Genetic Contribution to Heterogeneity in Brain Morphology
with Applications to Schizophrenia
by
Tristram Alexander Pierrepont Lett
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Institute of Medical Science
University of Toronto
© Copyright by Tristram Lett 2014
ii
Genetic Contribution to Heterogeneity in Brain Connectivity and
Plasticity in Schizophrenia
Tristram Alexander Pierrepont Lett
Doctor of Philosophy
Institute of Medical Science
University of Toronto
2014
Abstract
Schizophrenia is a highly heterogeneous disorder. Differences among patients with schizophrenia
have been reported in clinical features, cognitive functioning, and brain structure. This thesis
investigates the impact of the well supported genetic risk variants on brain structure and also
considers the role of imaging-genetic changes on heterogeneous phenotypes relevant to
schizophrenia. In the first study, the genome-wide supported NRXN1 gene, associated with
schizophrenia and autism spectrum disorders, was examined with magnetic resonance imaging
(MRI) volumetric measures and measures of sensorimotor function. Study two investigated the
effect of the genome-wide identified schizophrenia risk variant in the MIR137 gene for
association with age-at-onset and brain structures implicated in disease severity, as well as white
matter fractional anisotropy (FA) and cortical thickness. Risk allele carriers had earlier age-at-
onset and aberrant brain structure suggesting that MIR137 may predict a more severe
schizophrenia subphenotype. Study three examined the role of the GAD1 gene on brain structure
and executive function. Among patients and controls the GAD1 variant predicted changes in
white matter FA and multiple working memory tasks. Using voxel-wise mediation analysis, we
were able to infer the functional relevance of GAD1 on structural connectivity by showing these
iii
FA changes statistically cause impaired working memory performance. In the final study, we
investigated relationships among polygenic additive risk, brain-wide measures, and cognition in
schizophrenia patients and healthy controls. Schizophrenia patients with low polygenic risk were
similar in brain structure and cognitive performance to healthy controls. In contrast, high
polygenic risk in patients led to large reductions in cortical thickness and white matter skeleton
FA, in addition to impaired semantic memory and motor functioning. Taken together, these
studies suggest that neuroimaging and genetics can be used to meaningful disentangle
heterogeneity of schizophrenia phenotypes, and may move towards neurobiological based
treatment options.
iv
§
I would like to dedicate this thesis in loving memory of Margaret Depew
§
v
Acknowledgments
It is my pleasure to thank the many people for who made this thesis possible.
I would like to thank my PhD supervisors Drs. James Kennedy and Aristotle Voineskos. Dr.
Kennedy has been instrumental for my scientific development. He has taught me to navigate
through the complexity of scientific field, and I never wanted for any intellectual need under his
tutelage. Dr. Voineskos has been a true mentor as well as supervisor, and he was the inspiration
for my pursuit as a scientist. He provided the means and stellar direction necessary to maximize
my potential. As a result of their outstanding supervision, I believe I am well-prepared for a
career as an independent investigator. I have been extremely fortunate work and learn from them
these past few years.
I also want to thank my PhD committee members Drs. Jeff Daskalakis and Gary Remington. I
have benefitted greatly from their expertise and scientific input at every stage of my doctoral
training. Dr. Remington has provided invaluable clinical perspective to my research. I am
extremely grateful to Dr. Daskalakis who has acted in every way as supervisor and outstanding
mentor to me. He has providing novel understanding of translational and brain stimulation
research. It has been an honour to work in his lab.
I am grateful to all past and present members of the Psychiatric Neurogenetics Laboratory (Dr.
Kennedy), the Kimel Family Translational Imaging-genetics Laboratory (Dr. Voineskos), and
Temerty Centre for Therapeutic Brain Intervention (Dr. Daskalakis). Drs. Mallar Chakravarty
and Arun Tiwari deserve special mention.
vi
I am also thankful for the support provided by the Institute of Medical Science, and the Centre
for Addiction and Mental Health.
Last, I thank my wife Eva Brandl, my parents, my grandmother, and my siblings for their love
and support. Also, I appreciate the patience of my daughter, Hanna, who has spent many hours
bouncing on my knee while I wrote this thesis.
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Table of Contents
Acknowledgments ........................................................................................................................... v
Table of Contents .......................................................................................................................... vii
List of Figures .............................................................................................................................. xiv
List of Appendices ....................................................................................................................... xvi
List of Abbreviations .................................................................................................................. xvii
Chapter 1 ......................................................................................................................................... 1
1 Introduction ................................................................................................................................ 1
1.1 Overview ............................................................................................................................. 1
1.2 Schizophrenia ...................................................................................................................... 2
1.2.1 Impact, Epidemiology and Prognosis ..................................................................... 2
1.2.2 Treatment of Schizophrenia .................................................................................... 3
1.3 Sources of Schizophrenia Heterogeneity ............................................................................ 5
1.3.1 Onset of Psychotic Symptoms ................................................................................ 6
1.3.2 Cognitive Symptoms ............................................................................................... 8
1.3.3 Brain Structure ...................................................................................................... 10
1.4 Cortical Thickness and Diffusion Tensor Imaging Measures of White Matter
Structure ............................................................................................................................ 12
1.4.1 Analytic Approaches to Cortical Thickness and Diffusion Tensor Imaging ........ 12
1.4.2 Genetic Contribution to Cortical Thickness and White Matter Fractional
Anisotropy (FA) .................................................................................................... 15
1.5 Functional Integration in Schizophrenia ........................................................................... 16
1.6 Schizophrenia Genetics: An Update ................................................................................. 17
1.7 Important Genetic Modifiers of Schizophrenia Phenotypes ............................................. 23
1.7.1 Neurexin-1 (NRXN1) ............................................................................................ 23
1.7.2 Glutamate Decarboxylase 1 (GAD1) .................................................................... 23
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1.7.3 Brain-derived Neurotrophic Factor (BDNF) ........................................................ 24
1.7.4 MicroRNA 137 (MIR137) ..................................................................................... 25
1.7.5 L-type Voltage-dependent Calcium Channel CAv1.2 (CACNA1C) ..................... 26
1.7.6 Zinc-Finger 804A (ZNF804A) .............................................................................. 27
1.8 Multivariate Approaches to Neuroimaging ...................................................................... 29
1.8.1 Complex Genetic Analysis on Candidate Imaging Phenotypes ........................... 29
1.8.2 Single Variant Analysis of Whole Brain Imaging Phenotypes ............................. 32
1.8.3 Complex Genetic Analysis of Whole Brain Imaging Phenotypes ........................ 34
1.9 Application of polygenic risk models to imaging genetic studies in psychiatry .............. 38
1.10 Outline of Experiments ..................................................................................................... 40
Chapter 2 ....................................................................................................................................... 41
2 Overview of Experiments, and Hypothesis .............................................................................. 41
2.1 Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype
for Schizophrenia and Autism Spectrum Disorders .......................................................... 41
2.1.1 Background ........................................................................................................... 41
2.1.2 Hypothesis ............................................................................................................. 41
2.2 The Genome-Wide Supported MicroRNA-137 Variant Predicts Phenotypic
Heterogeneity within Schizophrenia ................................................................................. 42
2.2.1 Background ........................................................................................................... 42
2.2.2 Hypothesis ............................................................................................................. 42
2.3 Glutamate Decarboxylase 1 (GAD1) Variant Predicts a Neuroanatomical and
Working Memory Susceptibly Mechanism Relevant to Schizophrenia. .......................... 43
2.4 Background ....................................................................................................................... 43
2.5 Hypothesis ......................................................................................................................... 43
2.6 Additive Genetics Risk Predicts Widespread Changes in Brain Structure that Cause
Poorer Cognitive Function ................................................................................................ 44
2.6.1 Background ........................................................................................................... 44
2.6.2 Hypothesis ............................................................................................................. 44
ix
Chapter 3 ....................................................................................................................................... 45
3 Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for
Schizophrenia and Autism Spectrum Disorders ...................................................................... 45
3.1 Abstract ............................................................................................................................. 46
3.2 Introduction ....................................................................................................................... 47
3.3 Results ............................................................................................................................... 49
3.3.1 Genotypes ............................................................................................................. 49
3.3.2 Cognitive ............................................................................................................... 52
3.3.3 In silico Analysis ................................................................................................... 52
3.4 Discussion ......................................................................................................................... 52
3.5 Materials and Methods ...................................................................................................... 56
3.5.1 Participants ............................................................................................................ 56
3.5.2 Neuroimaging ....................................................................................................... 57
3.5.3 Image Processing .................................................................................................. 57
3.5.4 Genetics ................................................................................................................. 58
3.5.5 Cognitive Assessment ........................................................................................... 59
3.5.6 Statistical Analysis ................................................................................................ 59
3.5.7 In Silico Analysis .................................................................................................. 60
3.6 Acknowledgements ........................................................................................................... 60
Chapter 4 ....................................................................................................................................... 74
4 The Genome-Wide Supported MicroRNA-137 Variant Predicts Phenotypic Heterogeneity
Within Schizophrenia ............................................................................................................... 74
4.1 Abstract ............................................................................................................................. 75
4.2 Introduction ....................................................................................................................... 76
4.3 Subjects and Methods ....................................................................................................... 78
4.3.1 Participants for Genetic Investigation of Age-at-onset Phenotypes ..................... 78
4.3.2 Participants for Genetic Investigation of Neuroimaging Phenotypes ................... 79
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4.3.3 Image Acquisition ................................................................................................. 80
4.3.4 Cortical Volumes Processing ................................................................................ 80
4.3.5 Cortical Thickness Mapping ................................................................................. 80
4.3.6 Tract-Based Spatial Statistics (TBSS) .................................................................. 81
4.3.7 Whole-Brain TBSS Analysis ................................................................................ 82
4.3.8 Genetics ................................................................................................................. 82
4.3.9 Statistical Analysis ................................................................................................ 82
4.3.10 Mediation Analysis ............................................................................................... 84
4.4 Results ............................................................................................................................... 85
4.4.1 Genetics ................................................................................................................. 85
4.4.2 Age-at-onset .......................................................................................................... 85
4.4.3 Neuroimaging ....................................................................................................... 86
4.4.4 Mediation Analysis ............................................................................................... 88
4.5 Discussion ......................................................................................................................... 88
4.6 Acknowledgements ........................................................................................................... 92
4.7 Conflict of interest ............................................................................................................ 93
Chapter 5 ..................................................................................................................................... 106
5 GAD1 variant predicts a neuroanatomical and working memory susceptibly mechanism
relevant to schizophrenia ........................................................................................................ 106
5.1 Abstract ........................................................................................................................... 107
5.2 Introduction ..................................................................................................................... 108
5.3 Methods ........................................................................................................................... 109
5.3.1 Participants .......................................................................................................... 109
5.3.2 Genetics ............................................................................................................... 110
5.3.3 Image Acquisition ............................................................................................... 111
5.3.4 Cortical Thickness Mapping ............................................................................... 111
xi
5.3.5 Tract-Based Spatial Statistics (TBSS) ................................................................ 112
5.3.6 Assessment of Working Memory ....................................................................... 112
5.3.7 Statistics .............................................................................................................. 113
5.3.8 Voxel-wide mediation analysis ........................................................................... 114
5.4 Results ............................................................................................................................. 115
5.4.1 Participants .......................................................................................................... 115
5.4.2 Association between GAD1 and brain structure ................................................. 116
5.4.3 Association between GAD1 and working memory ............................................ 116
5.4.4 Voxel-wise mediation analysis ........................................................................... 117
5.5 Discussion ....................................................................................................................... 117
Chapter 6 ..................................................................................................................................... 126
6 Additive Genetics Risk Predicts Widespread Changes in Brain Structure Leading to
Poorer Cognitive Function ..................................................................................................... 126
6.1 Abstract ........................................................................................................................... 127
6.2 Introduction ..................................................................................................................... 128
6.3 Methods ........................................................................................................................... 132
6.3.1 Participants .......................................................................................................... 132
6.3.2 Image Acquisition ............................................................................................... 133
6.3.3 Cortical Thickness Mapping ............................................................................... 133
6.3.4 Tract-Based Spatial Statistics (TBSS) ................................................................ 134
6.3.5 Neuroimaging Dimension Reduction ................................................................. 134
6.3.6 Genetics ............................................................................................................... 135
6.3.7 Additive Model ................................................................................................... 135
6.3.8 Neuropsychological Assessment ........................................................................ 136
6.3.9 Statistical Analysis .............................................................................................. 136
6.3.10 Voxel-wise mediation analysis ........................................................................... 137
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6.4 Results ............................................................................................................................. 138
6.4.1 Demographics ..................................................................................................... 138
6.4.2 Genetics ............................................................................................................... 138
6.4.3 The effect of additive risk on whole brain measure of cortical thickness and
white matter FA .................................................................................................. 139
6.4.4 The effect of additive risk on general brain structure ......................................... 139
6.4.5 The effect of extreme additive risk loading on general brain structure and
cognitive performance ........................................................................................ 140
6.4.6 Voxel-wise mediation analysis ........................................................................... 140
6.5 Discussion ....................................................................................................................... 141
Chapter 7 ..................................................................................................................................... 159
7 General Discussion & Future Direction ................................................................................. 159
7.1 Summary of Results ........................................................................................................ 159
7.2 Can Imaging-genetics Dissect Clinically Meaningful Heterogeneity within
Schizophrenia? ................................................................................................................ 160
7.3 What Benefits does Translational Research Address? .................................................... 163
7.4 Can Imaging-genetics explain enough of the Heterogeneity to Guide Treatment
Decisions? ....................................................................................................................... 165
7.5 Limitations ...................................................................................................................... 166
7.6 Future Directions ............................................................................................................ 169
7.6.1 Functional Relevance of Genetic Variation ........................................................ 169
7.6.2 In Silico Prediction of SNP Function: Insight from ENCODE ........................... 170
7.6.3 Combining in vivo Biomarkers ........................................................................... 172
7.6.4 Towards Neurobiological Treatment .................................................................. 173
7.6.5 Conclusion .......................................................................................................... 174
References ................................................................................................................................... 175
Appendices .................................................................................................................................. 209
xiii
List of Tables
Table 3-1. Demographic Characteristics ...................................................................................... 61
Table 3-S1. Locations and Minor Allele Frequency in Toronto and Hapmap (CEU) Samples ... 67
Table 3-S2. T-test between rs1045881 T-Carriers Vs C/C and Demographics ........................... 68
Table 3-S3. Chi-squared Tests of Region by Genotype or Allele Interactions of rs1045881 and
rs858932 ....................................................................................................................................... 69
Table 3-S4. Haplotype Association between Frontal Lobe White Matter and rs1045881 (T/C) and
rs858932 (G/C) ............................................................................................................................ 70
Table 3-S5. Reported deletions within NRXN1 in Developmental Disorders, Schizophrenia and
Autism Spectrum Disorders .......................................................................................................... 71
Table 4-S1. Demographics for age at onset samples .................................................................... 98
Table 4-S2. Demographics and clinical characteristics for the Toronto imaging-genetics sample
....................................................................................................................................................... 99
Table 5-1. Demographics ........................................................................................................... 121
Table 5-2. The association between working memory related tasks and GAD1 genotype,
diagnosis, and their interaction ................................................................................................... 122
Table 6-1. Count and frequency of risk alleles by diagnosis ...................................................... 156
Table 6-2. Demographics and clinical characteristics ............................................................... 157
Table 6-3. The effect of high additive risk allele loading on general fluid intelligence (gF) and its
components ................................................................................................................................. 158
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List of Figures
Figure 3-1. Reported Deletions in the Neurexin-1α gene ............................................................. 62
Figure 3-2. The effect of rs1045881 on combined hemispheric volume of brain regions with total
brain volume (TBV) and age as covariates ................................................................................... 63
Figure 3-3. The effect of rs858932 on right and left thalamic volume with TBV and age as
covariates ...................................................................................................................................... 64
Figure 3-S1. The effect of rs858932 on combined hemispheric volume of brain regions with
TBV and age as covariates ............................................................................................................ 66
Figure 4-1. MIR137 rs1625579 risk variant homozygotes have earlier age-at-onset of
schizophrenia ................................................................................................................................ 94
Figure 4-2. MIR137 risk variant predicts poorer structural brain phenotypes in schizophrenia .. 95
Figure 4-3. Effect of MIR137 rs1625579 genotype on voxel-based white matter integrity in
patients with schizophrenia .......................................................................................................... 96
Figure 4-4. The effect of MIR137 risk variant on mean whole-brain fractional anisotropy (FA)
across the lifespan for four ‘diagnosis-genotype’ groups ............................................................ 97
Figure 4-S1. MIR137 rs1625579 risk variant homozygotes have earlier age-at-onset of
schizophrenia ............................................................................................................................. 101
Figure 4-S2. The main effect of MIR137 rs1625579 genotype on voxel-based white matter
integrity in healthy controls and patients with schizophrenia ................................................... 102
Figure 4-S3. Effect of MIR137 rs1625579 genotype by diagnosis interaction on voxel-based
white matter integrity in healthy controls and patients with schizophrenia .............................. 103
Figure 4-S4. Mediation Model .................................................................................................... 104
xv
Figure 4-S5. Mediation model examining the associations between MIR137 rs1625579 genotype,
age at onset and mean whole brain fractional anisotropy (FA) .................................................. 105
Figure 5-1. GAD1 rs3749034 risk A-allele predicts lower TBSS skeleton white matter FA in
healthy controls (N=115) and patients with schizophrenia (N=80) ............................................ 123
Figure 5-2. Higher TBSS skeleton white matter FA correlates with better digit span performance
..................................................................................................................................................... 124
Figure 5-3. Skeletal white matter FA regions that mediate the effect of GAD1 rs3749034 on digit
span performance ........................................................................................................................ 125
Figure 6-1. Greater additive risk predicts poorer white matter fractional anisotropy ............... 146
Figure 6-2. Significant additive score-by-diagnosis interaction for vertex-wide cortical thickness
..................................................................................................................................................... 147
Figure 6-3. The first principal component (PC1[FA]) of skeleton FA across additive model
scores in schizophrenia patients and healthy controls ................................................................ 148
Figure 6-4. PC1 Cortical Thickness across additive model scores in schizophrenia patients and
healthy controls ........................................................................................................................... 149
Figure 6-5. High additive risk allele load predicts lower PC1 fractional anisotropy in
schizophrenia patients ................................................................................................................. 150
Figure 6-6. High additive risk allele load predicts lower PC1 fractional anisotropy in
schizophrenia patients ................................................................................................................. 151
Figure 6-7. High additive risk allele load predicts lower poorer verbal fluency in schizophrenia
patients ........................................................................................................................................ 152
Figure 6-8. High additive risk allele load predicts lower PC1 of motor coordination in
schizophrenia patients ................................................................................................................. 153
Figure 6-9. Voxel-wise mediation analysis of verbal fluency in schizophrenia patients ........... 154
xvi
List of Appendices
Appendix 1: Lett TA, Tiwari AK, Meltzer HY, Lieberman JA, Potkin SG, Voineskos AN,
Kennedy JL, Müller DJ. The putative functional rs1045881 marker of neurexin-1 in
schizophrenia and clozapine response. Schizophr Res. 2011 Nov;132(2-3):121-4.
Appendix 2: Lett TA, Voineskos AN, Kennedy JL, Levine B, Daskalakis ZJ. Treating working
memory deficits in schizophrenia: a review of the neurobiology. Biol Psychiatry. 2014 Mar
1;75(5):361-70.
xvii
List of Abbreviations
AAO age-at-onset
ADHD attention deficit hyperactivity disorder
ADNI Alzheimer’s Disease Neuroimaging Initiative
AKT1 v-akt murine thymoma viral oncogene homolog 1
AMPA alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid
ANCOVA analysis of covariance
ASD autism spectrum disorder
BA Brodmann area
BDNF brain-derived neurotrophic factor
BOLD blood-oxygen-level dependent
B-SNIP Bipolar Schizophrenia Network on Intermediate Phenotypes
CACNA1C calcium channel, voltage-dependent, L-type, α 1C subunit
CACNA1D calcium channel, voltage-dependent, L type, alpha 1D subunit
CACNA1E calcium channel, voltage-dependent, R type, alpha 1E subunit
CACNA1S calcium channel, voltage-dependent, L type, alpha 1S subunit
CACNA2D2 calcium channel, voltage-dependent, alpha 2/delta subunit 2
CACNA2D4 calcium channel, voltage-dependent, alpha 2/delta subunit 4
CACNB2 calcium channel, voltage-dependent, beta 2 subunit
xviii
CATIE Clinical Antipsychotic Trials for Intervention Effectiveness
CBT cognitive behavioral therapy
CCA canonical component analysis
CIRS-G Clinical Information Rating Scale for Geriatrics
CNTRICS Cognitive Neuroscience Treatment Research to Improve Cognition
Schizophrenia
CNV copy number variant
COMT catecholamine-O-methyltransferanse
CONSIST Cognitive and Negative Symptoms in Schizophrenia Trial
COWAT controlled oral word association test
CNTNAP2 contactin associated protein-like 2
CSF cerebral spinal fluid
CRT cognitive remediation therapy
DISC1 disrupted in schizophrenia 1
DLPFC dorsolateral prefrontal cortex
DPYD dihydropyrimidine dehydrogenase
DTI diffusion tensor imaging
EDTA ethylenediametetraaecidic acid
EEG electroencephalography
ENCODE Encyclopedia of DNA Elements
ENIGMA Enhancing Neuro Imaging Genetics through Meta-Analysis
xix
ERBB4 v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 4
EZH2 enhancer of zeste 2 polycomb repressive complex 2 subunit
FA fractional anisotropy
FDR false discovery rate
FSL FMRIB Software Library
FWE family-wise error
GABA gamma-Aminobutyric acid
GAD1 glutamate decarboxylase 1
GAD67 glutamic acid decarboxylase-67
GRF Gaussian random field
GRM3 glutamate receptor, metabotropic 3
GSEA gene set enrichment analysis
GWAS genome-wide association study
HARDI high-angular-resolution diffusion imaging
HOXC8 homeobox C8
ICA independent component analysis
ITIH3 inter-alpha-trypsin inhibitor heavy chain 3
IQ intelligence quotient
LD linkage disequilibrium
LICI long interval intracortical inhibition
xx
LSD1 histone lysine-specific demethylase 1
LNS letter-number span
LPBA40 LONI probabilistic brain atlas
LRRTM2 leucine-rich repeat transmembrane protein
MAF Minor Allele Frequency
MATRICS Measurement and Treatment Research to Improve Cognition in
Schizophrenia
MIB1 mindbomb E3 ubiquitin protein ligase 1
MITF microphthalmia-associated transcription factor
MDR multifactor dimensionality reduction
MIR137 microRNA-137
MLA machine learning algorithms
MHC major histocompatibility complex
MMP16 matrix metallopeptidase 16 (membrane-inserted)
MMSE mini mental status exam
MRI magnetic resonance imaging
MTHFR methylenetetrahydrofolate reductase (NAD(P)H)
MULM mass-univariate linear modeling
NDEL1 nudE neurodevelopment protein 1-like 1
NLGN neuroligin
NMDA N-methyl-D-aspartate receptor
xxi
NRG1 neuregulin 1
NRXN1 neurexin-1
OR odds ratio
PCA principal component analysis
PFC prefrontal cortex
PLS partial least squares
PGC psychiatric genomics consortium
PV parvalbumin
RBANS repeatable battery for the assessment of neuropsychological status
RDoC Research Domain Criteria
RGS4 regulator of G-protein signaling 4
ROI region of interest
RRR reduced rank regression
rTMS repetitive transcranial magnetic stimulation
SDCCAG8 serologically defined colon cancer antigen 8
SGB stochastic gradient boosting
SPM statistical parameter mapping
SNP single nucleotide polymorphism
SNV single nucleotide variants
SVM support vector machines
xxii
TBV total brain volume
TFCE threshold-free clustering enhancement
TMT trail making test
TMS transcranial magnetic stimulation
TCF4 transcription factor 4
TURNS Treatment Units for Research on Neurocognition and Schizophrenia
TBSS tract-based spatial statistics
WBP1L WW domain binding protein 1-like
WTAR Wechsler test for adult reading
VIPR2 vasoactive intestinal peptide receptor 2
ZNF804A zinc finger protein 804A
1
Chapter 1
1 Introduction
1.1 Overview
Schizophrenia is a highly heritability disorder, although genetic studies of the disorder poses a
number of significant challenges (Burmeister, McInnis et al. 2008). A condition of any genetics
analysis is a valid and accurate phenotype; however, currently there are no neurobiological-based
tests for schizophrenia. Rather, the clinical diagnosis is usually made by structured interview
based on diagnostic criteria. Furthermore, the symptom heterogeneity among patients with
schizophrenia suggests that there are different subgroups within the disorder. The boundaries of
diagnostic criteria are not distinct, especially when symptoms in patients are not clear.
Environmental conditions can have significant impact in expression of schizophrenia including
prenatal or perinatal events, including but not limited to hypoxemia, infections, birth in winter,
and high-expressed negative emotions in families (Lewis and Levitt 2002). Difficulty arises in
overlapping symptoms with other disorders; for example, psychotic symptoms can be part of a
variety of diagnoses other than schizophrenia, such as mania or depression with psychotic
symptoms.
Schizophrenia is likely a spectrum of disorders that overlaps with autism spectrum disorder
(ASD), bipolar disorder, and schizoaffective disorder. Interestingly, genetic association studies
have mirrored these patterns. First, genetic associations have identified with diagnostic criteria
such as clinical symptoms. For example, the genome wide association study (GWAS) identified
variant rs1344709 of the zinc finger protein 804A (ZNF804A) gene is associated with psychosis
2
(Steinberg, Mors et al. 2010) and psychotic symptoms within bipolar patients (Lett, Zai et al.
2011). Second, there is a high overlap in genetic association findings between different
psychiatric disorders. For instance, there are more genetic variants common to bipolar disorder
and schizophrenia than those differentiating the two diseases (Carroll and Owen 2009, Cardno
and Owen 2014).
An approach to overcome the obstacles of phenotypic heterogeneity in psychiatric disorders is
the concept of intermediate and endophenotypes (e.g. neurophysiological, neuroanatomical, and
cognitive phenotypes). These are closer linked to biological mechanisms and genetic effects that
underlie schizophrenia. They are refined phenotypes with lower environmental and other
confounding influences compared to this complex diagnosis (Gottesman and Gould 2003,
Meyer-Lindenberg and Weinberger 2006). Understanding how schizophrenia risk genes impact
on specific phenotypes of the disorder such as changes in brain structure and how one risk gene
affects multiple subphenotypes might help to develop more biologically-based treatment options
in the future.
1.2 Schizophrenia
1.2.1 Impact, Epidemiology and Prognosis
Schizophrenia is a disabling brain disorder characterized by a diverse array of clinical features
and substantial comorbidity. It is one of the most devastating diseases for several reasons: (a) the
condition is one of the major contributors to global burden of disease (Murray and Lopez 1997).
This burden of the disease primarily due to the early mean onset of the disease in early adulthood
(discussed in detail in the following sections), and despite optimal treatment, approximately two-
3
thirds of affect individuals have chronic and unstable symptoms (American Psychiatric
Association 1994). Furthermore, 20-30 percent of patients fail to respond to treatment (Kane,
Honigfeld et al. 1988, Kane, Meltzer et al. 2007). (b), it is a common disorder. Systematic review
of the prevalence of schizophrenia estimate the lifetime morbid risk at 7.2 per 1000 persons
(Saha, Chant et al. 2005). (c), premature mortality. Twelve percent of the mortality of patients is
due to completed suicide (Brown 1997). Even after accounting for suicide, patients generally
have a much shorter (15-20 years) life expectancy than the general population (Tiihonen,
Lonnqvist et al. 2009). (d), the tremendous societal cost of patient care. Mental illness is the
second leading cause of disability and premature death in Canada (Waddell, McEwan et al.
2005), and the WHO ranks schizophrenia as one of the top ten causes of disability in developed
countries (Barbato and Organization 1998). The economic burden of schizophrenia in the USA
was estimated at $75 billion in 2012 dollars (Kennedy, Altar et al. 2014). (e), Current
pharmaceutical treatment of schizophrenia, antipsychotic medication, is effective against positive
symptoms (e.g., psychosis including hallucinations and delusions) but has little or no effect on
cognitive impairments (e.g., reduced executive functioning and concentration) (Mishara and
Goldberg 2004, Goldberg, Goldman et al. 2007, Keefe, Bilder et al. 2007, Keefe, Sweeney et al.
2007). This is particularly distressing since cognition is a key determinant of patient long-term
outcome and mortality in schizophrenia (Green 1996). (f), the heterogeneity of the schizophrenia
symptoms suggests a complex disorder (discussed below); therefore, no single treatment will be
efficacious for all patients.
1.2.2 Treatment of Schizophrenia
The introduction of chlorpromazine in the 1950s provided the first effective pharmacological
treatment of schizophrenia, and established antipsychotics as the primary intervention in many
4
psychiatric disorders. Numerous typical antipsychotics or first-generation antipsychotics were
subsequently developed, and collectively demonstrated successful treatment of psychotic
symptoms, in particular delusions and hallucinations. The benefits of first generation
antipsychotics were offset by the common occurrence of elevated prolactin and extrapyramidal
symptoms, such as parkinsonism and tardive dyskinesia. In the early 1970s, clozapine was
introduced as the first atypical antipsychotic or second-generation antipsychotic, which marked
an advantage over typical medications via increased efficacy and fewer neurological side effects
(Kane 1988, Foussias and Remington 2010). Among the second-generation antipsychotics that
have been developed, arguably only clozapine shows an increased efficacy of treatment
compared with first generation antipsychotics (Kane and Correll 2010). Unfortunately, most
second generation antipsychotic medication (in particular clozapine and olanzapine) have been
linked to substantial drug-induced weight gain, which confers risks of metabolic syndrome,
leading to diabetes mellitus type II and cardiovascular disease (Lett, Wallace et al. 2012).
Furthermore, cardiovascular disease is the leading cause of death in schizophrenia (Newcomer
2007).
In general, most second generation antipsychotic medication use a ‘pharmacological shotgun’
approach with strong affinity for the serotonin 5-HT2 receptor and concomitant affinities for
dopaminergic, muscarinic, histaminergic and adrenergic receptors (Meltzer, Matsubara et al.
1989). In contrast, first generation antipsychotics are more specific, and have greater affinity to
the dopamine D2 receptor. There are exceptions to this generalization including phenothiazines
(e.g., chlorpromazine) that have a more diverse binding profile and second generation
antipsychotics that have high D2 and D3 antagonism (e.g., risperidone and ziprasidone)
(Nasrallah 2008). Although second generation antipsychotics have preferential action on 5-HT2
receptors, the rapid dissociation of most second generation antipsychotics from the D2 receptor
5
has been suggested to account for the atypicality (Kapur and Remington 2001, Kapur and
Seeman 2001). Perhaps the major distinction is that second generation antipsychotics constitute a
major improvement in the avoidance of extrapyramidal symptoms through reduced D2 receptor
occupancy (Kapur, Zipursky et al. 2000).
Since antipsychotic medications mainly target positive symptoms, many adjunctive therapies
have been developed including: novel pharmacologic compounds (particularly for cognitive
dysfunction and negative symptoms), cognitive behavioral therapy, cognitive remediation
therapy, repetitive transcranial magnetic stimulation (rTMS), and electroconvulsive therapy (in
catatonic patients). For in-depth review please see (Lett, Voineskos et al. 2014) and the
Discussion section.
1.3 Sources of Schizophrenia Heterogeneity
It is now clear that the disease trajectory of schizophrenia carries tremendous heterogeneity.
There is wide variability from patient to patient in: age of onset of psychotic symptoms, rate of
onset, inter-episode residual impairment, long-term outcome, treatment response, treatment
emergent side effects, functional and structural brain abnormalities, and the severity or absence
of core symptoms of the disorder including positive symptoms, negative symptoms, and
cognitive impairment (Carpenter Jr and Kirkpatrick 1988, DeLisi, Hoff et al. 1991, DeLisi 1992,
Shenton, Dickey et al. 2001, Lett, Wallace et al. 2012, Lett, Brandl et al. 2014, Lett, Voineskos
et al. 2014). Therefore, it has been suggested that schizophrenia is a clinical syndrome rather
than a single disease entity (Carpenter Jr and Kirkpatrick 1988). The following section will
discuss some aspects of the heterogeneity within schizophrenia that may provide key insights
6
into the pathogenesis of schizophrenia. Here we focus on: (i) onset of illness, (ii) cognitive
dysfunction, and (iii) brain structure.
1.3.1 Onset of Psychotic Symptoms
There are many measures of age-at-onset (AAO) of schizophrenia (e.g., 1st psychotic episode, 1st
psychiatric interview, 1st hospitalization, 1st antipsychotic treatment, onset of negative symptoms,
and others), although, they tend to be highly correlated. There is a prodromal stage in which
cognitive symptoms are present (e.g. general fluid intelligence (gF), executive functioning), and
the prodromal cognitive symptoms may contribute to heterogeneity in patterns of cognitive
changes across illness phases and among individuals (Mesholam-Gately, Giuliano et al. 2009).
Furthermore, there are large cognitive deficits across most measures in first-episode
schizophrenia (Heinrichs and Zakzanis 1998, Mesholam-Gately, Giuliano et al. 2009, Rajji,
Ismail et al. 2009). The AAO of psychotic symptoms normally occurs between the ages of 15-24
years old (Messias, Chen et al. 2007). The mean AAO in men is approximately three years
earlier than in women. Furthermore, women tend to have a bimodal distribution of AAO with
second peak around 55-64 years of age. Schizophrenia is rare in children less than 12 years of
age (approximately 1%), and the prevalence in adolescents (12-18 years) is approximately 12-
33% (Kumra, Oberstar et al. 2008). However, it is likely that many patients have psychotic
symptoms before the age of 18 and seek treatment later (Schimmelmann, Schmidt et al. 2013).
The prevalence of late onset (greater than 40 years of age) schizophrenia is more difficult to
determine due to confounding factors associated with aging, although estimates range from 15%
to 32% of patients (Harris and Jeste 1988).
7
The AAO may have tremendous effects on social and vocational functioning. For instance, it
could be the difference between a patient finishing a degree, having a job, or being married.
Perhaps more importantly, AAO can provides can provide key insights into long-term clinical
outcome of schizophrenia. There is a relatively strong relationship between earlier AAO and
more severe cognitive symptoms of schizophrenia. Meta-analysis reveals that individuals with
youth-onset schizophrenia demonstrate larger deficits than those with first-episode schizophrenia
on arithmetic, executive function, IQ, psychomotor speed of processing, and verbal memory
(Rajji, Ismail et al. 2009). In contrast, late-onset patients schizophrenia patients tend to have
relatively conserved cognitive function (Rajji, Ismail et al. 2009). This is particularly important
as cognitive performance is an established predictor of functional outcome (Green 1996). Men
have greater negative symptom burden, worse clinical outcome, and poorer social functioning
than women (Abel, Drake et al. 2010), and these differences could be attributed to earlier AAO.
More specifically, early onset patients have greater symptoms at the onset of psychosis
(Ballageer, Malla et al. 2005). Early onset patients display further prognostic criteria including
more neurodevelopmental deficits and lower premorbid adjustment (Hans, Auerbach et al. 2000,
Joa, Johannessen et al. 2009), poorer social outcome (Szymanski, Lieberman et al. 1995,
Lauronen, Miettunen et al. 2007), and worse treatment response as well as poorer longitudinal
clinical outcome (Reichert, Kreiker et al. 2008, Vyas, Patel et al. 2011).
There is increasing evidence for a neurobiological basis to AAO of psychosis. Genetic factors
significantly contribute to the AAO of psychotic symptoms with a moderately high degree of
heritability (H2=0.33±0.9) (Hare, Glahn et al. 2010). There have been two genome-wide analysis
of AAO, although the results have been mixed (Wang, Liu et al. 2011, Bergen, O'Dushlaine et al.
2014). AAO also had an effect on clinically relevant brain structure. Earlier AAO has been
directly linked to enlarge left lateral ventricles, and together this suggested to be early predictors
8
of poor outcome (Crow 1980, DeLisi, Hoff et al. 1991, DeLisi 1992, Ho, Andreasen et al. 2003).
Furthermore, earlier AAO has been associated with reduced hippocampal volume (Giedd,
Jeffries et al. 1999), reduced gray matter volume (Marsh, Harris et al. 1997, Zipursky, Lambe et
al. 1998), and lower fractional anisotropy (FA) in the frontal cortex (Kumra, Ashtari et al. 2004,
Kyriakopoulos, Perez-Iglesias et al. 2009). All structure changes have arguably been associated
with a poorer outcome in schizophrenia (Ho, Andreasen et al. 2003, Szeszko, Robinson et al.
2008).
1.3.2 Cognitive Symptoms
Since the earliest conceptualization of schizophrenia, cognitive impairment has been viewed as a
core feature of schizophrenia (Bleuler 1950, Kraepelin 1971). Despite notable heterogeneity
among individuals with schizophrenia (Seidman 1990, Kremen, Seidman et al. 2004), it is
regarded as a core feature of the disorder (Elvevag and Goldberg 2000, Weickert, Goldberg et al.
2000). Neurocognitive dysfunction is prevalent in at least 70% of patients before disease onset
and after chronic treatment (Palmer, Heaton et al. 1997, Heinrichs and Zakzanis 1998,
Mesholam-Gately, Giuliano et al. 2009). Cognitive dysfunction is present in healthy relatives of
schizophrenia patients, and it has been suggested as a biomarker of schizophrenia (Barrantes-
Vidal, Aguilera et al. 2007). Compared to healthy controls, schizophrenia outpatients have a
generalized cognitive impairment. However, patient performance on the vast majority of
neurocognitive tests tends to show no deterioration over time; for example, performance on full-
scale IQ, attention, verbal and non-verbal memory, and visual skills are remarkably stable (Gold,
Arndt et al. 1999, Kurtz 2005). Cognitive dysfunction in schizophrenia shows high prevalence
and is relatively stable over time. For instance, measurements of neurocognitive impairment have
9
been more closely linked to community outcome, social problem solving, and social skill
acquisition rather than symptoms (Green 1996, Green, Kern et al. 2000, Gold 2004).
Currently approved pharmaceutical treatments for schizophrenia are typically effective for
positive symptoms, but have little or no effect on cognitive impairment (Keefe, Bilder et al.
2007). Although antipsychotic medications show small effects on cognitive performance with
treatment, some studies show therapeutic advantages of atypical antipsychotics compared to
typical antipsychotics(Woodward, Purdon et al. 2005); however, the large, multisite Clinical
Antipsychotic Trials for Intervention Effectiveness (CATIE) trial failed to find any advantage of
atypical antipsychotics in treating cognition (Keefe, Bilder et al. 2007). Clozapine, the only
second generation antipsychotic medication with any efficacy for treatment resistant
schizophrenia (Kane, Honigfeld et al. 1988), is no longer considered superior to other
antipsychotic mediation for cognitive deficits (Harvey, Sacchetti et al. 2008). These results were
driven by multiple pharmacologic initiatives such as the Measurement and Treatment Research
to Improve Cognition in Schizophrenia (MATRICS) (Green, Nuechterlein et al. 2004),
Treatment Units for Research on Neurocognition and Schizophrenia (TURNS) (Buchanan,
Freedman et al. 2007), and Cognitive Neuroscience Treatment Research to Improve Cognition
Schizophrenia (CNTRICS) (Barch and Smith 2008). Other pharmacological agents have been
studied as adjunctive treatment options for cognitive symptoms. For instance, results from the
Cognitive and Negative Symptoms in Schizophrenia Trial (CONSIST) suggest that either
glycine (binds to allosteric site of the N-methyl-D-aspartate receptor (NMDAR)) or D-
cycloserine (partial NMDAR agonist) were not effective in treating cognitive impairments
(Buchanan, Javitt et al. 2007). Catecholamine-O-methyltransferanse (COMT) has been directly
associated with prefrontal cortex dopamine turnover and working memory performance (Meyer-
Lindenberg, Kohn et al. 2005). COMT inhibitors, such as tolcapone, are a promising target
10
although they have unfortunately also been associated with hepatotoxicity (Goff, Hill et al.
2011). The selective agonist of the GABA (gamma-Aminobutyric acid) A receptor, MK-0777,
was shown to be effective in treating working memory deficits in a study with limited sample
size (Lewis, Cho et al. 2008); however, a subsequent study failed to replicate the enhancement of
working memory by MK-0777 in schizophrenia (Buchanan, Keefe et al. 2011). Beyond
pharmacological agents, cognitive remediation therapy consistently showed improvement in
social cognition (effect size ~ 0.65) and working memory (effect size ~ 0.35) (McGurk,
Twamley et al. 2007, Wykes, Huddy et al. 2011, Lett, Voineskos et al. 2014). Furthermore, there
is promising evidence that rTMS or anodal direct current stimulation may be effective at
improving cognitive functioning in schizophrenia (Utz, Dimova et al. 2010, Mulquiney, Hoy et
al. 2011, Barr, Farzan et al. 2013, Lett, Voineskos et al. 2014).
In summary, neurocognitive dysfunction predicts functional outcome in schizophrenia, and while
there are some promising treatment strategies, no staple treatment has been established yet.
Therefore, it is imperative for further investigation into the neurobiological mechanism of
dysfunction, and potentially which patients may best respond to a given treatment.
1.3.3 Brain Structure
Several meta-analyses have demonstrated replicable abnormalities in cross sectional structural
MRI studies of patients with first-episode and chronic schizophrenia. The most robust findings
comparing cases and controls are reductions in whole brain and gray matter volume (particularly
in limbic, paralimbic, and frontal cortical region as well as the thalamus), and enlargement of the
lateral ventricles (Wright, Rabe-Hesketh et al. 2000, Shenton, Dickey et al. 2001, Honea, Crow
et al. 2005, Steen, Mull et al. 2006, Vita, De Peri et al. 2006). Meta-analysis of diffusion tensor
11
imaging (DTI) MRI studies of white matter fractional anisotropy (FA) complement the gray
matter findings with lower FA in deep white matter frontal lobe (traversing white matter tracts
including the genu of the corpus callosum, cingulum bundle, left anterior thalamic radiation, left
inferior fronto-occipital fasciculus), and deep white matter in the left temporal lobe (splenium of
the corpus callosum, fornix, left inferior longitudinal fasciculus) (Ellison-Wright and Bullmore
2009). Furthermore, progressive brain abnormalities have been observed in longitudinal studies
in schizophrenia. Meta-analyses show a yearly decrease in whole brain gray matter, frontal white
matter, parietal white matter, and temporal white matter as well as increase in lateral ventricles
even compared to controls (Olabi, Ellison-Wright et al. 2011). It is important to note that both
white and gray matter volume decreases are inversely correlated to antipsychotic treatment, but
not duration of illness or severity in longitudinal studies of chronic and first episode and chronic
patients (Ho, Andreasen et al. 2011). Together, this suggests that antipsychotic treatment may be
causing these neuroanatomical changes, and is an important source of heterogeneity. The only
regions that moderated the effects of age (greater in patients versus controls) and illness duration
were the hippocampi (Fusar-Poli, Smieskova et al. 2013, Torres, Portela-Oliveira et al. 2013).
Furthermore, AAO predicts significant heterogeneity in frontal gray matter (please see the AAO
section) (Olabi, Ellison-Wright et al. 2011). Brain structure may also be an important
determinant of clinical symptoms within schizophrenia. For example, schizophrenia patients with
a deficit subtype have greater mean diffusivity in the right inferior longitudinal fasciculus, right
arcuate fasciculus, and left unicinate fasciculus compared to matched first episode, non-deficit
schizophrenia patients (Voineskos, Foussias et al. 2013).
12
1.4 Cortical Thickness and Diffusion Tensor Imaging Measures of White Matter Structure
The following sections will highlight the two major MRI methods employed in this thesis to
examine the genetic influence on in vivo brain structure: cortical thickness and DTI. The field of
structural neuroimaging is dynamic and the methodology discussed builds on earlier imaging
modalities applied in schizophrenia research including computed tomography (e.g., (Johnstone,
Crow et al. 1976)), volumetric analyses (e.g., (Smith, Calderon et al. 1984)), and voxel-based
morphometry (e.g., (Kubicki, Shenton et al. 2002)). For review of these modalities, please see
(Shenton, Dickey et al. 2001, Fusar-Poli, Radua et al. 2012). Next, we will discuss genetics
contribution of cortical thickness and white matter DTI, and its particular relevance in
schizophrenia heterogeneity.
1.4.1 Analytic Approaches to Cortical Thickness and Diffusion Tensor Imaging
Neuroimaging studies have shown volumetric reductions in a number of cortical regions (notably
in frontal and temporal cortices) as well as several subcortical regions (particularly in
hippocampal volumes). Advances in morphological imaging allow the estimation of structural
features in a sub-voxel range. This enables the reconstruction of cortical surfaces that allows
estimation of cortical thickness, surface area, and sulcal depth (Dale, Fischl et al. 1999, Fischl,
Sereno et al. 1999, Fischl and Dale 2000, Han, Jovicich et al. 2006). These measures are of
particular interest to schizophrenia research since: (a), abnormal architecture of the cortex has
been identified in post mortem studies of schizophrenia (Weinberger and Lipska 1995,
Rajkowska, Selemon et al. 1998, Selemon, Mrzljak et al. 2003); (b), they can provide enhance
understanding of the pathogenesis of the disorder (Murray and Lewis 1987, Weinberger 1987,
Lewis and Lieberman 2000, Lewis and Levitt 2002, Lewis, Hashimoto et al. 2005); and (c),
13
cortical volume are composed of surface area and cortical thickness that may under distinct
genetic (Panizzon, Fennema-Notestine et al. 2009), environmental (Raznahan, Cutter et al.
2010), and cellular (Chenn and Walsh 2002) influences. In particular, cortical thickness
represents the density and arrangement of cells (neurons, glia, and nerved fibers) (Chenn and
Walsh 2002). Aberrant neurogenesis, neuronal migration, differentiation, synaptogenesis, and
mechanisms involved in synaptic pruning have all been implicated in schizophrenia and reflect
changes in cortical thickness (Jakob and Beckmann 1986, Arnold 1999). Therefore, cortical
thickness is a means to potentially model neurodevelopmental contribution to schizophrenia with
high regional specificity. The most common approaches employ automated or semi-automated
procedures. In brief, a three dimensional polygonal mesh is applied over the cortical surface.
Cortical thickness is determined to be the distance between the white matter surface and the gray
matter-cerebral spinal fluid intersection. At each vertex (from the mesh) a scalar value (ranging
from approximately 1.5 to 4.5 mm in healthy controls) is outputted in millimeters representing
the cortical thickness at that point. Limitations of this method include the determining the inner
and out surface areas accurately using the resolutions of T1-weighted scan (normally around 1
mm3), and the fine detail of sulcal regions can be obscured by partial volume effects (regions of
poor definition due to the osmotic movement of water).
In addition to gray matter abnormalities observed in schizophrenia, microscopic and molecular
studies demonstrate oligodendrocyte abnormalities in schizophrenia (Hakak, Walker et al. 2001,
Uranova, Orlovskaya et al. 2001). Oligiodendrocytes are neuroglia that play a major role in
establishing the conductivity, and in protection of neuronal axons travelling within white matter
tracts by forming myelin sheets (Cotter, Pariante et al. 2001). Furthermore, they support cellular
metabolism as well as function in both neuronal migration and synaptic signalling regulation
(Fields and Burnstock 2006). All of these functions have also been reported to be disrupted in
14
schizophrenia (Lewis, Hashimoto et al. 2005). Therefore, it is conceivable that glial
abnormalities have a substantial contribution to the reduced neuronal size, reduced synaptic field
density, and functional dysconnectivity observed in schizophrenia (Benes, Davidson et al. 1986).
DTI MRI is thought to be, arguably, an indicator of white matter tract integrity, and currently is
the mainstay for assessing in vivo measures of white matter structure. The basic principal of DTI
is that diffusion weighted magnetic resonance sequence (gradient pulses) excite protons from
water molecules to vibrate in phase. The molecular diffusion of water in white matter tracts is
organized providing an estimation of diffusion. The degree of organized movement along white
matter tracts can be represented as a vector. The most common method of characterizing
diffusion within a voxel is via diffusion tensor (Basser, Mattiello et al. 1994). Three orthogonal
axes correspond to diffusivities along each axis over time (eigenvalues). When diffusion
eigenvalues are equal, the diffusion tensor is isotropic (equal diffusion in all directions);
whereas, unequal eigenvalues are anisotropic. Abnormalities detected using DTI-based measures
of anisotropy are thought to reflect coherence of white matter fibers; however, changes in density
and crossing of interconnecting fibers may affect the degree of anisotropy. Myelin is considered
to be the major (but not only) barrier to diffusion of white matter tracts (Beaulieu 2002). One of
the widely used metrics of diffusion anisotropy is fractional anisotropy (FA) (Basser, Mattiello et
al. 1994). FA measures the fraction of the tensor that is due to anisotropic diffusion. The FA
index is normalized (ranging from 0 to 1) and FA maps are created that can distinguish voxels
contain white matter fibers from gray matter and CSF.
There are two major categories of analyses that can be performed on DTI data: deterministic and
probabilistic. Deterministic tractography reconstructs three dimensional fiber trajectories of
anisotropic structures using voxel-based estimates of the continuous fiber orientation field
(Conturo, Lori et al. 1999). Deterministic methods normally involve either region of interest
15
(ROI) analyses or applying clustering algorithms to reconstruct white matter tracts. The average
FA value (or other DTI metrics) of the white matter tracts identified can then be assessed as
quantitative variables for statistical analysis. Probabilistic methods are generated by seed points
with random perturbations to the trajectory direction (Descoteaux, Deriche et al. 2009).
Probabilistic fiber tracking methods create a distribution of possible pathways that are weighted
by their likelihood. A newer voxel-based probabilistic approach is tract-based spatial statistics
(TBSS) (Smith, Jenkinson et al. 2006). TBSS creates a white matter FA skeleton for each subject
using peak FA values on a mean group template. TBSS provides batter alignment of tracts across
subjects and is robust again registration errors (Smith, Jenkinson et al. 2006). Voxel-wide
analyses can then be performed using a variety of statistical methodologies.
1.4.2 Genetic Contribution to Cortical Thickness and White Matter Fractional Anisotropy (FA)
Since there is a considerable genetic contribution to schizophrenia (See Chapter 1.6), it is of
strong interest if cortical thickness and FA are heritable (the proportion of variance of a given
traits that can be explained by genetics). The degree of heritability of any morphological
structure is dynamic, depending point of development and stability of environmental influences;
nevertheless, twin studies have provided estimations of heritability in adulthood.
Total cortical thickness and cortical surface have both been shown to highly heritable
(approximately 80%), and may be genetically distinct (Panizzon, Fennema-Notestine et al. 2009,
Rimol, Panizzon et al. 2010). Within schizophrenia patients, cortical thickness has also shown
high heritability (Goldman, Pezawas et al. 2009). However, first degree relatives of these
patients only demonstrated marginal differences from healthy controls (Goldman, Pezawas et al.
2009). These results suggest that cortical thickness may not be a strong intermediate phenotype
16
of schizophrenia, but within the disease cortical thickness still carries significant contribution
from genetic factors. DTI twin studies have consistently shown substantial heritability (40-80%)
among white matter tracts in different stages of development (Pfefferbaum, Sullivan et al. 2001,
Chiang, Barysheva et al. 2009, Brouwer, Mandl et al. 2010, Kochunov, Glahn et al. 2010,
Chiang, McMahon et al. 2011, Geng, Prom-Wormley et al. 2012). Meta-analytic studies support
heritability of white matter FA. Furthermore, graph theoretical approaches applied to FA
demonstrate brain topography is also a moderate heritable explaining (57-68%) (Bohlken, Mandl
et al. 2014). Results from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-
Analysis) consortium pooled five samples (4 twin and 1 family sample; N=2248) demonstrated
that all white matter tracts except the cortico-spinal tract are heritable (ranging from 40-
70%)(Kochunov, Jahanshad et al. 2014). Additive genetic variance explained over 50% of the
inter-subject variance in FA values (Kochunov, Jahanshad et al. 2014). Results from the B-SNIP
(Bipolar Schizophrenia Network on Intermediate Phenotypes) consortium supports the high
degree of white matter FA heritability (Skudlarski, Schretlen et al. 2013). Furthermore, first
degree relatives of patients had significant reductions in FA compared to healthy control,
suggesting that FA may be strong intermediate phenotype (Skudlarski, Schretlen et al. 2013).
Taken together, these studies suggest that both cortical thickness and white matter FA have
substantial contributions from genetic factors, although FA may be a more suitable intermediate
phenotype.
1.5 Functional Integration in Schizophrenia
There is overwhelming evidence that schizophrenia is, at least in part, a disorder of abnormal
functional integration of the brain (Pettersson-Yeo, Allen et al. 2011). This dysconnectivity of
brain processes may be due to aberrant wiring during development, due to aberrant plasticity, or
17
both. Features of this dysfunction are abnormal functional connectivity (e.g. frontotemporal
connectivity (Meyer-Lindenberg, Olsen et al. 2005), gamma synchrony (Spencer, Nestor et al.
2003)), abnormal structural connectivity (e.g. white matter integrity (Kubicki, Park et al. 2005,
Voineskos, Lobaugh et al. 2010), corpus callosum morphology (Woodruff, McManus et al.
1995), reduced brain asymmetry (Sommer, Ramsey et al. 2001)), synaptic plasticity (e.g.
pharmacological induced schizophrenia symptomology (Kapur 2003), and reduced dendritic
field size and density (Glantz and Lewis 2000). Neural connectivity is dynamically regulated by
signaling pathways relating neuronal activity to the expression of key activity regulated genes
(Flavell and Greenberg 2008, Leslie and Nedivi 2011). Genes associated with schizophrenia are
common to all of the abovementioned points of dysfunction suggesting that genetic
underpinnings potentiate dysconnectivity. Moreover, neural dysconnectivity may be a causative
factor in the more intractable deficits of schizophrenia, such as working memory functioning
(Tan, Choo et al. 2005). Thus, understanding how these schizophrenia liability genes influence
functional integration may describe an important underlying susceptibility mechanism of
schizophrenia.
1.6 Schizophrenia Genetics: An Update
The current knowledge on the complex genetic architecture of schizophrenia is rapidly
expanding. There are numerous excellent reviews on the genetic basis of schizophrenia (for
example, (Burmeister, McInnis et al. 2008, Sullivan, Daly et al. 2012, Horvath and Mirnics
2014)). This section will primarily focus on some of the most recent and exciting genome-wide
genetic findings.
Evidence from over 40 years of epidemiological and genetic studies has demonstrated that
schizophrenia is a complex genetic disorder, with both genetic and environmental determinants.
18
Twin and family studies of schizophrenia have demonstrated an estimated heritability ranging
from 0.60 to 0.80. The presence of a first degree relative with schizophrenia increases the
lifetime risk to 6.5%. Further, if one parent is affected the risk is 13%, and if both are affected
the risk increases to 50%. Meta-analyses of candidate gene studies and genome-wide analyses
have discovered: (a) common variants with low effect size (minor allele frequency (MAF)>0.1;
OR ~ 1.1-1.2), (b) rare variants (MAF <0.1) with greater effect sizes, and (c) rare copy number
variants (CNVs, insertions or deletion greater than 100 BP; OR ~ 4-20).
Recently, there has been a paradigm shift in genetic study of schizophrenia. The field is moving
towards large consortia examining risk of over tens of thousands of patients. Very large samples
are necessary to reliably detect the rare occurrence or small effect of these variants, and consortia
has been crucial for achieving improved statistical power. Perhaps the most ambitious of the
many consortia in psychiatry is the Psychiatric Genomics Consortium (PGC) (Psychiatric 2009).
The PGC has, currently, collected approximately 125,000 cases and controls with GWAS date
for mega-analyses of schizophrenia and other neuropsychiatric disorders (Giusti-Rodriguez and
Sullivan 2013). Most recently, it was found that 8300, mostly common SNPs, additively
accounted for 32% of the variance in schizophrenia liability (Ripke, O'Dushlaine et al. 2013),
suggesting massive sample size may be effective at explaining a high proportion of the ‘missing
heritability’ of schizophrenia (Manolio, Collins et al. 2009).
The most recent schizophrenia GWAS from the PGC identified 22 regions with genome-wide
significance including 13 novel regions and replication of previous GWAS findings (Ripke,
O'Dushlaine et al. 2013). The replication regions included the MHC (major histocompatibility
complex), CACNA1C (calcium channel, voltage-dependent, L-type, α 1C subunit), ITIH3 (inter-
alpha-trypsin inhibitor heavy chain 3), MIR137 (microRNA-137), MMP16 (matrix
19
metallopeptidase 16 (membrane-inserted)), SDCCAG8 (serologically defined colon cancer
antigen 8), and WBP1L (WW domain binding protein 1-like) (Ferreira, O'Donovan et al. 2008,
International Schizophrenia Consortium, Purcell et al. 2009, Shi, Levinson et al. 2009,
Stefansson, Ophoff et al. 2009, Psychiatric 2011, Ripke, Sanders et al. 2011, Hamshere, Walters
et al. 2013, Ripke, O'Dushlaine et al. 2013). Interestingly, MIR137 and CACNA1C provide key
insight into molecular pathways that may be disrupted in schizophrenia (discussed in detail in the
preceding sections). The MIR137 region has emerged as one of the best clues into the genetic
etiology of schizophrenia. First, it is strongly associated with schizophrenia in independent
genome-wide analyses similar effect sizes (combined analyses: OR = 0.89±0.02, p = 1.72 x 10-
12) (Ripke, O'Dushlaine et al. 2013). Second, microRNA-137 targets other schizophrenia risk
variants. Fourteen out of the 22 identified risk regions as well as CNVs associated with
schizophrenia (e.g. NRXN1) are putative targets of microRNA-137 (http://www.targetscan.org).
Moreover, this dynamic regulation has been confirmed in vitro for a number of these variants
(Kwon, Wang et al. 2011, Kim, Parker et al. 2012). Third, microRNA-137 has been implicated in
neurodevelopment, adult neural stem cell maturation, and dendritic arborisation (Szulwach, Li et
al. 2010, Sun, Ye et al. 2011, Willemsen, Valles et al. 2011, Sun, Gong et al. 2012).
There is now compelling evidence for disruption of the calcium signaling pathway increasing
liability multiple neuropsychiatric disorders. The CACNA1C and CACNB2 regions are associated
with schizophrenia in GWAS (Ripke, O'Dushlaine et al. 2013), and these regions also conferred
cross-disorder association in autism spectrum disorder, attention deficit-hyperactivity disorder,
bipolar disorder, major depressive disorder, and schizophrenia (Cross-Disorder Group of the
Psychiatric Genomics, Smoller et al. 2013). CACNA1C had been associated with schizophrenia
and bipolar disorder in multiple independent GWAS cohorts (Ferreira, O'Donovan et al. 2008,
Nyegaard, Demontis et al. 2010, Lett, Zai et al. 2011, Psychiatric 2011, Schizophrenia
20
Psychiatric Genome-Wide Association Study 2011, Hamshere, Walters et al. 2013, Ruderfer,
Fanous et al. 2013). In pathways based analysis, 20 of the 67 gene regions (CACNA1C,
CACNA1D, CACNA1E, CACNA1S, CACNA2D2, CACNA2D4, CACNB2) in the calcium activity
genes set were associated with the cross-disorder association (α < 1 x 10-3) (Cross-Disorder
Group of the Psychiatric Genomics, Smoller et al. 2013). Furthermore, calcium channel signaling
is critically important in learning, memory, and synaptic plasticity suggesting a common source
of neurocognitive dysfunction potentially independent of any particular neuropsychiatric
disorder(Moosmang, Haider et al. 2005, Baumgartel and Mansuy 2012, Bading 2013).
There are multiple lines of evidence implicating a role of the immune system in the neurobiology
of schizophrenia. Epidemiological studies point to maternal infections leading to an
inflammatory response that may elevate the risk for schizophrenia. For instance, influenza
infections lead to seven-fold increase in schizophrenia risk during the first trimester (Brown,
Begg et al. 2004). Prenatal exposure to rubella is associated with a five-fold elevated risk of
psychosis in adulthood (Brown, Cohen et al. 2000). Toxoplasma gondii infections are associated
with a two-fold increase of schizophrenia risk (Mortensen, Norgaard-Pedersen et al. 2007,
Torrey, Bartko et al. 2012). Last, maternal genital infections are associated with a five-fold
increase in disease liability (Babulas, Factor-Litvak et al. 2006). GWAS provides convergent
evidence for common SNPs being involved in immune function contributing to schizophrenia
etiology.
The region on the 6p22.1 that includes the major histocompatibility complex (MHC) has the
most consistently replicated association with schizophrenia (International Schizophrenia
Consortium, Purcell et al. 2009, Shi, Levinson et al. 2009, Stefansson, Ophoff et al. 2009, Irish
Schizophrenia Genomics and the Wellcome Trust Case Control 2012, Jia, Wang et al. 2012).
21
Indeed, the most recent PGC consortia findings pointing to the 6p22.1 region have been
overwhelmingly the strongest association (p<10-18 to P<10-31) (Schizophrenia Working Group of
the Psychiatric Genomics 2014). Despite the fact that 6p22.1 encompasses over 200 genes
spanning 8 Mb, most of the markers are in strong linkage disequilibrium. This creates substantial
difficulty in assessing the function significance of any specific marker as well as its relationship
to any particular gene. It is important to note that genes in this region may function beyond
immune regulation. 6p22.1 also includes histone protein genes that may be relevant in regulation
of transcription, or DNA repair via epigenetic regulation (Costa, Dong et al. 2007) and
antimicrobial defense (Kawasaki and Iwamuro 2008). Furthermore, the MHC region, particularly
the MHC class I family (MHCI), has an integral function in brain development and
neuroplasticity (Huh, Boulanger et al. 2000, Boulanger 2009, Deverman and Patterson 2009).
For example, MHCI proteins including TNF-α, IL-6, and IL-1β are essential for adult neural
stem cell regulation in the subventricular zone of the lateral ventricles and subgranular zone of
the hippocampus (Carpentier and Palmer 2009). This regulation is similar to the function of
microRNA-137 suggesting a potential common risk pathway (See Chapter 4.2). Taken together,
the GWAS findings in the 6p22.1 region provide one of most interesting clues into the genetic
and environmental contribution to the etiology of schizophrenia. Furthermore, since proteins
expressed in the 6p22.1 region have a critical and diverse role in neurodevelopment and
plasticity, they may predict heterogeneous features related to schizophrenia including differences
in brain structure or cognitive performance.
There is also building evidence that rare genomic variation may be playing a role in
neuropsychiatric disorders. CNVs are over-represented in schizophrenia and other
neuropsychiatric disorders. The 22q11.2 CNVs are robustly associated with schizophrenia
(Karayiorgou, Simon et al. 2010, Levinson, Duan et al. 2011). Other CNV have been
22
consistently been associated with schizophrenia including the NRXN1 and VIPR2 (vasoactive
intestinal peptide receptor 2) gene regions (Rujescu, Ingason et al. 2009, Vacic, McCarthy et al.
2011) as well as other multiplex gene regions (1q21, 3q29, 7q11, 15q11, 15q13, 16q13, 16p11,
17q12) (Walsh, McClellan et al. 2008, Malhotra and Sebat 2012, Sullivan, Daly et al. 2012). In
general, CNVs tend to be non-specific, and are risk factors for multiple disorders including
schizophrenia, mental retardation, and autism spectrum disorder. In contrast to CNVs, there is
less evidence for rare single nucleotide variants (SNV) associated with schizophrenia. Whole
exome sequencing studies have provided evidence for increased de novo SNVs in schizophrenia
including modest associations in the DPYD (dihydropyrimidine dehydrogenase) gene, a region
with SNPs that are in linkage disequilibrium (LD) with the MIR137 GWAS variants. However,
there have been some mixed findings, and further research in larger samples will provide better
evidence for the role of SNV in schizophrenia.
It is likely that the genetic architecture of schizophrenia includes contribution from common
variants, highly penetrant CNVs, and potentially exome SNVs. Future research will improve our
understanding. For example, preliminary results from the latest PGC schizophrenia mega-
analysis (25000 schizophrenia patients and 28000 controls) increased the number of significant
genome-wide associations to 62 (Anderson‐ Schmidt, Beltcheva et al. 2013), and many of the
new genome-wide associations have been identified by candidate gene analyses. As the genetic
findings become more concrete, it will be imperative to assess the biological relevance of these
genomic findings.
23
1.7 Important Genetic Modifiers of Schizophrenia Phenotypes
1.7.1 Neurexin-1 (NRXN1)
The NRXN1 gene is one of the largest known human genes (1.1 Mb) with 24 exons, located on
chromosome 2p16.3 (Südhof 2008). The NRXN1 gene encodes the neurexin-1α and neurexin-1β
proteins that function as pre-synaptic neural adhesion molecules. Neurexin-1α is reported to
interact with postsynaptic neuroligins mediating GABAergic and glutamatergic synapse function
(Südhof 2008). It also binds to leucine-rich repeat transmembrane protein (de Wit, Sylwestrak et
al. 2009), instructing presynaptic and mediating postsynaptic differentiation of glutamatergic
synapses. Substantial evidence implicates deletions in the NRXN1 gene in ASD (Feng, Schroer et
al. 2006, Szatmari, Paterson et al. 2007, Kim, Kishikawa et al. 2008, Marshall, Noor et al. 2008,
Morrow, Yoo et al. 2008, Yan, Noltner et al. 2008, Glessner and Hakonarson 2009), and in
schizophrenia (Vrijenhoek, Buizer-Voskamp et al. 2008, Glessner, Wang et al. 2009, Kirov,
Rujescu et al. 2009, Need, Ge et al. 2009, Ikeda, Aleksic et al. 2010, Shah, Tioleco et al. 2010).
Furthermore, common variants in NRXN1 have been linked to antipsychotic response in
schizophrenia patients (Souza, Meltzer et al. 2010, Lett, Tiwari et al. 2011, Jenkins, Apud et al.
2014).
1.7.2 Glutamate Decarboxylase 1 (GAD1)
The major determinant of GABA in the neocortex is glutamic acid decarboxylase-67 (GAD67;
encoded by the GAD1 gene). Convergent evidence suggests a compelling role for the GAD1
gene in cognition and dorsolateral prefrontal cortex (DLPFC) dysfunction in schizophrenia.
GAD1 codes for the glutamic acid decarboxylase (GAD67) enzyme that metabolizes glutamate to
GABA. One of the most consistent findings in schizophrenia is down-regulation of GAD1
mRNA and protein in the prefrontal cortex (Torrey, Barci et al. 2005). Furthermore, in
24
schizophrenia patients, DNA methylation profile of GAD1 in the prefrontal cortex (PFC) shows
an eight-fold increase in the promoter region leading to repressed expression (Huang and
Akbarian 2007). Genetic variation in the 5’ promoter and untranslated region of GAD1 was
associated with child-onset schizophrenia, and with an increased rate of cortical gray matter loss
over a two to-eight year period (Addington, Gornick et al. 2004). Last, optogenetics has revealed
that inhibition of fast-spiking parvalbumin (PV) interneurons results in suppression of gamma
activity (Sohal, Zhang et al. 2009), and there is a growing body of evidence suggesting abnormal
gamma-band oscillations are an endophenotype of schizophrenia related to cognition (Spencer,
Nestor et al. 2004, Lewis, Hashimoto et al. 2005, Spencer, Salisbury et al. 2008, Haenschel,
Bittner et al. 2009, Spencer 2009, Farzan, Barr et al. 2010, Farzan, Barr et al. 2010, Hall, Taylor
et al. 2011).
1.7.3 Brain-derived Neurotrophic Factor (BDNF)
Brain-derived neurotrophic factor (BDNF) is one of the key regulators of neuroplasticity,
synaptic structure, memory function and consolidation. Post-mortem studies have identified
reduced BDNF expression in the hippocampus and prefrontal cortex of schizophrenia patients
(Green, Matheson et al. 2011), and reduced BDNF levels in schizophrenia patients have been
associated with cognitive performance and clinical outcome (Chen da, Wang et al. 2009,
Vinogradov, Fisher et al. 2009). In healthy controls, it has been reported that the BDNF rs6265
SNP interacts with age to predict differences in cortical thickness, white matter FA, and episodic
memory relevant to Alzheimer’s disease (Voineskos, Lerch et al. 2011). Furthermore, there was
a significant schizophrenia diagnosis by rs6265 genotype interaction observed in resting and
working-memory related hippocampal regional cerebral blood flow, as well as on hippocampal
prefrontal coupling (Eisenberg, Ianni et al. 2013). The rs6265 SNP has also been shown to be
25
predict lower hippocampal volume, particularly within patients with schizophrenia in two
independent samples (Szeszko, Lipsky et al. 2005, Smith, Thornton et al. 2012).
1.7.4 MicroRNA 137 (MIR137)
MicroRNA-137 serves as a regulator of adult neural stem cell maturation and migration (Smrt,
Szulwach et al. 2010, Szulwach, Li et al. 2010, Sun, Ye et al. 2011) in the subventricular zones
in proximity to the lateral ventricles and the subgranular zone of the hippocampus. A single
nucleotide polymorphism (SNP), rs1625579, near the MIR137 gene (1p21.3) achieved genome-
wide significance for association with schizophrenia in approximately 50,000 subjects (p=1.6 x
10-11) (Ripke, Sanders et al. 2011), and the rs1198588 SNP (high LD with rs1625579; R2=0.79)
was further associated in the latest PCG results (Ripke, O'Dushlaine et al. 2013). Microdeletions
of the MIR137 region have also been associated with ASD and intellectual disability (Pinto,
Delaby et al. 2014). The risk allele of rs1625579 has consistently been associated with aberrant
dorsolateral prefrontal cortex (DLPFC) connectivity (Whalley, Papmeyer et al. 2012, Liu, Zhang
et al. 2014, van Erp, Guella et al. 2014). Furthermore, the variant along with severe negative
symptoms predicts an impaired neurocognitive subtype of schizophrenia(Green, Cairns et al.
2012). MIR137 has also been functionally shown to specifically regulate genes with replicated
genome-wide evidence for a role in schizophrenia, most notably CACNA1C (calcium channel,
voltage-dependent, L type, alpha 1C subunit), TCF4 (transcription factor 4) (Kwon, Wang et al.
2011) and ZNF804A (Zinc-Finger 804A) (Kim, Parker et al. 2012). The known role of
microRNAs as potent disease modifiers (Karres, Hilgers et al. 2007, Kim, Inoue et al. 2007, Lee,
Samaco et al. 2008, Williams, Valdez et al. 2009) raises the question of whether genetic
variation in the MIR137 gene might play a critical role in phenotypic expression of
schizophrenia.
26
1.7.5 L-type Voltage-dependent Calcium Channel CAv1.2 (CACNA1C)
The CACNA1C gene encodes the alpha subunit of the L-type voltage-dependent calcium channel
CAv1.2. The rs1006737 variant of CACNA1C was associated with bipolar disorder in a meta-
analysis of several large independent GWAS (p = 7.0x10-8, OR=1.18) (Ferreira, O'Donovan et al.
2008). In addition, the CACNA1C gene has recently been reported to be associated with
schizophrenia and unipolar affective disorder (Green, Grozeva et al. 2010, Nyegaard, Demontis
et al. 2010). Furthermore, recent results from the Cross-Disorder Group of the Psychiatric
Genomics Consortium show that two markers in genes involved in calcium regulation,
CACNA1C and CACNB2, reach genome-wide significance across five disorders (ASD, attention
deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder, and
schizophrenia) (Cross-Disorder Group of the Psychiatric Genomics, Smoller et al. 2013). At a
lower significance threshold (P<10-3), 20 of the 67 calcium active genes were associated with
these disorders, suggesting that calcium channels may have a pleiotropic effect on
psychopathology. The risk allele of the CACNA1C rs1006737 marker has been associated with
increased CACNA1C expression in the DLPFC, increased hippocampal activity during emotional
processing, and increased PFC activity during the n-back working memory task (Bigos, Mattay
et al. 2010). During reward and fear processing, healthy controls and first degree relative had
increased amygdala activation, while bipolar patients were reported to have reduced ventrolateral
PFC activation. Healthy risk variant carriers also showed reduced activation of the hippocampus
and the subgenual prefrontal cortex during an episodic memory task (Erk, Meyer-Lindenberg et
al. 2010). Most recently, it was found in healthy controls during the n-back working memory
task that the risk allele was associated with decreased activation in the DLPFC and increased
functional coupling between the DLPFC and the medial temporal lobe (Paulus, Bedenbender et
al. 2013). Furthermore, in schizophrenia patents and healthy controls the risk allele was
27
associated with poor working memory performance, whereas the association was in the opposite
direction in bipolar patients (Zhang, Shen et al. 2012). In summary, these results indicate that
CACNA1C may be a modulator of prefrontal function and cortical connectivity although the
direction of effects and their localization showed significant variance across subjects and tasks.
Considering the inconsistent direction of effect of the rs1006737 variant, more research is
necessary to understand how CACNA1C may impact working memory in different disease
populations.
1.7.6 Zinc-Finger 804A (ZNF804A)
The ZNF804A gene encodes the zinc-finger protein 804A that is expressed broadly in the brain,
especially in the developing hippocampus and cortex, as well as the adult cerebellum (Donohoe,
Morris et al. 2010). The rs1344706 variant has been implicated in schizophrenia in several
GWAS, and results became more significant when patients with bipolar disorder were included,
even though the odds ratio was still only slightly above one (p = 9.96 x 10-9; OR = 1.12)
(O'Donovan, Craddock et al. 2008). Furthermore, the variant may be particularly associated with
bipolar patients with psychotic symptoms (Lett, Zai et al. 2011). The conserved region around
the rs1344706 variant is a potential binding site for transcription factors, Myt1L zinc-finger
protein and POU3F1/Oct-6, which are involved in oligodendrocyte differentiation and
proliferation (Riley, Thiselton et al. 2010). Alternatively, the mouse homolog of ZNF804A,
zfp804a, is a target for HOXC8 (homeobox C8), suggesting that the ZNF804Agene may be
involved in early neurodevelopment; however the exact function of ZNF804A is unknown
(Chung, Lee et al. 2010). In an fMRI imaging-genetics study employing the n-back working
memory paradigm, healthy individuals (N=115) carrying the ZNF804A risk genotypes exhibited
no changes in regional activity although there was a pronounced gene dosage-dependent
28
alteration in functional connectivity (Esslinger, Walter et al. 2009). That is, risk allele carriers
had reduced connectivity between the right and left DLPFC and increased right DLPFC and left
hippocampal connectivity. Evidence for prefrontal-hippocampal connectivity as an intermediate
phenotype for schizophrenia comes from an independent replication study where it was shown
that the ZNF804A risk variant modulates connectivity in healthy controls (N=153), healthy
siblings (N=178) and patients with schizophrenia (N=78) (Rasetti, Sambataro et al. 2011).
Moreover, the impact of the ZNF804A risk variant on prefrontal-hippocampal connectivity was
specific to the working memory task, whereas right-left DLPFC connectivity was modulated by
the risk variants during working memory as well as during a face-matching paradigm and during
resting state (Esslinger, Kirsch et al. 2011). Indeed, during a theory of mind task (a measure of
social cognition) the risk variant was also associated with altered connectivity between medial
PFC and the angular gyrus (BA 39) (Walter, Schnell et al. 2011). Further, healthy controls
homozygous for the risk variant were reported to have reduced cortical thickness in the left
posterior cingulate cortex, left superior temporal gyrus and right anterior cingulate cortex, all
regions involved in attentional control and working memory (Voineskos, Lerch et al. 2011).
Together, these results support the notion that ZNF804A is conferring risk on basic brain
processes involved in proper cognitive function. ZNF804A may also impact heterogeneity within
schizophrenia. In a two-stage cognitive study examining cognitive function in schizophrenia
patients (N=297; N=165) and controls (N=165; N=1475), there was a significant gene-by-
diagnosis interaction in both episodic and working memory (Walters, Corvin et al. 2010). In both
samples, schizophrenia patients with the ZNF804A risk genotype performed worse in multiple
working memory and episodic memory tasks, although no effect was observed in healthy
controls. Moreover, this finding was stronger in patients with lower IQ. Notably, these findings
have been independently replicated in a Japanese sample (Hashimoto, Ohi et al. 2010). This
29
suggests that first, intermediate phenotypes may be sensitive to subtle effects of GWAS variants;
and second, ZNF804A may act in concert with other schizophrenia risk variants to impact
working memory function.
1.8 Multivariate Approaches to Neuroimaging
Bridging the gap between complex genetics information and whole brain neuroimaging measures
requires sophisticated multivariate analysis. Currently, within the field of psychiatric imaging-
genetics, there are many different strategies each with different assumptions, advantages, and
limitations. Three main experimental designs include: (1) complex genetics analyses (many
variants) on candidate imaging phenotypes, (2) single variant analysis of whole brain imaging
phenotypes, and (3) complex genetic analysis of whole brain imaging phenotypes. Furthermore,
there are a few strategies incorporating these imaging-genetic strategies to clinical and cognitive
phenotypes. Multivariate approaches either apply methods of data reduction (e.g. principal
component analysis (PCA)) to reduce multiple comparisons penalties for a priori hypothesis
testing, or data driven approaches to agnostically identify variables of interest (e.g., graph theory
analysis, machine learning).
1.8.1 Complex Genetic Analysis on Candidate Imaging Phenotypes
Identification of complex genetic variation influencing human brain structure may reveal
biological mechanisms underlying schizophrenia symptomology and cognitive dysfunction. One
strategy is to select a set of markers, in terms of genetic analysis, and tests these loci against
predefined neuroimaging phenotypes relevant to schizophrenia (e.g. mean hippocampal
volumes) based on a specific hypothesis. Selecting a specific brain-imaging phenotype as a
30
quantitative dependent variable and performing genome-wide analysis has been a successful
strategy that is particular useful for meta-analyses. For example, two meta-analyses of cohorts
with genetics and brain imaging have demonstrated significant associations have been observed
for hippocampal volume, intracranial volume, and total brain volume (Bis, DeCarli et al. 2012,
Stein, Medland et al. 2012). Furthermore, a gene expression and co-expression atlas has been
created that demonstrate gene transcription is highly variable across structures, but relatively
conserved across individuals (Hawrylycz, Lein et al. 2012). Therefore, providing a necessary
connection between expression and neuroanatomy, and thus, better rationale for selecting
candidate imaging phenotypes. The ENIGMA Consortium now has amalgamated advance
fMRI, structural MRI, and DTI-MRI scans on approximately 25000 subjects across healthy
controls and multiple neuropsychiatric disorders (Thompson, Stein et al. 2014). Currently,
genome-wide analyses have yielded very promising results, and in the near future more complex
multivariate analyses will be performed over multiple subgroups and imaging modalities.
Beyond genome-wide analyses, there have been many efforts to model complex genetics systems
or pathways of genes. A common approach is gene set enrichment analysis (GSEA) in which a
set of SNPs is selected based on common biological pathway (or ontology), then phenotype-
genotype associations are determine based on whether associations are enriched compared to the
null distribution (Subramanian, Tamayo et al. 2005).Thereby, pathways of interest can be
identified that may reveal be biological relevant, and potentially lead to novel treatment. Many
specific issues with this method have been addressed including biases due to gene size and LD
(Li, Gui et al. 2011). However, it should be noted that GSEA normally based on differences in
gene expression profile between psychiatric patients and healthy individuals. Gene expression
has its own drawbacks (Gunawardana and Niranjan 2013), and tissue selection may not be valid
31
for the candidate imaging phenotype. More recently, polygenic risk scores have been employed
in brain imaging based on a priori disease association.
Schizophrenia has long been theorized to be a polygenic disorder (Gottesman and Shields 1967),
and a polygenic model may influence individual differences across domains of brain
development and function; thus, polygenic risk may reflect within disease heterogeneity of
psychiatric disorders, and potentially shared genetic liability across disorders (e.g. bipolar
disorder and schizophrenia). There are many different types of strategies for deriving polygenic
scores including, but not limited to: quantitative models, linear regression, allele count, shrinkage
estimations, log-risk models, and model simulations (for a detailed review, see (Dudbridge
2013)). In imaging-genetics analysis, Whalley and colleagues employed the polygenic developed
by Purcell et al. (International Schizophrenia Consortium, Purcell et al. 2009) and found that
polygenic load was associated with increased limbic activity characteristic of bipolar disorder,
but was independent of diagnosis (Whalley, Papmeyer et al. 2012). Another polygenic risk score
selected variation associated with schizophrenia based on the meta-analyses from the
Schizophrenia Research Forum (www.schizophreniaforum.org) database, reported that
cumulative genetics risk explained 3.6% of the total variance in DLPFC activation (Walton,
Turner et al. 2013). A drawback of these polygenic models and GSEA is that they currently do
not take into account gene-gene interactions.
An alternative is multifactor dimensionality reduction (MDR) which was developed to identify
combinations of gene-gene and gene-environment interactions predicting a given phenotype.
MDR is a non-parametric machine learning strategy, similar to random forest and decision tree
models, that was specifically designed to detect interactions in the absence of marginal effects
(Moore, Asselbergs et al. 2010). To date, there are no published studies applying this data-driven
32
strategy to brain imaging, although it has been applied to quantitative phenotypes((Winham
2013)). Some drawbacks to MDR are that it can be difficult to interpret complex interactions, the
exploratory nature of MDR requires independent replication, and gene-gene interaction should
be validated in vitro. Furthermore, it has been argued the majority of genetics variance in
complex traits is due to additive effects (Hill, Goddard et al. 2008). Nevertheless, MDR remains
a potentially powerful strategy for examining epistasis that may have broad applications in
imaging-genetics.
1.8.2 Single Variant Analysis of Whole Brain Imaging Phenotypes
Perhaps the greatest advances in multivariate analyses in imaging-genetics is from whole brain
analyses of imaging-phenotypes. Two (minor carriers versus non-carries) or three genotypic
groups are created based on a single variant, then analyzed again using complex neuroimaging
phenotypes (e.g., voxel-wise FA). One of the major challenges of neuroimaging data is the high
degree correlation between brain regions even across imaging modalities. Therefore, in analysis
of candidate imaging phenotypes, it is difficult to know if a genetic association is a true effect, or
rather due to variance in the region of interest (ROI) explained by another region. Similar to
complex genetics analysis, there are two basic strategies: data reduction through incorporating
the high degree of covariance or clustering strategies to reduce the multiple comparison burden.
The latter is effectively utilized by many neuroimaging packages including FSL and SPM
(Friston, Holmes et al. 1994).
Component based analyses has been successfully used in fMRI studies, and are becoming more
common in structural neuroimaging. Principal component analysis (PCA) produces a linear set
of orthogonal principal components (latent variables) that explain the maximal variances in a set
33
of variables (e.g. a set of ROIs). Independent component analyses (ICA) extracts statistically
independent components and non-Gaussian, thus revealing hidden factors that can be a
particularly useful in noise reduction. In fMRI, ICA models provide an approximation of “true”
sources, thus within individuals it can correct for sources of systematic bias, such as cardiac
pulses in blood-oxygen-level dependent (BOLD) signals. Furthermore, several multi-subject ICA
methods have been developed that have been successfully applied to resting state fMRI data to
extract brain networks independent of a specific task (Calhoun and Adali 2012). ICA has also
been applied to DTI data and independent components have been identified that may represent
distinct white matter tracts (Wahl, Li et al. 2010), although more research is necessary to
understand the biological basis of the components. PCA on white matter tracts reveals that the
first factor explains approximately 45% of the variance that may be related to processing speed
and general fluid intelligence (Penke, Maniega et al. 2012). Thus, component based methods
may reveal biological relevant processes in terms of cognitive function. A major drawback of
these approaches is that the latent variable derived in these methods are derived from the sample.
Therefore, the results are not readily comparable among studies.
Recent advances in network-based statistical analysis and graph theory have had led to feasible
means to assess the human connectome via neuroimaging that could reveal how genetic factors
affect structural (and functional) connectivity (Hulshoff Pol and Bullmore 2013). Graph theory
permits the calculation of summary measures connectivity that underlie normal organization
processes that may break down in neuropsychiatric disease (Fornito, Zalesky et al. 2013).
Therefore, it is an effective, data-driven means to identify neural circuitry that may be dependent
on genetic factors. Indeed, small-work networks identified via resting state EEG have revealed
that local and global interconnectedness are moderately heritable (ranging from 32-89%) (Smit,
Stam et al. 2008), and similar heritability has been observed in global interconnectivity in
34
children via resting state fMRI (van den Heuvel, van Soelen et al. 2013). Furthermore,
genetically mediated relationships following small world architecture have been observed in
cortical thickness (Schmitt, Lenroot et al. 2008). Results from graph theoretical approaches can
be used to identify crucial network topography liable in neuropsychiatric disorder and
heterogeneity of this network can be examined in following genetic analysis. For example, DTI
analysis comparing complex networks of schizophrenia and healthy subjects revealed preserved
small world organization, yet longer path length in frontal and temporal regions. These results
suggest that schizophrenia patients have poor global integration in these regions resulting in a
limited capacity to integrate information across brain regions (van den Heuvel, Mandl et al.
2010). The regions with group differences in node global path length could then be used as seed
points for further DTI analysis examining the effect of a given genetic risk variant. An advantage
to this approach would be the identified DTI tract could be also applied to cognitive and clinical
phenotypes. An alternative would be to examine differences in global and regional connectivity
based on genotype. For instance, the autism risk gene CNTNAP2 (contactin associated protein-
like 2) was examined in 328 healthy individuals using high-angular-resolution diffusion imaging
(HARDI) revealed altered path length, small worldness, and global efficiency in risk carriers.
This approach provides detailed information on brain interconnectivity (Dennis, Jahanshad et al.
2011); however, it is difficult to assess what these measure represent in the phenotypic
expression of complex neuropsychiatric disorders.
1.8.3 Complex Genetic Analysis of Whole Brain Imaging Phenotypes
One of the ultimate goals of imaging-genetics is to analyze the whole variation of the genome
(greater than three billion base pairs) and voxel-wise imaging approaches (upwards of 140000
voxels). The advantages would include novel, data-drive findings as well as greater confidence
35
that the results are not confounded by regions that are not analyzed (either genetic or
anatomical). The complexity of the data, rather than the quality, poses significant challenges. It is
conceivable whole brain analysis of whole genome sequencing data would need to correct for
approximately 5 x 1014 multiple comparisons. Therefore, it is necessary to intelligently reduce
the complexity both genetic and imaging data. The approaches mentioned in the previous
sections can be combined to increase power. For instance, polygenic risk scores could be
regressed against clustering or component analyses of brain imaging. It should be noted that to
date no imaging-genetics studies have performed this type analysis. Alternatively, multivariate
methods have been developed to analyze this complex and confounded data.
There are at least three data driven approaches that could be utilized. The first is mass-univariate
linear modeling (MULM) in which all linear model combinations are fit. This approach has been
successful applied in imaging-genetic analysis (Stein, Hua et al. 2010). Beyond the need for
stringent correction for experiment-wise error, a drawback of MULM is that it independently
tests phenotypes and genotypes. Therefore, it is unable to capture cumulative effects from
multiple markers, and it does capitalize on power gains from confounded quantitative
phenotypes. The second approach is similar to component analyses in which latent variables are
extracted simultaneously from both imaging and genetics data to produce new genotype-
phenotype variables that are optimized using cost functions (e.g., partial least squares (PLS),
canonical component analysis (CCA), and reduced rank regression (RRR)). PLS maximizes the
covariance between latent variables, whereas CCA maximizes the correlation between them.
When the number of variables is significantly larger than the sample size (such as in imaging-
genetics), these methods become effectively equal (Le Floch, Guillemot et al. 2012). In RRR,
response and predictor variables are reduced and ranked according to latent variables and linear
regressions are performed. Given the data reduction strategies of these approaches, they have
36
more power with limited samples size, and they are especially effective for analyzing
confounded data; although, they may over-fit the data. PLS has successfully used to identify
genotype-phenotype differences between schizophrenia patients and controls through examining
genetic variation in the dopamine receptor D1 gene and DLPFC activation during the Serial Item
Recognition fMRI paradigm (Tura, Turner et al. 2008). Furthermore, PLS analysis has used to
parcellate genotype-phenotype associations in myelin associated genes and white matter FA that
predicted cognitive performance impaired in schizophrenia (Voineskos, Felsky et al. 2013). Last,
sparse RRR has been employed for genome-wide detection of markers associated with voxel-
wise longitudinal changes in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
(Vounou, Janousova et al. 2012).
The last approach is to employ data-mining strategies such as machine learning. Machine
learning is an iterative artificial system through which efficiency and effectiveness is improved
over time. There are a wide variety of techniques (e.g., support vector machines (SVM),
Gaussian random field (GRF), graphical models, autoregression, and others) (Nilsson 1996).
Major advantages of theses technique is that it is truly agnostic (does not require hypothesis
testing including what phenotype to examine), and the quality of the results increases with more
information. There have been a few studies applying machine learning algorithms (MLA) to
imaging-genetics studies. In fMRI analysis, MLA can be used to train classifiers to decode
stimuli, behaviors and other variables ((Pereira, Mitchell et al. 2009). For instance, components
obtained from ICA of resting state fMRI can classify patients with schizophrenia and healthy
controls (Shinkareva, Ombao et al. 2006). Furthermore, SVM had been used to distinguish
bipolar and schizophrenia patients from healthy controls using gene expression data as well as
demographic and clinical data (Struyf, Dobrin et al. 2008). Indeed, schizophrenia patients and
healthy controls have been successfully classified using structural MRI ((Davatzikos, Ruparel et
37
al. 2005), fMRI (Costafreda, Fu et al. 2011), and DTI (Ingalhalikar, Kanterakis et al. 2010).
More recently, SVM MLA has been used on voxel-wise ICA of fMRI resting state data and
genotypes from 384 SNPs, and imaging-genetic data provided better classification between
schizophrenia and healthy controls than either method alone (Yang, Liu et al. 2010). However, in
all of the mentioned studies the comparison group were healthy controls and given the overlap of
schizophrenia (clinical, neuroanatomical, genetic) it is likely that the high degree of sensitivity
and specificity would not remain comparing neuropsychiatic groups (e.g. schizophrenia versus
bipolar patients). MLAs can also be used to increase the power of voxel-wise genome-wide
association studies. A novel method of combining GRF for imaging data to SNP data is via least
square kernel machine. This allows for the joint effect of SNPs on imaging traits and assessment
of epistasis among SNPs (Ge, Feng et al. 2012). The approach was applied on the ADNI
database, and the top associations overlapped with Alzheimer’s candidate genes including
GRIN2B, although it did not survive multiple comparison testing (Ge, Feng et al. 2012). The
same approach was applied to data from the IMAGEN Consortium. The top variant in the
neuroplastin gene was associated with frontal and temporal lop thinning leading to verbal and
non-verbal intellectual disabilities in adolescents (Desrivieres, Lourdusamy et al. 2014).
Moreover, epistatic interactions were examined in over 14 candidate genes (810 SNPs) using
stochastic gradient boosting (SGB) (Andreasen, Wilcox et al. 2012). SGB identified SNPs that
had the strongest relationship with multiple measures of brain changes. Novel epistatic
interactions were discovered as well as five of the 17 SNPs identified by GSB had previously
implicated in cognitive processes relevant to schizophrenia, suggesting that this machine learning
algorithm may uncover meaningful epistasic relationships between genes (Andreasen, Wilcox et
al. 2012). Taken together, both approaches suggesting that MLA is a viable means to discovery
38
novel associations and a potentially powerful means to circumvent the large penalties studying
how complex genetic variation impacts voxel-wise imaging data.
There are caveats that should be considered using MLA. First, similar to component based
approaches, it is uncertain if the results from be over-fitted models. Second, there is no
consensus on which MLA method should be employed in imaging-genetic analysis. Therefore, it
is difficult to interpret the results among studies. Third, MLA approaches are by definition
exploratory, and confidence in the results requires replication or converging evidence. Indeed,
the majority of diagnostic studies using MLA tend to have a high degree of sensitivity and
specificity (Orru, Pettersson-Yeo et al. 2012). Given the small sample size of these studies it is
uncertain if the effect is rather due to population structure.
1.9 Application of polygenic risk models to imaging genetic studies in psychiatry
Lately, there has been an increasing trend towards applying polygenic risk models in imaging-
genetics. Earlier fMRI studies have divided their sample according to risk variant groups to
assess ‘epistasis’. Tan et al. reported that GRM3 risk allele homozygotes had greater DLPFC
activation in COMT rs4680 Val homozygotes but not Met homozygotes (Tan, Chen et al. 2007).
Similar three-way ‘interactions’ were observed in the NRG1, ERBB4, and AKT1 gene variants
(Nicodemus, Callicott et al. 2010). Furthermore, polygene influences on DLPFC activation have
been observed with DISC1 and NDEL1 (Nicodemus, Callicott et al. 2010), MTHFR and COMT
(Roffman, Weiss et al. 2008), and RGS4 and COMT (Buckholtz, Sust et al. 2007). Polygenic risk
scores have only recently been applied to functional and structural neuroimaging studies. The
most popular method involves selecting a group of SNPs at different p-value thresholds based on
large case-control disease association studies. Next, a set of risk scores (at different significance
39
thresholds) is created in which each risk variant is weighted by their odds ratio and accrued as a
polygenic score. Then, the polygenic scores are tested against brain phenotypes. Polygenic risk
for schizophrenia was also association with total brain volume and white matter across more than
14,000 SNPs among both healthy controls and schizophrenia patients (Terwisscha van
Scheltinga, Bakker et al. 2013). Walton et al. found a polygenic risk score, based on 600 SNPs
nominally associated with schizophrenia, predicted left DLPFC inefficiency in healthy controls
(Walton, Turner et al. 2013). Whalley and colleagues subsequently reported polygenic risk
scores derived from the PGC Bipolar Working Group (PGC-BD) predicting increased limbic
activation among both healthy controls and individuals with familial risk of bipolar disorder;
however, there was no significant association with limbic activation within each group (Whalley,
Papmeyer et al. 2012). In a subsequent study, polygenic score for major depressive disorder, but
not bipolar disorder, was associated with decreased white matter FA across healthy controls and
individuals with familial risk for mood disorders (Whalley, Sprooten et al. 2013). Furthermore,
in a relatively large healthy control sample (N=438), polygenic risk was associated with reduced
cortical thickness in the left medial prefrontal cortex (Holmes, Lee et al. 2012).
In general, these studies have provided important first steps to understanding polygenic risk on
brain function and structure, although there are some important caveats. First, the p-value
thresholds were arbitrarily selected; thus, there are potentially spurious associations included in
the polygenic scores. Second, to date, none of the studies included independent replication of
their imaging findings. Given the exploratory nature of these analyses, replication would greatly
bolster confidence in their results. Third, the scores were weighted according to disease
associations. It could be argued that disease risk may not be valid when assessing disease
heterogeneity. For instance, the effect on any particular brain region of a risk variant could be
different within schizophrenia than in healthy controls. Last, to assess the clinical utility of
40
polygenic risk score, it may be necessary to assess if the polygenic association with brain
phenotypes are mediating clinical and behavioral heterogeneity. Considering the wide variety of
methods to create polygenic scores (Dudbridge 2013), it may necessary to tailor the polygenic
methodology based to the specific hypothesis of the imaging-genetic study.
1.10 Outline of Experiments
The succeeding chapter will provide a brief background and hypothesis for the four manuscripts
in the thesis (Chapter 3-6). Chapters three and four have been published in the journals PLoS
One and Molecular Psychiatry, respectively. Chapter five and six are about to be submitted to
peer reviewed journals. Each of the studies are standalone articles; therefore, sections contained
in each studies may overlap with each other as well as with material presented in the
Introduction. Additionally, the appendix contains two manuscripts published in the journals
Schizophrenia Research and Biological Psychiatry. These articles complement the work
presented, but they are beyond the scope the specific hypotheses outlined in this thesis.
41
Chapter 2
2 Overview of Experiments, and Hypothesis
This thesis is composed of four independent projects that sought to examine heterogeneity
relevant to or within schizophrenia through combining neuroimaging and genetics, with
particular focus on how these impact variability in clinical and cognitive functioning.
2.1 Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders
2.1.1 Background
Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism spectrum
disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene variation may
be related to brain morphology to confer risk for ASD or schizophrenia is unknown. This study
examines the NRXN1 gene on brain structure and cognitive function, thereby attempting to
identify a neural and cognitive susceptibility mechanism by which the NRXN1 gene confers risk
for both schizophrenia and ASD.
2.1.2 Hypothesis
The intermediate phenotype approach permits us to examine how shared genetic underpinnings
of these two disorders may confer risk in the brain. Therefore, we used this approach to
investigate 11 single nucleotide polymorphisms (SNPs) of the NRXN1 gene lying within regions
overlapped by numerous deletions implicated in ASD and schizophrenia, and their effects on
brain morphometry in healthy individuals. Given that such deletions confer susceptibility to both
42
schizophrenia and ASD, we hypothesized that NRXN1 polymorphisms would confer an
intermediate phenotype related to schizophrenia and ASD, via effects on neural structures and
cognitive function altered in both disorders.
2.2 The Genome-Wide Supported MicroRNA-137 Variant Predicts Phenotypic Heterogeneity within Schizophrenia
2.2.1 Background
There is notable heterogeneity in the phenotypic presentation of schizophrenia including, but not
limited to, the onset of illness, severity of positive and negative symptoms, neurological soft
signs and cognition, course of illness, response to treatment, and functional and structural brain
abnormalities. This phenotypic heterogeneity has been a central challenge for schizophrenia
research and other neuropsychiatric disorders. MicroRNA regulate genetic expression and
translation over networks of gene, and thus, they are potent disease modifiers. A single
nucleotide polymorphism, rs1625579, near the MIR137 gene (microRNA 137; 1p21.3) is a top
genome-wide significance for association with schizophrenia, and MIR137 has also been
functionally shown to specifically regulate genes with replicated genome-wide significant
evidence for a role in schizophrenia, most notably CACNA1C and TCF4.
2.2.2 Hypothesis
The identification of the genetic sources of phenotype heterogeneity, such as the effects of a
genetic risk variant on phenotypes such as age-at-onset, or brain structure, may lead to early
identification of disease trajectory. Such identification, before disease progression, could then
serve as a platform to test earlier interventions, particularly within the subgroup at-risk for poorer
outcome. Given the recently established role of MIR137 as a central player in coordinating the
43
timing and expression of schizophrenia risk genes we hypothesized that MIR137 may be an
important determinant of age-at-onset of psychosis and brain structure in schizophrenia.
2.3 Glutamate Decarboxylase 1 (GAD1) Variant Predicts a Neuroanatomical and Working Memory Susceptibly Mechanism Relevant to Schizophrenia.
2.4 Background
Working memory dysfunction is a central feature of schizophrenia and many other psychiatric
disorders. In schizophrenia patients, working memory deficits are associated with dysfunction of
dorsolateral prefrontal cortex (DLPFC) as well as DLPFC connectivity with other regions and
disruption of neurotransmitter input such as GABA inhibitory neurotransmission. Convergent
evidence suggests a compelling role for the glutamate decarboxylase 1 (GAD1) gene in working
memory and dorsolateral prefrontal cortex (DLPFC) dysfunction in schizophrenia.
2.5 Hypothesis
We developed a novel method of voxel-wise mediation analysis that was used to examine the
relationship of GAD1 genetic variation, brain structure, and working memory performance. We
first hypothesized that the GAD1 rs3749034 risk variant would predict brain structure changes
relevant to schizophrenia and working memory function. Next, we examined the relationship
between the risk variant and working memory performance across multiple tasks relevant to
working memory (letter-number span, digit-span, Stroop). Last, we hypothesized that the effect
of the risk variant on brain structure would mediate its effect on working memory performance.
44
2.6 Additive Genetics Risk Predicts Widespread Changes in Brain Structure that Cause Poorer Cognitive Function
2.6.1 Background
There is growing theoretical and empirical evidence that additive genetic variation accounts for a
considerable proportion of the variance in complex traits. Therefore, examination of additive
genetic risk across several common variants might provide a better explanation for the high
degree of heterogeneity in neurocognitive dysfunction in schizophrenia that depends on brain
network connectivity. There is evidence to suggest that neuroanatomical changes and
neurocognitive dysfunction within schizophrenia are likely dependent on genetic load. Further,
these anatomical changes may mediate neurocognitive dysfunction.
2.6.2 Hypothesis
We hypothesized that increasing additive genetic risk loading may produce a more ‘severe’ brain
phenotype that may predict cognitive function. Furthermore, as we have previously shown, the
effect of schizophrenia risk variants on brain structure may be greater within schizophrenia
patients compared to healthy controls. Therefore, we examined the accrued effect of five
common genetic variants, implicated in schizophrenia, brain structure and cognitive function, for
association with brain-wide measures of white matter fraction anisotropy (FA) and cortical
thickness in healthy controls and patients. To compare genetic subsets with differences in brain
structure, we then isolated subjects with either low or high risk allele loading for association with
our neurocognitive battery. Last, we employ a novel voxel-wise mediation analysis to understand
how high risk allele loading explains poorer cognitive functioning via worse brain structure.
45
Chapter 3
3 Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders
Contents of this chapter have been published as:
Voineskos AN, Lett TA et al. Neurexin-1 and frontal lobe white matter: an overlapping
intermediate phenotype for schizophrenia and autism spectrum disorders. PLoS One.
2011;6(6):e20982.
A link to the published paper can be found at:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0020982#pone-
0020982-g003
This work is open access under the Creative Commons Attribution (CC BY) license
46
3.1 Abstract
Background: Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism
spectrum disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene
variation may be related to brain morphology to confer risk for ASD or schizophrenia is
unknown.
Method/ Principal Findings: 53 healthy individuals between 18-59 years of age were
genotyped at 11 single nucleotide polymorphisms of the NRXN1 gene. All subjects received
structural MRI scans, which were processed to determine cortical gray and white matter lobar
volumes, and volumes of striatal and thalamic structures. Each subject’s sensorimotor function
was also assessed. The general linear model was used to calculate the influence of genetic
variation on neural and cognitive phenotypes. Finally, in silico analysis was conducted to assess
potential functional relevance of any polymorphisms associated with brain measures. A
polymorphism located in the 3’ untranslated region of NRXN1 significantly influenced white
matter volumes in whole brain and frontal lobes after correcting for total brain volume, age and
multiple comparisons. Follow-up in silico analysis revealed that this SNP is a putative
microRNA binding site that may be of functional significance in regulating NRXN1 expression.
This variant also influenced sensorimotor performance, a neurocognitive function impaired in
both ASD and schizophrenia.
Conclusions: Our findings demonstrate that the NRXN1 gene, a vulnerability gene for SCZ and
ASD, influences brain structure and cognitive function susceptible in both disorders. In
conjunction with our in silico results, our findings provide evidence for a neural and cognitive
susceptibility mechanism by which the NRXN1 gene confers risk for both schizophrenia and
ASD.
47
3.2 Introduction
Autism Spectrum Disorders (ASDs) and schizophrenia are highly heritable disorders with
genetic factors comprising the majority of the known risk (Carroll and Owen 2009). Currently,
the gene with the best evidence for shared susceptibility for schizophrenia and ASD is the
Neurexin-1 (NRXN1) gene, one of the largest known human genes (1.1 Mb) with 24 exons,
located on chromosome 2p16.3 (Südhof 2008). The NRXN1 gene encodes the neurexin-1α and
neurexin-1β proteins that function as pre-synaptic neural adhesion molecules. Neurexin-1α is
reported to interact with postsynaptic neuroligins (NLGNs) mediating GABAergic and
glutamatergic synapse function (Südhof 2008). It also has been reported to bind to leucine-rich
repeat transmembrane protein (LRRTM2) (de Wit, Sylwestrak et al. 2009), instructing
presynaptic and mediating postsynaptic differentiation of glutamatergic synapses. Substantial
evidence implicates deletions in the NRXN1 gene in ASD (Feng, Schroer et al. 2006, Szatmari,
Paterson et al. 2007, Kim, Kishikawa et al. 2008, Marshall, Noor et al. 2008, Morrow, Yoo et al.
2008, Yan, Noltner et al. 2008, Glessner and Hakonarson 2009) and schizophrenia (Vrijenhoek,
Buizer-Voskamp et al. 2008, Glessner, Wang et al. 2009, Kirov, Rujescu et al. 2009, Need, Ge et
al. 2009, Rujescu, Ingason et al. 2009, Ikeda, Aleksic et al. 2010, Shah, Tioleco et al. 2010).
NRXN1 has also been associated with mental retardation (Zweier, de Jong et al. 2009, Ching,
Shen et al. 2010), nicotine dependence (Bierut, Madden et al. 2007, Nussbaum, Xu et al. 2008,
Novak, Boukhadra et al. 2009), alcoholism (Yang, Chang et al. 2005) and vertebral anomalies
(Zahir, Baross et al. 2008). Therefore, it is apparent that disruptions of the NRXN1 gene,
especially deletions, confer risk to a range of neurodevelopmental phenotypes, including ASDs,
schizophrenia, and mental retardation.
48
The results of neuroimaging studies suggest that schizophrenia and ASD patients also share
neural vulnerability, most notably in the frontal lobe and in frontal lobe circuitry (Minshew and
Keller 2010, Pettersson-Yeo, Allen et al. 2011). Therefore, genes that confer susceptibility to
both schizophrenia and ASD might contribute to altered brain structure and/or function common
to both disorders. Although few studies have included both ASD and schizophrenia patients,
overlapping findings between these illnesses occur most prominently in the frontal lobe and in
fronto-striatal circuitry (Minshew and Keller 2010, Pettersson-Yeo, Allen et al. 2011). Grey and
white matter in ASD has been associated with increased cortical grey to white matter ratio and
decreased volumes beyond childhood (Courchesne, Karns et al. 2001, Acosta and Pearl 2004).
Although both increases and decreases in grey and white matter volumes in ASD have been
reported, white matter abnormalities in the frontal lobe remain some of the most consistent
neuroimaging findings in ASD (McAlonan, Daly et al. 2002, Herbert, Ziegler et al. 2003,
Barnea-Goraly, Kwon et al. 2004, Herbert, Ziegler et al. 2004, McAlonan, Cheung et al. 2005,
Sundaram, Kumar et al. 2008, McAlonan, Cheung et al. 2009, Mengotti, D'Agostini et al. 2010).
Thus, developmental abnormalities in white matter growth seems important in the
etioneuropathology of ASD (Williams and Minshew 2007). Structural MRI findings in
schizophrenia populations are typically characterized by decreases in temporal and frontal lobe
volumes, and some reductions in total brain volume and parietal volumes (McCarley, Wible et
al. 1999, Shenton, Dickey et al. 2001). Although findings have not always been consistent, a
recent meta-analysis of 17 studies confirmed a frontal lobe white matter deficit in patients with
schizophrenia (Di, Chan et al. 2009). Furthermore, cytoarchitectural alterations of the prefrontal
cortex have been found in schizophrenia, and decreased thalamic volume and altered prefrontal-
thalamic circuitry are common findings in this disorder (Goldman-Rakic and Selemon 1997,
Jones 1997, Danos, Baumann et al. 2003, Brickman, Buchsbaum et al. 2004, James, James et al.
49
2004, McIntosh, Job et al. 2004, Rose, Chalk et al. 2006). Altogether, these findings suggest
abnormalities of frontal, thalamic, and striatal structure that may be shared in the neuropathology
of schizophrenia and ASD. Neurocognitively, sensorimotor deficits are shared by both disorders.
Such deficits are typically apparent in ASD patients (Sigman and Ungerer 1981). Cognitive
assessment (Rajji and Mulsant 2008) and birth cohort studies (Welham, Isohanni et al. 2009) also
identify impaired sensorimotor function in schizophrenia.
The intermediate phenotype approach permits us to examine how shared genetic underpinnings
of these two disorders may confer risk in the brain (Gottesman and Gould 2003, Meyer-
Lindenberg and Weinberger 2006, Tan, Callicott et al. 2008). Therefore, we used this approach
to investigate 11 single nucleotide polymorphisms (SNPs) of the NRXN1 gene lying within
regions overlapped by numerous deletions implicated in ASD and schizophrenia, and their
effects on brain morphometry in healthy individuals. Given that such deletions confer
susceptibility to both schizophrenia and ASD, we hypothesized that NRXN1 polymorphisms
would confer an intermediate phenotype related to schizophrenia and ASD, via effects on neural
structures and cognitive function altered in both disorders.
3.3 Results
3.3.1 Genotypes
Concordance for the 10% of re-genotyping of all 11 SNPs (Figure 3-1) was 100%. No SNP
deviated significantly from Hardy-Weinberg equilibrium. Four SNPs (rs10208208, rs12623467,
rs10490162, 10490227) were not included in further analysis since their minor allele frequency
(MAF) was below 15% (Table 3-S1). Furthermore, none of the SNPs was in linkage
50
disequilibrium (LD) (not shown), and their MAF was similar to the Hapmap CEU population
(Thorisson, Smith et al. 2005). For rs1045881 since only one TT homozygote was in the sample,
we combined T-allele carriers (T/T and T/C) and collectively analyzed in one cell. Post hoc
independent t-tests of rs1045881 genotype (T-Carriers vs. C/C) revealed no significant
differences in any demographics measured (Table 3-S2).
For lobar gray matter volumes, no genotype by brain region interactions or main effects of
genotype were found following repeated measure ANCOVAs conducted for each of the seven
SNPs with MAF > 15%, with age and total brain volume as covariates. Therefore, no follow-up
analysis was performed. When examining white matter volumes, we found that for each lobe, a
minimum of 85% of the variance in one hemisphere was explained by the white matter volume
of the other hemisphere (P<0.001, R2 (Pearson)>0.85); therefore, we combined lobar white matter
volumes across hemispheres. For lobar white matter volumes, a genotype by white matter lobe
volume interaction was found following repeated measures ANCOVA, at the rs1045881
(F2.25=5.498, p = 0.004) and rs858932 (F4.56= 3.802 , p=0.004) polymorphisms (Bonferroni
corrected alpha of 0.0071). We did not observe significant white matter region volume by
genotype interactions in any other NRXN1 variants examined. The results for the rs1045881 and
rs858932 SNPs were followed up using separate ANCOVAs for white matter volume at each
lobe. The rs1045881 variant was significantly associated with frontal lobe white matter volume
(Bonferroni corrected alpha = 0.0125 for four brain regions): F1,49=8.231, p=0.006; (Figure 3-2),
where ‘CC’ homozygotes demonstrated reduced frontal white matter volumes compared to ‘T’
allele carriers. Consistent with the direction of effect in frontal lobe, the rs1045881 was
nominally associated (as it did not survive Bonferroni correction) with change in parietal lobe
51
white matter volume (F1,49=4.089, p = 0.049). No association of this SNP with temporal or
occipital lobe white matter volume was observed.
The follow-up ANCOVA examining rs858932 genotype also predicted frontal lobe white matter
volume (F2,51=5.472, p=0.007), where ‘GG’ individuals had lower frontal lobe white matter
volume and nominal association in the parietal lobe also occurred in the same direction, but did
not survive Bonferroni correction (F48,2=3.719, p = 0.032; Figure 3-S1). Frontal lobe white
matter volumes were also associated at the allelic level: both the ‘C’ allele of rs1045881
(χ2=7.184, p=0.0074) and the ‘G’ allele of rs858932 (χ2=4.121, p=0.0432) predicted lower
frontal white matter volume (Table 3-S3). Similar results were shown in the haplotype analysis
(p(Global)<0.001; Table 3-S4).
Repeated measures analysis for striatal and thalamic structures revealed a significant volume by
region interaction for the rs858932 SNP only (F14,336 = 3.4, p < 0.001; Greenhouse-Geiser
correction: F4,99 = 3.4, p = 0.01). Follow-up ANCOVAs at left and right caudate, putamen,
globus pallidus, and thalamus revealed that this interaction was driven by the influence of the
rs858932 SNP on thalamic volumes only: for left thalamus (F2,48 = 8.9, p = 0.001), and for right
thalamus (F2,48 = 7.3, p = 0.002), significant at the Bonferroni corrected alpha for eight
comparisons (alpha= 0.0063, Figure 3-3). Here, ‘GG’ individuals had significantly lower
thalamic volumes compared to ‘T’ allele carriers. No significant effects were observed at
caudate, putamen, or globus pallidus.
52
3.3.2 Cognitive
Repeated measures ANCOVA showed a main effect of the rs1045881 SNP on sensorimotor
function (F1,49 = 4.8, p = 0.03). The ‘C/C’ homozygotes had reduced finger tapping scores
compared to ‘T’ allele carriers, consistent with the directional effect on white matter volumes.
No association was observed for the rs858932 SNP (F1,48 = 0.4, p = 0.67). No task by genotype
interaction was observed for either polymorphism.
Frontal lobe white matter volume was highly correlated with finger tapping (FT) score even after
accounting for age effects (Dominant Hand: R2 = 0.404, p = 0.003; Non-Dominant Hand: R2 =
0.469, p = 0.001).
3.3.3 In silico Analysis
The rs1045881 SNP is located in the 3’UTR of Neurexin-1. In silico prediction by miRBase
analysis revealed the presence of the C-allele creates a binding site for the microRNA hsa-miR-
1274a and hsa-miR-339-5p. Furthermore, alteration in exon splicing enhancer and other motifs
were observed. The rs858932 SNP was not sufficiently near any splice site (i.e. intron/exon
border) for in silico prediction.
3.4 Discussion
We found that genetic variation in the 3’untranslated region of the NRXN1 gene predicted an
intermediate risk phenotype in healthy individuals relevant to schizophrenia and ASD. Our
primary finding at the rs1045881 SNP in the 3’UTR of Neurexin1 demonstrated that the ‘C’
allele predicts reduced frontal white matter volume and sensorimotor function. Furthermore, our
53
in silico analysis suggested presence of the same ‘C’ allele predicted microRNA binding, thus
providing a potential mechanism for this allele’s effects on brain structure and cognitive
function. The gene variants that influenced brain morphology in our study are located in the
regions of NRXN1 susceptible to deletion in schizophrenia and ASD. The effects of these genetic
variants localized to brain structure and cognitive function that demonstrate overlapping
susceptibility in both schizophrenia and ASD, namely frontal lobe white matter abnormalities, as
shown in recent meta-analyses (Di, Chan et al. 2009, Radua, Via et al. 2010) and sensorimotor
function (Curcio 1978, Braff and Geyer 1990, Geyer, Swerdlow et al. 1990, Peng, Mansbach et
al. 1990). To our knowledge, this work provides the first evidence in vivo of how variation in the
NRXN1 gene may confer a potential neural risk mechanism for schizophrenia and ASD.
Schizophrenia and ASD patients share sensorimotor deficits and soft neurological signs
(Dumontheil, Burgess et al. 2008). Such shared deficits are almost certainly neurodevelopmental
in nature, as in ASD they present at a very early age, and when present in schizophrenia, they are
often present before illness onset. White matter, likely through myelination, plays a key role in
ensuring appropriate sensorimotor development, and motor tasks and motor speed are tightly
correlated with white matter indices on MRI (Barnea-Goraly, Menon et al. 2005, Takarae,
Minshew et al. 2007). Our finding correlating white matter volumes with sensorimotor
performance is consistent with previous investigations (Herbert, Ziegler et al. 2004, Douaud,
Smith et al. 2007, Shukla, Keehn et al. 2010). Moreover, the same NRXN1 allele that predicted
microRNA binding (and thus presumably increased enzymatic breakdown of NRXN1 mRNA and
reduced NRXN1 translation) also correlates with reduced white matter volumes and altered
sensorimotor function.
54
Our second finding was that the intronic rs858932 SNP, also located in a deletion site
(Vrijenhoek, Buizer-Voskamp et al. 2008, Rujescu, Ingason et al. 2009, Ching, Shen et al. 2010),
similarly influenced frontal lobe white matter volume, but also prominently influenced left and
right thalamic volumes. We consider this finding more preliminary due to the lower minor allele
frequency at this variant in our sample. Nevertheless, association of this variant with thalamic
volumes is consistent with overlapping neural vulnerability for ASD and schizophrenia as well
(Shenton, Dickey et al. 2001, Brambilla, Hardan et al. 2003), and suggests that the NRXN1 gene
may influence thalamocortical circuitry that is vulnerable in both disorders.
Little is known about how specific types of deletions within the NRXN1 gene may relate to a
given neuropsychiatric phenotype. Our in silico analysis demonstrated the 3’UTR SNP as a
putative microRNA binding site for hsa-miR-339 and hsa-miR-1274, thus suggesting a
functional role for this region of the gene that may relate to mRNA expression of NRXN1. This is
interesting since expression of miR-339 microRNA has been reported to be dysregulated in the
cortex of psychotic patients (Moreau, Bruse et al. 2011). Reduced NRXN1 mRNA may influence
white matter alterations by concomitant reductions in binding to the NRXN1 binding partner,
LRRTM2, which mediates postsynaptic differentiation of glutamatergic synapses (de Wit,
Sylwestrak et al. 2009, Ko, Fuccillo et al. 2009, Siddiqui, Pancaroglu et al. 2010). Glutamatergic
dysfunction is well established in schizophrenia (Coyle 1996); further, NXRN1 expression is
induced by AMPA receptors, and mediates recruitment of NMDA receptors, a hallmark of
synapse maturation(Thyagarajan and Ting 2010). Glutamatergic dysfunction can also lead to
white matter abnormalities. Oligodendrocytes possess glutamatergic receptors (both AMPA and
NMDA), and are highly sensitive to any form of stress or toxicity (McTigue and Tripathi 2008).
Therefore, NRXN1 may influence frontal white matter in schizophrenia and ASD through
55
disrupted interaction with its glutamatergically-related binding partners, or possibly via direct
glutamatergic involvement as the NRXN1 knock out mouse demonstrates decreased excitatory
synaptic strength and decreased prepulse inhibition (Etherton, Blaiss et al. 2009).
Recent imaging-genetics studies (Scott-Van Zeeland, Abrahams et al. 2010, Tan, Doke et al.
2010) have implicated a neurexin superfamily member, the contactin-associated protein-like 2
(CNTNAP2) gene in brain structure and function providing evidence for neural susceptibility
patterns relevant to ASD. These studies demonstrated volumetric reductions for CNTNAP2 risk
allele carriers particularly in frontal lobe (Scott-Van Zeeland, Abrahams et al. 2010, Tan, Doke
et al. 2010) and also showed altered frontal connectivity. One of these two studies (Scott-Van
Zeeland, Abrahams et al. 2010) demonstrated strong effects with sample sizes smaller than ours.
Our findings, in conjunction with the recent imaging-genetics findings of CNTNAP2 demonstrate
the value of examining common variants within known ASD risk genes to understand neural
susceptibility mechanisms conferred by these risk genes. The ‘added-value’ of this approach lies
in the neural localization of gene effects, providing information regarding how the genes may
confer brain risk patterns for these disorders.
There are several limitations in our study that should be considered. First, we imposed a
dominant model by combining genotypic groups C/T and T/T at rs1045881; however concern
regarding this model can be mitigated by our findings that allelic association analysis supported
such a model. Second, one could argue that our finding may constitute a ‘winner’s curse’, and
therefore we would encourage replication efforts. A third limitation of our study is that while
there was a clear effect of this putative risk variant on frontal lobe white matter volume, in a
direction consistent with cognitive function findings and in silico prediction, various MRI studies
56
have reported either reductions or increases in frontal lobe white matter for both populations.
Finally, given that we measured gray and white matter volumes for cortical lobar structures, we
were somewhat limited in obtaining more localized regional specificity for effects of NRXN1
variation. More detailed parcellation, white matter voxel-based morphometry, or other white
matter imaging techniques such as diffusion tensor imaging, magnetization transfer imaging, or
T2 techniques should help clarify further the manner in which NRXN1 influences frontal white
matter.
In summary, we found that variants within the NRXN1 gene influence brain morphometry with a
susceptibility pattern relevant to both schizophrenia and ASD. This finding is consistent with the
fact that NRXN1 is a vulnerability gene for both disorders. In addition to reporting that the
rs1045881 gene variant is associated with frontal white matter volume and sensorimotor
performance, we provide a putative mechanistic explanation for its effects in the brain. Taken
together, our findings provide evidence that genetic variation in NRXN1, a risk gene for
schizophrenia and ASD, may confer neural and cognitive susceptibility common to both
disorders.
3.5 Materials and Methods
3.5.1 Participants
Fifty-three healthy volunteers (15 women, 38 men) (Table 3-1) met the following eligibility
criteria: age between 18 and 59; right handedness; absence of any history of a mental disorder,
current substance abuse or a history of substance dependence, positive urine toxicology, history
of head trauma with loss of consciousness, seizure, or another neurological disorder; no first
57
degree relative with a history of psychotic mental disorder. All participants were assessed with
the Edinburgh handedness inventory (Oldfield 1971) for handedness, Wechsler Test for Adult
Reading (WTAR) for IQ, and Hollingshead index for socio-economic status (Hollingshead
1975). They were interviewed by a psychiatrist, and completed the Structured Clinical Interview
for DSM-IV Disorders (First MB 1995) . They also completed a urine toxicology screen. The
study was approved by the Research Ethics Board of the Centre for Addiction and Mental Health
(Toronto, Canada) and all participants provided informed, written consent.
3.5.2 Neuroimaging
High resolution magnetic resonance images were acquired as part of a multi-modal imaging
protocol using an eight-channel head coil on a 1.5 Tesla GE Echospeed system (General Electric
Medical Systems, Milwaukee, WI), which permits maximum gradient amplitudes of 40 mT/m.
Axial inversion recovery prepared spoiled gradient recall images were acquired: echo time (TE)
= 5.3, repetition time (TR) = 12.3, time to inversion (TI) = 300, flip angle = 20, number of
excitations (NEX) = 1 (124 contiguous images, 1.5 mm thickness).
3.5.3 Image Processing
Each subject’s T1 image was submitted to the CIVET pipeline (version 1.1.7)
(http://wiki.bic.mni.mcgill.ca/index.php/CIVET) developed at the Montreal Neurologic Institute
(Ad-Dab'bagh, Einarson et al. 2006). The processing steps included registration to the symmetric
ICBM 152 template (Mazziotta, Toga et al. 2001) with a 12-parameter linear
transformation(Collins 1994), correction for inhomogeneity artifact (Sled, Zijdenbos et al. 1998),
skull stripping(Smith, Zhang et al. 2002), tissue classification into white and grey matter,
58
cerebrospinal fluid and background (Zijdenbos, Forghani et al. 2002, Tohka, Zijdenbos et al.
2004) and neuroanatomical segmentation using ANIMAL (Collins, Holmes et al. 1995). Total
volumes for each cortical lobe and subcortical structures were estimated for each individuals by
non-linearly warping each T1 image towards a segmented atlas (Chakravarty, Sadikot et al.
2008). Volume (mL) was extracted from each of these regions using the RMINC package
(version 0.4) for reading and analyzing MINC2 output files. Total gray matter, white matter, and
CSF volumes were calculated, along with lobar cortical gray and white matter volumes (i.e., left
and right frontal, temporal, parietal, occipital), along with volumes of subcortical structures
related to the fronto-striato-thalamic loop implicated in both schizophrenia and ASD including
left and right caudate, putamen, globus pallidus, and thalamus.
3.5.4 Genetics
Genomic data was extracted from ethylenediametetraaecidic acid (EDTA) anticoagulated venous
blood according to standard procedures. Eleven SNPs were genotyped on an Applied Biosystems
ABI 7500 Real-Time PCR system, using Taqman 5’ nuclease assay. Genotyping accuracy was
assessed by running 10% of the sample in duplicate. Eleven SNPs were selected across the
NRXN1 gene (NC_000002.11). Each marker is located in reported regions within which multiple
rare deletions associated with ASD and schizophrenia (Figure 3-1, Table 3-S5).
The program Haploview 4.2 (Barrett, Fry et al. 2005) was used to determine pair-wise LD
between all SNPs with blocks determined by the Gabriel et al. method (Gabriel, Schaffner et al.
2002). Haploview 4.2 was also used to determine whether SNPs were in Hardy Weinberg
equilibrium.
59
3.5.5 Cognitive Assessment
Fifty-two of the study participants completed cognitive testing that included the finger-tapping
test (Reitan and Wolfson 1985, Reitan and Wolfson 1993, Lezak 1995). Although cognitive
deficits in ASD are not as well-characterized as those in schizophrenia, sensorimotor function is
disrupted in both disorders (Flashman, Flaum et al. 1996, Honey, Pomarol-Clotet et al. 2005,
Goldman, Wang et al. 2009, Mostofsky, Powell et al. 2009). Therefore, we used the finger-
tapping test to assess sensorimotor function.
3.5.6 Statistical Analysis
Statistical analysis was performed using SPSS for Windows 15.0. To test for effects of NRXN1
genotype on brain morphometry, three separate repeated measures ANCOVA (for cortical lobar
gray matter, cortical lobar white matter, and subcortical structures) were performed with
genotype as the between group factor, brain region volume as the within group factor, and age
and total brain volume (TBV) as covariates. To ensure adequate power, only markers with a
minor allele frequency (MAF) greater than 15% were tested. We used a Bonferroni correction
based on multiple comparisons of 7 SNPs to determine significance (alpha = 0.0071). Where the
repeated measures ANCOVA revealed a significant volume by genotype interaction, follow-up
ANCOVAs were performed and Bonferroni correction applied. When significant effect of a
genotype on brain volume was found, UNPHASED 3.1 was then used to examine allelic
association with brain phenotypes. Haplotype quantitative analysis of frontal lobe white matter
volume and the rs1045881 and rs858932 NRXN1 variants were calculated using haplotype score
(Methods S1). Finally, for those genotypes that significantly predicted brain measures, repeated
measures ANCOVA for sensorimotor function was performed (dominant and nondominant
60
finger-tapping scores as within group measures) with age as covariate. For any gene variant that
predicted both brain measures and cognitive performance, the relationship between that brain
measure and cognitive performance was examined using a linear regression model, accounting
for age effects.
3.5.7 In Silico Analysis
In order to enhance the understanding of the biological meaningfulness of the genetic
associations, we used in silico methods to predict potential function of the SNPs investigated in
this study. Depending on their location, SNPs were assessed for alteration in transcription factor
binding using MatInspector (Genomatix; promoter and intron 1). Presence of splicing enhancers,
repressors or intronic regulatory elements (intronic and exonic, synonymous and
nonsynonymous SNPs) were determined using F-SNP (http://compbio.cs/queensu.ca/F-SNP) and
Human Splicing Finder (http://www.umd.be/HSF). 3’UTR SNPs were also assessed for
alteration in microRNA binding sites (http://www.targetscan.org/).
3.6 Acknowledgements
The authors would like to thank Dielle Miranda for her help with this study. This work was
supported by the Canadian Institutes of Health Research Clinician Scientist Award (ANV);
APA/APIRE Astra-Zeneca Young Minds in Psychiatry Award (ANV), NARSAD (ANV, AKT,
TKR) and the Centre for Addiction and Mental Health (AKT). Dr. A. Voineskos and Mr. T. Lett
had full access to all of the data in the study and take responsibility for the integrity of the data
and the accuracy of the data analysis. The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
61
Table 3-1. Demographic Characteristics.
Mean± St. Dev. Range
Age 39.0 ± 13.1 19-59
Education (years) 15.6 ± 2.0 12-20
IQ (WTAR) 118.2 ±7.7 92-127
Socioeconomic Statusa 50.0 ± 9.8 27-66
WTAR, Wechsler Test of Adult Reading. aComposed of four factors are education, occupation,
sex, and marital status.
62
Figure 3-1. Reported Deletions in the Neurexin-1α gene. Figure contains the location of gene,
markers, and reported deletion in: developmental disorders (green; Ching et al.(Ching, Shen et
al. 2010)), schizophrenia (red; Rujescu et al. (Rujescu, Ingason et al. 2009), Vrijenhoek et al.
(Vrijenhoek, Buizer-Voskamp et al. 2008), Magri et al. (Magri, Sacchetti et al. 2010), Ikeda et al.
(Ikeda, Aleksic et al. 2010), Need et al.(Need, Ge et al. 2009)), and autism spectrum disorders
(blue; Pinto et al. (Pinto, Pagnamenta et al. 2010), Glessner et al. (Glessner, Wang et al. 2009).
The Autism Chromosome Rearrangement Database (Marshall, Noor et al. 2008)). Figure adapted
from the UCSC genome browser (GRCh37/hg19 assembly) (Kent, Sugnet et al. 2002).
63
Figure 3-2. The effect of rs1045881 on combined hemispheric volume of brain regions with
total brain volume (TBV) and age as covariates. Brain regions: (A) Frontal Lobe, (B)
Temporal Lobe, (C) Occipital Lobe, and (D) Parietal Lobe. Frontal lobe white matter volume
was significantly greater in T allele carriers (T/T +T/C) (ANCOVA F1,52 = 8.197, p = 0.006),
while other regions are non-significant after correcting for multiple comparisons. Covariates
appearing in the model are evaluated at the following values: TBV = 1364768.17, Age = 39.04,
(*) denotes significance of P<0.0125. Error bars represent +/- standard error of the marginal
means and percentages reflect the percent change in each brain region.
64
Figure 3-3. The effect of rs858932 on right and left thalamic volume with TBV and age as
covariates. There are approximately 10% and 9% percent differences between the G/G to G/C
and G/G and C/C genotypes for both thalamic hemispheres, respectively. Covariates appearing in
the model are evaluated at the following values: TBV = 1364768.17, Age = 39.04, (*) denotes
significance of P<0.0063. Error bars represent +/- standard error of the marginal means.
65
Methods 3-S1. Haplotype Analysis.
Haplotype quantitative analysis of frontal lobe white matter volume and the rs1045881 and
rs858932 NRXN1 variants were calculated using haplotype score algorithm in haplostats in the R
programming language (http://mayoresearch.mayo.edu/mayo/research/schaid_lab/software.cfm).
Schaid et al.(Schaid, Rowland et al. 2002) developed a score statistic that can test the
associations between haplotypes and a wide variety of traits, including binary, ordinal,
quantitative, and Poisson. This method also allows for adjustment for non-genetic covariates. In
our analysis, we used haplo.score to compute the global score statistic (that tests the significance
of association of all haplotypes) and haplotype specific statistic (that compares each haplotype
with selected common haplotypes). Our dependent variable was frontal lobe white matter
volume. Our covariates were the TBV and age of the subjects. All haplotypes with a frequency
less than 5% were dropped from the score test.
66
Figure 3-S1. The effect of rs858932 on combined hemispheric volume of brain regions with
TBV and age as covariates. Brain regions: (A) Frontal Lobe, (B) Temporal Lobe, (C) Occipital
Lobe, and (D) Parietal Lobe. Frontal and parietal lobe white matter volumes were significantly
greater in G allele carriers (T/T +T/C) (ANCOVA F2,52 = 7.074, p = 0.002; ANCOVA F2,52 =
5.724, p = 0.006). Other region are non-significant after correcting for multiple comparisons.
Covariates appearing in the model are evaluated at the following values: TBV = 1364768.17,
Age = 39.04, (*) denotes significance of P<0.0125. Error bars represent +/- standard error of the
marginal means and percentages reflect the percent change in each brain region.
67
Table 3-S1. Locations and Minor Allele Frequency in Toronto and Hapmap (CEU)
Samples.
Marker Positiona Alleles Strand Location MAF MAF (CEU)b
rs1995584 51263149 A/G + 5’ Pro A: 0.446 A: 0.446
rs10490162 51247657 A/G - Intron G: 0.054 G: 0.102
rs12623467 51225089 C/T + Intron T: 0.027 T: 0.050
rs2193225 51079482 A/G - Intron A: 0.446 A: 0.496
rs858932 50930063 C/G - Intron G: 0.420 G: 0.442
rs11125321 50852016 A/G + Intron G: 0.375 G: 0.403
rs2351765 50793780 A/C + Intron A: 0.312 A: 0.274
rs6721498 50713011 A/G + Intron G: 0.464 G: 0.492
rs10490227 50659515 A/G - Intron A: 0.143 A: 0.093
rs10208208 50593914 G/T + Intron T: 0.000 T: 0.175
rs1045881 50148972 A/G - 3’UTR T: 0.205 T: 0.129
MAF = Minor Allele Frequency; aAccording to dbSNP build 131; bHapmap CEU Sample
68
Table 3-S2. T-test between rs1045881 T-Carriers Vs C/C and Demographics.
Genotype N Mean Std. Dev. t df p-value
Age TT + TC 20 38.55 12.959 -0.209 51 0.836
CC 33 39.33 13.411
Education TT + TC 18 15.89 1.568 0.893 48 0.377
CC 32 15.38 2.136
WTAR TT + TC 19 118.53 6.230 0.370 50 0.713
CC 33 117.70 8.527
MMSE TT + TC 20 29.60 0.821 1.202 50 0.235
CC 32 29.28 0.991
CIRSG TT + TC 18 1.28 1.841 -0.232 48 0.817
CC 32 1.41 1.898
SE Status a TT + TC 17 51.12 8.667 0.736 43 0.466
CC 28 48.89 10.457
WTAR, Wechsler Test of Adult Reading; MMSE, Mini Mental State Examination; CIRS-G,
Cumulative Illness Rating Scale - Geriatrics; SE, Socioeconomic status. aComposed of four
factors: education, occupation, sex, and marital status.
69
Table 3-S3. Chi-squared Tests of Region by Genotype or Allele Interactions of rs1045881
and rs858932. Analysis was performed by Unphased 3.1 with total brain volume and age as
confounding factors.
rs1045881 rs858932
Allelic
(C vs T)a
Genotypic
(T-Carriers vs C/C)b
Allelic
(G vs C)c
Genotypic
(G/G vs G/C vs C/C)d
Region χ2 p-value χ2 p-value χ2 p-value χ2 p-value
Frontal Lobe 7.1840 0.0074 8.4151 0.0037 4.1213 0.0423 10.0033 0.0067
Temporal Lobe 1.8624 0.1723 2.4474 0.1177 2.4568 0.1170 3.9295 0.1402
Occipital Lobe 0.4720 0.4921 0.1402 0.5639 0.8973 0.3435 1.8958 0.3876
Parietal Lobe 2.8906 0.0891 4.2641 0.0389 4.0856 0.0432 8.5304 0.0140
Bold values are significant after Bonferroni correction alpha for multiple comparisons
(α=0.0125). a(T:C=21:85); b(T-carriers:C/C = 20:33); c(G:C = 45:61); d(G/G:G/C:C/C=6:33:14).
70
Table 3-S4. Haplotype Association between Frontal Lobe White Matter and rs1045881
(T/C) and rs858932 (G/C).
a Age and Total Brain Volume are covariates.
Haplotype Test of Overall Association with Frontal Lobe White Mattera
Global Score Statistic df p-value
21.92665 3 7 x 10-5
Estimates of Haplotype Main Effectsa
Haplotype Haplotype Score Freq p-value
T/G 2.12585 0.09992 0.03352
T/C 2.49622 0.09819 0.01255
C/G -3.9051 0.32461 9 x 10-5
C/C 1.10893 0.47728 0.26746
71
Table 3-S5. Reported deletions within NRXN1 in Developmental Disorders, Schizophrenia
and Autism Spectrum Disorders.
Start Stop Diagnosis
Ching et al. (2010) (Ching, Shen et al. 2010)
46938685 52015885 Moderate mental retardation
50128256 54050713 Global developmental delays, suspected autism
50897002 51212385 Gross motor delay, hypotonia
50936914 51167934 PDD-NOS, hypotonia
50920082 51059469 VACTERL
51059410 51316396 PDD-NOS, motor coordination delays
51090504 51212385 Autism, moderate mental retardation
50522892 50827767 Mild mental retardation
50689280 50853329 Language delay, prenatal substance exposure
50714297 50853329 PDD-NOS
50735499 50811018 Hypotonia, muscle weakness, large birth weight
50735499 50801233 Poor weight gain, mild craniofacial dysmorphism
Rujescu et al. (2009) (Rujescu, Ingason et al. 2009)
50856110 50900862 Schizophrenia
50890216 51116653 Schizophrenia, social contact problems in childhood
51147600 51225851 Schizophrenia
50071499 50208992 Schizophrenia, chronic, positive symptoms, low IQ (82),
low educational level
50822312 50948557 Schizophrenia, chronic, negative symptoms, episode
of aggression
51101161 51344213 Schizophrenia, alcoholism
72
50735657 50800548 Schizophrenia
50786446 50900862 Schizophrenia
51002576 51250922 Schizophrenia, episodic with partial remission and negative
symptoms
51024962 51251873 Schizophrenia
51211406 51299436 Schizophrenia, myoclonic seizures in the right shoulder
50711199 50756435 Schizophrenia
50836690 50936258 Schizophrenia
50850456 51225851 Schizophrenia, mental retardation (mild), Tardive dyskinesia
Vrijenhoek et al. (2008) (Vrijenhoek, Buizer-Voskamp et al. 2008)
51063670 51300517 Schizophrenia
Magri et al. (2010) (Magri, Sacchetti et al. 2010)
50952424 51280162 Schizophrenia
Ikeda et al. (2010) (Ikeda, Aleksic et al. 2010)
50743926 50911879 Schizophrenia
Need et al. (2009) (Need, Ge et al. 2009)
49999148 51113178 Schizophrenia
Pinto et al. (2010) (Pinto, Pagnamenta et al. 2010)
50912249 50955087 Autism Spectrum Disorder
50493827 50677835 Autism Spectrum Disorder
50539877 50730546 Autism Spectrum Disorder
50990306 51222043 Autism Spectrum Disorder
51002576 51157742 Autism Spectrum Disorder
50735657 50804497 Autism Spectrum Disorder
50822312 50886363 Autism Spectrum Disorder
73
50822312 50900862 Autism Spectrum Disorder
Glessner et al. (2009) (Glessner, Wang et al. 2009)
51120644 51147600 Autism Spectrum Disorder
The Autism Chromosome Rearrangement Database (Marshall, Noor et al. 2008)
50371853 50727153 Autism Spectrum Disorder
50722055 50801053 Autism Spectrum Disorder
50273117 50443987 Autism Spectrum Disorder
51086655 59969199 Autism Spectrum Disorder
51759493 52031003 Autism Spectrum Disorder
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Chapter 4
4 The Genome-Wide Supported MicroRNA-137 Variant Predicts Phenotypic Heterogeneity Within Schizophrenia
Contents of this chapter have been published as:
Lett TA et al. The genome-wide supported microRNA-137 variant predicts phenotypic
heterogeneity within schizophrenia. Mol Psychiatry. 2013 Apr;18(4):443-50
A link to the published paper can be found at:
http://www.nature.com.myaccess.library.utoronto.ca/mp/journal/v18/n4/full/mp201317a.html
Reprint by permission from Nature Publishing Group
75
4.1 Abstract
We examined the influence of the genome-wide significant schizophrenia risk variant rs1625579
near the microRNA-137 (MIR137) gene on well-established sources of phenotypic variability in
schizophrenia: age at onset of psychosis, and brain structure. We found that the MIR137 risk
genotype strongly predicts an earlier age-at-onset of psychosis across four independently
collected samples of patients with schizophrenia (n=510; F1,506=17.7, p = 3.1x10-5). In an
imaging-genetics subsample that included additional matched controls (n=213), patients with
schizophrenia who had the MIR137 risk genotype had reduced white matter integrity
(F3,209=13.6, p=3.88x10-8) throughout the brain as well as smaller hippocampi, and larger lateral
ventricles; the brain structure of patients who were carriers of the protective allele was no
different from healthy control subjects on these neuroimaging measures. Our findings suggest
that MIR137 substantially influences variation in phenotypes that are thought to play an
important role in clinical outcome and treatment response. Finally, the possible consequences of
genetic risk factors may be distinct in patients with schizophrenia compared to healthy controls.
Keywords: schizophrenia; age-at-onset; imaging; genetics; MIR137; heterogeneity
76
4.2 Introduction
There is notable heterogeneity in the phenotypic presentation of schizophrenia including, but not
limited to, the onset of illness, severity of positive and negative symptoms, neurological soft
signs and cognition, course of illness, response to treatment, and functional and structural brain
abnormalities(Carpenter Jr and Kirkpatrick 1988, DeLisi 1992, Shenton, Dickey et al. 2001).
This phenotypic heterogeneity has been a central challenge for schizophrenia research and other
neuropsychiatric disorders.
MicroRNAs (miRNAs) may be critically important genetic mechanisms contributing to
phenotypic heterogeneity.. Individual, small non-coding miRNAs regulate hundreds of genes in
tandem, and may fine-tune the activity of entire biological pathways (Chen and Rajewsky 2007).
Therefore, miRNAs may play an especially important role in contributing to phenotypic
heterogeneity via regulation of, or interaction with, risk gene pathways in neuropsychiatric
disorders (Kwon, Wang et al. 2011, Kim, Parker et al. 2012, Miller, Zeier et al. 2012). MiRNAs
function as crucial regulators of gene expression, and have been identified as potent disease
modifiers(Karres, Hilgers et al. 2007, Kim, Inoue et al. 2007, Lee, Samaco et al. 2008, Williams,
Valdez et al. 2009). MiRNAs function as crucial regulators of gene expression, and have been
identified as potent disease modifiers. For instance, knock out of miR-8 results in elevated
neuronal apoptosis and behavioral defects(Karres, Hilgers et al. 2007); a mouse model of
amyotrophic lateral sclerosis, miR-206 slows the progression of motor neuron
degeneration(Williams, Valdez et al. 2009); inhibition of microRNAs increase the toxicity of
CAG repeats in a spinocerebellar ataxia type 1 animal model(Lee, Samaco et al. 2008), and the
microRNA-133b regulates maturation and function of midbrain dopaminergic neurons relevant
to Parksinon’s disease(Kim, Inoue et al. 2007).
77
MicroRNA-137 (miR-137) serves as a regulator of adult neural stem cell maturation and
migration(Smrt, Szulwach et al. 2010, Szulwach, Li et al. 2010, Sun, Ye et al. 2011) in the
subventricular zones in proximity to the lateral ventricles and the subgranular zone of the
hippocampus. MiR-137 is also a regulator of gliogenesis(Silber, Lim et al. 2008). A single
nucleotide polymorphism, rs1625579, near the MIR137 gene (microRNA 137; 1p21.3) recently
achieved genome-wide significance for association with schizophrenia in a study of
approximately 50,000 subjects (p=1.6 x 10-11)(Ripke, Sanders et al. 2011). This polymorphism is
in the intronic region of the MIR137HG gene, MIR137 host gene (non-protein coding), that
includes MIR137. MIR137 has also been functionally shown to specifically regulate genes with
replicated genome-wide significant evidence for a role in schizophrenia, most notably CACNA1C
(calcium channel, voltage-dependent, L type, alpha 1C subunit) and TCF4 (transcription factor
4) (Kwon, Wang et al. 2011). The known role of microRNAs as potent disease modifiers raises
the question of whether genetic variation in the MIR137 gene might play a critical role in
phenotypic expression of schizophrenia, a psychiatric disorder known to show extensive
phenotypic heterogeneity.
In schizophrenia, age-at-onset of psychosis(DeLisi 1992) and brain structure(Shenton, Dickey et
al. 2001) are two well-established phenotypic measures, which are heritable,
heterogeneous(Brans, van Haren et al. 2008, Hare, Glahn et al. 2010), and related to disease
severity and outcome (Suvisaari, Haukka et al. 1998, Lieberman, Chakos et al. 2001, Lieberman,
Tollefson et al. 2005, Mitelman, Canfield et al. 2010). Brain structure including lateral ventricle
volume increases and hippocampal volume reductions in schizophrenia are well-established
sources of phenotypic heterogeneity and lateral ventricle volume, in particular, may predict
disease outcome (Lieberman, Chakos et al. 2001, Ho, Andreasen et al. 2003). A meta-analysis of
studies comparing schizophrenia patients and healthy controls showed reduced hippocampal
78
volumes and increased ventricular volumes in patients relative to controls(Steen, Mull et al.
2006). Similarly, cortical thickness reductions(Ehrlich, Brauns et al. 2011) and white matter
integrity changes(Voineskos, Foussias et al. 2013) can be also be heterogeneous among patients
with schizophrenia. Across these studies, the range of structural changes in schizophrenia
patients overlapped with controls.
The identification of the genetic sources of phenotypic heterogeneity, such as the effects of a
genetic risk variant on phenotypes such as age-at-onset, or brain structure, may lead to early
identification of disease trajectory. Such identification, before disease progression, could then
serve as a platform to test earlier interventions, particularly within the subgroup at-risk for poorer
outcome. Given the recently established role of MIR137 as a central player in coordinating the
timing and expression of schizophrenia risk genes (Kwon, Wang et al. 2011), we hypothesized
that MIR137 may be an important determinant of age-at-onset of psychosis and brain structure in
schizophrenia.
4.3 Subjects and Methods
4.3.1 Participants for Genetic Investigation of Age-at-onset Phenotypes
For the age-at-onset analysis, four independently collected samples were investigated. In total,
510 patients (346 Male, 154 Female) diagnosed with schizophrenia or schizoaffective disorder
according to DSM-III or DSM-IV criteria were included. Patients with a history of substance
abuse or dependence and those with a head injury with loss of consciousness >30 minutes or
neurological disorders were excluded from the study. Subjects were recruited from three clinical
sites in North America: the Centre for Addiction and Mental Health in Toronto, Canada (JLK,
Toronto schizophrenia sample: n=278; ANV, Toronto imaging-genetics sample: n=96). Case
Western Reserve University in Cleveland, Ohio (HYM, n=85); and Hillside Hospital in Glen
79
Oaks, New York (JAL, n=51). No overlapping subjects were present between the two Toronto
samples. Age-at-onset was ascertained in the same manner in all samples, recorded as the year of
the first psychotic episode. Ethnicity was assessed by self report.
4.3.2 Participants for Genetic Investigation of Neuroimaging Phenotypes
Participants from the Toronto imaging-genetics sample were recruited at the Centre for
Addiction and Mental Health (CAMH) in Toronto, Canada, via referrals, study registries, and
advertisements. All clinical assessments occurred at CAMH while DT-MRI scans were
performed at a nearby general hospital. Ninety-two of the 96 patients with a diagnosis of
schizophrenia or schizoaffective disorder and 121 healthy control subjects in this sample
completed all imaging, and genetics, protocols. All participants were administered the Structured
Clinical Interview for DSM-IV Disorders (First MB 1995) to determine diagnosis, and were
interviewed by a psychiatrist to ensure diagnostic accuracy. IQ was measured using the Wechsler
Test for Adult Reading (WTAR)(Wechsler 2001) and all participants were screened with the
Mini Mental Status Exam (MMSE) for dementia(Folstein, Folstein et al. 1975) and a urine
toxicology screen. Comorbid physical illness burden was measured by administration of the
Clinical Information Rating Scale for Geriatrics (CIRS-G)(Miller, Paradis et al. 1992).
Medication histories were initially recorded via self-report, and then verified either by the
patient’s treating psychiatrist or chart review. All subjects received urine toxicology screens and
anyone with current substance abuse or any history of substance dependence was excluded.
Individuals with previous head trauma with loss of consciousness, or neurological disorders were
also excluded. A history of a primary psychotic disorder in first-degree relatives was an
additional exclusion criterion for controls.
80
4.3.3 Image Acquisition
High-resolution axial inversion recovery-prepared spoiled gradient recall MR images were
acquired using a 1.5-T GE Echospeed system (General Electric Medical Systems, Milwaukee,
WI; echo time (TE): 5.3, repetition time (TR): 12.3, time to inversion: 300, flip angle 20,
number of excitations=1; 124 contiguous images, 1.5 mm thickness). For DTI acquisition, a
single-shot spin-echo planar sequence was used with diffusion gradients applied in 23 non-
collinear directions, b=1000 s/mm2, and two b=0 images. Fifty-seven slices were acquired for
whole-brain coverage oblique to the axial plane (2.6 mm isotropic voxels; field of view was
330 mm, 128 × 128 mm2 acquisition matrix; TE=85.5 ms, TR=15 000 ms; the sequence was
repeated three times to improve signal-to-noise ratio).
4.3.4 Cortical Volumes Processing
Automated measures of total cerebral and lateral ventricles volumes were derived via the CIVET
pipeline (version 1.1.10 developed at the Montreal Neurologic Institute)(Lerch, Pruessner et al.
2005, Lerch, Pruessner et al. 2008). Hippocampal volumes were processed using the FMRIB
Integrated Registration and Segmentation Tool (FIRST v1.2) automated subcortical
segmentation pipeline(Patenaude, Smith et al. 2011). Brain tissue volume, normalized for subject
head size, was estimated with SIENAX(Smith, Zhang et al. 2002), also part of the FSL toolkit.
4.3.5 Cortical Thickness Mapping
All MRIs were submitted to the CIVET pipeline. T1 images were registered to the ICBM152
nonlinear template with a 9-parameter linear transformation, intensity inhomogeneity corrected
(Sled, Zijdenbos et al. 1998) and tissue classified (for grey matter, white matter, and cerebral
spinal fluid)(Zijdenbos, Forghani et al. 2002, Tohka, Zijdenbos et al. 2004). Deformable models
81
were used to create white and gray matter surfaces for each hemisphere separately, resulting in 4
surfaces of 40,962 vertices each(MacDonald, Kabani et al. 2000, Kim, Singh et al. 2005). From
these surfaces, the t-link metric was derived for determining the distance between the white and
gray surfaces(Lerch and Evans 2005). The thickness data were blurred using a 20-mm surface-
based diffusion kernel in preparation for statistical analyses. Unnormalized, native-space
thickness values were used in all analyses owing to the poor correlation between cortical
thickness and brain volume (Ad-Dab'bagh, Singh et al. 2005).
4.3.6 Tract-Based Spatial Statistics (TBSS)
All diffusion tensor imaging (DTI) analysis was done using tools implemented in the FSL toolkit
v.4.1.10 (Smith, Jenkinson et al. 2004). All three repetitions were merged for each subject’s 4D
DTI volume. The resulting images were corrected for motion and eddy current distortion, and
then averaged. After brain extraction and skull stripping using BET (Smith, Zhang et al. 2002),
fractional anisotropy (FA) images were created by fitting a tensor model at each voxel using
DTIFit. FA quantifies directionality of water diffusion on a scale from zero (random diffusion) to
one (diffusion in one direction). Voxel-wise analysis of the FA data was carried out using Tract-
Based Spatial Statistics (TBSS, v1.2) (Smith, Jenkinson et al. 2006). TBSS projects all subjects'
fractional anisotropy (FA) data onto a mean FA tract skeleton, before applying voxelwise cross-
subject statistics. FA images then underwent nonlinear registration to the FMRIB58_FA target
image. Next, the mean FA image was iteratively generated from scans of healthy controls and
patients with schizophrenia separately. Each group was then aligned to MNI 152 standard space
using an affine transformation. An average white matter skeleton was then generated from the
mean of all subjects’ transformed FA images at a threshold of 0.2. For group comparisons, each
82
subject’s FA data was projected onto the white matter skeleton and voxel-wise statistics were
calculated using randomise (v2.1) with 10,000 permutations.
4.3.7 Whole-Brain TBSS Analysis
To compare genotype-by-diagnosis groups in all patients with schizophrenia and matched
controls, we extracted global whole-brain skeleton FA values. Univariate analysis of covariance
(ANCOVAs) were applied in the Statistical Program for the Social Sciences v. 15.0 software
(SPSS; Chicago, Illinois), comparing diagnosis-genotype group, with age as a covariate.
4.3.8 Genetics
Genotyping of the rs1625579 polymorphism was performed using a standard ABI (Applied
Biosystems Inc.) 5’ nuclease Taqman assay-on-demand protocol in a total volume of 10 µL.
Postamplification products were analyzed on the ABI 7500 Sequence Detection System (ABI,
Foster City, California, USA) and genotype calls were performed manually. Results were
verified independently by laboratory personnel blind to demographic and phenotypic
information.(Lahiri and Nurnberger 1991). Genotyping accuracy was assessed by repeating 10%
of the sample.
4.3.9 Statistical Analysis
Given the low frequency of GG genotype, these cases were collapsed with GT genotypes and
referred to as ‘G allele carriers’ for all statistical tests. Analyses were performed examining the
relationship between MIR137 genotype group (T allele homozygotes or G allele carriers) for age-
at-onset and brain morphology.
The influence of MIR137 as a predictor of age-at-onset was studied by ANCOVA with sex and
sample site as covariates using SPSS (Version 15). In complementary analyses, we further
83
performed a survival analysis (Cox proportional hazards model) to estimate the effect of
genotype on age-at-onset while controlling for sex. Ethnicity (Caucasian versus non-Caucasian),
sex (male versus female), and MIR137 genotype were compared among the four samples using
Chi-squared tests. To further exclude the potential confound of population stratification, we then
examined only subjects identified as Caucasian (3/4 grandparents from Caucasian descent) in a
separate analysis. Adherence to Hardy-Weinberg equilibrium was determined using Haploview
4.2(Barrett, Fry et al. 2005).
To assess the effect of MIR137 on brain morphology, we examined subjects from the Toronto
imaging-genetics sample: schizophrenia subjects that underwent structural MRI protocols (n=92)
and a healthy control sample (n=121). Repeated measure ANCOVAs were performed with
MIR137 genotype and diagnosis as the between-group factors, region as the within-group factors,
with age and total brain volume as a covariates. Using this model, we evaluated total brain
volume, ventricular volumes and hippocampal volumes. Using this model, we evaluated
between-subject interaction (genotype by diagnosis) and within-subject interaction (region by
genotype by diagnosis). For cortical thickness analyses, diagnosis and genotype were used as
between group factors, with age and sex as covariates. Finally, three separate TBSS analyses
were preformed. First, we examined a diagnosis by MIR137 genotype interaction, and main
effects of diagnosis and genotype on the FA skeleton. In follow-up analyses, separately in the
schizophrenia and control samples, we used TBSS to examine voxel-wise associations between
white matter integrity and MIR137 genotype with age as a covariate. An FDR correction of
q=0.05 was applied for cortical thickness and a family-wise error rate of 5% for white matter FA
comparisons. For lateral ventricle and hippocampal volumes, analyses were deemed significant
after Bonferroni correction for multiple comparisons. Multiple comparisons corrections were
performed within each analytic imaging modality. Post hoc partial correlations were performed
84
between hippocampal volumes, lateral ventricular volumes, and whole-brain white matter FA
after controlling for age and total brain volume.
4.3.10 Mediation Analysis
In post hoc analysis, a mediation model was used to evaluate if age-at-onset mediates the effect
of MIR137 rs1625579 genotype on white matter integrity. Others have shown that earlier age-at-
onset predicts reduced white matter integrity (Kyriakopoulos, Perez-Iglesias et al. 2009). We
used the multiple regression approach describe by Baron and Kenny (Baron and Kenny 1986).
There are four steps to establishing mediation (Figure 4-S4). This analysis is accomplished with
three regression equations: the dependent variable (white matter integrity) is regressed on the
independent variable (e.g. MIR137 genotype); the mediator (e.g. age-at-onset) is regressed on the
independent variable; and the dependent variable is regressed on both the mediator and
independent variables. Perfect mediation is defined as the case where the independent variable is
found to have no effect in the third equation (i.e., regression coefficient = 0); partial mediation is
the case where there is a significant reduction in the effect of the independent variable on the
dependent variable in the third equation. The Sobel test was used to assess the indirect effect of
the independent variable on the dependent variable via the mediator (Baron and Kenny 1986).
This test gives a Z score reflecting effect size and an associated p value.
85
4.4 Results
4.4.1 Genetics
For the schizophrenia subjects, the frequencies for rs1622579 genotype were 2.7% GG (n=14),
29.8% GT (n=152), and 67.5% TT (n=344). For the control subjects, the frequencies for
rs1622579 genotype were 4.1% GG (n=5), 33.1% GT (n=40), and 62.8% TT (n=76). There was
no significant deviation from Hardy-Weinberg equilibrium in either controls or patients with
schizophrenia (p>0.05). The minor allele frequency of rs1625579 in our schizophrenia
population (0.18) was virtually the same as the frequency observed in the Ripke et al. GWAS
(0.18) (Ripke, Sanders et al. 2011). In the Caucasian subsample our minor allele frequency was
0.164, which is nearly identical to the Hapmap CEU population (0.165; Hapmap Build #27)
(Gibbs, Belmont et al. 2003).
4.4.2 Age-at-onset
Frequencies and distribution of demographic data for the four age-at-onset samples are shown in
Table 4-S1. We found a significantly earlier mean age-at-onset of psychosis for T risk allele
homozygotes (20.8±5.8 years) compared to protective G allele carriers (23.4±8.5 years) after
covarying for sex and sample site: F1,506=17.7, p=3.1x10-5 (Figure 4-1a). The effect size of
rs1625579 genotype (Cohen’s D=0.38[95% CI: 0.20-0.57]) was greater than the effect size of
sex (Cohen’s D=0.20[0.02-0.39]), which to date has been considered among the most powerful
predictors of age-at-onset in schizophrenia(DeLisi 1992). A similar pattern was observed when
examining age-at-onset over time (using time dependent covariates). Our Cox regression model
showed a significant effects of progression to age-at-onset within schizophrenia patients across
the lifespan when grouped by rs1625579 genotype (β=0.37, p=1.43x10-4; Figure 4-1b) and sex
(β=0.23, p=0.015). A follow-up analysis was performed to exclude potential confounds of ethnic
86
admixture. Caucasian patients (n=379) yielded significant association between age-at-onset and
rs1625579 genotype (F1,375=12.7, p= 4.1x10-4; Cox-regression: p=0.003). It has been suggested
that schizophrenia with early (12 years or less; i.e. childhood-onset schizophrenia) or late
(greater than 45 years; i.e. paraphrenia) age-at-onset may constitute different forms of the
disorder based on severity and course of illness(Jeste, Harris et al. 1995, Hollis 2000). Thus, we
reanalyzed the sample excluding patients in these categories: early age-at-onset (n=15 [11 TT; 4
TG/GG]) and late age-at-onset (n=3 [0 TT; 3 TG/GG]). When considering our sample without
these individuals (n=492), the effect of rs1625579 genotype remained significant (F1,488=12.0,
p=5.7x10-4; Cox-regression: p=8.6x10-4; Figure 4-S1). Finally, in the subsample with brain
imaging data (n=92), we again found that T allele homozygotes had an earlier age-at-onset (22.5
± 6.9 years) compared to G allele carriers (27.8 ± 11.9 years; F1,89 = 7.8, p=0.006).
4.4.3 Neuroimaging
In the Toronto imaging-genetics sample, schizophrenia patients were not different from healthy
controls on age, sex, handedness, total brain volume, mini mental state examination (MMSE),
and CIRS-G, but had fewer years of education (p<0.05), and lower IQ (p<0.05; Table 4-S2).
There were no significant genotype differences on any demographic measure, PANSS score,
duration of illness, abnormal involuntary movement scale, chlorpromazine equivalent, MMSE,
and CIRS-G (p>0.05; Table 4-S2) with the exception of age-at-onset. Repeated measures
ANCOVA of the left and right lateral ventricles with age and total brain volume as covariates
revealed a significant MIR137 genotype by diagnosis interaction (F1,205=4.6, p=0.03). Further,
there was a significant within-group interaction between genotype and diagnosis (F1,205=4.3, p
=0.04). T allele homozygotes in the schizophrenia sample had larger left lateral ventricle
volumes than G allele carriers (F1,90=4.5, p=0.04). Repeated measures ANCOVA of left and right
87
hippocampal volumes with age and total brain volume as covariates revealed a main effect of
genotype (F1,205=4.2, p=0.05) and a significant genotype by diagnosis interaction (F1,205=4.2,
p=0.04). On follow-up within group analyses, repeated measure ANCOVA showed that T allele
homozyogtes in the schizophrenia sample had lower hippocampal volume (F1,87=5.6, p=0.02),
but there was no such effect in the control sample (F1,116=0.004, p=1.0). For both lateral ventricle
and hippocampal volumes, schizophrenia patients who carried the protective G allele were not
significantly different from healthy controls (Puncorrected>0.05; Figure 4-2a-d).
We found no effect of genotype or genotype by diagnosis interaction on cortical thickness at any
vertex using false discovery rate correction of 5%(Genovese, Lazar et al. 2002). Voxel-wise
statistical analysis of white matter integrity showed a prominent main effect of genotype on FA
throughout the brain using a family-wise error rate of 5% (Figure 4-S2). Most striking was the
widespread interaction between MIR137 genotype and diagnosis that was observed throughout
the brain (Figure 4-S3); we therefore proceeded to examine the effect of genotype separately in
the schizophrenia and control groups. In the schizophrenia sample, T allele homozygotes had
substantially lower FA than G allele carriers in an evident whole brain, rather than tract-based
pattern (Figure 4-3). No genotypic effect was found in the control group. Given the evident
effect of genotype across much of the white matter skeleton, we applied a whole brain TBSS
analysis to measure the aggregate of mean FA white matter skeleton for each subject, which
allowed us to treat white matter integrity as a single measurement. There was a global effect of
group (F3,209=13.60, p=3.88x10-8), and post hoc pairwise comparisons revealed that patients with
schizophrenia homozygous for the T risk allele have significantly lower whole-brain white
matter FA than all other genotype by diagnosis groups (Figure 4-3). Protective G allele carriers
in the schizophrenia group were no different in whole-brain white matter FA or in decline of FA
across the lifespan compared to the control group (p=0.72-0.84; Figure 4-3). Post hoc analysis
88
revealed that in patients homozygous for the risk allele, whole-brain white matter FA was
correlated with left and right ventricular volume (r=-0.54, p<0.001; r=-0.47, p<0.001).
4.4.4 Mediation Analysis
The effect of MIR137 genotype on whole-brain white matter FA was not significantly influenced
by controlling for age-at-onset. The main effect of genotype on FA (t=3.9, p=0.002) was similar
to the direct effect (t=3.4, p=0.009). Furthermore, the indirect effect of genotype on white matter
FA via age-at-onset was not significant (Z=0.90, p=0.37; Figure 4-S5) indicating that the effect
of MIR137 on mean whole-brain white matter FA was not significantly mediated by age-at-
onset. Age and age-at-onset alone were both significant predictors of whole-brain mean FA
(tage=-7.0,p=4.7x10-10; tAAO=2.1,p=0.04) and the model predicted 35% of the variance
(F2,92=24.6;p=3.1x10-9;R2=0.35). By adding MIR137 genotype to the model, we were then able
to explain 44% of the variance in FA (F3,92=23.1;p=3.8x10-11;R2=0.44).
4.5 Discussion
Our findings support an important role for the MIR137 rs1625579 variant in determining
phenotypic heterogeneity in schizophrenia via its effects on age-at-onset and brain structure.
Age-at-onset is a known predictor of disease severity in schizophrenia (DeLisi 1992). Similarly,
differences in brain structure in patients with schizophrenia have been related to disease severity
and disparate outcomes (Braff, Freedman et al. 2007) since the early classification based on
ventricular volume (Crow 1980), and correlation with functional outcomes (Ho, Andreasen et al.
2003). More recently, white matter FA has been found to be reduced in patients with poor
outcomes, but to a lesser degree in schizophrenia patients with good outcome (Mitelman,
Newmark et al. 2006). Therefore, our findings of T risk allele homozygotes with an earlier age-
at-onset, larger left lateral ventricle volume, smaller left hippocampal volume, and lower white
89
matter integrity, provide convergent evidence for MIR137 genotype as an important mechanism
of phenotypic variation in schizophrenia.
MiRNAs have emerged as integral regulators of expression of neuronal gene pathways involved
in brain function, plasticity and development (Olde Loohuis, Kos et al. 2012). There is increasing
evidence that miRNAs are implicated in both neurodevelopmental and neurodegenerative
disorders (Beveridge, Gardiner et al. 2009, Miller and Wahlestedt 2010, Geekiyanage and Chan
2011, Geekiyanage, Jicha et al. 2011, Moreau, Bruse et al. 2011, Santarelli, Beveridge et al.
2011). The role of MIR137 as a regulator of adult neural stem cell migration and maturation in
the subventricular zones and hippocampus (Smrt, Szulwach et al. 2010, Szulwach, Li et al. 2010,
Sun, Ye et al. 2011) supports the influence of this miRNA in neurodevelopmental processes
relevant to schizophrenia. Mir-137 interacts with several neurodevelopmental genes including
MIB1, MITF, TLX, LSD1, and EZH2 (Smrt, Szulwach et al. 2010, Szulwach, Li et al. 2010, Sun,
Ye et al. 2011, Willemsen, Valles et al. 2011). MIR137 is also involved in microRNA-
transcription factor regulatory networks within the Notch signalling pathway in neuroglia (Sun,
Gong et al. 2012). Therefore, one pathway through which MIR137 influences phenotypic
heterogeneity in schizophrenia may occur via neurodevelopmental gene networks.
The MIR137 gene has also been functionally shown to specifically regulate genome-wide
significant schizophrenia-associated liability genes such as CACNA1C and TCF4 (Kwon, Wang
et al. 2011). CACNA1C, a gene coding for a voltage-gated calcium channel, can influence
hippocampal function during an episodic memory task (Bigos, Mattay et al. 2010). CACNA1C
may also be relevant to white matter variation since calcium channel abnormalities have been
associated with white matter lesions in the brain (Matute 2010). Disruption of calcium channel
homeostasis can also play an important role in increasing oxidative stress, which is of particular
90
relevance to oligodendrocytes, which are the most susceptible cells to oxidative stress in the
central nervous system (McTigue and Tripathi 2008). TCF4 is of particular relevance to
oligodendrocytes at the neurodevelopmental stages since it promotes the initial stages of
oligodendrocyte differentiation (Emery 2010). In general, miRNAs form a positive feedback
loop during oligodendrocyte differentiation, such that key miRNAs induced early in
differentiation act to inhibit the expression of genes that promote oligodendrocyte precursor cell
maintenance, thus further inhibiting proliferation and promoting differentiation. These regulatory
interactions with other schizophrenia risk genes may provide a mechanistic explanation for our
findings of association of the MIR137 variant with hippocampal volume and white matter
integrity.
Our findings provide the first compelling evidence that MIR137 plays a sizeable role in
determining heterogeneity among schizophrenia patients in age-at-onset and brain structure.
Recent investigations have demonstrated association of this variant with symptom and cognitive
measures in schizophrenia. One recent publication found the MIR137 rs1625579 variant
associated with cognitive dysfunction in patients with high negative symptom burden(Green,
Cairns et al. 2012); however, the authors found protective G allele identified in the GWAS study
was associated with cognitive impairment (Ripke, Sanders et al. 2011). The risk variant has also
been recently associated with positive symptoms in a mixed sample of bipolar disorder I,
schizoaffective disorder, and schizophrenia patients (Cummings, Donohoe et al. 2012).
However, in both studies the associations were relatively weak, which is often the case in
explorations of genetic association with symptom or cognitive measures (Meyer-Lindenberg and
Weinberger 2006).
91
Age at onset has not been associated with gray matter deficits in several cross-sectional studies
(Lim, Harris et al. 1996, Marsh, Harris et al. 1997, Zipursky, Lambe et al. 1998). Larger
ventricular size has been associated with earlier age-at-onset (DeLisi, Hoff et al. 1991), and
differential effects have been observed in white matter FA between adolescent and adult onset
groups (Kyriakopoulos, Perez-Iglesias et al. 2009). In general, earlier onset patients (age-at-onset
before 18 years) are shown to have reduced hippocampal volume, increased lateral ventricle
volume, and decreased white matter integrity compared to matched controls (Giedd, Jeffries et
al. 1999, Kumra, Ashtari et al. 2004). However, it remains unclear whether these brain structure
abnormalities are related to the higher incidence of premorbid abnormalities (Hollis 1995,
Vourdas, Pipe et al. 2003), worse cognitive performance (Rajji, Ismail et al. 2009), and poorer
functional outcome (Hollis 2000) observed in early onset schizophrenia. Our mediation analysis
suggests that the effect of MIR137 genotype on earlier age-at-onset may not be driving lower
white matter FA. Rather, MIR137 may be an underlying neurobiological mechanism linking age-
at-onset and brain morphology within schizophrenia.
Phenotype choice notwithstanding, study of a single marker represents only a limited description
of the genetic variation within or near one gene. Furthermore, little is known regarding the
mechanism by which MIR137 may direct phenotypic heterogeneity. Therefore, future studies are
necessary to understand the precise interplay between miRNAs and transcription factors within
schizophrenia. Although MIR137 variation had a consistent effect across our schizophrenia
samples on both age-at-onset and neuroimaging phenotypes it is not entirely clear why a stronger
association was not found in the healthy control imaging sample, in line with the intermediate
phenotype approach. Although a main effect of genotype on brain structure was found in the
entire sample, the effect was more prominently observed in schizophrenia patients. One
speculative explanation might be that our finding is due to the interaction of MIR137 with other
92
schizophrenia risk genes, including one or more of ZNF804A, CACNA1C, and TCF4 (Kwon,
Wang et al. 2011, Kim, Parker et al. 2012), which might in turn influence age-at-onset and brain
structure. Therefore, the consequence of MIR137 variation may differ in schizophrenia patients
compared to healthy controls, and the mechanisms underlying such differences deserve further
exploration.
Phenotypic heterogeneity has impeded the study of neuropsychiatric disorders in general and
schizophrenia in particular. It poses a major challenge for consistent findings when comparing
schizophrenia patients to matched controls on any number of clinical or neurobiological
variables. Importantly, our data suggest MIR137 genotype may predict a schizophrenia
subphenotype with earlier age at onset and compromised brain structure. A better understanding
of phenotypic heterogeneity may lead to early identification of patients with more severe disease
trajectory for whom more aggressive treatment strategies may improve long-term outcome. In
addition, our results may help to provide a model for the role of miRNAs in phenotypic
heterogeneity of psychiatric disorders. Although it seems unlikely that a single genetic variant
could account for the phenotypic diversity of a disorder as complex as schizophrenia, MIR137
appears to be an important factor influencing the phenotypic expression in schizophrenia, and
potentially other related disorders.
4.6 Acknowledgements
This work was supported by the Canadian Institutes of Health Research Clinician Scientist
Award (ANV); NARSAD (ANV, TKR), Ontario Mental Health Foundation (ANV) and the
Centre for Addiction and Mental Health (CAMH) and the CAMH Foundation thanks to the
Kimel Family, Koerner New Scientist Award, and Paul E. Garfinkel New Investigator Catalyst
Award. We also like to thank Mr. Gabriel Oh for manuscript comments. Mr. Lett, Dr. Kennedy,
93
and Dr. Voineskos had full access to all of the data in the study and take responsibility for the
integrity of the data and the accuracy of the data analysis. No sponsor or funder played any role
in the design and conduct of the study; collection, management, analysis, and interpretation of
the data; and preparation, review, or approval of the manuscript.
4.7 Conflict of interest
We report the following conflicts of interest: JAL has received research funding or is a member
of the advisory board of Allon, Alkermes Bioline, GlaxoSmithKline Intracellular Therapies,
Lilly, Merck, Novartis, Pfizer, Pierre Fabre, Psychogenics, F. Hoffmann-La Roche LTD,
Sepracor (Sunovion) and Targacept. HYM reports having received research funding or is a
member of the advisory board of Novartis, Janssen, ACADIA, TEVA, Lilly, Jazz
Pharmaceuticals, Sunovion, Dainippon Sumitomo, Envivo. JLK has been a consultant to
GlaxoSmithKline, Sanofi-Aventis, and Dainippon-Sumitomo.
94
Figure 4-1. MIR137 rs1625579 risk variant homozygotes have earlier age-at-onset of
schizophrenia. (a) The mean age-at-onset of psychosis in T risk allele homozygotes (20.8 +/-
5.7 years) was significantly earlier than in G allele carriers (23.7 +/- 9.1 years; F1,540=21.4,
p=3.1x10-5). Circles represent each subject, and dotted lines mean AAO for each genotype
group. (b) Predicting survival curves of MIR137 rs1625579 genotype and AAO are based on a
Cox regression with sex as covariate. “Proportion surviving” refers to the proportion of
participants free of psychosis or not yet having onset of psychotic symptoms. There are
significant associations with both MIR137 genotype (β=0.37, p=1.4x10-4) and sex (β=0.23,
p=0.015).
95
Figure 4-2. MIR137 risk variant predicts poorer structural brain phenotypes in
schizophrenia. Marginal mean of (a) left lateral ventricle volume, (b) right lateral ventricle
volume, (c) left hippocampus volume, and (d) right hippocampal volume in control and
schizophrenia imaging samples. Covariates in the model are cerebral volume (1042204.12 mm3)
and age (45.9 years). Error bars represent s.e.m. * represents a significant difference between Scz
TT and other genotype-diagnostic groups (Scz GG/GT, Cnt TT, Cnt GG/GT) at an uncorrected
alpha of 0.05. SCZ = schizophrenia; CNT = control.
96
Figure 4-3. Effect of MIR137 rs1625579 genotype on voxel-based white matter integrity in
patients with schizophrenia. White matter regions in which schizophrenia subjects carrying the
T risk allele homozygotes have reduced fractional anisotropy than G allele carriers. Areas
coloured from red to yellow correspond to p-values ranging from 0.05 to less than 0.01 following
correction for multiple comparisons using family-wise error of 5%. Significant regions are
mapped onto the standard Montreal Neurological Institute atlas MN152 1-mm brain template.
Numbers refer to Z coordinates.
97
Figure 4-4. The effect of MIR137 risk variant on mean whole-brain fractional anisotropy
(FA) across the lifespan for four ‘diagnosis-genotype’ groups. There was a global effect of
group (F3,209=13.60, p=3.88x10-8). Post hoc pairwise comparisons revealed that patients with
schizophrenia that were T risk allele homozygotes had significantly lower mean FA than each of
the other genotype by diagnosis groups (CNT TT: p=6.05x10-8; CNT GT/GG: p=5.14x10-7; SCZ
GT/GG: p=1.05x10-5). No other pairwise comparisons were significant (p>0.05). Age was a
covariate in the model (45.9 years). SCZ = schizophrenia; CNT = control.
98
Table 4-S1. Demographics for age at onset samples
Sample Site Subjects, n Ethnicity, n Sex, n MIR137
genotype, n
AAO,
mean±SD
Toronto schizophrenia 278 203C,
75NC
193M, 85F 198 TT, 80
GT or GG
21.18±5.96
Toronto imaging-
genetics
96 74C, 22NC 63M, 33F 58 TT, 38
GT or GG
24.66±9.43
Case Western Reserve 85 61C,24NC 57M, 28F 48 TT, 37
GT or GG
20.72±5.86
Hillside Hospital in Glen
Oaks
51 41C, 10NC 33M, 18F 40 TT, 11
GT or GG
19.96±5.72
Total 510 379C,
131NC
346M, 164F 344 TT,
166 GT or
GG
21.64±6.86
AAO, Age-at-Onset of schizophrenia; C, Caucasian; F, Female; L, Left-handed; M, Male; NC,
Non-Caucasian; R, Right-handed.
99
Table 4-S2. Demographics and clinical characteristics for the Toronto imaging-genetics sample
Control Schizophrenia
TT,
mean±SD
GT or GG ,
mean±SD
Total,
mean±S
D
TT,
mean±SD
GT or GG ,
mean±SD
Total,
mean±SD
Age 44.95±19.7
5
45.18±17.8
7
45.03±1
9.00
44.18±16.71 48.78±17.6
2
45.72±17.
25
TBV (cm3) 1077.3±124
.1
1078.2±122
.0
1076.3±
124.1
1055.7±149.
7
1071.9±124
.4
1060.0±1
39.4
Education 15.53±1.58 15.20±2.32 15.40±1.
88
13.13±3.07 13.00±2.65 13.07±2.8
7
WTAR (IQ) 117.58±7.8
8
118.02±7.7
1
117.74±
7.79
107.60±17.2
3
110.47±13.
50
108.74±1
5.81
MMSE 29.31±0.89 29.44±0.89 29.36±0.
89
28.52±2.01 28.87±1.14 28.66±1.7
1
CIRS-G 2.05±2.08 1.78±2.13 1.95±2.1
0
2.42±2.09 2.61±2.27 2.48±2.15
AAO* 22.46±6.90 27.84±11.8
9
24.68±9.6
1
DOI 20.58±16.45 21.11±16.2
7
20.63±16.
29
AIMS 1.32±2.82 0.78±2.90 1.09±2.82
Chlorpromazine equivalent
(mg)
379.77±306.
99
361.75±209
.94
372.33±2
69.82
100
AAO, Age at Onset of schizophrenia; AIMS, Abnormal Involuntary Movement Scale; C,
Caucasian; CIRS-G, Cumulative Illness Rating Scale – Geriatrics; DOI – Duration of Illness, F,
Female; L, Left-handed; M, Male; MMSE, Mini Mental State Examination; NC, Non-Caucasian;
PANSS, Positive and Negative Syndrome Scale; FGA, first generation antipsychotic; SGA,
second generation antipsychotic; R, Right-handed; TBV, Total Brain Volume; WTAR, Wechsler
Test of Adult Reading. * denotes significant difference (p<0.05) in AAO by MIR137 genotype.
All other variables are non-significant (p>0.05).
Antipsychotic Treatment 45 SGA,4
FGA,1 Both
31 SGA,2
FGA,1 Both
76 SGA,6
FGA,2
Both
PANSS 52.96±14.78 55.84±16.0
5
54.13±15.
20
Positive 14.21±5.52 14.41±6.26 14.32±5.7
7
Negative 13.59±5.39 15.57±6.41 14.37±5.8
3
General 25.16±6.49 25.86±7.07 25.44±6.6
6
Sex, n 43M,32F 24M, 22F 67M,54
F
34M, 20F 26M, 12F 60M,32F
Handedness,
n
70R,4L 42R,3L 112R,7L 54R, 4L 34R, 4L 88R,8L
Ethnicity, n 71C, 4NC 44C, 2NC 115C,6N
C
41C, 13NC 31C, 7NC 72C,20N
C
101
Figure 4-S1. (a) Distribution of age-at-onset of psychosis based on MIR137 rs1625579.
Genotype is significantly associated with age-at-onset of psychotic symptoms (F1,488 = 12.0,
p=5.7x10-4). Bars represent estimated marginal mean for each genotypic group with sex and
sample site as a covariate. (b) Predicting survival curves of MIR137 rs1625579 genotype and
age-at-onset of psychosis, based on Cox regression with sex as covariate. “Proportion surviving”
refers to the proportion of participants free of psychosis or not yet having onset of psychotic
symptoms. There are significant associations with both MIR137 genotype (B=0.317, p=8.6x10-4)
and sex (B=0.247, p=0.009). Participants with an age-at-onset of 12 years or younger (n=15) or
over 45 years (n=3) were excluded for a total n=492 in this analysis.
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Figure 4-S2. The main effect of MIR137 rs1625579 genotype on voxel-based white matter
integrity in healthy controls and patients with schizophrenia. Areas coloured from red to yellow
correspond to corrected p-values ranging from 0.05 to less than 0.01 at a family-wise error rate
of 5%. Significant regions are mapped onto the standard Montreal Neurological Institute atlas
MN152 1-mm brain template. Numbers refer to Z coordinates
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Figure 4-S3. Effect of MIR137 rs1625579 genotype by diagnosis interaction on voxel-based
white matter integrity in healthy controls and patients with schizophrenia. Areas coloured from
red to yellow correspond to corrected p-values ranging from 0.05 to less than 0.01 at a family-
wise error rate of 5%. Significant regions are mapped onto the standard Montreal Neurological
Institute atlas MN152 1-mm brain template. Numbers refer to Z coordinates.
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Figure 4-S4. Mediation Model. The models describe a causal relationship in which the mediator
causes the outcome and not vice versa. Each path (a,b,c, and c’) represents the four steps to
establishing mediation. First, demonstrate that the independent variable is correlated with the
dependent variable (path c; total effect). Second, show that the independent variable is correlated
with the mediator (path a). Third, show that the mediator affects the dependent variable (path b).
Fourth, demonstrate that the effect of the independent variable on the dependent variable (path
c’; direct effect) is significantly reduced or eliminated when the mediator is controlled for. The
indirect effect is the remaining variance explained the independent variable explains after
removing the variance explained by the mediator variable (path ab).
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Figure 4-S5. Mediation model examining the associations between MIR137 rs1625579
genotype, age at onset and mean whole brain fractional anisotropy (FA). *Mean whole brain FA
has been corrected for age (45.9 years).
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Chapter 5
5 GAD1 variant predicts a neuroanatomical and working memory susceptibly mechanism relevant to schizophrenia
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5.1 Abstract
Cortical GABAergic dysfunction has been implicated as a key component in the
pathophysiology of schizophrenia and in working memory impairment. We examined the
influence of the functional rs3749034 variant in the glutamic acid decarboxylase 1 (GAD1) gene
on brain structure and working memory performance in schizophrenia patients and healthy
controls (N=197). We found that the rs3749034 A-allele carrier risk group predicted voxel-wise
lower white matter fractional anisotropy (FA) in frontal cortex region (Pcorrected<0.05), and
working memory performance (Digit-Span: p=0.005, LNS: p=0.026) as well as selective
attention (Stroop Ratio: p=0.009). White matter FA in the frontal cortex was associated with
digit-span performance. Last, our voxel-wise mediation analysis revealed that the effect of the
GAD1 risk variant on poorer digit-span performance was statistically caused by lower white
matter FA. Our findings converge on variation in the GAD1 predicting a susceptibility
mechanism through which genetic variation leads to reduced white matter FA and aberrant
working memory. These results also provide a plausible mechanism through which aberrant
GABA signaling in schizophrenia may potentiate working memory dysfunction.
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5.2 Introduction
Working memory dysfunction is a central feature of schizophrenia and other psychiatric
disorders. In schizophrenia patients, working memory deficits are associated with dysfunction of
dorsolateral prefrontal cortex (DLPFC) as well as DLPFC connectivity with other regions and
disruption of neurotransmitter input such as GABA inhibitory neurotransmission (Meyer-
Lindenberg, Poline et al. 2001, Callicott, Mattay et al. 2003, Lewis and Moghaddam 2006).
There is a genetic basis to working memory dysfunction. Unaffected co-twins of schizophrenia
patients perform significantly worse than controls on spatial and verbal working memory tasks
(Cannon, Huttunen et al. 2000, Tuulio-Henriksson, Haukka et al. 2002, Pirkola, Tuulio-
Henriksson et al. 2005). For example, the letter-number-sequencing task (a measure of working
memory) has been identified as an endophenotype of schizophrenia with a moderately high
heritability (h2=0.39) (Greenwood, Braff et al. 2007). Therefore, understanding the effect of
inhibitory neurotransmission in shaping structural connectivity in the DLPFC and other brain
regions may provide insights into the neuroanatomical changes underlying working memory
impairment in schizophrenia.
Convergent evidence suggests a compelling role for the glutamate decarboxylase 1 (GAD1) gene
in working memory and dorsolateral prefrontal cortex (DLPFC) dysfunction in schizophrenia.
GAD1 codes for the glutamic acid decarboxylase (GAD67) enzyme that metabolizes glutamate to
GABA and is responsible for the production of the majority of GABA in the brain (Lewis,
Hashimoto et al. 2005). Downregulation of GAD67 in the parvalbumin-positive (PV)
interneurons of the DLFPC is a well-replicated postmortem finding in schizophrenia (Torrey,
Barci et al. 2005). Optogenetics has revealed that inhibition of fast-spiking parvalbumin
interneurons results in suppression of gamma activity (Cardin, Carlen et al. 2009, Sohal, Zhang
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et al. 2009). Moreover, there is a growing body of evidence suggesting that abnormal gamma-
band oscillations are an endophenotype of schizophrenia related to working memory (Spencer,
Nestor et al. 2004, Lewis, Hashimoto et al. 2005, Spencer, Salisbury et al. 2008, Haenschel,
Bittner et al. 2009, Spencer 2009, Farzan, Barr et al. 2010, Farzan, Barr et al. 2010, Hall, Taylor
et al. 2011). The GAD1 rs3749034 SNP is associated with downregulation of GAD67 in the
DLPFC (Straub, Lipska et al. 2007), and reduced cortical thickness in the left parahippocampal
gyrus (Brauns, Gollub et al. 2013). Further, a 5’-promoter SNP in linkage disequilibrium with
rs3749034 is associated with variation in working memory performance and DLPFC function in
vivo (Straub, Lipska et al. 2007). In the mouse PFC, GAD67 deficits in the parvalbumin
interneurons has been causally link to inhibition transmission and network disinhibition
(Lazarus, Krishnan et al. 2013).
In the present study, we set out to provide convergent evidence that GABA signaling is integral
to brain structure related to working memory, and working memory performance. We first
applied an imaging-genetics approach to examine association between the GAD1 rs3749034 risk
variant and brain structure. Next, we examined the relationship between the risk variant and
working memory performance in the LNS and forward digit-span tasks, as well as the Stroop
selective attention task. Last, we connected the effect of GAD1 on working memory through
changes in brain structure via voxel-wise mediation analyses.
5.3 Methods
5.3.1 Participants
Participants from the Toronto imaging-genetics sample were recruited at the Centre for
Addiction and Mental Health (CAMH) in Toronto, Canada, via referrals, study registries, and
advertisements. All clinical assessments occurred at CAMH while diffusion tensor magnetic
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resonance imaging (MRI) scans were performed at a nearby general hospital. Eighty patients
with a diagnosis of schizophrenia or schizoaffective disorder and 115 healthy control subjects in
this sample completed all imaging and genetics protocols; three healthy controls did not
complete DT-MRI protocols. Seventy one patients and 118 healthy controls completed cognitive
testing, and genetics protocols. All participants were identified as Caucasian based on self-
reported ethnicity of three out of four grandparents.
All participants were administered the Structured Clinical Interview for DSM-IV Disorders
(First MB 1995) to confirm diagnosis, and were interviewed by a psychiatrist to ensure
diagnostic accuracy. IQ was measured using the Wechsler Test for Adult Reading (WTAR)
(Wechsler 2001) and all participants were screened with the Mini Mental Status Exam (MMSE)
for dementia (Folstein, Folstein et al. 1975) and a urine toxicology screen. The Hand Dominance
Questionnaire was used to examine handedness. Comorbid physical illness burden was measured
by administration of the Clinical Information Rating Scale for Geriatrics (CIRS-G) (Miller,
Paradis et al. 1992). All subjects received urine toxicology screens and anyone with current
substance abuse or any history of substance dependence was excluded. Individuals with previous
head trauma with loss of consciousness, or neurological disorders were also excluded. A history
of a primary psychotic disorder in first-degree relatives was an additional exclusion criterion for
controls.
5.3.2 Genetics
Genotyping of the rs3749034 variant (GAD1) was performed using a standard ABI (Applied
Biosystems Inc.) 5’ nuclease Taqman assay-on-demand protocol in a total volume of 10 µL.
Postamplification products were analyzed on the ABI 7500 Sequence Detection System (ABI,
Foster City, California, USA) and genotype calls were performed manually. Results were
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verified independently by laboratory personnel blind to demographic and phenotypic information
(Lahiri and Nurnberger 1991). Genotyping accuracy was assessed by repeating 10% of the
sample with 100% accordance in all genotype calls.
5.3.3 Image Acquisition
High-resolution axial inversion recovery-prepared spoiled gradient recall MR images were
acquired using a 1.5-T GE Echospeed system (General Electric Medical Systems, Milwaukee,
WI; echo time (TE): 5.3, repetition time (TR): 12.3, time to inversion: 300, flip angle 20,
number of excitations=1; 124 contiguous images, 1.5 mm thickness). For DTI acquisition, a
single-shot spin-echo planar sequence was used with diffusion gradients applied in 23 non-
collinear directions, b=1000 s/mm2, and two b=0 images. Fifty-seven slices were acquired for
whole-brain coverage oblique to the axial plane (2.6 mm isotropic voxels; field of view was
330 mm, 128 × 128 mm2 acquisition matrix; TE=85.5 ms, TR=15 000 ms; the sequence was
repeated three times to improve signal-to-noise ratio).
5.3.4 Cortical Thickness Mapping
All MRIs were submitted to the CIVET pipeline. T1 images were registered to the ICBM152
nonlinear template with a 9-parameter linear transformation, intensity inhomogeneity corrected
(Sled, Zijdenbos et al. 1998) and tissue classified (for grey matter, white matter, and cerebral
spinal fluid) (Zijdenbos, Forghani et al. 2002, Tohka, Zijdenbos et al. 2004). Deformable models
were used to create white and gray matter surfaces for each hemisphere separately, resulting in 4
surfaces of 40,962 vertices each (MacDonald, Kabani et al. 2000, Kim, Singh et al. 2005). From
these surfaces, the t-link metric was derived for determining the distance between the white and
gray surfaces (Lerch and Evans 2005). The thickness data were blurred using a 20-mm surface-
based diffusion kernel in preparation for statistical analyses. Unnormalized, native-space
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thickness values were used in all analyses owing to the poor correlation between cortical
thickness and brain volume (Ad-Dab'bagh, Singh et al. 2005).
5.3.5 Tract-Based Spatial Statistics (TBSS)
All diffusion tensor imaging (DTI) analyses were done using tools implemented in the FSL
toolkit v.4.1.10 (Smith, Jenkinson et al. 2004). The three repetitions for each subject’s 4D DTI
volume were merged. The images were corrected for motion and eddy current distortion, and
averaged. After skull stripping using BET (Smith, Zhang et al. 2002), fractional anisotropy (FA)
images were created by fitting a tensor model at each voxel using DTIFit. FA quantifies
directionality of water diffusion on a scale from zero (random diffusion) to one (diffusion in one
direction). Voxel-wise analysis of the FA data was carried out using Tract-Based Spatial
Statistics (TBSS, v1.2) (Smith, Jenkinson et al. 2006). TBSS projects all subjects' fractional
anisotropy (FA) data onto a mean FA tract skeleton, before applying voxel-wise cross-subject
statistics. FA images then underwent nonlinear registration to the FMRIB58_FA target image.
Next, the mean FA image was iteratively generated from scans and was then aligned to MNI 152
standard space using an affine transformation. An average white matter skeleton was then
generated from the mean of all subjects’ transformed FA images at a threshold of 0.2. For group
comparisons, each subject’s FA data was projected onto the white matter skeleton and voxel-
wise statistics were calculated using randomise (v2.1) with 10,000 permutations.
5.3.6 Assessment of Working Memory
All subjects underwent a battery of cognitive tests administered over approximately 1.5 hours.
This battery includes a wide range of cognitive domains with varying degrees of impairment in
schizophrenia (Rajji, Ismail et al. 2009), and has been previously described (Voineskos, Rajji et
al. 2012, Voineskos, Felsky et al. 2013). We chose two working memory span tasks (verbal
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working memory: the Letter-Number Span task (LNS); non-verbal working memory: the Digit
Span task (Digit-span)) (Randolph, Tierney et al. 1998, Hale, Hoeppner et al. 2002). The LNS
requires an understanding of order of the stimuli related to previous learning, whereas Digit-span
requires on the repetition of the forward order. We further assessed selective attention using the
Stroop Neuropsychological Screening Test (Stroop 1935, Golden and Freshwater 1978). We
assessed the Stroop interference effect by using the reaction time of the colour-word task (time
per item), and a ratio score (i.e. the Stroop difference score divided by the latency to colour-word
control items). This ratio score provides a more conservative estimate of Stroop interference
because it controls for differences in overall response latencies, both between and within groups
(Plude and Hoyer 1981, Perlstein, Carter et al. 1998).
5.3.7 Statistics
Given the low frequency of the AA genotype, these cases were grouped with GA genotypes and
referred to as ‘A allele carriers’ for all statistical tests. Analyses were performed examining the
relationship between GAD1 genotype group (G allele homozygotes or A allele carriers) for
cognitive performance and brain morphology. Adherence to Hardy-Weinberg equilibrium was
determined using Haploview 4.2(Barrett, Fry et al. 2005) for all healthy controls.
Differences in demographic characteristics between healthy controls and schizophrenia patients
were assessed by used independent t-tests for continuous variables and χ2 tests for count
variables (Table 5-1) using SPSS (Version 15). For cortical thickness analyses, diagnosis and
genotype were used as between group factors, with age as a covariate. For our TBSS analysis,
we examined a diagnosis by GAD1 genotype interaction, and main effects of diagnosis and
genotype on the FA skeleton with age as a covariate. An FDR correction of q=0.05 was applied
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for cortical thickness and a family-wise error rate of 5% for white matter FA comparisons.
Multiple comparison corrections were performed within each analytic imaging modality.
To determine potential confounders of working memory, we first evaluated if age, sex, IQ, and
three measures of education (participant, father, mother) were predictors of task performance
(LNS, Digit-Span) in healthy controls (N=117) or schizophrenia patients (N=71) via the general
linear model (GLM). Diagnostic groups were evaluated separately, and covariates of no interests
were only included in our model if they were below a significance threshold of p = 0.1. The
influence of GAD1 as a predictor of working memory performance, diagnosis, and their
interaction was then analyzed via the GLM with our covariates of no interest. Four GLMs were
performed with LNS score, Digit-Span, Stroop (Time/Item), and Stroop ratio. Associations were
deemed significant at P=0.05 after Bonferroni correction for four comparisons.
5.3.8 Voxel-wide mediation analysis
Voxel-wise mediation analysis was performed in MATLAB (R2013b). We used the multiple
regression approach described by Baron and Kenny (Baron and Kenny 1986), and applied this
approach across the entire TBSS FA skeleton. A 4D image containing the TBSS skeleton of the
subjects was loaded into MATLAB, and transformed into an array of all non-zero voxels across
each subject. To remove the confounding effects of age, we then regressed out the effect of mean
centered age for all voxels. Our mediation analysis was accomplished with three regression
equations applied across all voxels. First, we regressed the independent variable (risk group)
against white matter FA. A z-score was produced for each non-zero voxel and was used to
produce a 3D image of z-scores (‘Path A’). We then applied TFCE in the FSL ‘fslmaths’
function with E=2, H=1, and the neighbourhood-connectivity parameter = 26 as recommended in
TBSS analysis (Smith and Nichols 2009). 10,000 permutations (i.e. randomization analysis)
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were then performed and the maximum z-statistics for each permutation was used to assess
significance accounting for FWE. Second, we regressed the mediator variable (white matter FA
at each voxel) against cognitive performance at each voxel (‘Path B’). A 3D image of z-scores
were produced, and we tested significance using the same TFCE and permutation test technique.
Third, we regressed the independent variable (risk group) on cognitive performance (‘Path C’).
A significant association in all three sets of regressions then allowed us to proceed with the Sobel
equation to assess the indirect effect of the independent variable on the dependent variable via
the mediator (white matter FA at each voxel). We used the unstandardized regression
coefficients (beta) and the standard errors (SE) from ‘Path A’ and ‘Path B’ in order to produce a
z-value at each white matter FA voxel (Sobel equation: z-value = beta(Path A)*beta(Path B)/ √(beta(path
B)2 *SE(Path A)
2 + beta(Path A)2 *SE(Path B)
2)). A 3D image of z-values were produced, we applied
TFCE, and significant mediation was assessed using the max z-value of 10,000 permutations. It
should be noted that resampling strategies to assess significance of the Sobel equation are
considered to be a better alternative than parameter tests that impose distribution assumptions
(Preacher and Hayes 2008).
5.4 Results
5.4.1 Participants
Age, height, sex, and handedness were not significantly different between schizophrenia patients
and healthy controls. Patients had lower IQ, education, level of education of each parent (p<0.05;
Table 5-1). Patients also weighed more than controls (p<0.05; Table 5-1). There was no
significant deviation from Hardy-Weinberg equilibrium for the rs3749034 genotype in healthy
controls (p>0.05). The rs3749034 minor allele frequency of the healthy controls (0.22) was
similar to Hapmap CEU population (0.18; Hapmap Build #27) (Gibbs, Belmont et al. 2003).
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5.4.2 Association between GAD1 and brain structure
Because GAD1 has previously been associated with gray matter volume and cortical thickness,
we performed vertex-wise analysis of cortical thickness. We found no effect of genotype or
genotype by diagnosis interaction on cortical thickness after covarying for age and at any vertex
using false discovery rate correction of 5% (Genovese, Lazar et al. 2002). Since the effect of
GAD1 on white matter fraction anisotropy is unknown, we tested GAD1 and the interaction with
diagnosis on TBSS skeleton FA after covarying for age and correcting for FWE. We found a
significant main effect of GAD1 on voxel-wise FA predominately in the prefrontal cortex
including the genu of the corpus callosum (Figure 5-1). There was no significant GAD1
genotype-by-diagnosis interaction on FA after correcting for FWE.
5.4.3 Association between GAD1 and working memory
We included age and IQ as covariates of no interest in our analyses since they were significantly
associated with our working memory outcome measures (Digit-Span and LNS) in schizophrenia
patients and healthy controls (p<0.05). Sex and education (participant, father, and mother) were
not associated with Digit-span or LNS performance in either patients or controls (p>0.1);
therefore, they were not included in as covariates.
We tested the effect of the GAD1 rs3749034 SNP on working memory and attention across three
cognitive tasks. There was a significant effect of GAD1 on digit span performance (F1,188 = 7.97,
p = 0.005) and a nominally significant effect on the LNS task performance (F1,188 = 5.03, p =
0.026), after covarying for subject age and IQ. There also was a significant association with
Stroop ratio (F1,188 = 7.03, p=0.009), but not time per item. No significant GAD1 genotype-by-
diagnosis interaction was observed for any cognitive task (Table 5-2).
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5.4.4 Voxel-wise mediation analysis
Since there was a significant association between main effect of GAD1 risk genotype and
skeleton white matter FA, as well as working memory, we employed voxel-wise mediation
analysis across all subjects. For mediation to occur, we first needed to establish if skeleton whiter
matter FA predicted working memory performance (corrected for IQ). Higher skeleton FA
particularly in corpus callosum and right superior longitudinal fasciculus was associated with
better Digit-span performance after covarying for the effect of age and correcting for family-wise
error (pcorrected<0.05; Figure 5-2). Skeleton FA did not predict LNS performance or Stroop ratio
after correcting for family-wise error; therefore, we did not perform any mediation analyses for
the tasks. White matter skeleton FA mediated the association between GAD1 risk genotype and
Digit-span performance, particularly in the prefrontal and right inferior parietal regions,
suggesting that lower FA in this regions is statistically causing poorer working memory
(p[sobel]corrected<0.05).
5.5 Discussion
We have provided convergent evidence that the GAD1 risk variant leads to lower white matter
FA that may be related to poor working memory performance. Carriers of the GAD1 risk allele
had poorer working memory, and lower white matter FA in the prefrontal cortices. Moreover,
these effects tended to be independent from schizophrenia diagnosis. Further, we showed that
white matter FA positively correlates with digit-span performance. We can statistically infer a
causal relationship in which white matter FA mediates the effect on GAD1 on digit-span
performance. These results build upon previous work associated GABA inhibitory
neurotransmission in structural dysconnectivity and working memory dysfunction in
schizophrenia.
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To the best of our knowledge, our study is the first to examine the effect of GAD1 genotype on
white matter FA. Our finding that GAD1 risk genotype predicted lower FA, predominately in the
genu of the corpus callosum, is consistent with the literature on top-down modulation of
posterior brain regions during inhibition and attention tasks (Hopfinger, Buonocore et al. 2000,
Erickson, Prakash et al. 2009). This is bolstered by our convergent cognitive associations that
risk genotype had poorer working memory across multiple tasks. Furthermore, we found the
effect of GAD1 on FA and working memory performance was consistent across diagnostic
groups. This suggests that genotypic variation in GAD1 acts as a modifier of brain structure and
related cognitive function. GAD1 may explain some of the heterogeneity of brain structure and
cognitive function reported within schizophrenia, despite the potential confounders within
disease. We were unable to replicate previous association between the GAD1 risk variant and
cortical structure (Brauns, Gollub et al. 2013). This may due to stringent FDR correction in our
vertex-wise analysis, or different methodologies.
The GAD1 rs3749034 risk variant has repeatedly been associated with reduced expression in the
prefrontal cortex (Straub, Lipska et al. 2007, Hyde, Lipska et al. 2011). The rs3749034 variant is
associated with decoupling of progressive switching from expression of GAD25 to GAD67 that is
required for proper maturation of the GABA function in the prefrontal cortex (Hyde, Lipska et
al. 2011). Therefore, GAD1 genotype may predict progressive neurodevelopmental changes
similar to what is observed in schizophrenia. Furthermore, decreased GAD1 expression in PV
interneurons in the prefrontal cortex is consistently associated with schizophrenia (Lewis, Curley
et al. 2012), although the density of PV interneurons in the DLPFC patients is no different than
healthy controls ((Woo, Miller et al. 1997, Hashimoto, Volk et al. 2003, Tooney and Chahl
2004)). Therefore, PV interneurons may not be altered in schizophrenia, rather that GAD1 is
reduced in a subset of these neurons. It is also important to note that GAD1 expression is
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activity-regulated (Benson, Huntsman et al. 1994), and lower expression may be due reduced
cortical activity of secondary factors related to schizophrenia. Nevertheless, lower cortical GAD1
expression has not been associated with potential confounders (e.g. antipsychotic medication,
age of onset, functional outcome, and duration of illness) (Hashimoto, Arion et al. 2008, Curley,
Arion et al. 2011). Therefore, reduced GAD1 expression may be a core component of
schizophrenia, and our association with rs3749034 could be a marker of this disease process.
Our finding may have clinical implications in the treatment of working memory dysfunction.
Arguably the best supported treatment for working memory dysfunction (and general cognitive
function) in schizophrenia is cognitive remediation therapy (Lett, Voineskos et al. 2014).
Although the neurobiology mechanisms of CRT needs further research, CRT has been shown to
increase brain activation in the frontocortical regions associated with working memory function
(Wexler, Anderson et al. 2000, Wykes, Huddy et al. 2011, Penadés, Pujol et al. 2013). Also,
CRT has been associated with increased FA in the genu of the corpus callosum (Penadés, Pujol
et al. 2013). Nevertheless, not all schizophrenia patients respond to CRT with improved working
memory function. Bilateral rTMS treatment to the DLPFC has also been associated with
improved working memory performance, and GABAergic inhibitory function in schizophrenia
patients (Barr, Farzan et al. 2011, Barr, Farzan et al. 2013). Long-term rTMS treatment was also
associated with increased prefrontal cortex white matter FA (Allendorfer, Storrs et al. 2012,
Peng, Zheng et al. 2012). Considering our associations among GAD1, prefrontal cortex white
matter FA, and working memory, it could be speculated that GAD1 rs3749034 genotype may be
an important genetic predictor of adjunctive treatments including CRT and rTMS. Furthermore,
GAD1 genotype may be a potential confounder in future treatment trials, and therefore,
segregating subjects by genotype may improve trial outcome.
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This study has a number of limitations. First, the evidence for association between GAD1 SNPs
and schizophrenia is relatively weak with no reported genome-wide findings, although there is
strong evidence for disruption of GAD1 expression in schizophrenia. Our a priori hypothesis was
that GAD1 is an important modifier of the disease process, and we selected the rs3749034 based
on it known effect on GAD1 expression. That is, we sought to indirectly model for how altered
GAD1 expression may lead to neuroanatomical differences and working memory dysfunction via
imaging-genetics. Second, since the minor allele frequency of the rs3749034 variants is
relatively low, for statistical analysis we inferred a dominant model by grouping minor allele risk
homozygotes and heterozygotes. Third, we limited our analysis only to Caucasian subjects;
therefore, we are unable to assess the effect of GAD1 in other ethnic groups.
Working memory dysfunction is one of the most intractable symptoms of schizophrenia, with
severe consequences on functional outcome (Lett, Voineskos et al. 2014). We have provided
evidence that GAD1 may predict lower working memory function via changes in white matter
FA, and likely explains some of the heterogeneity in working memory dysfunction. In addition,
our results suggests that the relationship between inhibitory signaling and working memory
dysfunction may be independent of schizophrenia.
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Table 5-1. Demographics
Controls (N= 115) Schizophrenia (N = 80)
Mean SD Mean SD t193 P
Age (years) 46.79 19.32 45.49 16.00 0.49 0.63
IQ (WTAR score) 117.92 8.11 110.64 15.16 4.24 <0.05
Education (Years) 15.32 1.95 13.25 2.64 6.27 <0.05
Level of Education, Father 4.88 1.99 3.91 2.37 3.05 <0.05
Level of Education, Mother 4.67 1.75 3.79 2.12 3.93 <0.05
Weight (kg) 75.91 14.19 81.17 19.15 -2.13 <0.05
Height (m) 1.71 0.10 1.70 0.10 0.58 0.56
Count Frequency Count Frequency χ2 p
Handedness (R) 105 92.1% 71 89.9% 0.23 0.59
Sex (M) 61 53.0% 48 60.0% 0.93 0.38
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Table 5-2. The association between working memory related tasks and GAD1 genotype,
diagnosis, and their interaction.
GAD1 Diagnosis GAD1*Diagnosis
Working Memory Task F1,188 P F1,188 P F1,188 P
Letter-Number Span 5.03 0.026 18.04 <0.0001 2.89 0.091
Digit Span 7.97 0.005 5.06 0.026 0.02 0.89
Stroop (Time/Item) 1.17 0.28 24.41 <0.0001 0.17 0.68
Stroop Ratio 7.03 0.009 12.50 0.001 2.50 0.12
**Covariates included in the model include age, and IQ (WTAR).
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Figure 5-1. GAD1 rs3749034 risk A-allele predicts lower TBSS skeleton white matter FA in
healthy controls (N=115) and patients with schizophrenia (N=80). There was a significant
main effect of GAD1 genotype on prefrontal FA, and no significant effect of GAD1 genotype-
by-diagnosis interaction. Areas in yellow correspond to p<0.05 after correction for family-wise
error. Green represents the mean FA skeleton overlaid on the FMRIB58_FA standard space
image.
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Figure 5-2. Higher TBSS skeleton white matter FA correlates with better digit span
performance. Areas in yellow correspond to p<0.05 after correction for family-wise error.
Green represents the mean FA skeleton overlaid on the FMRIB58_FA standard space image.
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Figure 5-3. Skeletal white matter FA regions that mediate the effect of GAD1 rs3749034 on
digit span performance. Significant p-values indicated broad areas of the white matter skeleton
FA that mediated the effect of GAD1 risk A-allele risk genotype on poor digit span performance.
We created a z-statistic from voxel-wise Sobel tests for mediation based on the beta coefficients
and standard error from (i) GAD1 regressed on TBSS skeleton FA (Figure 5-1), and (ii) TBSS
skeleton FA regressed on digit-span performance (Figure 5-2). The z-values then underwent
threshold free clustering enhancement, and p-values are derived using permutation testing
(N=10000). Areas in yellow correspond to p<0.05 after correction for family-wise error. Green
represents the mean FA skeleton overlaid on the FMRIB58_FA standard space image.
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Chapter 6
6 Additive Genetics Risk Predicts Widespread Changes in Brain Structure Leading to Poorer Cognitive Function
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6.1 Abstract
There is growing theoretical and empirical evidence that additive genetic variation accounts for
the majority of the variance in complex traits. In healthy controls and schizophrenia patients
(N=198), we examined the association between an additive genetic model and brain structure via
brain-wide analysis of cortical thickness (vertex-wise analysis), and white matter FA (tract-based
spatial statistics), as well as cognitive performance. Our additive model included risk alleles
from MIR137 (rs1622579), CACNA1C (rs1006737), ZNF804A (rs1344706), GAD1 (rs3749034),
and BDNF (rs6265). Voxel-wise white matter FA mediation analysis was performed on
cognitive domains significant associated with additive genetic risk. We found that additive
schizophrenia risk score predicted white matter integrity throughout the brain (pcorrected<0.001),
and there was a significant model-by-diagnosis interaction predominately in the corpus callosum.
There was also a significant vertex-wise interaction between our additive risk score and
diagnosis in cortical thickness. High genetic risk loading predicted poor cognitive performance
and the effect was greater among schizophrenia patients for verbal fluency (F1,64=9.8, p=0.003;
interaction, F1,64=4.7, p=0.031) and motor functioning (F1,64=5.4, p=0.020; interaction,
F1,64=10.1, p=0.002)). Voxel-wise FA mediation analyses showed that genetic risk loading on
verbal fluency was statistically caused by white matter changes predominately in the corpus
callosum (Pcorrected[Sobel] < 0.001). Our findings suggest that our additive genetic risk model
predicts changes in brain structure and cognitive function. Furthermore, in a novel manner, our
results directly link our genetics association with cognitive performance through changes in
white matter FA.
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6.2 Introduction
Schizophrenia is a chronic and severe brain disorder that affects approximately 1% of the
population, and the disease carries a high degree of heritability (>80%). It is a complex genetic
disorder and genetic predisposition is likely to be determined both through genetic pathways and
environmental risk factors. To date, common risk gene variants only have shown limited effect
sizes explaining disease association (Ripke, Sanders et al. 2011, Ripke, O'Dushlaine et al. 2013),
and the same has been largely true for neuroimaging phenotypes and cognitive phenotypes
relevant to schizophrenia (Greenwood, Braff et al. 2007, Brans, van Haren et al. 2008, Lencz,
Knowles et al. 2014). The genetic variation in complex traits, such as brain morphology,
consists of many components due to additive, dominant, and interaction effects of genes. There
is growing theoretical and empirical evidence that additive genetic variation accounts for the
considerable portion of genetic variance (Hill, Goddard et al. 2008). Therefore, examination of
additive genetic risk across several common variants might provide a better explanation for the
high degree of heterogeneity in neurocognitive dysfunction in schizophrenia that depends on
brain network connectivity (Chan and Gottesman 2008, Rao, Di et al. 2008, Chan, Wang et al.
2009, Lett, Voineskos et al. 2014). Furthermore, assessing additive genetics may better means to
assessing multi-gene risk without necessarily needing large sample size necessary for examining
statistical interactions, especially when considering more than three variants (Smolka, Buhler et
al. 2007, Puls, Mohr et al. 2009, Button, Ioannidis et al. 2013). Last, associations between risk
variants and neuroimaging phenotypes are rarely consistent among studies that may be explained
by method or sample heterogeneity. It could also be speculated that the effect of any one genetic
risk variant on brain structure may, in part, depend on the additive effect other risk variants.
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In the present study, we investigate additive effects accrued across five risk variants implicated
in neuroanatomical and neurocognitive heterogeneity within schizophrenia. The rs1625579
variant near the microRNA-137 (MIR137) gene is among the top genome-wide associated SNPs
for schizophrenia, and MIR137 has been shown to regulate numerous other genome-wide
associated genes including: calcium channel, voltage-dependent, L-type, alpha 1C subunit
(CACNA1C) and zinc-finger 804A (ZNF804A)(Kwon, Wang et al. 2011, Ripke, Sanders et al.
2011, Kim, Parker et al. 2012, Ripke, O'Dushlaine et al. 2013). Recently, we have demonstrated
that the MIR137 risk variant (rs1625579) predicts clinical and neuroanatomical heterogeneity
within schizophrenia including poorer white matter structure, increased lateral ventricle volumes,
and lower hippocampal volume, as well as an earlier age-at-onset of psychosis. MIR137 has also
reported to be associated with frontal activation and cognitive function within schizophrenia
(Green, Cairns et al. 2012, Whalley, Papmeyer et al. 2012, van Erp, Guella et al. 2014). The
CACNA1C rs1006737 risk variant has been associated with poorer neurocognition in
schizophrenia patients but not healthy controls; however, the variant has been associated with
episodic memory circuit activation in healthy controls (Erk, Meyer-Lindenberg et al. 2010, Hori,
Yamamoto et al. 2012). It was recently reported that rs1006737 also confers greater frontolimbic
dysfunction in relatives of psychiatric patients compared to controls (Erk, Meyer-Lindenberg et
al. 2013). The ZNF804A rs1344706 risk allele has been associated with poorer episodic memory
and working memory in patients with schizophrenia but not healthy controls (Hashimoto, Ohi et
al. 2010, Walters, Corvin et al. 2010). Although in healthy controls during working memory, the
ZNF804A rs1344706 SNP risk allele carriers had reduced connectivity between the right and left
DLPFC, and increased right DLPFC and left hippocampal connectivity (Esslinger, Walter et al.
2009). ZNF804A is also associated with DLPFC greater inefficiency in siblings and patients with
schizophrenia than healthy controls; however, aberrant DLPFC-hippocampal connectivity was
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only observed siblings and patients (Rasetti, Sambataro et al. 2011). In healthy controls, our
group has reported that individuals homozygous for the risk variant have reduced cortical
thickness in the left posterior cingulate cortex, left superior temporal gyrus and right anterior
cingulate cortex, all regions associated with cognitive dysfunction within schizophrenia
(Voineskos, Lerch et al. 2011).
We further included two risk genes to our additive model that have been strongly associated with
schizophrenia in postmortem studies as well as brain structure and cognitive function.
Convergent evidence suggests a compelling role for the glutamate decarboxylase 1 (GAD1) gene
in cognition and dorsolateral prefrontal cortex (DLPFC) dysfunction in schizophrenia. The major
determinant of GABA in the neocortex is glutamic acid decarboxylase-67 (GAD67; encoded by
the GAD1 gene). One of the most consistent findings in schizophrenia is down-regulation of
GAD1 mRNA and protein in the prefrontal cortex (Torrey, Barci et al. 2005), and in the PFC,
eight-fold increase of GAD1 methylation has been reported (Huang and Akbarian 2007).
Conserved 2q31 chromosomal configuration is associated with GAD1 transcription, and this
spatial genome architecture is disrupted in the prefrontal cortex of schizophrenia patients
compared to controls (Bharadwaj, Jiang et al. 2013). GAD1 variants has been shown to be
associated with childhood-onset psychosis within schizophrenia, and increased rate of cortical
gray matter loss (Addington, Gornick et al. 2004). GAD1 influences multiple cognitive domains
including declarative memory, attention and working memory in families with schizophrenia,
and prefrontal activation during working memory (Straub, Lipska et al. 2007). Our final gene of
interest, the brain-derived neurotrophic factor (BDNF), is one of the key regulators of
neuroplasticity, synaptic structure, memory function and consolidation. Post-mortem studies
have identified reduced BDNF expression in the hippocampus and prefrontal cortex of
schizophrenia patients (Green, Matheson et al. 2011), and reduced BDNF levels in schizophrenia
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patients have been associated with cognitive performance and clinical outcome (Chen da, Wang
et al. 2009, Vinogradov, Fisher et al. 2009). In healthy controls, we recently have reported that
the BDNF rs6265 SNP interacts with age to predict differences in cortical thickness, white matter
FA, and episodic memory relevant to Alzheimer’s disease (Voineskos, Lerch et al. 2011).
Furthermore, there was a significant schizophrenia diagnosis by rs6265 genotype interaction
observed in resting and working-memory related hippocampal regional cerebral blood flow, as
well as on hippocampal prefrontal coupling (Eisenberg, Ianni et al. 2013).
There is evidence to suggest that neuroanatomical changes and neurocognitive dysfunction
within schizophrenia are likely dependent on genetic load. Further, these anatomical changes
may mediate neurocognitive dysfunction. We hypothesize that increasing additive genetic risk
loading may produce a more ‘severe’ brain phenotype that may predict cognitive function.
Furthermore, as we have previously shown, the effect of schizophrenia risk variants on brain
structure may be greater within schizophrenia patients (Lett, Chakavarty et al. 2013). Therefore,
we examine the accrued effect of five common genetic variants, implicated in schizophrenia,
brain structure and cognitive function, for association with brain-wide measures of white matter
fraction anisotropy (FA) and cortical thickness in healthy controls and patients. To compare
genetic subsets with differences in brain structure, we then isolated subjects with either low or
high risk allele loading for association with our neurocognitive battery. Last, we employ a novel
voxel-wise mediation analysis to understand how high risk allele loading explains poorer
cognitive functioning via worse brain structure.
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6.3 Methods
6.3.1 Participants
Participants from the Toronto imaging-genetics sample were recruited at the Centre for
Addiction and Mental Health (CAMH) in Toronto, Canada, via referrals, study registries, and
advertisements. All clinical assessments occurred at CAMH while DT-MRI scans were
performed at a nearby general hospital. Eighty-nine patients with a diagnosis of schizophrenia or
schizoaffective disorder and 109 healthy control subjects in this sample completed all imaging,
cognitive and genetics protocols. All participants were administered the Structured Clinical
Interview for DSM-IV Disorders (First MB 1995) to determine diagnosis, and were interviewed
by a psychiatrist to ensure diagnostic accuracy. IQ was measured using the Wechsler Test for
Adult Reading (WTAR) (Wechsler 2001) and all participants were screened with the Mini
Mental Status Exam (MMSE) for dementia (Folstein, Folstein et al. 1975) and a urine toxicology
screen. Comorbid physical illness burden was measured by administration of the Clinical
Information Rating Scale for Geriatrics (CIRS-G) (Miller, Paradis et al. 1992). Medication
histories were initially recorded via self-report, and then verified either by the patient’s treating
psychiatrist or chart review. All subjects received urine toxicology screens and anyone with
current substance abuse or any history of substance dependence was excluded. Individuals with
previous head trauma with loss of consciousness, or neurological disorders were also excluded.
A history of a primary psychotic disorder in first-degree relatives was an additional exclusion
criterion for controls. In previous imaging-genetics studies, we have examined MIR137
rs1625579 in healthy controls and schizophrenia, as well as ZNF804A rs1344706 and BDNF
rs6265 in healthy controls (Voineskos, Lerch et al. 2011, Voineskos, Lerch et al. 2011, Lett,
Chakavarty et al. 2013).
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6.3.2 Image Acquisition
High-resolution axial inversion recovery-prepared spoiled gradient recall MR images were
acquired using a 1.5-T GE Echospeed system (General Electric Medical Systems, Milwaukee,
WI; echo time (TE): 5.3, repetition time (TR): 12.3, time to inversion: 300, flip angle 20,
number of excitations=1; 124 contiguous images, 1.5 mm thickness). For DTI acquisition, a
single-shot spin-echo planar sequence was used with diffusion gradients applied in 23 non-
collinear directions, b=1000 s/mm2, and two b=0 images. Fifty-seven slices were acquired for
whole-brain coverage oblique to the axial plane (2.6 mm isotropic voxels; field of view was
330 mm, 128 × 128 mm2 acquisition matrix; TE=85.5 ms, TR=15 000 ms; the sequence was
repeated three times to improve signal-to-noise ratio).
6.3.3 Cortical Thickness Mapping
All MRIs were submitted to the CIVET pipeline. T1 images were registered to the ICBM152
nonlinear template with a 9-parameter linear transformation, intensity inhomogeneity corrected
(Sled, Zijdenbos et al. 1998) and tissue classified (for grey matter, white matter, and cerebral
spinal fluid) (Zijdenbos, Forghani et al. 2002, Tohka, Zijdenbos et al. 2004). Deformable models
were used to create white and gray matter surfaces for each hemisphere separately, resulting in 4
surfaces of 40,962 vertices each (MacDonald, Kabani et al. 2000, Kim, Singh et al. 2005). From
these surfaces, the t-link metric was derived for determining the distance between the white and
gray surfaces (Lerch and Evans 2005). The thickness data were blurred using a 20-mm surface-
based diffusion kernel in preparation for statistical analyses. Unnormalized, native-space
thickness values were used in all analyses owing to the poor correlation between cortical
thickness and brain volume (Ad-Dab'bagh, Singh et al. 2005).
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6.3.4 Tract-Based Spatial Statistics (TBSS)
All diffusion tensor imaging (DTI) analysis was done using tools implemented in the FSL toolkit
v.4.1.10 (Smith, Jenkinson et al. 2004). The three repetitions for each subject’s 4D DTI volume
were merged. The images were corrected for motion and eddy current distortion, and averaged.
After skull stripping using BET (Smith, Zhang et al. 2002), fractional anisotropy (FA) images
were created by fitting a tensor model at each voxel using DTIFit. FA quantifies directionality of
water diffusion on a scale from zero (random diffusion) to one (diffusion in one direction).
Voxel-wise analysis of the FA data was carried out using Tract-Based Spatial Statistics (TBSS,
v1.2) (Smith, Jenkinson et al. 2006). TBSS projects all subjects' fractional anisotropy (FA) data
onto a mean FA tract skeleton, before applying voxel-wise cross-subject statistics. FA images
then underwent nonlinear registration to the FMRIB58_FA target image. Next, the mean FA
image was iteratively generated from scans of healthy controls and patients with schizophrenia
separately. Each group was then aligned to MNI 152 standard space using an affine
transformation. An average white matter skeleton was then generated from the mean of all
subjects’ transformed FA images at a threshold of 0.2. For group comparisons, each subject’s FA
data was projected onto the white matter skeleton and voxel-wise statistics were calculated using
randomise (v2.1) with 10,000 permutations.
6.3.5 Neuroimaging Dimension Reduction
To assess the effect of our additive model on general brain structure, we employed a region of
interest (ROI) approach and performed separate factor analyses on average skeletal FA and
average cortical thickness among regions. We extracted the mean cortical thickness from 56
anatomical regions in the LONI Probabilistic Brain Atlas (LPBA40) (Shattuck, Mirza et al.
2008).From the TBSS FA skeleton, we extracted values of mean FA from 50 white-matter
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regions from the ICBM-DTI-81 atlas (Wakana, Caprihan et al. 2007). We applied separate factor
analyses for the mean skeleton FA and mean cortical thickness values using SPSS (version 15)
across all subjects, and extracted principal components that explained greater than 10% of the
variance.
6.3.6 Genetics
Genotyping of the rs1622579 (MIR137HG), rs1006737 (CACNA1C), rs1344706 (ZNF804A),
rs3749034 (GAD1), and rs6265 (BDNF) SNPs were performed using a standard ABI (Applied
Biosystems Inc.) 5’ nuclease Taqman assay-on-demand protocol in a total volume of 10 µL.
Postamplification products were analyzed on the ABI 7500 Sequence Detection System (ABI,
Foster City, California, USA) and genotype calls were performed manually. Results were
verified independently by laboratory personnel blind to demographic and phenotypic
information.(Lahiri and Nurnberger 1991). Genotyping accuracy was assessed by repeating 10%
of the sample with 100% accordance in genotype calls.
6.3.7 Additive Model
Risk scores for each gene variant were based on previous association with schizophrenia,
neuroimaging phenotypes, and cognitive function. The score range [0, 0.5, 1] corresponded to
the number of risk alleles. For each participant, we then calculated an additive risk score based
on the addition of the risk scores from each variant. The scores were not weighted for each
variant since it is unlikely these variants would affect cortical thickness and white matter FA in
proportion to their associations with schizophrenia.
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6.3.8 Neuropsychological Assessment
All subjects underwent a battery of cognitive tests administered over approximately 1.5 hours.
This battery includes a wide range of cognitive domains with varying degrees of impairment in
schizophrenia (Rajji, Ismail et al. 2009), and have been previously described (Voineskos, Rajji et
al. 2012). From the Repeatable Battery for the Assessment of Neuropsychological Status
(RBANS) battery, we selected measures of attention (Digit Span), processing efficiency (Digit-
Symbol Coding), immediate memory (Story memory), delayed memory (Story recall),
visuospatial construction (Line Orientation); we further selected standalone measures of verbal
fluency (Controlled oral word association test, COWAT; total words for F+A+S), working
memory (Letter-number span, LNS), processing speed (Trail Making Test A, TMT-A), and
executive function (Trail Making Test B, TMT-B) (Reitan and Wolfson 1985, Ruff, Light et al.
1989, Wechsler 2001, Hale, Hoeppner et al. 2002, Dickinson 2008). To assess motor
functioning, participants underwent test for fine motor speed (Finger tapping; dominant and non-
dominant hand) and visual-motor coordination (Grooved pegboard; dominant and non-dominant
hand) (Halstead 1947, Matthews and Klove 1964).
6.3.9 Statistical Analysis
Analysis of variance (ANOVA), χ2 Goodness-of-fit, and t-tests were performed using SPSS
(Version 15). Adherence to Hardy-Weinberg equilibrium for each marker was assessed using χ2
Goodness-of-fit. To test if our additive risk score was higher in patients with schizophrenia
compared to controls we used a one-way t-test. ANOVAs were performed to test for significant
differences between patients and controls for: age, education, IQ, MMSE, CIRS-G. χ2
Goodness-of-fit tests were used to assess significant differences in: sex, handedness, and
ethnicity (Caucasian versus non-Caucasian). For each brain measure, our independent variables
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were diagnosis (control or schizophrenia), additive risk score, their interactions and age as a
covariate of no interest. Vertex-wise cortical thickness analyses were performed separately on
left and right cortices, and we applied a false discovery rate (FDR) of q=0.05. TBSS analyses
were corrected for multiple comparisons using family-wise error (FWE).
6.3.10 Voxel-wise mediation analysis
Voxel-wise mediation analysis was performed in MATLAB (R2013b). We used the same
multiple regression approach described by Baron and Kenny (Baron and Kenny 1986), although
we applied this approach across the entire TBSS FA skeleton. A 4D image containing the TBSS
skeleton of the subjects was loaded into MATLAB, and transformed into an array of all non-zero
voxel across each subject. To remove confound of age, we then regressed out the effect of age
for all voxels. Our mediation analysis was accomplished with three regression equations applied
across all voxels. First, we regressed the independent variable (risk group) against white matter
FA. A z-score was produced for each non-zero voxel and was used to produce a 3D image of z-
scores (‘Path A’). We then applied TFCE in the FSL ‘fslmaths’ function with E=2, H=1, and the
neighbourhood-connectivity parameter = 26 as recommend in TBSS analysis (Smith and Nichols
2009). 10,000 permutations (i.e. randomization analysis) were then performed and the max z-
statistics for each permutation was used to assess significance accounting for FWE. Second, we
regressed the mediator variable (white matter FA at each voxel) against cognitive performance at
each voxel (‘Path B’). A 3D image of z-scores were produced, and we tested significance using
the same TFCE and permutation test technique. Third, we regressed the independent variable
(risk group) on cognitive performance (‘Path C’). A significant association in all three sets of
regressions then allowed us to proceed with the Sobel equation to assess the indirect effect of the
independent variable on the dependent variable via the mediator (white matter FA at each voxel).
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We used the unstandardized regression coefficients (beta) and the standard errors (SE) from
‘Path A’ and ‘Path B’ in order to produce a z-value at each white matter FA voxel (Sobel
equation: z-value = beta(Path A)*beta(Path B)/ √(beta(path B)2 *SE(Path A)
2 + beta(Path A)2 *SE(Path B)
2)). A
3D image of z-values were produced, we applied TFCE, and significant mediation was assessed
using the max z-value of 10,000 permutations. It should be noted that resampling strategies to
assess significance of the Sobel equation are considered to be a better alternative than parameter
tests that impose distribution assumptions (Preacher and Hayes 2008).
6.4 Results
6.4.1 Demographics
Frequencies and distribution of demographic data for our sample are shown in Table 6-2.
Schizophrenia patients were not different than controls with respect to age, IQ, sex, and
handedness, but had lower education, Mini Mental Status Exam (MMSE) score, Cumulative
Illness Rating Scale – Geriatrics (CIRS-G), and proportion of Caucasian subjects (p<0.05).
6.4.2 Genetics
None of the SNPs significantly deviated from Hardy-Weinberg equilibrium in the healthy
controls (p>0.05). There was no significant difference in the frequency for any of the SNPs
between healthy controls and schizophrenia patients, although it should be noted that all markers
were in the direction consistent with previous associations (Table 6-1). Our additive risk score
did not differ significantly between healthy controls (mean= 2.46±0.76) and schizophrenia
patients (mean=2.59±0.75), t196= 1.13, p(1-sided) = 0.131).
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6.4.3 The effect of additive risk on whole brain measure of cortical thickness and white matter FA
Vertex-wise analysis of cortical thickness revealed a significant score-by-diagnosis interaction
(q<0.05) predominately in the inferior parietal cortex (MNI co-ordinate [47 -73 12]): F1,197=16.5,
p=5.0x10-7) and right insular cortex (MNI co-ordinate [40 -7 6]): F1,197=9.8, p=0.002; Figure 6-
2). There was no main effect of additive risk (q>0.05). Voxel-wise TBSS of white matter FA
showed a prominent main effect of our additive risk score on FA throughout the brain (p<0.05 to
p<0.001) after correcting for family-wise error. Furthermore, there was a significant score-by-
diagnosis interaction predominately in the corpus callosum (P<0.05; Figure 6-1).
6.4.4 The effect of additive risk on general brain structure
To assess the effect of our additive model on general brain structure, we performed separate
factor analyses on average skeletal FA and average cortical thickness among regions. For cortical
thickness, the first principal component, PC1[CT], explained 62.6% of the variance. From the
TBSS FA skeleton the first principal component, PC1[FA], explained 46.5% of the variance. No
other components explained greater than 10% of the variance. PC1[FA] and PC1[CT]
significantly correlated with each other after removing the effect of age (r=0.30, p=1.6x10-5).
There was no significant main effect of additive risk score on PC1[CT] was observed, but a
significant diagnosis-by score interaction (t=2.42, p=0.016; Figure 6-4). Within schizophrenia
patients, risk score predicted 4.9% (R2=0.049, t=2.12, p=0.037) of the variance in PC1[CT].
There was a significant main effect of additive risk score on PC1[FA] (t=2.49 p=0.014; Figure 6-
3), and there was no significant diagnosis-by-score interaction. Within schizophrenia patients,
additive risk score predicted 6.7% (R2=0.067, t=2.50, p=0.014) of the variance in PC1[FA].
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6.4.5 The effect of extreme additive risk loading on general brain structure and cognitive performance
To examine the impact of multiple simultaneous risk allele hits on brain structure and cognitive
function, we re-analyzed our data including only subjects with high additive risk allele loading
(score>3; n=35) or low risk loading (score=<1.5; n=32). Significant diagnosis-by-score
interaction for PC1[CT] (F1,67=4.79, p=0.032; Figure 6-5), and a significant main effect of high
risk groups interactions were observed for PC1[FA] (F1,67=6.41, p=0.014; Figure 6-6).
The first principal component of our cognitive tasks, general fluid intelligence (gF), predicted
51.3% of the variance in the entire sample (Eigenvalue =4.62). There was no significant main
effect of high additive risk or diagnosis-by-group interactions for gF. In follow-up analyses in
our cognitive tasks composing gF, we did find a significant main effect association with verbal
fluency after Bonferroni correction for eight multiple comparisons (F1,67=9.84, p=0.003), and
nominally significant diagnosis-by-group interaction (F1,67=5.21, p=0.026; Figure 6-7). There
were no other significant associations (Table 6-3). The first principal component of our
psychomotor tasks (PC1[MC]) explained 70.1% of the variance in our entire sample
(Eigenvalue=2.81). There was a significant diagnosis-by-group interaction
(F1,63=10.07,p=0.002), and main effect of high additive risk on PC1[MC] (F1,63=5.77, p=0.020;
Figure 6-8). Because PC1[MC] explains a high degree of the variance, we did not examine these
tasks individually (dominant/non-dominant hand for grooved pegboard and finger-tapping).
6.4.6 Voxel-wise mediation analysis
Since there were significant associations between high risk allele loading and lower FA as well
poorer neurocognitive performance, we employed a novel approach of voxel-wise mediation
analysis on the TBSS skeleton. This allows for the testing of statistical inferences on whether the
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additive genetics effect on voxel-wise FA is causing the poorer verbal fluency and PC1[MC].
We analyzed only schizophrenia patients since the majority of effects on both cognition and
brain structure occurs in this group (Figure 6-5 to 6-8). High genetic risk loading strongly
predicted lower white matter skeleton FA after family-wise error correction for multiple
comparisons (Figure 6-9A). Poorer white matter skeleton FA predicted worse performance on
the controlled oral word association test (COWAT) for verbal fluency (Figure 6-9B). White
matter skeleton FA mediated the genetic association with verbal fluency over a widespread area,
suggesting that these regions (or FA regions in high correlation) are causally explaining the poor
performance in these schizophrenia patients (Figure 6-9C). For example, high risk allele loading
explained 33% of the variance in verbal fluency (R2=0.33, F1,24=11.12, p=0.003). When we co-
varied for the top associated voxel with high risk allele loading (MNI co-ordinates [108 148 98];
R2=0.50, p=7.5x10-5) the variance explained was 4% (R2=0.04, p=0.36); thus, 29% of the
variance was mediated after correction for family-wise error (PFWEcorrected[Sobel] < 0.001). There
were no significant associations between white matter skeleton FA voxels and PC1[MC] after
correcting for family-wise error; therefore, we did not perform any mediation analysis for motor
coordination.
6.5 Discussion
To our knowledge, this is the first study to apply additive genetic modelling on neuroimaging
phenotypes, and to causally show the effect of the additive risk on cognition using voxel-wide
mediation. Our findings support an important role for additive genetic risk in determining brain
structure and cognitive function within schizophrenia. Increasing number of schizophrenia risk
alleles lead to widespread reductions in FA, and in schizophrenia patients there were reductions
in cortical thickness. Our principal component analyses demonstrated that one latent variable
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explains a large percentage of the variance in both skeletal FA and cortical thickness. There were
significant main effect and diagnosis-by-score interaction on our principal component for
skeletal FA and cortical thickness, respectively. This suggests that additive risk broadly affects
brain structure, which we found to be especially true within schizophrenia patients. Lastly, our
results show that schizophrenia patients with high risk allele loading may constituent a more
severely impaired subgroup with reductions in white matter FA, cortical thickness motor
coordination and verbal fluency scores. Moreover, we showed a direct statistical relationship
suggesting that the reduced white matter FA in these individuals causes the impairment of verbal
fluency.
Our results show a differential effect of additive genetic risk between healthy controls and
patients with schizophrenia. There was a prominent main effect and diagnosis interaction of
additive risk loading in white FA. Conversely, we only detected an additive risk loading by
diagnosis interaction effect in cortical thickness. In fact, healthy subjects with high additive risk
loading tended to have greater thickness in our principal component analysis. Cortical thickness
reductions have a high genetic contribution, and there are cortical thickness reductions observed
in schizophrenia; however, reductions in thickness there are only trend-level reductions observed
in sibling of patients (Goldman, Pezawas et al. 2009, Panizzon, Fennema-Notestine et al. 2009,
Chen, Fiecas et al. 2013, Xiao, Lui et al. 2013). Thus, cortical thickness may not be a poor
schizophrenia intermediate phenotype. Moreover, cortical thickness has a high degree of
plasticity that may be dependent on schizophrenia treatment (Lett, Voineskos et al. 2014). Poor
outcome patients have more pronounced cortical thinning, and higher intake of antipsychotic
medication correlates with less cortical thinning over time (van Haren, Schnack et al. 2011). It
could be speculated that healthy individuals with high additive risk, may have higher compensate
via greater cortical thickness. Nevertheless, we observed interaction effects in both cortical
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thickness and white matter FA, suggesting that the effect of additive risk loading is greater
within schizophrenia patients. One potential explanation is that each risk gene included in our
additive model has been shown to be an important modifier of disease. Our previous results
showed that MIR137 had significant effects on white matter integrity, hippocampal volumes, and
lateral ventricle volumes, but only in schizophrenia (Lett, Chakavarty et al. 2013). Moreover in
each gene in our model, diagnosis-by-genotype interactions have been reported in cognitive
function (Hashimoto, Ohi et al. 2010, Walters, Corvin et al. 2010, Green, Cairns et al. 2012,
Hori, Yamamoto et al. 2012), imaging intermediate phenotypes (Addington, Gornick et al. 2004,
Smith, Thornton et al. 2012, Eisenberg, Ianni et al. 2013, Erk, Meyer-Lindenberg et al. 2013),
and genome regulation (Mill, Tang et al. 2008, Bharadwaj, Jiang et al. 2013). Taken together,
our results suggest that additive risk loading may be distinct within schizophrenia in comparison
to controls.
Although each one of the variants included in our model have been reported to have distinct
effects on brain structure and function, we found that an accrual of risk variants led to greater
effect on white matter FA and cortical thickness. The effect of additive risk was predominately
within schizophrenia patients, and therefore, may have important implications in explaining the
high degree of variability in brain structure finding within the disease (Ho, Andreasen et al.
2003, Kubicki, Westin et al. 2005, Kubicki, McCarley et al. 2007). It could be speculated that the
association between one variant and brain structure may be dependent on the additive risk from
other schizophrenia risk variants. Because our model takes the effects of multiple SNPs into
account, it may show effects which are closer to the actual genetic risk on brain structure
compared to analyses of single variants. It should be noted that although our SNPs could be
explain in terms of gene network interactions, none of the SNPs interact directly with each other.
That is, there are only indirect interactions through their gene products. We did find that patients
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with risk allele loading had robust reductions in cortical thickness and white matter FA.
Importantly, we were able to causally connect this change in white matter FA to poorer verbal
fluency. This has a number of important consequences. First, due to the high variability of brain
structure within and between patients groups, assumptions are made to what constitute poor brain
structure (e.g., lower FA). Our results definitely show that, in our sample, lower FA leads to
worse verbal fluency. Second, while the molecular genetic significance of our findings may
require more study, we do explain some of the neuroanatomical basis for language dysfunction
within schizophrenia.
We were unable to find statistically significant mediation of white matter structure and our motor
coordination first principal component (PC1[MC]). The voxel-wise skeleton FA analysis did not
significantly predict PC1[MC]; therefore, no voxels explain enough of the variance in PC1[MC]
for any meaningful mediation. It is possible that the effect was masked by our stringent
correction for multiple comparisons. Alternatively, other brain imaging modalities, such as
cortical surface area or subcortical volumes, may better correlated with PC1[MC], and thus,
potentially mediate the effect of high additive risk on motor coordination. Unfortunately, we
were unable to perform similar analysis in our vertex-wise analysis of cortical thickness.
This study has a number of important limitations. Many different analyses were conducted;
nevertheless, our study was strictly guided by our a priori hypotheses testing, and rigorous
correction for multiple comparison within each imaging modality. Moreover, our cognitive
analyses was performed in relatively smaller, genetically homogenous subgroups, and thus may
be potentially inconclusive. The fact that verbal fluency findings association did mediate through
brain structure support the validity of our findings. Furthermore, our subgroup analyses does
specifically address our research question. Namely, the impact of simultaneous risk alleles on
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brain structure and cognitive function. Last, we did not perform gene x gene interaction analysis.
Given that five SNPs were included in our model we would need a much larger sample to have
enough power to detect epistasis. Moreover, empirical and theoretic evidence suggest that
complex traits, such as morphology, are mainly due to additive genetic variance (Hill, Goddard
et al. 2008). Therefore, our approach may be a useful alternative to examining gene interaction
without massive sample sizes. These caveats notwithstanding, the data does suggests that
additive genetic risk has meaning impact on brain structure, and it may lead to reduced cognitive
function. Furthermore, the consequences of high additive risk for schizophrenia may differ in
schizophrenia patients compared to controls, and further research is necessary to explain the
mechanism underlying these differences.
To the best of our knowledge, this is the first study to examine the additive effect of these variant
on cortical structure and cognitive function. Via additive genetic modeling, we identified a
subgroup of patients with schizophrenia characterized by widespread reductions in white matter
FA, cortical thickness, motor coordination, and verbal fluency. Early identification of patients
with neurobiological markers of more severe disease trajectory may lead to better outcome either
through novel treatment tailored to these markers, or identification of patient whom may benefit
form more aggressive treatment strategies. Moreover, our additive modelling approach may
account for core features underlying the diverse features of schizophrenia and other psychiatric
disorders that may be difficult to detect with a single gene variant, and move us toward
biological or molecular subtyping of this heterogeneous disorder.
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Figure 6-1. Greater additive risk predicts poorer white matter fractional anisotropy.
Greater additive risk score predicted reduced fractional anisotropy across multiple brain regions,
and the effect was larger within schizophrenia patients. Areas coloured from red to yellow
correspond to p-values ranging from 0.05 and lower following correction for multiple
comparisons using family-wise error. Significant regions are mapped onto the standard Montreal
Neurological Institute atlas MN152 1-mm brain template.
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Figure 6-2. Significant additive score-by-diagnosis interaction for vertex-wide cortical
thickness. Schizophrenia subjects with greater additive risk have reduced cortical thickness.
Areas coloured from blue to yellow correspond to p-values ranging from 0.05 and lower
following FDR correction for multiple comparisons at q=0.05.
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Figure 6-3. The first principal component (PC1[FA]) of skeleton FA across additive model
scores in schizophrenia patients and healthy controls.
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Figure 6-4. PC1 Cortical Thickness across additive model scores in schizophrenia patients
and healthy controls.
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Figure 6-5. High additive risk allele load predicts lower PC1 fractional anisotropy in
schizophrenia patients. HC = Healthy Control; SCZ = Schizophrenia
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Figure 6-6. High additive risk allele load predicts lower PC1 fractional anisotropy in
schizophrenia patients. HC = Healthy Control; SCZ = Schizophrenia
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Figure 6-7. High additive risk allele load predicts lower poorer verbal fluency in
schizophrenia patients. HC = Healthy Control; SCZ = Schizophrenia
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Figure 6-8. High additive risk allele load predicts lower PC1 of motor coordination in
schizophrenia patients. HC = Healthy Control; SCZ = Schizophrenia
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Figure 6-9. Voxel-wise mediation analysis of verbal fluency in schizophrenia patients. Areas
corresponding from red to yellow correspond to p-values ranging from 0.05 and lower following
family-wise error correction for multiple comparisons. 10000 permutations were performed. (A)
High additive genetic risk loading predicted lower FA across multiple brain regions including the
corpus callosum. (B) White matter FA predicted verbal fluency across multiple white matter
tracts. (C) Significant p-values indicated broad areas of the white matter skeleton FA partially
mediate the effect of high genetics risk loading on cognitive function. We create a z-statistic
from voxel-wise Sobel tests for mediation based on the beta coefficients and standard error from
analyses preformed in A and B. The z-values then undergo threshold free clustering
enhancement, and p-values are derived using permutation testing.
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Table 6-1. Count and frequency of risk alleles by diagnosis
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Table 6-2. Demographics and Clinical Characteristics
Control (N=109) Schizophrenia (N=89)
Mean SD Mean SD T(196) P
Age (years) 45.62 19.01 45.47 17.42 0.06 0.95
Education 15.35 1.87 13.22 2.82 6.4 <0.05
IQ (WTAR) 111.44 7.85 108.39 16.27 1.7 0.09
MMSE 29.33 0.91 28.67 1.75 3.4 <0.05
CIRSG 1.89 2.00 2.49 2.17 2.0 <0.05
Sex (M) 62 56.90% 58 34.80% χ2 = 1.4 0.24
Handedness (R) 101 92.70% 81 91.00% χ2 = 2.5 0.28
Ethnicity (C) 103 94.50% 70 78.70% χ2 = 11.1 <0.05
Age at Onset (years) 25.15 9.52
Duration (years) 20.35 16.30
AIMS 0.99 2.38
PANSS 53.90 15.21
Positive 14.01 5.70
Negative 14.43 5.89
General 25.45 6.79
AAO, Age at Onset of schizophrenia; AIMS, Abnormal Involuntary Movement Scale; C,
Caucasian; CIRS-G, Cumulative Illness Rating Scale – Geriatrics; DOI – Duration of Illness, F,
Female; M, Male; MMSE, Mini Mental State Examination; PANSS, Positive and Negative
Syndrome Scale; R, Right-handed; WTAR, Wechsler Test of Adult Reading.
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Table 6-3. The effect of high additive risk allele loading on general fluid intelligence (gF)
and its components.
Neurocognitive Task Group Diagnosis Group*Diagnosis
F(1,64) P F(1,64) P F(1,64) P
gF (PC1) 1.219 0.274 24.535 <0.001 1.301 0.259
COWAT 9.84 0.003 4.86 0.031 5.21 0.026
LNS 2.25 0.139 16.3 <0.001 1.224 0.273
TMT-A 0.079 0.779 10.716 0.002 0.755 0.388
TMT-B 0.031 0.862 10.273 0.002 0.067 0.796
Digit Span 2.435 0.124 5.892 0.018 0.474 0.494
Digit Symbol Coding 0.299 0.587 17.096 <0.001 1.108 0.297
Story Memory 0.033 0.857 9.837 0.003 0.234 0.631
Story Recall 0.303 0.584 18.31 <0.001 0.003 0.954
Line Orientation 0.741 0.393 5.24 0.026 0.875 0.353
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Chapter 7
7 General Discussion & Future Direction
7.1 Summary of Results
The experiments presented in the previous chapters constitute several key findings. We
demonstrated that common genetic variants in the NRXN1 gene impact brain volume and
sensorimotor functioning integral to schizophrenia and other psychiatric disorders. Next, we
showed that the MIR137 risk variant robustly predicts across four independent sample the age-at-
onset of psychosis, a predictor of clinical outcome and cognitive function within schizophrenia.
The MIR137 risk variant also predicted neuroanatomical correlates of schizophrenia severity
(lateral ventricle and hippocampal volumes). Moreover, MIR137 was associated with worse
white matter FA across the lifespan. Importantly, patients carrying the protective genotype were
no different than healthy controls in cortical structure. Therefore, we demonstrated the MIR137
gene that influences neural development and regulates other strongly associated schizophrenia
risk variants plays a sizeable role in explaining heterogeneity among schizophrenia patients via
age-at-onset and brain structure. In the next project, we demonstrated that the GAD1 risk variant
was associated with verbal working memory, non-verbal working memory, and selective
attention among schizophrenia patients and healthy controls. GAD1 was also associated with
white matter FA in the prefrontal cortex. Furthermore, our results suggest that the GAD1
association with FA carries functional relevance to non-verbal working memory performance
using our voxel-wise mediation analysis. Thus, providing a potentially casual mechanism
through which a schizophrenia candidate gene influences some of the variance of working
memory functioning within the disorder. We then successfully demonstrated that additive genetic
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risk among five genes associated with schizophrenia heterogeneity strongly predicts cortical
thickness and white matter FA, particularly within schizophrenia patients. Using PCA on whole
brain measures of cortical thickness and white matter FA, additive risk was associated with core
changes in brain structure that provide a potential mechanism through which multiple common
risk variants enact a general effect on brain structure. Comparing genetically distinct
schizophrenia subgroups based on polygenic risk loading, we found that high risk loading
predicted reduced motor functioning, and poorer verbal fluency performance. Therefore, our
additive risk model may serve as a paradigm in which genetics can help reveal heterogeneity
within schizophrenia that may well predict different disease trajectory.
In summary, the results of the presented studies demonstrate that variation in schizophrenia risk
genes has appreciable effects on brain structure that may well affect cognitive function and
clinical heterogeneity within the disorder.
7.2 Can Imaging-genetics Dissect Clinically Meaningful Heterogeneity within Schizophrenia?
When imaging-genetics strategies were first developed over ten years ago it held tremendous
promise to better understanding the etiology of schizophrenia, and thus, provide better
description of the novel neurophysiological treatment. That is, characterizing the neural systems
affected by risk gene variants to understand quantitative measure of brain structure and function
related to psychiatric disease could provide novel treatment avenues via: novel molecular targets
of medication or better neuroanatomical outcome factors more specifically identifying patients
than may respond to current treatments. Importantly, imaging-genetics can target specific core
symptoms of schizophrenia, such as working memory deficits, in which there is arguably little
amelioration of dysfunction with antipsychotics treatment (Lett, Voineskos et al. 2014).
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Unfortunately to date, neurobiological research has not translated into novel treatment options in
psychiatric research have become standard of care.
The intermediate phenotype approach has been the dominant imaging-genetics method in
psychiatry. In the most common application, genetic variants associated with a disorder are
examined in healthy controls. Since common variants are present in healthy controls,
intermediate phenotypes should provide subclinical changes in brain structure and function
outside of confounds of the psychiatric disorder (e.g. socioeconomic status, medication, and/or
comorbidities). We have successfully used this approach with NRXN1, GAD1, and our additive
polygenic model. However, it could be argued that even in the presence of confounding factors
of schizophrenia (antipsychotic treatment, co-morbidities, stressful life events, and others),
meaningful imaging-genetic data can be derived in patient populations. Indeed, MIR137 had a
relatively small effect on brain structure in health controls, but in patients with schizophrenia the
risk genotype predicted early age-at-onset, larger lateral ventricles, lower hippocampal volume,
and lower white matter FA throughout the brain. This genotype-by-diagnosis interaction may be
due to the effect of microRNA-137 as a regulator of neurodevelopmental genes, epigenetics
machinery, and other genes strongly associated with schizophrenia liability genes (See Chapter
4.2). Importantly, the findings suggest the effect of common gene variants on brain structure and
cognitive function may be distinct within the disorder. It is possible the effect of a single risk
variant may be dependent on the presence of other risk variants. Our additive risk model
demonstrated some effects across healthy controls and schizophrenia patients although the
majority of effect was within schizophrenia. The effect sizes of white matter FA and cognitive
associations were greater in patients. Moreover, the association between additive risk loading
and cortical thickness was in opposite direction between controls and patients. The polygenic
risk by diagnosis interaction may be partially explained by the genetic contribution to cortical
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thickness. There is a high degree of cortical thickness heritability among healthy co-twins; thus,
likely determined through genetic variation (Panizzon, Fennema-Notestine et al. 2009, Rimol,
Panizzon et al. 2010). Among co-twins discordant for schizophrenia, cortical thickness
reductions observed in affected twins are not prevalent in the healthy sibling (Goldman, Pezawas
et al. 2009, Panizzon, Fennema-Notestine et al. 2009, Rimol, Panizzon et al. 2010) suggesting
that cortical thickness may be a poor intermediate phenotype. Alternatively, since cortical
thickness carries a high degree of plasticity it is also possible that the increased cortical thickness
may be a protective mechanism in healthy controls with high risk allele loading. Taken together,
our findings suggest that the intermediate phenotype approach (i.e., in healthy controls), although
useful, may be missing clinically relevant molecular genetic-by-diagnosis interactions.
Our results also suggest that the relationship between schizophrenia risk variants and brain
structure may be dependent on the presence of other schizophrenia risk variants. The MIR137
gene product functionally regulates other schizophrenia risk genes including NRXN1, CACNA1C,
and ZNF804A (Kwon, Wang et al. 2011, Kim, Parker et al. 2012); however, the functional
relevance of the rs1625579 variant is unclear. Furthermore, the association between disrupted
mir-137 expression and schizophrenia is controversial (Guella, Sequeira et al. 2013, Wright,
Turner et al. 2013). It should be noted though that mir-132, an upstream regulator of mir-137, is
associated with schizophrenia (Miller, Zeier et al. 2012). Therefore, the relationship between
rs1625779 genotype and the regulation of schizophrenia associated genes by mir-137 needs
further research. Alternatively, it could be hypothesized that the cumulative (or additive) effect
of the rs1625579 MIR137 variant and other common risk variants in neurodevelopmental gene
pathway may be driving the strong association within schizophrenia. Our additive modelling
results suggest that indeed while any one of the risk variants may independently have different
associations with brain structure, together they may have broad effects on the brain since
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polygenic risk predicted the first principal components of cortical thickness, and white matter
FA. Moreover, the results in voxel-wise FA mediation analysis suggest additive risk loading may
be clinical relevant in understanding how genetic effect on brain structure lead to verbal fluency
dysfunction in schizophrenia patients. In summary, the relative importance and the effect on the
brain of gene variants may be different in schizophrenia patients compared to healthy controls.
7.3 What Benefits does Translational Research Address?
Clinical diagnosis based on DSM and ICD currently relies on presenting symptoms that may not
reflect the neurobiology. It has been suggested that the boundaries between different diagnoses
fail to align with the clinical neuroscience and genetics. For example, the genetic effects shared
between schizophrenia and bipolar disorder are greater than the genetic effects differentiating the
disorders (Lichtenstein, Yip et al. 2009). Moreover, current diagnostic categories have not been
predictive of treatment response. One key strategy is to use translational research to describe one
core component of disease pathology or neural circuit that can be used to drive further research
or novel treatment targets. The best example is the NIMH Research Domain Criteria (RDoC)
initiative which addresses some these concerns by developing novel ways of classifying mental
disorders based on clinical neuroscience and genetics (Insel, Cuthbert et al. 2010, Cuthbert and
Insel 2013). Translational research among imaging, genetic, and neurocognitive data allows
modelling complex relation that may provide novel findings.
An important benefit would be driving new treatment strategies, and target individuals that may
better respond to treatment. Our NRXN1 findings suggest an intermediate phenotype with
disrupted sensorimotor function and frontal white matter volume that overlaps dysfunction
common to ASD and schizophrenia (See Chapter 3.4). In schizophrenia subjects, the rs1045881
NRXN1 marker was also associated with response to the antipsychotic medication clozapine in
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schizophrenia, and interestingly, this result was driven by improvement of negative symptoms
(Lett, Tiwari et al. 2011). Previous structural MRI brain imaging findings have reported that a
generalized reduction in frontal lobe white matter correlates with greater severity of negative
symptoms (Sanfilipo, Lafargue et al. 2000, Wolkin, Choi et al. 2003, Voineskos, Foussias et al.
2013). Thus, our NRXN1 imaging intermediate phenotype may be applicable to the treatment of
negative symptoms in schizophrenia. Given these findings are correlative it difficult to assess,
with confidence, if there is a direct relationship between the genetics effect on white matter and
negative symptoms within schizophrenia.
It may be necessary to not only describe heterogeneous phenotypes within schizophrenia, but
also to relate the genetic effect among different phenotypes. For example, we have shown that
the GAD1 rs3749034 risk variant, known to be a predictor of GABA levels in vivo, leads to
lower prefrontal white matter integrity and, at least statistically, causing working memory
dysfunction (See Chapter 5.5). Our voxel-wise mediation analysis importantly takes into account
variation in the entire white matter FA skeleton. Most imaging genetic modalities are highly
correlated; therefore, association in region of interest approaches may well due to the true
association in other regions of the brain. Our GAD1 voxel-wise mediation analysis identifies all
regions associated with working memory dysfunction. Therefore, this approach potentially
allows attachment of functional significance of cognitive performance tasks to white matter FA
imaging-genetics data. Since GAD1 is associated with lower FA that leads to impaired working
memory performance, this may be integral to future studies in which prefrontal white matter FA
could be used an outcome measure or early detector of treatment response (such as a GABAegic
pharmacological agent (Lett, Voineskos et al. 2014)).
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7.4 Can Imaging-genetics explain enough of the Heterogeneity to Guide Treatment Decisions?
The integration of research on neuroimaging and genetics holds tremendous promise for
improving outcome in schizophrenia by facilitating: (a) development of novel therapies based on
improved understanding of pathogenic mechanisms underlying core symptoms of schizophrenia,
and (b) more sensitive measure of treatment response in genetically defined patients groups.
However to date, common risk gene variants only have shown limited effect sizes explaining
disease association (Ripke, Sanders et al. 2011, Ripke, O'Dushlaine et al. 2013), and the same
has been largely true for neuroimaging phenotypes and cognitive phenotypes relevant to
schizophrenia (Greenwood, Braff et al. 2007, Brans, van Haren et al. 2008, Lencz, Knowles et al.
2014). It could be suggested that for clinically meaning for associations that a greater proportion
of the variance needs to explained. In the NRXN1 study (See Chapter 3.4) we were only able to
explain some proportion of the variation with rs1045581 risk allele homozygotes predicting
approximately 6% reduction in frontal lobe white matter volume in healthy controls. In contrast,
our MIR137 results (See Chapter 4.4.4) showed that within schizophrenia patients the risk
genotype predicted 9% of the variance in average white matter FA across the lifespan, and the
effect on age-at-onset of psychotic symptoms was approximately double that of sex (a well-
established predictor of age-at-onset). Our additive model results indicate that a clinically
relevant subgroup could be described based on polygenic risk. These results suggest enough of
the variance can be explained, but may be necessary for prospective studies to test the clinical
application of an imaging-genetics based algorithm for prediction of long term outcome in
schizophrenia.
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7.5 Limitations
Beyond the specific limitations discussion in each chapter, there are a number of important
limitations with the approaches and findings that should be considered. Most important is that
our positive findings require replication. Imaging-genetics studies has previously tended to have
sample sizes that are generally small, leading to low power and inflated effect size of genetic
association in discovery samples (Button, Ioannidis et al. 2013). For example, Munafo et al.
demonstrate that the effects of the 5-HTTLPR (serotonin-transporter-linked polymorphic region)
on amygdala activation in the discovery samples are usually much higher than in any replication
(Munafo, Freimer et al. 2009). It is possible that this “winner’s curse” occurred with our NRXN1
findings (Zollner and Pritchard 2007, Ioannidis 2008, Kraft 2008). Furthermore, considering the
modest sample size, our genotypic associations with brain volumes would be under-powered in
NRXN1 SNPs with a minor allele frequency less than 15%. Consequently, we excluded these
SNPs from our analyses to reduce penalties for multiple comparison. However, in all of
subsequent imaging analyses of this thesis our sample sizes were comparatively large (n~200).
Moreover, in each study there has been convergent evidence from clinical or cognitive
phenotypes to support the hypothesis. For example, our MIR137 age-at-onset findings were in
the same direction over four independent samples suggesting a true effect. The voxel-wise
mediation approach employed in final two studies (See Chapter 5.4 and Chapter 6.4) provides
even greater support since it is unlikely genetics association with a whole brain voxel-wise
measure would mediate the cognitive effects since (i) the stringent multiple comparison
correction performed, and (ii) the direction of the effect in the mediation model. The latter is
particularly important because it means the mediation not correlation (or moderation) between
our cognitive variables and white matter FA was not driving the effects. Therefore, we can state
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that within our own sample the genetic association with skeleton white matter FA structure has
functional relevance in cognitive performance.
Another important limitation was that we did not link the functional relevance of the genetic
variants via altered gene expression and cell biology. While some of the variant we examined
have well established effects on gene expression or function (e.g. BDNF rs6265 variant), it is
difficult to assess function on the genome-wide identified variants. Therefore, it is possible that
these variants are not be affecting expression or function of the gene products, rather it may
variant in linkage disequilibrium with genome-wide identified variant. This has a number of
important consequences. First, we have made an assumption that proximity of the variant to the
gene suggests a functional role although it may be associated with another gene. For example,
the MIR137 rs1625579 has SNPs in linkage disequilibrium (R2>0.8) over a region of
approximately 13,000 base pairs included in MIR137HG which codes for two microRNAs (mir-
137 and mir-2682). Second, we may be selecting the wrong variant thereby introducing noise
into our analysis leading to type II error. Third, it is difficult to ascertain how the action of the
variant leadings to dysfunction, such whether more or less expression leads to increased
schizophrenia liability. The Encyclopedia of DNA Elements (ENCODE) project should clarify
the functional relevance of many of the genome wide findings, and provide more target avenues
for in vitro analyses (Consortium, Bernstein et al. 2012). It is important to note that imaging-
genetic findings may provide some functional significance. For instance, we independently show
that rs16225579 MIR137 risk homozygotes lead to poorer brain structure and earlier age-at-
onset. Moreover, our voxel-wise mediation analyses indicate that even though may not have the
true risk locus, we do explain enough of the variance for genetic association to be useful in
explaining brain function.
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The last three studies in this thesis examined imaging-genetic associations within patients with
schizophrenia. Antipsychotic mediation may be an important confounder of our imaging findings
since it may have both direct and indirect consequences on brain structure. Grey matter loss,
higher neuronal density, and reduced glial cell number similar to what is histologically observed
in schizophrenia was reported in non-human primates exposed to olanzapine or haloperidol over
a two year period (Dorph-Petersen, Pierri et al. 2005, Konopaske, Dorph-Petersen et al. 2007).
Furthermore, longitudinal first episode schizophrenia study showed progressive decline of white
and gray matter volume correlating with antipsychotic medication dose (Ho, Andreasen et al.
2011). Longitudinal reductions in hippocampal volume and BDNF levels has also been reported
after eight months of antipsychotic treatment in first episode patients (Rizos, Papathanasiou et al.
2014). The effect of medication on white matter FA is more controversial with reports of
increase (Garver, Holcomb et al. 2008) and decrease FA with treatment (Wang, Cheung et al.
2013). Furthermore, antipsychotic medication causes significant weight gain in patients with
schizophrenia (Lett, Wallace et al. 2012), which may have significant impact on white matter FA
(Kuswanto, Sum et al. 2014). However, the lack of longitudinal, within-subject studies suggests
that further research is necessary. We found that chlorpromazine equivalents were not
significantly difference between groups. It would be expected the medication effects would
confound our results, and therefore not necessarily invalidate our significant findings. Moreover,
it is unlikely that the confounding effect of medication would be greater than the large effect size
of some of results (e.g. MIR137). Nevertheless, we cannot be certain if our genetics by disease
interactions may in part due to pharmacogenetic interactions, but our findings would arguably
remain just as clinically relevant.
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7.6 Future Directions
Imaging-genetics of schizophrenia is a dynamic field. Despite the aforementioned limitations it
has provided promising insights into how genetics may influence structure and function relevant
to the etiology and treatment of schizophrenia. Genome-wide identified variants may be of
especially high importance, since these schizophrenia risk variants might provide deeper insight
into the genetic mechanisms of phenotypic heterogeneity in the context of psychiatric disorders.
Future imaging-genetics studies will benefit from better description at the gene function (e.g.
regulation, expression, and interactions) and the brain function (e.g. connectivity, plasticity, and
relationship to behavior). The following section will discuss some of the recent advances in
which translational research may lead ultimately lead to novel treatment options.
7.6.1 Functional Relevance of Genetic Variation
Major leaps in increasing quality and affordability of newer sequencing technologies permit
unprecedented detail in the genetic variability. At the genomic level, whole-genome sequencing
is becoming affordable (currently $1000 USD per subject) with a relatively high degree of
coverage (average of 30 times), and such analyses are leading to discoveries of rare disruptive
mutations in synaptic pathways underlying schizophrenia (Fromer, Pocklington et al. 2014,
Purcell, Moran et al. 2014). The amount of information collected from each genome
(approximately three billion base pairs) and the complexity of voxel-wise brain imaging
approaches (e.g., 140,000 voxel for each skeleton FA) poses significant challenges in
multivariate analysis. One potential method could be to apply an additive risk model across a
gene network (e.g., neuronal plasticity, calcium regulation) in which sequencing data has
identified rare, functional variants and common risk associated variants. Next, this genetic
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information could be applied as a score to brain imaging phenotypes and symptom profiles to
provide a potential pathophysiological link between gene networks and phenotypes of interest.
One of the major concerns of genomic information is understanding the functional significance
of this static variation on the dynamic transciptome. RNA-sequencing and epigenetic methods
that have been very successfully applied to other domains such as cancer research are inherently
difficult in psychiatric research because of the challenges involved in sampling brain tissue.
These techniques are being applied to postmortem brain samples of healthy individuals across
the life span including: Brain Cloud (Colantuoni, Lipska et al. 2011, Numata, Ye et al. 2012),
Human Brain Transcriptome (Kang, Kawasawa et al. 2011), and the BrainSpan project
(http://www.brainspan.org/). These freely accessible resources provide invaluable information
regarding the timing of gene expression in the brain and the impact of genetic variants of interest
although there are caveats. First, epigenetic profile and RNA expression pattern vary
considerably, and the neurophysiology of the brain tissue can change dramatically depending on
the post-mortem interval and the pH of the tissue (Pidsley and Mill 2011). Second, in the
majority of the adult post-mortem samples clinical and neurocognitive assessments or in MRI
imaging may not have been collected. Third, most of the samples are not within clinical
populations. It is reasonable to suggest that the dynamic regulation of gene expression is
different between healthy controls and schizophrenia patients; therefore, the impact of any
variant may be dependent on diagnosis.
7.6.2 In Silico Prediction of SNP Function: Insight from ENCODE
It could be argued that our imaging-genetic analyses provide some degree of functional
relevance to significantly associated SNPs. However, establishing how these variants impact
gene expression would provide broader understanding of their impact in gene systems. We were
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able to demonstrate that NRXN1 rs1045881 likely affects binding of mir-339-5p, and thus may
affect expression of NRXN1. Furthermore, GAD1 rs3749034 has been associated with reduced
expression in vivo and in vitro. The BDNF rs6265 is a missense variant changing Valine to
Methionine at amino acid 66. In contrast, the functional relevance of the genome-wide identified
variants is poorly understood. It is unlikely that the top associated GWAS variants directly affect
gene expression, rather it may be another variant that is LD. Consequently, these SNPs could be
introducing noise into imaging-genetics analyses since the true association may be another SNP.
Recently, the Encyclopedia of DNA Elements (ENCODE) project is providing additional
insights into the true associated SNP via in silico prediction of protein binding. In silico analysis
using regulomeDB examining all SNPs with an R2 value greater than 60% (1000 genome phase
1, EUR population) from the GAD1, CACNA1C, MIR137, and ZNF804A markers reveals some
putative role in the expression of the co-localized genes. The GAD1 rs376255 variant likely
affects the binding of the repressor protein CTCF, and is in LD (R2=0.99) and 1080 bp away
from the GAD1 rs3749034. The putative relevance of the GWAS identified markers is less clear.
The MIR137 rs1625579 shares considerable variance with other two markers. 74% of the
variance (R2=0.74) in rs1625579 is explained by rs9324383 which is 17 kbp away. The
rs9324383 likely affects the binding of the transcription factor GATA2. The effect of GATA2 is
particularly interesting since the putative regulator of MIR137 and schizophrenia risk factor miR-
132 also regulates GATA2 expression (Miller, Zeier et al. 2012). 64% of the variance is also
shared by the rs4294451 MIR137 SNP that is 107 kbp away and likely affects the binding of
HNF4A, JUND, and P300. The rs1006737 marker also shared considerable variance with two
SNPs: rs139758774 and rs7308351. 62% of the variance at rs10066737 is explained by
rs139758774 that is 22 kbp away and affects the binding of the immune response transfactor
TRIM28. Further, 61% of variance is explained by rs7308351 that is 42 kbp away, and likely
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affects the binding of ZNF263. Last, there were no markers sharing more than 60% of the
variance with ZNF804A rs1344706 with any putative functional relevance. Potentially we would
have greater power in our imaging-genetic analysis by examining these putatively functional
markers. Nevertheless, in vitro analyses are necessary to confirm the functional influence of
these markers.
7.6.3 Combining in vivo Biomarkers
Structural imaging can provide “a window” through which neural connectivity can be assessed;
however, other methods may be better at understanding how circuits function. Cortical inhibitory
tone can be measured through combining transcranial magnetic stimulation (TMS) and
electroencephalogram (EEG) (Daskalakis, Christensen et al. 2002). Impaired network
synchronized activity has been reported in schizophrenia patients (Spencer, Nestor et al. 2003,
Spencer, Nestor et al. 2004, Spencer, Salisbury et al. 2008). In particular, long interval
intracortical inhibition (LICI; a measure closely associated with GABAB receptor
neurotransmission) at the DLPFC has been strongly correlated with performance on the N-back
(r=0.63, p=0.04) and LNS (r=0.68, p=0.005) working memory tasks in healthy controls
(Daskalakis, Farzan et al. 2008, Hoppenbrouwers, De Jesus et al. 2012). This is in line with
recent findings that age related working memory decline in rats correlates with GABAB receptor
expression, and the decline is reversible with administration of the CGP55845 GABAB receptor
antagonist (Banuelos, Beas et al. 2014). Furthermore, we recently found that LICI strongly
predict general fluid intelligence (gF), and in particular, working memory in healthy controls
(unpublished data; LNS: F1,22 =12.92, p=0.002; Digit-Span: F1,22 = 16.57, p=7.0x10-4). We also
found that frontothalamic FA was strongly associated with working memory only after covarying
for LICI and our overall model predicted approximately 65% of variance in working memory
173
(unpublished data; R2=0.65, F3,18=11.0, p=2.4x10-4). These results along with our GAD1 findings
(See Chapter 5) suggest multiple lines of evidence pointing to aberrant GABAergic signaling
potentiating working memory dysfunction in schizophrenia patients. Our findings also
demonstrate that combining in vivo biomarkers is an efficient strategy to dissecting complex
neural circuitry that may be at risk in psychiatric disorders. We are currently expanding our
TMS-EEG sample for genetic analysis. One important question is whether cortical inhibition and
white matter structure mediates the association between genetic variants and heterogeneous
phenotypes relevant to schizophrenia.
7.6.4 Towards Neurobiological Treatment
One of the major challenges in neuropsychiatric research is to identify biomarkers predicting or
directing better treatment response for evidence based decision making on treatment options. The
integration of research on neuroimaging and genetics holds tremendous promise for improving
outcome in schizophrenia by facilitating: (a) development of novel therapies based on improved
understanding of pathogenic mechanisms underlying core symptoms of schizophrenia, and (b)
more sensitive measure of treatment response in genetically defined patients groups. For
example, pharmacogenetic and non-pharmaceutical approaches (e.g., repetitive transcranial
magnetic stimulation (rTMS), cognitive remediation therapy (CRT), psychoeducation, cognitive
behavioral therapy (CBT)) could be employed in congruence with imaging methods.
Our GAD1 findings may be of particular relevance to neurobiological-guided treatment as they
point to aberrant GABAergic signaling potentiating PFC white matter structure changes and
working memory dysfunction relevant to schizophrenia (See Chapter 5). An inverse relationship
between FA in the genu of the corpus callosum and TMS-induced interhemispheric signal
propagation suggest functional asymmetry of the DLPFC depends white matter structure
174
(Voineskos, Farzan et al. 2010). TMS-induced LICI leads to suppression of gamma-band
oscillations in the DLPFC (Farzan, Barr et al. 2009). Also, rTMS treatment to the DLPFC
normalizes differences in gamma-band oscillatory activity between schizophrenia patients and
controls (Barr, Farzan et al. 2011). A recent 4-week, randomized, double-blind pilot study
suggests that rTMS to the DLPFC is effective at improving working memory performance in
schizophrenia patients (Barr, Farzan et al. 2013), and these findings are currently being followed-
up in large clinical trial. Considering the effects of GAD1 genotype on DLPFC inhibitory
function and working memory, it may be an important predictor of response to rTMS treatment
in schizophrenia patients. Furthermore, rTMS treatment has been shown to increase white matter
FA (Allendorfer, Storrs et al. 2012, Peng, Zheng et al. 2012). Therefore, changes in prefrontal
FA may well be a clinically relevant marker of plasticity, and potentially an important mediator
of response to rTMS treatment.
7.6.5 Conclusion
The combination of neuroimaging and genetics is a powerful strategy to parse out schizophrenia
heterogeneity. It provides clues into the neurobiological mechanism underlying psychiatric
disorders through characterization of at risk structures present in patients and controls. The
results of this thesis suggest that imaging-genetics can describe clinically meaningful
schizophrenia heterogeneity. Several previously identified variants in schizophrenia risk genes
explained significant differences in core features of the disorder and related brain abnormalities.
The multimodal approach of combing genetics, brain imaging, and clinical characterization was
more informative than any of the approaches alone in these studies. In the near future, it is likely
that this integrated approach will lead to better prognostic markers, and novel therapeutic
strategy for this complex and devastating disorder.
175
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Appendices
Appendix 1: Lett TA, Tiwari AK, Meltzer HY, Lieberman JA, Potkin SG, Voineskos AN,
Kennedy JL, Müller DJ. The putative functional rs1045881 marker of neurexin-1 in
schizophrenia and clozapine response. Schizophr Res. 2011 Nov;132(2-3):121-4.
Appendix 2: Lett TA, Voineskos AN, Kennedy JL, Levine B, Daskalakis ZJ. Treating working
memory deficits in schizophrenia: a review of the neurobiology. Biol Psychiatry. 2014 Mar
1;75(5):361-70.
The putative functional rs1045881 marker of neurexin-1 in schizophrenia andclozapine response
Tristram A.P. Lett a, Arun K. Tiwari a, Herbert Y. Meltzer b, Jeffrey A. Lieberman c, Steven G. Potkin d,Aristotle N. Voineskos a, James L. Kennedy a, Daniel J. Müller a,⁎a Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, Canadab Psychiatry and Pharmacology, Vanderbilt University, Nashville, TN, USAc Psychiatry, Columbia University, New York City, NY, USAd Department of Psychiatry & Human Behavior, University of California, Irvine, CA, USA
a b s t r a c ta r t i c l e i n f o
Article history:Received 15 May 2011Received in revised form 9 August 2011Accepted 12 August 2011Available online 3 September 2011
Keywords:Neurexin-1 geneNXRN1SchizophreniaGeneticsPharmacogeneticsAntipsychotic medicationClozapineProspective treatmentAntipsychotic responseBrief Psychiatric Rating Scale
Neurexin-1 (NRXN1) modulates recruitment of NMDA receptors. Furthermore, clozapine reduceshyperactivity of NMDA receptors. Thus, regulation of the NRXN1 gene may mediate the efficacy of clozapineat reducing cortical hyperactivity. We examined the putative functional SNP, rs1045881, for association withschizophrenia, and the potential role of this SNP in clozapine response. The rs1045881 variant was notsignificantly associated with schizophrenia (N=302 case–control pairs), but with clozapine response(N=163; p=0.030). Baseline and BPRS scores after six months revealed a trend for rs1045881 genotype bytreatment interaction (p=0.079). In the post hoc analysis, a significant association between BPRS negativesymptoms score and genotype was observed (p=0.033). These results suggest that the rs1045881 NRXN1polymorphism may influence clozapine response.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Deletions in the neurexin-1 (NRXN1; 2p16.3; gene size=1.1 mb)gene have been strongly associated with the etiology of schizophre-nia, and autism spectrumdisorder (Voineskos et al., 2011). TheNRXN1gene encodes the neurexin-1α protein that functions as a pre-synaptic neural adhesion molecule reported to interact with post-synaptic neuroligins mediating GABAergic and glutamatergic synapsefunction (Südhof, 2008). Neurexin-1α knockout mice exhibit anelectrophysiological phenotype consistent with a network disruptionthat presents as a presynaptic loss of synaptic strength in excitatorysynapses of the hippocampus (Kehrer et al., 2008; Etherton et al.,2009). Recent evidence suggests NRXN1 also binds to leucine-richrepeat transmembrane protein (LRRTM2), that modulates postsyn-aptic differentiation of glutamatergic synapses (de Wit et al., 2009).
Therefore, NRXN1 may, at least partially, direct excitatory synapseformation. These findings are interesting in light of reports thatclozapine prevents phencyclidine-induced functional hyperactivity ofN-methyl D-aspartate receptors (NMDAR) in pyramidal cells in ratmedial prefrontal cortex (Arvanov and Wang, 1999; Ninan et al.,2003). Furthermore, clozapine is reported to differentially regulatedendritic spine formation and synaptogenesis in the rat hippocampalneurons (Critchlow et al., 2006). Recently, we have reported thatrs1045881 is located in a putative miRNA binding site that influencesfrontal lobe structural white matter volume and sensorimotorfunction (Voineskos et al., 2011). Altogether, variation in regulationof the NRXN1 gene may influence response to clozapine treatedschizophrenia patients.
In this study, we analyzed associations of rs1045881 in schizo-phrenia (SCZ) matched case-controls. We have a strong a priorihypothesis to examine the association between this high-interestmarker and SCZ because of our in silico, neuroimaging andneurobehavioral findings. Second, given the effect of clozapine onNMDAR function, and the role of NRXN1 in NMDAR recruitment, weexamine the role of the rs1045881 in prospectively assessedEuropean-American schizophrenia patients for clozapine treatmentresponse.
Schizophrenia Research 132 (2011) 121–124
⁎ Corresponding author at: Neurogenetics Section, Centre for Addiction and MentalHealth, R31 250 College Street, Toronto, Ontario, Canada M5T 1R8. Tel.: +1 (416) 5358501; fax: +1 (416) 979 4666.
E-mail address: [email protected] (D.J. Müller).
0920-9964/$ – see front matter © 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.schres.2011.08.007
Contents lists available at SciVerse ScienceDirect
Schizophrenia Research
j ourna l homepage: www.e lsev ie r.com/ locate /schres
2. Experimental/materials and methods
2.1. Participants
The case–control study was composed of 302 matched pairs thatwere recruited at the Centre for Addiction andMental Health. Patientsand controls were matched for sex, ethnicity, and age at recruitment(Table S1). Research interviews were conducted using the StructuredClinical Interview for Diagnostic and Statistical Manual of MentalDisorder IV (DSM-IV) Disorders. Patients with a history of majorneurological disorders, major substance abuse, and head injury withsignificant loss of consciousness were excluded from the study. Eachindividual of the control group was screened for history of majorpsychiatric disorders using the SCID-I, and only those without majorpsychiatric disorders were entered as healthy controls. Our pairedcase–control sample had over 80% power to detect an odds ratio aslow as 1.65 (α=0.05, minor allele frequency=0.141, dominantmodel; Quanto v1.2.4 (Gauderman, 2002)).
Our clozapine response sample consisted of 169 European–Americanschizophrenia patients obtained from three research clinics: CaseWestern Reserve University in Cleveland, Ohio (HY Meltzer, N=68);Hillside Hospital in Glen Oaks, New York (JA Lieberman, N=73); andUniversity of California at Irvine (SG Potkin, N=28). These subjects hadDSM-III-R or DSM-IV diagnoses of SCZ andmet the criteria for treatmentrefractoriness or intolerance to traditional antipsychotic therapy. After a2- to 4-week washout period, patients were treated with clozapine andevaluated prospectively for 6 months and clozapine blood levels weremonitored. Treatment response was evaluated using the 18-item BriefPsychiatric Rating Scale (BPRS) at the time of enrolment into the study(baseline) and after 6 months of clozapine treatment. Differences inresponse rates across clinical sites were not observed (χ2=0.901,df=2, P=0.637, Table S2). Therefore, data from the three clinical siteswere analyzed together. Our sample had 80% power to detect an oddsratio of 2.0 at a non-responder frequency of 40% (unmatched case–control design; α=0.05, minor allele frequency=0.141, dominantmodel; Quanto v1.2.4 (Gauderman, 2002)). In our categorical responsemeasure sample, treatment response was analyzed as a dichotomousvariable. Responders were defined as a reduction N20% on the overallBPRS score after 6 monthsof treatmentbasedon the criteria proposedby(Kane et al., 1988). Quantitative treatment response data was availableonly for a subset (total BPRS [N=91]; positive/negative symptomssubscale [N=87]). All experimental procedures were approved by localethics committee and all patients signed informed consent prior to theirparticipation, in accordance with the Declaration of Helsinki.
2.2. Genetics
Genomic data was extracted from venous blood (Lahiri andNurnberger, 1991). The rs1045881 variant was genotyped, usingTaqman 5′ nuclease assay (Applied Biosystems; Foster City, CA, USA).Genotyping accuracy was assessed by repeating 10% of the sample, andresults showed 100% concordance.
2.3. Statistical analysis
Analysis of SCZ cases versus matched controls was done using log-likelihood χ2 ratio test both in terms of allele frequencies andgenotype frequencies in UNPHASED 3.1 (Dudbridge, 2008). Haplo-view 4.2 was used to determine Hardy Weinberg equilibrium (HWE)(Barrett et al., 2005).
To test the effect of NRXN1 genotype on quantitative treatmentresponse, a repeated measure analysis of variance (RM ANOVA) testswere performed. NRXN1 genotype was the between-group factor, andBPRS scores at baseline and 6 months were the within-group factor.These analyses were performed using Statistical Package for the SocialSciences 15.0.0 (Chicago, IL, USA).
3. Results
3.1. Association with Schizophrenia
The rs1045881 polymorphism was in HWE in both cases andcontrols (pN0.05). No significant allelic or genotypic associationsbetween rs1045881 and SCZ in our matched case–control sampleswere detected (p=0.37; p=0.27, respectively; Table 1). Further-more, we found no significant associations in European–Americansalone suggesting that our results are not masked by populationstratification (Table S3).
3.2. Influence of NRXN1 on clozapine response
The rs1045881 variant did not deviate from HWE in responders ornon-responders groups (pN0.05). Furthermore, therewasno significantdifference in gender, age at onset, and treatment duration based onrs1045881 genotype (Table S4). Our categorical analysis found thers1045881C allele of NRXN1 to be associated with clozapine response(p=0.012, OR=2.199 [1.185–4.080]; Table 2). Additionally, the C/Cgenotype showed association with treatment response under adominant model (p=0.030, OR=2.153 [1.077–4.304]; Table 2). Thiswas further supported by the trend observed in our quantitative mea-sure of treatment response (RM ANOVA: F1,87 Within-subject=3.151p=0.079; Fig. 1; Table S4).
In post hoc analysis, we examined positive and negative symptomsubscales of the BPRS. RM ANOVA of negative symptoms showed asignificant genotype association (F1,85 Between-subject=4.686,p=0.033) and a trend for genotype by treatment response (F1,85Within-subject=3.293, p=0.075; Fig. 2; Table S5). In contrast, therewas no genotype or genotype by treatment response effect forpositive symptoms (Fig. 2; Table S5).
4. Discussion
Our results show an association between the rs1045881 andclozapine response. The rs1045881T allele was over-represented inthe non-responder group, suggesting that the rs1045881 variant ofNRXN1 may influence clozapine response. This is consistent withrecent findings by our group that found two other markers of NRXN1to be nominally associated with clozapine response (Souza et al.,2010) and our observed trend of association between rs1045881 andquantitative total BPRS treatment response. There was no observableeffect of rs1045881T on change in negative symptoms; althoughoverall, T-allele carriers had lower negative symptom scores. Thetrend seen in the C allele homozygotes suggests their responsivenessto clozapine treatment.
The core clinical symptoms of schizophrenia are negativesymptoms (Andreasen, 1982) which do not respond well to existingtreatment (Murphy et al., 2006). Previous structural and diffusiontension imaging MRI brain imaging finding have reported that ageneralized reduction in frontal lobe white matter correlate withgreater severity of negative symptoms (Sanfilipo et al., 2000; Wolkinet al., 2003). This suggests that the increased frontal lobewhitematterwe previously reported in T-allele carriers could be related to the
Table 1The association between schizophrenia diagnosis and genotypic and allele frequenciesof rs1045881.
Case Control
Schizophrenia versuscontrol
T 88 99 χ2 Allelic p-valueC 514 501 0.811 0.368T/T+T/C 82 94 χ2 Genotypic p-valueC/C 219 206 1.214 0.270
T-allele carriers versus C/C homozygotes were compared in genotypic test because cellswith less than five T/T homozygotes were observed. χ2 = Likelihood ratio chi squared.
122 T.A.P. Lett et al. / Schizophrenia Research 132 (2011) 121–124
lower negative symptoms scores in our clozapine response sample(Voineskos et al., 2011).
Our findings are also concordant with the notion that NMDAantagonism may account for negative symptoms in SCZ (Olney et al.,1999). In silico analysis predicts that the rs1045881T allele eliminatesmiRNA binding sites, thus reducing mRNA decay and T allele carriersmay therefore have increased levels of NRXN1mRNA and protein. Thisincrease in NRXN1 could explain the protective effect observed in Tallele carriers against negative symptoms through facilitation ofNMDAR recruitment. This is important because dysfunction ofNMDARs has been implicated in memory function, cognitivedisturbances and negative symptoms (Olney et al., 1999). Thus, itcould be argued that clozapine action on negative symptoms may beattenuated in T-allele carriers because NRXN1 may already beexpressed at high levels. Alternatively, the clozapine response in C/Chomozygotes could be explained by higher initial negative symptomsproviding greater opportunity to respond to medication.
The association between NRXN1 and SCZ has only previously beenreported with respect to deletions within the gene. It is possible thatmultiple insults to the NRXN1 gene culminating across a threshold maybe involved inSCZpathogenesis. For instance, a recent studybyShahet al.(2010), in which the promoter region of NRXN1 was re-sequenced,reported that rare point mutations in the promoter region in addition tochromosomal alterations may contribute to the etiology of SCZ.Therefore, we may not have captured enough of the variance in NRXN1with a single SNP to observe a significant associationwith schizophrenia.
Our study has some limitations. First, we imposed a dominantmodel by combining genotypic groups T/T and T/C of rs1045881. Ourmodel would need to be confirmed by further analysis; however,results from our previous imaging analysis support such a model(Voineskos et al., 2011), and the allelic association we observed inclozapine response supports a dominant model. Second, quantitativeresponse data was only available for only a subset of our clozapine
response sample; therefore, our analysis could be underpowered.Furthermore, our sample size for categorical response was relativelylow compared tomost studies, even thoughwe had over 80% power todetect association with SCZ and responders to clozapine treatment.
In conclusion, we show that a common variant within the NRXN1gene with a predicted functional effect may be associated withclozapine response in European-American SCZ patients. This tenta-tively suggests with replication and further work, that NRXN1screening may provide more efficient treatment strategies throughpersonalized medicine.
Supplementarymaterials related to this article can be found onlineat 10.1016/j.schres.2011.08.007.
Role of funding sourceCIHR operating grant to DJM (Genetics of antipsychotics induced metabolic
syndrome, MOP 89853); NARSAD Young Investigator Award to DJM, CIHR MichaelSmith New Investigator Salary Prize for Research in Schizophrenia to DJM, OMHF New
Fig. 1. Interaction between clozapine treatment duration and rs1045881 genotype. Thepercent difference BPRS scores at baseline and after 6 months of clozapine treatment islisted for T allele carriers and C/C genotypes. (N[T/T+T/C]=28, N[C/C]=63; BPRS =Brief Psychiatric Ratings Scale). Repeated measures ANOVA of baseline and 6 monthBPRS scores reveal a trend in rs1045881 genotype by treatment response (F1,87 Within-subject=3.151 p=0.079).
Fig. 2.Mean positive (A) and negative (B) symptom subscale from the BPRS for baselineto 6 months by genotype. (N [T/T+T/C]=27, N [C/C]=60; BPRS = Brief PsychiatricRatings Scale). (A) Repeated measures ANOVA were not significant for within- orbetween-subject effects. Negative symptoms showed a significant genotype association(F1,85 (Between-subject)=4.686, p=0.033) and a trend for genotype by treatmentresponse (F1,85 (Within-subject)=3.293, p=0.075). * denotes pb0.05.
Table 2NRXN1 rs1045881 marker allelic and genotypic associations with clozapine response.
Non-responders Responders
Allele T C T C χ2 Allelic p-value OR (95% CI)27 107 21 183 6.301 0.012 2.199 (1.185–4.080)
Genotype T/T+T/C C/C T/T+T/C C/C χ2 Genotypic p-value OR (95% CI)24 43 21 81 4.736 0.030 2.153 (1.077–4.304)
OR = odds ratio.
123T.A.P. Lett et al. / Schizophrenia Research 132 (2011) 121–124
Investigator Fellowship to DJM.; the funding sources have no further role in studydesign; in the collection, analysis and interpretation of data; in the writing of thereport; and in the decision to submit the paper for publication.
ContributorsTAPL wrote the first draft of the manuscript performed the molecular genetic
analysis, statistical analysis, and managed the literature searches and analysis. AKTundertook statistical analysis and writing the manuscript. Author ANV contributed tothe literature search and writing of the manuscript. HYM, JAL, and SGP collected andclinically characterized the sample. Authors JLK and DJM designed the study and wrotethe protocol. All authors contributed to and have approved the final manuscript.
Conflict of interestHYM has received grants or is a consultant to Abbott Labs, ACADIA, Bristol Myers
Squibb, Eli Lilly, Janssen, Pfizer, Astra Zeneca, Glaxo Smith Kline, Memory, Cephalon,Minster, Aryx and BiolineRx. HYM is a shareholder of ACADIA. JAL reports that he serveson the Advisory Board of Bioline, GlaxoSmithKline, Intracellular Therapies, Eli Lilly,Pierre Fabre, Psychogenics and Wyeth. He does not receive financial compensation orsalary support for his participation as an advisor. He receives grant support from Allon,Forest Labs, Merck and Pfizer; he holds a patent from Repligen. JKL has one timehonorarium from Eli Lilly Corporation, and has been a consultant to GSK, Sanofi-Aventisand Dainippon-Sumitomo.
AcknowledgmentsNone.
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REVIEW
Treating Working Memory Deficits in Schizophrenia:A Review of the NeurobiologyTristram A. Lett, Aristotle N. Voineskos, James L. Kennedy, Brian Levine, andZafiris J. Daskalakis
Cognitive deficits are a core feature of schizophrenia. Among these deficits, working memory impairment is considered a centralcognitive impairment in schizophrenia. The prefrontal cortex, a region critical for working memory performance, has been demonstratedas a critical liability region in schizophrenia. As yet, there are no standardized treatment options for working memory deficits inschizophrenia. In this review, we summarize the neuronal basis for working memory impairment in schizophrenia, including dysfunctionin prefrontal signaling pathways (e.g., γ-aminobutyric acid transmission) and neural network synchrony (e.g., gamma/theta oscillations).We discuss therapeutic strategies for working memory dysfunction such as pharmacological agents, cognitive remediation therapy, andrepetitive transcranial magnetic stimulation. Despite the drawbacks of current approaches, the advances in neurobiological andtranslational treatment strategies suggest that clinical application of these methods will occur in the near future.
Key Words: Cognition, EEG, neurophysiology, schizophrenia, TMS,working memory
Schizophrenia is a common and chronic psychiatric disordercharacterized by delusions, hallucinations with concomitantcognitive, organizational, and motivational impairments.
Currently approved pharmaceutical treatments for schizophreniaare typically effective for positive symptoms but have little or noeffect on cognitive impairment (1). This is of particular concern,because cognitive performance is a key determinant of long-termoutcome and mortality in schizophrenia (2). Cognitive dysfunctionin schizophrenia shows high prevalence, is relatively stable overtime, and is independent of psychotic symptoms (3). Moreover,cognitive dysfunction is present in healthy relatives of schizo-phrenia patients, and it has been suggested as a biomarker ofschizophrenia (4). As a consequence, disturbances in criticalcognitive process, such as working memory, are regarded as acore feature of schizophrenia.
Of the demonstrated neurocognitive deficits in schizophrenia,research has focused on working memory, which has beendefined as the ability to transiently hold and manipulate infor-mation to guide goal-directed behavior (5). The contents ofworking memory are constitutively updated, monitored, andmanipulated in response to immediate processing demands (5).Working memory prolongs the impact of experience beyondimmediately accessible information to enable the incorporation ofinformation from long-term memory, lexical labels, and otherevents into goal-oriented behavior (6). The dorsolateral prefrontalcortex (DLPFC) is crucial to working memory function in healthyadults (7). In schizophrenia patients, working memory deficitsare associated with dysfunction of DLPFC as well as DLPFC
connectivity with other regions and disruption of neurotransmit-ter input (e.g., γ-aminobutyric acid [GABA], glutamate, anddopamine) (8–10). Working memory in schizophrenia might alsohave a genetic basis. Schizophrenia patients and their unaffectedco-twins perform significantly worse than control subjects onspatial working memory tasks (11). The letter-number-sequencingtask (a measure of working memory) has been identified asan endophenotype of schizophrenia with a heritability of .39(.25–.52) (12). Thus, improved identification of circuit disruption(from DLPFC to other regions) can help provide insights into thepathophysiology of working memory impairment in schizophre-nia and the development of novel therapeutic interventions.
In this article, we review the neuropsychological and neuro-anatomical basis of working memory and its relationship toschizophrenia. Next, the therapeutic approaches for treatmentof working memory deficits in schizophrenia are discussed,including pharmacological interventions and cognitive remedia-tion therapy (CRT). Finally, we establish the neurophysiologicalbasis for working memory deficits and present repetitive trans-cranial magnetic stimulation (rTMS) as a potential novel ther-apeutic strategy.
Working Memory
A key benefit of studying working memory is that psychologyand cognitive neuroscience have built a comprehensive frame-work for understanding the cognitive architecture of workingmemory and its neural correlates. For instance, studies in non-human primates suggest that lesions to the prefrontal cortex(PFC) cause marked reduction in working memory function andthat subdivisions of the PFC might represent multiple workingmemory domains, each having its own specialized processing orcontent-specific storage (13,14). According to the seminal theo-retical model by Baddeley (5), working memory functions can befractionalized into specialized systems that serve as buffers for thestorage and manipulation of information. The model is comple-mented by empirical evidence that most primate electrophysiol-ogy and neuroimaging studies, regardless of experimentalprocedure, report delay-period activity in the PFC [for examples,see (15,16)].
The PFC is an integral component of executive functioning(e.g., complex attention, planning, and mental flexibility) (17).The PFC contributes to working memory by exerting top-down control through filtering and strategic reorganization of
From the Centre for Addiction and Mental Health (TAL, ANV, JLK, ZJD);Institute of Medical Science (TAL, ANV, JLK, ZJD); Department ofPsychiatry (ANV, JLK, ZJD); Department of Psychology (BL), Universityof Toronto; and the Rotman Research Institute (BL), Baycrest CentreToronto, Toronto, Ontario, Canada.
Address correspondence to Zafiris J. Daskalakis, M.D., Ph.D., TemertyChair in Therapeutic Brain Intervention, Professor of Psychiatry,University of Toronto, Centre for Addiction and Mental Health(CAMH), 1001 Queen Street West, Toronto, Ontario, M6J 1H4 Canada;E-mail: [email protected].
Received Jan 3, 2013; revised and accepted Jul 22, 2013.
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information (18). Therefore, working memory performance woulddepend on efficient communication to the PFC and its capacity toinhibit extraneous information. Top-down attention relies onparietal and prefrontal regions that largely overlap with activationduring working memory tasks in both these regions (19). More-over, high global brain connectivity to the DLPFC predicts betterworking memory performance as well as general fluid intelligence(20). The PFC thus acts as a flexible hub by which frontalconnectivity is adjusted according to task demands (21).
More recently, research has emphasized recruitment ofextrafrontal regions involved in perceptual or long-termrepresentations in orchestration with DLPFC (22). Electroence-phalagram (EEG) studies show theta coupling between pre-frontal and parietal cortices is increased with more complexmanipulation (23), memory load (24), and predict individualworking memory capacity (25). Theta phase synchronybetween the prefrontal and temporal cortices occurs duringthe maintenance phase of working memory (26) in addition toencoding and retrieval (27). Further evidence for medialtemporal lobe involvement comes from intracranial EEGrecording in human epilepsy patients that shows cross-frequency coupling of oscillatory activity in the hippocampusbetween beta/gamma range and the theta band, and theprecision of coupling predicts working memory performance (28).This sustained phase synchronization between higher-order sen-sory, frontal, and temporal cortices and the hippocampus providesa mechanism for working memory maintenance by which activityin different brain regions is sustained in the absence of directsensory output (26). An important consequence of these findingsis that working memory depends on network-level activation andcoordination.
Working Memory and Schizophrenia
Schizophrenia patients are cognitively compromised on theorder of magnitude 1.0–1.8 SDs below the normal mean (29).Patients with an earlier onset have more severe cognitive deficitsthat persist throughout the course of the disorder (30). Cognitiveimpairments are present in the prodromal period and mightcontribute to heterogeneity in patterns of cognitive changesacross illness phases and among individuals (31). Meta-analyses inschizophrenia demonstrate large deficits in all 3 domains ofworking memory (phonological, visuospatial, and central execu-tive) with no clear differences across domains or tasks (32,33).There was also no consistent association between duration ofillness, antipsychotic medication, or symptom profile and workingmemory in schizophrenia (33).
The DLPFC has been identified as a key liability region forworking memory dysfunction in schizophrenia (34). In an earlystudy, healthy individuals demonstrated increased blood flow tothe DLPFC during the Wisconsin Card Sorting Task that was notobserved in medication-free schizophrenia patients (35). How-ever, recent neuroimaging studies generated conflicting findingswith regard to DLPFC activation during working memory tasks.Both “task-related hypofrontality” and “task-related hyperfrontal-ity” have been reported in patients with schizophrenia relative tohealthy subjects (34). These discrepancies are potentially drivenby study differences in task performance or difficulty, although itis possible that the findings are confounded by coupling andactivation in other cortical regions. For example, stronger activa-tion of deep brain structures [e.g., thalamus (36)] and the anteriorcingulate cortex (37) in schizophrenia patients might be a product
of compensatory mechanism for working memory deficits. There-fore, working memory dysfunction could be a result of reducedfunction of specific regions but also an impairment to engagefunctional networks synchronized to a given cognitive task.
The disruption of working memory networks in schizophrenia isstill poorly understood. As reviewed in the preceding text, dynamicnetwork connectivity is necessary for proper working memoryfunctioning. Given the functional and anatomical “dysconnectivity”observed in schizophrenia (38), especially to the DLPFC (39),working memory deficits in schizophrenia could be due todysfunction of establishing or changing brain networks. Thus,establishing a link between functional integration and workingmemory deficits is crucial to developing novel, neurobiological-based interventions to enhance working memory performance.
Current Treatments of Working Memory Deficits
Therapeutic strategies for working memory deficits in schizo-phrenia are of great interest, considering their predictive value forfunctional outcome. Nonpharmacological and pharmacologicaltreatment strategies have been investigated but demonstratemixed results.
Antipsychotic TreatmentPharmacological studies have examined differences in effects
of antipsychotic medications on cognitive functioning. Althoughshowing small effects toward improved cognitive performancewith treatment, some studies show therapeutic advantages ofatypical antipsychotics compared with typical antipsychotics (40);however, the large, multisite CATIE trial (Clinical AntipsychoticTrials of Intervention Effectiveness) failed to find any advantage ofatypical antipsychotics in treating cognition (1). Clozapine, theatypical antipsychotic agent for treatment resistant-schizophrenia(41), is no longer considered superior to other atypical antipsy-chotic agents for cognitive deficits (42). These results were drivenby multiple pharmacological initiatives, such as the MATRICS(Measurement and Treatment Research to Improve Cognition inSchizophrenia) (43), TURNS (Treatment Units for Research onNeurocognition and Schizophrenia) (44), and CNTRICS (CognitiveNeuroscience Treatment Research to Improve Cognition Schizo-phrenia) (45). These initiatives highlight continuing interest andcommitted resources currently dedicated for novel therapies forcognitive deficits in schizophrenia and, in particular, workingmemory deficits. It should be noted that the long-term con-sequences of antipsychotic treatment might be detrimental tocognition. Progressive declines in working memory performanceare observed in nonhuman primates undergoing chronic treat-ment of haloperidol over a 6-month period (46). Additionally, graymatter loss, higher neuronal density, and reduced glial cellnumber similar to that histologically observed in schizophreniawas reported in nonhuman primates exposed to olanzapine orhaloperidol over a 2-year period (47,48). A longitudinal first-episode schizophrenia study showed progressive decline of whiteand gray matter volume correlating with antipsychotic medica-tion dose (49). Thus, the evidence does not support a benefitfrom antipsychotic medication with regard to cognitive deficitsbut rather indicates a potential negative effect on workingmemory in schizophrenia during long-term treatment.
Pharmacological TargetsThe pharmacology of working memory dysfunction might
provide critical understanding for the development of new
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treatments (Figure 1) (50). Blockade of the glutamate-mediatedexcitatory neurotransmission by N-methyl-D-aspartate receptor(NMDAR) antagonists mimics positive and negative symptomsas well as cognitive deficits in schizophrenia. These findingssuggest that enhancing NMDAR neurotransmission mightreverse cognitive deficits (51,52). Furthermore, NMDAR ablationon GABA interneurons impairs hippocampal theta rhythmleading to impaired working memory (53). The NMDAR activa-tion also subserves persistent DLPFC neuronal firing duringworking memory (54), suggesting that glutamate function andconnectivity is integral to working memory performance.Results from the CONSIST (Cognitive and Negative Symptomsin Schizophrenia Trial), however, suggest that either glycine(binds to allosteric site of the NMDAR) or D-cycloserine (partialNMDA agonist) were not effective in treating cognitive impair-ments (55). Vis-à-vis dopamine, early preclinical work shows thatdopamine neurotransmission might be augmented to treat work-ing memory deficits in schizophrenia. Increased availability of PFCdopamine D1 receptors has been reported in schizophrenia andmight reflect a compensatory upregulation, due to reduced PFCdopamine release; furthermore, the increased expression hasbeen directly associated with poor working memory performance(56,57). In nonhuman primates, intermittent long-term D1 recep-tor agonist treatment yielded persistent improvements inhaloperidol-induced working memory deficits (46). The selectiveD1 receptor agonist, dihydrexidine, was reported to be well-tolerated in schizophrenia subjects (58). Single-dose administra-tion of dihydrexidine was reported to have no effect on neuro-cognition (59). Nevertheless, intermittent D1 receptor agonisttreatment remains a promising strategy. Catecholamine-O-meth-yltransferase has been directly associated with PFC dopamineturnover and working memory performance (60). Catecholamine-O-methyltransferase inhibitors, such as tolcapone, are a promisingtarget, although they have unfortunately also been associatedwith hepatotoxicity (61). Finally, GABAergic inhibitory neurotrans-mission in the DLPFC is altered in schizophrenia (62) and isintegral to organizing gamma oscillations associated with work-ing memory load (63). The major determinant of GABA in theneocortex, glutamic acid decarboxylase, is consistently down-regulated in postmortem studies of patients with schizophrenia(64). The selective agonist of the GABAA receptor, MK-0777, wasshown to be effective in treating working memory deficits andcould potentially modulate frontal gamma activity in a study withlimited sample size (65). A subsequent study failed to replicatethe enhancement of working memory by MK-0777 in schizophre-nia (66); however, modulating GABA neurotransmission remains apromising target.
Other pharmacological strategies including galantamine, acombined acetylcholinesterase inhibitor and allosteric potentiatorof the nicotinic receptor, show modest effect across severalcognitive domains (67) with no improvement in working memoryas confirmed by a recent Cochrane review (68). Furthermore,other pharmacological treatment attempts have failed, including:the novel neuropeptide davunetide; the nicotinic agonist vareni-cline; and pregnenolone (69–71). Although some improvement inworking memory performance was shown with pergolide, mino-cycline, amphetamine, and recombinant human erythropoietin,none of these findings have been replicated in a controlled study(72–75).
In summary, pharmacological investigations for working mem-ory deficits in schizophrenia could benefit from the use of novelagents, because existing studies have demonstrated limitedtreatment effects (61).
Cognitive TrainingPerhaps the best-supported strategy targeting working mem-
ory deficits (and cognitive dysfunction in general) in schizophre-nia is CRT. Cognitive remediation therapy employs drill or practiceexercises, teaching strategies to improve cognitive functioning, aswell as compensatory strategies and group discussions (76).A number of studies have investigated effects of CRT on differentcognitive domains (e.g., attention/vigilance, processing speed,verbal working memory, or social cognition) (77). Computerizedand noncomputerized training methods for these differentdomains of cognitive function have been described.
Although the neural mechanisms of action remain poorly under-stood, CRT might influence cortical connectivity and brain structurerelevant to the specific training involved. Wykes et al. (78) found that
Figure 1. Hypothesized prefrontal cortical circuit highlighting synapsesthat are implicated in working memory dysfunction and targets ofpharmacological cognitive enhancers. Direct and indirect disruptions ofdopamine, glutamate, and γ-aminobutyric acid (GABA) neurotransmittersignaling are reported in schizophrenia, and these synapses are integral toworking memory function. In the prefrontal cortex, chandelier cells(parvalbumin-containing, fast-spiking GABA interneurons) mediate GABAneurotransmission at the axon initial segment of pyramidal cells (excita-tory neurons) to GABAA receptors, including the α2 subunit. Pyramidalcells release glutamate to N-methyl-D-aspartate receptors (NMDARs) onchandelier cell forming a feedback mechanism responsible for gammaoscillation activity in the prefrontal cortex. Pyramidal cells synapse onbasket cells (parvalbumin-containing, fast-spiking GABA interneurons)with reciprocal GABAergic synapses of basket cells on the soma ofpyramidal interneurons. Pyramidal cells also release glutamate on striataldopamine neurons (and other regions, such as the hippocampus andventral tegmental area) leading to activation of dopamine D1 receptors(D1R) on prefrontal chandelier and pyramidal cells, thereby augmentingthe activation timing of these neurons. Examples of pharmacologicaltargets to improve working performance include: 1) restoring glutamatesignaling with glycine (binding to the allosteric site of the NMDAR) orD-cycloserine (partial NMDA agonist); 2) selectively increasing dopaminesignaling to the D1R with dihydrexidine (D1R agonist); and3) increasing GABAergic tone through agonism of the GABAA α2 subunitby MK-0777. For further review, please see Lewis and Gonzalez-Burgos(50) and Lisman et al. (149). GABA(A)α1, GABAA receptor including the α1subunit; GABA(A)α2, GABAA receptor including the α2 subunit.
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schizophrenia patients undergoing CRT for executive functioning (n¼ 6) over 3 months showed increased brain activation in fronto-cortical regions associated with working memory compared withcontrol therapy patients. Increased activation of the inferior frontalcortex after 10 weeks of verbal memory training in eight patientswith schizophrenia was associated with verbal working memoryimprovement (79). A 2-year randomized control trial of cognitiveenhancement therapy (combined neurocognitive and social cogni-tive remediation) in 121 patients reported strong, lasting effect oncognition and global functioning (Cohen’s d � 1.00), although therewas no association with working memory (80). Subsequently, it wasreported that the cognitive enhancement therapy group hadpreservation of left hippocampal, parahippocampal, and fusiformgyrus gray matter volume and increased amygdala volume (81).Furthermore, less gray matter loss in the parahippocampus andfusiform gyrus as well as greater amygdala gray matter volume wasrelated to improved cognition (81). In poor-reading children, 100hours of remedial reading training normalized left frontal fractionalanisotropy to that of normal reading children (82). More recently,strategy-learning-based CRT in 30 schizophrenia subjects normalizedactivation toward the pattern of healthy control subjects during anN-back working memory functional magnetic resonance imagingparadigm (83). Moreover, after CRT these subjects had increasedfractional anisotropy in the genu of the corpus callosum that wascorrelated with total cognition and executive function, although CRTwas not associated with working memory improvement (83). Takentogether, these results suggest that CRT initiates learning-inducedplasticity in cognitively compromised populations.
The most recent meta-analyses indicate that CRT can provide amoderate improvement in global cognition (effect size is approx-imately .4–.5) (77,84). Despite the difference between CRTapproaches in terms of methods used and targeted cognitivedomains, studies have shown consistent effect sizes (85); more-over, no single method (e.g., remediation approach, CRT duration)was superior in terms of cognitive outcome (77,84). Althoughsome CRT studies have shown no effect on working memory(77,86), computer-based programs that focused on the remedia-tion of verbal working memory in schizophrenia through auditorytraining exercises have shown promise (87–89). For instance,Fisher et al. (88) demonstrated significant improvements on theletter–number span working memory task in patients withschizophrenia after 50 hours of auditory training exercises,compared with a control group. This training also improvedauditory psychophysical performance that was related toimproved verbal working memory and global cognition. More-over, the active group had significantly elevated peripheral levelsof the brain-derived neurotrophic factor (BDNF), indicating CRTmight induce neuroplasticity. Six-month follow-up revealed aclear improvement of global cognition with lasting effects onauditory function, visual processing, and cognitive control (89).Although a recent study failed to replicate these results (90), CRTmight have enduring therapeutic value. Particularly when CRT isprovided with adjunctive psychiatric rehabilitation, such as socialgroup exercises (80), it is shown to be more effective (77).
The observed effects on neuroplasticity together with moder-ate effect sizes of the training suggest that CRT might best servein combination with neurophysiological-based interventionsinducing neuroplasticity, such as rTMS or with pharmacologicalapproaches. For example, CRT might act in concert with othermethods of inducing neuroplasticity to reinforce working memorypathways. However, to the best of our knowledge, there are nopublished studies examining CRT in combination with othercognitive neuroenhancement techniques.
TMS and Cognition
Transcranial magnetic stimulation is an investigational tool toexamine physiological brain processes in relation to cognitionand psychiatric illness (91). For example, rTMS intervention to theDLPFC transiently impairs encoding and retrieval mechanismswith visuospatial (92) and verbal stimuli (93); however, the samestimulation can facilitate cognition in picture naming, objectnaming, speed during reasoning puzzle, and cognitive reactiontasks (94–97). In this regard, rTMS modulates cortical excitabilitythrough local inhibitory circuits that could facilitate or inhibitbrain networks relevant to the cognitive task (98). Combining TMSwith EEG (TMS-EEG) allows measurement of both temporal andspatial activations at the targeted brain region (99). A linkbetween regional neurophysiology and cognitive function canbe examined, by assessing how TMS-induced modulation ofcortical activation of the DLPFC relates to working memoryfunction. For instance, long interval cortical inhibition (a measureclosely associated with GABAB receptor neurotransmission) hasbeen strongly correlated with performance on the N-back(r ¼ .63, p ¼ .04) and letter–number-sequencing (r ¼ .68, p ¼ .005)working memory tasks in healthy control subjects (100–102).Furthermore, this suggests that long interval cortical inhibitionmight be important in modulating high-frequency oscillations inthe DLPFC that influences working memory (102). Therefore, TMS-EEG measures of cortical inhibition and DLPFC synchrony mightprovide key insights into working memory function. The con-nection between targeted area and brain functioning is of greatimportance, because psychiatric disorders, such as schizophrenia,have abnormal neural oscillations and synchrony. This has beendemonstrated in rhythm-generating networks of GABA interneur-ons and in cortico–cortical connections (103).
Brain Networks Synchrony
Theories of schizophrenia emphasize deficits in the coordina-tion of distributed neuronal oscillatory activity that lead toworking memory dysfunction (104). Patients with schizophreniahave abnormal gamma oscillations and gamma–theta couplingthat might underlie independent cognitive and functional impair-ments (104–106). Gamma frequency oscillations are particularlyinteresting, because of their integral relationship with higherbrain processes (107). They provide a temporal structure forinformation processing in the brain, mediating storage and recallof information (108). One GABA interneuron typically connectsextensively with several pyramidal neurons forming neuronalnetworks that fire contemporaneously, a process that can beenrecorded over the surface of the cortex as gamma oscillatoryactivity (Figure 1) (109). The kinetics of inhibitory interneurons inthe cortex are such that their firing rate is much higher than thatof pyramidal cells, permitting higher rates of pyramidal cell firingcompared with baseline (110). Finally, inhibitory interneuronsform synaptic connections with pyramidal neurons at the cellbody, a synaptic relationship that allows greater control ofpyramidal neuron firing compared with synaptic terminations atmore distal regions of the neuron (111). As a result of this patternof connectivity, inhibitory interneurons exert fine control over thefiring of pyramidal neuron networks, which translates into high-frequency gamma oscillatory activity on EEG (112). Gammaoscillations are also coupled to theta rhythms (110), and thiscoupling has been found to be essential for working memory (27).This suggests that specific interplay between large ensembles ofneurons has clinical significance.
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In patients with schizophrenia, aberrant gamma oscillatoryactivity has been reported during a cognitive control task,compared with healthy subjects (107). Furthermore, inability tosupport stimulus-driven gamma oscillations in schizophreniapatients has been associated with working memory dysfunction(104). Excessive frontal activation of gamma oscillations werereported in schizophrenia and correlated with working memorytask difficulty (113). Finally, the increase in gamma oscillationswas associated with a later maintenance phase of workingmemory and induced gamma and theta activity during retrieval(114). Taken together, these results suggest that schizophreniapatients are not properly able to coordinate cortical activity that isappropriate cognitive demand.
Modulating Network Plasticity
There are several mechanisms for TMS to induce and measurecortical plasticity. The TMS activation of a population of neurons inthe same synaptic pathway (homosynaptic) or in different path-ways (heterosynaptic) is modulating synaptic efficacy either by:increased synaptic strength (long-term potentiation [LTP]); ordecreased synaptic strength (long-term depression) (115). Throughthese mechanisms, low-frequency rTMS will cause a decrease inbrain excitability (116); in contrast, high-frequency rTMS causesincreased brain excitability (117). Similarly, other magnetic brainstimulation protocols can produce changes in excitation orinhibition (Table S1 in Supplement 1). Furthermore, studies ofthe motor cortex have shown that TMS protocols potentiate lastingeffects on this excitability for 30 min to several hours (118). Arecent study suggests that one paradigm, known as paired-associated stimulation (PAS), might enhance motor learning at 1week post-PAS (119). The PAS-25, peripheral nerve stimulation 25msec before rTMS, induced LTP, leading to enduring enhancementof evoked motor potential. These lasting effects are particularlyexciting, because rTMS can modulate brain network oscillatoryactivity, thus providing evidence that PAS could trigger structuraland functional changes necessary for long-term improvement ofmotor performance. Similar findings have been reported in animalstudies. A recent study by Benali et al. (120) found that theintermittent theta-burst stimulation (a type of rTMS) (121) to the ratneocortex differently modulates gamma oscillations and proteinexpression. Intermittent theta-burst stimulation (excitatory)enhanced neural firing and EEG gamma power by reducingparvalbumin expression in fast spiking GABA interneurons; incontrast, continuous theta-burst stimulation (inhibitory) ratheraffected pyramidal neurons calbindin D-28k expression.
Taken together, the lasting cortical plasticity induced by TMS ispromising, especially because it can alleviate difficult-to-treatfacets of complex psychiatric disorders, such as cognitive deficitsin schizophrenia.
Remodeling of Connectivity in Schizophrenia by rTMS
There is overwhelming evidence that schizophrenia is, at least inpart, a disorder of dysconnectivity of the brain (122). This abnormalfunctional integration of processes might be due to aberrant wiringduring development or aberrant synaptic plasticity or both. Thisincludes abnormal functional connectivity [e.g., frontotemporalconnectivity, gamma synchrony (123,124)], abnormal structuralconnectivity [e.g., white matter integrity, reduced brain asymmetry(125–127)], and synaptic plasticity [e.g., pharmacological-inducedschizophrenia symptomology, reduced dendritic field size and
density (128,129)]. Genetic factors common to all of these pointsof dysfunction [e.g., disrupted-in-schizophrenia 1 (DISC1), glutamatedecarboxylase 1 (GAD1), neuregulin 1 (NRG1), microRNA 137(MIR137), and zinc finger protein 804A (ZNF804A) genes (130,131)]all point to the possibility that schizophrenia patients are predis-posed to dysconnectivity. Moreover, neural dysconnectivity mightbe a causative factor in the more intractable deficits of schizo-phrenia, such as working memory functioning (132). Importantly,rTMS as an external intervention might be used to activate neuraldevelopmental pathways sidestepping the normal modes ofsynaptic plasticity. In this regard, rTMS might galvanize plasticityin brain networks that are compromised in schizophrenia. Forinstance, intermittent theta-burst stimulation could remediateabnormalities of gamma oscillations of cognitive processing inschizophrenia patients (105). Indeed, high-frequency rTMS to theDLFPC results in reduced frontal gamma oscillation in schizophreniapatients during the N-back working memory task (Figure 2A) (133).
Activity-Dependent Regulation of Molecular Factorsby rTMS
In most cases, molecular factors that regulate plasticity relateneuronal activation to expression of activity-dependent genes (134).Knowledge of the molecular factors involved in rTMS induction ofneural plasticity is necessary to understand how rTMS might be usedto shape lasting effects on neural circuitry. Activity-dependent geneexpression is integral in the refinement of neuronal network indevelopment as well as in the adaptive, long-lasting modificationsnecessary for mature brain function, such as learning and memory. Ithas been well-established that the cyclic adenosine monophos-phate-response element binding-protein (CREB) plays a central role,at least in part, in mediating activity-dependent neuroplasticity(Figure 2B) (135). Ji et al. (136) reported that rTMS stimulation tothe rat brain activated CREB, leading to increased expression inparaventricular nucleus of the thalamus, cingulate cortex, and frontalcortex. Furthermore, CREB functionally regulates the BDNF gene(137), and theta-burst induction of LTP causes upregulation of BDNF(138). Most recently, it was shown that rTMS treatment to humanneuroblastoma cell lines (SH-SY5Y) resulted in activation of CREB(139). Interestingly, CREB regulates cellular fate by inducing expres-sion of the small, noncoding microRNA, miR-132, that controls themessenger RNA stability or translation of many genes involved inepigenetic regulation and neuronal morphogenesis including:dihydropyrimidinase-like 3 (DPYSL3), Rho GTPase activating protein32 (ARHGAP32), GATA binding protein 2 (GATA2), DNA(cytosine-5-)-methyltransferase-3-alpha (DNMT3A), and methyl CpG bindingprotein 2 (MECP2) (140–142).
Taken together, CREB and downstream factors might play acritical role in rTMS-induced plasticity. It should be noted that manyof the molecular factors potentially modulated by rTMS(e.g., MECP2, BDNF, and miR-132) are also schizophrenia risk factorsrelated to neuroplasticity. This relationship suggests that rTMS couldrescue normal function of neuroplasticity networks in schizophrenia;however, further research is imperative to establish causal relation-ships between rTMS gene networks involved in plasticity.
Treatment of Working Memory Deficits inSchizophrenia with rTMS
To date, there are only two published clinical trials examiningthe efficacy of rTMS for treatment of working memory dysfunctionin schizophrenia. In a 4-week sham-controlled rTMS trial, patients
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(n = 13 active; n = 12 sham) that received 20-Hz stimulation to theDLPFC (Brodmann area 46/6) had improved performance on the 3-back condition of the N-back working memory task (143). More-over, working memory performance in the active treatmentschizophrenia group normalized to that of healthy control subjects(143). In contrast, a 3-week sham-controlled 10-Hz rTMS trial to theleft posterior medial frontal gyrus in schizophrenia patients (n ¼13 active; n ¼ 12 sham) and control subjects (n ¼ 11 active; n ¼11 sham) reported no significant effect of treatment or treatment� diagnostic interaction in the 2-back condition (144). Thedisparity of results between these studies could be due to anumber of factors. First, rTMS-induced differences in gammaoscillations are reported to be more pronounced in the 3-backworking memory task (133); thus, treatment might be specific to
high working memory load. Second, rTMS treatment of workingmemory might be more efficacious when targeted to Brodmannarea 46/9. Last, 4 weeks of 20-Hz rTMS stimulation (in contrast to 3weeks of 10 Hz) might be a more effective mode of treatment.Anodal direct current stimulation (tDCS) treatment has beenpreviously associated with improvement in global cognitivefunction, attention, and enhancement of working memory [forreview, see (145)]. A single study has examined anodal tDCS fortreatment of working memory dysfunction in schizophreniapatients (n ¼ 12) and reported improvements in reaction timebut not accuracy (146). Transcranial alternating current (tACS) caninduce or disrupt theta phase-coupling and therefore might play arole in working memory function. In healthy subjects, tACSartificially induced frontoparietal phase coupling leading to
Figure 2. Neuroplasticity induction by repetitive transcranial magnetic stimulation (rTMS). (A) Topographical illustration of change in gamma power(30–50 Hz) during the 3-back working memory task (left, sham rTMS treatment to DLPFC; right, active treatment to DLPFC). Modified, with permission,from Barr et al. (150). (B) Potential molecular mechanism through which rTMS might induce plasticity. Synaptic activation of L-Type voltage-gated calciumchannel (VGCC) by rTMS leads to increased intracellular calcium initiating a signaling cascade causing activation of the transcription factor cAMP-responseelement binding protein (CREB) by phosphorylation, a hallmark of long-term potentiation (LTP)/long-term depression (LTD). Examples of downstreamchanges in gene expression and the effect these genes have on mediating neuronal plasticity are listed. Red, green, and yellow stars correspond to factorsshown to be associated with schizophrenia, modulated by rTMS, and involved in mediating LTP or LTD, respectively. BA, Brodmann area; BDNF, brain-derived neurotrophic factor; CaM, Calmodulin; CaMKII, CaM kinases II; DNMT3A, DNA (cytosine-5-)-methyltransferase 3 alpha; GABAAR, γ-aminobutyric acid(GABA) A receptor; GATA2, GATA binding protein 2; MECP2, methyl CpG binding protein 2 (Rett syndrome); miR-132, microRNA 132; NMDAR, N-methyl-D-aspartic acid receptor; p250GAP, ARHGAP32 Rho GTPase activating protein 32; TrkB, TrkB receptor.
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improved working memory, whereas desynchonization impairedworking memory (147). These early results suggest tDCS and tACStarget DLPFC functioning and connectivity, and thus, these treat-ments warrant further investigation. In major depressive disorder,mixed results are observed, although depressive patients are notas cognitively impaired as schizophrenia patients, and workingmemory has not been the primary outcome measure in any study(148). Therefore, results in major depressive disorder patientsmight not generalize to schizophrenia.
Conclusions
Several adjunctive pharmacological agents have shown prom-ising results, yet no agent has demonstrated efficacy in largeclinical trials. Cognitive remediation therapy fairly consistentlyshows improved cognition in schizophrenia, although the effectsseem to depend on domain. For example, CRT has large effect onsocial cognition (effect size is approximately .65), whereas meta-analyses reveal more moderate effects on working memory(effect size is approximately .35). Thus, CRT might be mosteffective in conjunction with other working memory treatments,such as rTMS, to produce large and durable effects whereby theDLPFC and related circuitry would be activated by rTMS andengaged by CRT concomitantly. Furthermore, cognitive enhance-ment drugs could enhance the efficacy of rTMS treatment ofworking memory. It could be speculated that GABAA receptoragonists, such as MK-0777, that affect gamma oscillatory tonecould act in concert with rTMS activation of the DLPFC tospecifically target local GABA signaling and coupling to otherbrain regions.
There are convergent lines of evidence suggesting that rTMSto the DLPFC might be efficacious treatment for working memorydeficits at multiple levels, including: synaptic (e.g., GABA signal-ing); cellular (e.g., GABA interneurons); neurophysiological (e.g.,inhibition); neural network (e.g., gamma oscillations); and func-tional neuroanatomy (e.g., DLPFC). Therefore, rTMS treatment forworking memory deficits in schizophrenia should garner moreresearch, both as an investigative to tool to understand howdysfunction might occur and as a powerful mechanism to induceneuroplasticity.
This work was supported by the Canadian Institutes of HealthResearch Clinician Scientist Award (ZJD, ANV); National Alliance forResearch on Schizophrenia and Depression (ANV), Ontario MentalHealth Foundation (ZJD, ANV) and the Centre for Addiction andMental Health, the Brain and Behaviour Research Foundation, andthe Centre for Addiction and Mental Health Foundation and theCampbell Institute, thanks to the Temerty Family, Grant Family,Kimel Family, Koerner New Scientist Award, and Paul E. GarfinkelNew Investigator Catalyst Award.
ZJD received external funding through Neuronetics and Brains-way and Aspect Medical and a travel allowance through Pfizer andMerck. ZJD has also received speaker funding through Sepracor andAstraZeneca and served on the advisory board for Hoffmann-LaRoche Limited. JLK has received honoraria from Eli Lilly, Roche, andNovartis. TAL, ANV, and BL report no biomedical financial interestsor potential conflicts of interest.
Supplementary material cited in this article is available online athttp://dx.doi.org/10.1016/j.biopsych.2013.07.026.
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