty (Thesis Committee Member) – mentorship; laboratory resources; guidance and assistance in planning, execution, and analysis of experiments as well as manuscript/thesis preparation.

Dr. Gary Remington (Thesis Committee Member) – mentorship; guidance and assistance in planning, execution, and analysis of experiments as well as manuscript/thesis preparation.

Dr. Sofia Chavez – mentorship; laboratory resources; guidance and assistance in planning, execution, and analysis of experiments as well as manuscript preparation.

Dr. Camilo de la Fuente-Sandoval – mentorship; laboratory resources; guidance and assistance in planning, execution, and analysis of the studies within Chapter 1, Chapter 3, and Chapter 4.

Dr. Shinichiro Nakajima – mentorship; guidance and assistance in planning, execution, and analysis of the studies within Chapter 1, Chapter 5, and Chapter 6.

Dr. Yusuke Iwata – assistance in executing the studies within Chapter 1, Chapter 5, and Chapter 6. Dr. Pablo León-Ortiz – assistance in executing the studies within Chapter 3 and Chapter 4.

Dr. Francisco Reyes-Madrigal – assistance in executing the studies within Chapter 3 and Chapter 4.

Dr. Gladys Gómez-Cruz – assistance in executing the studies within Chapter 3 and Chapter 4.

Dr. Youssef Alshehri – assistance in executing the study within Chapter 5.

Dr. Vincenzo De Luca – assistance in executing the study within Chapter 5.

Dr. Fernando Caravaggio – assistance in executing the studies within Chapter 1, Chapter 5, and Chapter 6.

Dr. Jun Ku Chung – assistance in executing the studies within Chapter 1, Chapter 4, Chapter 5, and Chapter 6.

Dr. Jane Kobylianskii – assistance in executing the study within Chapter 1.

Julia Kim – assistance in executing the study within Chapter 5 and Chapter 6.

Raihaan Patel – assistance in executing the study within Chapter 4.

Jon Pipitone – assistance in executing the study within Chapter 4.

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Table of Contents Acknowledgments ...... iv

Contributions ...... vi

Table of Contents ...... viii

List of Tables ...... xiv

List of Figures ...... xvi

List of Abbreviations ...... xx

Chapter 1 ...... 1

1 Introduction ...... 1 1.1 Schizophrenia ...... 1 1.1.1 Symptoms ...... 1 1.1.2 Diagnosis ...... 1 1.1.3 Illness Course ...... 2 1.1.4 Treatment ...... 3 1.1.5 Impact ...... 5 1.1.6 Epidemiology ...... 9 1.1.7 Etiology ...... 10 1.2 The Dopaminergic System ...... 11 1.3 The Glutamatergic System ...... 13 1.4 Magnetic Resonance Imaging ...... 15 1.4.1 Proton Magnetic Resonance Spectroscopy ...... 18 1.4.1.1 Glutamatergic Neurometabolites ...... 22 1.4.1.2 Other Neurometabolites ...... 22 1.4.1.3 Indices of Spectral Quality ...... 23 1.4.2 Structural Neuroimaging ...... 25 1.4.2.1 Volume ...... 25 1.4.2.2 Cortical Thickness ...... 28 1.5 Glutamate-mediated excitotoxicity in schizophrenia: a review ...... 29 1.5.1 Abstract ...... 30 1.5.2 Introduction ...... 30

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1.5.2.1 Schizophrenia ...... 31 1.5.2.2 Glutamatergic hypothesis of schizophrenia ...... 31 1.5.2.3 NMDA receptor hypofunction ...... 32 1.5.2.4 Glutamatergic dysfunction in schizophrenia ...... 32 1.5.2.5 Glutamate as an excitotoxic factor ...... 34 1.5.2.6 Structural changes in schizophrenia ...... 34 1.5.2.7 Aim of this review ...... 35 1.5.3 Experimental procedures ...... 35 1.5.4 Results ...... 36 1.5.5 Discussion ...... 39 1.5.5.1 Analysis of reviewed studies ...... 39 1.5.5.2 Limitations of reviewed studies: ...... 41 1.5.5.3 Evidence from preclinical literature: ...... 42 1.5.5.4 Future directions: ...... 44 1.5.5.5 Limitations of present review: ...... 44 1.5.6 Conclusion: ...... 45 1.6 Basal Ganglia ...... 46 1.6.1 Models of the Basal Ganglia ...... 46 1.6.2 Striatum and Subregions ...... 48 1.6.3 Dopaminergic Dysfunction within the Striatum in Schizophrenia ...... 50 1.6.4 Levels of Glutamatergic Neurometabolites within the Basal Ganglia in Schizophrenia ...... 52 1.6.5 Meta-Analytic 1H-MRS Findings within the Basal Ganglia in Schizophrenia ...... 54 1.6.6 Volume Findings within the Striatum in Schizophrenia ...... 55 1.7 Meta-Analytic 1H-MRS Findings in Schizophrenia ...... 55

Chapter 2 ...... 57

2 Rationale, Hypotheses, and Objectives ...... 57 2.1 Study One ...... 57 2.1.1 Rationale ...... 57 2.1.2 Hypotheses ...... 57 2.1.3 Objectives ...... 58 2.2 Study Two ...... 58 2.2.1 Rationale ...... 58 2.2.2 Hypotheses ...... 59

2.2.3 Objectives ...... 59 2.3 Study Three ...... 59 2.3.1 Rationale ...... 59 2.3.2 Hypotheses ...... 60 2.3.3 Objectives ...... 60

Chapter 3 ...... 61

3 Elevated Myo-Inositol, Choline, and Glutamate Levels in the Associative Striatum of Antipsychotic-Naïve Patients With First-Episode Psychosis: A Proton Magnetic Resonance Spectroscopy Study With Implications for Glial Dysfunction ...... 61 3.1 Abstract ...... 61 3.2 Introduction ...... 62 3.3 Methods ...... 63 3.3.1 Participants ...... 63 3.3.2 Clinical Assessment ...... 64 3.3.3 Magnetic Resonance Studies ...... 64 3.3.4 1H-MRS Data Analysis ...... 65 3.3.5 Statistical Analysis ...... 68 3.4 Results ...... 69 3.4.1 Demographic and Clinical Characteristics ...... 69 3.4.2 Neurometabolite Levels ...... 70 3.4.3 Relationships With Clinical Measures ...... 72 3.4.4 Relationships Between Neurometabolites ...... 73 3.4.5 CRLB, FWHM, Signal-to-Noise Ratios, and Tissue Heterogeneity ...... 74 3.5 Discussion ...... 75

Chapter 4 ...... 83

4 Glutamatergic Metabolites, Volume and Cortical Thickness in Antipsychotic-Naïve Patients with First-Episode Psychosis: Implications for Excitotoxicity ...... 83 4.1 Abstract ...... 83 4.2 Introduction ...... 84 4.3 Materials and Methods ...... 85 4.3.1 Participants ...... 85

4.3.2 Clinical Assessment ...... 86 4.3.3 Magnetic Resonance Studies ...... 86 4.3.4 1H-MRS Data Analysis ...... 87 4.3.5 Image Preprocessing ...... 88 4.3.6 PCV Analysis ...... 88 4.3.7 Subcortical Structure Volume Analysis ...... 90 4.3.8 Total Brain Volume Analysis ...... 91 4.3.9 CT Analysis: ...... 91 4.3.10 Statistical Analysis ...... 92 4.4 Results ...... 93 4.4.1 Demographic, Clinical, and 1H-MRS Group Differences ...... 93 4.4.2 Group Differences in PCV and TBV ...... 95 4.4.3 Group Differences in CT ...... 97 4.4.4 Relationships Between Neurometabolite Levels and Structural Measures ...... 98 4.4.5 Relationships Between Clinical Symptoms and Structural Measures ...... 99 4.5 Discussion ...... 100

Chapter 5 ...... 108

5 Striatal Neurometabolite Levels in Patients with Schizophrenia Undergoing Long-Term Antipsychotic Treatment: A Proton Magnetic Resonance Spectroscopy and Reliability Study 108 5.1 Abstract ...... 108 5.2 Introduction ...... 109 5.3 Methods ...... 110 5.3.1 Study design ...... 110 5.3.2 Participants ...... 111 5.3.3 Clinical assessment ...... 112 5.3.4 Magnetic resonance imaging ...... 112 5.3.5 1H-MRS data analysis ...... 113 5.3.6 Statistical analysis ...... 114 5.4 Results ...... 116 5.4.1 Demographic and clinical characteristics ...... 116 5.4.2 Group differences in neurometabolite levels ...... 117

5.4.3 Relationships between neurometabolite levels and participant characteristics ...... 119 5.4.4 CRLB, FWHM, signal-to-noise ratios, and tissue heterogeneity ...... 120 5.4.5 Reliability of neurometabolite levels ...... 121 5.4.6 Effect size and power analyses ...... 123 5.5 Discussion ...... 123 5.6 Conclusion ...... 127

Chapter 6 ...... 128

6 Discussion ...... 128 6.1 Summary of Findings ...... 128 6.2 The Relationship between Striatal Dopamine and Striatal Glutamate Levels ...... 130 6.3 Heuristic Mechanisms of Schizophrenia Pathophysiology in the Context of Glutamatergic Disturbances ...... 134 6.3.1 Neuroinflammation and Glial Activation in Schizophrenia ...... 134 6.3.2 Kynurenic Acid in Schizophrenia ...... 136 6.3.2.1 Abstract ...... 137 6.3.2.2 Introduction ...... 137 6.3.2.2.1 Schizophrenia ...... 137 6.3.2.2.2 Kynurenine Pathway ...... 138 6.3.2.2.3 KYNA Hypothesis of Schizophrenia ...... 139 6.3.2.2.4 Study Aims ...... 140 6.3.2.3 Methods ...... 140 6.3.2.3.1 Literature Search ...... 140 6.3.2.3.2 Inclusion Criteria ...... 140 6.3.2.3.3 Exclusion Criteria ...... 141 6.3.2.3.4 Outcome Measures ...... 141 6.3.2.3.5 Recorded Variables ...... 141 6.3.2.3.6 Data Analysis ...... 141 6.3.2.4 Results ...... 143 6.3.2.4.1 Included Individual Studies ...... 143 6.3.2.4.2 Risk of Bias ...... 146 6.3.2.4.3 Meta-analyses ...... 148 6.3.2.4.4 Moderator Analyses ...... 148 6.3.2.4.5 Sensitivity Analysis ...... 153 6.3.2.4.6 Publication Bias ...... 153

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6.3.2.5 Discussion ...... 154 6.3.2.5.1 Main Findings ...... 154 6.3.2.5.2 Analysis of Included Studies ...... 154 6.3.2.5.3 Analysis of Meta-regression Findings ...... 155 6.3.2.5.4 Putative Mechanisms of KYNA Elevation in Schizophrenia ...... 156 6.3.2.5.5 Implications of KYNA Dysregulation ...... 157 6.3.2.5.6 Limitations of Present Study ...... 161 6.3.2.5.7 Conclusion ...... 161 6.3.2.5.8 Future Directions ...... 162 6.4 Neurometabolite Modulation ...... 162 6.4.1 Adjunctive Treatment of Patients with Schizophrenia Using Glutamatergic Agents ...... 162 6.4.2 Adjunctive Treatment of Patients with Schizophrenia Using Non-Glutamatergic Agents Relevant to the Current Work ...... 165 6.4.3 Neurostimulation ...... 165 6.5 Glutamate and Other 1H-MRS Neurometabolites from a State and Trait Perspective ...... 168 6.6 Implications of Neurometabolic and Neuroanatomical Disturbances ...... 171 6.7 Other Noteworthy Neurometabolites to Consider ...... 174 6.8 Broad Limitations ...... 175 6.9 Conclusions ...... 179 6.10 Future Directions ...... 180 6.10.1 Next Steps for Investigation ...... 180 6.10.2 A Consideration for “Good” Quality 1H-MRS Research ...... 184 6.10.3 The “Perfect” Study Design to Better Understand 1H-MRS Neurometabolite Disturbances in Schizophrenia ...... 186

References ...... 189

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List of Tables

Table 1-1. Schizophrenia in DSM-5. Changes in diagnostic criteria from DSM-IV. Reproduced with permission from Tandon et al (2013) Schizophrenia Research (1).

Table 1-2. Pharmacological and selected side-effect profile of atypical antipsychotics (activity at D2 receptors is still the only property that unites atypical agents). Reproduced with permission from Kapur and Remington (2001) Annual Review of Medicine (2).

Table 1-3. Summary of studies that met inclusion criteria (n=7).

Table 3-1. Demographic and Clinical Characteristics of Study Participants.

Table 3-2. Neurometabolite Levels in Patients With First-Episode Psychosis and Healthy Controls.

Table 3-3. Relationships Between Neurometabolite Levels and PANSS Subscale Total Scores.

Table 3-4. Cramer-Rao Lower Bound Values, Full-Width at Half Maximum Values, and Signal- to-Noise Ratios.

Table 3-5. 1H-MRS Voxel Tissue Composition.

Table 3-6. Neurometabolite Levels Referenced to Total Creatine Levels in Patients With First- Episode Psychosis and Healthy Controls.

Table 3-7. Relationships Between Neurometabolite Levels Referenced to Total Creatine Levels and PANSS Scores.

Table 3-8. Relationships Among Neurometabolite Levels Referenced to Total Creatine Levels.

Table 3-9. Relationships Between Neurometabolite Levels and Age in Patients With First- Episode Psychosis, Healthy Controls, and Full Sample.

Table 4-1. Demographic and Clinical Characteristics of Study Participants.

Table 4-2. Glutamatergic Neurometabolite Levels and Cramer-Rao Lower Bound Values.

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Table 4-3. Full-Width at Half Maximum Values, Signal-to-Noise Ratios, and 1H-MRS Voxel Tissue Composition.

Table 4-4. Precommissural Caudate Volume and Total Brain Volume in Patients with First- Episode Psychosis and Healthy Controls.

Table 4-5. Relationships Between Glutamatergic Neurometabolite Levels and Precommissural Caudate Volume.

Table 4-6. Relationships Between Precommissural Caudate Volume and PANSS Subscale Total Scores.

Table 4-7. Relationships Between Glutamatergic Neurometabolite Levels and Subcortical Structure Volumes in Patients with First-Episode Psychosis.

Table 4-8. Relationships Between Glutamatergic Neurometabolite Levels and Subcortical Structure Volumes in Healthy Controls.

Table 5-1. Demographic and clinical characteristics of study participants.

Table 5-2. Neurometabolite levels in study participants.

Table 5-3. Relationships between striatal neurometabolite levels and participant characteristics.

Table 5-4. Cramer-Rao lower bound, full-width at half maximum, signal-to-noise ratios, and tissue heterogeneity in study participants.

Table 5-5. Reliability measures for striatal neurometabolite levels.

Table 5-6. Reliability measures for striatal spectral quality indices and tissue heterogeneity values.

Table 6-1. Summary of Included Studies (n = 13).

List of Figures

Figure 1-1. Illustration reflecting current understandings of schizophrenia phases of illness. Reproduced with permission from Tandon et al (2009) Schizophrenia Research (3).

Figure 1-2. Survival curves of expected age at death for men and women (n=31,728 patients with schizophrenia; n=4,581,311 persons free of psychiatric illness). Note: The Y-axis represents the proportion of the total cohort that is still alive, the yellow area represents the expected age at death for patients with schizophrenia, and the total area (yellow, green, orange) represents cohort members free of psychiatric illness. The green area, which represents patients with bipolar disorder, is not directly relevant to the current report. Reproduced with permission from Laursen (2011) Schizophrenia Research (4).

Figure 1-3. Estimated prevalence among patients with schizophrenia of several impacts associated with the illness. Note: Estimates are depicted using values provided within the text; where only ranges were provided, averages estimates were calculated. Comorbid mental illness values might be underestimated given that the average prevalence was determined considering only depression, panic disorder, posttraumatic stress disorder, and obsessive-compulsive disorder, whereas substance use disorder was separated for the purposes of this illustration.

Figure 1-4. Twelve-month and lifetime prevalence estimates of schizophrenia from thirty studies across fourteen countries. Note: Canadian estimates are demarcated in green. Reproduced with permission from Simeone et al (2015) BMC Psychiatry (5).

Figure 1-5. Schematic representation of the human central dopaminergic systems. Reproduced with permission from Scarr et al (2013) Frontiers in Cellular Neuroscience (6).

Figure 1-6. Tight physiological control is maintained over glutamatergic neurotransmission. Glutamine (Gln) is converted to glutamate (Glu) by glutaminase, although it can also be derived from the tricarboxylic acid cycle (not shown). Glu is packaged into presynaptic vesicles by the vesicular Glu transporters (VGLUTs) and released from the neuron in an activity-dependent manner through interactions with soluble N-ethylmaleimide-sensitive factor attachment receptor (SNARE) proteins. Glu is cleared from the extracellular space by excitatory amino-acid transporters (EAATs) present predominantly on glial cells. In glial cells Glu is converted to Gln by Gln synthetase. Various Glu receptors are present on presynaptic

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and postsynaptic neurons as well as on glial cells. These include both ionotropic receptors — AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid), NMDA (N-methyl-D- aspartate) and kainate receptors — as well as metabotropic Glu receptors (mGluRs). Reproduced with permission from Sanacora et al (2008) Nature Reviews Drug Discovery (7).

Figure 1-7. Pediatric patient positioned in the headcoil on the table of the MR system just before the table with patient and coil is moved into the magnetic bore for the MR investigation. Reproduced with permission from van der Graaf (2010) European Biophysics Journal (8).

Figure 1-8. Example of an LCModel output from a dorsolateral prefrontal cortex 1H-MRS voxel.

Figure 1-9. Example of a T1-weighted volumetric image.

Figure 1-10. Example of a MAGeT-Brain output, employing the Colin-27 Subcortical Atlas, from a single participant. Blue and red represent the striatum, green and purple represent the globus pallidus, and grey and yellow represent the thalamus.

Figure 1-11. Schematic illustrating an example of cortical thickness alterations in the left superior temporal gyrus.

Figure 1-12. Schematic representation of basal ganglia nuclei. Reproduced with permission from Jones (2012) Preface in Dopamine-Glutamate Interactions in the Basal Ganglia (9).

Figure 1-13. The classical “box and arrows” basal ganglia model, updated. Reproduced with permission from Obeso and Lanciego (2011) Frontiers in Neuroanatomy (10).

Figure 1-14. Diagram demonstrating the functional organization of A. frontal cortex and B. striatal afferent projections. (A) Schematic illustration of the functional connections linking frontal cortical brain regions. (B) Organization of cortical and subcortical inputs to the striatum. In both (A) and (B), the colors denote functional distinctions. Blue: motor cortex, execution of motor actions; green: premotor cortex, planning of movements; yellow: dorsal and lateral prefrontal cortex, cognitive and executive functions; orange: orbital prefrontal cortex, goal- directed behaviors and motivation; red: medial prefrontal cortex, goal-directed behaviors and emotional processing. Reproduced with permission from Haber (2003) Journal of Chemical Neuroanatomy (11).

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Figure 3-1. Location of right associative striatum voxel placement.

Figure 3-2. Example of a spectrum analyzed with LCModel 6.3-0E.

Figure 3-3. Neurometabolite levels in patients with first-episode psychosis and healthy controls. Cho, choline-containing compounds; Glu, glutamate; Glx, glutamate + glutamine; mI, myo- inositol; NAA, N-acetylaspartate; *P < .05; **P < .01; ***P < .001.

Figure 3-4. Relationships between myo-inositol levels and positive symptom subscale total (a), hallucinatory behavior (b), and grandiosity (c) scores. PANSS, Positive and Negative Syndrome Scale.

Figure 3-5. Relationships between levels of myo-inositol and choline (a), glutamate and myo- inositol (b), and glutamate and choline (c) in patients with first-episode psychosis and healthy controls.

Figure 4-1. Location of right precommissural dorsal caudate 1H-MRS voxel placement and delineation of right precommissural caudate volume measure. Abbreviations: R, right. Depicted images derived from one randomly selected study participant.

Figure 4-2. Precommissural caudate volume (PCV) in patients with first-episode psychosis and healthy controls.

Figure 4-3. Cortical thinning in patients with first-episode psychosis compared with healthy controls. Abbreviation: FDR, false discovery rate.

Figure 4-4. Relationship between glutamate+glutamine (Glx) levels and precommissural caudate volume (PCV) relative to total brain volume (TBV) in patients with first-episode psychosis.

Figure 5-1. Delineation of right striatum 1H-MRS voxel placement. Abbreviations: R, right. Note: Depicted images derived from one randomly selected study participant.

Figure 5-2. Neurometabolite levels in patients with schizophrenia and healthy controls. Abbreviations: Cho, choline-containing compounds; Cr, creatine-containing compounds; Glu, glutamate; Glx, glutamate+glutamine; mI, myo-inositol; NAA, N-acetylaspartate-containing compounds.

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Figure 5-3. Relationships between age and choline levels in study participants.

Figure 6-1. Flowchart illustrating literature search and exclusion process (PRISMA diagram).

Figure 6-2. Risk of bias summary of included studies. Risk of bias related to “selection of participants”, “measurement of exposure”, “blinding of outcome assessment”, and “selective outcome reporting” was considered “low”. Risk of bias related to “confounding variables” was “high” for six studies: one only reported age range, four had age differences, and one had gender differences. Risk of bias related to “incomplete outcome data” was deemed “unclear” for two studies that did not entirely report standard deviation. Collectively, six studies (46.2%) showed a “low” risk of bias.

Figure 6-3. Group differences in KYNA levels between patients with schizophrenia and healthy controls. CI, confidence interval; IV, inverse variance; Std, standardized.

Figure 6-4. Subgroup analysis of nonoverlapping samples.

Figure 6-5. Subgroup analyses of KYNA measurement technique.

Figure 6-6. Subgroup analyses of KYNA sample source.

Figure 6-7. Meta-regression results.

Figure 6-8. Funnel plot.

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List of Abbreviations DSM - Diagnostic and Statistical Manual of Mental Disorders DALYs - Disability-adjusted life years CVD - Cardiovascular disease cAMP - Cyclic adenosine monophosphate PET - Positron emission tomography AMPA - α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid NMDA - N-methyl-D-aspartate mGluR - Metabotropic glutamate receptor EAATs - Excitatory amino acid transporters MRI - Magnetic resonance imaging 1H-MRS - Proton magnetic resonance spectroscopy 1H - Protium CSF - Cerebrospinal fluid MRS - Magnetic resonance spectroscopy NAA - N-acetylaspartate mI - Myo-inositol Cho - Choline; choline-containing compounds Cr - Creatine; creatine-containing compounds ppm - Parts per million DLPFC - Dorsolateral prefrontal cortex PRESS - Point resolved excitation spin-echo sequence STEAM - Stimulated echo acquisition mode LCModel - Linear Combination Model GABA - Gamma-aminobutyric acid GSH - Glutathione NAAG - N-acetylaspartylglutamate FWHM - Full-width at half maximum CRLB - Cramer-Rao lower bound SNR - Signal-to-noise ratio GM - Grey matter WM - White matter

SPM - Statistical Parametric Mapping MAGeT-Brain - Multiple Automatically Generated Templates BEaST - Brain Extraction based on nonlocal Segmentation Technique TBV - Total brain volume PCP - Phencyclidine CT - Computed tomography VBR - Ventricle to brain ratio ARMS - At-risk mental state 13C-MRS - Carbon magnetic resonance spectroscopy STN - Subthalamic nucleus SNc - Substantia nigra pars compacta SNr - Substantia nigra pars reticulate GPe - Globus pallidus externa GPi - Globus pallidus interna FEP - First-episode of psychosis Glx - glutamate+glutamine PANSS - Positive and Negative Syndrome Scale PDC - Precommissural dorsal caudate PCV - Precommissural caudate volume INNN - Institute of Neurology and Neurosurgery of Mexico TE - Echo time TR - Repetition time Lip - Lipids MM - Macromolecules NMDAR - N-methyl-D-aspartate receptor CT - Cortical thickness GPC - Glycerophosphocholine PCh - Phosphocholine ANTS - Automatic Normalization Tools ANCOVA - Analysis of covariance FDR - False discovery rate CAMH - Centre for Addiction and Mental Health

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MINI - Mini-International Neuropsychiatric Interview MacCAT-T - MacArthur Competence Assessment Tool SAPS - Scale for the Assessment of Positive Symptoms WRAT III - Wide Range Achievement Test-III SANS - Scale for the Assessment of Negative Symptoms AC-PC - Anterior commissure-posterior commissure ICCs - Intraclass correlation coefficients TRS - Treatment-resistant schizophrenia KYNA - Kynurenic acid KYN - Kynurenine KATs - Kynurenine aminotransferases HCs - Healthy controls SMD - Standardized mean differences IDO - Indoleamine 2,3-dioxygenase TDO - Tryptophan 2,3-dioxygenase KMO - Kynurenine 3-monooxygenase 3-HK - 3-hydroxykynurenine CNS - Central nervous system α7nAChR - α7 nicotinic acetylcholine receptors CI – Confidence interval SA - Schizoaffective disorder NOS - Not otherwise specified BPRS - Brief Psychiatric Rating Scale COX - Cyclooxygenase QUIN - Quinolinic acid tDCS - Transcranial direct current stimulation rTMS - Repetitive transcranial magnetic stimulation ECT - Electroconvulsive therapy CV - Coefficient of variation

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Chapter 1

1 Introduction

1.1 Schizophrenia

1.1.1 Symptoms

Schizophrenia is a debilitating mental illness characterized by three symptom domains: positive, negative, and cognitive symptoms (3, 12-14). Positive symptoms are defined as a significant impairment in reality testing and are composed of hallucinations, delusions, and disordered thought (3, 12-14). Negative symptoms are defined as a blunting or loss of behaviours or emotions, and include apathy and amotivation (3, 12-14). Lastly, cognitive symptoms are deficits in domains such as attention, executive function, and memory (3, 12-14).

1.1.2 Diagnosis

Schizophrenia is diagnosed using the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases, the former of which is primarily utilized in Canada. According to the DSM-5 (12), a diagnosis of schizophrenia necessitates at least two of: 1) delusions, 2) hallucinations, 3) disorganized speech, 4) grossly disorganized or catatonic behaviour, or 5) negative symptoms. In addition, a loss of function in at least 1 major area (e.g. work, interpersonal relations, self-care) and a persistence of disturbance for at least 6 months are required. Complete DSM-5 diagnostic criteria are presented within Holder and Wayhs (2014) (15) and in Table 1-1.

Table 1-1. Schizophrenia in DSM-5. Changes in diagnostic criteria from DSM-IV. Reproduced with permission from Tandon et al (2013) Schizophrenia Research (1).

1.1.3 Illness Course

Patients typically experience premorbid and prodromal phases of illness characterized by cognitive deficits, behavioural alterations, mood changes, functional decline, and attenuated positive symptoms (3, 12-14, 16). Subsequently, the first episode of psychosis (FEP) emerges, which often marks the timing of patients’ first clinical contact, diagnosis, and initiation of treatment (3, 12-14). Age of onset differs for males and females, usually presenting in the early- to-mid 20s and the late 20s, respectively (12, 15). Although the majority of patients achieve remission following their FEP, most relapse within 3-5 years (17, 18). The natural history of schizophrenia consists of a life-long illness carrying a poor prognosis, with marked progressive functional deterioration and multiple recurrences of active psychotic episodes (Figure 1-1) (19-

21). However, with treatment, the illness course is heterogeneous, with interindividual variation in its remission and relapse (22, 23). Ultimately, the outcome of illness is variable; while 20-50% of patients follow a favourable course and have good outcomes, few recover entirely (12, 24-26).

Figure 1-1. Illustration reflecting current understandings of schizophrenia phases of illness. Reproduced with permission from Tandon et al (2009) Schizophrenia Research (3).

1.1.4 Treatment

There is currently no cure for schizophrenia. The mainstay of treatment is pharmacologic and primarily involves antipsychotic medications (13). These agents exert their therapeutic effects largely through dopamine receptor antagonism in an attempt to normalize a hyperdopaminergic state (27). Antipsychotics can be categorized broadly into first-generation and second-generation antipsychotics, which have different side-effect profiles (Table 1-2). Generally, first-generation antipsychotics (e.g. haloperidol) are associated with adverse extrapyramidal symptoms, whereas second-generation antipsychotics (e.g. olanzapine,

4 clozapine) have an increased risk of weight gain and metabolic side-effects, which consequently put patients at an increased risk for the development of diabetes mellitus, obesity, and metabolic syndrome (28-31). Although commonly effective for managing symptoms of psychosis, antipsychotics are limited in that they fail to treat negative and cognitive symptoms (32, 33). Also, approximately 20-40% of patients have a limited response to initial antipsychotic treatment and are classified as treatment-resistant (34, 35). Of this subpopulation, some improve following treatment with clozapine, an atypical antipsychotic typically reserved for those who have failed past treatment (36). Still, around 40-70% of patients do not respond to clozapine (35, 37). Other than antipsychotics, non-pharmacologic treatment options include psychosocial interventions, such as cognitive behavioural therapy, cognitive remediation, social skills training, assertive community treatment, family psychoeducation, peer-support, self-help strategies, and supported employment (38-43). While these approaches may improve symptomatology, their principal aim is to improve functioning; the evidence for their efficacy in this regard is promising (38, 39, 42). Overall, concomitant psychosocial and antipsychotic treatment has been found to optimize outcomes (41, 43). Notably, hospitalization also remains a consideration during acute psychotic episodes and when indications for admission are met (40).

1.1.5 Impact

Schizophrenia is a multifaceted mental illness with debilitating personal, familial, societal, and social implications. When examined at the population level, it is evident that schizophrenia has large social and financial implications. The World Health Organization has identified schizophrenia as a top 10 cause of worldwide disability and attributed 3% of the global burden of disease to the illness (44). Recently, the 2013 Global Burden of Disease Study recognized it as a top 25 cause of worldwide disability (45). Global estimates in 2010 suggested that schizophrenia contributed approximately 13.6 million disability-adjusted life years (DALYs) (46). In a recent systematic review, the annual economic burden of schizophrenia was found to range from US$94 million to US$102 billion across nations, with the majority of costs incurred stemming from indirect costs, defined as productivity losses related to morbidity and premature mortality (47). In Canada, the total cost estimate related to schizophrenia in 2004 was CAN$6.85 billion (48) and in 2012, using data from the previous decade, the burden of schizophrenia in Ontario was predicted to be 72,864 DALYs per year (49).

Schizophrenia also has a profound impact at the individual level. The life expectancy of patients with schizophrenia is reduced by 10-20 years and mortality rates are 2-4 times higher than that of the general population (4, 50-53) (Figure 1-2). One reason for this elevated mortality rate is an increased risk for physical illnesses, as demonstrated by elevated standardized mortality ratios for physical illnesses such as cardiovascular disease (CVD), respiratory disease, cancer, and diabetes mellitus, of which CVD typically accounts for the greatest proportion of deaths (50, 52, 54-56). Evidently, the increased disability and mortality statistics among patients with schizophrenia can be in part attributed to physical illness morbidity (50, 57-67). Three upstream factors principally contribute to the increased physical illness morbidity in patients with schizophrenia: genetic susceptibility, lifestyle factors, and antipsychotic medication side- effects (56, 68, 69). Lifestyle factors may include poor diet, lack of physical activity, tobacco smoking, and substance use (56, 69-71). In fact, recent reports suggest that roughly 50% of patients with schizophrenia have a comorbid substance use disorder within their lifetime (72-75). Besides lifestyle factors, antipsychotics – the principal pharmacologic treatment for schizophrenia – have adverse effects that contribute to physical illness (69). Evidence suggests that 30-40% of patients develop metabolic syndrome, with antipsychotic treatment contributing to the increased risk of metabolic derangements (57, 61). In turn, metabolic syndrome predisposes patients to an increased risk of developing aforementioned physical illnesses, effectively leading to increased mortality. In addition to physical morbidity, the reduced life expectancy in patients is in part attributable to an increased rate of suicide; roughly 30-40% of patients attempt suicide and 5-10% complete suicide (76-80). More detail on the causes of death in patients with schizophrenia relative to the general population can be found elsewhere (52).

Furthermore, individuals diagnosed with schizophrenia experience an appreciable impact on their functioning within society. For example, the cognitive impairment experienced by patients negatively impacts their education level and performance (81-83). Also, patients are at an increased risk for homelessness and unemployment. The rate of schizophrenia among Canadian and global homeless populations is 10-20% on average (84-86), and employment rates between 7-20% have been identified among the Canadian schizophrenia patient population, consistent with international reports (87-91). In addition, there exists a preponderance of patients with schizophrenia within the criminal justice system; the prevalence of the illness among the incarcerated Canadian population is estimated to be 3% on average, and to range from 0.3-13%, which is comparable with international literature (48, 92). Moreover, patients with schizophrenia have a demonstrable loss of functioning with respect to self-care and activities of daily living, including hygiene, health management, independent living, and driving (93, 94).

Patients with schizophrenia are also commonly affected by several psychiatric comorbidities. A recent report estimated that the prevalence of depression, panic disorder, posttraumatic stress disorder, and obsessive-compulsive disorder in schizophrenia patient populations is approximately 50%, 15%, 29%, and 23%, respectively (72). Additionally, patients frequently experience stigmatization and discrimination from the general public and health care practitioners, in addition to self-stigma and anticipated discrimination (95-98). Importantly, these can further worsen illness outcomes, reduce vocational and housing opportunities, and limit access to adequate health care (95-99). Finally, schizophrenia also carries significant implications for patients’ caregivers. Evidence suggests that caregivers are at increased risk for physical, mental, and financial distress, effectively worsening overall quality of life (100-103). Figure 1-3 delineates the prevalence of several impacts associated with schizophrenia mentioned above.

100 ) % ( a i n e r

h 80 p o z i h c S h t i 60 W s t n e i t a

P 40 g n o m A e c

n 20 e l a v e r P 0 r e e e s t t s e s d s n n s d a i id e e e e r ic ic n n o e u s m m ll is is u s y n I D S S e lo o l D d d l p is ta e ic e e e r n s l t t m m p e U o p le o e m b m p n I M e ta e H U d c e tt m i n o rb ta M A C s o b m u o S C

Figure 1-3. Estimated prevalence among patients with schizophrenia of several impacts associated with the illness (48, 57, 61, 72-80, 84-92). Note: Estimates are depicted using values provided within the text; where only ranges were provided, averages estimates were calculated. Comorbid mental illness values might be underestimated given that the average prevalence was determined considering only depression, panic disorder, posttraumatic stress disorder, and obsessive-compulsive disorder, whereas substance use disorder was separated for the purposes of this illustration.

1.1.6 Epidemiology

Although it is generally posited that schizophrenia affects 1% of the worldwide population, current epidemiological data estimate a lifetime prevalence closer to 0.5%, a 1-year prevalence

10 of 0.3%, and an incidence rate of 15.2 per 100,000 persons (Figure 1-4) (5, 14, 51, 104). It is believed that at least 26 million individuals are living with schizophrenia globally (105). Recently, a systematic review reported that the lifetime prevalence, 1-year prevalence, and incidence rates of schizophrenia in Canada, estimated to be approximately 1%, 0.4%, and 25.9 per 100,000 persons, respectively, significantly exceeded international rates (106). It is approximated that 300,000 Canadians and 120,000 Ontarians have schizophrenia (107, 108). Globally, the illness has a male:female incidence ratio of 1.4:1, although the prevalence is equal among the sexes (51).

1.1.7 Etiology

While the precise etiology of schizophrenia remains elusive, it is widely accepted that the illness involves both a genetic and environmental component (12-14, 109-111). In support of a

11 genetic predisposition, previous studies have reported a heritability of about 80%, an approximate 30-40% chance of an offspring developing schizophrenia if both parents have the illness, and a concordance rate in monozygotic twins of roughly 40-50% (109-111). Further, several environmental risk factors for schizophrenia have been described, including physical and psychosocial stressors in early life and prenatal development, season of birth, drug use, economic status, urbanicity, migration, and higher latitudes (12-14, 51, 112); the latter 2 risk factors were put forth as explanations for the aforementioned elevated Canadian prevalence and incidence rates (106).

1.2 The Dopaminergic System

Dopamine, also known as 3,4-dihydroxyphenethylamine, is a neurotransmitter that is vital for various physiological functions, including but not limited to movement, reward, and cognition (113). Dopamine receptors are a class of G-protein coupled receptors (114-116).

Generally, dopamine receptors can be classified as belonging to one of two families: the D1-like dopamine receptor family and the D2-like dopamine receptor family (114-116). The D1-like dopamine receptor family is composed of D1 and D5, whereas the D2-like dopamine receptor family is composed of D2, D3, and D4 (114-116). The D1-like dopamine receptor family activates the enzyme adenylyl cyclase, stimulating the production of the second messenger cyclic adenosine monophosphate (cAMP), whereas the D2-like dopamine receptor family inhibits adenylyl cyclase, decreasing the formation of cAMP (114-116). Notably, dopamine receptors can exist within high and low affinity states (114, 115).

Several dopaminergic projections have been identified within the literature; however, simplistic models highlight four notable dopaminergic pathways that produce and release dopamine: the mesocortical pathway, linking the ventral tegmental area to the frontal cortex; the mesolimbic pathway, linking the ventral tegmental area to the ventral striatum (nucleus accumbens), olfactory tubercle, and parts of the limbic system; the nigrostriatal pathway, linking the substantia nigra pars compacta (SNc) to the dorsal striatum (caudate and putamen); and the tuberoinfundibular pathway, linking the hypothalamus to the median eminence (113, 116-119) (Figure 1-5).

Figure 1-5. Schematic representation of the human central dopaminergic systems. Reproduced with permission from Scarr et al (2013) Frontiers in Cellular Neuroscience (6).

In schizophrenia, it has been posited that disruptions in mesocortical, nigrostriatal, tuberoinfundibular, and mesolimbic pathways are linked to negative and cognitive symptoms, extrapyramidal side effects, elevated prolactin levels, and positive symptoms, respectively (27). More details on dopaminergic dysfunction in schizophrenia are provided in coming sections.

Dopamine levels can be measured in vivo using positron emission tomography (PET) (27, 120, 121). PET is a functional and quantitative nuclear imaging technique that can be used to examine neurochemistry at the molecular level (120, 122-125). To do so, PET utilizes radiolabelled ligands, often called radiotracers, which are composed of positron-emitting radioisotopes that are chemically incorporated into biologically active molecules (120, 122-125). When introduced into an individual, this unstable radiotracer undergoes positron emission decay, leading to the release of a positron, which carries a positive charge and has the same mass as an

13 electron (120, 122-125). When a radioisotope emits a positron, it collides with a nearby electron, resulting in annihilation (120, 122-125). This leads to a pair of photons being released in diametrically opposed directions; the PET scanner detects these photons and is able to localize their source (120, 122-125). Some examples of common radiotracers used in dopamine PET imaging are [11C]-raclopride, [123I]-IBZM, [11C]-(+)-PHNO, and [18F]-DOPA (27, 120, 121). 11 123 [ C]-raclopride and [ I]-IBZM are typically used to study dopamine D2/D3 receptors (27, 120, 11 18 121), [ C]-(+)-PHNO is usually utilized to study D3 receptors (126, 127), and [ F]-DOPA is an index of presynaptic dopamine synthesis (27, 120, 121).

1.3 The Glutamatergic System

Glutamate is the most abundant excitatory neurotransmitter in the nervous system and is involved in numerous physiological functions (128). Glutamate acts on two main categories of receptors: ionotropic receptors, which are ligand-gated ion channels that open when activated by an agonist, and metabotropic receptors, which are G-protein coupled receptors (7, 129). Ionotropic receptors are fast acting; once glutamate binds to one, the receptor’s channel undergoes a conformational change, leading to the influx of extracellular sodium and other ions, along with the efflux of potassium ions (130). The ionotropic receptors can be further divided into α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, kainite receptors, and N-methyl-D-aspartate (NMDA) receptors (129, 130). NMDA receptors are composed of two NR1 subunits and two NR2 subunits; glutamate binds to the NR2 subunit, whereas glycine, a co-agonist, binds to the NR1 subunit (128, 130). Notably, NMDA receptors are additionally permeable to calcium ions, and under resting conditions, are blocked by magnesium (129, 130). Metabotropic receptors are categorized intro three groups: group I, which includes metabotropic glutamate receptor (mGluR)1 and mGluR5; group II, which includes mGluR2 and mGluR3; and group III, which includes mGluR4, mGluR6, mGluR7, and mGluR8 (128, 130).

Glutamate exists in large intracellular concentrations and is tightly controlled at the synapse and in extra-synaptic locations, as excess extracellular concentrations can have damaging effects (more details regarding excitotoxicity presented in subsequent sections) (119,

130). Glutamate transporters regulate glutamatergic neurotransmission and are called excitatory amino acid transporters (EAATs) (117, 130). EAATs play a role in glutamate-glutamine cycling, which is the process by which glutamate is recycled between neurons and glial cells (7, 117). EAATs on glial cells clear excess glutamate from the extracellular space (7, 117, 130). Then, via the enzyme glutamine synthetase, glutamate is converted to glutamine, which is subsequently transported to the neuron to be converted into glutamate (7, 130) (Figure 1-6).

15 present predominantly on glial cells. In glial cells Glu is converted to Gln by Gln synthetase. Various Glu receptors are present on presynaptic and postsynaptic neurons as well as on glial cells. These include both ionotropic receptors — AMPA (α-amino-3- hydroxy-5-methyl-4-isoxazole propionic acid), NMDA (N-methyl-D-aspartate) and kainate receptors — as well as metabotropic Glu receptors (mGluRs). Reproduced with permission from Sanacora et al (2008) Nature Reviews Drug Discovery (7).

In humans, glutamate and its metabolites can be measured non-invasively using a magnetic resonance imaging (MRI) technique called proton magnetic resonance spectroscopy (1H-MRS) (131). More discussion on 1H-MRS is provided within the next section.

1.4 Magnetic Resonance Imaging

MRI is a non-invasive imaging technique that can provide information about brain function and structure (132). In the context of the current work, only 1H-MRS and structural neuroimaging are discussed within this section. Information regarding techniques that are beyond the scope of the present thesis can be found elsewhere (133, 134). An illustration of an MRI machine is presented in Figure 1-7.

Generally, MRI is based on the interaction between an applied magnetic field and a particle with spin and charge (135). The human body is made up of tissues that are predominantly composed of water and fat, and both contain hydrogen (136). Notably, the most abundant isotope for hydrogen is protium (1H), which contains one proton in its nucleus that is orbited by one electron (137). For this reason, most MRI utilizes 1H, in addition to the fact that it has a spin (i.e. intrinsic spin angular momentum) of ½ (135-137). The positively charged nucleus rotates due to its nuclear spin, inducing a local magnetic field or magnetic moment of constant magnitude about the nucleus, parallel to the axis of rotation (132, 135, 137). When placed in an

external magnetic field, deemed B0, the magnetic moment of each proton will start to undergo a rotational motion around the magnetic field, a phenomenon called precession, at a constant rate that is proportional to the magnetic field strength applied (132, 135-138). The precession rate is determined by the Larmor equation, where Larmor frequency (i.e. precession frequency or resonant frequency) is the product of the magnetic field strength applied and a constant defined as the gyromagnetic ratio (132, 135-138). Also, when an external magnetic field is applied, proton spins will orient such that more protons will have a magnetic moment that is parallel to B0

(i.e. spin-up orientation), which is a lower energy state, than antiparallel to B0 (i.e. spin-down orientation), which is a higher energy state (132, 135-138).

To acquire information about the characteristics of the tissue being investigated, a manipulation of the above phenomena is subsequently performed. A radiofrequency pulse encompassing a narrow range of frequencies is applied perpendicular to B0 (132, 135, 136, 138, 139). When a proton absorbs a particular frequency, it is excited from its lower energy state (spin-up) to its higher energy state (spin-down) (132, 135, 136, 138, 139); the frequency absorbed is dependent on the above-referenced Larmor equation (132, 135, 136, 138, 139). The energy difference (delta E) between the lower and higher energy states is equal to Planck’s constant multiplied by precession frequency (132, 135, 139). The application of this pulse results in the net magnetic moment rotating into the transverse plane, and also causes the precession of the protons to become synchronized to some extent, deemed in-phase precession (132, 135, 136, 138, 139).

When the radiofrequency pulse is turned off, protons will gradually return to their starting orientation (132, 135, 136, 138, 139). Simply put, the rate at which protons return to their low energy state, termed T1, longitudinal relaxation, or spin-lattice relaxation, will differ depending on the tissue composition within the brain area (132, 135, 136, 140). For example, longitudinal relaxation time or T1 is relatively short in solid tissues such as white matter (WM), and longer in blood and cerebrospinal fluid (CSF) (132). Similarly, the rate at which protons change from in- phase precession to out-of-phase precession, termed T2, transverse relaxation, or spin-spin relaxation, will also vary based on the tissue composition within that region (132, 135, 136, 140).

Here, transverse relaxation time or T2 is long in free water within body fluids, and relatively shorter for larger, immobile molecules (132).

The radiofrequency pulse applied perpendicular to B0 introduces a small alteration to its strength, and therefore, its Larmor frequency (132, 136). The nuclei with a Larmor frequency that is equal to this applied Larmor frequency will be excited; this process thereby selects a particular slice of tissue, and is termed slice-selective excitation (132, 136). To achieve spatial encoding, frequency and pulse magnetic field gradients are subsequently applied to this slice, in two orthogonal directions (132, 136). The former (Gy) encodes position information into the frequency of the emitted signal, by causing the spins to rotate at a frequency that is dependent on the position of the nuclei along y (132, 136). The latter applies a short gradient pulse in the perpendicular direction (Gx) to produce a relative phase shift among the spins; this is repeated several times in succession with incremental amplitudes to encode the spatial position of nuclei along x (132, 136). Frequency and phase encoding is followed by processing of the resultant data through an analog-to-digital converter (132, 136). This generates the k-space, an array of raw data from which the image is then reconstructed through a 2D Fourier transformation (132, 136).

1.4.1 Proton Magnetic Resonance Spectroscopy

Magnetic resonance spectroscopy (MRS) is an MRI technique that permits the in vivo quantification of several neurometabolite concentrations (8, 141). Notably, there are several nuclei, including 1H, 31P, 19F, and 13C that can be utilized to acquire MRS data (8, 142). MRS investigations of the brain began in the 1980s in both humans and animals using 31P to assess energy metabolites such as phosphocreatine and ATP, and inorganic phosphate and phosphoesters (143). In the 1990s, 1H-MRS gained prevalence. Along with improvements in spatial localization and water suppression that led to higher sensitivity, the increased use of 1H- MRS was also driven by convenience, as no hardware modification was required on most MRI machines (143-146). Today, 1H-MRS is the most common form of MRS employed in biomedicine (8, 141-143), and primarily 1H-MRS is discussed within this thesis. The main neurometabolite concentrations measured using 1H-MRS are glutamate, glutamine, N- acetylaspartate (NAA), myo-inositol (mI), choline (Cho), and creatine (Cr) (8, 141-143). Using 1H-MRS, several disturbances in these neurometabolites have been identified across a variety of neuropsychiatric illnesses (147, 148). However, at present, 1H-MRS is not used clinically in patients with neuropsychiatric disorders (149), although it is utilized clinically in some capacity

19 for the management of brain tumors and for certain infectious, inflammatory, and demyelinating diseases (150, 151).

Notably, 1H-MRS is based on the notion that nuclei in dissimilar chemical environments can be differentiated on the basis of their resonant frequencies (132, 135, 141, 152). The electrons surrounding the nucleus generate a magnetic field, which opposes B0, and thus produces a shielding effect (132, 141, 152). However, the magnitude of this opposition and shielding varies depending on the surrounding chemical environment. Greater shielding ultimately results in a smaller effective magnetic field being experienced by a proton (Beff) than that which is actually applied (B0) (132, 139, 141). Consequently, the protons within different metabolites experience varying Beff, and thus will precess at varying Larmor frequencies. As described above, the frequency absorbed by a proton during the application of a radiofrequency pulse is related to its Larmor frequency. Differing frequencies will thus be detected in this process depending on which metabolites are present in the evaluated tissue; this phenomenon is referred to as chemical shift (132, 141, 142, 152). The frequencies detected upon investigation of a tissue sample, corresponding to various metabolites, are presented as peaks along a chemical shift spectrum; higher frequencies are downfield (141). Chemical shift of a metabolite is expressed relative to a reference compound and measured as parts per million (ppm) (8, 141-143, 146). Finally, this technique allows quantification of the metabolites present in a tissue sample, as the area under the peak at a particular frequency on the chemical shift spectrum reflects the metabolite’s concentration (8, 141-143, 146).

In practice, 1H-MRS utilizes identical hardware equipment as standard MRI (8, 141-143). In single-voxel spectroscopy, a 3-dimensional volume commonly referred to as a 1H-MRS voxel is placed within a region of interest (153). Some examples of common cortical 1H-MRS voxel placements include the anterior cingulate cortex and dorsolateral prefrontal cortex (DLPFC), while common subcortical voxel placements include the striatum, hippocampus, and thalamus (148). Single-voxel spectroscopy is especially useful when studying neuropsychiatric illnesses where hypotheses dictate the study of a particular region (146, 148). The 1H-MRS process involves a water suppression technique, which is followed by the application of three slice- selective radiofrequency pulses in orthogonal directions (Gx, Gy, and Gz), and then the recording and estimation of signal from the 1H-MRS voxel (8, 143, 153, 154). Common water suppression techniques are chemical shift-selective water suppression (CHESS) and water suppression

20 enhanced through T1 effects (WET) (8, 143, 153, 154). Next, the two most common single-voxel spectroscopy acquisition sequences are point resolved excitation spin-echo sequence (PRESS) and stimulated echo acquisition mode (STEAM) (8, 143, 153, 154). The PRESS sequence consists of a slice-selective 90° excitation pulse and two 180° slice-selective refocusing pulses, whereas the STEAM sequence consists of three 90° slice-selective pulses (8, 143, 153, 154). PRESS is used more often than STEAM; an advantage of the former is a larger signal-to-noise ratio, whereas a disadvantage is limited ability to utilize very short echo times (8, 143, 153, 154). Of note, the employed echo time influences the neurometabolites that are measurable (145). In particular, short echo times (e.g. 35 ms or less) permit the detection of glutamate, glutamine, and mI (8, 143, 145, 154). Field strength also plays a role in determining which neurometabolites are detectable (144, 146, 148); field strengths of 1.5 Tesla or greater are typically used (8, 143, 145, 154).

After application of pulses, an output displaying the chemical shift spectrum is generated (8, 143, 154, 155). Following the acquisition of 1H-MRS spectra, neurometabolite concentrations are often estimated using software packages (8, 143, 154, 155). One software package that is used by many previous 1H-MRS studies is Linear Combination Model (LCModel) (156). The program quantifies metabolite concentrations relative to both an unsuppressed water signal and total creatine levels, and spectral quality indices are also provided (8, 143, 154, 155). An example of an output from LCModel is presented in Figure 1-8.

Exam #4370-8 ID=GLTS074 09/29/2015 09:16 presscsi TE/TR/NS=35/2000/128 TG/R1/R2=142/13/28 13.5mL P11264.7 (CAMH) Data of: Research Imaging Centre, MRI Suite, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada

LCModel (Version 6.3-0E) Copyright: S.W. Provencher. Ref.: Magn. Reson. Med. 30:672-679 (1993). 30-September-2015 14:29 0.016 Conc. %SD /Cr+PCr Metabolite 8.23E-03 999% 1.2E-03 Ala 3.431 18% 0.486 Asp 1.856 19% 0.263 Cr

0.41 5.197 8% 0.737 PCr 0.000 999% 0.000 GABA 1.080 26% 0.153 Glc 2.563 18% 0.363 Gln 12.427 3% 1.762 Glu 1.539 3% 0.218 GPC 0.000 999% 0.000 PCh 3.384 6% 0.480 GSH 4.996 4% 0.708 Ins 0.000 999% 0.000 Lac 11.841 2% 1.679 NAA 0.000 999% 0.000 NAAG 0.272 17% 3.9E-02 Scyllo 0.104 242% 1.5E-02 Tau 2.679 8% 0.380 -CrCH2 1.539 3% 0.218 GPC+PCh 11.841 2% 1.679 NAA+NAAG 7.054 2% 1.000 Cr+PCr 14.990 4% 2.125 Glu+Gln 0.234 518% 3.3E-02 Lip13a 0.182 151% 2.6E-02 Lip13b 7.51E-03 999% 1.1E-03 Lip09 5.868 11% 0.832 MM09 0.000 999% 0.000 Lip20 9.584 9% 1.359 MM20 2.229 26% 0.316 MM12 6.063 18% 0.860 MM14 1.759 17% 0.249 MM17 0.416 258% 5.9E-02 Lip13a+Lip13b 8.708 14% 1.235 MM14+Lip13a+L 5.876 10% 0.833 MM09+Lip09 9.584 9% 1.359 MM20+Lip20

DIAGNOSTICS 1 info FINOUT 9 Doing Water-Scaling

MISCELLANEOUS OUTPUT FWHM = 0.043 ppm S/N = 37

0 Data shift = 0.019 ppm 4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.80 0.60 0.40 Ph: 2 deg 2.4 deg/ppm Chemical Shift (ppm) INPUT CHANGES deltat= 2.000e-04

Figure 1-8. Example of an LCModel output from a dorsolateral prefrontal cortex 1H-MRS voxel.

While the work within the present thesis focuses on single-voxel spectroscopy techniques, it deserves mention that multiple-voxel techniques exist, referred to as magnetic resonance spectroscopic imaging (157-159). A more detailed description of multiple-voxel techniques can be found within authoritative reviews (8, 143, 153, 154). Likewise, certain editing sequences (e.g. MEGA-PRESS) allow for the detection of other neurometabolites, such as gamma-aminobutyric acid (GABA), glutathione (GSH), and N-acetylaspartylglutamate (NAAG); more details regarding these editing sequences can also be found within authoritative reviews (143, 148, 153, 154).

1.4.1.1 Glutamatergic Neurometabolites

In addition to being the most prominent excitatory neurotransmitter, glutamate also functions as a precursor of GABA and GSH (160). As part of the glutamate-glutamine cycling process, glutamate can be localized to neurons, glia, and the extracellular space (160); notably, 1H-MRS cannot differentiate between extracellular and intracellular components and cannot distinguish glutamate located in different cell types (148, 161).

A summed measure of glutamate and glutamine, termed Glx, is often reported in 1H- MRS studies (143, 160). This is largely due to the difficulty associated with separating glutamate and glutamine peaks at lower field strengths, depending on the parameters used (143-145, 160). Glutamate resonates at 2.04, 2.11, 2.35, and 3.74 ppm, and glutamine resonates at 2.11, 2.13, 2.44, and 3.76 ppm (148).

1.4.1.2 Other Neurometabolites

NAA renders the largest 1H-MRS signal of all neurometabolites, resonating at 2.01 ppm (143, 145). NAA is believed to be created within neuronal mitochondria, and thus has often been interpreted to represent neuronal viability, integrity, density, or function (143, 145, 162). However, the full meaning of the NAA signal remains in question, as certain lines of evidence suggest that the neurometabolite may be more than just a neuronal marker (143, 162). NAA may hold roles as a precursor for the neurotransmitter NAAG, an osmolyte, and in the formation of myelin lipids (143, 162). Nonetheless, it is widely accepted within the bulk of 1H-MRS literature that NAA is a surrogate measure of neuronal health and function (143, 162). Importantly, decreases in the NAA signal may signify neuronal injury (145). Often, the contribution of NAAG is indistinguishable from the NAA signal, such that a summed measure of NAA and NAAG is utilized, and can be interpreted to reflect NAA-containing compounds (145).

Within short TE spectra, mI resonates between 3.5 to 3.6 ppm (143, 145, 163). Although not entirely conclusive, mI levels are often interpreted within the literature as being reflective of glial activity or function, due to their higher expression in glial cells than neurons (164, 165). Of note, disturbed mI levels are reported within various neuroinflammatory illnesses, and amongst

23 other functions, osmolytic roles have been posited for mI within the literature (163, 166). Furthermore, Cho resonates at 3.20 ppm and is a summed measure composed of choline- containing compounds (i.e. glycerophosphocholine, phosphocholine) (143, 145, 167). Due to the presence and involvement of these compounds within the processes of membrane synthesis and degradation, and the elevation of the Cho signal in illnesses where increased membrane turnover is implicated, the Cho signal from 1H-MRS is widely interpreted to reflect membrane turnover (143, 167). Akin to mI, Cho is also present within higher concentrations in glial cells than in neurons (168, 169), and elevated levels of Cho have also been previously found within neuroinflammatory conditions (166). Notably, elevations of mI and Cho, especially when concomitant, are often interpreted to reflect glial activation (166). Finally, Cr resonates at 3.03 and 3.91 ppm, and is a summed measure of creatine-containing compounds (i.e. creatine, phosphocreatine) (143, 145). In light of the involvement of these compounds in energy metabolism through the creatine kinase reaction, generating ATP, the 1H-MRS Cr signal is believed to represent energy metabolism (143, 170). There is also some suggestion within the literature that Cr levels may exist in higher proportions within glial cells (145, 169).

1.4.1.3 Indices of Spectral Quality

Several indices of spectral quality are essential in the acquisition and analysis of 1H-MRS data. During the acquisition, 1H-MRS spectra are typically shimmed, a process that adjusts the currents of shim coils to render the magnetic field homogenous within the 1H-MRS voxel (148, 152, 154). Shimming contributes towards reducing linewidth, defined by the full-width at half maximum (FWHM) (148, 152, 154, 171). A lower FWHM value reflects better spectral resolution, and often, at 3T, a water peak linewidth cutoff of 12 Hz or less is employed (148).

As mentioned above, external software packages estimate neurometabolite levels and present outputs. As seen in Figure 1-8, LCModel outputs include columns of neurometabolite concentration ratios, to water and total creatine concentration, as well as a %SD column (172). A %SD value lower than 20% is commonly deemed to be of acceptable quality (172, 173). Of note, %SD can also be referred to as relative Cramer-Rao lower bound (CRLB), and is given by the following equation:

�� %�� = 100 × ��

In this equation, absolute CRLB is a measure of goodness of fit (i.e. variance of the best estimator) and concentration estimate is the mean concentration of the neurometabolite in institutional units. Relative CRLB values are reported as either a fraction or percentage.

Further, LCModel also reports a FWHM value in ppm; certain studies utilize this FWHM value to employ a cutoff of 0.1 ppm or less (174). Another important measure to assess spectral quality is the signal-to-noise ratio (SNR), which is also given by LCModel (172). A higher SNR is preferred and indicates better spectral quality. That being said, the size and location of the 1H- MRS voxel carries influence on the SNR value; thus, caution should be exercised when comparing SNR values between differing 1H-MRS voxels.

Moreover, as referred to above, LCModel outputs provide neurometabolite ratios in reference to both water and total creatine concentration (143, 148, 172). Reference ratios are employed to account for confounding factors, particularly the heterogeneity in tissue composition that may exist between individuals in the location of the 1H-MRS voxel. For example, a 1H-MRS voxel placed in one participant may be composed of 33% grey matter (GM), 33% WM, and 33% CSF, while another participant may have 50% GM, 40% WM, and 10% CSF within the same brain location. Importantly, by limiting confounding factors, using reference ratios allows for better comparison between studies (148).

While several previous studies have utilized neurometabolite reference ratios to total Cr levels, it has been reported that Cr itself may differ between patients with schizophrenia and healthy controls (175). Thus, the field has largely shifted towards the use of water as a reference (148). However, this approach requires correction for CSF content within the 1H-MRS voxel. Notably, an underlying assumption of 1H-MRS is that neurometabolites are only present within brain tissue. Thus, neurometabolite levels are manipulated using a CSF correction factor through the following equation:

�� = (1 − ��)

Above, SV represents the spectroscopic values, CSF is the CSF fraction within the voxel, and SVcorr refers to the corrected spectroscopic values. In this fashion, the influence of CSF is taken into account. Consequently, the correction factor allows for the utilization of the water signal as a reference for neurometabolite quantification, adjusting for the confounding effect of variable CSF content. Similarly, the collection of GM proportion and WM proportion within the 1H-MRS voxel is vital for the assessment of 1H-MRS data. It is important to explore whether group differences exist in these measures of tissue heterogeneity and if these indices influence main findings.

Of note, more rigorous correction methods, accounting for partial volume and relaxation effects, have been described and utilized within the literature (176, 177). While these techniques were not applied in the present work, more information can be found elsewhere (178).

1.4.2 Structural Neuroimaging

Structural neuroimaging examines neuroanatomical compromise through a variety of measures. Within the present work, two neuroanatomical indices will be focused on: volume and cortical thickness.

1.4.2.1 Volume

Within the neuropsychiatric literature, volume is the most commonly investigated structural measure. Volumetric reductions have previously been linked with increased symptom severity and poorer functional outcome in patients with schizophrenia (179-185). Thus, the importance of further exploring volume in patients with schizophrenia is apparent and there have been several methodological advances in the techniques utilized to do so.

Volume is determined using a participant’s structural image, which is acquired during an MRI session. Most often, this image is a T1-weighted structural image, an example of which is shown in Figure 1-9. Several tools exist for volume analyses, including FreeSurfer and Statistical

Parametric Mapping (SPM), although one technique in particular, the Multiple Automatically Generated Templates (MAGeT-Brain) algorithm (186), appears to be superior (187).

Figure 1-9. Example of a T1-weighted volumetric image.

MAGeT-Brain is utilized to determine volumes of subcortical structures (i.e. striatum, thalamus, globus pallidus, hippocampus, amygdala) and operates by using a process referred to as multi-atlas segmentation (186, 188). Single atlas segmentation, also known as model-based segmentation, uses an atlas image and an atlas label to segment (i.e. decipher between GM, WM, and CSF) a participant’s T1-weighted image. The atlas image is registered to a participant’s T1- weighted image and, using this transformation, the atlas label is transformed to create a subject label.

In contrast, multi-atlas segmentation contains multiple atlases (e.g. five hippocampal atlases), each differing from one another (186, 188). Again, each atlas image is registered to the participant’s T1-weighted image; in other words, each atlas image is put into the space of the participant’s T1-weighted image. Here, for each registration, a subject label is generated; this process is called label propagation. Next, a majority vote is performed, through a process referred to as label fusion, where it is decided in a voxel-specific manner whether or not the structure of interest (e.g. hippocampus) is present within that voxel. This process is called

27 majority vote because any vote proportion exceeding 50% would result in that voxel being labeled as containing the structure of interest, whereas any vote proportion lower than 50% would result in that voxel being labeled as not containing the structure of interest. As such, after image registration and label propagation occur, the structure of interest is determined to be either present within or absent from the voxel. Notably, to prevent a vote proportion of 50%, an odd number of atlases is necessitated.

Still, the difference between MAGeT-Brain and typical multi-atlas segmentation is an additional step, involving the inclusion of template images (186, 188). An odd number – again, to prevent ties and thus increase the accuracy of the segmentation – of template images is chosen to be representative of the sample. Now, each atlas image is registered to the template image and atlas labels are propagated. Next, each of the template images is registered to the participant’s T1-weighted image and, using the matrix required to make that registration, the template-specific labels are transformed to create what are called candidate labels for each participant. Finally, as above, for each participant, a majority vote happens for these candidate labels on whether the structure of interest exists within each voxel. A vote proportion exceeding 50% would determine that the structure of interest exists within that voxel. An example of a MAGeT-Brain output is shown in Figure 1-10.

Another tool that is utilized to acquire volume is called the Brain Extraction based on nonlocal Segmentation Technique (BEaST) (189). BEaST works by multi-atlas segmentation but does not involve the inclusion of template images. BEaST is utilized to determine total brain volume (TBV), which is necessary to account for when investigating group differences in subcortical volumes.

1.4.2.2 Cortical Thickness

The cerebral cortex is a complicated structure composed of gyri and sulci, which have developed as per an evolutionary requirement to have a larger surface area without a consequent increase in intracranial size (190, 191). An array of literature has reported upon cortical GM volumetric reductions, predominantly in temporal and frontal regions, in patients with schizophrenia (192, 193). That being said, it is noteworthy that cortical volumes are a product of cortical thickness and surface area, the former of which is considered to be a sensitive measure of structural compromise, and has been posited to reflect the arrangement, size, and density of cells (194, 195). For this reason, cortical thickness is also directly studied to investigate neuroanatomical changes.

Generally, cortical thickness is defined as the distance from the cortical surface to the boundary between GM and WM (196). Cortical thickness varies across different regions of the brains, between hemispheres, and among individuals, and is often found to decrease with age (197, 198). Pathological differences in cortical thickness are also commonly observed across various neuropsychiatric illnesses, including schizophrenia, and recent evidence has linked cortical thinning with positive symptoms, negative symptoms, cognition, and outcomes in patients with schizophrenia (199-206).

One pipeline that can be used to calculate cortical thickness is CIVET, which has previously been utilized by our group and others to investigate schizophrenia and other neuropsychiatric illnesses (195, 203, 207-210). Notably, CIVET was recently compared to FreeSurfer, another popular cortical thickness extraction algorithm. Using a dataset from the

Alzheimer’s Disease Neuroimaging Initiative, the authors found both algorithms to be accurate, and CIVET to be slightly more sensitive to the typical Alzheimer’s disease atrophic pattern at the mild cognitive impairment stage (211). An example output for a cortical thickness vertex-wise analysis, performed using CIVET, is shown in Figure 1-11.

Figure 1-11. Schematic illustrating an example of cortical thickness alterations in the left superior temporal gyrus.

1.5 Glutamate-mediated excitotoxicity in schizophrenia: a review

Section 1.5 is reproduced with permission from the following: Plitman E, Nakajima S, de la Fuente-Sandoval C, Gerretsen P, Chakravarty MM, Kobylianskii J, Chung JK, Caravaggio F, Iwata Y, Remington G, Graff Guerrero A. Glutamate-mediated excitotoxicity in schizophrenia: a review. European

Neuropsychopharmacology 2014; 24(10):1591-1605.

1.5.1 Abstract

Findings from neuroimaging studies in patients with schizophrenia suggest widespread structural changes although the mechanisms through which these changes occur are currently unknown. Glutamatergic activity appears to be increased in the early phases of schizophrenia and may contribute to these structural alterations through an excitotoxic effect. The primary aim of this review was to describe the possible role of glutamate-mediated excitotoxicity in explaining the presence of neuroanatomical changes within schizophrenia. A Medline® literature search was conducted, identifying English language studies on the topic of glutamate-mediated excitotoxicity in schizophrenia, using the terms “schizophreni*” and “glutam*” and ((“MRS” or “MRI” or “magnetic resonance”) or (“computed tomography” or “CT”)). Studies concomitantly investigating glutamatergic activity and brain structure in patients with schizophrenia were included. Results are discussed in the context of findings from preclinical studies. Seven studies were identified that met the inclusion criteria. These studies provide inconclusive support for the role of glutamate-mediated excitotoxicity in the occurrence of structural changes within schizophrenia, with the caveat that there is a paucity of human studies investigating this topic. Preclinical data suggest that an excitotoxic effect may occur as a result of a paradoxical increase in glutamatergic activity following N-methyl-D-aspartate receptor hypofunction. Based on animal literature, glutamate-mediated excitotoxicity may account for certain structural changes present in schizophrenia, but additional human studies are required to substantiate these findings. Future studies should adopt a longitudinal design and employ magnetic resonance imaging techniques to investigate whether an association between glutamatergic activity and structural changes exists in patients with schizophrenia.

1.5.2 Introduction

This section provides a comprehensive explanation of topics relevant to the study of glutamate-mediated excitotoxicity in schizophrenia, beginning with a background of the illness

31 and its dopaminergic hypothesis. The limitations of the dopaminergic hypothesis are important in bringing forth the glutamatergic hypothesis of schizophrenia. Subsequently, N-methyl-D- aspartate (NMDA) receptor hypofunction is introduced as a model for schizophrenia, which is followed by an explanation of glutamatergic dysfunction in schizophrenia. Next, glutamate’s capacity to exert neurotoxic effects is presented. Lastly, common neuroanatomical deficits are noted. This broad introduction provides important background information for the contextualization of current research investigating glutamate-mediated excitotoxicity in schizophrenia.

1.5.2.1 Schizophrenia

Schizophrenia is a debilitating illness, present in approximately 1% of the global population and characterized by positive, negative and cognitive symptoms (111, 212). The primary treatment for schizophrenia is dopamine receptor antagonism with antipsychotic medication (13). The clinical effects of dopamine receptor antagonists have provided the basis for the dopamine hypothesis of schizophrenia (213, 214), which posits that patients with the illness have aberrant functioning of the dopaminergic system (215-218). The dopamine hypothesis is limited in that it only addresses positive symptoms (219); antipsychotics have minimal efficacy in the treatment of negative and cognitive symptoms (32, 33). Another limitation of the dopamine hypothesis is that 20-35% of patients show partial or no response to antipsychotic treatments (220, 221). In addition, this hypothesis does not appear to adequately explain the neuroanatomical changes in patients with schizophrenia (222). Thus, the dopaminergic system does not describe the illness in its entirety (223). The glutamatergic hypothesis provides an alternate mechanism to explain the pathophysiology of schizophrenia.

1.5.2.2 Glutamatergic hypothesis of schizophrenia

Glutamate antagonists, such as phencyclidine (PCP) and ketamine, are well known to transiently induce symptoms similar to those observed in patients with schizophrenia (224). Glutamate antagonists are unique in that they not only produce psychotomimetic effects, but also

32 elicit negative and cognitive symptoms (225, 226). Such effects have been reported following the acute administration of glutamate antagonists to healthy volunteers (227-231), while administration of these agents to patients with schizophrenia exacerbates symptoms (232, 233). The observed symptomatic effects of glutamate antagonists provide the basis for the glutamatergic hypothesis of schizophrenia (234).

1.5.2.3 NMDA receptor hypofunction

Glutamate antagonists induce schizophrenia-like symptoms through modulation of the NMDA receptor. PCP and ketamine are both non-competitive antagonists that exert their physiological effects by binding to the PCP receptor, a specific hydrophobic binding site coupled to the NMDA receptor (235). Through this binding, PCP and ketamine inhibit the action of glutamate at the NMDA receptor, suggesting that the pathophysiology of schizophrenia may similarly result from dysregulation of the NMDA receptor (219). Current proponents of the glutamatergic hypothesis postulate that hypofunctional NMDA receptors located on gamma- aminobutyric acid (GABA)–ergic inhibitory interneurons disinhibit pyramidal neurons, leading to a paradoxical increase in glutamatergic activity (222, 236, 237).

1.5.2.4 Glutamatergic dysfunction in schizophrenia

The role of the NMDA receptor in increasing glutamate is supported by both preclinical and human studies using NMDA receptor antagonists. Acute treatment of rodents with NMDA receptor antagonists results in increased extracellular glutamate in the striatum and prefrontal cortex (238, 239), and increased glutamine (the main metabolite of glutamate) in the prefrontal cortex (240). Studies in healthy human participants employing proton magnetic resonance spectroscopy (1H-MRS) report increased glutamate and glutamine in the anterior cingulate after the acute administration of a sub-anesthetic dose of ketamine (241, 242). In addition, agents that inhibit glutamate release reverse behavioral, cognitive, and cerebral blood flow changes induced by NMDA receptor antagonists in healthy human volunteers (243-245).

The aforementioned findings in rodents and healthy humans following acute treatment with NMDA receptor antagonists are comparable to 1H-MRS studies in patients with schizophrenia, which report increased glutamate levels in antipsychotic-free and naïve subjects during their first episodes of psychosis, as well as in subjects at ultra-high risk for psychosis (246-250). 1H-MRS studies have also demonstrated higher glutamine levels in antipsychotic- naïve patients with schizophrenia (251, 252). While there is strong evidence to support increased glutamatergic activity in patients with untreated schizophrenia, it should be noted that studies investigating medicated patients with schizophrenia have reported glutamatergic marker decreases or levels similar to healthy controls (157, 174, 248, 253-256). Thus far, two studies have made direct comparisons between unmedicated and medicated patients, both showing elevated glutamate levels in the unmedicated state and normal glutamate levels in the medicated state. Using a longitudinal within-subject comparison, one study in particular administered clinically effective antipsychotic treatment (reduction of at least 30% on the total score of the Positive and Negative Syndrome Scale after 4 weeks) to antipsychotic-naïve patients with first- episode psychosis, significantly decreasing elevated baseline glutamate in the associative striatum, such that levels following treatment did not differ from controls (253). Notably, this study specifically included patients who responded to treatment. Another study utilized a cross- sectional approach to compare unmedicated patients, medicated patients and healthy controls, reporting increased Glx in the medial prefrontal cortex region of unmedicated patients, in comparison to controls, whereas no such difference existed between medicated patients and the control group (248). To further elucidate the role of treatment in changing glutamatergic activity, a recent review noted that glutamatergic levels are elevated in antipsychotic naïve patients but are similar to those of healthy controls in medicated patients with schizophrenia, independent of stage of illness (131). This is contrasted by a meta-analysis that demonstrated that glutamate and glutamine concentrations decrease at a faster rate with age in patients with schizophrenia, as compared to healthy controls (257).

However, it should be noted that recent research observed higher glutamate levels in the anterior cingulate cortex of antipsychotic-treated first episode patients with unremitted psychotic symptoms and in treatment-resistant patients than in medication responders (258, 259). These findings suggest that an alternative underlying pathophysiology may exist in patients with

34 treatment-resistant schizophrenia than in patients who respond well to antipsychotics – one that similarly involves the glutamatergic system, yet is not modulated by dopaminergic regulation.

1.5.2.5 Glutamate as an excitotoxic factor

Glutamate has the potential to induce neuronal dysfunction and degeneration when present in abnormally high extracellular concentrations (260-262). This process is referred to as excitotoxicity, a term coined by John Olney (263, 264), who posited that excessive stimulation by glutamate has the capacity to vastly increase intracellular calcium, affecting calcium homeostatic mechanisms and triggering a cascade of events that ultimately result in cell death (261). Though the exact mechanisms of this phenomenon are only partially known, calcium influx is highly implicated (265-267). In schizophrenia, the disruption in glutamatergic signalling may result in an excitotoxic effect secondary to excess stimulation of non-NMDA glutamate receptors (i.e AMPA and Kainate), leading to the structural findings associated with the illness (144, 268).

1.5.2.6 Structural changes in schizophrenia

Neuroanatomical changes are often reported in patients with schizophrenia; for example, progressive loss of grey matter volume is common in both early and chronic stages of the illness (269-272). Recent meta-analyses investigating grey matter losses in schizophrenia most commonly identify volumetric reduction within superior temporal, medial temporal, superior prefrontal, medial prefrontal and insular regions, along with the thalamus and basal ganglia (273- 279). Whole brain volume reductions, ventricular enlargement and white-matter alterations are also frequently reported (278, 280-284).

In addition, reductions in cortical thickness are common in patients with schizophrenia. Various studies have observed cortical thinning in schizophrenia, particularly within frontal, temporal, parietal and cingulate regions, though insular and occipital areas are also affected (201, 206, 285-289).

The occurrence of these neuroanatomical changes is largely unexplained. Though the changes may conceivably result from medication intake and prolonged illness progression (290- 294), studies utilizing first episode schizophrenia patients have provided evidence that structural changes occur irrespective of continuous antipsychotic treatment and years of illness duration. First episode schizophrenia patients with little or no exposure to antipsychotics exhibit neuroanatomical alterations within a number of brain regions in comparison with healthy controls (279, 287, 294-300). Glutamate-mediated excitotoxicity may contribute to these structural changes present in patients with schizophrenia (129, 144, 222).

1.5.2.7 Aim of this review

The glutamatergic hypothesis offers a mechanism through which neuroanatomical changes may occur: glutamate-mediated excitotoxicity. In short, elevated glutamatergic neurotransmission, which is highly implicated in the pathology of schizophrenia, may have neurotoxic effects. The primary aim of this review was to describe the potential role of glutamate-mediated excitotoxicity as an explanatory mechanism for the neuroanatomical changes observed in patients with schizophrenia. To do so, findings from human studies were reviewed and discussed, followed by a presentation of the evidence from preclinical literature. Limitations of both human and preclinical studies were considered in drawing conclusions and providing future research directions.

1.5.3 Experimental procedures

A Medline® literature search (1946-April Week 3 2014) was performed to identify studies, reviews or case reports relevant to glutamate-mediated excitotoxicity in patients with schizophrenia. The search was conducted using the terms “schizophreni*” (Subheadings: schizophrenia, antipsychotic agents and psychotic disorders) and “glutam*” (Subheading: magnetic resonance spectroscopy) and ((“MRS” or “MRI” or “magnetic resonance”) or (“computed tomography or “CT”)). Only English language human publications were included. Reference sections of major review articles (131, 222, 235, 257, 268, 301) were examined for

36 additional, relevant articles that were overlooked by the search strategy. Articles were included if they concomitantly measured markers of glutamatergic activity and brain structure using magnetic resonance imaging (MRI) or computed tomography (CT), and investigated the relationship between the two measurements. Studies utilizing participants deemed to be at risk for schizophrenia were included. The last search was conducted on April 10th 2014. Findings resulting from this search are discussed in the context of established preclinical data.

1.5.4 Results

The Medline® search yielded 622 publications. All titles and abstracts were read by two of the authors (E.P. and S.N.). Thirteen papers concurrently investigated glutamatergic activity and brain structure, and were thus selected and reviewed. Six articles were excluded because they failed to include statistics regarding the relationship between glutamatergic markers and neuroanatomical measures (302-307). The supplementary search through the reference sections of the specified review articles resulted in no additional articles that concurrently measured glutamatergic activity and brain structure. Thus, all remaining studies (n=7) (249, 308-313) that reported on the relationship between glutamatergic activity and brain structure in patients with schizophrenia were retained. These studies are summarized in Table 1-3. Six of the studies utilized 1H-MRS to assess glutamatergic markers, while one measured cerebrospinal fluid (CSF) glutamate. Six of the studies used volumetric measurements to assess structure – four of which were specific to grey matter – and one study assessed structure through ventricle to brain ratio (VBR) and prefrontal atrophy.

Table 1-3. Summary of studies that met inclusion criteria (n=7).

volume in volume loss

volume in hippocampus

GM GM volume in

volume reduction volume reduction in , left temporal pole and glutamate and and

GM GM lx and caudate volume volume in two hippocampal

glutamine and GM GM volume in left prefrontal

GM campal campal glutamate and volume in and Glx and GM volume

anterior cingulate tGL and GM

halamic Glx and thalamic volume e glutamine and GM volume in right CSF glutamate and prefrontal atrophy or

mpal

lutamate indings f lobes hippoca anterior cingulate thalamic loss of thalamic glutamine and Key

ation between hippo nterior cingulate glutamine hippocampal Glx and thalamic glutamine loss and thalamic glutamine loss and and limbic between between between between orrelation between t orrelation between caudate G orrelation between hippo orrel orrelation between

between CSF glutamate and VBR

parietal ,

correlation

rrelation

lved introduction of medication

temporal

significant negative c significant negative c significant negative c significant negative c significant correlations between voxel glutamine loss and voxel GM volume loss significant correlations between voxel glutamine loss and voxel GM volume loss significant negative c ------

significant correlation significant significant correlation significant correlation

Negative correlation between clusters No Non Non Positive correlation between frontal, No No volume loss Study involved introduction of medication No reduction Non hippocampus Non hippocampus Positive correlation between thalamic g cortex, left insula, left cingulate, left superior temporal gyrus bilaterally in cerebellum and lingual gyrus Negative correlation between thalamic glutamate and GM volume in dorsal anterior cingulate extending to posterior cingulate gyrus Positive correlation between a posterior cingulate gyrus Negative correlation between anterior cingulate glutamine and GM volume in left cerebellum No significant correlation between thalamic glutamate and GM volume Positive correlation between anterior cingulat temporal cortex Negative correlation between anterior cingulate glutamine and GM volume in medial frontal and orbitofrontal cortex Positive correlation between left angular gyrus, right precuneus and left superior and inferior temporal gyrus Non Study invo Non Negative co Non

In SCZ: • In HC: • In HR: • • In SCZ: • • • • In HC: • In SCZ: • In HC: • In ARMS: • • • • In HC: • • • In SCZ: • • • In HC: • In SCZ: • •

. a MRI

. . . and

olume MRS voxel volume v volume easure(s) atrophy. - Structural volume GM volume GM volume. H m Hippocampal Hippocampal Thalamic and 1 ssessed by MRI Assessed by CT VBR; prefrontal Whole brain GM Whole brain GM Whole brain GM caudate volume. Assessed by MRI Assessed by Assessed by MRI Assessed by MRI Assessed by MRI A

a . .

MRS MRS MRS MRS MRS MRS ------Glx in Glx in Glutamate H H H H H H thalamus arker(s) 1 1 1 1 1 1 anterior anterior anterior caudate. cingulate cingulate. cingulate thalamus; m in Assessed by Assessed by Assessed by Assessed by Assessed by Assessed by and anterior glutamine in Glutamate in Glutamate in Glutamine in Glutamine in thalamus and thalamus and thalamus and cingulate; tGL hippocampus. hippocampus. Glutamatergic CSF

; ; - -

at at at at and 80 and 30 tatus

s - - month month at initial at initial nmedicated nmedicated nmedicated nmedicated assessment assessment assessment assessment assessment 10 10 assessment assessment assessments assessments Medicated at medicated at medicated at U Unmedicated U Unmedicated U U Antipsychotic

isk isk r r FES FES llness Mixed Mixed i At At Chronic Phase of

CS CS CS CS CS esign L; 80 L; 30 Study d months months

ge,

a (4) (7); (5); (8); (10) (12) (SD) (7.9) (6.8); (7.1); (9.39) (3.99) (10.1) HC: 29 HC: 25 HC: 29 (9.28); (4.21); SCZ: 25 SCZ: 25 HC: 30.9 HC: 35.8 SCZ: 27.6 SCZ: 37.7 ARMS: HC: 32.85 HC: 15.57 HR: 15.92 Mean SCZ: 32.63

;

n 27 HC 24 HC 17 HC 44 HC 27 HC 16 HC 23 HC 23 HR; 27 SCZ; 17 SCZ; 29 SCZ; 16 SCZ; 61 SCZ 27 ARMS;

. . .

J .

ear,

. . J mage

. r et al. Res berge et Br a JAMA ournal j (2013), (2010), Tsai et al. Kl al. (2013), al. (2007), Schizophr Psychiatry Psychiatry Psychiatry Psychiatry Psychiatry Stone et al. Kraguljac et The NeuroI (2009), Biol (1998), Biol (2011), Br Tandon et al. Aoyama et al. Authors,

ARMS: at risk mental state; CS: cross-sectional; CT: computed tomography; FES: first episode schizophrenia; Glx: Glutamate+Glutamine; GM: grey matter; HC: healthy controls; 1H-MRS: proton magnetic resonance spectroscopy; HR: high-risk; L: longitudinal; MRI: magnetic resonance imaging; SCZ: schizophrenia; SD: standard deviation; tGL: Glutamate+Glutamine; VBR: ventricle–brain ratio. aOnly measures that were utilized for the investigation of the relationship between glutamatergic markers and brain structure, and were subsequently reported upon, are included in this table. bIncluded schizoaffective patients.

Studies that met the inclusion criteria indicate that glutamate, along with its metabolite glutamine, may have a relationship with neuroanatomical measurements. One study identified a negative correlation between two clusters of grey matter volume in the hippocampus and hippocampal Glx, a combined measure of glutamate and glutamine concentrations (249). By contrast, another study that measured glutamate within the hippocampus and hippocampal volume failed to find such a relationship (309).

Two other publications report on a study that employed a longitudinal design and found a relationship between thalamic glutamine and grey matter volume (308, 312). Théberge et al. (2007) noted an association between decreased thalamic glutamine levels and parietal and temporal grey matter volume loss over the course of 30 months, beginning with a never-treated state. Aoyama et al. (2011) extended these findings to an 80-month follow-up, and noted a positive relationship between change in both thalamic glutamine and grey matter volume within frontal, parietal, temporal and limbic regions; thalamic glutamine levels and grey matter volume both decreased over the course of 80 months. Notably, no relationships between changes in grey matter volume and anterior cingulate glutamine or tGL, a summed measure of glutamate and glutamine levels, were reported within this study. This longitudinal design included the introduction of antipsychotic medication over the duration of the study.

A study involving individuals with an at-risk mental state (ARMS) investigated the relationship between grey matter volume and glutamate in the thalamus and glutamine in the anterior cingulate (310). This study identified positive associations in participants with an ARMS between thalamic glutamate and grey matter volume in the left prefrontal cortex, insula,

39 cingulate, superior temporal gyrus and temporal pole, and bilaterally in the cerebellum and lingual gyrus. In the same study, negative correlations were found between thalamic glutamate and grey matter volume in the dorsal anterior cingulate extending to the posterior cingulate gyrus. Anterior cingulate glutamine was positively correlated with grey matter volume in the posterior cingulate gyrus and negatively correlated with grey matter volume in the left cerebellum. In another study that included individuals at familial high risk for schizophrenia, non-significant negative correlations were observed in the at-risk group between thalamic and caudate Glx, and thalamic and caudate volumes, respectively (311).

Lastly, a CT investigation assessing CSF glutamate and VBR in antipsychotic-free chronic schizophrenia patients reported an inverse relationship between CSF glutamate and VBR (313). Overall, the identified studies suggest that an association between markers of glutamatergic activity and structural measures may exist, although conflicting findings are reported within available literature.

1.5.5 Discussion

The overall aim of this review was to explore the evidence in humans for the relationship between glutamate related compounds (glutamate, glutamine and Glx) and structural brain measurements in patients with schizophrenia. A review of existing literature was conducted to elucidate the role of glutamate-mediated excitotoxicity in the structural brain changes associated with schizophrenia. Unexpectedly, the search yielded only seven studies that met inclusion criteria, reflecting the paucity of literature available to effectively address this topic in humans.

1.5.5.1 Analysis of reviewed studies

Of the seven studies identified from the search, Kraguljac et al. (2013) offered the most direct evidence for a glutamate-mediated excitotoxic effect. The authors attributed structural changes observed in the hippocampus to increases in glutamatergic activity by reporting a negative correlation between two clusters of grey matter volume and Glx within the hippocampus in the schizophrenia group, whereas no such relationship existed in the healthy

40 control group (249). In this study, the patient group was unmedicated. In contrast, Klär et al. (2010), which included medicated patients, failed to find an association between hippocampal glutamate and volume; however, the study may have been underpowered to find a significant relationship (r=-0.356, p=0.074).

Interestingly, in other studies that resulted from the search, measures of thalamic glutamatergic activity were associated with volumetric loss in a number of different brain regions. Decreases in thalamic glutamine paralleled grey matter volume decreases within frontal, temporal, parietal and limbic areas (308, 312). Based on the regions in which volume loss occurred and the involvement of the thalamus, excitotoxic damage was considered in the explanation of these findings. Results suggest that neurodegeneration secondary to glutamate- mediated excitotoxicity may result from decreased levels of thalamic glutamatergic activity. Consistent with this notion, a decrease in thalamic glutamatergic markers was observed in both studies, which may have resulted in diminished stimulation of NMDA receptors on GABAergic interneurons in the thalamus. This hypostimulation could subsequently result in toxicity induced by paradoxically high levels of glutamate in cortical regions through the disinhibition of thalamocortical circuits that use glutamate as a neurotransmitter.

Two additional studies offer evidence that the relationship between glutamatergic markers and volumetric measures is present in the early stages of schizophrenia. In one study of participants with an ARMS, thalamic glutamate and anterior cingulate glutamine levels were associated with grey matter volume, demonstrating both positive and negative correlations depending on the brain region (310). Another study, which included individuals with familial high-risk for schizophrenia, reported that Glx in both the thalamus and the caudate was negatively associated with regional brain volume, though both correlations were not significant (311).

Finally, one study measured CSF glutamate and VBR using CT in antipsychotic-free chronic schizophrenia patients (313). An inverse relationship was reported between CSF glutamate and VBR, suggesting that glutamatergic cells may have degenerated over the duration of the illness as a result of toxic levels of glutamate, resulting in a lower glutamatergic measure at the time of assessment, consistent with an excitotoxic effect.

1.5.5.2 Limitations of reviewed studies:

Overall, the studies listed in Table 1-3 have several limitations, and as such, do not individually offer sufficient evidence to make conclusive claims about the role of glutamatergic markers in the structural alterations observed in schizophrenia. The publications by Théberge et al. (2007) and Aoyama et al. (2011) were limited largely by the introduction of medication during the study. Antipsychotic treatment very likely confounded the relationship between markers of glutamatergic activity and volumetric losses; although patients were monitored throughout the study, it is expected that the introduction of medications influenced the serial neurochemical and structural measurements. This said, we acknowledge the practical challenges associated with performing a longitudinal study in medication-free patients. The studies conducted by Stone et al. (2009) and Tandon et al. (2013) were limited by the nature of the participant population. The authors included at-risk individuals, and although useful, this sample is not necessarily generalizable to patients with schizophrenia, as many at-risk participants may not progress to the illness (314); one study reported a 35% transition rate to a psychotic disorder over a 10-year follow-up (315). The studies by Kraguljac et al. (2013) and Klär et al. (2010) were limited by their targeted focus on glutamatergic levels and grey matter volume in the hippocampus. A common limitation shared among the aforementioned studies was the use of 1H- MRS, which quantifies the concentration of glutamate or glutamate-related compounds but cannot distinguish between intracellular and extracellular glutamate pools, therefore failing to precisely measure glutamate neurotransmission (161). Finally, the study performed by Tsai et al. (1998) was limited by the use of VBR, as this index of brain structure lacks sensitivity to minor neuroanatomical alterations, though this measurement was necessitated by the usage of CT. Further, the same study included participants with chronic schizophrenia; in this population, ongoing medication exposure and/or illness progression very likely confounded the relationship between glutamate levels and structural irregularities. Due to the limitations present within the reviewed studies, and the general dearth of literature exploring this topic, it remains unclear whether glutamate-mediated excitotoxicity is responsible for the structural changes observed in schizophrenia.

1.5.5.3 Evidence from preclinical literature:

In contrast to currently available human studies, preclinical studies offer more conclusive evidence for the role of glutamate-mediated excitotoxicity in schizophrenia. Pharmacological NMDA receptor antagonism is believed to reflect a similar state to schizophrenia, as evidenced by changes in symptomology, blood flow and cognition (230, 232, 316, 317). Established and replicated preclinical studies in rodents have reported increased extracellular measures of glutamatergic activity within cortical and subcortical brain regions following NMDA receptor antagonism (238, 239, 318, 319). The administration of NMDA receptor antagonists also consistently leads to neurotoxic injury, characterized by neuronal vacuolization, neurodegeneration and the appearance of heat-shock protein in affected cells (320-325). Moreover, preventing glutamate release in these preclinical models, using agonists of metabotropic glutamate receptors types 2 and 3, has been shown to block the neurotoxic effect of NMDA receptor antagonists (326-329). A study by Schobel et al. (2013) utilized a preclinical rodent model to test the hypothesis that excess glutamate serves as a common upstream mechanism for hippocampal hypermetabolism and atrophy, after these phenomena were identified in participants who fulfilled “clinical high-risk” criteria. Acute ketamine administration led to hippocampal hypermetabolism, whereas chronic ketamine administration additionally resulted in hippocampal atrophy and parvalbumin-expressing interneuron downregulation; pre-treatment with an agonist of metabotropic glutamate receptors types 2 and 3 prevented the effects of ketamine. Thus, this study provided translational evidence to implicate increased glutamate in certain metabolic and structural abnormalities present in schizophrenia, notably concluding that excess glutamate may drive hippocampal atrophy.

Furthermore, the injury induced by NMDA receptor antagonists in preclinical models is comparable to the damage seen in schizophrenia in that both are age-dependent and affect similar locations within the brain. In schizophrenia, symptoms and certain structural changes usually appear after puberty, in late adolescence (26, 330, 331). The psychotomimetic properties of NMDA receptor antagonists are also age-dependent (332, 333). NMDA receptor antagonists begin to cause injury to rodent brain cells around the time of puberty (approximately 45 days of age), an effect that becomes more severe as rodents progress into adulthood (334, 335). Moreover, NMDA receptor antagonism in preclinical models damages regions similar to those affected in patients with schizophrenia, including limbic and neocortical brain areas (322, 336-

339). These findings offer support for the role of glutamate-mediated excitotoxicity in causing structural changes, and are consistent with the NMDA receptor hypofunction theory of schizophrenia.

The use of this preclinical model of schizophrenia also offers an opportunity to explore the mechanism by which glutamate-mediated excitotoxicity causes neuroanatomical changes. Rodent studies have suggested that the thalamus may be a primary site of NMDA receptor hypofunction. NMDA receptor hypofunction on thalamic GABAergic interneurons may lead to decreased GABA production and subsequent disinhibition of thalamocortical circuits, which utilize glutamate as a neurotransmitter (339). As the thalamus has a multitude of projection neurons, NMDA receptor hypofunction on thalamic GABAergic interneurons provides one explanation for the increase in glutamatergic markers across multiple brain regions in schizophrenia. This is supported by studies where a GABAA agonist is injected into the thalamus, preventing NMDA receptor antagonist-induced neurotoxicity (339, 340). Also, NMDA receptor antagonist administration into the anterior nucleus of the thalamus results in cortical degeneration, while injection into cortical regions has no effect (339, 340). Notably, unlike systemic treatment with NMDA antagonists, cortical injection of ketamine also does not induce glutamate release (341).

The surge in glutamate following NMDA receptor hypofunction is believed to act on AMPA/kainate receptors, possibly resulting in calcium influx that leads to an excitotoxic effect. In support of this theory, administration of AMPA/kainate receptor antagonists and calcium channel blockers individually protects against neuronal injury by NMDA receptor antagonism (268, 321, 342).

The ability to generalize these findings from preclinical studies to patients with schizophrenia is dependent on both the validity of the NMDA receptor hypothesis of schizophrenia and on the comparability of acute NMDA receptor deficits to the chronic NMDA receptor hypofunction presumed to exist in the illness. Thus, though findings from preclinical studies are not wholly generalizable to patients with schizophrenia, they certainly suggest that an excitotoxic effect may occur as a result of a paradoxical increase in glutamatergic activity following NMDA receptor hypofunction.

1.5.5.4 Future directions:

Results from these preclinical studies provide sufficient evidence for future research to investigate the role of glutamate-mediated excitotoxicity in patients with schizophrenia. Several prior studies have assessed the relationship between functional imaging measures and glutamatergic markers (343-345). The challenge in transferring this strategy to analysing structural imaging data relates to the fact that structural changes likely do not evolve acutely. As such, future studies should adopt a longitudinal design, serially measuring glutamatergic markers and brain structure in individuals at high risk for psychosis as they progress to their first episode of schizophrenia. In addition, the use of carbon magnetic resonance spectroscopy (13C-MRS) along with 1H-MRS would provide a complementary approach to understanding glutamatergic activity and excitotoxicity in schizophrenia. 13C-MRS has the capacity to assess neuronal and astrocyte metabolic activity, and has been previously employed to study the glutamatergic system in rodent models of schizophrenia and human participants (346-348). Lastly, it is for a future review to investigate the relationship between markers of glutamatergic activity and N- acetyl-aspartate measures, as the latter is believed to reflect neuronal viability (349). Further, N- acetyl-aspartate is altered in schizophrenia and has been shown to decrease following PCP administration in rodents (350-354).

1.5.5.5 Limitations of present review:

This review is not without limitations. The principal aim may have been too narrow, yielding few studies specifically examining glutamate-mediated excitotoxicity in schizophrenia, thus rendering it challenging to make meaningful conclusions. Similarly, relevant articles may have been omitted from the Medline® search due to selection of the search terms.

Further, this review is biased to studies that used 1H-MRS. It may have benefited from including additional imaging techniques other than MRI and CT, such as PET and SPECT. However, the few studies that have employed these methods to investigate the glutamatergic system in schizophrenia have not addressed the relationship between structure and glutamatergic activity (355, 356). Moreover, MRI is currently regarded as the most sensitive, non-invasive

45 imaging technique and does not utilize ionizing radiation. Thus, it is likely that the majority of subsequent research will be performed using this modality.

1.5.6 Conclusion:

The glutamatergic hypothesis of schizophrenia provides an alternate or complementary mechanism to the dopaminergic hypothesis of schizophrenia. Excitotoxic levels of glutamate secondary to NMDA receptor hypofunction on GABAergic inhibitory interneurons may contribute to the structural abnormalities observed in schizophrenia. However, currently available literature from human studies fails to adequately address this topic.

The present review performed a Medline® search to investigate whether glutamate- mediated excitotoxicity is supported by findings from human schizophrenia studies. This search resulted in a small number of studies that met inclusion criteria: MRI or CT studies that concomitantly measured glutamatergic activity and brain structure. From the articles that met these criteria, it remains inconclusive whether glutamate-mediated excitotoxicity adequately explains the neuroanatomical changes observed in patients with schizophrenia. It is possible that publication bias exists within the literature, such that negative findings on this topic are less likely to be published.

In contrast, a subsequent discussion of preclinical studies does provide sufficient evidence of increased glutamatergic activity and associated structural alterations in response to the administration of NMDA receptor antagonists. Based on this body of literature, further studies in humans are warranted to determine the degree to which glutamate-mediated excitotoxicity could explain the structural deficits present in schizophrenia. Determining whether glutamate-mediated excitotoxicity contributes to the structural changes in schizophrenia may aid in the understanding of illness progression, along with the development of adjunctive therapies. Studies investigating the use of glutamatergic modulators to this point have provided useful, yet inconclusive evidence and as such, a better understanding of glutamatergic dysfunction within schizophrenia is required (219, 357-359).

Future studies investigating the relationship between glutamatergic function and brain structure should employ MRI techniques and adopt a longitudinal study design, including individuals at a high risk for schizophrenia as they transition into the illness. Future research would also benefit from performing MRS studies to assess glutamatergic activity in unmedicated patients. Glutamatergic activity is commonly, reliably and non-invasively evaluated through MRS, and structure can be assessed through analyses of T1-weighted images.

1.6 Basal Ganglia

1.6.1 Models of the Basal Ganglia

The basal ganglia are a collection of subcortical nuclei that interact through various pathways. The structures within the basal ganglia have been conceptualized to be involved in motor, as well as a diversity of other functions (e.g. cognitive, motivational). The classical model of the basal ganglia was developed in the 1980s and involves the interplay between the cerebral cortex, striatum, thalamus, subthalamic nucleus (STN), SNc, substantia nigra pars reticulate (SNr), globus pallidus externa (GPe), and globus pallidus interna (GPi) (10) (Figure 1-12). The interaction among these structures is believed to occur via parallel cortico-striatal-thalamic- cortico loops, executing different functions of the basal ganglia, including the skeletomotor, associative, and limbic circuits (10, 360).

Figure 1-12. Schematic representation of basal ganglia nuclei. Reproduced with permission from Jones (2012) Preface in Dopamine-Glutamate Interactions in the Basal Ganglia (9).

Within this model, the cerebral cortex and thalamus provide excitatory stimulation to medium spiny neurons, GABA-ergic projection neurons that are the predominant neuron type in the striatum (10, 361). There are posited to be two pathways of projection for these medium spiny neurons (9, 10, 362, 363). The “direct” pathway involves the inhibition of the SNr/GPi, which has tonic inhibitory control on the thalamus (9, 10, 362, 363). Thus, this leads to increased activity of the thalamus and increased excitatory communication with the cortex. Of note, a modulating effect is provided by the SNc and the STN (9, 10, 363). Medium spiny neurons involved in the “direct” pathway express dopamine D1 receptors (9, 10, 362, 363). Dopaminergic projections from the SNc have an excitatory effect through binding to dopamine D1 receptors and thereby lead to enhanced inhibition of the SNr/GPi (9, 10, 362, 363). Further, the STN has a modulating effect on the SNc through excitatory projections, which lead to increased dopaminergic activity from the SNc (9).

On the other hand, the medium spiny neurons involved in the “indirect” pathway project to the GPe (9, 10, 362, 363). In response to excitatory glutamatergic stimulation from the cerebral cortex, these neurons provide inhibitory signalling towards GABAergic neurons within the GPe, which project to the STN (9, 10, 363). As a result, under reduced inhibitory control, the STN increases excitatory stimulation of the SNr/GPi, leading to increased inhibition of thalamic activity, and thereby reducing excitatory signalling to the cerebral cortex (9, 10, 362, 363). As above, the SNc has modulating effects on the striatum through dopaminergic signalling. The medium spiny neurons within the “indirect” pathway express dopamine D2 receptors, and through dopaminergic signalling from the SNc, the inhibitory activity of the medium spiny neurons is reduced (9, 10, 362, 363). As described in the “direct” pathway, the STN also modulates dopaminergic activity of the SNc via excitatory projections (9).

Since the introduction of the classical model, there has been significant progress in our understanding of the basal ganglia and its components (10). Some of these developments are shown in Figure 1-13. An update on the state of the literature can be found within an authoritative review on this topic (10).

Figure 1-13. The classical “box and arrows” basal ganglia model, updated. Reproduced with permission from Obeso and Lanciego (2011) Frontiers in Neuroanatomy (10).

1.6.2 Striatum and Subregions

Broadly, the striatum is a component of the basal ganglia that has dorsal and ventral divisions. The dorsal striatum is composed of the caudate and putamen, which are separated by the internal capsule, and the ventral striatum includes the nucleus accumbens (118, 362). The striatum is purported to be involved in reward, motivation, and cognition, and is often further divided into three subregions: the associative striatum, the limbic striatum, and the sensorimotor striatum (364-367). These subregions are named based on the brain structures from which their afferent inputs originate; the associative striatum receives projections from the association/cognitive cortex (e.g. DLPFC), the limbic striatum receives projections from limbic areas (e.g. hippocampus, amygdala), and the sensorimotor striatum receives projections from the sensory and motor cortex (360, 364, 367) (Figure 1-14). As seen in Figure 1-14, the limbic striatum, associative striatum, and sensorimotor striatum are represented by red, yellow-green, and green-blue regions, respectively (364). Further, the associative striatum is composed of the precommissural dorsal caudate, the postcommissural caudate, and the precommissural dorsal

49 putamen, the limbic striatum includes the ventral striatum, and the sensorimotor striatum consists of the postcommissural putamen (360, 367).

Figure 1-14. Diagram demonstrating the functional organization of A. frontal cortex and B. striatal afferent projections. (A) Schematic illustration of the functional connections linking frontal cortical brain regions. (B) Organization of cortical and subcortical inputs to the striatum. In both (A) and (B), the colors denote functional distinctions. Blue: motor cortex, execution of motor actions; green: premotor cortex, planning of movements; yellow: dorsal and lateral prefrontal cortex, cognitive and executive functions; orange: orbital prefrontal cortex, goal-directed behaviors and motivation; red: medial prefrontal cortex, goal- directed behaviors and emotional processing. Reproduced with permission from Haber (2003) Journal of Chemical Neuroanatomy (11).

1.6.3 Dopaminergic Dysfunction within the Striatum in Schizophrenia

The striatum is known to be a dopamine-rich brain region and is part of the mesolimbic and nigrostriatal dopaminergic pathways, which are linked with psychosis and extrapyramidal side effects of antipsychotic treatment, respectively. As such, several PET studies have strongly implicated the striatum within the pathophysiology of schizophrenia. A meta-analysis examining PET or single-photo emission computed tomographic studies measuring in vivo striatal dopaminergic function found a large elevation in presynaptic dopaminergic function in patients with schizophrenia and a modest elevation in dopamine D2/3 receptor availability, while no differences with respect to the dopamine transporter or other dopamine receptors were identified (121). In addition, another meta-analysis examining in vivo molecular studies of extrastriatal dopaminergic function in schizophrenia reported that a scarcity of evidence existed supporting dopaminergic alterations outside of the striatum (368).

Furthermore, the role of striatal dopamine in psychosis has been clearly evaluated through the manipulation of dopaminergic neurotransmission. The administration of psychostimulants, such as amphethamine, leads to the increase of dopaminergic activity; these agents result in the emergence of psychotic symptoms in healthy controls and the worsening of symptoms in patients with schizophrenia (27). It has previously been demonstrated that an amphetamine challenge leads to increased striatal radiotracer displacement from striatal dopamine D2 receptors in patients with schizophrenia compared to healthy controls, indicating a greater degree of increase in dopaminergic neurotransmission in this patient population (215,

218). Further, the extent of radiotracer displacement from striatal dopamine D2 receptors in response to an amphetamine challenge was related to the change in positive symptoms (215, 218). The dopaminergic system can also be modulated with depleting agents, such as α-methyl- para-tyrosine (369, 370), which have been shown to alleviate psychotic symptoms. Past studies have shown that after depletion, more striatal dopamine D2 receptor availability existed in patients with schizophrenia than in healthy controls, suggesting greater basal dopamine levels in the patient group (366, 371). After dopamine depletion, there was an improvement in positive symptoms in the patient groups (366, 371).

Furthermore, as noted above, the primary form of pharmacological management of schizophrenia is antipsychotic medication. In 1975, it was discovered that all antipsychotics

51 inhibited binding of radiolabelled haloperidol within the striatum in direct relation to their clinical potencies (214, 372). Notably, dopamine was the most potent endogenous compound to inhibit the binding of haloperidol; thus, it was evident that the antipsychotic receptor was in fact a dopamine receptor (214, 372). Today, while antipsychotic medications differ in terms of their receptor binding and side-effect profile, a shared property among antipsychotics is their blockade of striatal dopamine D2 receptors (27).

Further, studies introducing medication to a previously unmedicated sample have reported correlations between striatal dopamine D2 receptor occupancy and change in positive symptoms (373, 374); this relationship is also confirmed by two meta-analyses (375, 376). Notably, such associations are not identified in extrastriatal regions.

Moreover, PET studies particularly implicate the associative striatum in the pathophysiology of schizophrenia (364-366). In support, elevated striatal 18F-dopa uptake, representative of subcortical dopamine synthesis capacity, was found within the associative striatum of individuals with prodromal symptoms and in patients with schizophrenia; this effect was not present in the limbic or sensorimotor striatum (365). Also, dopamine synthesis capacity was greater in the associative striatum of individuals with prodromal symptoms who transitioned to a psychotic disorder, in comparison to patients who did not transition to a psychotic disorder and healthy controls; this effect was also not present in the limbic or sensorimotor striatum (364). Moreover, after acute pharmacologically induced dopamine depletion, a larger increase in dopamine D2 receptor availability was reported within the associative striatum of untreated patients with schizophrenia, in comparison to healthy controls, whereas no group differences were observed in the limbic and sensorimotor striatum (366). Another PET study found that dopamine synthesis capacity in the associative striatum was reduced in patients with schizophrenia within whom treatment resistance to first-line antipsychotics was established compared to patients who responded to antipsychotic treatment; dopamine synthesis capacity in patients with treatment resistance also did not differ from controls (377). Likewise, in a prospective cohort that included patients with FEP, dopamine synthesis capacity prior to antipsychotic treatment in the associative striatum was higher in antipsychotic treatment responders compared to non-responders and healthy controls; in this study, associative striatum dopamine synthesis capacity was also positively related to improvements in symptomatology and functioning (378). Additionally, pilot data using the agonist radiotracer [11C]-(+)-PHNO suggests

52 that endogenous dopamine levels in the associative striatum remain elevated in patients with schizophrenia who have responded to an atypical antipsychotic (379). Collectively, this lends biological support to the clinical observation that worse and more frequent relapses in symptoms can occur in initial antipsychotic responders after episodes of antipsychotic discontinuation (380). These findings strongly contribute towards implicating the associative striatum in illness pathophysiology.

As mentioned above, the associative striatum consists of the precommissural dorsal caudate, the postcomissural caudate, and the precommissural dorsal putamen (360). Of these, evidence particularly implicates the precommissural dorsal caudate (the rostral and dorsal part of the caudate nuclei). In the aforementioned dopamine depletion study, the increase in dopamine

D2 receptor availability was most prominent in the precommissural dorsal caudate (366). Additionally, the precommissural dorsal caudate has been reported to specifically contain communications with the DLPFC and to regulate circuitry affecting its function (366); of note, DLPFC dysfunction is strongly linked to the cognitive impairment that exists in patients with schizophrenia (381).

1.6.4 Levels of Glutamatergic Neurometabolites within the Basal Ganglia in Schizophrenia

In this section, findings from accessible studies utilizing 1H-MRS to measure levels of glutamatergic neurometabolites within the basal ganglia in participants at high risk for psychosis, patients with FEP, or patients with schizophrenia, are exclusively discussed. Within these studies, concomitant group differences in levels of other neurometabolites are also discussed below. It deserves mention that, at times, nomenclature describing the location of 1H-MRS voxel placement across studies might be inconsistent.

Among those investigating the basal ganglia, previous 1H-MRS studies have provided strong evidence that the striatum (i.e. caudate, putamen), and especially its associative subregion, plays an important role in schizophrenia. First, in 2003, no differences in putamen glutamate+glutamine (Glx) levels were identified between groups of medicated patients with schizophrenia and healthy controls (382). Similarly, in 2009, no differences in caudate Glx levels

53 were found in first-degree relatives of patients with schizophrenia, although reductions in Cho, NAA, and Cr were reported (383). However, in 2011, it was reported that higher levels of glutamate and Cho were found in the associative striatum of antipsychotic-naïve patients experiencing their FEP, in comparison to healthy controls (247). The same study found elevated levels of glutamate and NAA in participants at ultra high-risk for schizophrenia or with prodromal symptoms of schizophrenia, compared to healthy controls (247). Next, in 2013, increased glutamate levels were found in the associative striatum of participants at ultra-high risk for psychosis who transitioned to a psychotic disorder compared to participants at ultra-high risk for psychosis who did not exhibit psychotic symptoms over a 2-year period (246). Then, in the same year, earlier cross-sectional findings of patients with FEP were replicated in a longitudinal study, in which increased baseline levels of glutamate and Cho were again observed in the associative striatum within the FEP group, in addition to elevated baseline levels of Glx and mI (253). Within the same study, effective antipsychotic treatment (reduction of at least 30% on the total score of the Positive and Negative Syndrome Scale (PANSS) after 4 weeks) decreased elevated baseline glutamate in this area, such that levels following treatment did not differ from controls (253); notably, previously elevated Glx levels also no longer differed from healthy controls following antipsychotic treatment. Moreover, also in 2013, individuals at high familial risk for schizophrenia were found to have elevated caudate Glx levels, along with reductions in NAA (311). More recently, in 2015, higher levels of Glx and GABA were found in the dorsal caudate of ultra-high risk participants (384). Finally, in 2018, a study found higher Glx and GABA levels in the dorsal caudate of antipsychotic-naïve patients with FEP and, after 4 weeks of antipsychotic treatment, both Glx and GABA levels did not differ between the patient group and healthy controls (385).

Beyond the striatum, other 1H-MRS studies have investigated levels of glutamatergic neurometabolites in voxels characterized as being placed within the basal ganglia more generally. In 2000 and 2009, two studies investigating patients with schizophrenia failed to observe group differences in levels of glutamatergic neurometabolites within the basal ganglia (386, 387). Contrastingly, in 2012, a study reported increased levels of basal ganglia Glx in patients with early-stage first-episode schizophrenia (254). Then, in 2017, a 7T study that included patients with chronic schizophrenia, healthy first-degree relatives of patients with

54 schizophrenia, and healthy controls, found no group differences in glutamate, glutamine or GABA within the basal ganglia (388).

1.6.5 Meta-Analytic 1H-MRS Findings within the Basal Ganglia in Schizophrenia

Altogether, there is a limited number of 1H-MRS studies specifically isolating and targeting the striatum, a brain region that is key to the present work. To allow for a combined investigation of comparable studies through a meta-analytic approach, previous meta-analyses group together studies within the basal ganglia. With respect to glutamatergic disturbances, a recent meta-analysis reported elevated levels of glutamate and Glx within the basal ganglia; notably, this investigation included patients at varying stages of illness, although Glx elevations were also found independently in the FEP group (389). Beyond glutamatergic neurometabolites, NAA, Cho, and Cr have been assessed in the basal ganglia through a meta-analytic approach, and no group differences have been identified (147, 349, 350, 390).

However, studies using 1H-MRS to investigate the basal ganglia have several key methodological differences, resulting in vast heterogeneity across this field of work. In particular, certain differing factors that contribute towards study heterogeneity include: patients’ medication status (i.e. antipsychotic-naïve, antipsychotic-free, minimal antipsychotic exposure, long-term antipsychotic exposure), patients’ stage of illness (i.e. high risk, FEP, chronic), patients’ onset of illness, 1H-MRS voxel placement, neurometabolite concentration (i.e. absolute, relative to Cr, relative to water), 1H-MRS acquisition parameters, MRI Tesla strength, and study sample size (131, 147, 161, 389-391). As a result of these several factors, synthesizing 1H-MRS findings in a meaningful manner is challenging. While many studies were conducted near the turn of the century, more recent work has placed a greater focus on accounting for and explaining heterogeneity, reducing confounders, and improving reproducibility.

1.6.6 Volume Findings within the Striatum in Schizophrenia

Striatal regions are often investigated within volumetric studies in patients with schizophrenia. Surprisingly, a recent meta-analysis found no group differences between patients with schizophrenia and healthy controls in terms of caudate or putamen volumes (184). However, in other meta-analyses, volumetric loss within the caudate, an area accounting for the majority of the associative striatum, is often identified in both antipsychotic-naïve patients with schizophrenia and patients with FEP (192, 274, 275, 392). Interestingly, elevations in glutamatergic compounds within these two subgroups, as suggested by the 1H-MRS findings discussed above, provide support for the role of excitotoxicity in explaining some of these deficits (192, 275).

1.7 Meta-Analytic 1H-MRS Findings in Schizophrenia

To best summarize the existing 1H-MRS findings in patients with schizophrenia beyond the basal ganglia, the main results from meta-analyses will be presented in this section. In terms of glutamatergic neurometabolites, a meta-analysis published by Merritt et al in 2016 found increases in glutamate within the basal ganglia, increases in glutamine within the thalamus, and increases in Glx within the basal ganglia and medial temporal lobe (389). Of note, these findings considered patients within the high risk, FEP, or chronic schizophrenia stages of illness; however, when analyses were limited to patient groups, no additional findings were noted. The authors further found elevated medial frontal Glx levels in individuals at high risk for schizophrenia, increased basal ganglia Glx levels in patients with FEP, and elevated medial temporal and frontal WM Glx levels in patients with chronic schizophrenia. Also, Merritt et al found no association with age, symptom severity, or antipsychotic dose in a meta-regression analysis. This meta-analysis follows from one that was published in 2013 by Marsman et al, which found decreased medial frontal glutamate and increased medial frontal glutamine in patients with schizophrenia (257). In this study, the authors also found that glutamate and glutamine decreased with age in patients with schizophrenia at a faster rate than healthy controls.

Furthermore, other meta-analyses have reported upon NAA, Cho, and Cr in patients with schizophrenia. NAA reductions predominantly within frontal and temporal lobes, and certain

56 subcortical regions, have been reported within this patient population in meta-analyses published in 2005, 2011, 2012, and 2013 (147, 257, 349, 350). Moreover, a recent meta-analysis identified lower thalamic NAA levels in antipsychotic-naïve/free patients with schizophrenia (390). In contrast, the one existing meta-analysis that has examined Cho and Cr levels in patients with schizophrenia to-date, published in 2012 by Kraguljac et al, suggested no alterations in either neurometabolite (147). Notably, mI levels in patients with schizophrenia have yet to be assessed in any published meta-analysis.

Chapter 2

2 Rationale, Hypotheses, and Objectives

2.1 Study One

2.1.1 Rationale

Myo-inositol (mI), a putative marker of glial cells, and choline (Cho), representative of membrane turnover, both exist in larger concentrations within glial cells than in neurons, and their elevation is frequently taken to represent glial activation. Proton magnetic resonance spectroscopy (1H-MRS) permits the assessment of mI and Cho levels, along with levels of other neurometabolites, including glutamate, glutamate+glutamine (Glx), and N-acetylaspartate (NAA). A collective examination of these neurometabolites in the associative striatum, a brain region highly implicated in the pathophysiology of schizophrenia, within a sample of antipsychotic-naïve patients experiencing their first non-affective episode of psychosis can advance the knowledge surrounding glial dysfunction and its implications, particularly in terms of the disruption of glutamatergic neurotransmission, in the early stages of schizophrenia. Our group previously examined neurometabolite differences within this brain region and in this patient population. First, we found higher Cho and glutamate levels in patients with first-episode psychosis (FEP) in comparison to healthy controls. These findings were replicated in a second, longitudinal study, wherein we also observed elevated baseline mI and Glx levels in the patient group. The present investigation advanced past work by including what we believed to be the largest sample to date of antipsychotic-naïve patients experiencing their first non-affective episode of psychosis and a group of age- and sex-matched healthy controls (FEP: n=60; controls: n=60), and by exploring glial dysfunction.

2.1.2 Hypotheses

We hypothesized that neurometabolites predominantly present in glial cells would be elevated in the FEP group along with levels of glutamatergic neurometabolites. In addition, we

58 hypothesized that dysregulated neurometabolite levels would correlate with clinical symptom severity. Finally, we hypothesized that abnormal levels of glial neurometabolites would be linked with disrupted levels of glutamatergic neurometabolites in the patient group, such that the relationships between these measures would differ from those in healthy controls.

2.1.3 Objectives

The objectives of this study were threefold: 1) to compare associative striatum mI, Cho, glutamate, Glx, and NAA levels, as assessed by 1H-MRS, between a sample of antipsychotic- naïve patients with FEP and a group of age- and sex-matched healthy controls; 2) to explore the relationships between disrupted neurometabolite levels and measures of clinical symptomatology; and 3) to examine the associations among levels of dysregulated neurometabolites.

2.2 Study Two

2.2.1 Rationale

Past work has reported substantial neuroanatomical compromise in patients with schizophrenia, including volumetric deficits and cortical thinning. However, the mechanisms through which these reductions occur are currently unclear. Additionally, illness chronicity and medication intake may confound the evaluation of these structural indices, and as a result, the assessment of true illness pathophysiology. Notably, elevated levels of glutamatergic neurometabolites may have an excitotoxic effect. Having previously reported upon elevations in levels of glutamatergic neurometabolites within the precommissural dorsal caudate (PDC), an area thought to be involved in the schizophrenia disease process, in a sample of antipsychotic- naïve patients with FEP, within whom the above-mentioned confounding factors would be appreciably reduced, the present study explored whether elevations in glutamatergic neurometabolites are associated with local and widespread neuroanatomical compromise.

2.2.2 Hypotheses

We hypothesized precommissural caudate volume (PCV) loss and widespread cortical thinning within the FEP group. Additionally, we hypothesized a negative relationship between elevated glutamatergic neurometabolites and local volumetric deficits but not cortical thinning in the FEP group. Lastly, we hypothesized that reduced PCV and cortical thinning would be associated with greater clinical symptom severity.

2.2.3 Objectives

The objectives of this study were threefold: 1) to compare PCV and cortical thickness between a sample of antipsychotic-naïve patients with FEP and a group of age- and sex-matched healthy controls; 2) to explore the associations between levels of glutamatergic neurometabolites (i.e. glutamate, Glx) in the PDC, as assessed by 1H-MRS, and neuroanatomical indices (i.e. PCV, cortical thickness); and 3) to investigate the relationships between neuroanatomical indices and measures of clinical symptomatology.

2.3 Study Three

2.3.1 Rationale

Neurometabolic disturbances are believed to play a key role in schizophrenia and the striatum is particularly implicated in the pathophysiology of the illness. Previous literature has suggested that striatal glutamatergic neurometabolites may be elevated in the early, unmedicated stages of schizophrenia, yet decrease to levels comparable to those of healthy controls in later, medicated stages of illness. To complement our previous findings within antipsychotic-naïve patients with FEP, and to further contribute to the body of literature, we sought to examine striatal neurometabolite levels, as assessed by 1H-MRS, in patients with schizophrenia undergoing long-term antipsychotic treatment.

2.3.2 Hypotheses

We hypothesized that levels of glutamatergic neurometabolites would not differ between the group of antipsychotic-treated patients with schizophrenia and the group of healthy controls.

2.3.3 Objectives

The objectives of this study were twofold: 1) to compare levels of striatal neurometabolites between patients with schizophrenia receiving at least one year of antipsychotic treatment and healthy controls, and 2) to assess the reliability of striatal neurometabolite levels, 1H-MRS spectral quality indices, and tissue heterogeneity values.

Chapter 3

This chapter is reproduced with permission from the following: Plitman E, de la Fuente-Sandoval C, Reyes-Madrigal F, Chavez S, Gómez-Cruz G, León- Ortiz P, Graff Guerrero A. Elevated Myo-Inositol, Choline, and Glutamate Levels in the Associative Striatum of Antipsychotic-Naïve Patients With First-Episode Psychosis: A Proton Magnetic Resonance Spectroscopy Study With Implications for Glial Dysfunction. Schizophrenia Bulletin 2016; 42(2): 415-424.

3.1 Abstract

Glial disturbances are highly implicated in the pathophysiology of schizophrenia and may be linked with glutamatergic dysregulation. Myo-inositol (mI), a putative marker of glial cells, and choline (Cho), representative of membrane turnover, are both present in larger concentrations within glial cells than in neurons, and their elevation is often interpreted to reflect glial activation. Proton magnetic resonance spectroscopy (1H-MRS) allows for the evaluation of mI, Cho, glutamate, glutamate + glutamine (Glx), and N-acetylaspartate (NAA). A collective investigation of these measures in antipsychotic-naive patients experiencing their first nonaffective episode of psychosis (FEP) can improve the understanding of glial dysfunction and its implications in the early stages of schizophrenia. 3-Tesla 1H-MRS (echo time = 35ms) was performed in 60 antipsychotic-naive patients with FEP and 60 age- and sex-matched healthy

62 controls. mI, Cho, glutamate, Glx, and NAA were estimated using LCModel and corrected for cerebrospinal fluid composition within the voxel. mI, Cho, and glutamate were elevated in the FEP group. After correction for multiple comparisons, mI positively correlated with grandiosity. The relationships between mI and glutamate, and Cho and glutamate, were more positive in the FEP group. These findings are suggestive of glial activation in the absence of neuronal loss and may thereby provide support for the presence of a neuroinflammatory process within the early stages of schizophrenia. Dysregulation of glial function might result in the disruption of glutamatergic neurotransmission, which may influence positive symptomatology in patients with FEP.

3.2 Introduction

Glial disturbances are highly implicated in the pathophysiology of schizophrenia (393, 394). Myo-inositol (mI) and choline-containing compounds (Cho) act as markers of glial cells and membrane metabolism, respectively (146, 166, 395, 396). Both mI and Cho are present in higher concentrations within glial cells than in neurons (164, 165, 169) and have been investigated in patients with schizophrenia using proton magnetic resonance spectroscopy (1H- MRS) (147, 391, 397). Elevated levels of these neurometabolites have been proposed to reflect glial activation and have been observed in several neuroinflammatory disorders (166).

Astrocytes (a sub-type of glial cells) contribute to the regulation of glutamatergic neurotransmission (398, 399). Glutamatergic dysregulation is thought to be involved in the schizophrenia disease process (129, 161, 301, 400) and has been evaluated in patients with schizophrenia using 1H-MRS through the measurement of glutamate, glutamine, and glutamate + glutamine (Glx) (148, 176, 247-249). 1H-MRS has also been used to ascertain levels of N- acetylaspartate (NAA), which serves as an index of neuronal integrity (401, 402).

Existing 1H-MRS literature is heterogeneous in terms of voxel placement, stage of illness, and medication status; antipsychotic treatment has been suggested to influence the assessment of mI, Cho, glutamatergic markers, and NAA levels (248, 256, 403, 404). Our group has previously investigated neurometabolic differences in antipsychotic-naive patients experiencing their first nonaffective episode of psychosis (FEP) within the right associative striatum, an area rich in

dopamine afferents and dopamine D2 receptors, which is involved in the pathophysiology of schizophrenia (365, 366) and is often included in the quantification of in vivo occupancy studies of antipsychotics (405, 406). Our first study found higher Cho and glutamate levels in the FEP group in comparison to controls (247). We replicated these findings in a longitudinal study, in which we also found elevated baseline mI and Glx levels in the FEP group (253).

In the present study, we used a larger sample to compare mI, Cho, glutamate, Glx, and NAA levels in the associative striatum between antipsychotic-naive patients with FEP and a group of age- and sex-matched healthy controls. We also explored the associations between neurometabolite levels and clinical measures, as well as the relationships amongst levels of neurometabolites. We hypothesized that neurometabolites predominantly present in glial cells would be elevated in the FEP group along with levels of glutamatergic compounds, in accordance with our previous findings. Our additional hypotheses were exploratory. We hypothesized that dysregulated neurometabolite levels would correlate with clinical symptom severity. Also, we hypothesized that abnormal levels of glial neurometabolites would be linked with disrupted glutamatergic levels in the patient group, such that the relationships between these measures would differ from healthy controls. The assessment of unmedicated patients is vital towards characterizing the pathophysiology of schizophrenia in that the confounding effects of medication are eliminated (407). To the best of our knowledge, this is the largest sample to date of antipsychotic-naive patients with FEP in which 1H-MRS was performed.

3.3 Methods

3.3.1 Participants

This study received approval from the Ethics and Scientific Committees of the National Institute of Neurology and Neurosurgery of Mexico (INNN). Individuals were included after providing informed written consent, which was obtained from both parents for participants under 18 years old. Participants did not receive a stipend.

Sixty-four patients were recruited during their FEP from inpatient or outpatient services at the INNN between 2008 and 2013. The Structured Clinical Interview for DSM-IV was utilized

64 to determine inclusion. Patients met inclusion criteria if they were antipsychotic-naive; all but 3 patients had less than 2 years of psychotic symptoms. Exclusion criteria included a concomitant medical or neurological illness, current substance abuse or history of substance dependence (excluding nicotine), comorbidity with other Axis I disorders, a high risk for suicide, and psychomotor agitation. Sixty-three age- and sex-matched healthy controls were also enrolled and assessed in the same manner as the patients. Controls with a history of psychiatric illness or a family history of psychosis were excluded.

Each participant was screened for drugs of abuse, including cannabis, cocaine, heroin, opioids, and benzodiazepines at the time of inclusion and 1 hour prior to the magnetic resonance imaging (MRI) scan. The current sample included a subset of participants (FEP: n = 35; controls: n = 35) previously reported upon (247, 253); additional subjects were added to increase statistical power.

3.3.2 Clinical Assessment

Patients’ psychopathology was assessed by research psychiatrists (C.d.l.F.-S., F.R.-M., P.L.-O.) using the Positive and Negative Syndrome Scale (PANSS) (408).

3.3.3 Magnetic Resonance Studies

Participants were scanned at the INNN in a 3T GE whole-body scanner (Signa Excite HDxt; GE Healthcare) with a high-resolution 8-channel head coil. The participant’s head was positioned along the canthomeatal line and immobilized using a forehead strap. Each participant was scanned using a T1-weighted spoiled gradient-echo 3-dimensional axial acquisition (SPGR, echo time [TE] = 5.7ms, repetition time [TR] = 13.4ms, inversion time = 450ms, flip angle = 20°, field of view = 25.6cm, ≥256 × ≥256 matrix, slice thickness ≤ 1.2mm), oriented above and parallel to the anterior-posterior commissure line. These T1-weighted SPGR images were reformatted to sagittal and coronal views and were subsequently used for 1H-MRS voxel localization.

1H-MRS spectra were obtained using point-resolved spectroscopy (PRESS, TE = 35ms, TR = 2000ms, spectral width = 5000 Hz, 4096 data points used, 128 water-suppressed, and 16 water-unsuppressed averages) centered on the right dorsal-caudate nucleus in volume elements (voxels) of 8ml (2 × 2 × 2cm). The lower end of the dorsal-caudate voxel (associative striatum) was located 3mm dorsal to the anterior commissure to include maximum grey matter (GM) and with a dorsal extension (thickness) of 2 cm. Voxel placement is identified in Figure 3-1. During the acquisition, 1H-MRS spectra were shimmed to achieve a full-width at half maximum (FWHM) of 12 Hz or less, measured on the unsuppressed water signal from the voxel. Spectra with larger FWHM were excluded from ensuing analyses (148).

Figure 3-1. Location of right associative striatum voxel placement.

3.3.4 1H-MRS Data Analysis

All water-suppressed spectra were analyzed using LCModel version 6.3-0E (156). Spectra were normalized to the unsuppressed water signal, allowing for neurometabolite quantification, expressed in institutional units. A standard basis set of metabolites, consisting of

L-alanine, aspartate, creatine, creatine methylene group, γ-aminobutyric acid, glucose, glutamate, glutamine, glutathione, glycerophosphocholine, guanidinoacetate, L-lactate, myo-inositol, N- acetylaspartate, N-acetylaspartylglutamate, phosphocholine, phosphocreatine, scyllo-inositol, and taurine, along with lipids (Lip), and macromolecules (MM) (Lip09, Lip13a, Lip13b, Lip20, MM09, MM12, MM14, MM17 and MM20) was used for analysis. LCModel included this basis set, which was acquired with the same sequence parameters used in our study. In this study, Cho is the sum of glycerophosphocholine + phosphocholine, NAA is the sum of NAA + N- acetylaspartylglutamate, and creatine-containing compounds (Cr) is the sum of creatine + phosphocreatine. One analyzed spectrum is included in Figure 3-2.

Figure 3-2. Example of a spectrum analyzed with LCModel 6.3-0E.

Spectra with %SD values of 20% or greater for neurometabolites of interest were considered poor quality and excluded from subsequent analyses (172, 173). Four patients and 3 controls were excluded due to either rejection by LCModel analysis or a FWHM greater than 12 Hz, resulting in the inclusion of 60 patients and 60 healthy controls; of the 7 participants/spectra removed in total, 1 was previously reported upon. Glutamine was not analyzed because of poor spectra fitting. To control for correlations introduced by the LCModel fitting procedure, the triangular table of correlation coefficients was used. All reported metabolite correlations did not

68 show strong negative pairwise correlations in the triangular table: no correlational coefficients were less than -0.5 (172).

T1-weighted MRI scans used for voxel localization were segmented into GM, white matter (WM), and cerebrospinal fluid (CSF) using Statistical Parametric Mapping 8 (SPM8, Wellcome Department of Imaging Neurosciences, University College London, UK). The size and location of each area were extracted from the spectra file headers to calculate the percentage of GM, WM, and CSF content within the voxel using an in-house software, allowing for the correction of the CSF fraction of the spectroscopic values (247).

3.3.5 Statistical Analysis

Statistical analyses were performed using SPSS Statistics version 20 (IBM Corporation). Demographic and clinical characteristics, Cramer-Rao lower bounds (CRLBs), FWHM values, signal-to-noise ratios, GM, WM, and CSF percentages, and GM/(GM + WM) were compared between groups using independent-sample t tests. Frequency data were analyzed using χ2 or Fisher’s exact tests. Neurometabolite levels were compared between groups using analyses of variance. To check for confounders, tobacco use, GM content, GM/(GM + WM), and age were each investigated as covariates. Outliers were defined as greater than 3 times the interquartile range and were removed in a neurometabolite-specific manner; 2 mI outliers, 1 glutamate outlier, and 1 Cr outlier were removed. Due to a priori hypotheses, neurometabolite level group comparisons were conducted with a significance level of P < .05.

Pearson correlations were performed to investigate the association between PANSS subscale total scores and neurometabolite levels that differed significantly between groups. If any correlation reached an uncorrected P < .05, Pearson correlations between the neurometabolite and items within the specific PANSS subscale were also examined. All investigations were corrected for multiple comparisons and a statistical threshold of P < .05 ÷ n was used, where n = # of comparisons (n = 16; 3 neurometabolites with 3 subscale total scores and 1 neurometabolite with 7 subscale items).

The relationships amongst levels of neurometabolites that differed significantly between groups were assessed using Pearson correlations. A statistical threshold of P < .05 ÷ n was used, where n = # of comparisons (n = 6; 3 neurometabolites investigated separately for each group).

Group differences in correlational coefficients were evaluated by converting correlational coefficients with Fisher's transformation (Equation 1) and comparing them using Fisher’s z test (Equation 2), which allowed for z score acquisition. Here, comparisons were conducted with a significance level of P < .05. 1+ r 1. r'= (0.5) loge 1− r

r 1'−r 2' 2. z = 1 1 + n 1 − 3 n 2 − 3

Above, r represents the sample correlational coefficient, r’ is the transformed value of r, n indicates sample size, and z refers to z score.

3.4 Results

3.4.1 Demographic and Clinical Characteristics

Participants’ demographic and clinical characteristics are reported in Table 3-1. Patients’ DSM-IV diagnoses were: brief psychotic disorder (n = 14), schizophreniform disorder (n = 21), and schizophrenia (n = 25). Education years were higher in the control group (t(118) = 6.40, P < .001), while tobacco use was greater in the FEP group (χ2 = 5.21, P = .039). Age, sex, handedness, and cannabis use did not differ between groups. The FEP group had a mean duration of untreated psychosis of 33.03 ± 52.70 weeks, and mean PANSS positive, negative, and general psychopathology subscale total scores of 24.13 ± 4.97, 24.33 ± 5.66, and 48.75 ± 8.38, respectively.

Table 3-1. Demographic and Clinical Characteristics of Study Participants.

Variable FEP group (n = 60) HC group (n = 60) Age, mean (SD) [range], y 24.67 (7.68) [13-47] 23.03 (4.87) [15-42] Educational level, mean 11.48 (3.13)* 15.00 (2.88) (SD), y Sex, No. Male 37 38 Female 23 22 Handedness, No. Right 60 60 Left 0 0 Ever used, No./total No. Tobacco 17/60* 7/60 Cannabis 4/60 0/60 Duration of untreated 33.03 (52.70) NA psychosis, mean (SD), wk PANSS subscale total, mean (SD), score Positive 24.13 (4.97) NA Negative 24.33 (5.66) NA General 48.75 (8.38) NA psychopathology

Note: FEP, first-episode psychosis; HC, healthy control; NA, not applicable; No., number; PANSS, Positive and Negative Syndrome Scale. *P < .05.

3.4.2 Neurometabolite Levels

Neurometabolite levels are reported in Table 3-2 and displayed in Figure 3-3. mI, Cho, and glutamate levels were higher in the FEP group (F(1,116) = 5.66, P = .019; F(1,118) = 10.66, P = .001; F(1,117) = 11.63, P < .001, respectively). Glx and NAA levels did not differ between groups (F(1,118) = 1.84, P = .18; F(1,118) = 0.29, P = .59, respectively). Results were unaffected when tobacco use, GM content, GM/(GM + WM), and age were included as covariates.

Figure 3-3. Neurometabolite levels in patients with first-episode psychosis and healthy controls. Cho, choline-containing compounds; Glu, glutamate; Glx, glutamate + glutamine; mI, myo-inositol; NAA, N-acetylaspartate; *P < .05; **P < .01; ***P < .001.

Table 3-2. Neurometabolite Levels in Patients With First-Episode Psychosis and Healthy Controls.

Mean (SD)

mI Cho Glu Glx NAA Cr

FEP group 5.81 (1.10)* 2.53 (0.29)** 13.10 (1.31)*** 16.61 (1.69) 11.00 (1.08) 8.61 (0.74)

HC group 5.31 (1.19) 2.36 (0.28) 12.39 (0.95) 16.21 (1.50) 10.90 (0.96) 8.49 (0.75)

Note: Cho, choline-containing compounds; Cr, creatine-containing compounds; FEP, first- episode psychosis; Glu, glutamate; Glx, glutamate + glutamine; HC, healthy control; mI, myo- inositol; NAA, N-acetylaspartate. *P < .05. **P < .01. ***P < .001.

3.4.3 Relationships With Clinical Measures

The relationships between neurometabolites and PANSS subscale total scores are presented in Table 3-3. After correction for multiple comparisons, mI levels were positively correlated at a trend-level significance with PANSS positive total score (r(57) = .31, P- uncorrected = .017) (Figure 3.4a) and PANSS item P3 (Hallucinatory Behavior) score (r(57) = .37, P-uncorrected = .004) (Figure 3-4b). mI levels were also positively correlated with PANSS item P5 (Grandiosity) score (r(57) = .49, P-uncorrected < .001) (Figure 3-4c). mI levels were not related to PANSS negative or general psychopathology total scores. Removing a potential outlier (mI > 8) did not alter findings. Cho and glutamate levels were not related to any PANSS subscale total scores. Including tobacco use, GM content, GM/(GM + WM), and age as covariates did not affect results.

Table 3-3. Relationships Between Neurometabolite Levels and PANSS Subscale Total Scores.

Correlational Coefficient (r)

Variable mI Cho Glu PANSS subscale

Positive r(57) = .31, P = .017* r(58) = .11, P = .40 r(58) = -.05, P = .71

Negative r(57) = -.04, P = .78 r(58) = .03, P = .82 r(58) = -.01, P = .91

General r(57) = .08, P = .56 r(58) = .06, P = .65 r(58) = .001, P = .99 psychopathology

Note: Cho, choline-containing compounds; Glu, glutamate; mI, myo-inositol; PANSS, Positive and Negative Syndrome Scale.

*P-uncorrected < .05.

Figure 3-4. Relationships between myo-inositol levels and positive symptom subscale total (a), hallucinatory behavior (b), and grandiosity (c) scores. PANSS, Positive and Negative Syndrome Scale.

3.4.4 Relationships Between Neurometabolites

mI levels positively correlated with Cho levels in both groups (FEP: r(57) = .58, P- uncorrected < .001; control: r(57) = .69, P-uncorrected < .001) (Figure 3-5a). mI levels positively correlated with glutamate levels in the FEP group only (FEP: r(57) = .29, P- uncorrected = .024; control: r(56) = -.13, P-uncorrected = .32) (Figure 3-5b), though this

74 relationship did not survive correction for multiple comparisons. Cho levels positively correlated with glutamate in the FEP group only (FEP: r(58) = .48, P-uncorrected < .001; control: r(57) = .13, P-uncorrected = .32) (Figure 3-5c). Results were unaffected by the addition of tobacco use, GM content, GM/(GM + WM), and age as covariates.

r-to-Z transformations identified that the relationships between levels of mI and glutamate, and Cho and glutamate, were more positive in the FEP group (Z = 2.26, P = .024; Z = 2.08, P = .038, respectively). The relationships between levels of mI and Cho were not different between groups (Z = 0.98, P = .33).

Figure 3-5. Relationships between levels of myo-inositol and choline (a), glutamate and myo-inositol (b), and glutamate and choline (c) in patients with first-episode psychosis and healthy controls.

3.4.5 CRLB, FWHM, Signal-to-Noise Ratios, and Tissue Heterogeneity

CRLBs, FWHM values, signal-to-noise ratios, GM, WM, and CSF percentages, and GM/(GM + WM) did not differ between groups (Tables 3-4 and 3-5).

Table 3-4. Cramer-Rao Lower Bound Values, Full-Width at Half Maximum Values, and

Signal-to-Noise Ratios.

Mean (SD) FWHM mI Cho Glu Glx NAA Cr SNR (ppm) FEP group 0.33 (0.06) 0.07 (0.02) 0.75 (0.11) 0.83 (0.18) 0.32 (0.09) 0.23 (0.04) 0.08 (0.02) 14.32 (2.45)

HC group 0.33 (0.07) 0.07 (0.02) 0.74 (0.12) 0.83 (0.17) 0.33 (0.07) 0.24 (0.05) 0.08 (0.02) 14.68 (2.63)

Note: Cho, choline-containing compounds; Cr, creatine-containing compounds; FEP, first- episode psychosis; FWHM, full-width at half maximum; Glu, glutamate; Glx, glutamate + glutamine; HC, healthy control; mI, myo-inositol; NAA, N-acetylaspartate; SNR, signal-to-noise ratio.

Table 3-5. 1H-MRS Voxel Tissue Composition.

Variable FEP group (n = 60) HC group (n = 60) 1H-MRS voxel, mean (SD), % GM 0.43 (0.06) 0.43 (0.04) WM 0.47 (0.08) 0.48 (0.06) CSF 0.11 (0.08) 0.09 (0.06) GM/(GM + WM), mean (SD) 0.48 (0.06) 0.48 (0.05)

Note: CSF, cerebrospinal fluid; FEP, first-episode psychosis; GM, grey matter; HC, healthy control; WM, white matter.

3.5 Discussion

The present study investigated neurometabolite levels in the associative striatum within the largest sample of antipsychotic-naive patients with FEP to date and a group of age- and sex- matched healthy controls. We found elevations in mI, Cho, and glutamate levels in the FEP group. Additionally, mI levels positively correlated with grandiosity, while positive symptom total score and hallucinatory behavior positively correlated with mI levels at a trend-level

76 significance. Lastly, the correlations between levels of mI and glutamate, and Cho and glutamate, were more positive in the FEP group.

We previously reported increases in mI and Cho levels within the associative striatum of antipsychotic-naive patients with FEP (247, 253). Though most 1H-MRS studies have found unaltered levels of mI and Cho (147, 391, 397, 409), others have reported deviations in these neurometabolites within several brain regions (157, 387, 410-412). To the best of our knowledge, no previous 1H-MRS study has observed statistical differences in mI levels within the striatum of patients with schizophrenia. However, in terms of Cho, one study found increased levels in the caudate nucleus of antipsychotic-naive patients with schizophrenia (413), while others have reported increases in the basal ganglia, encompassing caudate and lenticular nucleus regions, within medicated patients (414, 415).

Both mI and Cho are present in greater concentrations within glial cells than in neurons (164, 165, 169). The strong correlation between mI and Cho levels in both groups suggests that these neurometabolites are linked, although these results must be interpreted with caution due to the potential for spurious correlations (416). Elevated levels of mI and Cho are often interpreted as glial activation, which is commonly associated with a neuroinflammatory response (166); accordingly, mI and Cho levels are elevated in several neuroinflammatory disorders (166, 417- 420). Recently, Chiappelli et al reported that mI levels within frontal WM were negatively correlated with fractional anisotropy of WM in both patients with schizophrenia and in healthy controls (421); in support of the link between mI and neuroinflammation, this finding was interpreted by the authors as evidence for a general effect of inflammation on WM microstructure. Further, previous literature has suggested that schizophrenia may have a neuroinflammatory component and that antipsychotics may have anti-inflammatory effects (422- 424). Additionally, anti-inflammatory agents might have beneficial effects on symptomatology as adjunctive therapies (425, 426). In our study, the concomitant elevation of mI and Cho levels in the patient group may provide an 1H-MRS finding in support of early neuroinflammation that either accompanies or precedes the FEP in schizophrenia.

Furthermore, consistent with our previous reports (247, 253), we found elevated glutamate levels in the FEP group. Notably, this result was also identified in the subjects not included in previous reports (F(1,48) = 13.53, P < .001; Cohen’s d = 1.05). This finding is in

77 accordance with previous 1H-MRS literature suggesting increased levels of glutamatergic markers in antipsychotic-naive patients with schizophrenia, minimally treated patients with schizophrenia, and individuals at ultra-high risk for psychosis who later transitioned to psychosis (148, 246, 248, 249, 251, 252) – levels that may subsequently normalize to or decrease below those of healthy controls following antipsychotic treatment (131, 148, 174, 248, 256, 352). While some previous 1H-MRS studies investigating the basal ganglia (including lenticular nucleus, putamen, and substantia nigra regions) in patients with schizophrenia have failed to find differences in glutamatergic markers (387, 427), our findings are comparable to those of Goto et al, who reported increased basal ganglia Glx in patients with first-episode schizophrenia (254); however, it is important to distinguish between glutamate and Glx levels in this comparison, especially since only the former was found to be elevated in our study and the latter is a composite measure of both glutamate and glutamine levels.

The present study was also supplemented by the investigation of the relationships between levels of glutamate and levels of mI and Cho. Studies localizing mI and Cho to glial cells specifically found elevated concentrations of these neurometabolites within astrocytes (164, 165, 169). Typically, synaptic glutamate is taken up by astrocytes and converted to glutamine (398, 399). Thus, the parallel increase of mI and Cho levels in patients with FEP may support a mechanism wherein astrocytic function is abnormally altered in response to a pathological process and glutamatergic neurotransmission is consequently disturbed, as suggested by our observation of increased glutamate levels. This notion is reinforced by our findings of positive correlations between levels of mI and glutamate, and Cho and glutamate, in the FEP group only, and the fact that the correlational coefficients of these relationships were more positive in the FEP group. Previous studies have observed increases in S100B, a marker for astrocyte function, in patients with schizophrenia during acute psychosis stages, in addition to concomitant increases in mI levels, supporting the notion that astrocytic activation with associated mI elevation may exist in schizophrenia (428). Likewise, elevated Cho levels have been interpreted to represent increased astrocytic turnover of glutamatergic compounds (176).

Though the exact mechanism by which astrocytic dysfunction might lead to glutamatergic dysregulation has not been characterized, evidence in patients with schizophrenia has suggested a role for astrocytic overproduction of kynurenic acid, an endogenous N-methyl- D-aspartate receptor (NMDAR) antagonist (429, 430). Given that the administration of

78 exogenous NMDAR antagonists leads to increased levels of glutamatergic compounds (241, 242), elevated kynurenic acid may connect the aforementioned phenomena (431-433). Additionally, astrocyte dysfunction might disturb glutamate transporter function, preventing the reuptake of extracellular glutamate (434-437) and thereby contributing to dysregulated glutamatergic neurotransmission.

The relationships between mI levels and positive symptom total score, hallucinatory behavior, and grandiosity also warrant discussion. Though 2 of these correlations did not retain significance after correction for multiple comparisons, our results provide some suggestion that mI levels may be linked with positive symptomatology, reinforcing the notion that the group difference in mI levels is related to illness pathophysiology. We believe this is the first study to suggest that mI might be related to positive symptomatology, while associations with other symptom domains have been observed. Our group previously reported trend-level reductions in mI levels following clinically effective antipsychotic treatment (PANSS total score reduction of at least 30%) of antipsychotic-naive patients with FEP (253). In medicated patients with schizophrenia, Homan et al found a negative relationship between mI levels in Broca’s area and total PANSS scores (438). Furthermore, Chiappelli et al found a negative correlation between trait depressive symptoms and anterior cingulate cortex mI levels in patients with schizophrenia spectrum disorders and in healthy controls (439). The authors also observed lower mI levels in patients with at least one major depressive episode, suggesting that mI may be a biomarker of depressive symptoms in this patient population. In the present study, we propose that within the associative striatum, mI is related to positive symptomatology through astrocytic dysregulation of glutamatergic neurotransmission (440). However, despite the elevation in glutamate levels within the FEP group, glutamate was unexpectedly not associated with symptomatology. Thus, the exact mechanism connecting increased mI levels and positive symptoms remains elusive and necessitates further investigation.

In terms of NAA levels, we failed to find group differences, contrasting previous 1H- MRS studies that report reductions (147, 350). Our finding suggests preserved neuronal integrity in the associative striatum at an early stage of schizophrenia. We posit that neuronal loss occurs later in the illness, resulting from either glutamate-mediated excitotoxicity or the advancement and chronicity of neuroinflammation and glial activation (166, 441). These processes would align with literature suggesting progressive NAA reductions in schizophrenia (391).

One methodological consideration is that neurometabolite concentrations were referenced relative to water. While mI, Cho, and glutamate levels were elevated in the FEP group, it deserves emphasis that Glx and NAA levels did not differ between groups. Thus, even though neurometabolite concentrations were corrected for CSF, decreased water content likely did not drive group differences, as further evidenced by the similar voxel CSF content between groups. Notably, when referenced to Cr levels, which importantly did not differ between groups (F(1,117) = 0.70, P = .41) (147), results did not differ for analyses concerning group differences in neurometabolite levels (mI: F(1,116) = 4.45, P = .037; Cho: F(1,118) = 6.06, P = .015; glutamate: F(1,118) = 4.82, P = .030; Glx: F(1,118) = 0.02, P = .90; NAA: F(1,118) = 0.40, P = .53; Table 3-6) and their relationships with clinical symptoms (Table 3-7), whereas findings related to the relationships among neurometabolite levels were not identical (Table 3-8).

Table 3-6. Neurometabolite Levels Referenced to Total Creatine Levels in Patients With First -Episode Psychosis and Healthy Controls.

Mean (SD)

mI Cho Glu Glx NAA

FEP group 0.68 (0.12)* 0.29 (0.03)* 1.52 (0.13)* 1.92 (0.15) 1.28 (0.15)

HC group 0.63 (0.13) 0.28 (0.03) 1.46 (0.17) 1.92 (0.17) 1.29 (0.17)

Note: Cho, choline-containing compounds; FEP, first-episode psychosis; Glu, glutamate; Glx, glutamate + glutamine; HC, healthy control; mI, myo-inositol; NAA, N-acetylaspartate. *P < .05.

Table 3-7. Relationships Between Neurometabolite Levels Referenced to Total Creatine

Levels and PANSS Scores.

Correlational Coefficient (r)

Variable mI Cho Glu PANSS subscale

Positive r(57) = .38, P = .003* r(58) = .19, P= .15 r(58) = .01, P = .96

Negative r(57) = .05, P = .72 r(58) = .20, P= .12 r(58) = .18, P = .18

General r(57) = .14, P = .30 r(58) = .16, P= .22 r(58) = .09, P = .50 psychopathology

P3 (Hallucinatory r(57) = .36, P = .006* - - behavior) P5 (Grandiosity) r(57) = .45, P < .001* - - Note: Cho, choline-containing compounds; Glu, glutamate; mI, myo-inositol; PANSS, Positive and Negative Syndrome Scale. *Denotes P < .05.

Table 3-8. Relationships Among Neurometabolite Levels Referenced to Total Creatine

Levels.

Correlational Coefficient (r)

Variable Cho Glu mI FEP: r(57) = .45, P < .001*; FEP: r(57) = .09, P = .50; HC: r(57) = .65, P < .001* HC: r(57) = -.23, P = .078

Cho - FEP: r(58) = .36, P = .005*; HC: r(58) = .16, P = .23 Note: Cho, choline-containing compounds; FEP, first-episode psychosis; Glu, glutamate; HC, healthy control; mI, myo-inositol. *P < .05.

Our study is not without limitations. First, the functions assigned to neurometabolites do not comprehensively delineate their physiological roles. Particularly, the involvement of mI and Cho extend beyond glial cells and neuroinflammation to include an extensive range of other structural and signaling functions. Second, the wide age range in our sample is a source of biographic inhomogeneity. While age has been shown to influence neurometabolite levels (157, 257, 411, 421, 442), it was not presently a primary focus, though analyses concerning age are included in Table 3-9. Of note, in the full sample, mI levels were positively correlated with age at a trend-level significance (r(116) = 0.17, P = .066), a finding consistent with past work (421). Third, 1H-MRS cannot distinguish between extracellular and intracellular measurements and does not directly assess neurotransmission. Fourth, using a TE of 35ms at 3T renders glutamate and glutamine difficult to distinguish; thus, the glutamate peak may be contaminated by glutamine. This was evidenced by the correlational coefficients between glutamate and glutamine in the triangular table, which were close to or less than -0.5. Fifth, at a TE of 35ms, Glx levels may contaminate the NAA peak (443). Sixth, not all patients with FEP progress to schizophrenia, affecting generalizability, although 73% of the FEP group (44 patients) received a follow-up diagnosis of schizophrenia. Seventh, only the right associative striatum was studied to reduce imaging time in patients with active psychosis. However, previous 1H-MRS studies did not observe laterality differences in levels of mI, Cho, or glutamatergic markers in patients with schizophrenia (411, 444). Eighth, the group difference in nicotine smoking presents an important limitation. Ninth, chemical shift artifacts were not specifically addressed, though it is noteworthy that with PRESS at 3T, they could potentially account for a reduced water signal in the patient group. Tenth, simulated macromolecular resonances may not be representative of the true macromolecular spectrum. Finally, since all neurometabolite levels are relative to water, spurious correlations may exist between pairs of neurometabolites. However, similar to the reasoning provided by Kraguljac et al (445), our hypothesis was initially formulated in terms of ratios and primarily concerns a difference in correlations between patients and controls, which we were able to assess using r-to-Z transformations.

Table 3-9. Relationships Between Neurometabolite Levels and Age in Patients With First-

Episode Psychosis, Healthy Controls, and Full Sample.

Correlational Coefficient (r)

mI Cho Glu Glx NAA

FEP group r(57) = .14, P = .31 r(58) = .05, P = .71 r(58) = -.06, P = .66 r(58) = .05, P = .70 r(58) = .25, P = .056

HC group r(57) = .18, P = .17 r(58) = .09, P = .51 r(57) = -.06, P = .68 r(58) = -.08, P = .54 r(58) = .19, P = .15

r(116) = .17, r(118) = .10, r(117) = -.02, r(118) = .02, r(118) = .23, Full sample P = .066 P = .30 P = .86 P = .84 P = .012* Note: Cho, choline-containing compounds; FEP, first-episode psychosis; Glu, glutamate; Glx, glutamate + glutamine; HC, healthy control; mI, myo-inositol; NAA, N-acetylaspartate. *P < .05.

Taken together, our findings are suggestive of glial activation in the absence of neuronal loss and may thereby provide support for the presence of a neuroinflammatory process within the early stages of schizophrenia. Astrocytic dysfunction might disrupt glutamatergic neurotransmission, which may subsequently influence positive symptomatology in patients with FEP and may have an excitotoxic effect in later stages of the illness. Considering that approximately 20% to 35% of patients have unremitting positive symptoms following antipsychotic treatment (220, 221), the development of a fuller picture of schizophrenia and its neurochemical underpinnings is vital towards understanding the pathophysiology of the illness and improving treatment interventions. Future research should continue to investigate neuroinflammation and glial abnormalities in schizophrenia, as well as their impact on glutamatergic neurotransmission.

Chapter 4

4 Glutamatergic Metabolites, Volume and Cortical Thickness in Antipsychotic-Naïve Patients with First- Episode Psychosis: Implications for Excitotoxicity

This chapter is reproduced with permission from the following: Plitman E, Patel R, Chung JK, Pipitone J, Chavez S, Reyes-Madrigal F, Gómez-Cruz G, León-Ortiz P, Chakravarty MM, de la Fuente-Sandoval C, Graff Guerrero A. Glutamatergic Metabolites, Volume and Cortical Thickness in Antipsychotic-Naive Patients with First-Episode Psychosis: Implications for Excitotoxicity. Neuropsychopharmacology 2016; 41(10): 2606-2613.

4.1 Abstract

Neuroimaging studies investigating patients with schizophrenia often report appreciable volumetric reductions and cortical thinning, yet the cause of these deficits is unknown. The association between subcortical and cortical structural alterations, and glutamatergic neurometabolites is of particular interest due to glutamate’s capacity for neurotoxicity; elevated levels may be related to neuroanatomical compromise through an excitotoxic process. To this end, we explored the relationships between glutamatergic neurometabolites and structural measures in antipsychotic-naive patients experiencing their first non-affective episode of psychosis (FEP). Sixty antipsychotic-naive patients with FEP and 60 age- and sex-matched healthy controls underwent a magnetic resonance imaging session, which included a T1- weighted volumetric image and proton magnetic resonance spectroscopy in the precommissural dorsal caudate. Group differences in precommissural caudate volume (PCV) and cortical thickness (CT), and the relationships between glutamatergic neurometabolites (ie, glutamate+glutamine (Glx) and glutamate) and these structural measures, were examined. PCV

84 was decreased in the FEP group (p < 0.001), yet did not differ when controlling for total brain volume. Cortical thinning existed in the FEP group within frontal, parietal, temporal, occipital, and limbic regions at a 5% false discovery rate. Glx levels were negatively associated with PCV only in the FEP group (p = 0.018). The observed relationship between Glx and PCV in the FEP group is supportive of a focal excitotoxic mechanism whereby increased levels of glutamatergic markers are related to local structural losses. This process may be related to the prominent structural deficits that exist in patients with schizophrenia.

4.2 Introduction

Previous studies have identified appreciable volumetric deficits and cortical thinning in patients with schizophrenia (184, 201). However, chronicity and medication intake may confound the assessment of these measures and thus true illness pathophysiology. The investigation of antipsychotic-naive patients experiencing their first episode of psychosis (FEP) is beneficial in that confounding effects are substantially reduced. Previous meta-analyses have specifically identified volumetric deficits in patients with FEP (275, 392) and in antipsychotic- naive patients with schizophrenia (192). Similarly, cortical thickness (CT) alterations have been observed in antipsychotic-naive patients with FEP (202, 203). However, the mechanisms through which these structural changes occur are presently unknown.

Glutamate is an abundant excitatory neurotransmitter that exists in large intracellular concentrations in the brain (262). When present in abnormally high extracellular concentrations, glutamate may have a neurotoxic effect through a process referred to as excitotoxicity, whereby overstimulation by glutamate increases intracellular calcium and triggers a cascade of events that lead to cell death (260, 262). Several studies have quantified glutamate in patients with schizophrenia using proton magnetic resonance spectroscopy (1H-MRS). Studies investigating patients in the early stages of the disorder, in which subjects are either antipsychotic-naive or have been minimally exposed to antipsychotic medication, have observed increased levels of glutamatergic neurometabolites (247-249, 253, 446); this increase may have an excitotoxic effect in several brain regions. However, as previously reviewed by our group, only a few human studies currently exist that have investigated the relationship between glutamatergic

85 neurometabolites and measures of brain structure, collectively providing inconclusive support for the role of glutamate-mediated excitotoxicity in the structural deficits present in schizophrenia (441). Our group has also previously reported increases in glutamate and glutamate+glutamine (Glx) levels in patients with FEP within the precommissural dorsal caudate (PDC) (247, 253, 446), an area highly implicated in the pathophysiology of schizophrenia that has been reported to specifically contain communications with cortical brain regions (366).

Given the overwhelming evidence for structural compromise in patients with schizophrenia, there is a pressing need to better understand the role of glutamate-mediated excitotoxicity in neuroanatomical alteration. To answer this question, this study examined the relationships between levels of glutamatergic neurometabolites (ie, glutamate and Glx) in the PDC and structural measures (ie, precommissural caudate volume (PCV) and CT) in the largest sample to date of antipsychotic-naive patients with FEP in which 1H-MRS was performed. The study of PCV provides a specific and local examination of this phenomenon, as the 1H-MRS voxel was preferentially located within the PDC, while CT assesses a more global excitotoxic mechanism. To the best of our knowledge, no previous study has investigated these relationships. On the basis of previous literature (192, 202, 203, 275, 392), we hypothesized that the FEP group would demonstrate PCV deficits in addition to widespread cortical thinning. Also, in accordance with a local excitotoxic process (249, 441), we hypothesized that increased levels of glutamatergic neurometabolites would be associated with PCV loss in the patient group, while no such relationship would exist with CT. Finally, as an exploratory investigation, we examined the relationships between structural measures and clinical symptoms, hypothesizing that lower PCV and cortical thinning would be related to higher symptom scores.

4.3 Materials and Methods

4.3.1 Participants

Approval for this study was received from the Ethics and Scientific Committees of the National Institute of Neurology and Neurosurgery of Mexico (INNN). For inclusion, individuals successfully completed an informed consent procedure; for participants under 18 years old, written consent was obtained from both parents.

The present study included participants previously reported upon by our group (247, 253, 446). In total, 64 right-handed FEP patients were recruited during their first non-affective psychosis episode from inpatient or outpatient services at the INNN. The Structured Clinical Interview for DSM-IV was used to assess participants’ inclusion into this study. Patients satisfied inclusion criteria if they were antipsychotic-naive; all but 3 patients had less than 2 years of psychotic symptoms. Exclusion criteria included a high risk for suicide, a concomitant medical or neurological illness, current substance abuse or history of substance dependence (excluding nicotine), comorbidity with other Axis I disorders, and psychomotor agitation. Sixty-three right- handed, age- and sex-matched healthy controls were also included. Any control with a history of psychiatric illness or a family history of psychosis was excluded. Participants from both groups were screened for drugs of abuse at inclusion and 1 hour prior to the magnetic resonance imaging (MRI) scan.

4.3.2 Clinical Assessment

The Positive and Negative Syndrome Scale (PANSS) was used by research psychiatrists (C.d.l.F.-S., F.R.-M., P.L.-O.) to measure patients’ psychopathology (408).

4.3.3 Magnetic Resonance Studies

MRI parameters were carried out in accordance with previous publications (246, 247, 253, 446). Briefly, MRI scans were performed at the Neuroimaging Department of the INNN in a 3T GE whole-body scanner (Signa Excite HDxt; GE Healthcare, Milwaukee, WI) with a high- resolution 8-channel head coil. The participant’s head was positioned along the canthomeatal line and immobilized using a forehead strap. MRI scans included a T1-weighted spoiled gradient-echo 3-dimensional axial acquisition (SPGR, TE = 5.7 ms, TR = 13.4 ms, TI = 450 ms, flip angle = 20°, FOV = 25.6 cm, ≥256 × ≥256 matrix, slice thickness ≤ 1.2 mm), oriented parallel to the anterior-posterior commissure line. Three patterns of voxel dimensions existed across the dataset: 0.47mm × 0.47mm × 1.2mm; 0.47mm × 0.47mm × 0.6mm; 1mm × 1mm ×

1mm. T1-weighted SPGR images were reformatted to sagittal and coronal views and were utilized for 1H-MRS voxel placement.

1H-MRS spectra were acquired using point-resolved spectroscopy (PRESS, TE = 35 ms, TR = 2000 ms, spectral width = 5000 Hz, 4096 data points used, 128 water-suppressed, and 16 water-unsuppressed averages) centered on the right dorsal-caudate nucleus in volume elements (voxels) of 8 mℓ (2 × 2 × 2 cm). The lower end of the dorsal-caudate voxel (associative striatum) was located 3 mm dorsal to the anterior commissure so that the maximum amount of gray matter (GM) was included and with a dorsal extension (thickness) of 2 cm. 1H-MRS spectra were shimmed during the acquisition to achieve a full-width at half maximum (FWHM) of 12 Hz or less, measured on the unsuppressed water signal from the voxel.

4.3.4 1H-MRS Data Analysis

Each participant’s water suppressed spectra were analyzed with LCModel version 6.3-0E (156), using a standard basis set of metabolites acquired with the same sequence parameters as used in the present study. Spectra were normalized to the unsuppressed water signal, allowing for the quantification of neurometabolite levels, expressed in institutional units. The standard basis set of metabolites included L-alanine, aspartate, creatine (Cr), Cr methylene group, γ- aminobutyric acid, glucose, glutamate, glutamine, glutathione, glycerophosphocholine (GPC), guanidinoacetate, L-lactate, myo-inositol, N-acetylaspartate (NAA), N-acetylaspartylglutamate acid (NAAG), phosphocholine (PCh), phosphocreatine, scyllo-inositol, taurine, GPC+PCh, Glx, NAA+NAAG, along with the following lipids (Lip) and macromolecules (MM): Lip09, Lip13a, Lip13b, Lip20, MM09, MM12, MM14, MM17 and MM20. Participants with %SD values of 20% or greater for any neurometabolite of interest were interpreted as poor quality and were excluded from statistical analyses (172, 173). Four patients with FEP and 3 healthy controls were removed as a result of either rejection by LCModel analysis or due to a FWHM exceeding 12Hz (148). Thus, 60 patients with FEP and 60 healthy controls progressed to statistical analysis.

T1-weighted MRI scans utilized for voxel localization were segmented into GM, white matter (WM), and cerebrospinal fluid (CSF) with Statistical Parametric Mapping 8 (SPM8, Wellcome Department of Imaging Neuroscience, University College London, UK). The size and

88 location of each area were obtained from the spectra file headers to determine the percentage of GM, WM, and CSF content within the 1H-MRS voxel using an in-house software, which allowed spectroscopic values to be corrected for CSF fraction (247).

4.3.5 Image Preprocessing

All structural imaging analysis was done in the minc format. T1-weighted structural images were first preprocessed using an intensity correction tool. Intensity correction was performed using the N4ITK algorithm (447), an improved version of the popular N3 correction algorithm (448). This updated version provides improved bias field correction via a robust B- spline approximation routine with the capability to handle a range of resolutions. It also provides an optimization scheme that leads to better convergence and a more accurate estimation of the overall bias field (447).

4.3.6 PCV Analysis

For volume analyses, T1-weighted structural images were additionally preprocessed using an autocrop tool part of the minc toolkit (https://github.com/BIC-MNI/mni_autoreg), which resampled each image to a 1-mm isotropic slicing. This resampling was done in order to satisfy computational resource limits, as well as achieve consistency throughout the dataset. Note that this is a stage that is carried out in various other image processing tool chains (196). Fully- automated segmentation of striatal subdivisions was carried out using the Multiple Automatically Generated Templates (MAGeT-Brain) algorithm (188). This technique is a modified multi-atlas segmentation technique designed to use a limited number of high-quality manually segmented atlases as input. In this case, the striatal subdivisions atlas – based on a histology-based atlas warped to fit the Colin-27 Subcortical Atlas (449) – was used as the single atlas input. A subset of the population under study is used as a template library through which the final segmentation is bootstrapped. Each subject in the template library is segmented through non-linear atlas-to- template registration followed by label propagation, yielding a unique definition of the subdivisions for each of the templates. For this work, 21 templates were used from the overall

89 subject pool. A matched set of 10 patients with FEP and 11 healthy controls were chosen so as to ensure a representative template set. The bootstrapping of the final segmentations through the template library results in 21 candidate labels produced for each subject and labels are then fused using a majority vote to complete the segmentation process. Non-linear registration was performed using a version of the Automatic Normalization Tools (ANTS) registration technique (450) that is compatible with the minc toolkit (https://github.com/vfonov/mincANTS). To focus on most local phenomena, only right PCV was examined in primary analyses, given that the 1H- MRS voxel was preferentially placed in the right PDC (Figure 4-1).

4.3.7 Subcortical Structure Volume Analysis

These analyses were performed in the same manner as those described for the PCV. To determine volumes of the postcommissural caudate, precommissural putamen, postcommissural

91 putamen, nucleus accumbens/ventral striatum, total striatum, globus pallidus, and thalamus, the Colin-27 Subcortical Atlas (449) was utilized. To determine volumes of the hippocampus, the hippocampal subfields atlas (451) was used. Notably, 1 healthy control was excluded from hippocampal analyses due to failed subfield segmentation.

4.3.8 Total Brain Volume Analysis

Total brain volume (TBV) was obtained using the Brain Extraction based on non-local Segmentation Technique (BEaST) method (189), which is based on non-local segmentation in a multi-resolution framework. In BEaST, each voxel is labeled based on the similarity of its neighborhood of voxels to all the neighborhoods in a library of pre-defined priors and a nonlocal means estimator is used to estimate the label at the voxel. Inputs are down-sampled to a lower resolution, segmentation is performed, and results are propagated up to higher resolutions (189). BEaST is designed to include CSF (in the ventricles, cerebellar cistern, deep sulci, along surface of brain, and brainstem), the brainstem, and cerebellar WM and GM in the brain mask, while excluding the skull, skin, fat, muscles, dura, eyes, bone, exterior blood vessels, and exterior nerves. Images preprocessed for MAGeT were similarly used for BEaST.

4.3.9 CT Analysis:

CT was estimated using the CIVET processing pipeline (version 1.1.12; Montreal Neurological Institute). T1-weighted images were aligned linearly to the ICBM 152 average template through a nine-parameter transformation (3 translations, rotations, and scales) (452) and preprocessed to reduce intensity non-uniformity effects (448). Next, images were classified into GM, WM, and CSF (453). Hemispheres were then modeled as GM and WM surfaces using a deformable model strategy, which generates 4 separate surfaces, each defined by 40 962 vertices (454). CT was determined in native space through non-linear surface-based normalization that uses a midsurface between pial and WM surfaces. Images were then smoothed with a 20-mm surface-based diffusion kernel and non-linearly registered to a minimally biased surface-based

92 template (455). Native-space thicknesses were used in all analyses, considering that normalizing for head or brain volume has little relationship to CT and risks introducing noise.

4.3.10 Statistical Analysis

Analyses were performed using SPSS Statistics version 21 (IBM Corporation). Independent-sample t tests were used to compare demographic characteristics, Cramer-Rao lower bounds (CRLBs), FWHM values, signal-to-noise ratios, and GM, WM, and CSF percentages between groups. χ2 or Fisher’s exact tests were used to compare frequency data. Glutamatergic neurometabolite levels were compared between groups using analyses of variance.

Group differences in TBV were assessed using an analysis of covariance (ANCOVA), controlling for original T1-weighted image voxel dimensions (henceforth, dimensions), age, and sex. Group differences in PCV were assessed with an ANCOVA, controlling for dimensions, TBV, age, and sex; here, analyses were also performed without TBV as a covariate. Additionally, multiple regressions were performed separately for each group to investigate the relationships between glutamatergic neurometabolite levels and PCV, controlling for dimensions, TBV, age, and sex. Finally, multiple regressions were utilized to measure the relationships between PCV and PANSS subscale total scores, controlling for dimensions, TBV, age, and sex. Owing to our a priori hypotheses that PCV would be smaller in the FEP group and that glutamatergic neurometabolites would have a negative relationship with PCV in the FEP group, significance thresholds set at p<0.05 and p<0.025 (p = 0.05÷2 to correct for the examination of both glutamate and Glx), respectively, were used for these investigations. The assessment of the relationships between PCV and clinical symptoms was considered exploratory; thus, a Bonferroni correction for multiple comparisons was employed.

CT vertexwise analyses were performed with the RMINC package (https://github.com/mcvaneede/RMINC). Using a general linear model, separate CT regression analyses were carried out for diagnosis, glutamate, Glx, and PANSS subscale total scores, each controlling for dimensions, age, and sex. Glutamate and Glx analyses were performed independently for FEP and control groups. Maps of t statistics at each vertex were projected onto

93 an average brain template. Analyses were corrected for multiple comparisons using a false discovery rate (FDR<0.05).

Outliers were defined as greater than three times the interquartile range; where applicable, outliers were removed in a neurometabolite-specific manner.

4.4 Results

4.4.1 Demographic, Clinical, and 1H-MRS Group Differences

Demographic, clinical, and 1H-MRS results have been reported upon in another publication (446), although data pertinent to the present study are included in Tables 4-1, 4-2, and 4-3. DSM-IV diagnoses of patients with FEP were brief psychotic disorder (n = 14), schizophreniform disorder (n = 21), and schizophrenia (n = 25). No group differences existed in age, sex, handedness, and cannabis use. Education years were higher in the control group (t(118) = 6.40, p<0.001), whereas tobacco use was greater in the FEP group (χ2 = 5.21, p = 0.039). The FEP group had a mean duration of untreated psychosis of 33.03 ± 52.70 weeks, and mean PANSS positive, negative, and general psychopathology subscale total scores of 24.13 ± 4.97, 24.33 ± 5.66, and 48.75 ± 8.38, respectively. One glutamate outlier was removed before analysis; however, removal of this outlier did not affect results. Glutamate levels were higher in the FEP group (F(1,117) = 11.63, p<0.001), whereas Glx levels were not different between groups (F(1,118) = 1.84, p = 0.18). Neurometabolite CRLBs, FWHM values, signal-to-noise ratios, and GM, WM, and CSF voxel percentages did not differ between groups.

Table 4-1. Demographic and Clinical Characteristics of Study Participants.

Variable FEP group (n = 60) HC group (n = 60) Age, mean (SD) [range], y 24.67 (7.68) [13-47] 23.03 (4.87) [15-42] Educational level, mean (SD), y 11.48 (3.13)a 15.00 (2.88) Sex, No. Male 37 38 Female 23 22 Ever used, No./total No. Tobacco 17/60* 7/60 Cannabis 4/60 0/60 Handedness, No. Right 60 60 Left 0 0 PANSS subscale total, mean (SD), score Positive 24.13 (4.97) NA Negative 24.33 (5.66) NA General 48.75 (8.38) NA Psychopathology Duration of untreated psychosis, 33.03 (52.70) [1-312] NA mean (SD) [range], wk

Abbreviations: FEP, first-episode psychosis; HC, healthy control; NA, not applicable; No., number; PANSS, Positive and Negative Syndrome Scale. aDenotes p<0.05.

Table 4- 2. Glutamatergic Neurometabolite Levels and Cramer-Rao Lower Bound Values.

Mean (SD) Glu Glx CRLB – Glu CRLB – Glx %SD – Glu %SD – Glx FEP group 13.10 (1.31)a 16.61 (1.69) 0.75 (0.11) 0.83 (0.18) 6.48 (1.16) 5.67 (1.17) HC group 12.39 (0.95) 16.21 (1.50) 0.74 (0.12) 0.83 (0.17) 6.69 (1.44) 5.72 (1.33)

Abbreviations: CRLB, Cramer-Rao lower bound; FEP, first-episode psychosis; Glu, glutamate; Glx, glutamate+glutamine; HC, healthy control. aDenotes p<0.05.

Table 4-3. Full-Width at Half Maximum Values, Signal-to-Noise Ratios, and 1H-MRS Voxel Tissue Composition.

Variable FEP group (n = 60) HC group (n = 60) FWHM, mean (SD), ppm 0.08 (0.02) 0.08 (0.02) SNR, mean (SD) 14.32 (2.45) 14.68 (2.63) 1H-MRS voxel, mean (SD), % GM 0.43 (0.06) 0.43 (0.04) WM 0.47 (0.08) 0.48 (0.06)

CSF 0.11 (0.08) 0.09 (0.06) Abbreviations: CSF, cerebrospinal fluid; FEP, first-episode psychosis; FWHM, full-width at half maximum; GM, gray matter; HC, healthy control; SNR, signal-to-noise ratio; WM, white matter.

4.4.2 Group Differences in PCV and TBV

No outliers were identified for PCV or TBV. PCV and TBV values are presented in Table 4-4; mean PCV values are also displayed in Figure 4-2. Without TBV as a covariate, PCV was smaller in the FEP group (F(1,114) = 11.88, p<0.001). However, controlling for TBV rendered the group difference insignificant (F(1,113) = 1.18, p = 0.28). TBV was smaller in the FEP group (F(1,114) = 16.80, p<0.001).

Table 4-4. Precommissural Caudate Volume and Total Brain Volume in Patients with

First -Episode Psychosis and Healthy Controls.

Mean (SD) (mm3) PCV TBV FEP group 2109.80 (320.91)a 1 354 333.20 (142 317.05)a HC group 2302.63 (287.98) 1 442 801.00 (137 349.56)

Abbreviations: FEP, first-episode psychosis; HC, healthy control; PCV, precommissural caudate volume; TBV, total brain volume. aDenotes p<0.001.

Figure 4-2. Precommissural caudate volume (PCV) in patients with first-episode psychosis and healthy controls.

4.4.3 Group Differences in CT

Widespread thinning was identified in the FEP group within the frontal gyri (right superior, bilateral middle, bilateral inferior, bilateral precentral), parietal gyri (bilateral postcentral, bilateral supramarginal, bilateral angular), temporal gyri (bilateral superior, right middle, right inferior), bilateral gyrus rectus, bilateral orbitofrontal cortex, bilateral precuneus, bilateral cuneus, right superior parietal lobule, right paracentral lobule, right parahippocampal gyrus, right lingual gyrus, and right fusiform gyrus (Figure 4-3). CT was not increased at any vertex in the FEP group.

Figure 4-3. Cortical thinning in patients with first-episode psychosis compared with healthy controls. Abbreviation: FDR, false discovery rate.

4.4.4 Relationships Between Neurometabolite Levels and Structural Measures

The relationships between glutamatergic neurometabolite levels and PCV are presented in Table 4-5. Glutamate levels were not related to PCV in either group. Glx levels were negatively associated with PCV only in the FEP group (β = -0.25, t = -2.44, p = 0.018). The relationship between PCV/TBV and Glx levels in the FEP group is displayed in Figure 4-4. No relationships were identified between glutamatergic markers and CT in either group.

Table 4-5. Relationships Between Glutamatergic Neurometabolite Levels and Precommissural Caudate Volume.

Variable Glu Glx

FEP group PCV β=-0.08, t=-0.81, p=0.42a β=-0.25, t=-2.44, p=0.018b,c HC group PCV =0.11, t=0.88, p=0.38d =0.01, t=0.09, p=0.93e β β Abbreviations: FEP, first-episode psychosis; Glu, glutamate; Glx, glutamate+glutamine; HC, healthy control; PCV, precommissural caudate volume. cDenotes p<0.05. a 2 b 2 Model statistics: adjusted R = 0.558, F5,54 = 15.92, p<0.001; adjusted R = 0.597, F5,54 = 18.51, d 2 e 2 p<0.001; adjusted R = 0.264, F6,52 = 4.46, p=0.001; adjusted R = 0.254, F6,53 = 4.34, p=0.001.

Figure 4-4. Relationship between glutamate+glutamine (Glx) levels and precommissural caudate volume (PCV) relative to total brain volume (TBV) in patients with first-episode psychosis.

4.4.5 Relationships Between Clinical Symptoms and Structural Measures

No relationships between PANSS subscale total scores and PCV were identified (Table 4-6). Similarly, no relationships between PANSS subscale total scores and CT were identified.

100

Table 4-6. Relationships Between Precommissural Caudate Volume and PANSS Subscale

Total Scores.

Variable PCV PANSS subscale Positive β=-0.08, t=-0.92, p=0.36a Negative β=-0.07, t=-0.77, p=0.44b General psychopathology =-0.11, t=-1.25, p=0.22c β Abbreviations: PANSS, Positive and Negative Syndrome Scale; PCV, precommissural caudate volume. a 2 b 2 Model statistics: adjusted R = 0.560, F5,54 = 16.02, p<0.001; adjusted R = 0.558, F5,54 = 15.89, c 2 p<0.001; adjusted R = 0.566, F5,54 = 16.36, p<0.001

4.5 Discussion

The present study aimed to investigate whether glutamatergic markers in the PDC were related to PCV and CT in a sample of antipsychotic-naive patients with FEP. Expectedly, we found decreased PCV and widespread cortical thinning within the FEP group; however, when TBV was included as a covariate, PCV did not differ between groups. Glx levels were negatively associated with PCV in the FEP group, while no relationships involving glutamate or CT were identified.

To the best of our knowledge, this is the first study to examine the relationships between glutamatergic neurometabolite levels and volume in this population within the precommissural caudate. Also, though MAGeT-Brain has been recently used to investigate patients with childhood-onset schizophrenia and their non-psychotic siblings (456), the present study is the first to use this algorithm to assess a measure of striatal volume in an antipsychotic-naive sample of patients with FEP. Likewise, we believe this is the first study to investigate the relationships between glutamatergic neurometabolite levels and CT in patients with FEP or schizophrenia.

Our finding of decreased PCV in the FEP group is consistent with the current literature. Two meta-analyses found bilateral caudate head GM volume decreases in patients with FEP

101

(275, 392), one of which identified that deficits were more pronounced in FEP than in chronic schizophrenia (275). Another meta-analysis reported that caudate nucleus reductions were greater in antipsychotic-naive patients than in medicated patients with schizophrenia (192). Caudate volume reductions have been similarly observed in studies including antipsychotic- naive patients with FEP that have adjusted for TBV-analogous measures (457, 458). However, other studies considering the effect of TBV have shown no caudate volumetric deficits in antipsychotic-naive or minimally treated patients with FEP (459, 460). In our study, group differences in PCV failed to reach significance once TBV was considered as a covariate, suggesting that group volumetric differences may not be specific to PCV and might be occurring simultaneously in other brain regions, such that controlling for TBV masks the PCV deficit in the patient population.

Only one prior study has investigated striatal subdivision volumes in patients with schizophrenia; this study examined medicated male patients with chronic schizophrenia and found no reduction in PDC volume relative to total intracranial contents (367). This is in line with a recent meta-analysis that found no caudate volumetric abnormalities in a large sample of mostly medicated patients with schizophrenia regardless whether intracranial volume was included as a covariate (184). Of note, in this meta-analysis, the sample proportions of medication-naive patients were not associated with caudate volume.

Furthermore, the PDC has been reported to have elevated dopaminergic function and is highly implicated in the pathophysiology of schizophrenia (366). Excitotoxicity in this region may impact dopaminergic activity and thereby contribute to symptomatology. In the present study, we observed a negative relationship between Glx levels in the PDC and PCV in the FEP group, providing support for a local excitotoxic process. Glutamate-mediated excitotoxicity may contribute to the neuroanatomical deviations present in schizophrenia. It is currently posited that hypofunctioning N-methyl-D-aspartate receptors (NMDARs) on gamma-aminobutyric acid-ergic inhibitory interneurons lead to the disinhibition of downstream pyramidal neurons, a subsequent increase in glutamate release, and consequent excitotoxicity (222). Evidence for this phenomenon arises from pharmacological studies using NMDAR antagonists, which lead to the emergence of schizophrenia-like symptomatology in healthy volunteers (229, 461) and elicit symptom exacerbation in patients with schizophrenia (233, 461). Studies in which rodents are exposed to an NMDAR antagonist have shown elevations in extracellular glutamatergic markers

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(238, 318). Comparably, 1H-MRS studies in healthy humans have shown increased glutamate and glutamine following acute exposure to ketamine (241, 242). Moreover, studies administering an NMDAR antagonist to rodents have demonstrated resultant neurotoxic injury, which resembles features of schizophrenia and is attenuated by agents inhibiting glutamate release (441).

While preclinical literature strongly supports the role of glutamate-mediated excitotoxicity in schizophrenia, human studies have provided insufficient evidence for the phenomenon (441). Kraguljac et al. (2013) reported a negative relationship between left hippocampal Glx and two clusters of left hippocampal GM volume adjusted for total intracranial volume, only in the unmedicated patient group and not in controls (249). However, Klar et al. (2010) did not find a relationship between hippocampal glutamate and hippocampal volume in a medicated sample (309). Two longitudinal studies investigating a sample of initially unmedicated patients who were then medicated at follow-up visits noted positive correlations between decreasing thalamic glutamate and GM volume within frontal, temporal, parietal, and limbic areas (308, 312). The observed negative relationship between Glx levels and local PCV in the present study is most comparable to the findings of Kraguljac et al. (2013) and supports an excitotoxic process in schizophrenia. Our analysis similarly included TBV as a covariate, suggesting that whole brain effects do not drive the relationship between Glx and PCV, and indicating a localized effect. However, the lack of a group difference in PCV when TBV was included as a covariate is not wholly supportive of a specific excitotoxic process in this brain region.

Notably, Glx levels were not significantly elevated in the FEP group, though mean levels were higher than the control group; this group difference may have been underpowered to achieve statistical significance. Thus, the proposed excitotoxicity in the patient group might be a result of elevated Glx levels or a pathological process specific to individuals with FEP that increases susceptibility to the excitotoxicity phenomenon. It is unclear why a similar relationship did not exist for glutamate levels, which were significantly higher in the FEP group. We speculate that glutamine’s influence on the Glx peak had a contributing effect; abnormalities in glutamatergic metabolism might account for the observed results in addition to or instead of excitotoxicity, especially considering that 1H-MRS cannot distinguish synaptic from extrasynaptic pools of neurometabolites. Further, neurotransmitter glutamate likely has a greater role

103 in excitotoxicity than intracellular glutamate, and it has previously been suggested that glutamine (contained in Glx) may more robustly reflect the neurotransmitter pool of glutamate than 1H- MRS glutamate itself, which may be mainly intracellular (148).

The cortical thinning identified in the FEP group was diffuse, involving frontal, parietal, temporal, occipital, and limbic brain regions. Our findings are akin to those of previous studies investigating CT in antipsychotic-naive patients with FEP, in which widespread thinning has been reported (202, 203). Within our investigation, the lack of any observed relationships between glutamatergic markers and CT suggests that PDC glutamatergic activity does not account for widespread cortical thinning. Thus, cortical thinning might occur through an alternate mechanism. However, glutamatergic neurometabolites were not measured in cortical areas; consequently, a local inverse relationship between cortical glutamatergic neurometabolites and cortical structural measures cannot be excluded.

Finally, neither PCV nor CT was related to any PANSS subscale total score. Previous studies have similarly failed to identify an association between caudate volumes and symptom scores in untreated patients with schizophrenia (462, 463). With respect to CT, while this null finding is comparable to those from previous CT investigations of patients with FEP or chronic schizophrenia (201, 202), it contrasts a recent CIVET examination of antipsychotic-naive FEP patients in which cortical thinning within frontal regions was related to higher PANSS-positive symptom scores (203).

This study must be considered in light of its limitations. First, the adopted study design does not provide insight towards illness progression or causation. Second, tobacco use was greater in the FEP group. Importantly, when tobacco use was examined as a covariate in all analyses, the only deviation from the presented results was additional cortical thinning observed in the patient group within the left parahippocampal gyrus. Third, to reduce imaging time in patients with active psychosis, 1H-MRS was only performed in the right PDC, although glutamatergic neurometabolite laterality differences were not previously observed in this region (444). Fourth, the inclusion of patients with brief psychotic disorder presents an important limitation given their short duration of illness. Fifth, since the 1H-MRS voxel was located in the right PDC, the present study focused on the examination of right PCV. However, an exploratory analysis involving left PCV demonstrated no laterality effects: left PCV was reduced in the FEP

104 group without TBV as a covariate (F(1,114) = 8.27, p = 0.005); controlling for TBV rendered this difference insignificant; Glx levels were negatively associated with left PCV only in the FEP 2 group (β = -0.23, t = -2.17, p = 0.035; Model Statistics: adjusted R = 0.541, F5,54 = 14.91, p<0.001); and no relationships existed between left PCV and glutamate or PANSS subscale total scores. Sixth, despite the preferential placement of the 1H-MRS voxel in the PDC, it encompassed other associative striatum subregions and included components of other structures (eg, putamen and internal capsule). The 1H-MRS voxel location was referred to as the PDC to maintain consistency in nomenclature. Seventh, to focus on most local phenomena and examine a highly implicated subregion in schizophrenia, the current investigation employed an a priori hypothesis concerning the precommissural caudate. Although the present study is limited in its investigation of regionally specific excitotoxicity in that 1H-MRS was not collected elsewhere, other subcortical structure volumes were examined in exploratory analyses to provide support for this phenomenon. As shown in Tables 4-7 and 4-8, no associations were identified between PDC glutamatergic neurometabolites and other subcortical structure volumes after correcting for multiple comparisons. Eighth, with PRESS at 3T, chemical shift artifact might result in varying volumes of interest between neurometabolites and the water signal; however, we anticipate that such would be the case for both patients and controls. Finally, though our sample size was not small, our study may have been underpowered.

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Table 4-7. Relationships Between Glutamatergic Neurometabolite Levels and Subcortical Structure Volumes in Patients with First-Episode Psychosis.

Variable Glu Glx

FEP Rpostcau β=0.19, t=1.80, p=0.08 β=0.11, t=0.98, p=0.33 Rpreput β=-0.06, t=-0.49, p=0.63 β=-0.17, t=-1.33, p=0.19 Rpostput β=0.03, t=0.27, p=0.79 β=-0.04, t=-0.32, p=0.75 RNA/VS β=-0.13, t=-1.38, p=0.17 β=-0.21, t=-2.25, p=0.029 Lpostcau β=0.17, t=1.64, p=0.11 β=0.05, t=0.50, p=0.62 Lpreput β=-0.03, t=-0.25, p=0.80 β=-0.16, t=-1.24, p=0.22 Lpostput β=-0.01, t=-0.05, p=0.96 β=-0.08, t=-0.72, p=0.48 LNA/VS β=-0.17, t=-1.70, p=0.09 β=-0.21, t=-2.11, p=0.040 RS β=-0.002, t=-0.03, p=0.98 β=-0.12, t=-1.31, p=0.20 RGP β=-0.01, t=-0.11, p=0.92 β=-0.06, t=-0.51, p=0.61 RT β=-0.02, t=-0.22, p=0.83 β=0.50, t=0.52, p=0.60 LS β=-0.01, t=-0.15, p=0.88 β=-0.14, t=-1.55, p=0.13 LGP β=0.02, t=0.21, p=0.83 β=0.03, t=0.30, p=0.77 LT β=-0.06, t=-0.62, p=0.54 β=-0.03, t=-0.28, p=0.78 RHIP β=0.13, t=1.26, p=0.21 β=0.07, t=0.64, p=0.52 LHIP =0.15, t=1.33, p=0.19 =0.08, t=0.67, p=0.51 β β Abbreviations: FEP, first-episode psychosis; Glu, glutamate; Glx, glutamate+glutamine; LGP, left globus pallidus; LHIP, left hippocampus; LNA/VS, left nucleus accumbens/ventral striatum; Lpreput, left precommissural putamen; Lpostcau, left postcommissural caudate; Lpostput, left postcommissural putamen; LS, left striatum; LT, left thalamus; RGP, right globus pallidus; RHIP, right hippocampus; RNA/VS, right nucleus accumbens/ventral striatum; Rpreput, right precommissural putamen; Rpostcau, right postcommissural caudate; Rpostput, right postcommissural putamen; RS, right striatum; RT, right thalamus. 2 Model statistics range: adjusted R = 0.296-0.676, F5,54 = 5.96-25.62, all p<0.001

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Table 4-8. Relationships Between Glutamatergic Neurometabolite Levels and Subcortical Structure Volumes in Healthy Controls.

Variable Glu Glx

HC Rpostcau β=0.11, t=0.97, p=0.33a β=0.09, t=0.76, p=0.45a Rpreput β=-0.09, t=-0.69, p=0.49a β=-0.20, t=-1.47, p=0.15a Rpostput β=-0.09, t=-0.73, p=0.47a β=-0.14, t=-1.07, p=0.29a RNA/VS β=0.18, t=1.46, p=0.15a β=0.04, t=0.30, p=0.77a Lpostcau β=0.11, t=0.95, p=0.35a β=0.08, t=0.63, p=0.53a Lpreput β=-0.07, t=-0.50, p=0.62a β=-0.05, t=-0.36, p=0.72a Lpostput β=-0.12, t=-1.03, p=0.31a β=-0.16, t=-1.31, p=0.20a LNA/VS β=0.18, t=1.48, p=0.15a β=0.06, t=0.42, p=0.67a RS β=0.03, t=0.24, p=0.81a β=-0.06, t=-0.49, p=0.62a RGP β=-0.04, t=-0.30, p=0.76a β=-0.10, t=-0.83, p=0.41a RT β=0.03, t=0.34, p=0.74a β=-0.02, t=-0.20, p=0.84a LS β=0.05, t=0.48, p=0.64a β=-0.03, t=-0.29, p=0.77a LGP β=-0.02, t=-0.21, p=0.83a β=-0.16, t=-1.36, p=0.18a LT β=-0.05, t=-0.69, p=0.50a β=-0.06, t=-0.71, p=0.48a RHIP β=-0.07, t=-0.57, p=0.57b β=-0.05, t=-0.38, p=0.71b LHIP =-0.09, t=-0.65, p=0.52b =-0.14, t=-0.99, p=0.33b β β Abbreviations: Glu, glutamate; Glx, glutamate+glutamine; HC, healthy control; LGP, left globus pallidus; LHIP, left hippocampus; LNA/VS, left nucleus accumbens/ventral striatum; Lpreput, left precommissural putamen; Lpostcau, left postcommissural caudate; Lpostput, left postcommissural putamen; LS, left striatum; LT, left thalamus; RGP, right globus pallidus; RHIP, right hippocampus; RNA/VS, right nucleus accumbens/ventral striatum; Rpreput, right precommissural putamen; Rpostcau, right postcommissural caudate; Rpostput, right postcommissural putamen; RS, right striatum; RT, right thalamus. a 2 Model statistics range: adjusted R = 0.156-0.712, Glutamate: F6,52 = 2.79-24.87; Glx: F6,53 = 2.90-24.63, p<0.001-0.02. b 2 Model statistics range: adjusted R = 0.227-0.406, Glutamate: F6,51 = 3.79-7.45; Glx: F6,52 = 4.21-7.60, p<0.001-0.003.

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Taken together, our findings offer evidence in support of a local excitotoxic process in schizophrenia. This mechanism may contribute to structural compromise in the illness. Going forward, a longitudinal study following FEP patients as they transition to schizophrenia, investigating several brain regions using 1H-MRS, is warranted. An improved understanding of dysregulated neurometabolites and their consequent impact on structural losses in schizophrenia may aid in furthering our knowledge of illness pathophysiology and might identify future diagnostic and therapeutic strategies.

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Chapter 5

5 Striatal Neurometabolite Levels in Patients with Schizophrenia Undergoing Long-Term Antipsychotic Treatment: A Proton Magnetic Resonance Spectroscopy and Reliability Study

This chapter is reproduced with permission from the following: Plitman E, Chavez S, Nakajima S, Iwata Y, Chung JK, Caravaggio F, Kim J, Alshehri Y, Chakravarty MM, De Luca V, Remington G, Gerretsen P, Graff Guerrero A. Striatal Neurometabolite Levels in Patients with Schizophrenia Undergoing Long-Term Antipsychotic Treatment: A Proton Magnetic Resonance Spectroscopy and Reliability Study. Psychiatry Research: Neuroimaging 2018; 273: 16-24.

5.1 Abstract

Previous proton magnetic resonance spectroscopy (1H-MRS) studies have reported disrupted levels of various neurometabolites in patients with schizophrenia. An area of particular interest within this patient population is the striatum, which is highly implicated in the pathophysiology of schizophrenia. The present study examined neurometabolite levels in the striatum of 12 patients with schizophrenia receiving antipsychotic treatment for at least 1 year and 11 healthy controls using 3-Tesla 1H-MRS (PRESS, TE = 35 ms). Glutamate, glutamate+glutamine (Glx), myo-inositol, choline, N-acetylaspartate, and creatine levels were estimated using LCModel, and corrected for fraction of cerebrospinal fluid in the 1H-MRS voxel. Striatal neurometabolite levels were compared between groups. Multiple study visits permitted a reliability assessment for neurometabolite levels (days between paired 1H-MRS acquisitions: average = 90.33; range = 7-306). Striatal neurometabolite levels did not differ between groups. Within the whole sample, intraclass correlation coefficients for glutamate, Glx, myo-inositol,

109 choline, and N-acetylaspartate were fair to excellent (0.576-0.847). The similarity in striatal neurometabolite levels between groups implies a marked difference from the antipsychotic-naïve first-episode state, especially in terms of glutamatergic neurometabolites, and might provide insight regarding illness progression and the influence of antipsychotic medication.

5.2 Introduction

Schizophrenia is a disabling illness that is currently treated using antipsychotics, which exert their effect primarily through dopamine D2 receptor antagonism (13, 214). Accordingly, dopaminergic disturbances are well documented in patients with schizophrenia (121). However, using proton magnetic resonance spectroscopy (1H-MRS), various disturbances in other neurometabolites have also been observed in patients with schizophrenia, including glutamate, glutamate+glutamine (Glx), myo-inositol (mI), choline (Cho), N-acetylaspartate (NAA), and creatine (Cr) (147, 157, 175, 387, 410-413). In particular, glutamatergic disturbances are frequently reported in patients with schizophrenia and are highly implicated in the pathophysiology of the illness (148, 161, 176, 247-249, 253, 257-259, 389, 446, 464).

The striatum is a brain region that is posited to play a key role in schizophrenia. Previous dopamine-related positron emission tomography studies have implicated the striatum in the illness. Past work has demonstrated a relationship between striatal dopamine D2 receptor occupancy and clinical response (375, 376), along with elevations in striatal presynaptic dopaminergic function and dopamine D2/3 receptor availability, in patients with schizophrenia (121). Accordingly, studies have observed increased dopamine synthesis capacity (as measured by 18F-dopa uptake) in individuals with prodromal symptoms and in patients with schizophrenia (365), and found that this increase may be relevant to conversion from an ultra-high risk state to a psychotic disorder (364). The relationship between striatal neurochemistry and psychosis is further supported by the observation of a larger increase in dopamine D2 receptor availability within this brain region after acute pharmacologically induced dopamine depletion in untreated patients with schizophrenia compared to healthy controls (366).

Within the striatum, previous 1H-MRS studies have identified elevated levels of glutamatergic neurometabolites, mI, and Cho in antipsychotic-naïve patients experiencing their

110 first non-affective episode of psychosis (247, 253, 446), and increased levels of glutamate and NAA in participants with prodromal symptoms of schizophrenia (247). It has also been reported that levels of glutamatergic neurometabolites normalized in antipsychotic-naïve patients with first-episode psychosis after 4 weeks of effective antipsychotic treatment (253). Moreover, in the basal ganglia, increased Glx levels have been observed in first-episode schizophrenia, while reports suggest increased Cho levels, decreased NAA levels, or no differences in neurometabolite levels in later stages of the illness (254, 382, 386, 387, 414, 415).

There are numerous studies of antipsychotic medication-treated patients, with variable findings. However, controlled studies comparing medicated patients, unmedicated patients, and healthy controls reported increased glutamatergic neurometabolite levels in untreated patients, while treated patients showed lower levels, similar to those of healthy controls (248, 253). Thus, the primary aim of the present study was to examine group differences in striatal neurometabolite levels between patients with schizophrenia receiving antipsychotic treatment for at least 1 year and healthy controls. The secondary aim of the present study was to explore the reliability of neurometabolite levels over time within the striatum, to assess the utility of 1H-MRS as an investigative tool in schizophrenia.

5.3 Methods

5.3.1 Study design

The present investigation was part of a larger parent study evaluating the effect of single session transcranial direct current stimulation on illness awareness in patients with schizophrenia through a within-subject study design. As part of this parent study, subjects participated in 3 magnetic resonance imaging (MRI) study visits, separated by at least 1 week. On each study visit, participants were randomly assigned to 1 of 3 stimulation conditions (frontal, parietal, or sham). 1H-MRS was collected at each study visit. Importantly, only pre-stimulation 1H-MRS results were utilized across all analyses within the current manuscript. Participants’ first acceptable 1H-MRS acquisitions were used for group comparisons and pairs of 1H-MRS acquisitions were selected for reliability analyses from available data according to the criterion of proximity in time. The average time elapsed between paired 1H-MRS acquisitions was 90.33

111 days (SD = 74.85 days, range = 7-306 days).

5.3.2 Participants

This study obtained approval from the Centre for Addiction and Mental Health (CAMH) Research Ethics Board and was conducted between 2013 and 2016. All participants provided written informed consent and received a stipend for their involvement.

Twelve symptomatic patients with schizophrenia were recruited using the CAMH research registry, from flyers, or through referral. Inclusion and exclusion criteria were driven by the principal aims of the parent study. Patients met inclusion criteria if they: were male or female inpatients or outpatients ≥ 18 years of age; had a DSM-IV diagnosis of schizophrenia or schizoaffective disorder (confirmed by assessment with the Mini-International Neuropsychiatric Interview (MINI)); were capable of consenting to participation in the research study, as assessed with the MacArthur Competence Assessment Tool (MacCAT-T) (465); were fluent in English; and had a moderate-to-severe lack of illness awareness (≥ 3 on Positive and Negative Syndrome Scale G12 Insight and Judgment item). Exclusion criteria for patients with schizophrenia included: a serious unstable medical illness or any concomitant major medical or neurological illness, including a history of seizures or a first degree relative with a history of a seizure disorder; acute suicidal and/or homicidal ideation; a formal thought disorder rating of ≥ 2 on the Scale for the Assessment of Positive Symptoms (SAPS); a DSM-IV substance dependence (except caffeine and nicotine) within 1 month prior to entering the study; a positive urine drug screen for drugs of abuse; pregnancy; current antiepileptic intake; any contraindications to MRI (e.g. metal implants that would preclude an MRI, claustrophobia); and a score < 32 on the Wide Range Achievement Test-III (WRAT III).

Eleven healthy controls were additionally enrolled. Healthy controls met inclusion criteria if they were: males or females ≥ 18 years of age; healthy and did not have first degree relatives with primary psychotic disorders; capable of consenting to participation in the research study, as assessed with the MacCAT-T; and fluent in English. Exclusion criteria for healthy controls included: a serious unstable medical illness or any concomitant major medical or neurological illness, including a history of seizures or a first degree relative with a history of a

112 seizure disorder; a current or past psychiatric disorder, as assessed with the MINI and medical history; DSM-IV substance dependence (except caffeine and nicotine) within 1 month prior to entering the study; pregnancy; a positive urine drug screen for drugs of abuse; any contraindications to MRI (e.g. metal implants that would preclude an MRI, claustrophobia); or a score < 32 on the WRAT III.

Of the 23 included participants, 20 completed 3 MRI visits. One patient contributed 4 MRI visits. One patient and 1 control each underwent only 1 MRI visit; of these, both participants were lost to follow-up.

5.3.3 Clinical assessment

Patients’ symptomatology was assessed by research psychiatrists (P.G., S.N., Y.I., Y.A.) using the SAPS and the Scale for the Assessment of Negative Symptoms (SANS). SAPS and SANS total and summary scores were calculated according to previous literature (466).

5.3.4 Magnetic resonance imaging

Each participant was scanned at CAMH in a 3T GE Discovery MR750 scanner (General Electric, Waukesha, WI) equipped with an 8-channel head coil. Participants underwent a 3- dimensional IR-prepared T1-weighted MRI scan (BRAVO, TE = 3.00 ms, TR = 6.74 ms, TI = 650 ms, flip angle = 8°, FOV = 23 cm, 256×256 matrix, slice thickness = 0.9 mm).

1H-MRS spectra were acquired using point-resolved spectroscopy (PRESS, TE = 35 ms, TR = 2000 ms, spectral width = 5000 Hz, 4096 data points used, 128 water-suppressed, and 16 water-unsuppressed averages, 8 NEX). 1H-MRS voxels were localized to the right striatum. The striatum voxel was 9.4 mL and was placed on an oblique axial image obtained parallel to the anterior commissure-posterior commissure (AC-PC) line; the center of the voxel was 14 mm superior to the AC-PC line. Placement of the 1H-MRS voxel is displayed in Figure 5-1. 1H-MRS spectra were shimmed before the acquisition to attain a full-width at half maximum (FWHM) ≤ 12 Hz, measured on the unsuppressed water signal from the voxel.

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Figure 5-1. Delineation of right striatum 1H-MRS voxel placement. Abbreviations: R, right. Note: Depicted images derived from one randomly selected study participant.

5.3.5 1H-MRS data analysis

Water-suppressed spectra were analyzed with LCModel version 6.3-0E (156). Spectra were normalized to the unsuppressed water signal, permitting quantification of neurometabolite levels, expressed in institutional units. A field appropriate basis set with matching TE (TE = 35 ms) was used, and contained L-alanine, aspartate, Cr, Cr methylene group, γ-aminobutyric acid, glucose, glutamate, glutamine, glutathione, glycerophosphocholine, L-lactate, mI, NAA, N- acetylaspartylglutamate, phosphocholine, phosphocreatine, scyllo-inositol, and taurine, as well as the following lipids (Lip) and macromolecules (MM): Lip09, Lip13a, Lip13b, Lip20, MM09, MM12, MM14, MM17, and MM20. In the present work, Cho represents glycerophosphocholine+phosphocholine, NAA represents NAA+N-acetylaspartylglutamate, and Cr represents Cr+phosphocreatine.

For the current study, neurometabolites of interest included glutamate, Glx, mI, Cho, NAA, and Cr. Due to poor spectra fitting, glutamine was not analyzed alone. A %SD cutoff value of ≥ 15% was employed. Of the total 66 1H-MRS acquisitions, 5 scans (1 scan from 3 patients and 1 scan from 2 controls) were excluded due to either incorrect voxel placement, rejection by

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LCModel analysis, or a FWHM > 12 Hz. Utilization of participants’ first acceptable 1H-MRS acquisition permitted the inclusion of all 12 patients and 11 controls into group comparisons of striatal neurometabolite levels.

T1-weighted MRI scans were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using the FIRST tool (467) from FSL (FMRIB Software Library v5.0, Oxford, UK) (468). A freely distributed, MATLAB (The MathWorks Inc., Natick, MA) – based, software package called “Gannet” (http://github.com/cjohnevans/Gannet2.0) was utilized to determine the size and location of each 1H-MRS voxel, permitting correction of neurometabolite levels for fraction of CSF in the 1H-MRS voxel (247).

5.3.6 Statistical analysis

Analyses were performed using SPSS Statistics version 21 (IBM Corporation). Demographic and clinical characteristics were compared between groups using independent- sample t-tests, and where appropriate, χ2 or Fisher’s exact tests.

Neurometabolite outliers were defined as greater than 2 times the interquartile range; where outliers were identified, they were replaced in a spectra-specific manner. Amongst all of the 1H-MRS data, a total of 5 outliers (2 glutamate, 3 Glx) were identified, spread amongst a total of 3 1H-MRS acquisitions (2 separate acquisitions in 1 patient and 1 acquisition in 1 control).

Neurometabolite levels were compared between groups using analyses of variance; here, participants’ first acceptable 1H-MRS acquisition was utilized. To assess potential confounders, age and 1H-MRS voxel GM fraction were each investigated as covariates. Group differences in Cramer-Rao lower bounds (CRLBs), FWHM values, signal-to-noise ratios (SNRs), and GM, WM, and CSF fractions were also examined using independent-sample t-tests. CRLBs were calculated as (%SD × Concentration Estimate) ÷ 100. Group comparisons were conducted with a significance level of p < 0.05.

Additionally, using the same data as above, Pearson correlations were used to explore the relationships between neurometabolite levels and age, symptom scores, duration of illness, and

115 chlorpromazine equivalent dose, the latter of which was calculated in reference to international, expert consensus-based recommendations (469). Given the exploratory nature of these investigations, a Bonferroni correction for multiple comparisons was employed and a statistical threshold of p < 0.0009 was used (p < 0.05 ÷ n, where n = # of comparisons (n = 54)). Where relationships achieved statistical significance, group differences in correlational coefficients were tested using r-to-Z transformations.

Neurometabolite reliability was assessed for the whole sample and separately for each group using the following test-retest measures: absolute percentage difference, [(point1 level - point2 level) ÷ ((point1 level + point2 level) ÷ 2)] × 100; absolute percentage error, [(point1 level - point2 level) ÷ point2 level] × 100; and coefficient of variation, standard deviation of point1 level and point2 level ÷ mean of point1 level and point2 level. Intraclass correlation coefficients (ICCs) were also calculated using a 2-way random-effects model (average measure). For reliability analyses, pairs were selected from available 1H-MRS acquisitions according to the criterion of proximity in time. Of note, while outliers were generally removed in a spectra-specific manner, in 1 control, 2 of the 3 spectra contained outliers (1 had 2 outliers, 1 had 1 outlier); here, the spectra with 1 outlier was chosen to make up the pair and the Glx outlier was removed in a neurometabolite-specific manner. Using the same pairs, reliability analyses were also performed as described above for spectral quality indices (i.e. signal-to-noise ratio, full-width at half maximum) and tissue heterogeneity values (i.e. 1H-MRS voxel GM, WM, and CSF fraction) in the whole sample. For reliability investigations, a significance level of p < 0.05 was utilized; p < 0.1 was considered to reflect trend-level significance. ICC values were interpreted according to established criteria (470).

Finally, effect size and power analyses were performed. Using the results acquired from the above-mentioned neurometabolite level comparisons between groups, effect sizes were calculated. Utilizing these observed effect sizes, an alpha probability of 0.05, a power of 0.8, and an allocation ratio of 1, we determined the approximate number of participants required to show group differences in a two-tailed investigation comparing the difference between two independent means (two groups).

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5.4 Results

5.4.1 Demographic and clinical characteristics

Participant characteristics are described in Table 5-1. Patients’ diagnoses were: schizophrenia (n = 9) and schizoaffective disorder (n = 3). Age, handedness, tobacco use, and sex did not differ between groups. Education years were lower in the patient group (t(21) = 3.02, p = 0.007). The patient group had mean SAPS and SANS total scores of 19.50 ± 12.43 and 21.92 ± 13.81, respectively, and mean SAPS and SANS summary scores of 3.08 ± 2.02 and 6.17 ± 3.16, respectively. Patients’ mean duration of illness was 19.58 ± 12.87 years and mean chlorpromazine equivalent dose was 471.25 ± 191.77 mg/day (469). Patients’ antipsychotic medication use was: clozapine (n = 5), olanzapine (n = 2), risperidone (n = 2), clozapine, quetiapine, and risperidone (n = 1), perphenazine (n = 1), and quetiapine (n = 1).

Table 5-1. Demographic and clinical characteristics of study participants.

Variable SCZ Group (n = 12) HC Group (n = 11) Age, mean (SD) [range], y 45.00 (12.09) [28-64] 40.73 (12.95) [23-64] Educational level, mean (SD), y 13.63 (2.27)a 16.00 (1.34) Sex, No. Male 7 8 Female 5 3 Tobacco Smoking, No. 4 3 Handedness, No. Right 10 8 Left 0 0 Ambidextrous 2 3 SAPS total, mean (SD), score 19.50 (12.43) NA SAPS summary, mean (SD), score 3.08 (2.02) NA SANS total, mean (SD), score 21.92 (13.81) NA SANS summary, mean (SD), score 6.17 (3.16) NA Duration of Illness, mean (SD) [range], y 19.58 (12.87) [2-39] NA CPZ Equivalent Dose, mean (SD), mg/d 471.25 (191.77) NA

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Abbreviations: CPZ, chlorpromazine; HC, healthy control; NA, not applicable; No., number; SANS, Scale for the Assessment of Negative Symptoms; SAPS, Scale for the Assessment of Positive Symptoms; SCZ, schizophrenia. Note: baseline screening data. a Denotes p<0.05.

5.4.2 Group differences in neurometabolite levels

Neurometabolite levels are reported in Table 5-2 and displayed in Figure 5-2. Glutamate, Glx, mI, Cho, NAA, and Cr levels did not differ between groups (F(1,21) = 0.70, p = 0.41; F(1,21) = 0.45, p = 0.51; F(1,21) = 2.13, p = 0.16; F(1,21) = 3.85, p = 0.063; F(1,21) = 1.12, p = 0.30; F(1,21) = 0.91, p = 0.35, respectively). Results were unaffected by the inclusion of age or 1H-MRS voxel GM fraction as covariates.

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Table 5-2. Neurometabolite levels in study participants.

Mean (SD) Glu Glx mI Cho NAA Cr SCZ Group 11.28 (1.18) 15.32 (1.87) 6.54 (1.24) 2.68 (0.35) 11.07 (0.89) 8.58 (0.87) HC Group 11.69 (1.19) 15.83 (1.78) 5.60 (1.82) 2.40 (0.33) 10.73 (0.60) 8.20 (1.05)

Abbreviations: Cho, choline-containing compounds; Cr, creatine-containing compounds; Glu, glutamate; Glx, glutamate+glutamine; HC, healthy control; mI, myo-inositol; NAA, N- acetylaspartate-containing compounds; SCZ, schizophrenia.

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5.4.3 Relationships between neurometabolite levels and participant characteristics

The relationships between neurometabolite levels and participant characteristics are presented in Table 5-3. Age was positively related to Cho levels in the healthy control group (p = 0.0005) (Figure 5-3). An r-to-Z transformation revealed a significant difference between groups in terms of the association between age and Cho levels (Z = 2.74, p = 0.006). No relationships were identified between levels of any neurometabolites and duration of illness, chlorpromazine equivalent dose, SAPS scores, or SANS scores.

Table 5-3. Relationships between striatal neurometabolite levels and participant characteristics.

Correlational Coefficient (r) Glu Glx mI Cho NAA Cr Whole Sample N=23 N=23 N=23 N=23 N=23 N=23 Age r = -0.10, p = 0.64 r = 0.04, p = 0.87 r = 0.26, p = 0.22 r = 0.45, p = 0.033 r = 0.20, p = 0.36 r = 0.18, p = 0.42 SCZ Group N=12 N=12 N=12 N=12 N=12 N=12 Age r = -0.18, p = 0.59 r = 0.24, p = 0.46 r = -0.18, p = 0.58 r = 0.003, p = 0.99 r = 0.17, p = 0.60 r = -0.25, p = 0.43 Duration of Illness r = 0.20, p = 0.53 r = 0.38, p = 0.22 r = 0.21, p = 0.52 r = 0.18, p = 0.58 r = 0.50, p = 0.10 r = -0.08, p = 0.80 CPZ Equivalent Dose r = 0.47, p = 0.12 r = 0.14, p = 0.67 r = -0.03, p = 0.93 r = 0.29, p = 0.35 r = 0.27, p = 0.40 r = 0.23, p = 0.47 SAPS Total Score r = -0.003, p = 0.99 r = -0.10, p = 0.76 r = -0.05, p = 0.87 r = -0.32, p = 0.30 r = -0.22, p = 0.50 r = -0.22, p = 0.48 SAPS Summary Score r = 0.07, p = 0.82 r = -0.25, p = 0.42 r = -0.31, p = 0.33 r = 0.05, p = 0.88 r = -0.08, p = 0.81 r = -0.10, p = 0.75 SANS Total Score r = -0.24, p = 0.46 r = -0.37, p = 0.24 r = -0.19, p = 0.54 r = -0.21, p = 0.50 r = -0.23, p = 0.48 r = -0.49, p = 0.11 SANS Summary Score r = -0.27, p = 0.40 r = -0.42, p = 0.18 r = -0.40, p = 0.20 r = -0.18, p = 0.57 r = -0.22, p = 0.48 r = -0.46, p = 0.13 HC Group N=11 N=11 N=11 N=11 N=11 N=11

a Age r = 0.03, p = 0.93 r = -0.13, p = 0.71 r = 0.51, p = 0.11 r = 0.87, p = 0.0005 r = 0.17, p = 0.61 r = 0.48, p = 0.13 Abbreviations: Cho, choline-containing compounds; CPZ, chlorpromazine; Cr, creatine- containing compounds; Glu, glutamate; Glx, glutamate+glutamine; HC, healthy control; mI, myo-inositol; NAA, N-acetylaspartate-containing compounds; SANS, Scale for the Assessment of Negative Symptoms; SAPS, Scale for the Assessment of Positive Symptoms; SCZ, schizophrenia. a Denotes pcorrected<0.05.

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4 Schizophrenia Healthy Controls s t i n

U 3 l a n o i t u t i t s n I 2 , l e v e L e n i l

o 1 h C

0 0 20 40 60 80 Age, Years

Figure 5-3. Relationships between age and choline levels in study participants.

5.4.4 CRLB, FWHM, signal-to-noise ratios, and tissue heterogeneity

Spectral quality indices and tissue heterogeneity values are described in Table 5-4. Group differences in CRLB, FWHM, SNR, and GM, WM, and CSF fraction were not identified.

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Table 5-4. Cramer-Rao lower bound, full-width at half maximum, signal-to-noise ratios, and tissue heterogeneity values in study participants.

Mean (SD) Glu – Glx – mI – Cho – NAA – Cr – FWHM SNR GM WM CSF CRLB CRLB CRLB CRLB CRLB CRLB 0.81 0.93 0.35 0.08 0.32 0.24 0.08 13.75 0.29 0.63 0.09 SCZ Group (0.19) (0.20) (0.08) (0.02) (0.07) (0.06) (0.02) (3.33) (0.05) (0.07) (0.06) 0.87 0.99 0.38 0.08 0.34 0.26 0.08 12.36 0.28 0.64 0.07 HC Group (0.20) (0.22) (0.09) (0.02) (0.08) (0.07) (0.02) (3.07) (0.04) (0.05) (0.04) Abbreviations: Cho, choline-containing compounds; Cr, creatine-containing compounds; CRLB, Cramer-Rao lower bound; CSF, cerebrospinal fluid fraction; FWHM, full-width at half maximum; Glu, glutamate; Glx, glutamate+glutamine; GM, gray matter fraction; HC, healthy control; mI, myo-inositol; NAA, N-acetylaspartate-containing compounds; SCZ, schizophrenia; SNR, signal-to-noise ratio; WM, white matter fraction.

5.4.5 Reliability of neurometabolite levels

Neurometabolite reliability results are presented for the whole sample and separately for each group in Table 5-5. In the whole sample, the ICCs for glutamate, Glx, mI, Cho, and NAA levels were 0.742, 0.633, 0.576, 0.847, and 0.747, respectively. At a trend-level significance, the ICC for Cr was 0.503. Within the schizophrenia group, the ICCs for mI, Cho, and NAA were 0.655, 0.815, and 0.752, respectively. At a trend-level significance, the ICCs for glutamate and Glx were 0.628 and 0.533, respectively. Within the healthy control group, the ICCs for glutamate, Glx, Cho, and NAA were 0.806, 0.779, 0.840, and 0.759, respectively. At a trend- level significance, the ICC for Cr was 0.633.

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Table 5-5. Reliability measures for striatal neurometabolite levels.

Mean (SD) Glu Glx mI Cho NAA Cr Whole Sample N=21 N=20 N=21 N=21 N=21 N=21 Point 1 11.78 (1.78) 15.86 (1.72) 6.46 (1.35) 2.60 (0.32) 11.04 (1.01) 8.69 (1.13) Point 2 12.18 (1.57) 16.96 (1.90) 6.75 (1.09) 2.60 (0.33) 10.97 (0.78) 8.94 (0.90) Absolute percentage difference 8.58 (6.40) 10.47 (6.56) 15.26 (13.22) 7.13 (6.22) 6.05 (4.19) 10.90 (8.14) Absolute percentage error 8.39 (6.13) 10.03 (6.13) 14.74 (12.91) 7.14 (6.12) 6.09 (4.29) 10.59 (7.38) Coefficient of variation 0.061 (0.045) 0.074 (0.046) 0.108 (0.094) 0.050 (0.044) 0.043 (0.030) 0.077 (0.058) ICC (95% CI), p-value 0.742 (0.384 – 0.633 (0.088 – 0.576 (- 0.033 – 0.847 (0.620 – 0.747 (0.368 – 0.503 (- 0.212 – 0.894), p = 0.001b 0.854), p = 0.006b 0.827), p = 0.031b 0.938), p < 0.001b 0.898), p = 0.002b 0.797), p = 0.064a

SCZ Group N=11 N=11 N=11 N=11 N=11 N=11 Point 1 11.65 (0.92) 15.72 (1.14) 6.47 (0.83) 2.68 (0.25) 11.05 (1.11) 8.55 (0.47) Point 2 11.79 (1.49) 17.23 (2.17) 6.98 (0.90) 2.72 (0.22) 11.20 (0.78) 9.15 (0.54) Absolute percentage difference 8.77 (6.25) 12.80 (5.19) 10.64 (9.25) 5.32 (4.52) 6.25 (4.24) 8.50 (6.08) Absolute percentage error 8.78 (6.34) 12.22 (4.86) 9.89 (8.06) 5.18 (4.11) 6.17 (4.18) 8.05 (5.53) Coefficient of variation 0.062 (0.044) 0.090 (0.037) 0.075 (0.065) 0.038 (0.032) 0.044 (0.030) 0.060 (0.043) ICC (95% CI), p-value 0.628 (- 0.502 – 0.533 (- 0.289 – 0.655 (- 0.090 – 0.815 (0.318 – 0.752 (0.061 – -0.027 (- 0.893 – 0.902), p = 0.078a 0.862), p = 0.056a 0.902), p = 0.031b 0.950), p = 0.008b 0.934), p = 0.022b 0.620), p = 0.526 HC Group N=10 N=9 N=10 N=10 N=10 N=10 Point 1 11.92 (1.45) 16.03 (2.30) 6.46 (1.82) 2.51 (0.37) 11.03 (0.96) 8.84 (1.59) Point 2 12.61 (1.62) 16.62 (1.56) 6.49 (1.26) 2.47 (0.38) 10.72 (0.73) 8.71 (1.16) Absolute percentage difference 8.38 (6.89) 7.63 (7.22) 20.34 (15.46) 9.13 (7.40) 5.83 (4.34) 13.53 (9.56) Absolute percentage error 7.96 (6.20) 7.35 (6.70) 20.08 (15.43) 9.28 (7.40) 5.99 (4.64) 13.39 (8.41) Coefficient of variation 0.059 (0.049) 0.054 (0.051) 0.144 (0.109) 0.065 (0.052) 0.041 (0.031) 0.096 (0.068) ICC (95% CI), p-value 0.806 (0.265 – 0.779 (0.104 – 0.559 (- 1.081 – 0.840 (0.349 – 0.759 (0.127 – 0.633 (- 0.657 – 0.951), p = 0.006b 0.949), p = 0.023b 0.894), p = 0.136 0.960), p = 0.007b 0.939), p = 0.019b 0.911), p = 0.089a Abbreviations: Cho, choline-containing compounds; Cr, creatine-containing compounds; Glu, glutamate; Glx, glutamate + glutamine; HC, healthy control; ICC, intraclass correlation coefficient; mI, myo-inositol; NAA, N-acetylaspartate-containing compounds; SCZ, schizophrenia. Note: days between paired 1H-MRS acquisitions: whole sample, average = 90.33 (74.85), range = 7-306; schizophrenia group, average = 94.09 (78.55), range = 27-306; healthy control group, average = 86.20 (74.55), range = 7-234. a Denotes p<0.1. b Denotes p<0.05.

Spectral quality and tissue heterogeneity reliability results are reported in Table 5-6. For SNR, FWHM acquired from the MRI scanner (in Hz), and GM, WM, and CSF fraction, the ICCs were 0.819, 0.731, 0.664, 0.729, and 0.898, respectively. At a trend-level significance, FWHM collected from LCModel outputs (in ppm) was 0.511.

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Table 5-6. Reliability measures for striatal spectral quality indices and tissue heterogeneity values.

Mean (SD) SNR FWHM (ppm) FWHM (Hz) GM WM CSF N=21 N=21 N=21 N=21 N=21 N=21 Point 1 13.14 (3.02) 0.08 (0.02) 9.76 (1.64) 0.29 (0.04) 0.61 (0.06) 0.10 (0.06) Point 2 12.24 (3.32) 0.09 (0.02) 10.24 (1.37) 0.28 (0.04) 0.61 (0.06) 0.10 (0.05) Absolute percentage difference 15.20 (15.14) 16.94 (14.46) 11.91 (9.22) 11.25 (8.31) 6.26 (5.73) 30.29 (25.74) Absolute percentage error 17.36 (20.21) 16.37 (14.21) 11.43 (8.52) 11.45 (8.76) 6.35 (5.98) 29.22 (20.01) Coefficient of variation 0.108 (0.107) 0.120 (0.102) 0.084 (0.065) 0.080 (0.059) 0.044 (0.041) 0.214 (0.182) ICC (95% CI), p-value 0.819 (0.560 – 0.511 (- 0.140 – 0.731 (0.359 – 0.664 (0.156 – 0.729 (0.320 – 0.898 (0.749 – b a b b b b 0.926), p < 0.001 0.797), p = 0.052 0.889), p = 0.002 0.865), p = 0.011 0.891), p = 0.003 0.959), p < 0.001 Abbreviations: CSF, cerebrospinal fluid fraction; FWHM, full-width at half maximum; GM, gray matter fraction; SNR, signal-to-noise ratio; WM, white matter fraction. a Denotes p<0.1. b Denotes p<0.05.

5.4.6 Effect size and power analyses

Based on the largest observed effect size (0.82 for Cho), roughly 25 participants would be necessitated in each group to demonstrate a difference in Cho levels. Based on the observed effect sizes for glutamate (0.35), Glx (0.28), mI (0.60), NAA (0.45), and Cr (0.39), approximately 130, 202, 45, 79, and 105 participants, respectively, would be required in each group to show differences.

5.5 Discussion

As its primary aim, the present study explored whether striatal neurometabolite levels differed between patients with schizophrenia receiving antipsychotic treatment for at least 1 year and healthy controls. No group differences were identified for any neurometabolite. Also, the

124 reliability of several neurometabolite levels, as well as spectral quality indices and tissue heterogeneity values, was demonstrated across study visits.

The striatum is known to have pathophysiological significance in schizophrenia. As noted above, previous 1H-MRS studies have reported various neurometabolite disturbances within the striatum of antipsychotic-naïve patients experiencing their first non-affective episode of psychosis (247, 253, 446). Further, elevated glutamate levels have been identified in this brain region among individuals at ultra-high risk for psychosis who later transitioned to psychosis (246).

Akin to previous literature investigating antipsychotic-treated patients with schizophrenia (382, 386, 387), we found neurometabolite levels in the patient group to resemble those of healthy controls. Previous investigations found no differences in levels of neurometabolites within the basal ganglia compared to healthy controls (386, 387). Similarly, a previous study found no differences in neurometabolite levels within the putamen in comparison to healthy controls (382). Specifically with regard to glutamatergic neurometabolites, our finding is consistent with the notion that levels might be increased in the antipsychotic-naïve or antipsychotic-free state (247-249, 251-253, 446), yet normalize to or decrease below healthy control levels with antipsychotic treatment (248, 253). Notably, a previous study reported elevated Glx levels in the basal ganglia in patients with early-stage first-episode schizophrenia (254). Overall, the present findings may be interpreted to reflect the effects of prolonged antipsychotic exposure, antipsychotic treatment response, or a combination of these factors (248, 253, 254, 258, 308, 464, 471).

It is well accepted that the therapeutic effect of antipsychotics is mediated by striatal dopamine D2 receptor blockade (373-376). While the precise relationship between striatal dopamine and striatal glutamate levels remains elusive, the dampening effect of long-term antipsychotic treatment on levels of glutamatergic neurometabolites is consistent with the classical model of the basal ganglia, as well as a recent review discussing the effect of striatal dopamine depletion on striatal glutamate (472). This review concluded that chronic dopamine depletion decreases striatal glutamate levels (472), consistent with the findings from the present work, which imply a normalization of striatal glutamate levels in patients with schizophrenia undergoing long-term antipsychotic treatment. Beyond the effects of antipsychotic medication,

125 age may have contributed to the lack of difference in glutamatergic neurometabolites between groups (257, 421), although a recent meta-analysis did not find a moderating effect of age on group differences in glutamatergic levels (389).

Furthermore, we identified Cho elevations in the patient group that approached statistical significance. This finding is comparable to those of previous studies that identified increased Cho levels in patients with schizophrenia within the striatum, caudate nucleus, and basal ganglia (413-415, 446). Additionally, a strong positive association between Cho levels and age was found in the healthy control group. Previous studies have reported a negative relationship between age and Cho levels in patients with schizophrenia (256, 411). Nevertheless, in the present study, a noteworthy group difference in the relationship between Cho levels and age was found using r-to-Z transformations.

Using participants’ 2 closest 1H-MRS acquisitions in terms of temporal proximity, the reliability of glutamate, Glx, mI, Cho, NAA, and Cr level measurements ranged from fair to excellent in the whole sample. Similarly, the reliability of SNR, FWHM, and GM, WM, and cerebrospinal fluid fraction across study visits ranged from fair to excellent. Collectively, our results suggest that these neurometabolite levels, spectral quality indices, and tissue heterogeneity values do not fluctuate considerably across study visits, highlighting the reliability of their assessment with 1H-MRS.

When reliability analyses were conducted within each group, varying neurometabolite patterns were identified. Within the schizophrenia group, we observed excellent reliability for Cho and NAA, good reliability for glutamate and mI, and fair reliability for Glx. Within the healthy control group, excellent reliability was seen for glutamate, Glx, Cho, and NAA, with good reliability observed for Cr. These findings are in part comparable and should be considered alongside a previous test-retest investigation, which examined the same brain region within a healthy control group (253). It is noteworthy that the specific underpinnings of change for most neurometabolites remain largely unknown. Neurometabolite levels could be influenced by several factors, including various physiological alterations or slight deviations in 1H-MRS voxel placement between study visits.

Importantly, while the findings discussed above are generally indicative of reliability in neurometabolite measurements between 1H-MRS acquisitions separated by less than 1 year,

126 future work must consider the influence that age and longer lapses between acquisitions may have on neurometabolite levels. For example, previous studies have reported negative relationships – often more marked in patients with schizophrenia – between age and levels of glutamatergic neurometabolites, Cho, NAA, and Cr (257, 411, 421). Overall, the present results should be considered alongside prior work that suggests associations between neurometabolite levels and age.

This study is limited by several factors. First, medication type and dosing were not uniform across the patient sample. Different antipsychotic medications may have varying effects on neurometabolite levels. Second, patients with schizophrenia and schizoaffective disorder were both included. These illnesses may have differing profiles of neurometabolite dysregulation. Third, only patients with moderate-to-severe lack of illness awareness were included, as per the principal aims of the parent study, potentially affecting generalizability of findings. Fourth, the present study might be underpowered, although it should be noted that the main implication of the current work remains despite the limitation of sample size, i.e. the observation of comparable levels of striatal glutamatergic neurometabolites between patients with schizophrenia undergoing long-term antipsychotic treatment and healthy controls. This is of relevance due to the previously reported observation of elevated striatal glutamatergic neurometabolite levels in antipsychotic- naïve patients within the early stages of illness. Indeed, a larger sample size may have revealed a further lowering of glutamatergic neurometabolite levels, below that of healthy controls, as has been demonstrated in previous studies and is suggested by the lower mean glutamate and Glx levels in the patient group within the present work. Similarly, investigations concerning group differences in levels of other neurometabolites may have also been underpowered. While it was not feasible for the present study, future work should strive to reach the sample sizes described in the effect size and power analyses. Fifth, the time separating participants’ 2 1H-MRS acquisitions differed among the sample; however, no relationships were found between the time interval and any of the measures presented in Table 5-5. Moreover, with the shortest time interval between 2 1H-MRS acquisitions being 1 week, potential carry-over effects of tDCS on neurometabolite measures should be considered. However, it is currently believed that the effects of a single session of tDCS would not induce changes that would last this duration (473-478). Sixth, the current investigation could not assess the degree of response to antipsychotic treatment, thus precluding the attribution of findings to treatment response, antipsychotic exposure, or both.

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Similarly, the present study did not classify patients as treatment-responders or treatment- resistant. Seventh, the current work is limited by its lack of a medication-free group or pre- and post-treatment data, which are necessitated for more conclusive interpretations to be made regarding the aforementioned mechanism wherein glutamatergic neurometabolites are proposed to be elevated in the antipsychotic-naïve or antipsychotic-free state and normalize to that of healthy controls with appropriate antipsychotic treatment. Eighth, while the 1H-MRS voxel location was referred to as the striatum, it is noteworthy that it included components of other structures as well, such as the internal capsule. Finally, the present study interprets the results of analyses across multiple study visits to reflect reliability. That being said, it deserves mention that these results, especially for neurometabolite levels, can additionally be interpreted to reflect stability over time, including the influence of physiological fluctuations.

5.6 Conclusion

Taken together, in contrast to previous findings from antipsychotic-naïve patients with first-episode psychosis, the present study observed that patients with schizophrenia undergoing long-term antipsychotic treatment appear to have levels of neurometabolites within the striatum that resemble those of healthy controls, ultimately contributing to an enhanced understanding of illness pathophysiology and the effects of antipsychotic medication. The current work also suggests that most neurometabolite levels do not vary over time periods shorter than 1 year and demonstrates reliable acquisition of 1H-MRS data. Future studies should continue to elucidate the reliability and stability of these measures and should be careful to employ appropriate technical methodology and quality control parameters to maximize reproducibility.

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Chapter 6 6 Discussion

Given the cumulative format used for this thesis, each chapter presenting original research contains a discussion section. Thus, to avoid repetition, the current section is devoted towards discussing the collective body of work within this thesis.

6.1 Summary of Findings

In Chapter 3, elevated levels of myo-inositol (mI), choline (Cho), and glutamate were observed in the associative striatum within a sample of antipsychotic-naïve patients experiencing their first-episode of psychosis (FEP) compared to a group of age- and sex-matched healthy controls. Additionally, mI levels positively correlated with Positive and Negative Syndrome Scale (PANSS) item P5 (grandiosity) score, and positively correlated at a trend-level significance with PANSS positive total score and PANSS item P3 (hallucinatory behaviour) score. Lastly, the relationships between mI and glutamate levels, and Cho and glutamate levels, were found to be more positive in the patient group in comparison to the healthy control group.

In Chapter 4, precommissural caudate volume (PCV) loss and cortical thinning were found within a sample of antipsychotic-naïve patients experiencing their FEP compared to a group of age- and sex-matched healthy controls. Notably, PCV group differences were not identified when total brain volume (TBV) was included as a covariate. Cortical thinning was vast and widespread in the patient group. Importantly, precommissural dorsal caudate (PDC) glutamate+glutamine (Glx) levels were negatively associated with PCV in the FEP group but not the healthy control group, and no relationships were identified between levels of glutamatergic neurometabolites and cortical thickness.

In Chapter 5, no differences in striatal neurometabolites were found between patients with schizophrenia receiving antipsychotic treatment for at least 1 year and healthy controls. Multiple study visits permitted a reliability assessment, which showed fair to excellent reliability for glutamate, Glx, mI, Cho, and N-acetylaspartate (NAA) levels, and various proton magnetic resonance spectroscopy (1H-MRS) indices.

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Collectively, the main findings from Chapters 3, 4, and 5 contribute five important takeaways. First, evidence is put forth supporting glial dysfunction in patients who have yet to be exposed to antipsychotic medication and are within the early stages of schizophrenia. This is of relevance to literature suggesting glial activation, along with neuroinflammation, in patients with schizophrenia (422, 423, 428, 479). While elevations in glutamatergic neurometabolites have been previously shown in antipsychotic-naïve or antipsychotic-free patients with schizophrenia (247-249, 251-253, 446), evidence is provided for a mechanism by which these might occur: glial dysfunction that has consequent effects on the disruption of glutamatergic neurotransmission.

Second, further evidence is provided for a pattern often described within the literature, wherein levels of glutamatergic neurometabolites are elevated during antipsychotic-naïve or antipsychotic-free states of illness, yet decrease to or below healthy control levels following antipsychotic treatment (131, 144, 146, 248, 253, 480). The findings from the current thesis provide support for this phenomenon in the striatum.

Third, consistent with previous literature, vast structural compromise is shown in the form of subcortical structure volume loss and widespread cortical thinning (184, 192, 201-203, 275, 392). However, using Multiple Automatically Generated Templates (MAGeT-Brain), volumetric loss is presently shown specifically within the precommissural caudate in antipsychotic-naïve patients with FEP. Also, evidence is provided to suggest the importance of considering TBV when performing volumetric analyses.

Fourth, an excitotoxic mechanism is suggested within the precommissural caudate, whereby increased levels of glutamatergic neurometabolites are related to local structural compromise. Notably, similar findings have previously been reported in other brain regions, such as the hippocampus (249, 441).

Fifth, reliable acquisition of 1H-MRS data is demonstrated within the striatum, in terms of neurometabolite levels, spectral quality indices, and tissue heterogeneity values. These findings are comparable to those from prior studies investigating striatal and extrastriatal regions in patients with schizophrenia (481, 482).

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6.2 The Relationship between Striatal Dopamine and Striatal Glutamate Levels

As previously mentioned, dopaminergic disturbances are well-documented in patients with schizophrenia. In addition to the notion that all antipsychotic drugs block dopamine D2 receptors (483), a recent meta-analysis reported elevations in striatal presynaptic dopaminergic function and dopamine D2/3 receptor availability in patients with schizophrenia (121).

Additionally, previous evidence demonstrates a close relationship between striatal dopamine D2 receptor occupancy and clinical response (375, 376).

With respect to striatal glutamatergic neurometabolites, the findings presented in this thesis within Chapter 3 show elevated glutamate levels in antipsychotic-naïve patients experiencing their FEP compared to healthy controls, whereas those in Chapter 5 suggest no group differences between patients with schizophrenia undergoing long-term antipsychotic treatment and healthy controls. This is supportive of a pattern previously purported in the literature wherein antipsychotic treatment reduces elevated levels of striatal glutamatergic neurometabolites to levels that either resemble or are below those of healthy controls (131, 480). As such, considering the dopaminergic findings discussed above and the glutamatergic findings presented within this work, a close interaction between striatal dopamine and striatal glutamate may be inferred.

Generally, glutamatergic projections are sent from cortical areas to the striatum, which sends inhibitory gamma-aminobutyric acid (GABA)–ergic projections to the globus pallidum. The globus pallidum ultimately projects to the thalamus, which then sends glutamatergic projections back to the cortex (484). On the other hand, dopaminergic projections are sent from: the substantia nigra to the dorsal striatum (known as the nigrostriatal system); the ventral tegmental area to limbic areas, including the hippocampus, amygdala, and ventral striatum (known as the mesolimbic system); and the ventral tegmental area to the cortex (known as the mesocortical system) (484).

The dopaminergic and glutamatergic systems are known to interact with one another; as previously described, some of the interplay between these systems is commonly understood through the classical model of the basal ganglia (9, 10, 362, 363, 472). In vivo brain imaging studies investigating both healthy controls and neuropsychiatric patient populations, including

131 patients with Parkinson’s disease, addiction, and schizophrenia, provide some support for this model (472). Also, in healthy participants, a recent study employing both 1H-MRS and positron emission tomography (PET) observed a positive relationship between striatal glutamate and ventral striatum dopamine synthesis capacity (485). In terms of schizophrenia, the literature review above has independently discussed elevations in both dopamine and glutamate in antipsychotic-naïve or antipsychotic-free patients with schizophrenia. However, it remains elusive which of dopamine or glutamate disturbances are primary to the other in the pathophysiology of the illness (222, 483).

In terms of the effect of glutamatergic dysregulation on the dopaminergic system, it is posited that excess extrastriatal glutamate may lead to a secondary increase in striatal dopamine release (222, 486, 487). One mechanism that has been proposed regarding a way in which the glutamatergic system may carry influence over dopaminergic signalling is through hypofunctioning N-methyl-D-aspartate (NMDA) receptors on GABAergic inhibitory neurons, resulting in overactivity of glutamatergic projections to midbrain dopaminergic nuclei and thereby producing a hyperdopaminergic state (483). In preclinical studies, the administration of NMDA receptor antagonists, such as phencyclidine and ketamine, leads to altered dopamine synthesis and firing patterns, and increased dopamine release in response to an amphetamine challenge (483, 488, 489). Similarly, in humans, ketamine administration has resulted in greater dopamine release, and baseline dopamine D2/3 receptor availability was found to be linked with greater symptomatic effects of ketamine (483, 490-492).

Previous work also suggests a modulatory role of the dopaminergic system on glutamatergic signalling. One mechanism by which this is posited to occur is via glutamatergic and dopaminergic projections towards the striatal GABAergic medium spiny interneurons within the striatum (235, 493). Activation of the postsynaptic NMDA and dopamine D2 receptors is understood to produce opposite effects on the inhibitory signalling of these neurons towards thalamic glutamatergic neurons (235, 493). In this way, dopamine D2 receptor agonism reduces the inhibitory control over thalamic glutamatergic neurons, thus increasing glutamatergic signalling towards the cortex; this produces the same effect as NMDA receptor antagonism or a hypoglutamatergic state (235, 493). Similarly, dopamine D2 receptor antagonism or a hypodopaminergic state is posited to increase the inhibition of glutamatergic signalling, yielding a reduction in release of glutamate towards the cortex, and mirroring the effect of NMDA

132 receptor stimulation (235, 493). This mechanism suggests that decreases in striatal dopamine will decrease glutamate levels in the striatum and cortex, while increases in striatal dopamine will increase striatal and cortical glutamate levels (9, 10, 362). In keeping, a recent review by our group examined the effect of striatal dopamine depletion on striatal and cortical glutamate levels, concluding that chronic dopamine depletion decreases striatal glutamate levels (472). This may also be consistent with the observation within the literature that antipsychotic treatment, known to function primarily through dopamine D2 receptor blockade, results in a reduction of levels of glutamatergic neurometabolites (480).

However, the mechanism underlying the reduction in glutamatergic neurometabolites with antipsychotic treatment is yet to be fully elucidated. A recent systematic review examining longitudinal changes in levels of glutamatergic neurometabolites within participants undergoing antipsychotic treatment found that most studies observe numerical reductions; half of the studies included in the review reported a reduction in glutamatergic neurometabolites, and no included study identified an increase in levels of glutamatergic neurometabolites (480). The authors determined an estimated overall mean reduction of 6.5% in Glx levels across various brain regions (480). In addition to alterations within the dopaminergic and glutamatergic systems, the authors discussed a mechanism whereby antipsychotic-mediated 5HT2A receptor modulation may play a role (480, 494, 495). In support, preclinical studies have demonstrated that the administration of atypical antipsychotics leads to a reduction in glutamate levels, possibly influenced by 5HT2A receptor antagonism (327, 480, 495, 496). Additionally, the administration of a selective 5HT2A receptor antagonist has been found to oppose the effect of NMDA receptor antagonism on behavioural stimulation (497). Moreover, an additional putative mechanism by which antipsychotic treatment produces a reduction in glutamatergic signalling may be through modulation of glutamate release by the alteration of glutamatergic receptor density or activity (480, 493, 498).

Furthermore, a body of work within the 1H-MRS literature has put forth strong evidence that deserves consideration in light of the proposed pattern of elevated levels of glutamatergic neurometabolites in early, unmedicated stages of schizophrenia, which then normalize to or decrease beyond the levels of healthy controls in later, medicated stages of the illness. In such discussions, it is important to consider the brain region being investigated and the glutamatergic neurometabolite of interest. For example, a recent meta-analysis found elevated medial temporal

133 and frontal white matter (WM) Glx levels in patients with chronic schizophrenia compared to controls (389). As well, there is strong evidence from a large single-voxel 1H-MRS study suggesting glutamine level elevations in the dorsal anterior cingulate of patients with chronic schizophrenia (176). Similarly, utilizing the largest existing sample to date, recent 1H-MRS imaging work has found increased Glx levels in patients with schizophrenia compared to healthy controls within medial frontal and parietal grey matter and frontal WM brain regions (158). These findings, taken together with others in the field, as well as those of the present work, suggest that residual increases in glutamatergic neurometabolite levels remain even after antipsychotic treatment (158).

It is also noteworthy that recent evidence in patients with treatment-resistant schizophrenia (TRS) identifies subgroups of patients with schizophrenia who have striatal dopamine synthesis elevations without any identified glutamate disturbances, and patients with schizophrenia with elevated glutamate levels in the anterior cingulate cortex without any identified dopamine disturbances. Demjaha et al found that patients responding to antipsychotic treatment had elevated striatal dopamine synthesis capacity compared to patients who were deemed to be treatment-resistant and healthy controls (377). In the same sample, the authors observed elevated glutamate levels in the anterior cingulate of patients with TRS, in comparison to healthy controls, while glutamate levels in treatment responders did not differ from healthy controls (258). Similarly, Egerton et al reported higher glutamate levels in the anterior cingulate cortex of antipsychotic-treated patients with FEP who remained symptomatic compared to those who achieved remission (259). Lastly, Mouchlianitis et al found higher anterior cingulate cortex glutamate levels in patients with TRS compared to treatment-responsive patients (464). Currently, such findings within patients with TRS are not easily explained by existing knowledge of dopamine-glutamate interactions, and are suggestive of differing phenotypes of schizophrenia.

Additionally, it is worth repeating that the dopamine hypothesis of schizophrenia only accounts for positive symptoms, whereas the glutamate model (i.e. NMDA receptor hypofunction) accounts for positive, negative, and cognitive symptoms in patients with schizophrenia. This is noteworthy in the discussion of dopamine-glutamate interactions, as it appears plausible that the two systems have modulating effects on one another as well as independent effects on symptomatology.

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6.3 Heuristic Mechanisms of Schizophrenia Pathophysiology in the Context of Glutamatergic Disturbances

6.3.1 Neuroinflammation and Glial Activation in Schizophrenia

Over the past two decades, there has been an accumulation of evidence suggesting neuroinflammation to be involved in the underlying pathophysiology of schizophrenia (423, 499). The literature in this field has invoked neuroinflammation based on several lines of evidence, including the identification of changes in levels of various pro-inflammatory markers, structural changes, and genetic associations within this patient population, as well as the substantial body of literature suggesting that perinatal bacterial or viral infections and obstetric complications have an impact on the risk of developing schizophrenia later in life (422-424, 499- 501). Recent literature investigating patients with schizophrenia has provided some suggestion of disruptions in translocator protein 18 kDA, known as TSPO, which is a PET marker believed to reflect microglial activation and neuroinflammation, although study findings and methodologies are somewhat inconsistent (502-510). One of the most comprehensive studies using TSPO to date failed to find microglial activation in untreated patients with FEP (505), suggesting caution regarding the inflammatory hypothesis.

The brain is considered an immune privileged area; under normal conditions, the blood brain barrier largely prevents immune cells from entering the brain parenchyma from the peripheral circulation (423, 501). As such, the brain has a separate innate immune system, which relies predominantly on the functioning of glial cells (423, 501). The activation of this system, termed neuroinflammation, can occur as a response to an acute insult such as infection or injury and may resolve once the insult has ceased (423, 501, 511). However, persistence of this response over an extended period may occur for various reasons as part of a chronic neuroinflammatory process (423, 501, 511). Several neurological disorders, especially those with a neurodegenerative course, have been observed to involve chronic neuroinflammation within their pathophysiology (501).

Generally, at least three classes of glial cells exist: oligodendrocytes, microglia, and astrocytes (393, 512, 513). Amongst other functions, the primary role of oligodendrocytes is the formation of myelin (393, 514). Microglia are conceptualized as resident macrophages of the

135 central nervous system and, when activated, they initiate phagocytosis and produce immunomodulatory cytokines (514-517). Astrocytes provide neuronal support and contribute to nervous system repair, in addition to other roles (513, 518). The activation of glial cells, as occurs during response to injury or infection, characterizes neuroinflammation (166, 423, 511, 514, 515, 517). Glial activation is observed in many neuroinflammatory conditions (e.g. Human Immunodeficiency Virus, multiple sclerosis, Alzheimer’s disease) (166, 501, 514). Within schizophrenia, glial activation through a neuroinflammatory process has been posited to be involved in the pathophysiology of the illness, and may have a role in the etiology of glutamatergic dysregulation (393, 432, 499-501, 513). Although dysregulation of the glutamatergic system is well-described in schizophrenia (131, 161, 389), the mechanism through which it occurs remains unclear.

Astrocytes, in addition to the roles described above, regulate the cycling of glutamate and its primary metabolite glutamine (347, 399, 519). 1H-MRS allows for the measurement of mI and Cho, both of which are present in higher concentrations in astrocytes than in neurons and are consequently interpreted to reflect astrocyte function (164, 166, 169). As such, elevated levels of mI and Cho are often believed to be indicative of astrocyte activation (166), and several studies have reported mI and Cho elevations (amongst other neurometabolic disruptions) in the investigation of various neuroinflammatory disorders (166, 417-420).

In schizophrenia, reported deviations in mI and Cho levels are inconsistent across the literature (147, 391), though elevations in both neurometabolites have been reported in the associative striatum of antipsychotic-naïve patients experiencing their FEP (253). In addition to elevations in mI levels, concomitant increases in S100B, a marker for astrocyte function, have been found in patients with schizophrenia during acute psychosis stages, suggesting that increased astrocytic activity with associated mI elevation may exist in this patient population (428). Also, a recent 1H-MRS study interpreted a negative correlation between mI levels within frontal WM and fractional anisotropy of WM within both patients with schizophrenia and healthy controls to reflect a general inflammatory effect on WM microstructure (421). Moreover, Cho levels are related to membrane turnover; in patients with schizophrenia, elevated levels of Cho have been interpreted to be indicative of increased astrocytic turnover of glutamatergic compounds (176). Taken together, dysregulated astrocytic activity in patients with schizophrenia, as evidenced by abnormal mI and Cho levels, might consequently disrupt

136 glutamatergic neurotransmission. In Chapter 3, within a sample of sixty antipsychotic-naïve patients with FEP and sixty age- and sex-matched healthy controls, it was found that associative striatum glutamate, mI, and Cho levels were elevated in the FEP group. Also, mI and Cho levels were positively correlated with glutamate in the patient group only. Using r-to-Z transformations, the relationships between levels of mI and glutamate, and Cho and glutamate, were reported to be more positive in the FEP group. These findings provide support for astrocyte activation in the FEP group and may suggest that dysregulated astrocyte function might contribute to the observed disruption of glutamatergic tone in this patient population.

6.3.2 Kynurenic Acid in Schizophrenia

In light of the above described neuroinflammatory phenomena and the potential association between astrocytic activation and glutamatergic dysfunction, a neurometabolite of interest is kynurenic acid (KYNA). It is produced through the kynurenine (KYN) pathway of tryptophan degradation (520, 521). Within this pathway, the conversion of KYN to KYNA occurs primarily within astrocytes, as they contain kynurenine aminotransferases, which irreversibly transaminate KYN to KYNA (522, 523). Notably, KYNA is the only known endogenous NMDA receptor antagonist; administration of exogenous NMDA receptor antagonists is known to mimic glutamatergic dysregulation and symptomatology seen in patients with schizophrenia (524). Further, the production of KYNA may be affected by the release of immunomodulatory cytokines and their effect on enzymes within the tryptophan degradation pathway as well as through astrocytic activation (430, 525). Given the potential for KYNA to contribute towards a better understanding of glutamatergic dysregulation in patients with schizophrenia (526), a comprehensive investigation of its potential role within the illness is warranted. We recently performed a systematic review and meta-analysis to better understand the role of KYNA and its dysregulation in schizophrenia, which is presented below.

Section 6.3.2 is reproduced with permission from the following:

Plitman E, Iwata Y, Caravaggio F, Nakajima S, Chung JK, Gerretsen P, Kim J, Takeuchi H, Chakravarty MM, Remington G, Graff Guerrero A. Kynurenic Acid in Schizophrenia:

137

A Systematic Review and Meta-analysis. Schizophrenia Bulletin 2017; 43(4):764-777.

6.3.2.1 Abstract

Kynurenic acid (KYNA) is an endogenous antagonist of N-methyl-D-aspartate and α7 nicotinic acetylcholine receptors that is derived from astrocytes as part of the kynurenine pathway of tryptophan degradation. Evidence suggests that abnormal KYNA levels are involved in the pathophysiology of schizophrenia. However, this has never been assessed through a meta- analysis. A literature search was conducted through Ovid using Embase, Medline, and PsycINFO databases (last search: December 2016) with the search terms: (kynuren* or KYNA) and (schizophreni* or psychosis). English language studies measuring KYNA levels using any method in patients with schizophrenia and healthy controls (HCs) were identified. Standardized mean differences (SMDs) were calculated to determine differences in KYNA levels between groups. Subgroup analyses were separately performed for nonoverlapping participant samples, KYNA measurement techniques, and KYNA sample source. The influences of patients’ age, antipsychotic status (%medicated), and sex (%male) on study SMDs were assessed through a meta-regression. Thirteen studies were deemed eligible for inclusion in the meta-analysis. In the main analysis, KYNA levels were elevated in the patient group. Subgroup analyses demonstrated that KYNA levels were increased in nonoverlapping participant samples, and centrally (cerebrospinal fluid and brain tissue) but not peripherally. Patients’ age, %medicated, and %male were each positively associated with study SMDs. Overall, KYNA levels are increased in patients with schizophrenia, specifically within the central nervous system. An improved understanding of KYNA in patients with schizophrenia may contribute to the development of novel diagnostic approaches and therapeutic strategies.

6.3.2.2 Introduction

6.3.2.2.1 Schizophrenia

While schizophrenia is characterized by positive, negative, and cognitive symptoms, neurometabolic abnormalities have also been identified as key features of the illness (161, 483). The longstanding dopamine hypothesis of schizophrenia suggests that dysregulated functioning

138 of the dopaminergic system underlies its pathophysiology (214, 217, 218, 377, 527). However, the dopamine hypothesis does not readily explain negative and cognitive symptoms (33, 528). Moreover, a subset of patients (20%-35%) show partial or no response to standard antipsychotic treatments, which exert their effect primarily through dopamine receptor antagonism (220, 221).

Another widely purported pathophysiological mechanism is the glutamatergic hypothesis of schizophrenia. Evidence for this hypothesis arises from pharmacological studies in which N- methyl-D-aspartate receptor (NMDAR) antagonist administration leads to the emergence of positive, negative, and cognitive symptoms in human volunteers (227-231, 461). These agents also elicit symptom exacerbation in patients with schizophrenia (232, 233, 461). Olney and Farber proposed that hypofunctioning NMDARs on gamma-aminobutyric acid (GABA)-ergic inhibitory interneurons result in the disinhibition of downstream pyramidal neurons, increasing presynaptic glutamate release within various brain regions (322). In support, disturbed glutamatergic signaling has been observed in healthy volunteers following acute exposure to an NMDAR antagonist (241, 242) and in patients with schizophrenia (247-249, 251, 252, 446). The known effects of exogenous NMDAR antagonists on glutamatergic dysregulation and schizophrenia-like symptomatology have resulted in increased attention towards kynurenic acid (KYNA), the only currently known endogenous NMDAR antagonist.

6.3.2.2.2 Kynurenine Pathway

KYNA is produced through the kynurenine (KYN) pathway of tryptophan (TRP) degradation, accounting for over 90% of the metabolism of this essential amino acid (529). TRP is oxidized to N-formylkynurenine by 1 of 3 enzymes: indoleamine 2,3-dioxygenase 1 (IDO1), IDO2, or tryptophan 2,3-dioxygenase (TDO2). Next, deformylation of N-formylkynurenine by formamidase produces KYN. KYN is thereafter metabolized through 3 distinct branches of the KYN pathway. KYN can be irreversibly transaminated to KYNA by 4 kynurenine aminotransferases (KATs). KYN can also be oxidized by kynurenine 3-monooxygenase (KMO) to produce 3-hydroxykynurenine (3-HK). Lastly, KYN can undergo oxidative cleavage by kynureninase to form anthranilic acid (for additional details on this pathway, see reviews by Dounay et al (522), Schwarcz et al (430), and Vécsei et al (521)).

139

The KYN pathway of TRP degradation is initiated by IDO and TDO2 (522). These enzymes are known to exist at higher levels in the periphery compared to the central nervous system (CNS) (430). Downstream, KYN readily crosses the blood-brain barrier through the large neutral amino acid transporter (530); approximately 60% of brain KYN is believed to be contributed from the periphery (531). In contrast, due to its polar structure, KYNA does not cross the blood-brain barrier (530). Thus, brain KYNA is predominantly derived from brain KYN (430). The conversion of KYN to KYNA takes place primarily within astrocytes, as these cells contain KATs but not KMO and therefore cannot degrade KYN to 3-HK and its metabolites (532). Of the 4 existing KATs, KAT II is thought to be the main enzyme of KYNA production (533).

KYNA acts as an antagonist of all 3 ionotropic glutamate receptors, including NMDARs, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors, and kainate receptors (534). However, of these, KYNA preferentially and competitively inhibits the glycine site of the NMDAR (535, 536). KYNA is also an antagonist of α7 nicotinic acetylcholine receptors (α7nAChR) (537); its inhibitory effect on these receptors is achieved noncompetitively through its interaction with an allosteric potentiating site, which is oppositely stimulated by galantamine, an α7nAChR positive allosteric modulator (525). KYNA also activates the G-protein-coupled receptor GPR 35 and the aryl hydrocarbon receptor (538, 539). Additionally, KYNA functions as a free radical scavenger and an antioxidant (540). Given its capacity to block neuronal excitation and scavenge free radicals, KYNA is widely considered to have neuroprotective and anticonvulsant properties (541).

6.3.2.2.3 KYNA Hypothesis of Schizophrenia

The KYNA hypothesis of schizophrenia posits that disrupted KYNA levels are implicated in the pathophysiology of the illness (524). This hypothesis is supported by the notion that KYNA, as an endogenous glutamate receptor antagonist, may mimic schizophrenia-like phenomena induced by exogenous glutamate receptor antagonists, along with evidence from both preclinical and clinical literature (525, 542, 543). Preclinical studies manipulating levels of KYNA have demonstrated its influence on both behavior (eg, cognitive functioning) and neurotransmission (eg, glutamatergic, dopaminergic) observed to be aberrant in patients with

140 schizophrenia (525, 543). Furthermore, KYNA levels have also been measured in schizophrenia patient populations and deviations from healthy controls (HCs) have often been reported (525).

6.3.2.2.4 Study Aims

Although individual studies have reported KYNA disruptions in patients with schizophrenia, their findings have not been assessed through a meta-analysis. The primary aim of this systematic review and meta-analysis was to evaluate the difference in KYNA levels between patients with schizophrenia and HCs. As secondary aims, subgroup analyses examined nonoverlapping participant samples, KYNA measurement techniques, and KYNA sample source. Also, the influences of patients’ age, antipsychotic status, and sex were explored through a meta-regression.

6.3.2.3 Methods

6.3.2.3.1 Literature Search

This meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analysis group (544). Two authors (E.P., J.K.) independently performed the search (last search: December 2016) and assessed eligibility, and 2 authors (E.P., J.K.C.) independently extracted data. English language human published articles were searched for using Embase, Medline, and PsycINFO. The Ovid search was conducted using the following terms: (kynuren* or KYNA) and (schizophreni* or psychosis). The reference sections of major review articles (430, 520-526, 542, 543, 545, 546) were also searched.

6.3.2.3.2 Inclusion Criteria

Full-length English language articles were included if: (1) they included patients with schizophrenia or related disorders, (2) they included a HC group, (3) KYNA levels were measured in both groups using any method, and (4) data were sufficient to calculate standardized mean differences (SMDs).

141

6.3.2.3.3 Exclusion Criteria

When studies reported upon a sample completely overlapping with another study, as described within their texts, the study with the largest sample size was used and the other excluded. Where publications reported partially overlapping samples, both were included in the primary analysis. Studies missing baseline KYNA levels or examining KYNA production were excluded.

6.3.2.3.4 Outcome Measures

The main outcome measure was KYNA levels. We aimed to investigate group differences in KYNA between patients with schizophrenia and HCs.

6.3.2.3.5 Recorded Variables

The variables recorded from each included study were KYNA levels, diagnoses, age, sex, antipsychotic status, method of KYNA measurement, and participant sample overlap with other studies.

6.3.2.3.6 Data Analysis 6.3.2.3.6.1 Meta-analysis

The primary meta-analysis, subgroup analyses, and sensitivity analyses were conducted using Review Manager Version 5.2 (http://tech.cochrane.org/revman). The meta-regression was carried out using Comprehensive Meta Analysis (www.meta-analysis.com). Differences in KYNA levels between patients with schizophrenia and HCs were determined by calculating SMDs (547). If the total number of study participants exceeded the number that underwent KYNA measurement, only subjects in whom KYNA was measured were included. When studies separately reported KYNA levels from multiple brain areas, average SMDs were calculated and

142 utilized. Where mean values were not stated, authors were contacted for additional data or, if reported, median values were utilized. Where SD values were not reported, values were obtained through calculations from available data according to the Cochrane Handbook for Systematic Reviews of Interventions (http://www.handbook.cochrane.org). Effects were interpreted as small (SMD = 0.2), moderate (SMD = 0.5) or large (SMD = 0.8) (547), with positive values indicating elevated KYNA levels in the schizophrenia group. To adjust for study heterogeneity, the inverse variance statistical method and random effects model were employed (548). Significance was assessed using 2-sided 95% confidence intervals (CIs).

The I2 statistic was utilized to assess study heterogeneity for the primary analysis; I2 ≥ 50% represented significant heterogeneity. If heterogeneity was found, one-leave-out sensitivity analyses were performed to examine influences of any single study on the pooled SMD and associated P values. The possibility of publication bias was assessed using funnel plots and Egger’s regression test (549); if identified, the trim-and-fill procedure (550) was utilized.

6.3.2.3.6.2 Moderator Analyses

Moderator analyses were conducted to investigate the influence of study and patient characteristics on KYNA levels. Subgroup analyses were separately examined for: (1) nonoverlapping participant samples using the study with the largest sample size, (2) KYNA measurement technique (ie, cerebrospinal fluid (CSF), brain tissue, plasma/serum, saliva), and (3) KYNA sample source (ie, central, peripheral). Meta-regression analyses were conducted for patients’ age, the proportion of antipsychotic-medicated patients (%medicated), and the proportion of male patients (%male). When participant information was presented only for the full sample, this data was used for meta-regression analyses.

6.3.2.3.6.3 Risk of Bias

The Risk of Bias Assessment tool for Non-randomized Studies (551) was employed, using the following factors: participant selection, confounding variables, measurement of

143 exposure, blinding of outcome assessment, incomplete outcome data, and selective outcome reporting.

Significance for all tests was set at P < 0.05 (2-tailed). Continuous variables are reported as mean ± SD.

6.3.2.4 Results

6.3.2.4.1 Included Individual Studies

Thirteen studies were deemed eligible for inclusion in the meta-analysis (total number of subjects, n = 961) (429, 542, 552-562). The PRISMA flow diagram is presented in figure 6-1 and characteristics of included studies are summarized in Table 6-1. The average number of subjects was 73.9 ± 47.1 (range: 26 to 174). Average age and %male of the patient group were 37.7 ± 7.0 years and 68.0% ± 17.5%, respectively. Average age and %male of the control group were 34.2 ± 9.7 years and 64.0% ± 18.6%, respectively. Average %medicated was 69.0% ± 35.3%. Four studies measured KYNA in CSF (429, 556, 558, 562), 3 in brain tissue (557, 560, 561), 5 in plasma/serum (552, 554, 555, 559, 563), and 1 in saliva (553). Of the 13 included studies, 10 had completely nonoverlapping samples (552-555, 557-561, 563).

144 Supplementary Figure 1. Flowchart Illustrating Literature Search and Exclusion Process (PRISMA Diagram)

Records identified through database Additional records identified searching (n = 558) through other sources (n = 1)

Records after duplicates removed (n = 361)

361 records screened Records excluded (n = 313)

Full-text articles excluded with reasons (n = 35) - KYNA not measured (n = 27) - Complete participant sample overlap with included study Full-text articles assessed using a larger sample size (n = 3) for eligibility (n = 48) - KYNA production examined or missing baseline KYNA levels (n = 2)

- No control group (n = 2) - KYNA not measured in schizophrenia group (n = 1)

Studies included in quantitative synthesis (meta-analysis) (n = 13) - Cerebrospinal fluid (n = 4) - Brain tissue (n = 3) - Plasma or serum (n = 5) - Saliva (n = 1)

Figure 6-1. Flowchart illustrating literature search and exclusion process (PRISMA d iagram).

145

Table 6-1. Summary of Included Studies (n = 13).

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43 ] Mean (SD)Mean [Units], ] (0.32) [pmol/10mg tissue] (0.32) [pmol/10mg : SCZ: 2.9 SCZ: :

e 1.7 (0.77) [pmol/mg protein]; protein]; 1.7 (0.77) [pmol/mg 4

10, SCZ: 4.03 [pmol/mg protein]; HC: protein]; 4.03 [pmol/mg 10, SCZ: protein]; 2.50 [pmol/mg 9, SCZ: 9 pr 2.7 (2.2) [pmol/mg SCZ: 10: protein]; 1.1 (0.6) [pmol/mg SCZ: 19:

SCZ: 3.79 (2.03) [ng/ml]; HC: 3.28 3.79 (2.03) [ng/ml]; SCZ: [ng/ml (IQR),1.87 (1. SCZ: Median HC: 1.50 (1.14 7.40 (1.05) [nM]; HC: 6.02 (0.74) [nM] SCZ: HC: 28.7 26.5 (11.95) [nmol/L]; SCZ: [nmol/L] 2.1 (0.87) [nM]; HC: 1.6 (0.51) [nM] SCZ: 2.03 (0.92) [nM]; HC: 1.36 (0.43) [nM] SCZ: HC: 35.95 26.90 (16.38) [nmol/L]; SCZ: (9.49) [nmol/L] BA protein]; 2.2 (0.77) [pmol/mg BA HC: 6. SCZ: [ng/mL 1.719 SCZ: 1.03 1.45 (0.95) [nM]; HC: 1.06 (0.42) [nM] SCZ: BA protein] HC: 1.9 (1.3) [pmol/mg BA protein] HC: 2.0 (1.3) [pmol/mg BA protein] HC: 0.9 (0.4) [pmol/mg HC: 172.60 271.21 (22.44) [ng/ml]; SCZ: (16.46) [ng/ml] SCZ KYNA (SD) Mean [Units], HC KYNA

CSF CSF CSF CSF Saliva Serum Serum KYNA Plasma Plasma Plasma Technique Brain tissue tissue Brain tissue Brain tissue Brain Measurement Measurement

% % % % patients patients ed for ed at

% .0 .0 .0 .0 0 .0 edicated) 0 100 100 100 100 M %; 3 patients 3 patients %; 5 patients %; 3 patients %; 9 patients %; least 21 d least edicat %; 11 patients 11 patients %; least 1 mo least 4 mo least 6 mo least %; all patients patients all %; 7%; 3 7%; .0 .0 56 patients 8%; .0 unmedicated unmedicated unmedicated unmedicated . (% .0 nmedicated for at nmedicated for at nmedicated 0 80 85.3 75 70 66 82.8 37. unm u for at unmedicated u Antipsychotic Status Status Antipsychotic

%; %; %; % % %; %; %; %; %; %; %; %; %; %; %; %; % % % % % % % % % ale) .0 .0 .0 7% .0 3% .4 .0 M 66.7 62.2 66. 64.5

: 68.9 : HC: 44.0 HC: HC: 54.7 HC: 44.4 HC: 69.2 HC: 43.8 HC: HC: 72.2 HC: 57.1 HC: HC: 53. HC: 100 HC: 100 SCZ 65.2 SCZ: 65.6 SCZ: 44.0 SCZ: 57.1 SCZ: 43 SCZ: 73.3 SCZ: 76.5 SCZ: SCZ: 70 SCZ: 53.3 SCZ: Sex (% SCZ: 100 SCZ: 100 SCZ:

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25.5) 3 (12.2 .0 .0 .0 - SCZ: SCZ: .0 32.8 (10.4 ); HC: 23 ); .0 (22 HC: 26.5 (5.6) HC: 24.9 (5.8) HC: 25.4 (7.3) HC: 33.7 (6.6) HC: 27 Median (IQR), Median 41 HC: HC: 38.9 (12.9) HC: 32.6 (10.3 HC: 46.7 HC: 48.6 (10.9 HC: 49.8 (15.9) Mean Age (SD)Mean SCZ: 28.2 (4.4); 28.2 (4.4); SCZ: 37.5 (7.5); SCZ: 36.8 (7.9); SCZ: 37.3 (8.9); SCZ: 29.9 (8.5 SCZ: SCZ: 35 SCZ: SCZ: 33.4 (11.7 SCZ: 37.7 (12.4); SCZ: 33. SCZ: 50 SCZ: 43.8 (12.8); SCZ: 49.8 (15.1); SCZ: Range, Range,

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cz et al et cz ler et al et ler e Authors Fazio et al et Fazio Barry et al et Barry Myint et al et Myint Miller et al et Miller Kegel et al et Kegel Nilsson et al Nilsson et

Schwar Chiappelli et al et Chiappelli Schwi Ravikumar et al et Ravikumar Fukushima et al et Fukushima Linderholm Linderholm Sathyasaikumar et al et Sathyasaikumar

146

Note: BA, Brodmann area; HC, healthy controls; KYNA, kynurenic acid; SCZ, schizophrenia. aWhere P values were utilized to calculate SD, corresponding P values are presented in this table. bFor ease of presentation, only findings concerning group differences in KYNA levels are included in this table. cConsist of partially overlapping samples. dIncluded patients with schizoaffective disorder. eSample size presented here does not reflect total sample size, from which variables for meta- regression analyses were utilized. fIncluded patients with schizoaffective disorder and psychosis not otherwise specified.

6.3.2.4.2 Risk of Bias

Six (46.2%) of 13 studies showed a “low” risk of bias for all items. The detailed assessment is displayed in figure 6-2.

147

148 differences, and one had gender differences. Risk of bias related to “incomplete outcome data” was deemed “unclear” for two studies that did not entirely report standard deviation. Collectively, six studies (46.2%) showed a “low” risk of bias.

6.3.2.4.3 Meta-analyses

KYNA levels were moderately higher in patients with schizophrenia in comparison to HCs (SMD = 0.66, CI = 0.25 to 1.06, P = .001) (figure 6-3).

Figure 6-3. Group differences in KYNA levels between patients with schizophrenia and healthy controls. CI, confidence interval; IV, inverse variance; Std, standardized.

6.3.2.4.4 Moderator Analyses

6.3.2.4.4.1 Subgroup Analyses

6.3.2.4.4.1.1 Nonoverlapping Samples

Excluding 2 studies (429, 556) with smaller, partially overlapping samples with another study (562), KYNA levels were still moderately elevated in patients with schizophrenia compared to HCs (SMD = 0.62, CI = 0.17 to 1.07, P = .007) (figure 6-4).

149

Supplementary Figure 3. Subgroup Analysis of Non-Overlapping Samples

Figure 6-4. Subgroup analysis of nonoverlapping samples.

6.3.2.4.4.1.2 KYNA Measurement Technique

KYNA levels were moderately increased in patients with schizophrenia compared to HCs in studies using CSF (SMD = 0.66, CI = 0.42 to 0.91, P < .00001) and brain tissue samples (SMD = 0.55, CI = 0.31 to 0.79, P < .0001). KYNA levels did not differ between groups in studies using plasma/serum measurement techniques (SMD = 0.51, CI = -0.32 to 1.33, P = .23) (figure 6-5). There were insufficient studies using saliva to permit an analysis in this subgroup.

150

Supplementary Figure 4. Subgroup Analyses of KYNA Measurement Technique a. Cerebrospinal Fluid

b. Brain Tissue

c. Plasma/Serum

Figure 6-5. Subgroup analyses of KYNA measurement technique.

6.3.2.4.4.1.3 KYNA Sample Source

In the 7 studies measuring KYNA centrally, KYNA levels were moderately higher in patients with schizophrenia in comparison to HCs (SMD = 0.61, CI = 0.43 to 0.78, P < .00001). In contrast, in the 6 studies measuring KYNA peripherally, KYNA levels did not differ between groups (SMD = 0.74, CI = -0.12 to 1.59, P = .09) (figure 6-6).

151

Supplementary Figure 5. Subgroup Analyses of KYNA Sample Source a. Central

b. Peripheral

Figure 6-6. Subgroup analyses of KYNA sample source.

6.3.2.4.4.2 Meta-regression Analyses.

Meta-regression analyses showed that the higher the patients’ age, the higher (ie, more positive) the study SMD (12 studies, n = 931, slope = 0.022, 95% CI: 0.005 to 0.039, P = .012). Also, the higher the %medicated, the higher the study SMD (13 studies, n = 961, slope = 0.008, 95% CI: 0.004 to 0.013, P < .001). Lastly, the higher the patients’ %male, the higher the study SMD (13 studies, n = 961, slope = 0.012, 95% CI: 0.004 to 0.020, P = .002) (figure 6-7). Notably, excluding the study with the lowest SMD (563) led to the loss of significance for the meta-regression analyses mentioned above (all P values > .17). In contrast, excluding the study with the highest SMD (553, 559) did not alter findings (all P values < .012).

152

Supplementary Figure 6. Meta-Regression Results a. Age Regression of Age on Std diff in means 2.00 1.72 1.44 1.16 0.88 0.60 0.32 0.04 Stddiff inmeans -0.24

StandardizedMean Difference -0.52 -0.80 26.02 28.64 31.25 33.87 36.48 39.10 41.72 44.33 46.95 49.56 52.18 Age of Patient GroupAge (Years) b. %male Regression of %Male on Std diff in means 6.00 5.32 4.64 3.96 3.28 2.60 1.92 1.24 Stddiff inmeans 0.56

StandardizedMean Difference -0.12 -0.80 37.74 44.53 51.32 58.12 64.91 71.70 78.49 85.28 92.08 98.87 105.66 Proportion of Male%M aPatients l e (%) c. %medicatedRegression of %Medicated on Std diff in means 6.00 5.32 4.64 3.96 3.28 2.60 1.92 1.24 Stddiff inmeans 0.56

StandardizedMean Difference -0.12 -0.80 -10.00 2.00 14.00 26.00 38.00 50.00 62.00 74.00 86.00 98.00 110.00 Proportion of Medicated%M e d i c a t ePatients d (%)

Figure 6-7. Meta-regression results.

153

6.3.2.4.5 Sensitivity Analysis

Significant study heterogeneity existed in the main analysis (I2 = 90%). Sensitivity analyses indicated that no single study significantly contributed to heterogeneity.

6.3.2.4.6 Publication Bias

Egger’s test showed no publication bias in the analysis. The funnel plot is displayed in figure 6-8.

Supplementary Figure 7. Funnel Plot

Figure 6-8. Funnel plot.

1 154

6.3.2.5 Discussion

6.3.2.5.1 Main Findings

This is the first meta-analysis to compare KYNA levels between patients with schizophrenia and HCs. The main analysis found elevated KYNA in patients with schizophrenia. Subgroup analyses demonstrated that: (1) this group difference remained when studies with partially overlapping samples were removed, (2) KYNA was increased in patients with schizophrenia when measured in CSF and brain tissue samples, and (3) KYNA was increased in patients with schizophrenia when measured in the CNS but not in the periphery. Lastly, meta- regression analyses revealed that the higher patients’ age, %medicated, and %male, the more positive the SMDs comparing KYNA between groups. Upon removing the study with the lowest SMD, significance for these relationships was lost.

6.3.2.5.2 Analysis of Included Studies

Four included studies measured KYNA in CSF. Nilsson et al (558) found elevated KYNA levels in a mostly unmedicated sample of patients with schizophrenia compared to HCs. The other 3 studies measuring KYNA in CSF used partially overlapping participant samples, each investigating a unique primary objective. In their samples of olanzapine-treated patients with schizophrenia or schizoaffective disorder (SA), each of the 3 studies found increased KYNA levels in the patient group compared to HCs (429, 556, 562).

Three included studies measured KYNA in brain tissue samples. In a seminal study, Schwarcz et al (561) found increased KYNA in a sample of mostly medicated patients with schizophrenia within Brodmann area (BA) 9 but not 10 or 19, although a trend towards an increase was seen in the latter 2 areas. Sathyasaikumar et al (560) found increased KYNA within BA 10 but not 9 in a mostly medicated sample of patients with schizophrenia; the elevation in BA 9 approached significance. Miller et al (557) noted an increase in KYNA in samples of the anterior cingulate gyrus from mostly medicated patients with schizophrenia as compared to HCs, but the study was only powered to assess significance for a greater degree of change than that seen.

Five included studies measured KYNA in the plasma or serum. Fazio et al (554) reported

155 increased KYNA levels in a mostly medicated sample of patients with schizophrenia. Ravikumar et al (559) found elevated plasma KYNA levels in unmedicated patients with schizophrenia. Contrastingly, Myint et al (563) reported decreased plasma KYNA in antipsychotic-naïve or antipsychotic-free patients with schizophrenia. Also, Fukushima et al (555) reported no difference in serum KYNA between medicated patients with schizophrenia and HCs, and Barry et al (552) found no difference in plasma KYNA between mostly medicated patients with schizophrenia, SA, or psychosis not otherwise specified (NOS) and HCs.

Lastly, 1 included study measured KYNA in saliva. Chiappelli et al (553) reported higher mean saliva KYNA in a mostly medicated sample of patients with schizophrenia or SA compared to HCs.

6.3.2.5.3 Analysis of Meta-regression Findings

The findings from meta-regression analyses suggest that patients’ age, %medicated, and %male are positively related to study SMDs. First, with respect to age, the current meta- regression results are in line with previous studies that report a positive correlation between age and KYNA in patients with schizophrenia (558, 564). This supports the notion that increasing KYNA levels may explain cognitive deterioration with age (520, 545). Also, given that α7nAChRs may be the preferred target of endogenous KYNA (537), and have been linked to cognitive impairment, increases in KYNA with age may also explain why cognitive symptoms arise early in the course of schizophrenia (431). However, it should be noted that not all studies find an association between age and KYNA levels (429, 561). Second, in terms of antipsychotic status, these findings contrast those of previous studies suggesting that antipsychotic medication reduces brain KYNA levels (561, 565). One included study showed a trend towards decreased KYNA within brain tissue samples of treated vs untreated patients (557), although other studies have found no relationship between antipsychotic status and KYNA levels (429, 553). Finally, with respect to sex, results from the present meta-analysis contrast those of a previous study that found higher KYNA levels in female HCs than male HCs (566).

However, the removal of Myint et al (563) from the meta-regression analysis led to the loss of significance in each of the aforementioned relationships. Thus, the meta-regression results

156 may in fact hold greater implications for interpreting the findings from Myint et al (563) than those of the entire meta-analysis. It is proposed that the results of Myint et al (563), which was the only study to report decreased KYNA in the patient group, were influenced by their comparatively young, unmedicated, and mostly female patient sample.

6.3.2.5.4 Putative Mechanisms of KYNA Elevation in Schizophrenia

One explanation for elevated KYNA in schizophrenia might be a greater availability of KYN to be metabolized by KAT II to KYNA. In keeping with the notion that schizophrenia has an inflammatory component (423, 424, 567), evidence suggests that inflammatory processes activate KYN pathway enzymes in the periphery, leading to increases in peripheral KYN (430, 433, 523, 542, 568). As KYN readily crosses the blood-brain barrier, elevated peripheral KYN may contribute to elevated brain KYNA. Accordingly, elevated KYN has been detected centrally and peripherally in patients with schizophrenia (429, 555-557, 561, 562) and has been shown to correlate with brain KYNA (429, 561).

Further, studies examining KYN pathway enzyme expression and activity within brain areas highly implicated in schizophrenia pathophysiology have reported increased TDO2 and decreased KMO, with no change in IDO or KAT II (557, 560, 569, 570). An increase in TDO2 would contribute to elevated brain KYN. This would be increasingly directed towards KYNA production in the presence of KMO disturbances, as supported by genetic studies that report KMO gene alterations to be related to increased KYNA (571, 572). Likewise, preclinical studies administering a KMO blocker or genetically disrupting KMO have observed KYNA elevations (573-576).

In addition, astrocytic activation may have an important role in explaining elevated KYNA. As previously described, KAT II, the enzyme primarily responsible for converting KYN to KYNA in the brain, has been found to exist preferentially in astrocytes. Thus, astrocytic activation may increase KYNA production. In support, increases in S100B, a marker for astrocyte function, have been found in patients with schizophrenia, reflecting increased astrocytic activity (428). Moreover, administration of interleukin 6 to cultured human astrocytes has been shown to increase KYNA, consistent with the aforementioned inflammation and

157 astrocyte mechanisms (562).

Overall, peripheral inflammation, altered brain TDO2 and KMO, and astrocytic activation may provide a framework through which to understand elevated KYNA in schizophrenia.

6.3.2.5.5 Implications of KYNA Dysregulation

6.3.2.5.5.1 KYNA and Behavior

KYNA has a demonstrated capacity to affect behavior and has been posited to be especially influential in cognitive dysfunction (430, 526). In the present review, 4 included studies reported upon relationships between KYNA and behavior. Fazio et al (554) found negative correlations between KYNA levels and Positive and Negative Syndrome Scale (PANSS) positive symptom scores, and between KYNA levels and speed of processing, in subgroups of patients with multi-episode schizophrenia and first-episode schizophrenia, respectively; the authors identified no other relationships between KYNA levels and measures of symptomatology and functioning. Chiappelli et al (553) noted that patients who experienced distress intolerance had higher KYNA levels both at baseline and following a stressor paradigm than patients who tolerated the psychological stressor and HCs. Also, in patients with distress intolerance, the change in KYNA was positively related to Brief Psychiatric Rating Scale (BPRS) total scores; however, baseline KYNA levels were not related to BPRS total scores. In addition, neither baseline KYNA nor change in KYNA levels were correlated with processing speed or working memory in patients or HCs. Linderholm et al (429) noted no relationship between KYNA levels and BPRS and the Global Assessment of Functioning scores. Finally, Myint et al (563) found that initial plasma KYNA levels were associated with a greater reduction in PANSS positive symptom scores as well as Korean Version of the Calgary Depression Scale for Schizophrenia depressive symptom scores after 6 weeks of antipsychotic treatment, though no cross-sectional relationships existed.

Beyond the included studies, other investigations in humans have provided evidence for associations between KYNA and behavior in patients with schizophrenia. Wonodi et al (570) found that a single-nucleotide polymorphism in the KMO gene (the rate-limiting enzyme of

158

KYN breakdown) was related to impaired smooth pursuit eye movement and visuospatial working memory in a clinical sample. Similarly, Wonodi et al (577) found an association between variations in the KMO gene and deficits in cognitive function, an effect that was more marked in patients with schizophrenia than in HCs.

While human studies provide some evidence for the role of KYNA in modulating schizophrenia-like behavior, stronger support arises from preclinical work. In animal studies, KYNA levels can be raised through focal application of KYNA, administration of KYN, genomic KMO elimination, or KMO blockade (525, 526). These manipulations cause cognitive impairments similar to those observed in patients with schizophrenia, including deficits in prepulse inhibition (578, 579), auditory sensory-gating (580), stimulus processing and conditioned responding (581), spatial working memory (582), contextual fear conditioning and context discrimination (583), spatial learning and memory (584-586), and cognitive flexibility (587-589). In addition, KYNA increases have been shown to enhance spontaneous and amphetamine-induced locomotor activity (590).

Conversely, experimentally induced reductions in KYNA by genetic deletion or acute inhibition of KAT II have led to improved cognitive functioning. Improvements have been noted in contextual memory and spatial discrimination (591), spatial learning and memory (586), sustained attention, amphetamine- and ketamine-induced disruptions in auditory gating, and ketamine-induced deficits in working memory and spatial memory (592).

In summary, while human literature on the topic is emergent, preclinical studies provide evidence to suggest that increased KYNA levels may account for certain schizophrenia-like behaviors, specifically those observed within cognitive and social domains.

6.3.2.5.5.2 KYNA and Neurotransmission

Of the studies included in the current review, only one measured indices of neurotransmission. Among other neurometabolites, Fukushima et al (555) found decreased plasma serotonin and increased glutamate in the schizophrenia group, although relationships with KYNA were not reported. Moreover, another human study reported positive correlations between CSF KYNA and CSF homovanillic acid and 5-hydroxy-indoleacetic acid, indicative of

159 dopamine and serotonin turnover, respectively (593).

Unlike currently available human studies, preclinical literature has provided ample evidence to suggest that KYNA has inverse bi-directional relationships with several neurotransmitters, including glutamate, dopamine, acetylcholine, and GABA. Studies have demonstrated that increasing KYNA results in decreased glutamate (585, 586, 588, 594-598); notably galantamine administration normalizes this effect (588, 596, 598). Accordingly, decreasing KYNA leads to increased glutamate (586, 591, 594, 596, 598).

Similarly, studies investigating dopaminergic neurotransmission have found that increasing KYNA results in decreased dopamine levels (574, 599, 600) – an effect that can also be attenuated by galantamine (599, 600) – whereas decreasing KYNA increases dopamine levels (600, 601). Furthermore, KYNA’s influence on midbrain dopamine neurons has been thoroughly studied; reliably, increased KYNA leads to increased firing rate and burst firing activity (579, 602-605), whereas decreased KYNA has an opposite effect (605, 606). These effects are believed to result from KYNA’s blockade of glutamate receptors (602, 604, 605). Moreover, the influence of KYNA on the dopamine system has been explored through the assessment of its effect on amphetamine-induced responses. Akin to NMDAR antagonists, KYNA has an amplifying effect on amphetamine-induced dopamine release through a mechanism involving reduced inhibition by amphetamine on firing rate and burst activity of ventral tegmental area dopamine neurons (607, 608). Finally, it deserves mention that KYNA modulates the effects of clozapine – an atypical antipsychotic with particular efficacy in patients with treatment-resistant schizophrenia – and nicotine on midbrain dopamine neurons (524, 606, 609).

Further, an inverse relationship between KYNA and acetylcholine is observed, with a decrease in KYNA leading to increased acetylcholine levels (610). Lastly, a bi-directional relationship between KYNA and GABA has been noted. An increase in KYNA results in decreased GABA – an effect prevented by galantamine – while a decrease in KYNA increases GABA (594, 611).

In summary, preclinical literature supports KYNA’s inverse effects on glutamate, dopamine, acetylcholine, and GABA, through its antagonism of α7nAChRs. In contrast, KYNA’s influence on midbrain dopamine neurons’ firing rate and burst firing activity, and amphetamine-induced responses, is likely related to its antagonism of glutamate receptors. These

160 inverse associations remain unclear in schizophrenia, as hallmark neurotransmitter disruptions such as elevated striatal dopamine synthesis and release (218, 377), and increased subcortical glutamate (161), seem incongruent with observed elevations in KYNA levels; heterogeneous brain KYNA distribution might explain this discrepancy.

6.3.2.5.5.3 KYNA and Drugs

Several pharmacological agents can be utilized to manipulate KYNA levels. As per the above evidence, treatments that are intended to benefit patients with schizophrenia might aim to reduce KYNA levels. Given that there are no known KYNA degradation enzymes or specific targetable reuptake sites, the optimal method to lower KYNA appears to be via KAT II inhibition (525). KAT II has been shown to be highly substrate-specific, further making it an attractive target (525). Preclinical studies have shown that agents inhibiting KAT II lower KYNA levels by approximately 30% to 40% (526, 586, 592, 598, 610, 612-614). Additionally, preclinical work suggests that these agents are procognitive (586, 592, 614) and increase neurotransmitter levels described to be influenced by KYNA above (586, 594, 596, 598, 601, 610, 611).

Pharmacological treatments might also attempt to counter KYNA’s mechanism of action. Preclinical findings presented above suggest that agonism of α7nAChRs or NMDARs might mitigate schizophrenia-like behavior and/or neurotransmission derangements. However, studies examining such agents in patients with schizophrenia have shown minimal efficacy to-date (547, 615-617).

Other possible targets to reduce KYNA are peripheral IDO and TDO2. Decreasing their activity might attenuate overproduction of peripheral KYN, thereby preventing increases in brain KYN and ultimately, brain KYNA. While IDO and TDO2 inhibitors have been studied as possible cancer treatments, their use in psychiatric diseases has been limited (430). IDO and TDO2 have important physiological functions, including immunomodulation and NAD production, respectively, and their inhibition can cause significant adverse effects (618-621).

Nonselective inhibitors of cyclooxygenase (COX)-1 and COX-2 have also been found to influence KYNA levels: the former elevating KYNA and the latter decreasing it (622). COX-2 inhibitors have also been suggested to rebalance a disrupted immune response (431, 432) and

161 have demonstrated beneficial effects for patients with schizophrenia (568, 623); however, the latter notion may be influenced by publication bias (624).

Another strategy is activation of central KMO and its downstream enzymes along the quinolinic acid (QUIN)-producing branch, which may be decreased in schizophrenia (560). Doing so may shift brain KYN degradation towards QUIN and away from KYNA production. However, this would increase production of potentially harmful neurotoxins (520, 521, 523).

Finally, nonspecific reduction of KYNA through TRP depletion has been attempted. Thus far, it has produced mixed results with respect to symptoms in patients with schizophrenia (625-627).

6.3.2.5.6 Limitations of Present Study

The present work should be considered in light of its limitations. First, the primary aim may have been too narrow in that other KYN pathway metabolites were not evaluated. Second, some studies included patients with SA and psychosis NOS, which may have alternate pathophysiologies. Third, since some included studies did not report upon certain variables, such as duration of illness, antipsychotic dose, and symptom severity, and multiple measurement scales were utilized for the latter, the present study was unable to include these variables in meta- regression analyses. Fourth, some included studies did not account for the influence of food, smoking, or drug use. Fifth, compared to other major meta-analyses, our sample size was small. This may be especially relevant for the interpretation of subgroup analyses, as accumulating evidence may also reveal disruptions in peripheral KYNA levels. Finally, the possibility of publication bias should not be discounted.

6.3.2.5.7 Conclusion

The present meta-analysis found increased KYNA levels in patients with schizophrenia, a phenomenon that appears to be localized to the CNS. While age, antipsychotic status, and sex may have modulating effects, elevated central KYNA might help to explain disruptions in behavior and neurotransmission in patients with schizophrenia, thereby providing further clarity

162 towards the understanding of schizophrenia pathophysiology and contributing to the development of novel potential treatment targets.

6.3.2.5.8 Future Directions

Future clinical studies should aim to replicate preclinical findings by testing the relationships between KYNA levels and measures of behavior and neurotransmission. The former can be achieved by utilizing well-characterized symptom (eg, PANSS (408)) and neuropsychological (eg, MATRICS (628)) batteries while the latter can be studied using in vivo brain imaging. Moreover, the regional distribution of KYNA levels should be explored in these relationships. Specifically, the investigation of possible relationships among KYNA levels, elevated striatal presynaptic dopamine synthesis, and increased subcortical glutamatergic neurometabolites early in the illness is warranted. Furthermore, measuring KYNA longitudinally over the course of illness would help define its role in schizophrenia pathophysiology. Additionally, future investigations should clarify the relationship between CSF, brain, plasma, and saliva KYNA levels, while ensuring that methodological issues such as fasting status are accounted for. This may elucidate whether studies in patients with schizophrenia should employ a particular KYNA sampling method. It may also be beneficial for future work to concurrently measure KYNA with other KYN pathway components to further examine pathway dysregulation. Overall, a better understanding of the cause and consequences of elevated KYNA in patients with schizophrenia may lead to the development of improved diagnostic and therapeutic strategies.

6.4 Neurometabolite Modulation

6.4.1 Adjunctive Treatment of Patients with Schizophrenia Using Glutamatergic Agents

In light of the above findings concerning disruptions in glutamatergic neurometabolites, one might consider whether treatment with glutamatergic agents or modulation of glutamatergic activity might be beneficial for patients with schizophrenia. Such agents include those that act upon the glycine allosteric site of NMDA receptors (e.g. D-glycine, D-alanine, D-serine, D-

163 cycloserine, the latter of which is a partial agonist), those that modulate α-amino-3-hydroxy-5- methyl-4-isoxazolepropionic acid (AMPA) receptors (i.e. ampakines), and those that inhibit the glycine transporter (e.g. sarcosine, bitopertin) (629). Given the plethora of data available on this topic within the literature, only findings from meta-analyses will be discussed in this section.

Meta-analyses from 2005 and 2006 conducted by the same group assessed the efficacy of glutamatergic drugs in the treatment of schizophrenia. The authors concluded that all glutamatergic agents appeared to be ineffective in further ameliorating positive symptoms when used as adjunctive treatments to antipsychotic therapies. However, their findings suggested that adjunctive administration of glycine and D-serine, two NMDA receptor co-agonists, may be beneficial in the treatment of negative symptoms (630, 631).

These findings are also in line with more recent meta-analyses examining glutamatergic medications in the treatment of patients with schizophrenia. In 2015, Fusar-Poli et al reported that adjunctive glutamatergic medications have greater efficacy for the treatment of negative symptoms than placebo; however, such interventions were not found to be clinically significant (528). In 2013, Choi et al found that glutamate receptor agonists (i.e. D-serine, D-cycloserine, CX516) had a small positive impact on general symptoms and a moderate positive impact on negative symptoms (615). Further, meta-analyses in 2010 and 2011 indicated that adjunctive NMDA receptor modulators (i.e. D-alanine, D-cycloserine, D-serine, glycine, sarcosine, CX516, memantine, and N-acetyl-cysteine) might have therapeutic benefits for a wide range of symptoms, although predominantly for negative and total symptoms (632, 633). Notably, recent meta-analyses on memantine, a NMDA receptor antagonist, find adjunctive memantine to outperform placebo in terms of alleviating negative symptoms and improving neurocognitive performance (634, 635). Further, a meta-analysis examining N-acetylcysteine, a precursor of glutathione (GSH) that may enhance NMDA receptor signalling through its redox site, reported improved total psychopathology in schizophrenia when administered as an adjunct to antipsychotics compared to placebo (636). A recent meta-analysis by our group assessed the influence of glutamate positive modulators on cognitive deficits in patients with schizophrenia. This meta-analysis showed that glutamate positive modulators were not superior to placebo in terms of overall cognitive function or any individual cognitive domain (547).

164

Other compounds that have been found to modulate glutamatergic signalling have also been investigated for the treatment of schizophrenia. Minocycline, a tetracycline antibiotic that has broad-spectrum antimicrobial activity, has been suggested to increase GluR1 subunit phosphorylation and AMPA receptor potentiation (7, 637). Two recent meta-analyses showed that adjunctive minocycline was superior to placebo in the treatment of negative, general, and total symptoms (638, 639).

Other compounds that may modulate glutamatergic activity are less broadly studied. For example, L-carnosine, a dipeptide that co-localizes with glutamate (640-643), and pregnenolone, a neurosteroid that acts as a precursor to pregnenolone sulphate, may positively modulate NMDA receptors (644, 645). To date, these compounds have been included within one meta- analysis, which investigated the effects of these agents on cognition (547).

Additionally, an area of ongoing development within the field has been treatment of patients with schizophrenia by administration of metabotropic glutamate receptor (mGluR) modulators. The three groups of mGluRs have been explored in the hopes of identifying potential treatment targets for schizophrenia, which has generated a substantial body of literature – dominated by preclinical investigations – implicating these receptors in the pathophysiology of the illness (646-653). To date, the most promise has been within the study of mGluR2/3 agonists and selective mGluR2 positive allosteric modulators, both of which were investigated in phase II clinical trials (646-653). However, while preclinical evidence is supportive of the efficacy of these treatments in improving schizophrenia symptomatology, this has yet to translate to reproducible findings within clinical trials (646-653).

Overall, treatments involving modulation of the glutamatergic system are promising. However, it deserves mention that nearly all are predominantly investigated as adjunctive treatments, highlighting the distance between the state of the literature surrounding these potential therapies and that of current antipsychotic medications, which remain the standard of care for patients with schizophrenia.

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6.4.2 Adjunctive Treatment of Patients with Schizophrenia Using Non- Glutamatergic Agents Relevant to the Current Work

Several other adjunctive treatments for schizophrenia have received support within the existing literature. Of relevance to the present thesis, given that neuroinflammation is posited to be involved in the pathophysiology of schizophrenia, anti-inflammatory agents may contribute to the clinical picture. A meta-analysis investigating several anti-inflammatory agents (i.e. aspirin, celecoxib, davunetide, fatty acids, estrogens, minocycline, N-acetylcysteine), some of which are dually classified as modulators of glutamatergic activity above (i.e. minocycline, N- acetylcysteine), reported aspirin, estrogens, and N-acetylcysteine to have beneficial effects on symptom scores (426). Another meta-analysis similarly reported that adjunctive nonsteroidal anti-inflammatory drugs might have superiority over placebo for positive symptoms (425). Recently, another meta-analysis focusing solely on celecoxib, a non-competitive anti- inflammatory drug, found that adjunctive celecoxib outperformed placebo for total psychopathology, positive symptoms, negative symptoms, and general psychopathology, especially in FEP (654). Likewise, statins may similarly have anti-neuroinflammatory effects (655, 656); a recent meta-analysis examining the adjunctive effect of statins in the treatment of patients with schizophrenia found reductions in positive and negative symptoms compared to placebo (657). In Chapter 3, mI levels, which serve as a putative marker of glial activity, were positively associated with positive symptomatology scores. In light of this, one might speculate that anti-inflammatory agents may have an effect on glial activity, which might have resultant influence on glutamatergic tone or symptomatology.

Furthermore, as described above, some pharmacological agents targeting the manipulation of KYNA or its mechanism of action might have beneficial effects for patients with schizophrenia (658). However, it is noteworthy that this may occur through indirect modulation of glutamatergic activity, as KYNA functions as an endogenous NMDA receptor antagonist (535, 536).

6.4.3 Neurostimulation

In addition to pharmacological agents, it is worthwhile to discuss other means by which modulation of glutamatergic neurometabolites might occur. In light of the results presented

166 within this thesis, it is possible that the alteration of dysregulated neurometabolite levels may have therapeutic potential. Additionally, the findings from the present work may help to elucidate the mechanisms of action for certain therapies that have evidence of benefit in the treatment of patients with schizophrenia. One such example is transcranial direct current stimulation (tDCS).

tDCS is a non-invasive method of modifying brain function that is being utilized increasingly for the treatment of neuropsychiatric disorders (659-661). In this technique, a current is applied between an anodal and cathodal electrode positioned on the scalp (661, 662). Amongst other phenomena, it is often posited that an increase in cortical excitability occurs beneath the anode and a decrease occurs beneath the cathode (662-665). Several previous studies have demonstrated the safety of tDCS (660, 661, 666) and recent reports have found the technique to have a beneficial impact in patients with schizophrenia (659, 660, 662, 667-670). However, the mechanism through which tDCS exerts its therapeutic effects in patients with schizophrenia remains elusive. Notably, several lines of evidence have proposed that tDCS might exert its influence through the glutamatergic system (671, 672).

Previous single session tDCS studies examining healthy controls have reported neurometabolite level changes using 1H-MRS following stimulation, albeit methodologies and results vary. One study examined the online effects of tDCS by measuring neurometabolite levels during anodal left dorsolateral prefrontal cortex (DLPFC) and cathodal right DLPFC stimulation (475). The authors found increased striatal Glx and left DLPFC NAA; however, DLPFC changes normalized after cessation of stimulation (475).

Moreover, studies exploring the offline effects of tDCS have found that anodal stimulation to the right parietal cortex increases Glx levels beneath the electrode (673, 674). Meanwhile, cathodal stimulation to the left M1 has been observed to decrease both glutamate and Glx levels beneath the electrode (675). Further, anodal stimulation to the right parietal cortex and right frontal lobe has been found to increase levels of NAA and mI, respectively (673, 676). Also, reductions in GABA levels have been reported following both anodal and cathodal stimulation to the left M1 (675, 677, 678), anodal stimulation to the right temporal cortex (679), and anodal stimulation to the left sensorimotor region (680). While the referenced studies have reported positive findings with respect to changes in various neurometabolite levels as described

167 above, it should be noted that several of these reports concomitantly describe negative findings for other neurometabolites, with limited homogeneity of findings within this field (475, 673, 675, 676, 678-680). Similarly, other studies have also reported no change in neurometabolite levels following treatment with tDCS (681, 682). Thus, the effects of a single tDCS session on neurometabolite levels remain elusive.

Importantly, the majority of studies in which an improvement in clinical symptomatology is noted amongst patients with schizophrenia use a study design involving multiple repetitive and consecutive tDCS sessions (659, 663, 667). Often, benefits in positive symptoms (predominantly hallucinations) and/or negative symptoms are observed, and in several studies, improvements are sustained (659, 663, 667). Notably, the results of three recent meta-analyses provide support for the beneficial effects of this technique on negative symptoms (668-670).

To the best of our knowledge, only one study to date has investigated neurometabolite level changes using 1H-MRS following multiple tDCS sessions. In female patients with fibromyalgia, following 5 days of active tDCS compared to 5 days of sham tDCS, Glx levels were lower in the anterior cingulate and at a trend-level in the thalamus (683). In this study, the anode and cathode were placed over the left motor cortex and the contralateral supraorbital cortex, respectively (683). Additionally, compared to baseline, a trend-level increase in GABA levels was observed within the anterior insula following 5 days of active tDCS, and an increase in NAA levels was observed within the posterior insula following 5 days of sham tDCS (683).

Repetitive transcranial magnetic stimulation (rTMS) is another non-invasive neuromodulation technique. rTMS functions by inducing an electric current in underlying brain tissue through the use of alternating magnetic fields (684, 685). In patients with schizophrenia, recent meta-analyses have observed a beneficial effect of rTMS administration on negative symptoms (668-670, 686, 687). Unlike the evidence base surrounding tDCS, one study has performed 1H-MRS in patients with schizophrenia pre- and post-rTMS in active bilateral prefrontal rTMS and sham rTMS groups (688). The authors found increased DLPFC Glx in the active treatment group and decreased Glx in the same brain region within the sham group. Of note, no changes in NAA were found.

Lastly, electroconvulsive therapy (ECT) is an effective neurostimulation technique in which electrical current is applied to specific brain regions to evoke a therapeutic grand mal

168 seizure (689-691). Akin to rTMS, the effects of ECT have been investigated using 1H-MRS in patients with schizophrenia. A recent study found that modified ECT reversed deficits in NAA levels within the left prefrontal cortex in the patient population, paralleling the effects of atypical antipsychotic treatment (692). In fact, NAA levels were higher after treatment in the group of patients who received modified ECT than healthy controls, in contrast to the group that received atypical antipsychotic treatment (692). Of note, no alterations in Cho levels were found and glutamatergic neurometabolite levels were not reported (692). However, other longitudinal studies in patients with major depressive disorder have observed reversal of reduced levels of glutamatergic neurometabolites following ECT (693).

Overall, despite its promise, the effects of neurostimulation on patients with schizophrenia require further study. Although preliminary, findings to date suggest an improvement in symptomatology with neurostimulation, potentially through modulation of the glutamatergic system. However, it is noteworthy that substantial heterogeneity exists across studies, possibly contributing to certain observed inconsistencies within the field. Additionally, at the present moment, all interventions are being tested as adjunctive treatments; despite their limited impact on negative and cognitive symptoms, antipsychotics remain the mainstay therapy for patients with schizophrenia.

6.5 Glutamate and Other 1H-MRS Neurometabolites from a State and Trait Perspective

An interesting topic of discussion within the literature relates to the state and trait features of certain 1H-MRS neurometabolites, such as glutamate, within schizophrenia. Glutamate may be considered a state marker of the illness in accordance with findings from studies that longitudinally observe the normalization of elevated levels of glutamate following clinically effective treatment to levels that resemble those of healthy controls. For example, de la Fuente- Sandoval et al found elevated associative striatum glutamate and Glx levels in a sample of antipsychotic-naïve patients experiencing their FEP (253). Following 4 weeks of clinically effective antipsychotic treatment, defined as a reduction of at least 30% on patients’ total PANSS scores, glutamate and Glx levels were similar between the patient group and healthy controls (253). These findings are directly comparable to those within the current thesis, which present

169 cross-sectional support for this phenomenon. If we are to assume that glutamatergic neurometabolites are altered by effective treatment, while acknowledging that the definition of effective treatment itself remains a point of debate, then it may be reasonable to infer that such indices are state markers of schizophrenia. Similarly, if levels of glutamatergic neurometabolites were to be closely related to symptom severity scores, they might be categorized as state markers of schizophrenia. While the existing literature is largely inconsistent in this respect, some evidence has provided support for relationships between levels of glutamatergic neurometabolites and symptomatology (131, 161, 176, 480, 694).

Simultaneously, there is some evidence suggesting that glutamate is a trait marker. In support of this notion, glutamatergic disturbances have been found in relatives of patients with schizophrenia (250, 311, 383, 388, 412). Also, increased levels of glutamatergic neurometabolites have been found in patients with schizophrenia who have undergone antipsychotic treatment, suggesting that elevations may persist in medicated patients and might thus be a trait marker of the illness (158, 176). Additionally, recent literature posits that elevated glutamate levels might be a stable neurobiological trait of treatment resistance (464). As noted above, two studies have found elevated glutamate levels in the anterior cingulate of TRS patients: one study reported increased glutamate levels in the TRS group compared to healthy controls, along with no identifiable group differences between treatment responders and healthy controls (258), while another study reported increased glutamate levels in patients with TRS compared to treatment-responsive patients (464). Similarly, another study observed elevated anterior cingulate glutamate levels in antipsychotic-treated patients with FEP who remained symptomatic compared to those who achieved remission (259). Likewise, a longitudinal study found elevated baseline Glx levels within the frontal lobe in antipsychotic non-responders compared to responders (695). As a whole, these findings can be interpreted to reflect a stable neurobiological trait in patients with antipsychotic treatment resistance (464).

Moreover, preliminary data from our group provides further support for this notion; in a sample composed of treatment-resistant patients with schizophrenia who are clozapine-resistant, treatment-resistant patients with schizophrenia who are clozapine-responsive, patients with schizophrenia who respond to a first-line antipsychotic, and healthy controls, we identified higher anterior cingulate cortex Glx levels in the clozapine-resistant group compared to healthy controls. Notably, when treatment-resistant patients with schizophrenia (i.e. clozapine-resistant

170 patients, clozapine-responsive patients) were combined into one group, this subset of patients had elevated anterior cingulate cortex glutamate and Glx levels compared with healthy controls. Of note, no group differences were identified involving the first-line antipsychotic responder group. In contextualizing findings from the present thesis and preliminary work from our group within the existing literature, it is plausible that glutamatergic disturbances in patients with schizophrenia might exist on a continuum, wherein levels are most elevated in patients who are resistant to all antipsychotics, including clozapine, followed by those who are resistant to first- line antipsychotics but will respond to clozapine, followed by those who will respond to first-line antipsychotics.

A recent study that deserves mention is a multicentre 1H-MRS study that investigated glutamate levels in the anterior cingulate cortex and thalamus of antipsychotic-naïve or minimally medicated patients with FEP at three different sites (696). In between 1H-MRS scans, patients underwent treatment with amisulpride for 4 weeks. Interestingly, the authors demonstrated that higher baseline anterior cingulate cortex glutamate levels were associated with more severe symptoms and a lower likelihood of being in remission at 4 weeks. However, the authors concomitantly showed longitudinal reductions in both anterior cingulate cortex and thalamus glutamate levels over the treatment period, which were not related to therapeutic response. Overall, the results of this study seem to suggest that glutamate may hold roles as both a state and a trait marker.

In light of the above, additional longitudinal work will be useful to further elucidate the state and trait characteristics of glutamatergic disturbances as markers within schizophrenia. It is presently unclear whether the group differences described above are driven by factors such as symptom severity and treatment response, or whether they represent distinct neurobiological variations within schizophrenia that may predict how an individual will respond to a particular treatment or even separate subtypes of the illness altogether. Caution is warranted in this discussion given the heterogeneity that exists among 1H-MRS studies in terms of voxel placement, 1H-MRS parameters, and other methodological factors. Importantly, patterns of 1H- MRS results may differ across brain regions; notably, the findings within patients with TRS providing the basis for the role of glutamate as a trait marker above are within the anterior cingulate cortex.

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Aside from glutamatergic neurometabolites, other 1H-MRS neurometabolites may be of interest in this discussion. For example, basal ganglia Cho levels might prove to be a trait marker of schizophrenia. In antipsychotic-naïve patients experiencing their FEP as well as patients with schizophrenia who have undergone long-term antipsychotic treatment, reports have provided support for elevated Cho levels within the striatum, caudate nucleus, and basal ganglia (413-415, 446, 697). While Cho differences were not identified in the latest meta-analysis (147), recent findings provide evidence for increased Cho within the striatum (446, 697, 698). Overall, among several other points of controversy within the literature, Cho elevations within the basal ganglia appear to be a promising area of investigation going forward.

6.6 Implications of Neurometabolic and Neuroanatomical Disturbances

Despite the vast neurochemical abnormalities and neuroanatomical compromise that are often reported in patients with schizophrenia, the implications – clinical and non-clinical – of these alterations in patients with schizophrenia are largely still unclear. From a neurochemical perspective, beyond striatal dopamine disturbances and their well-established relationship with positive symptoms, the implications of other neurometabolic disturbances remain mostly elusive to researchers and clinicians within the field.

Considering glutamatergic disturbances, the results from Chapter 4 suggest a meaningful implication of elevated glutamatergic neurometabolites: excitotoxicity. Now shown in multiple clinical samples (441, 699) within brain regions that are posited to be highly implicated in the pathophysiology of schizophrenia (e.g. hippocampus, striatum), it appears reasonable to purport that elevated glutamatergic neurometabolites contribute at least in part to the widely observed, and mostly unexplained, structural deficits that exist in patients with schizophrenia. While such findings certainly contribute a better understanding of schizophrenia pathophysiology, their impact is limited by an apparent lack of association with clinical implications in the present state of the literature.

Notably, evidence suggesting progressive structural changes in the illness is highlighted by rigorous meta-analyses of longitudinal studies (700-702). Similarly, progressive cortical

172 thinning has already been reported (206). It deserves mention that abundant support within the literature implicates a variety of factors in the progressive brain alterations that exist in patients with schizophrenia. An important factor is antipsychotic medication. Strong evidence has linked greater antipsychotic exposure, assessed by both cumulative antipsychotic intake and dose at the time of scan, with cortical losses, and interestingly, class of antipsychotic may influence the degree of deficits (192, 206, 700, 702, 703). Additional factors that necessitate consideration in this patient population are marijuana and alcohol; studies in patients with schizophrenia have found a link between use of these substances and structural compromise (704-707). That being said, it is clear that there are several factors, including antipsychotics, cannabis, and alcohol, that play a role in the progressive structural alterations that are observed in patients with schizophrenia. However, an interesting caveat is that meta-analyses identify volumetric loss within the caudate that is more marked in unmedicated and FEP patients than in medicated patients with chronic schizophrenia (192, 275). To that effect, the literature is suggestive that antipsychotics may have an effect on increasing basal ganglia volumes in this patient population, although additional investigation is required (192, 703, 708). Thus, given that the influence of antipsychotics may instead increase caudate volume, and that the patients within Chapter 4 were antipsychotic-naïve, we suggest that glutamate-mediated excitotoxicity may contribute to the volumetric deficits observed in this area. That being said, it is noteworthy that many of the factors described above play a role, especially in the copious cortical deficits reported upon within the literature.

Furthermore, despite several reports of group differences in levels of glutamatergic neurometabolites between patients with schizophrenia and healthy controls, only a portion of studies identify associations between levels of glutamatergic neurometabolites and symptom scores and, among those that do, findings are inconsistent (131, 161, 480, 694). A recent systematic review examined longitudinal changes in levels of glutamatergic neurometabolites and found that reported relationships between the degree of change in glutamate and the degree of clinical improvement are inconsistent (480). However, the authors concluded that, although limited, there is evidence that antipsychotic treatment may lead to a reduction in glutamatergic neurometabolite levels, and that antipsychotic response may be related to lower glutamate levels prior to the initiation of treatment as well as a larger degree of glutamatergic reduction during treatment (480). Within the review, some studies did observe relationships between levels of

173 glutamatergic neurometabolites and symptomatology (253, 308, 695, 709), while others did not (254, 710-712).

Moreover, even more heterogeneity exists in the literature surrounding non-glutamatergic neurometabolites, such as mI, Cho, and Cr, and the clinical implications of their dysregulation in patients with schizophrenia. It is plausible that neurometabolic disturbances, as assessed by 1H- MRS, may exist upstream of disturbances in dopaminergic signalling, which have been posited to be the final common pathway for schizophrenia (527). However, this explanation may potentially be less relevant for relationships with negative and cognitive symptoms, which are not readily explained by the dopamine hypothesis of schizophrenia (32, 33, 219). Evidently, there are several factors influencing the heterogeneity of findings with respect to relationships between neurometabolite levels and symptom scores, not least of which are disparate 1H-MRS methodologies and heterogeneity within the patient population.

Patterns also emerge when reviewing the literature with respect to neuroanatomical findings in patients with schizophrenia. In this patient population, a recent ENIGMA consortium meta-analysis reported smaller total intracranial, hippocampus, amygdala, thalamus, and accumbens volumes, as well as larger pallidum and lateral ventricle volumes (184). The group also recently reported widespread WM microstructural changes in patients with schizophrenia (713). However, unlike 1H-MRS results, structural studies have suggested specific neuroanatomical correlates related to symptomatology. Recently, the ENIGMA consortium reported meta-analytical findings showing that greater positive symptom severity was related to superior temporal gyrus thinning in both hemispheres (204) and that greater negative symptom severity was related to left medial orbitofrontal cortex thinning (205). While PET studies have clearly linked dopamine levels to symptomatology in patients with schizophrenia, they have not examined how these abnormal dopamine levels are related to the aforementioned changes in structural brain morphology observed in the disease (714). Although a challenge for 1H-MRS researchers over the next decade will be to similarly identify reliable relationships with symptomatology, a challenge for those investigating structural compromise will be to translate current neuroanatomical findings to the clinical arena.

Within Chapters 3, 4, and 5 of this thesis, the only identified relationship with symptom scores existed between mI levels and grandiosity within a sample of antipsychotic-naïve patients

174 with FEP; associations between mI levels and hallucinatory behaviour and total positive symptomatology scores were identified at a trend-level significance within the same sample. Importantly, no relationships were found between glutamatergic neurometabolites and symptom scores in either antipsychotic-naïve patients with FEP or patients with schizophrenia undergoing long-term antipsychotic treatment. Similarly, no relationships were found between symptom scores and cortical thickness or PCV in the sample of antipsychotic-naïve patients with FEP, keeping in mind that the cortical thickness analysis employed a rigorous statistical correction. It should be noted that neuroanatomical changes in patients with schizophrenia may also be upstream of several consequent neurochemical alterations that may be the final drivers of symptomatology, and thus, it may be difficult to routinely identify subtle relationships between neuroanatomical changes and symptomatology.

6.7 Other Noteworthy Neurometabolites to Consider

In addition to the neurometabolites discussed within Chapters 3, 4, and 5, levels of other neurometabolites can be measured in patients with schizophrenia with 1H-MRS by using certain editing sequences, such as the MEGA-PRESS sequence. One such example is GABA, which is the main inhibitory neurotransmitter in the brain (715). Support from both preclinical and postmortem findings suggests that dysfunction of the GABAergic system is important in the pathophysiology of schizophrenia (716). That being said, a recent meta-analysis by Egerton et al replicated the finding of a previous meta-analysis, failing to find significant group differences in GABA levels, as measured by 1H-MRS, across the medial prefrontal cortex, parietal/occipital cortex, and striatum, while noting substantial heterogeneity across studies (717, 718).

Another neurometabolite of interest that may be measured using 1H-MRS is GSH, which primarily serves as an antioxidant and has a modulating effect on glutamate receptors (161). A recent study using 1H-MRS at 7T reported reductions in anterior cingulate cortex GSH levels in patients with schizophrenia compared to healthy controls (719), contrasting another 7T study that failed to find differences in anterior cingulate cortex GSH levels between patients with schizophrenia and healthy controls (720). Further, two previous studies using 1H-MRS at 3T found that medial temporal GSH levels were higher in patients with FEP compared to healthy

175 controls (721) and that posterior medial frontal cortex GSH levels were negatively correlated with negative symptoms (722).

N-acetylaspartylglutamate (NAAG) concentrations can also be assessed using 1H-MRS. NAAG is a neuropeptide that is thought to be an agonist of mGluR3 receptors and an antagonist of the NMDA receptor (148). Two studies have used 1H-MRS to measure NAAG in patients with schizophrenia. One study found an elevated NAAG/NAA ratio within the anterior cingulate cortex of patients with schizophrenia, while NAAG was elevated at a trend-level significance; a negative correlation was identified between frontal lobe NAAG and negative symptoms (723). Another study reported no group differences in levels of NAAG within the anterior cingulate cortex, but did observe elevated NAAG levels within the centrum semiovale of younger patients with schizophrenia compared to younger healthy controls, and decreased NAAG levels in older patients with schizophrenia compared to older healthy controls (174). A positive correlation was also identified between levels of NAAG within the centrum semiovale and negative symptom scores (174).

Lastly, an authoritative review discussed the use of 1H-MRS to measure glycine and serine, which both function to activate NMDA receptors at the glycine-binding site and may play a role in the pathophysiology of schizophrenia (148). While no studies to date have used 1H- MRS to measure these compounds, doing so in the future may contribute to a better understanding of the illness.

6.8 Broad Limitations

There are several limitations of the current work that should be considered during its interpretation and broader contextualization within the field. First, different 1H-MRS voxel sizes were utilized among the presented studies. Chapters 3 and 4 employed an 8mL 1H-MRS voxel, whereas Chapter 5 utilized a 9.4mL 1H-MRS voxel. Second, different nomenclature was employed to describe the 1H-MRS voxel placement across the studies (i.e. associative striatum, PDC, and striatum in Chapters 3, 4, and 5, respectively). In Chapter 3, the term associative striatum was utilized to best describe the contents of the 1H-MRS voxel. In Chapter 4, the term PDC was used to maintain consistency throughout the study. In Chapter 5, the term striatum was

176 employed to account for the larger voxel. Third, across all three studies, structures not captured within the employed nomenclature – for example, components of the internal capsule – were included within the 1H-MRS voxel. Fourth, another broad limitation of the current work is the difficulty in distinguishing the glutamate and glutamine peak using a TE of 35ms at 3T. As a result, the glutamate peak, which is a focus of the present thesis, may be influenced by glutamine. To account for this limitation, all investigations were performed for both glutamate and Glx, a summed measure of glutamate and glutamine. Fifth, all of the studies in this thesis are limited by the sole investigation of right striatal regions, which was necessitated by time constraints and the needs of our patient population. However, most evidence suggests that laterality differences across hemispheres are unlikely (411, 444). Sixth, akin to much of the existing 1H-MRS literature, the studies within the current thesis may be underpowered. While the data that made up Chapters 3 and 4 put forth the largest sample of antipsychotic-naïve patients with FEP in which 1H-MRS was performed, our work may yet be underpowered, which may speak to the magnitude of effect sizes, the resource-intensive nature of magnetic resonance imaging (MRI)-based investigations, and the challenges that may be involved in recruiting participants suffering from severe mental illness. Seventh, the heterogeneity among the investigated patient samples presents a noteworthy limitation. In Chapters 3 and 4, patient samples included individuals with brief psychotic disorder, schizophreniform disorder, and schizophrenia, whereas the patient sample in Chapter 5 was composed of individuals with schizoaffective disorder and schizophrenia. Eighth, the failure to include multiple 1H-MRS voxels in any of the studies included within this thesis presents a limitation. It was therefore not possible to explore the interplay between the striatum and another brain region highly implicated in the pathophysiology of schizophrenia, such as the anterior cingulate cortex or the thalamus. Ninth, the work within this thesis is limited by the cross-sectional study designs employed in each study. As a result, causation, illness progression, antipsychotic exposure, or the degree of response to antipsychotic treatment could not be directly examined. Tenth, the current thesis is limited by the lack of testing of the glutamate-mediated excitotoxicity hypothesis using NAA. While the present work was driven by reports of caudate volumetric deficits in the included patient population, many benefits of examining the relationships between levels of glutamatergic neurometabolites and NAA might have been taken advantage of if such an analysis had been conducted. Most notably, levels would have been acquired from the same 1H-MRS voxel and thus the same brain area, and NAA would have provided insight towards neuronal integrity.

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Some limitations of such analyses include: potential contamination between NAA and Glx levels, that NAA and Glx relationships may be impacted by schizophrenia pathophysiology in a manner that is not presently understood, that NAA levels may reflect a function beyond neuronal integrity, and the need to account for spurious correlations in the analysis (352, 416, 443). It also deserves mention that NAA reductions have not been identified within the basal ganglia in existing meta-analyses (147, 350). Interestingly, if an inverse relationship had been identified between glutamate and volume, but not glutamate and NAA, it would suggest glutamate- mediated excitotoxicity without neuronal loss. This would provide support for previous findings of preserved neuronal number and increased neuronal density, consistent with microstructural changes and the reduced neuropil hypothesis (724). Such density increases might be in line with NAA elevations observed in early stages of schizophrenia within the basal ganglia and reductions seen in later stages of the illness (247, 414).

Also, several limitations of this work, presented below, are ubiquitous amongst 1H-MRS studies. 1H-MRS as a technique cannot distinguish between intracellular and extracellular neurometabolite concentrations, thus making it difficult to accurately assess neurotransmission. Also, within the interpretation of 1H-MRS results, particular roles are often assigned to certain neurometabolites; for example, mI is often interpreted in the literature to reflect glial activity. However, in most cases, neurometabolites of interest (e.g. mI, Cho, NAA, Cr, glutamatergic neurometabolites, etc.) have numerous known physiological roles. Both within the present work and other 1H-MRS studies, the interpretation of particular 1H-MRS signals representing neurometabolites of interest to reflect one or more of its functions may not be accurate or sufficiently comprehensive. It is necessary to gain more insight into the factors that influence 1H- MRS measurements of neurometabolite concentrations to be able to improve our understanding and interpretation regarding which functions of a particular neurometabolite are being reflected by changes in its 1H-MRS measurement. Particularly, the evidence suggesting that mI and Cho are glial markers is limited and requires further dissection. For example, a 1H-MRS study of cultured brain cells by Brand et al is most often referred to in support of mI’s role as a glial marker (164). The authors found a higher concentration of mI in astrocytes and a negligible amount of the metabolite in neurons. However, this study is not without limitations and, as detailed by an authoritative review, several key methodological factors necessitate consideration (725). First, the cultured astrocytes were in a more mature stage of development than the

178 neurons. Second, neither astrocytes nor neurons were able to synthesize mI from glucose. As such, mI was added and concentrations may instead be reflective of ability to uptake mI. Third, the acidic conditions in the study may have rendered the mI uptake transporter in neurons less active. Furthermore, a systematic review reported that median concentrations of mI were higher in glial cells, despite there being no significant difference between glia and neurons (726). Additionally, associative evidence links mI to astroglial function; a study by Rothermundt et al concomitantly investigated levels of mI and S100B, the latter of which is purported to be an astrocytic protein and an indicator of astroglial function (727). In this study, which notably had a small sample size, mI and S100B levels were correlated and mI levels were higher in the high S100B group. Thus, mI’s role as a glial marker is suggestive but certainly requires further investigation. The same notion is true for Cho; its role as a glial marker relates in part to evidence of increases in malignant tumours (167), as well as studies that report that it is present within higher concentrations in glial cells than neurons (169). In the present work, evidence of mI and Cho increases in neuroinflammatory illnesses was also put forth as associative evidence to suggest a link between these neurometabolites and glial activity (166). That being said, both mI and Cho likely have other physiological roles: for example, mI as an osmolyte and part of a second messenger system, and Cho as an indicator of cell density (725).

Furthermore, there are also limitations to consider within the field of 1H-MRS in schizophrenia as a whole. A noteworthy limitation that was previously alluded to is the heterogeneity that exists among studies, which is especially relevant when interpreting the findings from the present work in the context of available literature. While understandably seeking to answer different research questions, study designs differ in terms of factors such as 1H-MRS voxel placement, patient sample, 1H-MRS parameters utilized, and 1H-MRS quality control criteria. In terms of 1H-MRS voxel placement, many brain regions can be investigated with 1H-MRS, the most common of which are the DLPFC, anterior cingulate cortex, frontal WM, striatum, hippocampus, and thalamus. As several decades of PET research have shown, dopaminergic disturbances in patients with schizophrenia are predominantly localized to the striatum. Notably, similar patterns within specific brain regions are steadily emerging for neurometabolites assessed by 1H-MRS, although they may remain somewhat elusive at present due to the noted heterogeneity within the field. Thus, it is paramount that voxel placement be considered when contextualizing 1H-MRS findings within the relevant literature. Likewise,

179 differences in patient sample (e.g. medication status, illness stage, drug use), 1H-MRS acquisition and analysis methodologies (e.g. echo time length, sequence specifications, software), and quality control criteria (e.g. %SD, Cramer-Rao lower bound (CRLB), full-width at half maximum, signal-to-noise ratio) must be considered. As more data are collected, additional focus should be placed on replicating findings using comparable study designs and methodologies.

Additionally, the field of 1H-MRS research is limited by the difficulties that arise in trying to conduct multi-site investigations. Unlike other modalities, for which multi-site studies are gaining prevalence, there are several challenges that exist in performing multi-site 1H-MRS studies. These obstacles often include the fact that sites may utilize different MRI machines and scanning parameters, as well as 1H-MRS data output being delivered in institutional units. As a result, 1H-MRS data may not be readily comparable between institutions. Notably, a recent multicentre 1H-MRS study, which utilized creatine-scaled values, strove to overcome these challenges (696). Still, sharing 1H-MRS data across institutions remains difficult, in part limiting the ability to answer 1H-MRS research questions by analyzing large samples.

6.9 Conclusions

With a particular focus on striatal glutamatergic neurometabolites in patients with schizophrenia, the present thesis examined neurochemical and neuroanatomical alterations in patients with schizophrenia using MRI. First, support was provided for glial activation in antipsychotic-naïve patients experiencing their first non-affective episode of psychosis, which may have consequent effects on the dysregulation of glutamatergic neurotransmission within the striatum. Next, in the same patient population, evidence was put forth in support of neuroanatomical compromise and a striatal excitotoxic effect was proposed, whereby elevated levels of striatal glutamatergic neurometabolites contribute to local volumetric deficits. Lastly, no striatal neurometabolite differences were identified between patients with schizophrenia who had undergone long-term antipsychotic treatment and healthy controls.

Taken together, the findings within the presented studies contribute novel evidence to the field, advancing our understanding of schizophrenia pathophysiology. The studies within this

180 thesis support the notion that levels of striatal glutamatergic neurometabolites are elevated in antipsychotic-naïve states but decrease following antipsychotic treatment. Additionally, the work within this thesis contributes to a growing body of literature implicating neuroinflammation in schizophrenia. The studies within this thesis also offer evidence for a mechanism by which at least some of the vast and widespread structural compromise seen in patients with schizophrenia might occur. One possible link between glial dysfunction and NMDA receptor hypofunction that results in downstream hyperglutamatergia and excitotoxicity may be KYNA. Activated astrocytes may produce more KYNA, an endogenous NMDA receptor antagonist that may contribute to the symptoms of schizophrenia and lead to downstream glutamatergic disruptions. Finally, this thesis further demonstrates the utility of employing 1H-MRS in the study of neuropsychiatric illnesses.

Future studies are warranted to further investigate dysregulation of the glutamatergic system, glial activation, neuroinflammation, and 1H-MRS methodology in patients with schizophrenia. Of critical importance going forward will be assessing the clinical implications of altered neurometabolite levels as measured by 1H-MRS, the potential therapeutic effects of modulating the glutamatergic system, the utility of 1H-MRS in categorizing subtypes of schizophrenia or assessing treatment response, and the associations between neurometabolites measured by 1H-MRS and those measured by other modalities (e.g. dopamine, KYNA).

6.10 Future Directions

6.10.1 Next Steps for Investigation

Overall, the main findings within the present work put forth evidence supporting: 1) glial activation that may influence the disruption of glutamatergic neurotransmission in antipsychotic- naïve patients with FEP; 2) striatal glutamate-mediated excitotoxicity in antipsychotic-naïve patients with FEP; 3) a pattern of elevated glutamatergic neurometabolites in early, antipsychotic-naïve stages of illness followed by a lowering of these neurometabolites in later, treated stages of illness; and 4) structural compromise in the form of cortical thinning and subcortical volumetric reductions in antipsychotic-naïve patients with FEP. All that considered,

181 there are several noteworthy future directions for subsequent work to pursue in order to be convinced of each of these phenomena and further these lines of investigation.

First, future 1H-MRS studies should aim to concomitantly utilize 1H-MRS and other modalities to continue to explore glial activation and neuroinflammation, and further test mI and Cho as putative markers of these phenomena. Notably, as discussed above, PET studies have assessed glial activation by utilizing radioligands that target TSPO, which is often interpreted to reflect microglial activation, in patients with schizophrenia. Although conflicting findings have been presented (502-510), this is a developing area within the field that will hopefully provide further insight towards glial activation within the illness. Beyond TSPO, there are several emerging techniques to test glial activation and neuroinflammation in patients with schizophrenia (728). Ideally, whether using PET measurements of TSPO or other means to study these phenomena, 1H-MRS will be employed in parallel. Moreover, it is also imperative that future 1H-MRS research examines to what extent mI and Cho represent glial activity. Specifically, it would be beneficial to understand how reflective these neurometabolites are of astrocytic activity. To this effect, Rothermundt et al showed concomitant elevations in mI levels and S100B, a marker of astrocyte activation, in patients with schizophrenia, providing associative support for the role of mI in this regard (727).

Also, the notion that glial activation or neuroinflammation in patients with schizophrenia is linked with disruptions in the glutamatergic system requires further testing. Most importantly, studies should investigate whether an association or causation exists between glial activation or neuroinflammation and elevated glutamatergic neurometabolite levels, as well as the temporal onset of each of these phenomena within the illness. To do so, longitudinal studies following individuals at high risk for psychosis to the onset of the FEP, and ultimately schizophrenia, would be necessitated. Alternatively, experimental studies using modulators of neuroinflammation and glial activity could be explored. An important consideration in the further testing of glial activation or neuroinflammation and associated glutamatergic disruptions is the precise brain location in which the phenomena are identified. While the present work suggests that these might be occurring with the striatum, future work should also examine the interplay with other brain regions implicated in the pathophysiology of schizophrenia.

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Similarly, future MRI studies should further investigate disruptions in KYNA as a promising player in the mechanism underlying glutamatergic dysregulation, possibly through neuroinflammation and glial activation. Neuroimaging studies should look to explore the relationships between KYN pathway metabolites and 1H-MRS neurometabolites. This approach has recently been adopted by one study, which found that in both patients and controls, KYN/tryptophan ratios were negatively associated with frontal WM glutamate levels (729).

Second, in terms of glutamate-mediated excitotoxicity in patients with schizophrenia, temporality must be investigated in order to better understand whether it has a causative role in structural deficits within the illness. As above, a longitudinal study examining individuals at high risk for developing psychosis, who later transition to the FEP and ultimately schizophrenia, should be performed in order to discern whether the relationships between elevated glutamatergic neurometabolites and structural compromise within early stages of illness are associative or whether one of these components might cause the other to develop. In doing so, attention should also be directed towards investigating a potential dose-response effect, to elucidate whether higher levels of glutamatergic neurometabolites might contribute greater toxicity and more dramatic structural losses.

It will also be important to understand whether glutamate-mediated excitotoxicity occurs solely as a local phenomenon, can affect only particular brain regions, or can have effects on distant structures in a reliable pattern. A future 1H-MRS study would greatly contribute towards this body of evidence by investigating several brain regions, such as the anterior cingulate cortex, hippocampus, and striatum, and assessing the relationships among levels of glutamatergic neurometabolites in these areas and structural changes both in these brain regions and elsewhere.

Third, future work is required to further investigate whether antipsychotic treatment lowers previously elevated levels of glutamatergic neurometabolites. Given that antipsychotic medications primarily modulate the dopaminergic system, more 1H-MRS studies assessing glutamatergic neurometabolites should aim to concomitantly investigate dopamine levels using PET. Although PET studies present notable challenges, co-investigations with 1H-MRS and PET in patients with schizophrenia would prove extremely beneficial towards elucidating the influence of antipsychotic treatment on glutamatergic neurometabolites, the relationship between

183 dopamine and glutamate levels, and the neurochemical mechanisms involved in the pathophysiology of schizophrenia as a whole.

Moreover, the proposed pattern of glutamatergic dysregulation within schizophrenia, with elevated levels of striatal glutamatergic neurometabolites in the early, untreated stage of the illness and a decrease in levels in the medicated state, should be further investigated through 1H- MRS studies that seek to optimize reproducibility. With respect to factors such as 1H-MRS voxel placement, 1H-MRS quality control criteria, participants’ age and duration of illness, and confounding factors (e.g. drug use, concomitant medications), future studies should design their methodology with the aim to replicate existing studies and provide support for or against this pattern. Especially important to such investigations will be the brain region being investigated, as the precise locations in which antipsychotics might have a lowering effect on levels of glutamatergic neurometabolites remain elusive.

Additionally, future studies should aim to explore and distinguish the potential contributions of antipsychotic exposure and antipsychotic response towards the lowering of glutamatergic neurometabolites in patients with schizophrenia, ideally through a longitudinal approach. A potential dose-response effect should also be investigated by examining whether a relationship exists between the amount of antipsychotic exposure and the resultant degree of clinical treatment response and reduction in glutamate levels.

Furthermore, studies should attempt to utilize sample sizes that provide sufficient power. In light of this, it would be beneficial for potentially underpowered studies to calculate and detail the sample sizes that would have been required.

Fourth, it is uniformly accepted within the field that structural compromise in patients with schizophrenia is a key aspect of the illness. However, an important consideration for structural analyses is whether the deficits that are often observed within the literature reflect strictly local losses in certain brain regions or exist diffusely throughout the brain. In Chapter 4, we showed that the consideration of TBV as a covariate rendered the loss of significance for group differences in PCV. Typically, findings within the field are based on analyses that either do or do not adjust for the influence of a TBV-like measure. While reducing redundancy within a study is understandable, it deserves mention that showing results with and without the inclusion of TBV potentially provides insight into whether findings are specific to a brain region or occur

184 simultaneously in other areas. Of note, recent literature does consider the results from both analyses (184). We suggest that, as appropriate, future studies consider reporting results with and without the inclusion of TBV.

6.10.2 A Consideration for “Good” Quality 1H-MRS Research

As a technique, 1H-MRS is constantly improving and as field strengths continue to increase, its capabilities will undoubtedly continue to grow. However, there are a few important considerations to keep in mind to conduct high quality 1H-MRS research going forward. As supported by the reliability investigation in Chapter 5, it is essential to utilize quality control criteria and consider the removal of outliers to ensure sufficient quality and reliability of 1H- MRS data. Below, the CRLB measure in the context of Linear Combination Model (LCModel) analyses is discussed in greater detail.

As previously stated, a %SD value less than 20% is typically considered to be of sufficient quality. %SD, also referred to as relative CRLB, is calculated by dividing the absolute CRLB by the mean concentration of the neurometabolite. Notably, there is inconsistency in how CRLB is reported in the 1H-MRS literature (absolute versus relative) and this has led to misinterpretations of data when group comparisons are performed.

While %SD can be a biased measure of spectral quality (730) for a given neurometabolite, it is commonly used to provide insight towards reliability within an individual spectrum. Furthermore, Stephen Provencher suggests that only metabolites with a %SD less than 20%, as reported by LCModel, can be considered reliable (172). Despite the fact that this filtering of data is problematic, in particular when wishing to compare across groups with low levels of neurometabolites (731), here, another aspect of misinterpretation of %SD that is common in the neuropsychiatric literature is discussed.

In particular, comparing %SD values between groups (where enough neurometabolite is present such that the 20% cut-off yields enough data per group to apply statistics on the results) may be inappropriate, especially if a group difference in metabolite concentration exists. For example, a group difference in glutamate concentrations, with comparable absolute CRLB values between groups, might render a group difference in %SD values. This finding might

185 inappropriately be interpreted to reflect a difference in the quality of spectral fit between groups. A more appropriate measurement to compare spectral fit between groups would be absolute CRLB, which can be determined by multiplying %SD by the neurometabolite concentration and dividing by 100.

Further, given that %SD differences between groups are not independent of neurometabolite concentration, it is often incorrect to account for the influence of %SD in group comparisons involving neurometabolite concentration – as a covariate, for example. If necessary, the inclusion of neurometabolite-specific absolute CRLB values as covariates should be preferred.

The assertions above regarding the use and misuse of the relative measure %SD are comparable to comments from previous literature discussing another relative measure, the coefficient of variation (CV). The CV is defined as the standard deviation divided by the mean; as a result, both the standard deviation and the mean have independent influences on the CV. Past work has similarly highlighted the problematic use of CV to compare variation across groups (732, 733).

Importantly, relative measures, such as CV, are useful in circumstances where groups differ in such a manner that renders absolute measures meaningless for group comparison. For example, if groups differ in terms of measurement scale, it would be futile to compare an absolute measure of variability between groups, given the expected difference in means between groups that would in part be attributed to the use of different scales. This is also true for %SD, which is valuable in assessing different neurometabolites within individual spectra; given the expected difference in means between neurometabolite levels (e.g. glutamate versus NAA) within individual spectra, a relative measure must be employed for meaningful comparison in this instance. However, when searching for subtle mean differences in levels of a neurometabolite between groups that are mostly identical (e.g. MRI collected at same institution, same 1H-MRS parameters used, comparable demographic factors, etc.), a more informative variable to present and compare is the absolute CRLB. In such cases, the interpretation of %SD comparisons might be confounded by potential group mean differences.

In summary, %SD and absolute CRLB should not be used interchangeably. %SD is a relative measure that is calculated using both CRLB and the metabolite concentration. Contrarily,

186 absolute CRLB is a measure of goodness of fit of the metabolite signature into the acquired spectrum. If absolute CRLB does not differ across groups, one can infer that the measurement error is not different amongst groups. Future studies should report both %SD and absolute CRLB values, use %SD < 20% to ensure reliability within each individual spectra, and utilize absolute CRLB to assess potential group differences in measurement error unless otherwise warranted by the data.

6.10.3 The “Perfect” Study Design to Better Understand 1H-MRS Neurometabolite Disturbances in Schizophrenia

The following section describes an ideal 1H-MRS study design to better understand neurometabolite level disturbances in patients with schizophrenia. It is acknowledged that the presented design carries with it several challenges, which may render the full execution of such a study implausible. These challenges will also be discussed below.

Longitudinal work is required in order to provide further insight towards the effects of illness progression, treatment response, and antipsychotic exposure, and to assess the potential of neurometabolites as biomarkers. Thus, an ideal study would adopt a longitudinal design to examine the transition from high risk states to the onset of the FEP, and ultimately the development of schizophrenia, by including individuals at high risk for psychosis and following their progression through the illness course. Ideally, healthy controls matched for age and sex should also be included and followed in the study. Furthermore, attention should be paid towards characterizing subsets of patients who respond to first-line antipsychotic treatment, patients who fail to respond to first-line antipsychotic treatment but respond to clozapine, and patients who fail to respond to first-line antipsychotic treatment and clozapine. Notably, treatment resistance has at times been posited to predate the point of first antipsychotic exposure or to develop over the course of the illness; these varying mechanisms and their potential implications should be kept in mind.

With respect to the MRI parameters employed, an ideal study design should aim to better separate glutamate and glutamine peaks using strategies that may include employing field strengths greater than 3 Tesla and utilizing short echo times. Similarly, the use of editing sequences such as MEGA-PRESS should be incorporated into study designs to concomitantly

187 investigate GABA, GSH, and NAAG levels. Additionally, future 1H-MRS studies should consider the interplay between different brain areas. To do so, rather than placing 1H-MRS voxels within one particular area of interest, acquisitions from several 1H-MRS voxels, each within a separate brain region, should be collected. If using single-voxel spectroscopy, 1H-MRS voxels should be placed within several brain regions implicated in the pathophysiology of schizophrenia, some of which might include the striatum, thalamus, hippocampus, anterior cingulate cortex, DLPFC, frontal WM, and superior temporal gyrus. Alternatively, multiple- voxel techniques may be considered. Moreover, appropriate 1H-MRS quality control criteria, as discussed above, should be employed throughout. Finally, the concomitant usage of 13C-MRS and PET would allow for the parallel assessment of glutamatergic neurotransmission and dopaminergic neurotransmission, respectively. A more holistic understanding of the neurochemical dysregulation underlying schizophrenia pathophysiology may be acquired if future work examines the interplay between multiple neurotransmitter systems.

Naturally, the proposed study design has several noteworthy challenges. First, given that only a portion of individuals in high risk states transition to a FEP, and similarly, only a portion of patients with FEP transition to schizophrenia, a very large sample size would be required to execute such an investigation. Undoubtedly, attaining such large sample sizes is challenging due to the significant resources that are required to do so. Similarly, the design would necessitate a prolonged follow-up period, which increases the likelihood of loss to follow-up due to participant dropout. This challenge might be overcome by dividing the overall time frame of the proposed study into segments by stage of illness. Moreover, it is also essential that the study have sufficient statistical power; data sharing initiatives and multi-site examinations should be explored going forward. Finally, the assessment of multiple brain regions using single-voxel spectroscopy would necessitate a long scan time, which may be difficult for participants with severe psychosis to endure; employing multiple-voxel imaging techniques could address this challenge.

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The copyrighted status of material that was reproduced from previous publications is acknowledged alongside its presentation within the current work.

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References

1. Tandon R, Gaebel W, Barch DM, Bustillo J, Gur RE, Heckers S, et al. Definition and description of schizophrenia in the DSM-5. Schizophr Res. 2013;150(1):3-10. 2. Kapur S, Remington G. Atypical antipsychotics: new directions and new challenges in the treatment of schizophrenia. Annu Rev Med. 2001;52:503-17. 3. Tandon R, Nasrallah HA, Keshavan MS. Schizophrenia, "just the facts" 4. Clinical features and conceptualization. Schizophr Res. 2009;110(1-3):1-23. 4. Laursen TM. Life expectancy among persons with schizophrenia or bipolar affective disorder. Schizophr Res. 2011;131(1-3):101-4. 5. Simeone JC, Ward AJ, Rotella P, Collins J, Windisch R. An evaluation of variation in published estimates of schizophrenia prevalence from 1990 horizontal line 2013: a systematic literature review. BMC Psychiatry. 2015;15:193. 6. Scarr E, Gibbons AS, Neo J, Udawela M, Dean B. Cholinergic connectivity: it's implications for psychiatric disorders. Front Cell Neurosci. 2013;7:55. 7. Sanacora G, Zarate CA, Krystal JH, Manji HK. Targeting the glutamatergic system to develop novel, improved therapeutics for mood disorders. Nat Rev Drug Discov. 2008;7(5):426- 37. 8. van der Graaf M. In vivo magnetic resonance spectroscopy: basic methodology and clinical applications. Eur Biophys J. 2010;39(4):527-40. 9. Jones S. Dopamine-Glutamate Interactions in the Basal Ganglia: CRC Press/Taylor & Francis Group; 2012. 10. Obeso JA, Lanciego JL. Past, present, and future of the pathophysiological model of the Basal Ganglia. Front Neuroanat. 2011;5:39. 11. Haber SN. The primate basal ganglia: parallel and integrative networks. J Chem Neuroanat. 2003;26(4):317-30. 12. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders - Fifth Edition. 2013. 13. Frangou S. Schizophrenia. Medicine. 2008;36(8):405-9. 14. Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016. 15. Holder SD, Wayhs A. Schizophrenia. Am Fam Physician. 2014;90(11):775-82. 16. Klosterkotter J, Schultze-Lutter F, Ruhrmann S. Kraepelin and psychotic prodromal conditions. Eur Arch Psychiatry Clin Neurosci. 2008;258 Suppl 2:74-84. 17. Alvarez-Jimenez M, Priede A, Hetrick SE, Bendall S, Killackey E, Parker AG, et al. Risk factors for relapse following treatment for first episode psychosis: a systematic review and meta- analysis of longitudinal studies. Schizophr Res. 2012;139(1-3):116-28. 18. Robinson D, Woerner MG, Alvir JM, Bilder R, Goldman R, Geisler S, et al. Predictors of relapse following response from a first episode of schizophrenia or schizoaffective disorder. Arch Gen Psychiatry. 1999;56(3):241-7. 19. Cechnicki A, Cichocki L, Kalisz A, Bladzinski P, Adamczyk P, Franczyk-Glita J. Duration of untreated psychosis (DUP) and the course of schizophrenia in a 20-year follow-up study. Psychiatry Res. 2014;219(3):420-5. 20. Karson C, Duffy RA, Eramo A, Nylander AG, Offord SJ. Long-term outcomes of antipsychotic treatment in patients with first-episode schizophrenia: a systematic review. Neuropsychiatr Dis Treat. 2016;12:57-67.

190

21. Penttila M, Jaaskelainen E, Hirvonen N, Isohanni M, Miettunen J. Duration of untreated psychosis as predictor of long-term outcome in schizophrenia: systematic review and meta- analysis. Br J Psychiatry. 2014;205(2):88-94. 22. Jager M, Weiser P, Becker T, Frasch K, Langle G, Croissant D, et al. Identification of psychopathological course trajectories in schizophrenia. Psychiatry Res. 2014;215(2):274-9. 23. Levine SZ, Lurie I, Kohn R, Levav I. Trajectories of the course of schizophrenia: from progressive deterioration to amelioration over three decades. Schizophr Res. 2011;126(1-3):184- 91. 24. Bellack AS. Scientific and consumer models of recovery in schizophrenia: concordance, contrasts, and implications. Schizophr Bull. 2006;32(3):432-42. 25. Lang FU, Kosters M, Lang S, Becker T, Jager M. Psychopathological long-term outcome of schizophrenia -- a review. Acta Psychiatr Scand. 2013;127(3):173-82. 26. van Os J, Kapur S. Schizophrenia. Lancet. 2009;374(9690):635-45. 27. Ginovart N, Kapur S. Role of dopamine D(2) receptors for antipsychotic activity. Handb Exp Pharmacol. 2012(212):27-52. 28. Hartling L, Abou-Setta AM, Dursun S, Mousavi SS, Pasichnyk D, Newton AS. Antipsychotics in adults with schizophrenia: comparative effectiveness of first-generation versus second-generation medications: a systematic review and meta-analysis. Ann Intern Med. 2012;157(7):498-511. 29. Leucht S, Cipriani A, Spineli L, Mavridis D, Orey D, Richter F, et al. Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta- analysis. Lancet. 2013;382(9896):951-62. 30. Leucht S, Corves C, Arbter D, Engel RR, Li C, Davis JM. Second-generation versus first- generation antipsychotic drugs for schizophrenia: a meta-analysis. Lancet. 2009;373(9657):31- 41. 31. Svard A, Lahti J, Rahkonen O, Lahelma E, Lallukka T. Obesity and psychotropic medication: a prospective register linkage study among midlife women and men. BMC Psychiatry. 2016;16:185. 32. George M, Amrutheshwar R, Rajkumar RP, Kattimani S, Dkhar SA. Newer antipsychotics and upcoming molecules for schizophrenia. Eur J Clin Pharmacol. 2013;69(8):1497-509. 33. Miyamoto S, Miyake N, Jarskog LF, Fleischhacker WW, Lieberman JA. Pharmacological treatment of schizophrenia: a critical review of the pharmacology and clinical effects of current and future therapeutic agents. Mol Psychiatry. 2012;17(12):1206-27. 34. Meltzer HY. Treatment-resistant schizophrenia--the role of clozapine. Curr Med Res Opin. 1997;14(1):1-20. 35. Miyamoto S, Jarskog LF, Fleischhacker WW. New therapeutic approaches for treatment- resistant schizophrenia: a look to the future. J Psychiatr Res. 2014;58:1-6. 36. Kane JM, Correll CU. Past and present progress in the pharmacologic treatment of schizophrenia. J Clin Psychiatry. 2010;71(9):1115-24. 37. Sommer IE, Begemann MJ, Temmerman A, Leucht S. Pharmacological augmentation strategies for schizophrenia patients with insufficient response to clozapine: a quantitative literature review. Schizophr Bull. 2012;38(5):1003-11. 38. Addington J, Piskulic D, Marshall C. Psychosocial treatments for schizophrenia. Curr Dir Psychol Sci. 2010;19(4):260-3. 39. Chien WT, Leung SF, Yeung FK, Wong WK. Current approaches to treatments for schizophrenia spectrum disorders, part II: psychosocial interventions and patient-focused perspectives in psychiatric care. Neuropsychiatr Dis Treat. 2013;9:1463-81.

191

40. Fleischhacker WW, Arango C, Arteel P, Barnes TR, Carpenter W, Duckworth K, et al. Schizophrenia--time to commit to policy change. Schizophr Bull. 2014;40 Suppl 3:S165-94. 41. Guo X, Zhai J, Liu Z, Fang M, Wang B, Wang C, et al. Effect of antipsychotic medication alone vs combined with psychosocial intervention on outcomes of early-stage schizophrenia: A randomized, 1-year study. Arch Gen Psychiatry. 2010;67(9):895-904. 42. Mueser KT, Deavers F, Penn DL, Cassisi JE. Psychosocial treatments for schizophrenia. Annu Rev Clin Psychol. 2013;9:465-97. 43. Valencia M, Fresan A, Juarez F, Escamilla R, Saracco R. The beneficial effects of combining pharmacological and psychosocial treatment on remission and functional outcome in outpatients with schizophrenia. J Psychiatr Res. 2013;47(12):1886-92. 44. Murray CJ, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: Harvard School of Public Health; 1996. 45. Vos T, Barber, RM, Bell, B, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(9995):743-800. 46. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382(9904):1575-86. 47. Chong HY, Teoh SL, Wu DB, Kotirum S, Chiou CF, Chaiyakunapruk N. Global economic burden of schizophrenia: a systematic review. Neuropsychiatr Dis Treat. 2016;12:357- 73. 48. Goeree R, Farahati F, Burke N, Blackhouse G, O'Reilly D, Pyne J, et al. The economic burden of schizophrenia in Canada in 2004. Curr Med Res Opin. 2005;21(12):2017-28. 49. Ratnasingham S, Cairney J, Rehm J, Manson H, Kurdyak PA. Opening Eyes, Opening Minds: The Ontario Burden of Mental Illness and Addictions Report. An ICES/PHO Report. 2012. 50. Crump C, Winkleby MA, Sundquist K, Sundquist J. Comorbidities and mortality in persons with schizophrenia: a Swedish national cohort study. Am J Psychiatry. 2013;170(3):324- 33. 51. McGrath J, Saha S, Chant D, Welham J. Schizophrenia: a concise overview of incidence, prevalence, and mortality. Epidemiol Rev. 2008;30:67-76. 52. Olfson M, Gerhard T, Huang C, Crystal S, Stroup TS. Premature Mortality Among Adults With Schizophrenia in the United States. JAMA Psychiatry. 2015;72(12):1172-81. 53. Saha S, Chant D, McGrath J. A systematic review of mortality in schizophrenia: is the differential mortality gap worsening over time? Arch Gen Psychiatry. 2007;64(10):1123-31. 54. Bushe CJ, Taylor M, Haukka J. Mortality in schizophrenia: a measurable clinical endpoint. J Psychopharmacol. 2010;24(4 Suppl):17-25. 55. Latoo J, Mistry M, Dunne F. Physical morbidity and mortality in people with mental illness. British Journal of Medical Practitioners. 2013;6(3):621-3. 56. Ringen PA, Engh JA, Birkenaes AB, Dieset I, Andreassen OA. Increased mortality in schizophrenia due to cardiovascular disease - a non-systematic review of epidemiology, possible causes, and interventions. Front Psychiatry. 2014;5:137. 57. Bener A, Al-Hamaq AO, Dafeeah EE. A two fold risk of metabolic syndrome in a sample of patients with schizophrenia: do consanguinity and family history increase risk? Diabetes Metab Syndr. 2014;8(1):24-9.

192

58. De Hert M, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry. 2011;10(1):52-77. 59. De Hert M, Schreurs V, Vancampfort D, R VANW. Metabolic syndrome in people with schizophrenia: a review. World Psychiatry. 2009;8(1):15-22. 60. Fan Z, Wu Y, Shen J, Ji T, Zhan R. Schizophrenia and the risk of cardiovascular diseases: a meta-analysis of thirteen cohort studies. J Psychiatr Res. 2013;47(11):1549-56. 61. Mitchell AJ, Vancampfort D, Sweers K, van Winkel R, Yu W, De Hert M. Prevalence of metabolic syndrome and metabolic abnormalities in schizophrenia and related disorders--a systematic review and meta-analysis. Schizophr Bull. 2013;39(2):306-18. 62. Partti K, Vasankari T, Kanervisto M, Perala J, Saarni SI, Jousilahti P, et al. Lung function and respiratory diseases in people with psychosis: population-based study. Br J Psychiatry. 2015;207(1):37-45. 63. Schoepf D, Uppal H, Potluri R, Heun R. Physical comorbidity and its relevance on mortality in schizophrenia: a naturalistic 12-year follow-up in general hospital admissions. Eur Arch Psychiatry Clin Neurosci. 2014;264(1):3-28. 64. Suvisaari J, Keinanen J, Eskelinen S, Mantere O. Diabetes and Schizophrenia. Curr Diab Rep. 2016;16(2):16. 65. Urhan M, Ergun C, Aksoy M, Ayer A. Effects of weight loss diet therapy on anthropometric measurements and biochemical variables in schizophrenic patients. Nord J Psychiatry. 2015;69(5):323-30. 66. Vancampfort D, Correll CU, Galling B, Probst M, De Hert M, Ward PB, et al. Diabetes mellitus in people with schizophrenia, bipolar disorder and major depressive disorder: a systematic review and large scale meta-analysis. World Psychiatry. 2016;15(2):166-74. 67. Ward M, Druss B. The epidemiology of diabetes in psychotic disorders. Lancet Psychiatry. 2015;2(5):431-51. 68. Henderson DC, Vincenzi B, Andrea NV, Ulloa M, Copeland PM. Pathophysiological mechanisms of increased cardiometabolic risk in people with schizophrenia and other severe mental illnesses. Lancet Psychiatry. 2015;2(5):452-64. 69. Ventriglio A, Gentile A, Stella E, Bellomo A. Metabolic issues in patients affected by schizophrenia: clinical characteristics and medical management. Front Neurosci. 2015;9:297. 70. de Leon J, Diaz FJ. A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr Res. 2005;76(2-3):135-57. 71. Laursen TM, Nordentoft M, Mortensen PB. Excess early mortality in schizophrenia. Annu Rev Clin Psychol. 2014;10:425-48. 72. Buckley PF, Miller BJ, Lehrer DS, Castle DJ. Psychiatric comorbidities and schizophrenia. Schizophr Bull. 2009;35(2):383-402. 73. Kerner B. Comorbid substance use disorders in schizophrenia: a latent class approach. Psychiatry Res. 2015;225(3):395-401. 74. Swofford CD, Scheller-Gilkey G, Miller AH, Woolwine B, Mance R. Double jeopardy: schizophrenia and substance use. Am J Drug Alcohol Abuse. 2000;26(3):343-53. 75. Thoma P, Daum I. Comorbid substance use disorder in schizophrenia: a selective overview of neurobiological and cognitive underpinnings. Psychiatry Clin Neurosci. 2013;67(6):367-83. 76. Fuller-Thomson E, Hollister B. Schizophrenia and Suicide Attempts: Findings from a Representative Community-Based Canadian Sample. 2016;2016:3165243. 77. Hor K, Taylor M. Suicide and schizophrenia: a systematic review of rates and risk factors. J Psychopharmacol. 2010;24(4 Suppl):81-90.

193

78. Palmer BA, Pankratz VS, Bostwick JM. The lifetime risk of suicide in schizophrenia: a reexamination. Arch Gen Psychiatry. 2005;62(3):247-53. 79. Siris SG. Suicide and schizophrenia. J Psychopharmacol. 2001;15(2):127-35. 80. Suokas JT, Perala J, Suominen K, Saarni S, Lonnqvist J, Suvisaari JM. Epidemiology of suicide attempts among persons with psychotic disorder in the general population. Schizophr Res. 2010;124(1-3):22-8. 81. Fuller R, Nopoulos P, Arndt S, O'Leary D, Ho BC, Andreasen NC. Longitudinal assessment of premorbid cognitive functioning in patients with schizophrenia through examination of standardized scholastic test performance. Am J Psychiatry. 2002;159(7):1183-9. 82. Kahn RS, Keefe RS. Schizophrenia is a cognitive illness: time for a change in focus. JAMA Psychiatry. 2013;70(10):1107-12. 83. Keefe RS. The longitudinal course of cognitive impairment in schizophrenia: an examination of data from premorbid through posttreatment phases of illness. J Clin Psychiatry. 2014;75 Suppl 2:8-13. 84. Dealberto MJ, Middlebro A, Farrell S. Symptoms of schizophrenia and psychosis according to foreign birth in a Canadian sample of homeless persons. Psychiatr Serv. 2011;62(10):1187-93. 85. Folsom D, Jeste DV. Schizophrenia in homeless persons: a systematic review of the literature. Acta Psychiatr Scand. 2002;105(6):404-13. 86. Foster A, Gable J, Buckley J. Homelessness in schizophrenia. Psychiatr Clin North Am. 2012;35(3):717-34. 87. Abdel-Baki A, Lesage A, Nicole L, Cossette M, Salvat E, Lalonde P. Schizophrenia, an illness with bad outcome: myth or reality? Can J Psychiatry. 2011;56(2):92-101. 88. Bouwmans C, de Sonneville C, Mulder CL, Hakkaart-van Roijen L. Employment and the associated impact on quality of life in people diagnosed with schizophrenia. Neuropsychiatr Dis Treat. 2015;11:2125-42. 89. Caron J, Mercier C, Diaz P, Martin A. Socio-demographic and clinical predictors of quality of life in patients with schizophrenia or schizo-affective disorder. Psychiatry Res. 2005;137(3):203-13. 90. Evensen S, Wisloff T, Lystad JU, Bull H, Ueland T, Falkum E. Prevalence, Employment Rate, and Cost of Schizophrenia in a High-Income Welfare Society: A Population-Based Study Using Comprehensive Health and Welfare Registers. Schizophr Bull. 2016;42(2):476-83. 91. Marwaha S, Johnson S. Schizophrenia and employment - a review. Soc Psychiatry Psychiatr Epidemiol. 2004;39(5):337-49. 92. Brink JH, Doherty D, Boer A. Mental disorder in federal offenders: a Canadian prevalence study. Int J Law Psychiatry. 2001;24(4-5):339-56. 93. Harvey PD, Heaton RK, Carpenter WT, Jr., Green MF, Gold JM, Schoenbaum M. Functional impairment in people with schizophrenia: focus on employability and eligibility for disability compensation. Schizophr Res. 2012;140(1-3):1-8. 94. Steinert T, Veit F, Schmid P, Jacob Snellgrove B, Borbe R. Participating in mobility: People with schizophrenia driving motorized vehicles. Psychiatry Res. 2015;228(3):719-23. 95. Brain C, Sameby B, Allerby K, Quinlan P, Joas E, Lindstrom E, et al. Stigma, discrimination and medication adherence in schizophrenia: results from the Swedish COAST study. Psychiatry Res. 2014;220(3):811-7. 96. Cechnicki A, Angermeyer MC, Bielanska A. Anticipated and experienced stigma among people with schizophrenia: its nature and correlates. Soc Psychiatry Psychiatr Epidemiol. 2011;46(7):643-50.

194

97. Mestdagh A, Hansen B. Stigma in patients with schizophrenia receiving community mental health care: a review of qualitative studies. Soc Psychiatry Psychiatr Epidemiol. 2014;49(1):79-87. 98. Ucok A, Brohan E, Rose D, Sartorius N, Leese M, Yoon CK, et al. Anticipated discrimination among people with schizophrenia. Acta Psychiatr Scand. 2012;125(1):77-83. 99. Tang IC, Wu HC. Quality of life and self-stigma in individuals with schizophrenia. Psychiatr Q. 2012;83(4):497-507. 100. Awad AG, Voruganti LN. The burden of schizophrenia on caregivers: a review. Pharmacoeconomics. 2008;26(2):149-62. 101. Caqueo-Urizar A, Miranda-Castillo C, Lemos Giraldez S, Lee Maturana SL, Ramirez Perez M, Mascayano Tapia F. An updated review on burden on caregivers of schizophrenia patients. Psicothema. 2014;26(2):235-43. 102. Caqueo-Urizar A, Gutierrez-Maldonado J, Miranda-Castillo C. Quality of life in caregivers of patients with schizophrenia: a literature review. Health Qual Life Outcomes. 2009;7:84. 103. Chan SW. Global perspective of burden of family caregivers for persons with schizophrenia. Arch Psychiatr Nurs. 2011;25(5):339-49. 104. Saha S, Chant D, Welham J, McGrath J. A systematic review of the prevalence of schizophrenia. PLoS Med. 2005;2(5):e141. 105. World Health Organization. The global burden of disease: 2004 update. 2008. 106. Dealberto MJ. Are the rates of schizophrenia unusually high in Canada? A comparison of Canadian and international data. Psychiatry Res. 2013;209(3):259-65. 107. Ng R, Maxwell CJ, Yates EA, Nylen K, Antflick J, Jetté N, et al. Brain Disorders in Ontario: Prevalence, Incidence and Costs from Health Administrative Data. Toronto: Institute for Clinical Evaluative Sciences; 2015. 108. Statistics Canada. Table 105-1101 - Mental Health Profile, Canadian Community Health Survey - Mental Health (CCHS), by age group and sex, Canada and provinces, occasional. 109. Gejman PV, Sanders AR, Kendler KS. Genetics of schizophrenia: new findings and challenges. Annu Rev Genomics Hum Genet. 2011;12:121-44. 110. Gottesman, II, Laursen TM, Bertelsen A, Mortensen PB. Severe mental disorders in offspring with 2 psychiatrically ill parents. Arch Gen Psychiatry. 2010;67(3):252-7. 111. Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry. 2003;60(12):1187-92. 112. van Os J, Rutten BP, Myin-Germeys I, Delespaul P, Viechtbauer W, van Zelst C, et al. Identifying gene-environment interactions in schizophrenia: contemporary challenges for integrated, large-scale investigations. Schizophr Bull. 2014;40(4):729-36. 113. Jucaite A, Nyberg S. Dopaminergic Hypothesis of Schizophrenia: A Historical Perspective. In: Albert JS, Wood MW, editors. Targets and emerging therapies for schizophrenia: John Wiley & Sons; 2012. p. 5-35. 114. Beaulieu JM, Espinoza S, Gainetdinov RR. Dopamine receptors - IUPHAR Review 13. Br J Pharmacol. 2015;172(1):1-23. 115. D'Souza U. Gene and Promoter Structures of the Dopamine Receptors. In: Neve KA, editor. The Dopamine Receptors: Springer; 2010. p. 23-46. 116. Vallone D, Picetti R, Borrelli E. Structure and function of dopamine receptors. Neurosci Biobehav Rev. 2000;24(1):125-32. 117. Iversen LL, Iversen SD, Bloom FE, Roth RH. Introduction to Neuropsychopharmacology: Oxford University Press; 2009.

195

118. Mawlawi O, Martinez D, Slifstein M, Broft A, Chatterjee R, Hwang DR, et al. Imaging human mesolimbic dopamine transmission with positron emission tomography: I. Accuracy and precision of D(2) receptor parameter measurements in ventral striatum. J Cereb Blood Flow Metab. 2001;21(9):1034-57. 119. von Bohlen und Halbach O, Dermietzel R. Neurotransmitters. Neurotransmitters and Neuromodulators: Wiley-VCH Verlag GmbH & Co. KGaA; 2003. p. 40-115. 120. Hinz R. Tracer Imaging. In: Salzer R, editor. Biomedical Imaging: Principles and Applications: John Wiley & Sons; 2012. p. 215-47. 121. Howes OD, Kambeitz J, Kim E, Stahl D, Slifstein M, Abi-Dargham A, et al. The nature of dopamine dysfunction in schizophrenia and what this means for treatment. Arch Gen Psychiatry. 2012;69(8):776-86. 122. Cherry SR, Dalhbom M. PET: Physics, Instrumentation, and Scanners. In: Phelps ME, editor. PET: Springer; 2006. p. 1-117. 123. Das BK. Positron Emission Tomography: A Guide for Clinicians: Springer; 2015. 124. Jadvar H, Parker JA. Clinical PET and PET/CT: Springer; 2005. 125. Townsend DW. Basic Science of PET and PET/CT. In: Valk PE, editor. Positron emission tomography: clinical practice: Springer; 2006. p. 1-16. 126. Graff-Guerrero A, Mamo D, Shammi CM, Mizrahi R, Marcon H, Barsoum P, et al. The effect of antipsychotics on the high-affinity state of D2 and D3 receptors: a positron emission tomography study With [11C]-(+)-PHNO. Arch Gen Psychiatry. 2009;66(6):606-15. 127. Nakajima S, Caravaggio F, Boileau I, Chung JK, Plitman E, Gerretsen P, et al. Lack of age-dependent decrease in dopamine D3 receptor availability: a [(11)C]-(+)-PHNO and [(11)C]- raclopride positron emission tomography study. J Cereb Blood Flow Metab. 2015;35(11):1812- 8. 128. Stone JM. Glutamatergic antipsychotic drugs: a new dawn in the treatment of schizophrenia? Therapeutic advances in psychopharmacology. 2011;1(1):5-18. 129. Goff DC, Coyle JT. The emerging role of glutamate in the pathophysiology and treatment of schizophrenia. Am J Psychiatry. 2001;158(9):1367-77. 130. Institute of Medicine Forum on Neuroscience and Nervous System Disorders. Glutamate- Related Biomarkers in Drug Development for Disorders of the Nervous System: Workshop Summary. Washington (DC): National Academies Press; 2011. 131. Poels EM, Kegeles LS, Kantrowitz JT, Javitt DC, Lieberman JA, Abi-Dargham A, et al. Glutamatergic abnormalities in schizophrenia: a review of proton MRS findings. Schizophr Res. 2014;152(2-3):325-32. 132. Storey P. Introduction to Magnetic Resonance Imaging and Spectroscopy. In: Prasad PV, editor. Magnetic Resonacne Imaging: Methods and Biologic Applications: Humana Press; 2006. p. 3-57. 133. Neuroimaging Part I: Elsevier; 2016. 134. Wise RG. Neuroimaging Modalities: Description, Comparisons, Strengths, and Weaknesses. In: McArthur RA, editor. Translational Neuroimaging: Tools for CNS Drug Discovery, Development and Treatment: Elsevier; 2012. p. 1-22. 135. Brown RW, Cheng YN, Haacke EM, Thompson MR, Venkatesan R. Magnetic Resonance Imaging: A Preview. Magnetic resonance imaging: physical principles and sequence design: Wiley-Blackwell; 2014. p. 1-17. 136. Tomanek B, Sharp JC. Magnetic Resonance Technology. In: Salzer R, editor. Biomedical Imaging: Principles and Applications: John Wiley & Sons; 2012. p. 131-79. 137. Dale BM, Brown MA, Semelka RC. Production of net magnetization. In: Blackwell W, editor. MRI : basic principles and applications: John Wiley & Sons; 2015. p. 1-9.

196

138. Weishaupt D, Köchli VD, Marincek B. Spin and the Nuclear Magnetic Resonance Phenomenon. How does MRI work?: Springer; 2006. p. 1-5. 139. Dale BM, Brown MA, Semelka RC. Concepts of magnetic resonance. In: Blackwell W, editor. MRI : basic principles and applications: John Wiley & Sons; 2015. p. 10-6. 140. Weishaupt D, Köchli VD, Marincek B. Relaxation. How does MRI work?: Springer; 2006. p. 7-10. 141. Juchem C, Rothman DL. Basis of Magnetic Resonance. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 3-14. 142. Prost RW. Magnetic resonance spectroscopy. Med Phys. 2008;35(10):4530-44. 143. Zhu H, Barker PB. MR spectroscopy and spectroscopic imaging of the brain. Methods Mol Biol. 2011;711:203-26. 144. Abbott C, Bustillo J. What have we learned from proton magnetic resonance spectroscopy about schizophrenia? A critical update. Curr Opin Psychiatry. 2006;19(2):135-9. 145. Barker PB, Bizzi A, De Stefano N, Gullapalli R, Lin DDM. Introduction to MR spectroscopy in vivo. Clinical MR Spectroscopy: Cambridge University Press; 2010. p. 1-18. 146. Bustillo JR. Use of proton magnetic resonance spectroscopy in the treatment of psychiatric disorders: a critical update. Dialogues Clin Neurosci. 2013;15(3):329-37. 147. Kraguljac NV, Reid M, White D, Jones R, den Hollander J, Lowman D, et al. Neurometabolites in schizophrenia and bipolar disorder - a systematic review and meta-analysis. Psychiatry Res. 2012;203(2-3):111-25. 148. Wijtenburg SA, Yang S, Fischer BA, Rowland LM. In vivo assessment of neurotransmitters and modulators with magnetic resonance spectroscopy: Application to schizophrenia. Neurosci Biobehav Rev. 2015;51:276-95. 149. Taylor M. MRS of Psychiatric Disorders. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 222-8. 150. De Stefano N, Giorgio A. Multiple Sclerosis and Inflammatory Diseases. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 162-71. 151. Majós C, Julià-Sapé M, Arús C. Usefulness of Proton Magnetic Resonance Spectroscopy in the Clinical Management of Brain Tumors. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 141-61. 152. Dale BM, Brown MA, Semelka RC. Magnetic resonance spectroscopy. In: Blackwell W, editor. MRI : basic principles and applications: John Wiley & Sons; 2015. p. 162-76. 153. Barker PB, Bizzi A, De Stefano N, Gullapalli R, Lin DDM. Pulse sequences and protocol design. Clinical MR Spectroscopy: Cambridge University Press; 2010. p. 19-33. 154. Lei H, Xin L, Gruetter R, Mlynárik V. Localized Single-Voxel Magnetic Resonance Spectroscopy, Water Suppression, and Novel Approaches for Ultrashort Echo-Time Measurements. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 15-30. 155. Barker PB, Bizzi A, De Stefano N, Gullapalli R, Lin DDM. Spectral analysis, methods, quantitiation, and common artifacts. Clinical MR Spectroscopy: Cambridge University Press; 2010. p. 34-50. 156. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed. 2001;14(4):260-4. 157. Bustillo JR, Chen H, Gasparovic C, Mullins P, Caprihan A, Qualls C, et al. Glutamate as a marker of cognitive function in schizophrenia: a proton spectroscopic imaging study at 4 Tesla. Biol Psychiatry. 2011;69(1):19-27.

197

158. Bustillo JR, Jones T, Chen H, Lemke N, Abbott C, Qualls C, et al. Glutamatergic and Neuronal Dysfunction in Gray and White Matter: A Spectroscopic Imaging Study in a Large Schizophrenia Sample. Schizophr Bull. 2017;43(3):611-9. 159. Bustillo JR, Patel V, Jones T, Jung R, Payaknait N, Qualls C, et al. Risk-Conferring Glutamatergic Genes and Brain Glutamate Plus Glutamine in Schizophrenia. Front Psychiatry. 2017;8:79. 160. Shen J. Glutamate. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 111-21. 161. Poels EM, Kegeles LS, Kantrowitz JT, Slifstein M, Javitt DC, Lieberman JA, et al. Imaging glutamate in schizophrenia: review of findings and implications for drug discovery. Mol Psychiatry. 2014;19(1):20-9. 162. Moffett JR, Ariyannur P, Arun P, Namboodiri AMA. N-Acetylaspartate and N- Acetylaspartylglutamate in Central Nervous System Health and Disease. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 71-90. 163. Best JG, Stagg CJ, Dennis A. Other Significant Metabolites: Myo-Inositol, GABA, Glutamine, and Lactate. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 122-38. 164. Brand A, Richter-Landsberg C, Leibfritz D. Multinuclear NMR studies on the energy metabolism of glial and neuronal cells. Dev Neurosci. 1993;15(3-5):289-98. 165. Griffin JL, Bollard M, Nicholson JK, Bhakoo K. Spectral profiles of cultured neuronal and glial cells derived from HRMAS (1)H NMR spectroscopy. NMR Biomed. 2002;15(6):375- 84. 166. Chang L, Munsaka SM, Kraft-Terry S, Ernst T. Magnetic resonance spectroscopy to assess neuroinflammation and neuropathic pain. J Neuroimmune Pharmacol. 2013;8(3):576-93. 167. Lin JC, Gant N. The Biochemistry of Choline. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 104-10. 168. Gill SS, Thomas DG, Van Bruggen N, Gadian DG, Peden CJ, Bell JD, et al. Proton MR spectroscopy of intracranial tumours: in vivo and in vitro studies. J Comput Assist Tomogr. 1990;14(4):497-504. 169. Urenjak J, Williams SR, Gadian DG, Noble M. Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types. J Neurosci. 1993;13(3):981-9. 170. Turner CE, Gant N. The Biochemistry of Creatine. In: Stagg C, Rothman D, editors. Magnetic Resonance Spectroscopy. San Diego: Academic Press; 2014. p. 91-103. 171. Kreis R. Issues of spectral quality in clinical 1H-magnetic resonance spectroscopy and a gallery of artifacts. NMR Biomed. 2004;17(6):361-81. 172. Provencher SW. LCModel & LCMgui User’s Manual. 2012. 173. Jiru F, Skoch A, Klose U, Grodd W, Hajek M. Error images for spectroscopic imaging by LCModel using Cramer-Rao bounds. MAGMA. 2006;19(1):1-14. 174. Rowland LM, Kontson K, West J, Edden RA, Zhu H, Wijtenburg SA, et al. In vivo measurements of glutamate, GABA, and NAAG in schizophrenia. Schizophr Bull. 2013;39(5):1096-104. 175. Ongur D, Prescot AP, Jensen JE, Cohen BM, Renshaw PF. Creatine abnormalities in schizophrenia and bipolar disorder. Psychiatry Res. 2009;172(1):44-8. 176. Bustillo JR, Chen H, Jones T, Lemke N, Abbott C, Qualls C, et al. Increased glutamine in patients undergoing long-term treatment for schizophrenia: a proton magnetic resonance spectroscopy study at 3 T. JAMA Psychiatry. 2014;71(3):265-72.

198

177. Wijtenburg SA, Wright SN, Korenic SA, Gaston FE, Ndubuizu N, Chiappelli J, et al. Altered Glutamate and Regional Cerebral Blood Flow Levels in Schizophrenia: A (1)H-MRS and pCASL study. Neuropsychopharmacology. 2017;42(2):562-71. 178. Gasparovic C, Song T, Devier D, Bockholt HJ, Caprihan A, Mullins PG, et al. Use of tissue water as a concentration reference for proton spectroscopic imaging. Magn Reson Med. 2006;55(6):1219-26. 179. Flaum M, O'Leary DS, Swayze VW, 2nd, Miller DD, Arndt S, Andreasen NC. Symptom dimensions and brain morphology in schizophrenia and related psychotic disorders. J Psychiatr Res. 1995;29(4):261-76. 180. Ho BC, Andreasen NC, Nopoulos P, Arndt S, Magnotta V, Flaum M. Progressive structural brain abnormalities and their relationship to clinical outcome: a longitudinal magnetic resonance imaging study early in schizophrenia. Arch Gen Psychiatry. 2003;60(6):585-94. 181. Jaaskelainen E, Juola P, Kurtti J, Haapea M, Kyllonen M, Miettunen J, et al. Associations between brain morphology and outcome in schizophrenia in a general population sample. Eur Psychiatry. 2014;29(7):456-62. 182. Padmanabhan JL, Tandon N, Haller CS, Mathew IT, Eack SM, Clementz BA, et al. Correlations between brain structure and symptom dimensions of psychosis in schizophrenia, schizoaffective, and psychotic bipolar I disorders. Schizophr Bull. 2015;41(1):154-62. 183. Prasad KM, Sahni SD, Rohm BR, Keshavan MS. Dorsolateral prefrontal cortex morphology and short-term outcome in first-episode schizophrenia. Psychiatry Res. 2005;140(2):147-55. 184. van Erp TG, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. 2016;21(4):547-53. 185. Yao L, Lui S, Deng W, Wu M, Chen L, Xiao Y, et al. Association of white matter deficits with clinical symptoms in antipsychotic-naive first-episode schizophrenia: an optimized VBM study using 3T. MAGMA. 2014;27(4):283-90. 186. Pipitone J, Park MT, Winterburn J, Lett TA, Lerch JP, Pruessner JC, et al. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. NeuroImage. 2014;101:494-512. 187. Makowski C, Beland S, Kostopoulos P, Bhagwat N, Devenyi GA, Malla AK, et al. Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation. NeuroImage. 2017. 188. Chakravarty MM, Steadman P, van Eede MC, Calcott RD, Gu V, Shaw P, et al. Performing label-fusion-based segmentation using multiple automatically generated templates. Hum Brain Mapp. 2013;34(10):2635-54. 189. Eskildsen SF, Coupe P, Fonov V, Manjon JV, Leung KK, Guizard N, et al. BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage. 2012;59(3):2362-73. 190. Armstrong E, Schleicher A, Omran H, Curtis M, Zilles K. The ontogeny of human gyrification. Cereb Cortex. 1995;5(1):56-63. 191. Zilles K, Armstrong E, Schleicher A, Kretschmann HJ. The human pattern of gyrification in the cerebral cortex. Anat Embryol (Berl). 1988;179(2):173-9. 192. Haijma SV, Van Haren N, Cahn W, Koolschijn PC, Hulshoff Pol HE, Kahn RS. Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr Bull. 2013;39(5):1129-38. 193. Shenton ME, Whitford TJ, Kubicki M. Structural neuroimaging in schizophrenia: from methods to insights to treatments. Dialogues Clin Neurosci. 2010;12(3):317-32.

199

194. Narr KL, Bilder RM, Toga AW, Woods RP, Rex DE, Szeszko PR, et al. Mapping cortical thickness and gray matter concentration in first episode schizophrenia. Cereb Cortex. 2005;15(6):708-19. 195. Wheeler AL, Chakravarty MM, Lerch JP, Pipitone J, Daskalakis ZJ, Rajji TK, et al. Disrupted prefrontal interhemispheric structural coupling in schizophrenia related to working memory performance. Schizophr Bull. 2014;40(4):914-24. 196. Lerch JP, Evans AC. Cortical thickness analysis examined through power analysis and a population simulation. NeuroImage. 2005;24(1):163-73. 197. Kabani N, Le Goualher G, MacDonald D, Evans AC. Measurement of cortical thickness using an automated 3-D algorithm: a validation study. NeuroImage. 2001;13(2):375-80. 198. Sussman D, Leung RC, Chakravarty MM, Lerch JP, Taylor MJ. The developing human brain: age-related changes in cortical, subcortical, and cerebellar anatomy. Brain and behavior. 2016;6(4):e00457. 199. Ehrlich S, Brauns S, Yendiki A, Ho BC, Calhoun V, Schulz SC, et al. Associations of cortical thickness and cognition in patients with schizophrenia and healthy controls. Schizophr Bull. 2012;38(5):1050-62. 200. Hartberg CB, Lawyer G, Nyman H, Jonsson EG, Haukvik UK, Saetre P, et al. Investigating relationships between cortical thickness and cognitive performance in patients with schizophrenia and healthy adults. Psychiatry Res. 2010;182(2):123-33. 201. Rimol LM, Hartberg CB, Nesvag R, Fennema-Notestine C, Hagler DJ, Jr., Pung CJ, et al. Cortical thickness and subcortical volumes in schizophrenia and bipolar disorder. Biol Psychiatry. 2010;68(1):41-50. 202. Song X, Quan M, Lv L, Li X, Pang L, Kennedy D, et al. Decreased cortical thickness in drug naive first episode schizophrenia: in relation to serum levels of BDNF. J Psychiatr Res. 2015;60:22-8. 203. Xiao Y, Lui S, Deng W, Yao L, Zhang W, Li S, et al. Altered cortical thickness related to clinical severity but not the untreated disease duration in schizophrenia. Schizophr Bull. 2015;41(1):201-10. 204. Walton E, Hibar DP, van Erp TG, Potkin SG, Roiz-Santianez R, Crespo-Facorro B, et al. Positive symptoms associate with cortical thinning in the superior temporal gyrus via the ENIGMA Schizophrenia consortium. Acta Psychiatr Scand. 2017;135(5):439-47. 205. Walton E, Hibar DP, van Erp TGM, Potkin SG, Roiz-Santianez R, Crespo-Facorro B, et al. Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium. Psychol Med. 2018;48(1):82-94. 206. van Haren NE, Schnack HG, Cahn W, van den Heuvel MP, Lepage C, Collins L, et al. Changes in cortical thickness during the course of illness in schizophrenia. Arch Gen Psychiatry. 2011;68(9):871-80. 207. Gerretsen P, Menon M, Chakravarty MM, Lerch JP, Mamo DC, Remington G, et al. Illness denial in schizophrenia spectrum disorders: a function of left hemisphere dominance. Hum Brain Mapp. 2015;36(1):213-25. 208. Makowski C, Bodnar M, Malla AK, Joober R, Lepage M. Age-related cortical thickness trajectories in first episode psychosis patients presenting with early persistent negative symptoms. NPJ schizophrenia. 2016;2:16029. 209. Park MTM, Raznahan A, Shaw P, Gogtay N, Lerch JP, Chakravarty MM. Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia. J Psychiatry Neurosci. 2018;43(2):170094. 210. Rajaprakash M, Chakravarty MM, Lerch JP, Rovet J. Cortical morphology in children with alcohol-related neurodevelopmental disorder. Brain and behavior. 2014;4(1):41-50.

200

211. Redolfi A, Manset D, Barkhof F, Wahlund LO, Glatard T, Mangin JF, et al. Head-to- head comparison of two popular cortical thickness extraction algorithms: a cross-sectional and longitudinal study. PLoS One. 2015;10(3):e0117692. 212. Weiser M, Davidson M, Noy S. Comments on risk for schizophrenia. Schizophr Res. 2005;79(1):15-21. 213. Creese I, Burt DR, Snyder SH. Dopamine receptor binding predicts clinical and pharmacological potencies of antischizophrenic drugs. Science. 1976;192(4238):481-3. 214. Seeman P, Lee T. Antipsychotic drugs: direct correlation between clinical potency and presynaptic action on dopamine neurons. Science. 1975;188(4194):1217-9. 215. Abi-Dargham A, Gil R, Krystal J, Baldwin RM, Seibyl JP, Bowers M, et al. Increased striatal dopamine transmission in schizophrenia: confirmation in a second cohort. Am J Psychiatry. 1998;155(6):761-7. 216. Breier A, Su TP, Saunders R, Carson RE, Kolachana BS, de Bartolomeis A, et al. Schizophrenia is associated with elevated amphetamine-induced synaptic dopamine concentrations: evidence from a novel positron emission tomography method. Proc Natl Acad Sci U S A. 1997;94(6):2569-74. 217. Hietala J, Syvalahti E, Vuorio K, Rakkolainen V, Bergman J, Haaparanta M, et al. Presynaptic dopamine function in striatum of neuroleptic-naive schizophrenic patients. Lancet. 1995;346(8983):1130-1. 218. Laruelle M, Abi-Dargham A, Van Dyck CH, Gil R, D'Souza CD, Erdos J, et al. Single photon emission computerized tomography imaging of amphetamine-induced dopamine release in drug-free schizophrenic subjects. Proc Natl Acad Sci U S A. 1996;93(17):9235. 219. Javitt DC, Zukin SR, Heresco-Levy U, Umbricht D. Has an angel shown the way? Etiological and therapeutic implications of the PCP/NMDA model of schizophrenia. Schizophr Bull. 2012;38(5):958-66. 220. Lindenmayer JP. Treatment refractory schizophrenia. Psychiatr Q. 2000;71(4):373-84. 221. Suzuki T, Remington G, Mulsant BH, Rajji TK, Uchida H, Graff-Guerrero A, et al. Treatment resistant schizophrenia and response to antipsychotics: a review. Schizophr Res. 2011;133(1-3):54-62. 222. Stone JM, Morrison PD, Pilowsky LS. Glutamate and dopamine dysregulation in schizophrenia--a synthesis and selective review. J Psychopharmacol. 2007;21(4):440-52. 223. Moghaddam B, Javitt D. From revolution to evolution: the glutamate hypothesis of schizophrenia and its implication for treatment. Neuropsychopharmacology. 2012;37(1):4-15. 224. Coyle JT, Tsai G, Goff D. Converging evidence of NMDA receptor hypofunction in the pathophysiology of schizophrenia. Ann N Y Acad Sci. 2003;1003:318-27. 225. Javitt DC, Zukin SR. Recent advances in the phencyclidine model of schizophrenia. Am J Psychiatry. 1991;148(10):1301-8. 226. Vollenweider FX, Geyer MA. A systems model of altered consciousness: integrating natural and drug-induced psychoses. Brain Res Bull. 2001;56(5):495-507. 227. Adler CM, Malhotra AK, Elman I, Goldberg T, Egan M, Pickar D, et al. Comparison of ketamine-induced thought disorder in healthy volunteers and thought disorder in schizophrenia. Am J Psychiatry. 1999;156(10):1646-9. 228. Krystal JH, Bennett A, Abi-Saab D, Belger A, Karper LP, D'Souza DC, et al. Dissociation of ketamine effects on rule acquisition and rule implementation: possible relevance to NMDA receptor contributions to executive cognitive functions. Biol Psychiatry. 2000;47(2):137-43. 229. Krystal JH, Karper LP, Seibyl JP, Freeman GK, Delaney R, Bremner JD, et al. Subanesthetic effects of the noncompetitive NMDA antagonist, ketamine, in humans.

201

Psychotomimetic, perceptual, cognitive, and neuroendocrine responses. Arch Gen Psychiatry. 1994;51(3):199-214. 230. Krystal JH, Perry EB, Jr., Gueorguieva R, Belger A, Madonick SH, Abi-Dargham A, et al. Comparative and interactive human psychopharmacologic effects of ketamine and amphetamine: implications for glutamatergic and dopaminergic model psychoses and cognitive function. Arch Gen Psychiatry. 2005;62(9):985-94. 231. Malhotra AK, Pinals DA, Weingartner H, Sirocco K, Missar CD, Pickar D, et al. NMDA receptor function and human cognition: the effects of ketamine in healthy volunteers. Neuropsychopharmacology. 1996;14(5):301-7. 232. Lahti AC, Holcomb HH, Medoff DR, Tamminga CA. Ketamine activates psychosis and alters limbic blood flow in schizophrenia. Neuroreport. 1995;6(6):869-72. 233. Lahti AC, Koffel B, LaPorte D, Tamminga CA. Subanesthetic doses of ketamine stimulate psychosis in schizophrenia. Neuropsychopharmacology. 1995;13(1):9-19. 234. Kantrowitz J, Javitt DC. Glutamatergic transmission in schizophrenia: from basic research to clinical practice. Curr Opin Psychiatry. 2012;25(2):96-102. 235. Javitt DC. Glutamate and Schizophrenia: Phencyclidine, N-Methyl-d-Aspartate Receptors, and Dopamine-Glutamate Interactions. Int Rev Neurobiol. 2007;78:69-108. 236. Moghaddam B, Krystal JH. Capturing the angel in "angel dust": twenty years of translational neuroscience studies of NMDA receptor antagonists in animals and humans. Schizophr Bull. 2012;38(5):942-9. 237. Nakazawa K, Zsiros V, Jiang Z, Nakao K, Kolata S, Zhang S, et al. GABAergic interneuron origin of schizophrenia pathophysiology. Neuropharmacology. 2012;62(3):1574-83. 238. Bustos G, Abarca J, Forray MI, Gysling K, Bradberry CW, Roth RH. Regulation of excitatory amino acid release by N-methyl-D-aspartate receptors in rat striatum: in vivo microdialysis studies. Brain Res. 1992;585(1-2):105-15. 239. Moghaddam B, Adams B, Verma A, Daly D. Activation of glutamatergic neurotransmission by ketamine: a novel step in the pathway from NMDA receptor blockade to dopaminergic and cognitive disruptions associated with the prefrontal cortex. J Neurosci. 1997;17(8):2921-7. 240. Iltis I, Koski DM, Eberly LE, Nelson CD, Deelchand DK, Valette J, et al. Neurochemical changes in the rat prefrontal cortex following acute phencyclidine treatment: an in vivo localized (1)H MRS study. NMR Biomed. 2009;22(7):737-44. 241. Rowland LM, Bustillo JR, Mullins PG, Jung RE, Lenroot R, Landgraf E, et al. Effects of ketamine on anterior cingulate glutamate metabolism in healthy humans: a 4-T proton MRS study. Am J Psychiatry. 2005;162(2):394-6. 242. Stone JM, Dietrich C, Edden R, Mehta MA, De Simoni S, Reed LJ, et al. Ketamine effects on brain GABA and glutamate levels with 1H-MRS: relationship to ketamine-induced psychopathology. Mol Psychiatry. 2012;17(7):664-5. 243. Anand A, Charney DS, Oren DA, Berman RM, Hu XS, Cappiello A, et al. Attenuation of the neuropsychiatric effects of ketamine with lamotrigine: support for hyperglutamatergic effects of N-methyl-D-aspartate receptor antagonists. Arch Gen Psychiatry. 2000;57(3):270-6. 244. Deakin JF, Lees J, McKie S, Hallak JE, Williams SR, Dursun SM. Glutamate and the neural basis of the subjective effects of ketamine: a pharmaco-magnetic resonance imaging study. Arch Gen Psychiatry. 2008;65(2):154-64. 245. Doyle OM, De Simoni S, Schwarz AJ, Brittain C, O'Daly OG, Williams SC, et al. Quantifying the attenuation of the ketamine pharmacological magnetic resonance imaging response in humans: a validation using antipsychotic and glutamatergic agents. J Pharmacol Exp Ther. 2013;345(1):151-60.

202

246. de la Fuente-Sandoval C, Leon-Ortiz P, Azcarraga M, Favila R, Stephano S, Graff- Guerrero A. Striatal glutamate and the conversion to psychosis: a prospective 1H-MRS imaging study. Int J Neuropsychopharmacol. 2013;16(2):471-5. 247. de la Fuente-Sandoval C, Leon-Ortiz P, Favila R, Stephano S, Mamo D, Ramirez- Bermudez J, et al. Higher levels of glutamate in the associative-striatum of subjects with prodromal symptoms of schizophrenia and patients with first-episode psychosis. Neuropsychopharmacology. 2011;36(9):1781-91. 248. Kegeles LS, Mao X, Stanford AD, Girgis R, Ojeil N, Xu X, et al. Elevated prefrontal cortex gamma-aminobutyric acid and glutamate-glutamine levels in schizophrenia measured in vivo with proton magnetic resonance spectroscopy. Arch Gen Psychiatry. 2012;69(5):449-59. 249. Kraguljac NV, White DM, Reid MA, Lahti AC. Increased hippocampal glutamate and volumetric deficits in unmedicated patients with schizophrenia. JAMA Psychiatry. 2013;70(12):1294-302. 250. Purdon SE, Valiakalayil A, Hanstock CC, Seres P, Tibbo P. Elevated 3T proton MRS glutamate levels associated with poor Continuous Performance Test (CPT-0X) scores and genetic risk for schizophrenia. Schizophr Res. 2008;99(1-3):218-24. 251. Bartha R, Williamson PC, Drost DJ, Malla A, Carr TJ, Cortese L, et al. Measurement of glutamate and glutamine in the medial prefrontal cortex of never-treated schizophrenic patients and healthy controls by proton magnetic resonance spectroscopy. Arch Gen Psychiatry. 1997;54(10):959-65. 252. Theberge J, Bartha R, Drost DJ, Menon RS, Malla A, Takhar J, et al. Glutamate and glutamine measured with 4.0 T proton MRS in never-treated patients with schizophrenia and healthy volunteers. Am J Psychiatry. 2002;159(11):1944-6. 253. de la Fuente-Sandoval C, Leon-Ortiz P, Azcarraga M, Stephano S, Favila R, Diaz-Galvis L, et al. Glutamate levels in the associative striatum before and after 4 weeks of antipsychotic treatment in first-episode psychosis: a longitudinal proton magnetic resonance spectroscopy study. JAMA Psychiatry. 2013;70(10):1057-66. 254. Goto N, Yoshimura R, Kakeda S, Nishimura J, Moriya J, Hayashi K, et al. Six-month treatment with atypical antipsychotic drugs decreased frontal-lobe levels of glutamate plus glutamine in early-stage first-episode schizophrenia. Neuropsychiatr Dis Treat. 2012;8:119-22. 255. Ohrmann P, Siegmund A, Suslow T, Spitzberg K, Kersting A, Arolt V, et al. Evidence for glutamatergic neuronal dysfunction in the prefrontal cortex in chronic but not in first-episode patients with schizophrenia: a proton magnetic resonance spectroscopy study. Schizophr Res. 2005;73(2-3):153-7. 256. Theberge J, Al-Semaan Y, Williamson PC, Menon RS, Neufeld RW, Rajakumar N, et al. Glutamate and glutamine in the anterior cingulate and thalamus of medicated patients with chronic schizophrenia and healthy comparison subjects measured with 4.0-T proton MRS. Am J Psychiatry. 2003;160(12):2231-3. 257. Marsman A, van den Heuvel MP, Klomp DW, Kahn RS, Luijten PR, Hulshoff Pol HE. Glutamate in schizophrenia: a focused review and meta-analysis of (1)H-MRS studies. Schizophr Bull. 2013;39(1):120-9. 258. Demjaha A, Egerton A, Murray RM, Kapur S, Howes OD, Stone JM, et al. Antipsychotic treatment resistance in schizophrenia associated with elevated glutamate levels but normal dopamine function. Biol Psychiatry. 2014;75(5):e11-3. 259. Egerton A, Brugger S, Raffin M, Barker GJ, Lythgoe DJ, McGuire PK, et al. Anterior cingulate glutamate levels related to clinical status following treatment in first-episode schizophrenia. Neuropsychopharmacology. 2012;37(11):2515-21.

203

260. Lahti AC, Reid MA. Is there evidence for neurotoxicity in the prodromal and early stages of schizophrenia? Neuropsychopharmacology. 2011;36(9):1779-80. 261. Lau A, Tymianski M. Glutamate receptors, neurotoxicity and neurodegeneration. Pflugers Arch. 2010;460(2):525-42. 262. Mehta A, Prabhakar M, Kumar P, Deshmukh R, Sharma PL. Excitotoxicity: bridge to various triggers in neurodegenerative disorders. Eur J Pharmacol. 2013;698(1-3):6-18. 263. Olney JW. Brain lesions, obesity, and other disturbances in mice treated with monosodium glutamate. Science. 1969;164(3880):719-21. 264. Olney JW, Sharpe LG. Brain lesions in an infant rhesus monkey treated with monsodium glutamate. Science. 1969;166(3903):386-8. 265. Belousov AB. Novel model for the mechanisms of glutamate-dependent excitotoxicity: role of neuronal gap junctions. Brain Res. 2012;1487:123-30. 266. Choi DW. Glutamate neurotoxicity and diseases of the nervous system. Neuron. 1988;1(8):623-34. 267. Hardingham GE, Bading H. Synaptic versus extrasynaptic NMDA receptor signalling: implications for neurodegenerative disorders. Nat Rev Neurosci. 2010;11(10):682-96. 268. Deutsch SI, Rosse RB, Schwartz BL, Mastropaolo J. A revised excitotoxic hypothesis of schizophrenia: therapeutic implications. Clin Neuropharmacol. 2001;24(1):43-9. 269. Arango C, Rapado-Castro M, Reig S, Castro-Fornieles J, Gonzalez-Pinto A, Otero S, et al. Progressive brain changes in children and adolescents with first-episode psychosis. Arch Gen Psychiatry. 2012;69(1):16-26. 270. Hulshoff Pol HE, Kahn RS. What happens after the first episode? A review of progressive brain changes in chronically ill patients with schizophrenia. Schizophr Bull. 2008;34(2):354-66. 271. Meyer-Lindenberg A. Neuroimaging and the question of neurodegeneration in schizophrenia. Prog Neurobiol. 2011;95(4):514-6. 272. van Haren NE, Hulshoff Pol HE, Schnack HG, Cahn W, Brans R, Carati I, et al. Progressive brain volume loss in schizophrenia over the course of the illness: evidence of maturational abnormalities in early adulthood. Biol Psychiatry. 2008;63(1):106-13. 273. Bora E, Fornito A, Radua J, Walterfang M, Seal M, Wood SJ, et al. Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophr Res. 2011;127(1-3):46-57. 274. Chan RC, Di X, McAlonan GM, Gong QY. Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta- analysis of illness progression. Schizophr Bull. 2011;37(1):177-88. 275. Ellison-Wright I, Glahn DC, Laird AR, Thelen SM, Bullmore E. The anatomy of first- episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. Am J Psychiatry. 2008;165(8):1015-23. 276. Glahn DC, Laird AR, Ellison-Wright I, Thelen SM, Robinson JL, Lancaster JL, et al. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry. 2008;64(9):774-81. 277. Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry. 2005;162(12):2233-45. 278. Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of MRI findings in schizophrenia. Schizophr Res. 2001;49(1-2):1-52.

204

279. Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiatry. 2006;188:510-8. 280. Colibazzi T, Wexler BE, Bansal R, Hao X, Liu J, Sanchez-Pena J, et al. Anatomical abnormalities in gray and white matter of the cortical surface in persons with schizophrenia. PLoS One. 2013;8(2):e55783. 281. Connor CM, Crawford BC, Akbarian S. White matter neuron alterations in schizophrenia and related disorders. Int J Dev Neurosci. 2011;29(3):325-34. 282. Lawrie SM, Abukmeil SS. Brain abnormality in schizophrenia. A systematic and quantitative review of volumetric magnetic resonance imaging studies. Br J Psychiatry. 1998;172:110-20. 283. Nazeri A, Chakravarty MM, Felsky D, Lobaugh NJ, Rajji TK, Mulsant BH, et al. Alterations of superficial white matter in schizophrenia and relationship to cognitive performance. Neuropsychopharmacology. 2013;38(10):1954-62. 284. Voineskos AN, Foussias G, Lerch J, Felsky D, Remington G, Rajji TK, et al. Neuroimaging evidence for the deficit subtype of schizophrenia. JAMA Psychiatry. 2013;70(5):472-80. 285. Goldman AL, Pezawas L, Mattay VS, Fischl B, Verchinski BA, Chen Q, et al. Widespread reductions of cortical thickness in schizophrenia and spectrum disorders and evidence of heritability. Arch Gen Psychiatry. 2009;66(5):467-77. 286. Kuperberg GR, Broome MR, McGuire PK, David AS, Eddy M, Ozawa F, et al. Regionally localized thinning of the cerebral cortex in schizophrenia. Arch Gen Psychiatry. 2003;60(9):878-88. 287. Narr KL, Toga AW, Szeszko P, Thompson PM, Woods RP, Robinson D, et al. Cortical thinning in cingulate and occipital cortices in first episode schizophrenia. Biol Psychiatry. 2005;58(1):32-40. 288. Rais M, van Haren NE, Cahn W, Schnack HG, Lepage C, Collins L, et al. Cannabis use and progressive cortical thickness loss in areas rich in CB1 receptors during the first five years of schizophrenia. Eur Neuropsychopharmacol. 2010;20(12):855-65. 289. White T, Andreasen NC, Nopoulos P, Magnotta V. Gyrification abnormalities in childhood- and adolescent-onset schizophrenia. Biol Psychiatry. 2003;54(4):418-26. 290. Moncrieff J, Leo J. A systematic review of the effects of antipsychotic drugs on brain volume. Psychol Med. 2010;40(9):1409-22. 291. Navari S, Dazzan P. Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings. Psychol Med. 2009;39(11):1763-77. 292. Torres US, Portela-Oliveira E, Borgwardt S, Busatto GF. Structural brain changes associated with antipsychotic treatment in schizophrenia as revealed by voxel-based morphometric MRI: an activation likelihood estimation meta-analysis. BMC Psychiatry. 2013;13:342. 293. van Haren NE, Cahn W, Hulshoff Pol HE, Kahn RS. The course of brain abnormalities in schizophrenia: can we slow the progression? J Psychopharmacol. 2012;26(5 Suppl):8-14. 294. Vita A, De Peri L, Deste G, Sacchetti E. Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies. Transl Psychiatry. 2012;2:e190. 295. Chen Z, Deng W, Gong Q, Huang C, Jiang L, Li M, et al. Extensive brain structural network abnormality in first-episode treatment-naive patients with schizophrenia: morphometrical and covariation study. Psychol Med. 2014:1-13.

205

296. Fornito A, Yucel M, Wood SJ, Adamson C, Velakoulis D, Saling MM, et al. Surface- based morphometry of the anterior cingulate cortex in first episode schizophrenia. Hum Brain Mapp. 2008;29(4):478-89. 297. Ren W, Lui S, Deng W, Li F, Li M, Huang X, et al. Anatomical and functional brain abnormalities in drug-naive first-episode schizophrenia. Am J Psychiatry. 2013;170(11):1308- 16. 298. Schultz CC, Koch K, Wagner G, Roebel M, Schachtzabel C, Gaser C, et al. Reduced cortical thickness in first episode schizophrenia. Schizophr Res. 2010;116(2-3):204-9. 299. Sprooten E, Papmeyer M, Smyth AM, Vincenz D, Honold S, Conlon GA, et al. Cortical thickness in first-episode schizophrenia patients and individuals at high familial risk: a cross- sectional comparison. Schizophr Res. 2013;151(1-3):259-64. 300. Venkatasubramanian G, Jayakumar PN, Gangadhar BN, Keshavan MS. Automated MRI parcellation study of regional volume and thickness of prefrontal cortex (PFC) in antipsychotic- naive schizophrenia. Acta Psychiatr Scand. 2008;117(6):420-31. 301. Tsai G, Coyle JT. Glutamatergic mechanisms in schizophrenia. Annu Rev Pharmacol Toxicol. 2002;42:165-79. 302. Bartha R, al-Semaan YM, Williamson PC, Drost DJ, Malla AK, Carr TJ, et al. A short echo proton magnetic resonance spectroscopy study of the left mesial-temporal lobe in first-onset schizophrenic patients. Biol Psychiatry. 1999;45(11):1403-11. 303. Duncan NW, Wiebking C, Tiret B, Marjanska M, Hayes DJ, Lyttleton O, et al. Glutamate concentration in the medial prefrontal cortex predicts resting-state cortical-subcortical functional connectivity in humans. PLoS One. 2013;8(4):e60312. 304. Gruber O, Hasan A, Scherk H, Wobrock T, Schneider-Axmann T, Ekawardhani S, et al. Association of the brain-derived neurotrophic factor val66met polymorphism with magnetic resonance spectroscopic markers in the human hippocampus: in vivo evidence for effects on the glutamate system. Eur Arch Psychiatry Clin Neurosci. 2012;262(1):23-31. 305. Kegeles LS, Shungu DC, Anjilvel S, Chan S, Ellis SP, Xanthopoulos E, et al. Hippocampal pathology in schizophrenia: magnetic resonance imaging and spectroscopy studies. Psychiatry Res. 2000;98(3):163-75. 306. Rusch N, Tebartz van Elst L, Valerius G, Buchert M, Thiel T, Ebert D, et al. Neurochemical and structural correlates of executive dysfunction in schizophrenia. Schizophr Res. 2008;99(1-3):155-63. 307. Wood SJ, Kennedy D, Phillips LJ, Seal ML, Yucel M, Nelson B, et al. Hippocampal pathology in individuals at ultra-high risk for psychosis: a multi-modal magnetic resonance study. NeuroImage. 2010;52(1):62-8. 308. Aoyama N, Theberge J, Drost DJ, Manchanda R, Northcott S, Neufeld RW, et al. Grey matter and social functioning correlates of glutamatergic metabolite loss in schizophrenia. Br J Psychiatry. 2011;198(6):448-56. 309. Klar AA, Ballmaier M, Leopold K, Hake I, Schaefer M, Bruhl R, et al. Interaction of hippocampal volume and N-acetylaspartate concentration deficits in schizophrenia: a combined MRI and 1H-MRS study. NeuroImage. 2010;53(1):51-7. 310. Stone JM, Day F, Tsagaraki H, Valli I, McLean MA, Lythgoe DJ, et al. Glutamate dysfunction in people with prodromal symptoms of psychosis: relationship to gray matter volume. Biol Psychiatry. 2009;66(6):533-9. 311. Tandon N, Bolo NR, Sanghavi K, Mathew IT, Francis AN, Stanley JA, et al. Brain metabolite alterations in young adults at familial high risk for schizophrenia using proton magnetic resonance spectroscopy. Schizophr Res. 2013;148(1-3):59-66.

206

312. Theberge J, Williamson KE, Aoyama N, Drost DJ, Manchanda R, Malla AK, et al. Longitudinal grey-matter and glutamatergic losses in first-episode schizophrenia. Br J Psychiatry. 2007;191:325-34. 313. Tsai G, van Kammen DP, Chen S, Kelley ME, Grier A, Coyle JT. Glutamatergic neurotransmission involves structural and clinical deficits of schizophrenia. Biol Psychiatry. 1998;44(8):667-74. 314. McGorry PD, Nelson B, Amminger GP, Bechdolf A, Francey SM, Berger G, et al. Intervention in individuals at ultra-high risk for psychosis: a review and future directions. J Clin Psychiatry. 2009;70(9):1206-12. 315. Nelson B, Yuen HP, Wood SJ, Lin A, Spiliotacopoulos D, Bruxner A, et al. Long-term follow-up of a group at ultra high risk ("prodromal") for psychosis: the PACE 400 study. JAMA Psychiatry. 2013;70(8):793-802. 316. Anticevic A, Gancsos M, Murray JD, Repovs G, Driesen NR, Ennis DJ, et al. NMDA receptor function in large-scale anticorrelated neural systems with implications for cognition and schizophrenia. Proc Natl Acad Sci U S A. 2012;109(41):16720-5. 317. Nagels A, Kirner-Veselinovic A, Krach S, Kircher T. Neural correlates of S-ketamine induced psychosis during overt continuous verbal fluency. NeuroImage. 2011;54(2):1307-14. 318. Adams B, Moghaddam B. Corticolimbic dopamine neurotransmission is temporally dissociated from the cognitive and locomotor effects of phencyclidine. J Neurosci. 1998;18(14):5545-54. 319. Adams BW, Moghaddam B. Effect of clozapine, haloperidol, or M100907 on phencyclidine-activated glutamate efflux in the prefrontal cortex. Biol Psychiatry. 2001;50(10):750-7. 320. Farber NB, Kim SH, Dikranian K, Jiang XP, Heinkel C. Receptor mechanisms and circuitry underlying NMDA antagonist neurotoxicity. Mol Psychiatry. 2002;7(1):32-43. 321. Ikonomidou C, Bosch F, Miksa M, Bittigau P, Vockler J, Dikranian K, et al. Blockade of NMDA receptors and apoptotic neurodegeneration in the developing brain. Science. 1999;283(5398):70-4. 322. Olney JW, Farber NB. Glutamate receptor dysfunction and schizophrenia. Arch Gen Psychiatry. 1995;52(12):998-1007. 323. Olney JW, Newcomer JW, Farber NB. NMDA receptor hypofunction model of schizophrenia. J Psychiatr Res. 1999;33(6):523-33. 324. Sharp FR, Butman M, Koistinaho J, Aardalen K, Nakki R, Massa SM, et al. Phencyclidine induction of the hsp 70 stress gene in injured pyramidal neurons is mediated via multiple receptors and voltage gated calcium channels. Neuroscience. 1994;62(4):1079-92. 325. Sharp FR, Jasper P, Hall J, Noble L, Sagar SM. MK-801 and ketamine induce heat shock protein HSP72 in injured neurons in posterior cingulate and retrosplenial cortex. Ann Neurol. 1991;30(6):801-9. 326. Carter K, Dickerson J, Schoepp DD, Reilly M, Herring N, Williams J, et al. The mGlu2/3 receptor agonist LY379268 injected into cortex or thalamus decreases neuronal injury in retrosplenial cortex produced by NMDA receptor antagonist MK-801: possible implications for psychosis. Neuropharmacology. 2004;47(8):1135-45. 327. Moghaddam B, Adams BW. Reversal of phencyclidine effects by a group II metabotropic glutamate receptor agonist in rats. Science. 1998;281(5381):1349-52. 328. Okamura N, Hashimoto K, Shimizu E, Koike K, Ohgake S, Koizumi H, et al. Protective effect of LY379268, a selective group II metabotropic glutamate receptor agonist, on dizocilpine-induced neuropathological changes in rat retrosplenial cortex. Brain Res. 2003;992(1):114-9.

207

329. Schobel SA, Chaudhury NH, Khan UA, Paniagua B, Styner MA, Asllani I, et al. Imaging patients with psychosis and a mouse model establishes a spreading pattern of hippocampal dysfunction and implicates glutamate as a driver. Neuron. 2013;78(1):81-93. 330. Kessler RC, Amminger GP, Aguilar-Gaxiola S, Alonso J, Lee S, Ustun TB. Age of onset of mental disorders: a review of recent literature. Curr Opin Psychiatry. 2007;20(4):359-64. 331. Lieberman JA, Perkins D, Belger A, Chakos M, Jarskog F, Boteva K, et al. The early stages of schizophrenia: speculations on pathogenesis, pathophysiology, and therapeutic approaches. Biol Psychiatry. 2001;50(11):884-97. 332. Farber NB. The NMDA receptor hypofunction model of psychosis. Ann N Y Acad Sci. 2003;1003:119-30. 333. Reich DL, Silvay G. Ketamine: an update on the first twenty-five years of clinical experience. Can J Anaesth. 1989;36(2):186-97. 334. Auer RN. Effect of age and sex on N-methyl-D-aspartate antagonist-induced neuronal necrosis in rats. Stroke. 1996;27(4):743-6. 335. Farber NB, Wozniak DF, Price MT, Labruyere J, Huss J, St Peter H, et al. Age-specific neurotoxicity in the rat associated with NMDA receptor blockade: potential relevance to schizophrenia? Biol Psychiatry. 1995;38(12):788-96. 336. Ellison G. Competitive and non-competitive NMDA antagonists induce similar limbic degeneration. Neuroreport. 1994;5(18):2688-92. 337. Hargreaves RJ, Rigby M, Smith D, Hill RG, Iversen LL. Competitive as well as uncompetitive N-methyl-D-aspartate receptor antagonists affect cortical neuronal morphology and cerebral glucose metabolism. Neurochem Res. 1993;18(12):1263-9. 338. Olney JW, Labruyere J, Price MT. Pathological changes induced in cerebrocortical neurons by phencyclidine and related drugs. Science. 1989;244(4910):1360-2. 339. Sharp FR, Tomitaka M, Bernaudin M, Tomitaka S. Psychosis: pathological activation of limbic thalamocortical circuits by psychomimetics and schizophrenia? Trends Neurosci. 2001;24(6):330-4. 340. Tomitaka S, Tomitaka M, Tolliver BK, Sharp FR. Bilateral blockade of NMDA receptors in anterior thalamus by dizocilpine (MK-801) injures pyramidal neurons in rat retrosplenial cortex. Eur J Neurosci. 2000;12(4):1420-30. 341. Lorrain DS, Baccei CS, Bristow LJ, Anderson JJ, Varney MA. Effects of ketamine and N-methyl-D-aspartate on glutamate and dopamine release in the rat prefrontal cortex: modulation by a group II selective metabotropic glutamate receptor agonist LY379268. Neuroscience. 2003;117(3):697-706. 342. Sharp JW, Petersen DL, Langford MT. DNQX inhibits phencyclidine (PCP) and ketamine induction of the hsp70 heat shock gene in the rat cingulate and retrosplenial cortex. Brain Res. 1995;687(1-2):114-24. 343. Hutcheson NL, Reid MA, White DM, Kraguljac NV, Avsar KB, Bolding MS, et al. Multimodal analysis of the hippocampus in schizophrenia using proton magnetic resonance spectroscopy and functional magnetic resonance imaging. Schizophr Res. 2012;140(1-3):136-42. 344. Reid MA, Stoeckel LE, White DM, Avsar KB, Bolding MS, Akella NS, et al. Assessments of function and biochemistry of the anterior cingulate cortex in schizophrenia. Biol Psychiatry. 2010;68(7):625-33. 345. Valli I, Stone J, Mechelli A, Bhattacharyya S, Raffin M, Allen P, et al. Altered medial temporal activation related to local glutamate levels in subjects with prodromal signs of psychosis. Biol Psychiatry. 2011;69(1):97-9.

208

346. Eyjolfsson EM, Nilsen LH, Kondziella D, Brenner E, Haberg A, Sonnewald U. Altered 13C glucose metabolism in the cortico-striato-thalamo-cortical loop in the MK-801 rat model of schizophrenia. J Cereb Blood Flow Metab. 2011;31(3):976-85. 347. Kondziella D, Brenner E, Eyjolfsson EM, Markinhuhta KR, Carlsson ML, Sonnewald U. Glial-neuronal interactions are impaired in the schizophrenia model of repeated MK801 exposure. Neuropsychopharmacology. 2006;31(9):1880-7. 348. Rothman DL, De Feyter HM, de Graaf RA, Mason GF, Behar KL. 13C MRS studies of neuroenergetics and neurotransmitter cycling in humans. NMR Biomed. 2011;24(8):943-57. 349. Steen RG, Hamer RM, Lieberman JA. Measurement of brain metabolites by 1H magnetic resonance spectroscopy in patients with schizophrenia: a systematic review and meta-analysis. Neuropsychopharmacology. 2005;30(11):1949-62. 350. Brugger S, Davis JM, Leucht S, Stone JM. Proton magnetic resonance spectroscopy and illness stage in schizophrenia--a systematic review and meta-analysis. Biol Psychiatry. 2011;69(5):495-503. 351. Bustillo J, Galloway MP, Ghoddoussi F, Bolognani F, Perrone-Bizzozero N. Medial- frontal cortex hypometabolism in chronic phencyclidine exposed rats assessed by high resolution magic angle spin 11.7 T proton magnetic resonance spectroscopy. Neurochem Int. 2012;61(1):128-31. 352. Kraguljac NV, Reid MA, White DM, den Hollander J, Lahti AC. Regional decoupling of N-acetyl-aspartate and glutamate in schizophrenia. Neuropsychopharmacology. 2012;37(12):2635-42. 353. Natsubori T, Inoue H, Abe O, Takano Y, Iwashiro N, Aoki Y, et al. Reduced Frontal Glutamate + Glutamine and N-Acetylaspartate Levels in Patients With Chronic Schizophrenia but not in Those at Clinical High Risk for Psychosis or With First-Episode Schizophrenia. Schizophr Bull. 2013. 354. Reynolds LM, Cochran SM, Morris BJ, Pratt JA, Reynolds GP. Chronic phencyclidine administration induces schizophrenia-like changes in N-acetylaspartate and N- acetylaspartylglutamate in rat brain. Schizophr Res. 2005;73(2-3):147-52. 355. Pilowsky LS, Bressan RA, Stone JM, Erlandsson K, Mulligan RS, Krystal JH, et al. First in vivo evidence of an NMDA receptor deficit in medication-free schizophrenic patients. Mol Psychiatry. 2006;11(2):118-9. 356. Stone JM, Erlandsson K, Arstad E, Bressan RA, Squassante L, Teneggi V, et al. Ketamine displaces the novel NMDA receptor SPET probe [(123)I]CNS-1261 in humans in vivo. Nucl Med Biol. 2006;33(2):239-43. 357. Heresco-Levy U. Glutamatergic neurotransmission modulators as emerging new drugs for schizophrenia. Expert Opin Emerg Drugs. 2005;10(4):827-44. 358. Large CH, Webster EL, Goff DC. The potential role of lamotrigine in schizophrenia. Psychopharmacology (Berl). 2005;181(3):415-36. 359. Weiser M, Heresco-Levy U, Davidson M, Javitt DC, Werbeloff N, Gershon AA, et al. A multicenter, add-on randomized controlled trial of low-dose d-serine for negative and cognitive symptoms of schizophrenia. J Clin Psychiatry. 2012;73(6):e728-34. 360. Martinez D, Slifstein M, Broft A, Mawlawi O, Hwang DR, Huang Y, et al. Imaging human mesolimbic dopamine transmission with positron emission tomography. Part II: amphetamine-induced dopamine release in the functional subdivisions of the striatum. J Cereb Blood Flow Metab. 2003;23(3):285-300. 361. Kreitzer AC. Physiology and pharmacology of striatal neurons. Annu Rev Neurosci. 2009;32:127-47.

209

362. Gerfen CR, Surmeier DJ. Modulation of striatal projection systems by dopamine. Annu Rev Neurosci. 2011;34:441-66. 363. Smith Y, Bevan MD, Shink E, Bolam JP. Microcircuitry of the direct and indirect pathways of the basal ganglia. Neuroscience. 1998;86(2):353-87. 364. Howes OD, Bose SK, Turkheimer F, Valli I, Egerton A, Valmaggia LR, et al. Dopamine synthesis capacity before onset of psychosis: a prospective [18F]-DOPA PET imaging study. Am J Psychiatry. 2011;168(12):1311-7. 365. Howes OD, Montgomery AJ, Asselin MC, Murray RM, Valli I, Tabraham P, et al. Elevated striatal dopamine function linked to prodromal signs of schizophrenia. Arch Gen Psychiatry. 2009;66(1):13-20. 366. Kegeles LS, Abi-Dargham A, Frankle WG, Gil R, Cooper TB, Slifstein M, et al. Increased synaptic dopamine function in associative regions of the striatum in schizophrenia. Arch Gen Psychiatry. 2010;67(3):231-9. 367. Levitt JJ, Rosow LK, Nestor PG, Pelavin PE, Swisher TM, McCarley RW, et al. A volumetric MRI study of limbic, associative and sensorimotor striatal subregions in schizophrenia. Schizophr Res. 2013;145(1-3):11-9. 368. Kambeitz J, Abi-Dargham A, Kapur S, Howes OD. Alterations in cortical and extrastriatal subcortical dopamine function in schizophrenia: systematic review and meta- analysis of imaging studies. Br J Psychiatry. 2014;204(6):420-9. 369. Caravaggio F, Kegeles LS, Wilson AA, Remington G, Borlido C, Mamo DC, et al. Estimating the effect of endogenous dopamine on baseline [(11) C]-(+)-PHNO binding in the human brain. Synapse. 2016;70(11):453-60. 370. Caravaggio F, Nakajima S, Borlido C, Remington G, Gerretsen P, Wilson A, et al. Estimating endogenous dopamine levels at D2 and D3 receptors in humans using the agonist radiotracer [(11)C]-(+)-PHNO. Neuropsychopharmacology. 2014;39(12):2769-76. 371. Abi-Dargham A, Rodenhiser J, Printz D, Zea-Ponce Y, Gil R, Kegeles LS, et al. Increased baseline occupancy of D2 receptors by dopamine in schizophrenia. Proc Natl Acad Sci U S A. 2000;97(14):8104-9. 372. Seeman P. Targeting the dopamine D2 receptor in schizophrenia. Expert Opin Ther Targets. 2006;10(4):515-31. 373. Agid O, Mamo D, Ginovart N, Vitcu I, Wilson AA, Zipursky RB, et al. Striatal vs extrastriatal dopamine D2 receptors in antipsychotic response--a double-blind PET study in schizophrenia. Neuropsychopharmacology. 2007;32(6):1209-15. 374. Kegeles LS, Slifstein M, Frankle WG, Xu X, Hackett E, Bae SA, et al. Dose-occupancy study of striatal and extrastriatal dopamine D2 receptors by aripiprazole in schizophrenia with PET and [18F]fallypride. Neuropsychopharmacology. 2008;33(13):3111-25. 375. Uchida H, Takeuchi H, Graff-Guerrero A, Suzuki T, Watanabe K, Mamo DC. Dopamine D2 receptor occupancy and clinical effects: a systematic review and pooled analysis. J Clin Psychopharmacol. 2011;31(4):497-502. 376. Yilmaz Z, Zai CC, Hwang R, Mann S, Arenovich T, Remington G, et al. Antipsychotics, dopamine D(2) receptor occupancy and clinical improvement in schizophrenia: a meta-analysis. Schizophr Res. 2012;140(1-3):214-20. 377. Demjaha A, Murray RM, McGuire PK, Kapur S, Howes OD. Dopamine synthesis capacity in patients with treatment-resistant schizophrenia. Am J Psychiatry. 2012;169(11):1203- 10. 378. Jauhar S, Veronese M, Nour MM, Rogdaki M, Hathway P, Turkheimer FE, et al. Determinants of treatment response in first-episode psychosis: an (18)F-DOPA PET study. Mol Psychiatry. 2018.

210

379. Caravaggio F, Borlido C, Wilson A, Graff-Guerrero A. Examining endogenous dopamine in treated schizophrenia using [(1)(1)C]-(+)-PHNO positron emission tomography: A pilot study. Clin Chim Acta. 2015;449:60-2. 380. Tiihonen J, Tanskanen A, Taipale H. 20-Year Nationwide Follow-Up Study on Discontinuation of Antipsychotic Treatment in First-Episode Schizophrenia. Am J Psychiatry. 2018:appiajp201817091001. 381. Barch DM, Ceaser A. Cognition in schizophrenia: core psychological and neural mechanisms. Trends Cogn Sci. 2012;16(1):27-34. 382. Yamasue H, Fukui T, Fukuda R, Kasai K, Iwanami A, Kato N, et al. Drug-induced parkinsonism in relation to choline-containing compounds measured by 1H-MR spectroscopy in putamen of chronically medicated patients with schizophrenia. Int J Neuropsychopharmacol. 2003;6(4):353-60. 383. Keshavan MS, Dick RM, Diwadkar VA, Montrose DM, Prasad KM, Stanley JA. Striatal metabolic alterations in non-psychotic adolescent offspring at risk for schizophrenia: a (1)H spectroscopy study. Schizophr Res. 2009;115(1):88-93. 384. de la Fuente-Sandoval C, Reyes-Madrigal F, Mao X, Leon-Ortiz P, Rodriguez-Mayoral O, Solis-Vivanco R, et al. Cortico-Striatal GABAergic and Glutamatergic Dysregulations in Subjects at Ultra-High Risk for Psychosis Investigated with Proton Magnetic Resonance Spectroscopy. Int J Neuropsychopharmacol. 2015;19(3):pyv105. 385. de la Fuente-Sandoval C, Reyes-Madrigal F, Mao X, Leon-Ortiz P, Rodriguez-Mayoral O, Jung-Cook H, et al. Prefrontal and Striatal Gamma-Aminobutyric Acid Levels and the Effect of Antipsychotic Treatment in First-Episode Psychosis Patients. Biol Psychiatry. 2018;83(6):475-83. 386. Block W, Bayer TA, Tepest R, Traber F, Rietschel M, Muller DJ, et al. Decreased frontal lobe ratio of N-acetyl aspartate to choline in familial schizophrenia: a proton magnetic resonance spectroscopy study. Neurosci Lett. 2000;289(2):147-51. 387. Tayoshi S, Sumitani S, Taniguchi K, Shibuya-Tayoshi S, Numata S, Iga J, et al. Metabolite changes and gender differences in schizophrenia using 3-Tesla proton magnetic resonance spectroscopy (1H-MRS). Schizophr Res. 2009;108(1-3):69-77. 388. Thakkar KN, Rosler L, Wijnen JP, Boer VO, Klomp DW, Cahn W, et al. 7T Proton Magnetic Resonance Spectroscopy of Gamma-Aminobutyric Acid, Glutamate, and Glutamine Reveals Altered Concentrations in Patients With Schizophrenia and Healthy Siblings. Biol Psychiatry. 2017;81(6):525-35. 389. Merritt K, Egerton A, Kempton MJ, Taylor MJ, McGuire PK. Nature of Glutamate Alterations in Schizophrenia: A Meta-analysis of Proton Magnetic Resonance Spectroscopy Studies. JAMA Psychiatry. 2016;73(7):665-74. 390. Iwata Y, Nakajima S, Plitman E, Mihashi Y, Caravaggio F, Chung JK, et al. Neurometabolite levels in antipsychotic-naive/free patients with schizophrenia: A systematic review and meta-analysis of (1)H-MRS studies. Prog Neuropsychopharmacol Biol Psychiatry. 2018. 391. Schwerk A, Alves FD, Pouwels PJ, van Amelsvoort T. Metabolic alterations associated with schizophrenia: a critical evaluation of proton magnetic resonance spectroscopy studies. J Neurochem. 2014;128(1):1-87. 392. Olabi B, Ellison-Wright I, Bullmore E, Lawrie SM. Structural brain changes in First Episode Schizophrenia compared with Fronto-Temporal Lobar Degeneration: a meta-analysis. BMC Psychiatry. 2012;12:104.

211

393. Bernstein HG, Steiner J, Guest PC, Dobrowolny H, Bogerts B. Glial cells as key players in schizophrenia pathology: recent insights and concepts of therapy. Schizophr Res. 2015;161(1):4-18. 394. Goudriaan A, de Leeuw C, Ripke S, Hultman CM, Sklar P, Sullivan PF, et al. Specific glial functions contribute to schizophrenia susceptibility. Schizophr Bull. 2014;40(4):925-35. 395. Malhi GS, Valenzuela M, Wen W, Sachdev P. Magnetic resonance spectroscopy and its applications in psychiatry. Aust N Z J Psychiatry. 2002;36(1):31-43. 396. Vance AL, Velakoulis D, Maruff P, Wood SJ, Desmond P, Pantelis C. Magnetic resonance spectroscopy and schizophrenia: what have we learnt? Aust N Z J Psychiatry. 2000;34(1):14-25. 397. Kim H, McGrath BM, Silverstone PH. A review of the possible relevance of inositol and the phosphatidylinositol second messenger system (PI-cycle) to psychiatric disorders--focus on magnetic resonance spectroscopy (MRS) studies. Human psychopharmacology. 2005;20(5):309- 26. 398. Kondziella D, Brenner E, Eyjolfsson EM, Sonnewald U. How do glial-neuronal interactions fit into current neurotransmitter hypotheses of schizophrenia? Neurochem Int. 2007;50(2):291-301. 399. Xia M, Abazyan S, Jouroukhin Y, Pletnikov M. Behavioral sequelae of astrocyte dysfunction: focus on animal models of schizophrenia. Schizophr Res. 2016;176(1):72-82. 400. Stone JM. Imaging the glutamate system in humans: relevance to drug discovery for schizophrenia. Curr Pharm Des. 2009;15(22):2594-602. 401. Meyerhoff DJ, MacKay S, Bachman L, Poole N, Dillon WP, Weiner MW, et al. Reduced brain N-acetylaspartate suggests neuronal loss in cognitively impaired human immunodeficiency virus-seropositive individuals: in vivo 1H magnetic resonance spectroscopic imaging. Neurology. 1993;43(3 Pt 1):509-15. 402. Tsai G, Coyle JT. N-acetylaspartate in neuropsychiatric disorders. Prog Neurobiol. 1995;46(5):531-40. 403. Gan JL, Cheng ZX, Duan HF, Yang JM, Zhu XQ, Gao CY. Atypical antipsychotic drug treatment for 6 months restores N-acetylaspartate in left prefrontal cortex and left thalamus of first-episode patients with early onset schizophrenia: A magnetic resonance spectroscopy study. Psychiatry Res. 2014;223(1):23-7. 404. Wood SJ, Berger GE, Wellard RM, Proffitt T, McConchie M, Velakoulis D, et al. A 1H- MRS investigation of the medial temporal lobe in antipsychotic-naive and early-treated first episode psychosis. Schizophr Res. 2008;102(1-3):163-70. 405. Farde L, Wiesel FA, Halldin C, Sedvall G. Central D2-dopamine receptor occupancy in schizophrenic patients treated with antipsychotic drugs. Arch Gen Psychiatry. 1988;45(1):71-6. 406. Graff-Guerrero A, Willeit M, Ginovart N, Mamo D, Mizrahi R, Rusjan P, et al. Brain region binding of the D2/3 agonist [11C]-(+)-PHNO and the D2/3 antagonist [11C]raclopride in healthy humans. Hum Brain Mapp. 2008;29(4):400-10. 407. Carpenter WT, Jr., Schooler NR, Kane JM. The rationale and ethics of medication-free research in schizophrenia. Arch Gen Psychiatry. 1997;54(5):401-7. 408. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):261-76. 409. Bustillo JR, Rowland LM, Jung R, Brooks WM, Qualls C, Hammond R, et al. Proton magnetic resonance spectroscopy during initial treatment with antipsychotic medication in schizophrenia. Neuropsychopharmacology. 2008;33(10):2456-66.

212

410. Auer DP, Wilke M, Grabner A, Heidenreich JO, Bronisch T, Wetter TC. Reduced NAA in the thalamus and altered membrane and glial metabolism in schizophrenic patients detected by 1H-MRS and tissue segmentation. Schizophr Res. 2001;52(1-2):87-99. 411. Chang L, Friedman J, Ernst T, Zhong K, Tsopelas ND, Davis K. Brain metabolite abnormalities in the white matter of elderly schizophrenic subjects: implication for glial dysfunction. Biol Psychiatry. 2007;62(12):1396-404. 412. Lutkenhoff ES, van Erp TG, Thomas MA, Therman S, Manninen M, Huttunen MO, et al. Proton MRS in twin pairs discordant for schizophrenia. Mol Psychiatry. 2010;15(3):308-18. 413. Bustillo JR, Rowland LM, Lauriello J, Petropoulos H, Hammond R, Hart B, et al. High choline concentrations in the caudate nucleus in antipsychotic-naive patients with schizophrenia. Am J Psychiatry. 2002;159(1):130-3. 414. Fujimoto T, Nakano T, Takano T, Takeuchi K, Yamada K, Fukuzako T, et al. Proton magnetic resonance spectroscopy of basal ganglia in chronic schizophrenia. Biol Psychiatry. 1996;40(1):14-8. 415. Shioiri T, Hamakawa H, Kato T, Murashita J, Fujii K, Inubushi T, et al. Proton magnetic resonance spectroscopy of the basal ganglia in patients with schizophrenia: a preliminary report. Schizophr Res. 1996;22(1):19-26. 416. Maddock R. The problem of spurious correlations between pairs of brain metabolite values measured in the same voxel with magnetic resonance spectroscopy. JAMA Psychiatry. 2014;71(3):338-9. 417. Chang L, Jiang C, Cunningham E, Buchthal S, Douet V, Andres M, et al. Effects of APOE epsilon4, age, and HIV on glial metabolites and cognitive deficits. Neurology. 2014;82(24):2213-22. 418. Harezlak J, Buchthal S, Taylor M, Schifitto G, Zhong J, Daar E, et al. Persistence of HIV-associated cognitive impairment, inflammation, and neuronal injury in era of highly active antiretroviral treatment. AIDS. 2011;25(5):625-33. 419. Inglese M, Li BS, Rusinek H, Babb JS, Grossman RI, Gonen O. Diffusely elevated cerebral choline and creatine in relapsing-remitting multiple sclerosis. Magn Reson Med. 2003;50(1):190-5. 420. Miller BL, Moats RA, Shonk T, Ernst T, Woolley S, Ross BD. Alzheimer disease: depiction of increased cerebral myo-inositol with proton MR spectroscopy. Radiology. 1993;187(2):433-7. 421. Chiappelli J, Hong LE, Wijtenburg SA, Du X, Gaston F, Kochunov P, et al. Alterations in frontal white matter neurochemistry and microstructure in schizophrenia: implications for neuroinflammation. Transl Psychiatry. 2015;5:e548. 422. Fan X, Goff DC, Henderson DC. Inflammation and schizophrenia. Expert Rev Neurother. 2007;7(7):789-96. 423. Kirkpatrick B, Miller BJ. Inflammation and schizophrenia. Schizophr Bull. 2013;39(6):1174-9. 424. Tourjman V, Kouassi E, Koue ME, Rocchetti M, Fortin-Fournier S, Fusar-Poli P, et al. Antipsychotics' effects on blood levels of cytokines in schizophrenia: a meta-analysis. Schizophr Res. 2013;151(1-3):43-7. 425. Nitta M, Kishimoto T, Muller N, Weiser M, Davidson M, Kane JM, et al. Adjunctive use of nonsteroidal anti-inflammatory drugs for schizophrenia: a meta-analytic investigation of randomized controlled trials. Schizophr Bull. 2013;39(6):1230-41. 426. Sommer IE, van Westrhenen R, Begemann MJ, de Witte LD, Leucht S, Kahn RS. Efficacy of anti-inflammatory agents to improve symptoms in patients with schizophrenia: an update. Schizophr Bull. 2014;40(1):181-91.

213

427. Reid MA, Kraguljac NV, Avsar KB, White DM, den Hollander JA, Lahti AC. Proton magnetic resonance spectroscopy of the substantia nigra in schizophrenia. Schizophr Res. 2013;147(2-3):348-54. 428. Rothermundt M, Ahn JN, Jorgens S. S100B in schizophrenia: an update. Gen Physiol Biophys. 2009;28 Spec No Focus:F76-81. 429. Linderholm KR, Skogh E, Olsson SK, Dahl ML, Holtze M, Engberg G, et al. Increased levels of kynurenine and kynurenic acid in the CSF of patients with schizophrenia. Schizophr Bull. 2012;38(3):426-32. 430. Schwarcz R, Bruno JP, Muchowski PJ, Wu HQ. Kynurenines in the mammalian brain: when physiology meets pathology. Nat Rev Neurosci. 2012;13(7):465-77. 431. Muller N, Schwarz M. Schizophrenia as an inflammation-mediated dysbalance of glutamatergic neurotransmission. Neurotox Res. 2006;10(2):131-48. 432. Muller N. Inflammation and the glutamate system in schizophrenia: implications for therapeutic targets and drug development. Expert Opin Ther Targets. 2008;12(12):1497-507. 433. Steiner J, Bogerts B, Sarnyai Z, Walter M, Gos T, Bernstein HG, et al. Bridging the gap between the immune and glutamate hypotheses of schizophrenia and major depression: Potential role of glial NMDA receptor modulators and impaired blood-brain barrier integrity. World J Biol Psychiatry. 2012;13(7):482-92. 434. Aparicio-Legarza MI, Cutts AJ, Davis B, Reynolds GP. Deficits of [3H]D-aspartate binding to glutamate uptake sites in striatal and accumbens tissue in patients with schizophrenia. Neurosci Lett. 1997;232(1):13-6. 435. Lauriat TL, McInnes LA. EAAT2 regulation and splicing: relevance to psychiatric and neurological disorders. Mol Psychiatry. 2007;12(12):1065-78. 436. Shan D, Lucas EK, Drummond JB, Haroutunian V, Meador-Woodruff JH, McCullumsmith RE. Abnormal expression of glutamate transporters in temporal lobe areas in elderly patients with schizophrenia. Schizophr Res. 2013;144(1-3):1-8. 437. Simpson MD, Slater P, Royston MC, Deakin JF. Regionally selective deficits in uptake sites for glutamate and gamma-aminobutyric acid in the basal ganglia in schizophrenia. Psychiatry Res. 1992;42(3):273-82. 438. Homan P, Vermathen P, Van Swam C, Federspiel A, Boesch C, Strik W, et al. Magnetic resonance spectroscopy investigations of functionally defined language areas in schizophrenia patients with and without auditory hallucinations. NeuroImage. 2014;94:23-32. 439. Chiappelli J, Rowland LM, Wijtenburg SA, Muellerklein F, Tagamets M, McMahon RP, et al. Evaluation of Myo-Inositol as a Potential Biomarker for Depression in Schizophrenia. Neuropsychopharmacology. 2015. 440. Hugdahl K, Craven AR, Nygard M, Loberg EM, Berle JO, Johnsen E, et al. Glutamate as a mediating transmitter for auditory hallucinations in schizophrenia: a (1)H MRS study. Schizophr Res. 2015;161(2-3):252-60. 441. Plitman E, Nakajima S, de la Fuente-Sandoval C, Gerretsen P, Chakravarty MM, Kobylianskii J, et al. Glutamate-mediated excitotoxicity in schizophrenia: A review. Eur Neuropsychopharmacol. 2014;24(10):1591-605. 442. Rowland LM, Krause BW, Wijtenburg SA, McMahon RP, Chiappelli J, Nugent KL, et al. Medial frontal GABA is lower in older schizophrenia: a MEGA-PRESS with macromolecule suppression study. Mol Psychiatry. 2016;21(2):198-204. 443. Clementi V, Tonon C, Lodi R, Malucelli E, Barbiroli B, Iotti S. Assessment of glutamate and glutamine contribution to in vivo N-acetylaspartate quantification in human brain by (1)H- magnetic resonance spectroscopy. Magn Reson Med. 2005;54(6):1333-9.

214

444. de la Fuente-Sandoval C, Favila R, Alvarado P, Leon-Ortiz P, Diaz-Galvis L, Amezcua C, et al. [Glutamate increase in the associative striatum in schizophrenia: a longitudinal magnetic resonance spectroscopy preliminary study]. Gac Med Mex. 2009;145(2):109-13. 445. Kraguljac NV, Cutter GR, Morgan C, Lahti AC. In reply. JAMA Psychiatry. 2014;71(3):339-40. 446. Plitman E, de la Fuente-Sandoval C, Reyes-Madrigal F, Chavez S, Gomez-Cruz G, Leon- Ortiz P, et al. Elevated Myo-Inositol, Choline, and Glutamate Levels in the Associative Striatum of Antipsychotic-Naive Patients With First-Episode Psychosis: A Proton Magnetic Resonance Spectroscopy Study With Implications for Glial Dysfunction. Schizophr Bull. 2016;42(2):415- 24. 447. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310-20. 448. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17(1):87-97. 449. Chakravarty MM, Bertrand G, Hodge CP, Sadikot AF, Collins DL. The creation of a brain atlas for image guided neurosurgery using serial histological data. NeuroImage. 2006;30(2):359-76. 450. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12(1):26-41. 451. Winterburn JL, Pruessner JC, Chavez S, Schira MM, Lobaugh NJ, Voineskos AN, et al. A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging. NeuroImage. 2013;74:254-65. 452. Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18(2):192- 205. 453. Zijdenbos AP, Forghani R, Evans AC. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging. 2002;21(10):1280-91. 454. Kim JS, Singh V, Lee JK, Lerch J, Ad-Dab'bagh Y, MacDonald D, et al. Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage. 2005;27(1):210-21. 455. Boucher M, Whitesides S, Evans A. Depth potential function for folding pattern representation, registration and analysis. Med Image Anal. 2009;13(2):203-14. 456. Chakravarty MM, Rapoport JL, Giedd JN, Raznahan A, Shaw P, Collins DL, et al. Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: a longitudinal study. Hum Brain Mapp. 2015;36(4):1458-69. 457. Ebdrup BH, Glenthoj B, Rasmussen H, Aggernaes B, Langkilde AR, Paulson OB, et al. Hippocampal and caudate volume reductions in antipsychotic-naive first-episode schizophrenia. J Psychiatry Neurosci. 2010;35(2):95-104. 458. Keshavan MS, Rosenberg D, Sweeney JA, Pettegrew JW. Decreased caudate volume in neuroleptic-naive psychotic patients. Am J Psychiatry. 1998;155(6):774-8. 459. Glenthoj A, Glenthoj BY, Mackeprang T, Pagsberg AK, Hemmingsen RP, Jernigan TL, et al. Basal ganglia volumes in drug-naive first-episode schizophrenia patients before and after short-term treatment with either a typical or an atypical antipsychotic drug. Psychiatry Res. 2007;154(3):199-208. 460. Gunduz H, Wu H, Ashtari M, Bogerts B, Crandall D, Robinson DG, et al. Basal ganglia volumes in first-episode schizophrenia and healthy comparison subjects. Biol Psychiatry. 2002;51(10):801-8.

215

461. Lahti AC, Weiler MA, Tamara Michaelidis BA, Parwani A, Tamminga CA. Effects of ketamine in normal and schizophrenic volunteers. Neuropsychopharmacology. 2001;25(4):455- 67. 462. Ballmaier M, Schlagenhauf F, Toga AW, Gallinat J, Koslowski M, Zoli M, et al. Regional patterns and clinical correlates of basal ganglia morphology in non-medicated schizophrenia. Schizophr Res. 2008;106(2-3):140-7. 463. Venkatasubramanian G, Gangadhar BN, Jayakumar PN, Janakiramaiah N, Keshavan MS. Reduced Caudate Volume in Never-Treated Schizophrenia : Evidence for Neuro developmental Etiopathogenesis. Indian J Psychiatry. 2003;45(2):20-6. 464. Mouchlianitis E, Bloomfield MA, Law V, Beck K, Selvaraj S, Rasquinha N, et al. Treatment-Resistant Schizophrenia Patients Show Elevated Anterior Cingulate Cortex Glutamate Compared to Treatment-Responsive. Schizophr Bull. 2016;42(3):744-52. 465. Grisso T, Appelbaum PS, Hill-Fotouhi C. The MacCAT-T: a clinical tool to assess patients' capacities to make treatment decisions. Psychiatr Serv. 1997;48(11):1415-9. 466. van Erp TG, Preda A, Nguyen D, Faziola L, Turner J, Bustillo J, et al. Converting positive and negative symptom scores between PANSS and SAPS/SANS. Schizophr Res. 2014;152(1):289-94. 467. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage. 2011;56(3):907-22. 468. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, et al. Bayesian analysis of neuroimaging data in FSL. NeuroImage. 2009;45(1 Suppl):S173-86. 469. Gardner DM, Murphy AL, O'Donnell H, Centorrino F, Baldessarini RJ. International consensus study of antipsychotic dosing. Am J Psychiatry. 2010;167(6):686-93. 470. Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994;6(4):284-90. 471. Szulc A, Galinska-Skok B, Waszkiewicz N, Bibulowicz D, Konarzewska B, Tarasow E. Proton magnetic resonance spectroscopy changes after antipsychotic treatment. Curr Med Chem. 2013;20(3):414-27. 472. Caravaggio F, Nakajima S, Plitman E, Gerretsen P, Chung JK, Iwata Y, et al. The effect of striatal dopamine depletion on striatal and cortical glutamate: A mini-review. Prog Neuropsychopharmacol Biol Psychiatry. 2016;65:49-53. 473. Andrews SC, Hoy KE, Enticott PG, Daskalakis ZJ, Fitzgerald PB. Improving working memory: the effect of combining cognitive activity and anodal transcranial direct current stimulation to the left dorsolateral prefrontal cortex. Brain Stimul. 2011;4(2):84-9. 474. Bastani A, Jaberzadeh S. Within-session repeated a-tDCS: the effects of repetition rate and inter-stimulus interval on corticospinal excitability and motor performance. Clin Neurophysiol. 2014;125(9):1809-18. 475. Hone-Blanchet A, Edden RA, Fecteau S. Online Effects of Transcranial Direct Current Stimulation in Real Time on Human Prefrontal and Striatal Metabolites. Biol Psychiatry. 2015. 476. Monte-Silva K, Kuo MF, Hessenthaler S, Fresnoza S, Liebetanz D, Paulus W, et al. Induction of late LTP-like plasticity in the human motor cortex by repeated non-invasive brain stimulation. Brain Stimul. 2013;6(3):424-32. 477. Nitsche MA, Nitsche MS, Klein CC, Tergau F, Rothwell JC, Paulus W. Level of action of cathodal DC polarisation induced inhibition of the human motor cortex. Clin Neurophysiol. 2003;114(4):600-4. 478. Nitsche MA, Koschack J, Pohlers H, Hullemann S, Paulus W, Happe S. Effects of frontal transcranial direct current stimulation on emotional state and processing in healthy humans. Front Psychiatry. 2012;3:58.

216

479. De Picker LJ, Morrens M, Chance SA, Boche D. Microglia and Brain Plasticity in Acute Psychosis and Schizophrenia Illness Course: A Meta-Review. Front Psychiatry. 2017;8:238. 480. Egerton A, Bhachu A, Merritt K, McQueen G, Szulc A, McGuire P. Effects of Antipsychotic Administration on Brain Glutamate in Schizophrenia: A Systematic Review of Longitudinal (1)H-MRS Studies. Front Psychiatry. 2017;8:66. 481. Bustillo JR, Rediske N, Jones T, Rowland LM, Abbott C, Wijtenburg SA. Reproducibility of phase rotation stimulated echo acquisition mode at 3T in schizophrenia: Emphasis on glutamine. Magn Reson Med. 2016;75(2):498-502. 482. Mullins PG, Rowland L, Bustillo J, Bedrick EJ, Lauriello J, Brooks WM. Reproducibility of 1H-MRS measurements in schizophrenic patients. Magn Reson Med. 2003;50(4):704-7. 483. Howes O, McCutcheon R, Stone J. Glutamate and dopamine in schizophrenia: an update for the 21st century. J Psychopharmacol. 2015;29(2):97-115. 484. Abi-Dargham A, Meyer JM. Schizophrenia: the role of dopamine and glutamate. J Clin Psychiatry. 2014;75(3):274-5. 485. Gleich T, Deserno L, Lorenz RC, Boehme R, Pankow A, Buchert R, et al. Prefrontal and Striatal Glutamate Differently Relate to Striatal Dopamine: Potential Regulatory Mechanisms of Striatal Presynaptic Dopamine Function? J Neurosci. 2015;35(26):9615-21. 486. Laruelle M, Kegeles LS, Abi-Dargham A. Glutamate, dopamine, and schizophrenia: from pathophysiology to treatment. Ann N Y Acad Sci. 2003;1003:138-58. 487. Lisman JE, Coyle JT, Green RW, Javitt DC, Benes FM, Heckers S, et al. Circuit-based framework for understanding neurotransmitter and risk gene interactions in schizophrenia. Trends Neurosci. 2008;31(5):234-42. 488. Jackson ME, Homayoun H, Moghaddam B. NMDA receptor hypofunction produces concomitant firing rate potentiation and burst activity reduction in the prefrontal cortex. Proc Natl Acad Sci U S A. 2004;101(22):8467-72. 489. Miller DW, Abercrombie ED. Effects of MK-801 on spontaneous and amphetamine- stimulated dopamine release in striatum measured with in vivo microdialysis in awake rats. Brain Res Bull. 1996;40(1):57-62. 490. Breier A, Adler CM, Weisenfeld N, Su TP, Elman I, Picken L, et al. Effects of NMDA antagonism on striatal dopamine release in healthy subjects: application of a novel PET approach. Synapse. 1998;29(2):142-7. 491. Kegeles LS, Abi-Dargham A, Zea-Ponce Y, Rodenhiser-Hill J, Mann JJ, Van Heertum RL, et al. Modulation of amphetamine-induced striatal dopamine release by ketamine in humans: implications for schizophrenia. Biol Psychiatry. 2000;48(7):627-40. 492. Vernaleken I, Klomp M, Moeller O, Raptis M, Nagels A, Rosch F, et al. Vulnerability to psychotogenic effects of ketamine is associated with elevated D2/3-receptor availability. Int J Neuropsychopharmacol. 2013;16(4):745-54. 493. Laruelle M, Frankle WG, Narendran R, Kegeles LS, Abi-Dargham A. Mechanism of action of antipsychotic drugs: from dopamine D(2) receptor antagonism to glutamate NMDA facilitation. Clin Ther. 2005;27 Suppl A:S16-24. 494. Abekawa T, Ito K, Koyama T. Different effects of a single and repeated administration of clozapine on phencyclidine-induced hyperlocomotion and glutamate releases in the rat medial prefrontal cortex at short- and long-term withdrawal from this antipsychotic. Naunyn Schmiedebergs Arch Pharmacol. 2007;375(4):261-71. 495. Lopez-Gil X, Artigas F, Adell A. Role of different monoamine receptors controlling MK- 801-induced release of serotonin and glutamate in the medial prefrontal cortex: relevance for antipsychotic action. Int J Neuropsychopharmacol. 2009;12(4):487-99.

217

496. Ceglia I, Carli M, Baviera M, Renoldi G, Calcagno E, Invernizzi RW. The 5-HT receptor antagonist M100,907 prevents extracellular glutamate rising in response to NMDA receptor blockade in the mPFC. J Neurochem. 2004;91(1):189-99. 497. Carlsson ML, Carlsson A, Nilsson M. Schizophrenia: from dopamine to glutamate and back. Curr Med Chem. 2004;11(3):267-77. 498. Molteni R, Calabrese F, Racagni G, Fumagalli F, Riva MA. Antipsychotic drug actions on gene modulation and signaling mechanisms. Pharmacol Ther. 2009;124(1):74-85. 499. Monji A, Kato TA, Mizoguchi Y, Horikawa H, Seki Y, Kasai M, et al. Neuroinflammation in schizophrenia especially focused on the role of microglia. Prog Neuropsychopharmacol Biol Psychiatry. 2013;42:115-21. 500. Najjar S, Pearlman DM. Neuroinflammation and white matter pathology in schizophrenia: systematic review. Schizophr Res. 2014. 501. Pasternak O, Kubicki M, Shenton ME. In vivo imaging of neuroinflammation in schizophrenia. Schizophr Res. 2016;173(3):200-12. 502. Bloomfield PS, Selvaraj S, Veronese M, Rizzo G, Bertoldo A, Owen DR, et al. Microglial Activity in People at Ultra High Risk of Psychosis and in Schizophrenia: An [(11)C]PBR28 PET Brain Imaging Study. Am J Psychiatry. 2016;173(1):44-52. 503. Collste K, Plaven-Sigray P, Fatouros-Bergman H, Victorsson P, Schain M, Forsberg A, et al. Lower levels of the glial cell marker TSPO in drug-naive first-episode psychosis patients as measured using PET and [(11)C]PBR28. Mol Psychiatry. 2017;22(6):850-6. 504. Coughlin JM, Wang Y, Ambinder EB, Ward RE, Minn I, Vranesic M, et al. In vivo markers of inflammatory response in recent-onset schizophrenia: a combined study using [(11)C]DPA-713 PET and analysis of CSF and plasma. Transl Psychiatry. 2016;6:e777. 505. Hafizi S, Tseng HH, Rao N, Selvanathan T, Kenk M, Bazinet RP, et al. Imaging Microglial Activation in Untreated First-Episode Psychosis: A PET Study With [(18)F]FEPPA. Am J Psychiatry. 2017;174(2):118-24. 506. Holmes SE, Hinz R, Drake RJ, Gregory CJ, Conen S, Matthews JC, et al. In vivo imaging of brain microglial activity in antipsychotic-free and medicated schizophrenia: a [(11)C](R)-PK11195 positron emission tomography study. Mol Psychiatry. 2016;21(12):1672-9. 507. Kenk M, Selvanathan T, Rao N, Suridjan I, Rusjan P, Remington G, et al. Imaging neuroinflammation in gray and white matter in schizophrenia: an in-vivo PET study with [18F]- FEPPA. Schizophr Bull. 2015;41(1):85-93. 508. Schifani C, Hafizi S, Da Silva T, Watts JJ, Khan MS, Mizrahi R. Using molecular imaging to understand early schizophrenia-related psychosis neurochemistry: a review of human studies. International review of psychiatry (Abingdon, England). 2017;29(6):555-66. 509. Turkheimer FE, Rizzo G, Bloomfield PS, Howes O, Zanotti-Fregonara P, Bertoldo A, et al. The methodology of TSPO imaging with positron emission tomography. Biochem Soc Trans. 2015;43(4):586-92. 510. van Berckel BN, Bossong MG, Boellaard R, Kloet R, Schuitemaker A, Caspers E, et al. Microglia activation in recent-onset schizophrenia: a quantitative (R)-[11C]PK11195 positron emission tomography study. Biol Psychiatry. 2008;64(9):820-2. 511. Allaman I, Belanger M, Magistretti PJ. Astrocyte-neuron metabolic relationships: for better and for worse. Trends Neurosci. 2011;34(2):76-87. 512. Jakel S, Dimou L. Glial Cells and Their Function in the Adult Brain: A Journey through the History of Their Ablation. Front Cell Neurosci. 2017;11:24. 513. Takahashi N, Sakurai T. Roles of glial cells in schizophrenia: possible targets for therapeutic approaches. Neurobiol Dis. 2013;53:49-60.

218

514. Jha MK, Suk K. Glia-based biomarkers and their functional role in the CNS. Expert review of proteomics. 2013;10(1):43-63. 515. Colonna M, Butovsky O. Microglia Function in the Central Nervous System During Health and Neurodegeneration. Annu Rev Immunol. 2017;35:441-68. 516. Takeuchi H, Suzumura A. Gap junctions and hemichannels composed of connexins: potential therapeutic targets for neurodegenerative diseases. Front Cell Neurosci. 2014;8:189. 517. Watkins CC, Sawa A, Pomper MG. Glia and immune cell signaling in bipolar disorder: insights from neuropharmacology and molecular imaging to clinical application. Transl Psychiatry. 2014;4:e350. 518. Sofroniew MV, Vinters HV. Astrocytes: biology and pathology. Acta Neuropathol. 2010;119(1):7-35. 519. Shan D, Yates S, Roberts RC, McCullumsmith RE. Update on the neurobiology of schizophrenia: a role for extracellular microdomains. Minerva Psichiatr. 2012;53(3):233-49. 520. Stone TW, Stoy N, Darlington LG. An expanding range of targets for kynurenine metabolites of tryptophan. Trends Pharmacol Sci. 2013;34(2):136-43. 521. Vecsei L, Szalardy L, Fulop F, Toldi J. Kynurenines in the CNS: recent advances and new questions. Nat Rev Drug Discov. 2013;12(1):64-82. 522. Dounay AB, Tuttle JB, Verhoest PR. Challenges and Opportunities in the Discovery of New Therapeutics Targeting the Kynurenine Pathway. J Med Chem. 2015;58(22):8762-82. 523. Stone TW, Darlington LG. The kynurenine pathway as a therapeutic target in cognitive and neurodegenerative disorders. Br J Pharmacol. 2013;169(6):1211-27. 524. Erhardt S, Schwieler L, Nilsson L, Linderholm K, Engberg G. The kynurenic acid hypothesis of schizophrenia. Physiol Behav. 2007;92(1-2):203-9. 525. Pocivavsek A, Notarangelo FM, Wu H-Q, Bruno JP, Schwarcz R. Chapter 25 - Astrocytes as Pharmacological Targets in the Treatment of Schizophrenia: Focus on Kynurenic Acid. In: Mikhail VP, John LW, editors. Handbook of Behavioral Neuroscience. Vol 23: Elsevier; 2016. p. 423-43. 526. Schwarcz R. Kynurenines and Glutamate: Multiple Links and Therapeutic Implications. Adv Pharmacol. 2016. 527. Howes OD, Kapur S. The dopamine hypothesis of schizophrenia: version III--the final common pathway. Schizophr Bull. 2009;35(3):549-62. 528. Fusar-Poli P, Papanastasiou E, Stahl D, Rocchetti M, Carpenter W, Shergill S, et al. Treatments of Negative Symptoms in Schizophrenia: Meta-Analysis of 168 Randomized Placebo-Controlled Trials. Schizophr Bull. 2015;41(4):892-9. 529. Leklem JE. Quantitative aspects of tryptophan metabolism in humans and other species: a review. Am J Clin Nutr. 1971;24(6):659-72. 530. Fukui S, Schwarcz R, Rapoport SI, Takada Y, Smith QR. Blood-brain barrier transport of kynurenines: implications for brain synthesis and metabolism. J Neurochem. 1991;56(6):2007- 17. 531. Gal EM, Sherman AD. L-kynurenine: its synthesis and possible regulatory function in brain. Neurochem Res. 1980;5(3):223-39. 532. Guillemin GJ, Kerr SJ, Smythe GA, Smith DG, Kapoor V, Armati PJ, et al. Kynurenine pathway metabolism in human astrocytes: a paradox for neuronal protection. J Neurochem. 2001;78(4):842-53. 533. Guidetti P, Amori L, Sapko MT, Okuno E, Schwarcz R. Mitochondrial aspartate aminotransferase: a third kynurenate-producing enzyme in the mammalian brain. J Neurochem. 2007;102(1):103-11.

219

534. Perkins MN, Stone TW. An iontophoretic investigation of the actions of convulsant kynurenines and their interaction with the endogenous excitant quinolinic acid. Brain Res. 1982;247(1):184-7. 535. Birch PJ, Grossman CJ, Hayes AG. Kynurenic acid antagonises responses to NMDA via an action at the strychnine-insensitive glycine receptor. Eur J Pharmacol. 1988;154(1):85-7. 536. Kessler M, Terramani T, Lynch G, Baudry M. A glycine site associated with N-methyl- D-aspartic acid receptors: characterization and identification of a new class of antagonists. J Neurochem. 1989;52(4):1319-28. 537. Hilmas C, Pereira EF, Alkondon M, Rassoulpour A, Schwarcz R, Albuquerque EX. The brain metabolite kynurenic acid inhibits alpha7 nicotinic receptor activity and increases non- alpha7 nicotinic receptor expression: physiopathological implications. J Neurosci. 2001;21(19):7463-73. 538. DiNatale BC, Murray IA, Schroeder JC, Flaveny CA, Lahoti TS, Laurenzana EM, et al. Kynurenic acid is a potent endogenous aryl hydrocarbon receptor ligand that synergistically induces interleukin-6 in the presence of inflammatory signaling. Toxicol Sci. 2010;115(1):89-97. 539. Wang J, Simonavicius N, Wu X, Swaminath G, Reagan J, Tian H, et al. Kynurenic acid as a ligand for orphan G protein-coupled receptor GPR35. J Biol Chem. 2006;281(31):22021-8. 540. Lugo-Huitron R, Blanco-Ayala T, Ugalde-Muniz P, Carrillo-Mora P, Pedraza-Chaverri J, Silva-Adaya D, et al. On the antioxidant properties of kynurenic acid: free radical scavenging activity and inhibition of oxidative stress. Neurotoxicol Teratol. 2011;33(5):538-47. 541. Foster AC, Vezzani A, French ED, Schwarcz R. Kynurenic acid blocks neurotoxicity and seizures induced in rats by the related brain metabolite quinolinic acid. Neurosci Lett. 1984;48(3):273-8. 542. Myint AM. Kynurenines: from the perspective of major psychiatric disorders. FEBS J. 2012;279(8):1375-85. 543. Wonodi I, Schwarcz R. Cortical kynurenine pathway metabolism: a novel target for cognitive enhancement in Schizophrenia. Schizophr Bull. 2010;36(2):211-8. 544. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62(10):1006-12. 545. Erhardt S, Olsson SK, Engberg G. Pharmacological manipulation of kynurenic acid: potential in the treatment of psychiatric disorders. CNS Drugs. 2009;23(2):91-101. 546. Muller N, Myint AM, Schwarz MJ. Kynurenine pathway in schizophrenia: pathophysiological and therapeutic aspects. Curr Pharm Des. 2011;17(2):130-6. 547. Iwata Y, Nakajima S, Suzuki T, Keefe RS, Plitman E, Chung JK, et al. Effects of glutamate positive modulators on cognitive deficits in schizophrenia: a systematic review and meta-analysis of double-blind randomized controlled trials. Mol Psychiatry. 2015;20(10):1151- 60. 548. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177-88. 549. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-34. 550. Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455-63. 551. Kim SY, Park JE, Lee YJ, Seo HJ, Sheen SS, Hahn S, et al. Testing a tool for assessing the risk of bias for nonrandomized studies showed moderate reliability and promising validity. J Clin Epidemiol. 2013;66(4):408-14. 552. Barry S, Clarke G, Scully P, Dinan TG. Kynurenine pathway in psychosis: evidence of increased tryptophan degradation. J Psychopharmacol. 2009;23(3):287-94.

220

553. Chiappelli J, Pocivavsek A, Nugent KL, Notarangelo FM, Kochunov P, Rowland LM, et al. Stress-induced increase in kynurenic acid as a potential biomarker for patients with schizophrenia and distress intolerance. JAMA Psychiatry. 2014;71(7):761-8. 554. Fazio F, Lionetto L, Curto M, Iacovelli L, Cavallari M, Zappulla C, et al. Xanthurenic Acid Activates mGlu2/3 Metabotropic Glutamate Receptors and is a Potential Trait Marker for Schizophrenia. Sci Rep. 2015;5:17799. 555. Fukushima T, Iizuka H, Yokota A, Suzuki T, Ohno C, Kono Y, et al. Quantitative analyses of schizophrenia-associated metabolites in serum: serum D-lactate levels are negatively correlated with gamma-glutamylcysteine in medicated schizophrenia patients. PLoS One. 2014;9(7):e101652. 556. Kegel ME, Bhat M, Skogh E, Samuelsson M, Lundberg K, Dahl ML, et al. Imbalanced kynurenine pathway in schizophrenia. Int J Tryptophan Res. 2014;7:15-22. 557. Miller CL, Llenos IC, Dulay JR, Weis S. Upregulation of the initiating step of the kynurenine pathway in postmortem anterior cingulate cortex from individuals with schizophrenia and bipolar disorder. Brain Res. 2006;1073-1074:25-37. 558. Nilsson LK, Linderholm KR, Engberg G, Paulson L, Blennow K, Lindstrom LH, et al. Elevated levels of kynurenic acid in the cerebrospinal fluid of male patients with schizophrenia. Schizophr Res. 2005;80(2-3):315-22. 559. Ravikumar A, Deepadevi KV, Arun P, Manojkumar V, Kurup PA. Tryptophan and tyrosine catabolic pattern in neuropsychiatric disorders. Neurol India. 2000;48(3):231-8. 560. Sathyasaikumar KV, Stachowski EK, Wonodi I, Roberts RC, Rassoulpour A, McMahon RP, et al. Impaired kynurenine pathway metabolism in the prefrontal cortex of individuals with schizophrenia. Schizophr Bull. 2011;37(6):1147-56. 561. Schwarcz R, Rassoulpour A, Wu HQ, Medoff D, Tamminga CA, Roberts RC. Increased cortical kynurenate content in schizophrenia. Biol Psychiatry. 2001;50(7):521-30. 562. Schwieler L, Larsson MK, Skogh E, Kegel ME, Orhan F, Abdelmoaty S, et al. Increased levels of IL-6 in the cerebrospinal fluid of patients with chronic schizophrenia--significance for activation of the kynurenine pathway. J Psychiatry Neurosci. 2015;40(2):126-33. 563. Myint AM, Schwarz MJ, Verkerk R, Mueller HH, Zach J, Scharpe S, et al. Reversal of imbalance between kynurenic acid and 3-hydroxykynurenine by antipsychotics in medication- naive and medication-free schizophrenic patients. Brain Behav Immun. 2011;25(8):1576-81. 564. Erhardt S, Blennow K, Nordin C, Skogh E, Lindstrom LH, Engberg G. Kynurenic acid levels are elevated in the cerebrospinal fluid of patients with schizophrenia. Neurosci Lett. 2001;313(1-2):96-8. 565. Ceresoli-Borroni G, Rassoulpour A, Wu HQ, Guidetti P, Schwarcz R. Chronic neuroleptic treatment reduces endogenous kynurenic acid levels in rat brain. Journal of neural transmission (Vienna, Austria : 1996). 2006;113(10):1355-65. 566. Nilsson LK, Nordin C, Jonsson EG, Engberg G, Linderholm KR, Erhardt S. Cerebrospinal fluid kynurenic acid in male and female controls - correlation with monoamine metabolites and influences of confounding factors. J Psychiatr Res. 2007;41(1-2):144-51. 567. Upthegrove R, Manzanares-Teson N, Barnes NM. Cytokine function in medication-naive first episode psychosis: a systematic review and meta-analysis. Schizophr Res. 2014;155(1- 3):101-8. 568. Muller N, Weidinger E, Leitner B, Schwarz MJ. The role of inflammation in schizophrenia. Front Neurosci. 2015;9:372. 569. Miller CL, Llenos IC, Dulay JR, Barillo MM, Yolken RH, Weis S. Expression of the kynurenine pathway enzyme tryptophan 2,3-dioxygenase is increased in the frontal cortex of individuals with schizophrenia. Neurobiol Dis. 2004;15(3):618-29.

221

570. Wonodi I, Stine OC, Sathyasaikumar KV, Roberts RC, Mitchell BD, Hong LE, et al. Downregulated kynurenine 3-monooxygenase gene expression and enzyme activity in schizophrenia and genetic association with schizophrenia endophenotypes. Arch Gen Psychiatry. 2011;68(7):665-74. 571. Holtze M, Saetre P, Engberg G, Schwieler L, Werge T, Andreassen OA, et al. Kynurenine 3-monooxygenase polymorphisms: relevance for kynurenic acid synthesis in patients with schizophrenia and healthy controls. J Psychiatry Neurosci. 2012;37(1):53-7. 572. Lavebratt C, Olsson S, Backlund L, Frisen L, Sellgren C, Priebe L, et al. The KMO allele encoding Arg452 is associated with psychotic features in bipolar disorder type 1, and with increased CSF KYNA level and reduced KMO expression. Mol Psychiatry. 2014;19(3):334-41. 573. Giorgini F, Huang SY, Sathyasaikumar KV, Notarangelo FM, Thomas MA, Tararina M, et al. Targeted deletion of kynurenine 3-monooxygenase in mice: a new tool for studying kynurenine pathway metabolism in periphery and brain. J Biol Chem. 2013;288(51):36554-66. 574. Rassoulpour A, Wu HQ, Ferre S, Schwarcz R. Nanomolar concentrations of kynurenic acid reduce extracellular dopamine levels in the striatum. J Neurochem. 2005;93(3):762-5. 575. Rover S, Cesura AM, Huguenin P, Kettler R, Szente A. Synthesis and biochemical evaluation of N-(4-phenylthiazol-2-yl)benzenesulfonamides as high-affinity inhibitors of kynurenine 3-hydroxylase. J Med Chem. 1997;40(26):4378-85. 576. Speciale C, Wu HQ, Cini M, Marconi M, Varasi M, Schwarcz R. (R,S)-3,4- dichlorobenzoylalanine (FCE 28833A) causes a large and persistent increase in brain kynurenic acid levels in rats. Eur J Pharmacol. 1996;315(3):263-7. 577. Wonodi I, McMahon RP, Krishna N, Mitchell BD, Liu J, Glassman M, et al. Influence of kynurenine 3-monooxygenase (KMO) gene polymorphism on cognitive function in schizophrenia. Schizophr Res. 2014;160(1-3):80-7. 578. Erhardt S, Schwieler L, Emanuelsson C, Geyer M. Endogenous kynurenic acid disrupts prepulse inhibition. Biol Psychiatry. 2004;56(4):255-60. 579. Nilsson LK, Linderholm KR, Erhardt S. Subchronic treatment with kynurenine and probenecid: effects on prepulse inhibition and firing of midbrain dopamine neurons. Journal of neural transmission (Vienna, Austria : 1996). 2006;113(5):557-71. 580. Shepard PD, Joy B, Clerkin L, Schwarcz R. Micromolar brain levels of kynurenic acid are associated with a disruption of auditory sensory gating in the rat. Neuropsychopharmacology. 2003;28(8):1454-62. 581. Chess AC, Bucci DJ. Increased concentration of cerebral kynurenic acid alters stimulus processing and conditioned responding. Behav Brain Res. 2006;170(2):326-32. 582. Chess AC, Simoni MK, Alling TE, Bucci DJ. Elevations of endogenous kynurenic acid produce spatial working memory deficits. Schizophr Bull. 2007;33(3):797-804. 583. Chess AC, Landers AM, Bucci DJ. L-kynurenine treatment alters contextual fear conditioning and context discrimination but not cue-specific fear conditioning. Behav Brain Res. 2009;201(2):325-31. 584. Pocivavsek A, Thomas MA, Elmer GI, Bruno JP, Schwarcz R. Continuous kynurenine administration during the prenatal period, but not during adolescence, causes learning and memory deficits in adult rats. Psychopharmacology (Berl). 2014;231(14):2799-809. 585. Pocivavsek A, Wu HQ, Elmer GI, Bruno JP, Schwarcz R. Pre- and postnatal exposure to kynurenine causes cognitive deficits in adulthood. Eur J Neurosci. 2012;35(10):1605-12. 586. Pocivavsek A, Wu HQ, Potter MC, Elmer GI, Pellicciari R, Schwarcz R. Fluctuations in endogenous kynurenic acid control hippocampal glutamate and memory. Neuropsychopharmacology. 2011;36(11):2357-67.

222

587. Alexander KS, Pocivavsek A, Wu HQ, Pershing ML, Schwarcz R, Bruno JP. Early developmental elevations of brain kynurenic acid impair cognitive flexibility in adults: reversal with galantamine. Neuroscience. 2013;238:19-28. 588. Alexander KS, Wu HQ, Schwarcz R, Bruno JP. Acute elevations of brain kynurenic acid impair cognitive flexibility: normalization by the alpha7 positive modulator galantamine. Psychopharmacology (Berl). 2012;220(3):627-37. 589. Pershing ML, Bortz DM, Pocivavsek A, Fredericks PJ, Jorgensen CV, Vunck SA, et al. Elevated levels of kynurenic acid during gestation produce neurochemical, morphological, and cognitive deficits in adulthood: implications for schizophrenia. Neuropharmacology. 2015;90:33- 41. 590. Olsson SK, Larsson MK, Erhardt S. Subchronic elevation of brain kynurenic acid augments amphetamine-induced locomotor response in mice. Journal of neural transmission (Vienna, Austria : 1996). 2012;119(2):155-63. 591. Potter MC, Elmer GI, Bergeron R, Albuquerque EX, Guidetti P, Wu HQ, et al. Reduction of endogenous kynurenic acid formation enhances extracellular glutamate, hippocampal plasticity, and cognitive behavior. Neuropsychopharmacology. 2010;35(8):1734-42. 592. Kozak R, Campbell BM, Strick CA, Horner W, Hoffmann WE, Kiss T, et al. Reduction of brain kynurenic acid improves cognitive function. J Neurosci. 2014;34(32):10592-602. 593. Nilsson-Todd LK, Nordin C, Jonsson EG, Skogh E, Erhardt S. Cerebrospinal fluid kynurenic acid in male patients with schizophrenia - correlation with monoamine metabolites. Acta Neuropsychiatr. 2007;19(1):45-52. 594. Beggiato S, Antonelli T, Tomasini MC, Tanganelli S, Fuxe K, Schwarcz R, et al. Kynurenic acid, by targeting alpha7 nicotinic acetylcholine receptors, modulates extracellular GABA levels in the rat striatum in vivo. Eur J Neurosci. 2013;37(9):1470-7. 595. Carpenedo R, Pittaluga A, Cozzi A, Attucci S, Galli A, Raiteri M, et al. Presynaptic kynurenate-sensitive receptors inhibit glutamate release. Eur J Neurosci. 2001;13(11):2141-7. 596. Konradsson-Geuken A, Wu HQ, Gash CR, Alexander KS, Campbell A, Sozeri Y, et al. Cortical kynurenic acid bi-directionally modulates prefrontal glutamate levels as assessed by microdialysis and rapid electrochemistry. Neuroscience. 2010;169(4):1848-59. 597. Konradsson-Geuken A, Gash CR, Alexander K, Pomerleau F, Huettl P, Gerhardt GA, et al. Second-by-second analysis of alpha 7 nicotine receptor regulation of glutamate release in the prefrontal cortex of awake rats. Synapse. 2009;63(12):1069-82. 598. Wu HQ, Pereira EF, Bruno JP, Pellicciari R, Albuquerque EX, Schwarcz R. The astrocyte-derived alpha7 nicotinic receptor antagonist kynurenic acid controls extracellular glutamate levels in the prefrontal cortex. J Mol Neurosci. 2010;40(1-2):204-10. 599. Lopes C, Pereira EF, Wu HQ, Purushottamachar P, Njar V, Schwarcz R, et al. Competitive antagonism between the nicotinic allosteric potentiating ligand galantamine and kynurenic acid at alpha7* nicotinic receptors. J Pharmacol Exp Ther. 2007;322(1):48-58. 600. Wu HQ, Rassoulpour A, Schwarcz R. Kynurenic acid leads, dopamine follows: a new case of volume transmission in the brain? Journal of neural transmission (Vienna, Austria : 1996). 2007;114(1):33-41. 601. Amori L, Wu HQ, Marinozzi M, Pellicciari R, Guidetti P, Schwarcz R. Specific inhibition of kynurenate synthesis enhances extracellular dopamine levels in the rodent striatum. Neuroscience. 2009;159(1):196-203. 602. Erhardt S, Engberg G. Increased phasic activity of dopaminergic neurones in the rat ventral tegmental area following pharmacologically elevated levels of endogenous kynurenic acid. Acta Physiol Scand. 2002;175(1):45-53.

223

603. Erhardt S, Oberg H, Mathe JM, Engberg G. Pharmacological elevation of endogenous kynurenic acid levels activates nigral dopamine neurons. Amino Acids. 2001;20(4):353-62. 604. Linderholm KR, Andersson A, Olsson S, Olsson E, Snodgrass R, Engberg G, et al. Activation of rat ventral tegmental area dopamine neurons by endogenous kynurenic acid: a pharmacological analysis. Neuropharmacology. 2007;53(8):918-24. 605. Schwieler L, Erhardt S, Nilsson L, Linderholm K, Engberg G. Effects of COX-1 and COX-2 inhibitors on the firing of rat midbrain dopaminergic neurons--possible involvement of endogenous kynurenic acid. Synapse. 2006;59(5):290-8. 606. Schwieler L, Linderholm KR, Nilsson-Todd LK, Erhardt S, Engberg G. Clozapine interacts with the glycine site of the NMDA receptor: electrophysiological studies of dopamine neurons in the rat ventral tegmental area. Life Sci. 2008;83(5-6):170-5. 607. Liu XC, Holtze M, Powell SB, Terrando N, Larsson MK, Persson A, et al. Behavioral disturbances in adult mice following neonatal virus infection or kynurenine treatment--role of brain kynurenic acid. Brain Behav Immun. 2014;36:80-9. 608. Olsson SK, Andersson AS, Linderholm KR, Holtze M, Nilsson-Todd LK, Schwieler L, et al. Elevated levels of kynurenic acid change the dopaminergic response to amphetamine: implications for schizophrenia. Int J Neuropsychopharmacol. 2009;12(4):501-12. 609. Schwieler L, Erhardt S. Inhibitory action of clozapine on rat ventral tegmental area dopamine neurons following increased levels of endogenous kynurenic acid. Neuropsychopharmacology. 2003;28(10):1770-7. 610. Zmarowski A, Wu HQ, Brooks JM, Potter MC, Pellicciari R, Schwarcz R, et al. Astrocyte-derived kynurenic acid modulates basal and evoked cortical acetylcholine release. Eur J Neurosci. 2009;29(3):529-38. 611. Beggiato S, Tanganelli S, Fuxe K, Antonelli T, Schwarcz R, Ferraro L. Endogenous kynurenic acid regulates extracellular GABA levels in the rat prefrontal cortex. Neuropharmacology. 2014;82:11-8. 612. Amori L, Guidetti P, Pellicciari R, Kajii Y, Schwarcz R. On the relationship between the two branches of the kynurenine pathway in the rat brain in vivo. J Neurochem. 2009;109(2):316- 25. 613. Pellicciari R, Rizzo RC, Costantino G, Marinozzi M, Amori L, Guidetti P, et al. Modulators of the kynurenine pathway of tryptophan metabolism: synthesis and preliminary biological evaluation of (S)-4-(ethylsulfonyl)benzoylalanine, a potent and selective kynurenine aminotransferase II (KAT II) inhibitor. ChemMedChem. 2006;1(5):528-31. 614. Wu HQ, Okuyama M, Kajii Y, Pocivavsek A, Bruno JP, Schwarcz R. Targeting kynurenine aminotransferase II in psychiatric diseases: promising effects of an orally active enzyme inhibitor. Schizophr Bull. 2014;40 Suppl 2:S152-8. 615. Choi KH, Wykes T, Kurtz MM. Adjunctive pharmacotherapy for cognitive deficits in schizophrenia: meta-analytical investigation of efficacy. Br J Psychiatry. 2013;203(3):172-8. 616. Conley RR, Boggs DL, Kelly DL, McMahon RP, Dickinson D, Feldman S, et al. The effects of galantamine on psychopathology in chronic stable schizophrenia. Clin Neuropharmacol. 2009;32(2):69-74. 617. Lindenmayer JP, Khan A. Galantamine augmentation of long-acting injectable risperidone for cognitive impairments in chronic schizophrenia. Schizophr Res. 2011;125(2- 3):267-77. 618. Ball HJ, Jusof FF, Bakmiwewa SM, Hunt NH, Yuasa HJ. Tryptophan-catabolizing enzymes - party of three. Front Immunol. 2014;5:485.

224

619. Fatokun AA, Hunt NH, Ball HJ. Indoleamine 2,3-dioxygenase 2 (IDO2) and the kynurenine pathway: characteristics and potential roles in health and disease. Amino Acids. 2013;45(6):1319-29. 620. Miller CL. The evolution of schizophrenia: a model for selection by infection, with a focus on NAD. Curr Pharm Des. 2009;15(1):100-9. 621. Yu CP, Pan ZZ, Luo DY. TDO as a therapeutic target in brain diseases. Metab Brain Dis. 2016;31(4):737-47. 622. Schwieler L, Erhardt S, Erhardt C, Engberg G. Prostaglandin-mediated control of rat brain kynurenic acid synthesis--opposite actions by COX-1 and COX-2 isoforms. Journal of neural transmission (Vienna, Austria : 1996). 2005;112(7):863-72. 623. Akhondzadeh S, Tabatabaee M, Amini H, Ahmadi Abhari SA, Abbasi SH, Behnam B. Celecoxib as adjunctive therapy in schizophrenia: a double-blind, randomized and placebo- controlled trial. Schizophr Res. 2007;90(1-3):179-85. 624. Rappard F, Müller N. Celecoxib add-on therapy does not have beneficial antipsychotic effects over risperidone alone in schizophrenia. Neuropsychopharmacology. 2004;29:S222. 625. Golightly KL, Lloyd JA, Hobson JE, Gallagher P, Mercer G, Young AH. Acute tryptophan depletion in schizophrenia. Psychol Med. 2001;31(1):75-84. 626. Hitsman B, Spring B, Wolf W, Pingitore R, Crayton JW, Hedeker D. Effects of acute tryptophan depletion on negative symptoms and smoking topography in nicotine-dependent schizophrenics and nonpsychiatric controls. Neuropsychopharmacology. 2005;30(3):640-8. 627. Sharma RP, Shapiro LE, Kamath SK, Soll EA, Watanabe MD, Davis JM. Acute dietary tryptophan depletion: effects on schizophrenic positive and negative symptoms. Neuropsychobiology. 1997;35(1):5-10. 628. Green MF, Nuechterlein KH, Gold JM, Barch DM, Cohen J, Essock S, et al. Approaching a consensus cognitive battery for clinical trials in schizophrenia: the NIMH- MATRICS conference to select cognitive domains and test criteria. Biol Psychiatry. 2004;56(5):301-7. 629. Bumb JM, Enning F, Leweke FM. Drug repurposing and emerging adjunctive treatments for schizophrenia. Expert Opin Pharmacother. 2015;16(7):1049-67. 630. Tuominen HJ, Tiihonen J, Wahlbeck K. Glutamatergic drugs for schizophrenia. The Cochrane database of systematic reviews. 2006(2):Cd003730. 631. Tuominen HJ, Tiihonen J, Wahlbeck K. Glutamatergic drugs for schizophrenia: a systematic review and meta-analysis. Schizophr Res. 2005;72(2-3):225-34. 632. Singh SP, Singh V. Meta-analysis of the efficacy of adjunctive NMDA receptor modulators in chronic schizophrenia. CNS Drugs. 2011;25(10):859-85. 633. Tsai GE, Lin PY. Strategies to enhance N-methyl-D-aspartate receptor-mediated neurotransmission in schizophrenia, a critical review and meta-analysis. Curr Pharm Des. 2010;16(5):522-37. 634. Kishi T, Matsuda Y, Iwata N. Memantine add-on to antipsychotic treatment for residual negative and cognitive symptoms of schizophrenia: a meta-analysis. Psychopharmacology (Berl). 2017;234(14):2113-25. 635. Zheng W, Li XH, Yang XH, Cai DB, Ungvari GS, Ng CH, et al. Adjunctive memantine for schizophrenia: a meta-analysis of randomized, double-blind, placebo-controlled trials. Psychol Med. 2018;48(1):72-81. 636. Zheng W, Zhang QE, Cai DB, Yang XH, Qiu Y, Ungvari GS, et al. N-acetylcysteine for major mental disorders: a systematic review and meta-analysis of randomized controlled trials. Acta Psychiatr Scand. 2018.

225

637. Imbesi M, Uz T, Manev R, Sharma RP, Manev H. Minocycline increases phosphorylation and membrane insertion of neuronal GluR1 receptors. Neurosci Lett. 2008;447(2-3):134-7. 638. Solmi M, Veronese N, Thapa N, Facchini S, Stubbs B, Fornaro M, et al. Systematic review and meta-analysis of the efficacy and safety of minocycline in schizophrenia. CNS spectrums. 2017;22(5):415-26. 639. Xiang YQ, Zheng W, Wang SB, Yang XH, Cai DB, Ng CH, et al. Adjunctive minocycline for schizophrenia: A meta-analysis of randomized controlled trials. Eur Neuropsychopharmacol. 2017;27(1):8-18. 640. Bakardjiev A. Carnosine and beta-alanine release is stimulated by glutamatergic receptors in cultured rat oligodendrocytes. Glia. 1998;24(3):346-51. 641. Panzanelli P, Cantino D, Sassoe-Pognetto M. Co-localization of carnosine and glutamate in photoreceptors and bipolar cells of the frog retina. Brain Res. 1997;758(1-2):143-52. 642. Sassoe-Pognetto M, Cantino D, Panzanelli P, Verdun di Cantogno L, Giustetto M, Margolis FL, et al. Presynaptic co-localization of carnosine and glutamate in olfactory neurones. Neuroreport. 1993;5(1):7-10. 643. Smythies J. Redox mechanisms at the glutamate synapse and their significance: a review. Eur J Pharmacol. 1999;370(1):1-7. 644. Marx CE, Keefe RS, Buchanan RW, Hamer RM, Kilts JD, Bradford DW, et al. Proof-of- concept trial with the neurosteroid pregnenolone targeting cognitive and negative symptoms in schizophrenia. Neuropsychopharmacology. 2009;34(8):1885-903. 645. Smith CC, Martin SC, Sugunan K, Russek SJ, Gibbs TT, Farb DH. A role for picomolar concentrations of pregnenolone sulfate in synaptic activity-dependent Ca2+ signaling and CREB activation. Mol Pharmacol. 2014;86(4):390-8. 646. Ellaithy A, Younkin J, Gonzalez-Maeso J, Logothetis DE. Positive allosteric modulators of metabotropic glutamate 2 receptors in schizophrenia treatment. Trends Neurosci. 2015;38(8):506-16. 647. Fell MJ, McKinzie DL, Monn JA, Svensson KA. Group II metabotropic glutamate receptor agonists and positive allosteric modulators as novel treatments for schizophrenia. Neuropharmacology. 2012;62(3):1473-83. 648. Herman EJ, Bubser M, Conn PJ, Jones CK. Metabotropic Glutamate Receptors for New Treatments in Schizophrenia. In: Geyer MA, editor. Novel Antischizophrenia Treatments: Springer; 2012. p. 297-366. 649. Javitt DC. Current and emergent treatments for symptoms and neurocognitive impairment in schizophrenia. Current treatment options in psychiatry. 2015;1(2):107-20. 650. Maksymetz J, Moran SP, Conn PJ. Targeting metabotropic glutamate receptors for novel treatments of schizophrenia. Molecular brain. 2017;10(1):15. 651. Matosin N, Newell KA. Metabotropic glutamate receptor 5 in the pathology and treatment of schizophrenia. Neurosci Biobehav Rev. 2013;37(3):256-68. 652. Muguruza C, Meana JJ, Callado LF. Group II Metabotropic Glutamate Receptors as Targets for Novel Antipsychotic Drugs. Front Pharmacol. 2016;7:130. 653. Stansley BJ, Conn PJ. The therapeutic potential of metabotropic glutamate receptor modulation for schizophrenia. Curr Opin Pharmacol. 2018;38:31-6. 654. Zheng W, Cai DB, Yang XH, Ungvari GS, Ng CH, Muller N, et al. Adjunctive celecoxib for schizophrenia: A meta-analysis of randomized, double-blind, placebo-controlled trials. J Psychiatr Res. 2017;92:139-46. 655. Adamson P, Greenwood J. How do statins control neuroinflammation? Inflamm Res. 2003;52(10):399-403.

226

656. Stuve O, Youssef S, Steinman L, Zamvil SS. Statins as potential therapeutic agents in neuroinflammatory disorders. Curr Opin Neurol. 2003;16(3):393-401. 657. Shen H, Li R, Yan R, Zhou X, Feng X, Zhao M, et al. Adjunctive therapy with statins in schizophrenia patients: A meta-analysis and implications. Psychiatry Res. 2018;262:84-93. 658. Plitman E, Iwata Y, Caravaggio F, Nakajima S, Chung JK, Gerretsen P, et al. Kynurenic Acid in Schizophrenia: A Systematic Review and Meta-analysis. Schizophr Bull. 2017;43(4):764-77. 659. Kekic M, Boysen E, Campbell IC, Schmidt U. A systematic review of the clinical efficacy of transcranial direct current stimulation (tDCS) in psychiatric disorders. J Psychiatr Res. 2016;74:70-86. 660. Tortella G, Casati R, Aparicio LV, Mantovani A, Senco N, D'Urso G, et al. Transcranial direct current stimulation in psychiatric disorders. World J Psychiatry. 2015;5(1):88-102. 661. Utz KS, Dimova V, Oppenlander K, Kerkhoff G. Electrified minds: transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non- invasive brain stimulation in neuropsychology--a review of current data and future implications. Neuropsychologia. 2010;48(10):2789-810. 662. Brunoni AR, Shiozawa P, Truong D, Javitt DC, Elkis H, Fregni F, et al. Understanding tDCS effects in schizophrenia: a systematic review of clinical data and an integrated computation modeling analysis. Expert Rev Med Devices. 2014;11(4):383-94. 663. Lefaucheur JP, Antal A, Ayache SS, Benninger DH, Brunelin J, Cogiamanian F, et al. Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clin Neurophysiol. 2017;128(1):56-92. 664. Ponde PH, de Sena EP, Camprodon JA, de Araujo AN, Neto MF, DiBiasi M, et al. Use of transcranial direct current stimulation for the treatment of auditory hallucinations of schizophrenia - a systematic review. Neuropsychiatr Dis Treat. 2017;13:347-55. 665. Stagg CJ, Nitsche MA. Physiological basis of transcranial direct current stimulation. Neuroscientist. 2011;17(1):37-53. 666. Gbadeyan O, Steinhauser M, McMahon K, Meinzer M. Safety, Tolerability, Blinding Efficacy and Behavioural Effects of a Novel MRI-Compatible, High-Definition tDCS Set-Up. Brain Stimul. 2016. 667. Agarwal SM, Shivakumar V, Bose A, Subramaniam A, Nawani H, Chhabra H, et al. Transcranial direct current stimulation in schizophrenia. Clin Psychopharmacol Neurosci. 2013;11(3):118-25. 668. Aleman A, Enriquez-Geppert S, Knegtering H, Dlabac-de Lange JJ. Moderate effects of noninvasive brain stimulation of the frontal cortex for improving negative symptoms in schizophrenia: Meta-analysis of controlled trials. Neurosci Biobehav Rev. 2018. 669. Kennedy NI, Lee WH, Frangou S. Efficacy of non-invasive brain stimulation on the symptom dimensions of schizophrenia: A meta-analysis of randomized controlled trials. Eur Psychiatry. 2018;49:69-77. 670. Osoegawa C, Gomes JS, Grigolon RB, Brietzke E, Gadelha A, Lacerda ALT, et al. Non- invasive brain stimulation for negative symptoms in schizophrenia: An updated systematic review and meta-analysis. Schizophr Res. 2018. 671. Liebetanz D, Nitsche MA, Tergau F, Paulus W. Pharmacological approach to the mechanisms of transcranial DC-stimulation-induced after-effects of human motor cortex excitability. Brain. 2002;125(Pt 10):2238-47. 672. Nitsche MA, Jaussi W, Liebetanz D, Lang N, Tergau F, Paulus W. Consolidation of human motor cortical neuroplasticity by D-cycloserine. Neuropsychopharmacology. 2004;29(8):1573-8.

227

673. Clark VP, Coffman BA, Trumbo MC, Gasparovic C. Transcranial direct current stimulation (tDCS) produces localized and specific alterations in neurochemistry: a (1)H magnetic resonance spectroscopy study. Neurosci Lett. 2011;500(1):67-71. 674. Hunter MA, Coffman BA, Gasparovic C, Calhoun VD, Trumbo MC, Clark VP. Baseline effects of transcranial direct current stimulation on glutamatergic neurotransmission and large- scale network connectivity. Brain Res. 2015;1594:92-107. 675. Stagg CJ, Best JG, Stephenson MC, O'Shea J, Wylezinska M, Kincses ZT, et al. Polarity- sensitive modulation of cortical neurotransmitters by transcranial stimulation. J Neurosci. 2009;29(16):5202-6. 676. Rango M, Cogiamanian F, Marceglia S, Barberis B, Arighi A, Biondetti P, et al. Myoinositol content in the human brain is modified by transcranial direct current stimulation in a matter of minutes: a 1H-MRS study. Magn Reson Med. 2008;60(4):782-9. 677. Bachtiar V, Near J, Johansen-Berg H, Stagg CJ. Modulation of GABA and resting state functional connectivity by transcranial direct current stimulation. Elife. 2015;4:e08789. 678. Kim S, Stephenson MC, Morris PG, Jackson SR. tDCS-induced alterations in GABA concentration within primary motor cortex predict motor learning and motor memory: a 7 T magnetic resonance spectroscopy study. NeuroImage. 2014;99:237-43. 679. Barron HC, Vogels TP, Emir UE, Makin TR, O'Shea J, Clare S, et al. Unmasking Latent Inhibitory Connections in Human Cortex to Reveal Dormant Cortical Memories. Neuron. 2016;90(1):191-203. 680. Antonenko D, Schubert F, Bohm F, Ittermann B, Aydin S, Hayek D, et al. tDCS-induced Modulation of GABA Levels and Resting-State Functional Connectivity in Older Adults. J Neurosci. 2017. 681. Tremblay S, Lafleur LP, Proulx S, Beaule V, Latulipe-Loiselle A, Doyon J, et al. The effects of bi-hemispheric M1-M1 transcranial direct current stimulation on primary motor cortex neurophysiology and metabolite concentration. Restor Neurol Neurosci. 2016;34(4):587-602. 682. Wilke S, List J, Mekle R, Lindenberg R, Bukowski M, Ott S, et al. No Effect of Anodal Transcranial Direct Current Stimulation on Gamma-Aminobutyric Acid Levels in Patients with Recurrent Mild Traumatic Brain Injury. J Neurotrauma. 2017;34(2):281-90. 683. Foerster BR, Nascimento TD, DeBoer M, Bender MA, Rice IC, Truong DQ, et al. Excitatory and inhibitory brain metabolites as targets of motor cortex transcranial direct current stimulation therapy and predictors of its efficacy in fibromyalgia. Arthritis Rheumatol. 2015;67(2):576-81. 684. Chervyakov AV, Chernyavsky AY, Sinitsyn DO, Piradov MA. Possible Mechanisms Underlying the Therapeutic Effects of Transcranial Magnetic Stimulation. Front Hum Neurosci. 2015;9:303. 685. Lefaucheur JP, Andre-Obadia N, Antal A, Ayache SS, Baeken C, Benninger DH, et al. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS). Clin Neurophysiol. 2014;125(11):2150-206. 686. Dlabac-de Lange JJ, Knegtering R, Aleman A. Repetitive transcranial magnetic stimulation for negative symptoms of schizophrenia: review and meta-analysis. J Clin Psychiatry. 2010;71(4):411-8. 687. Shi C, Yu X, Cheung EF, Shum DH, Chan RC. Revisiting the therapeutic effect of rTMS on negative symptoms in schizophrenia: a meta-analysis. Psychiatry Res. 2014;215(3):505-13. 688. Dlabac-de Lange JJ, Liemburg EJ, Bais L, van de Poel-Mustafayeva AT, de Lange-de Klerk ESM, Knegtering H, et al. Effect of Bilateral Prefrontal rTMS on Left Prefrontal NAA and Glx Levels in Schizophrenia Patients with Predominant Negative Symptoms: An Exploratory Study. Brain Stimul. 2017;10(1):59-64.

228

689. Bolwig TG. How does electroconvulsive therapy work? Theories on its mechanism. Can J Psychiatry. 2011;56(1):13-8. 690. Haskett RF. Electroconvulsive therapy's mechanism of action: neuroendocrine hypotheses. J ECT. 2014;30(2):107-10. 691. Trevino K, McClintock SM, Husain MM. A review of continuation electroconvulsive therapy: application, safety, and efficacy. J ECT. 2010;26(3):186-95. 692. Gan JL, Duan HF, Cheng ZX, Yang JM, Zhu XQ, Gao CY, et al. Neuroprotective Effect of Modified Electroconvulsive Therapy for Schizophrenia: A Proton Magnetic Resonance Spectroscopy Study. J Nerv Ment Dis. 2017;205(6):480-6. 693. Abbott CC, Gallegos P, Rediske N, Lemke NT, Quinn DK. A review of longitudinal electroconvulsive therapy: neuroimaging investigations. J Geriatr Psychiatry Neurol. 2014;27(1):33-46. 694. Merritt K, McGuire P, Egerton A. Relationship between Glutamate Dysfunction and Symptoms and Cognitive Function in Psychosis. Front Psychiatry. 2013;4:151. 695. Szulc A, Konarzewska B, Galinska-Skok B, Lazarczyk J, Waszkiewicz N, Tarasow E, et al. Proton magnetic resonance spectroscopy measures related to short-term symptomatic outcome in chronic schizophrenia. Neurosci Lett. 2013;547:37-41. 696. Egerton A, Broberg BV, Van Haren N, Merritt K, Barker GJ, Lythgoe DJ, et al. Response to initial antipsychotic treatment in first episode psychosis is related to anterior cingulate glutamate levels: a multicentre (1)H-MRS study (OPTiMiSE). Mol Psychiatry. 2018. 697. Plitman E, Chavez S, Nakajima S, Iwata Y, Chung JK, Caravaggio F, et al. Striatal neurometabolite levels in patients with schizophrenia undergoing long-term antipsychotic treatment: A proton magnetic resonance spectroscopy and reliability study. Psychiatry Res. 2018;273:16-24. 698. Sivaraman S, Kraguljac NV, White DM, Morgan CJ, Gonzales SS, Lahti AC. Neurometabolic Abnormalities in the Associative Striatum in Antipsychotic-naïve First Episode Psychosis Patients. Psychiatry Research: Neuroimaging. 699. Plitman E, Patel R, Chung JK, Pipitone J, Chavez S, Reyes-Madrigal F, et al. Glutamatergic Metabolites, Volume and Cortical Thickness in Antipsychotic-Naive Patients with First-Episode Psychosis: Implications for Excitotoxicity. Neuropsychopharmacology. 2016;41(10):2606-13. 700. Fusar-Poli P, Smieskova R, Kempton MJ, Ho BC, Andreasen NC, Borgwardt S. Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev. 2013;37(8):1680-91. 701. Olabi B, Ellison-Wright I, McIntosh AM, Wood SJ, Bullmore E, Lawrie SM. Are there progressive brain changes in schizophrenia? A meta-analysis of structural magnetic resonance imaging studies. Biol Psychiatry. 2011;70(1):88-96. 702. Vita A, De Peri L, Deste G, Barlati S, Sacchetti E. The Effect of Antipsychotic Treatment on Cortical Gray Matter Changes in Schizophrenia: Does the Class Matter? A Meta-analysis and Meta-regression of Longitudinal Magnetic Resonance Imaging Studies. Biol Psychiatry. 2015;78(6):403-12. 703. Huhtaniska S, Jaaskelainen E, Hirvonen N, Remes J, Murray GK, Veijola J, et al. Long- term antipsychotic use and brain changes in schizophrenia - a systematic review and meta- analysis. Human psychopharmacology. 2017;32(2). 704. James A, James C, Thwaites T. The brain effects of cannabis in healthy adolescents and in adolescents with schizophrenia: a systematic review. Psychiatry Res. 2013;214(3):181-9.

229

705. Mathalon DH, Pfefferbaum A, Lim KO, Rosenbloom MJ, Sullivan EV. Compounded brain volume deficits in schizophrenia-alcoholism comorbidity. Arch Gen Psychiatry. 2003;60(3):245-52. 706. Rais M, Cahn W, Van Haren N, Schnack H, Caspers E, Hulshoff Pol H, et al. Excessive brain volume loss over time in cannabis-using first-episode schizophrenia patients. Am J Psychiatry. 2008;165(4):490-6. 707. Van Haren NE, Cahn W, Hulshoff Pol HE, Kahn RS. Confounders of excessive brain volume loss in schizophrenia. Neurosci Biobehav Rev. 2013;37(10 Pt 1):2418-23. 708. Smieskova R, Fusar-Poli P, Allen P, Bendfeldt K, Stieglitz RD, Drewe J, et al. The effects of antipsychotics on the brain: what have we learnt from structural imaging of schizophrenia?--a systematic review. Curr Pharm Des. 2009;15(22):2535-49. 709. Choe BY, Suh TS, Shinn KS, Lee CW, Lee C, Paik IH. Observation of metabolic changes in chronic schizophrenia after neuroleptic treatment by in vivo hydrogen magnetic resonance spectroscopy. Invest Radiol. 1996;31(6):345-52. 710. Goff DC, Hennen J, Lyoo IK, Tsai G, Wald LL, Evins AE, et al. Modulation of brain and serum glutamatergic concentrations following a switch from conventional neuroleptics to olanzapine. Biol Psychiatry. 2002;51(6):493-7. 711. Szulc A, Galinska B, Tarasow E, Dzienis W, Kubas B, Konarzewska B, et al. The effect of risperidone on metabolite measures in the frontal lobe, temporal lobe, and thalamus in schizophrenic patients. A proton magnetic resonance spectroscopy (1H MRS). Pharmacopsychiatry. 2005;38(5):214-9. 712. Szulc A, Galinska B, Tarasow E, Waszkiewicz N, Konarzewska B, Poplawska R, et al. Proton magnetic resonance spectroscopy study of brain metabolite changes after antipsychotic treatment. Pharmacopsychiatry. 2011;44(4):148-57. 713. Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, et al. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry. 2017. 714. Caravaggio F, Ku Chung J, Plitman E, Boileau I, Gerretsen P, Kim J, et al. The relationship between subcortical brain volume and striatal dopamine D2/3 receptor availability in healthy humans assessed with [(11) C]-raclopride and [(11) C]-(+)-PHNO PET. Hum Brain Mapp. 2017;38(11):5519-34. 715. Schmidt MJ, Mirnics K. Neurodevelopment, GABA system dysfunction, and schizophrenia. Neuropsychopharmacology. 2015;40(1):190-206. 716. Taylor SF, Tso IF. GABA abnormalities in schizophrenia: a methodological review of in vivo studies. Schizophr Res. 2015;167(1-3):84-90. 717. Egerton A, Modinos G, Ferrera D, McGuire P. Neuroimaging studies of GABA in schizophrenia: a systematic review with meta-analysis. Transl Psychiatry. 2017;7(6):e1147. 718. Schur RR, Draisma LW, Wijnen JP, Boks MP, Koevoets MG, Joels M, et al. Brain GABA levels across psychiatric disorders: A systematic literature review and meta-analysis of (1) H-MRS studies. Hum Brain Mapp. 2016;37(9):3337-52. 719. Kumar J, Liddle EB, Fernandes CC, Palaniyappan L, Hall EL, Robson SE, et al. Glutathione and glutamate in schizophrenia: a 7T MRS study. Mol Psychiatry. 2018. 720. Brandt AS, Unschuld PG, Pradhan S, Lim IA, Churchill G, Harris AD, et al. Age-related changes in anterior cingulate cortex glutamate in schizophrenia: A (1)H MRS Study at 7 Tesla. Schizophr Res. 2016;172(1-3):101-5. 721. Wood SJ, Berger GE, Wellard RM, Proffitt TM, McConchie M, Berk M, et al. Medial temporal lobe glutathione concentration in first episode psychosis: a 1H-MRS investigation. Neurobiol Dis. 2009;33(3):354-7.

230

722. Matsuzawa D, Obata T, Shirayama Y, Nonaka H, Kanazawa Y, Yoshitome E, et al. Negative correlation between brain glutathione level and negative symptoms in schizophrenia: a 3T 1H-MRS study. PLoS One. 2008;3(4):e1944. 723. Jessen F, Fingerhut N, Sprinkart AM, Kuhn KU, Petrovsky N, Maier W, et al. N- acetylaspartylglutamate (NAAG) and N-acetylaspartate (NAA) in patients with schizophrenia. Schizophr Bull. 2013;39(1):197-205. 724. Bakhshi K, Chance SA. The neuropathology of schizophrenia: A selective review of past studies and emerging themes in brain structure and cytoarchitecture. Neuroscience. 2015;303:82- 102. 725. Maddock RJ, Buonocore MH. MR spectroscopic studies of the brain in psychiatric disorders. Curr Top Behav Neurosci. 2012;11:199-251. 726. Fisher SK, Novak JE, Agranoff BW. Inositol and higher inositol phosphates in neural tissues: homeostasis, metabolism and functional significance. J Neurochem. 2002;82(4):736-54. 727. Rothermundt M, Ohrmann P, Abel S, Siegmund A, Pedersen A, Ponath G, et al. Glial cell activation in a subgroup of patients with schizophrenia indicated by increased S100B serum concentrations and elevated myo-inositol. Prog Neuropsychopharmacol Biol Psychiatry. 2007;31(2):361-4. 728. Janssen B, Vugts DJ, Windhorst AD, Mach RH. PET Imaging of Microglial Activation- Beyond Targeting TSPO. Molecules (Basel, Switzerland). 2018;23(3). 729. Chiappelli J, Postolache TT, Kochunov P, Rowland LM, Wijtenburg SA, Shukla DK, et al. Tryptophan Metabolism and White Matter Integrity in Schizophrenia. Neuropsychopharmacology. 2016;41(10):2587-95. 730. Tisell A, Leinhard OD, Warntjes JB, Lundberg P. Procedure for quantitative (1)H magnetic resonance spectroscopy and tissue characterization of human brain tissue based on the use of quantitative magnetic resonance imaging. Magn Reson Med. 2013;70(4):905-15. 731. Kreis R. The trouble with quality filtering based on relative Cramer-Rao lower bounds. Magn Reson Med. 2016;75(1):15-8. 732. Sørensen JB. The Use and Misuse of the Coefficient of Variation in Organizational Demography Research. Sociological Methods & Research. 2002;30(4):475-91. 733. Trickey KA. Structural Models of Coefficient of Variation Matrices: University of California, Los Angeles; 2015.