Dissecting Shared and Unique Neural Circuitry Underlying ... · Dissecting Shared and Unique Neural...
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Dissecting Shared and Unique Neural Circuitry Underlying Negative Symptoms, Social Cognition, and Functional Outcome in Schizophrenia by Tina Behdinan A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Medical Science University of Toronto © Copyright by Tina Behdinan 2015
Transcript of Dissecting Shared and Unique Neural Circuitry Underlying ... · Dissecting Shared and Unique Neural...
Dissecting Shared and Unique Neural Circuitry Underlying Negative Symptoms, Social Cognition, and Functional
Outcome in Schizophrenia
by
Tina Behdinan
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Science University of Toronto
© Copyright by Tina Behdinan 2015
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Dissecting Shared and Unique Neural Circuitry Underlying
Negative Symptoms, Social Cognition, and Functional Outcome in
Schizophrenia
Tina Behdinan
Master of Science
Institute of Medical Science University of Toronto
2015
Abstract
Schizophrenia is a devastating illness with significant disability and poor long-term clinical and
functional outcome. Negative symptoms and social cognitive impairments are two key symptom
domains that affect functional outcome in schizophrenia. This thesis explores the shared and
unique neural circuitry related to negative symptoms, social cognition, and functional outcome in
schizophrenia. In study one, the relationship between white matter fractional anisotropy, negative
symptoms, and functional outcome in schizophrenia participants was investigated. Study two
includes a broad sample of patient and healthy control populations, which is in line with the
RDoC methodology. In this study, the relationship between white matter tracts implicated in
functional outcome and social cognitive domains was investigated in a sample of schizophrenia,
bipolar disorder, and healthy control participants on a continuum of social cognitive
performance. Taken together, these studies elucidate circuitry that may be impaired in
schizophrenia, and may represent neurobiological correlates of negative symptoms, social
cognition, and functional outcome.
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Acknowledgments
First and foremost, I would like to thank my supervisor and mentor, Dr. Aristotle
Voineskos, for showing me the ropes, for the countless meetings and discussions, for giving me
the space to grow and learn, and for leading by example as a dedicated clinician-scientist.
Without your support, I wouldn’t be where I am today.
Thank you to my program advisory committee members, Dr. Mallar Chakravarty, Dr.
George Foussias, Dr. Gary Remington, and Dr. Tarek Rajji. You have been invaluable sources of
knowledge and guidance and I thank you for your commitment and contributions to this work.
Thank you to my labmates Nikhil Bhagwat, Daniel Felsky, Jon Pipitone, Joseph Viviano,
Julie Winterburn, David Rotenberg, Arash Nazeri, Tina Roostaie, Anne Wheeler, Melissa
Levesque, Vincent Man, Colin Hawco, Yuliya Nikolova, and Sejal Patel. Thank you for all your
help and support, the laughs and the friendship.
My good friends Alise, Priya, RJ, Shanza, Tamara, Louise, and Kristen, thank you for
being with me on this journey. You all inspire me. And to Joshua Lister, my dear friend, thank
you for your unwavering support. You are dearly missed.
To my loving family, thank you for putting up with my madness and for helping me pick
myself up when I’m down. Asha, you are my best friend and I’m so proud and lucky to have you
as my sister.
Thank you to Dr. Andrew Spice, Dr. Erin McDonough, Dr. Suvercha Pasricha, Dr. Claire
Fantus, Dr. Ellen Fergusson, Susan Green, and Dr. Carolina Vidal. Words cannot express how
grateful I am to each and every one of you.
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And thank you to our research participants and others who make this work possible. To
all who struggle with mental illness, service users, and consumer/survivors, I hope you find your
recovery, however you choose to define it.
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Contributors
Tina Behdinan wrote the manuscripts and performed majority of the image
processing and statistical analysis for studies one and two.
George Foussias, Gary Remington, Tarek Rajji, and Laura Stefanik recruited
participants and conducted clinical and neuropsychological assessments for study one
participants. Judy Kwan and Mikko Mason recruited and conducted clinical and
neuropsychological assessments for study two participants.
Daniel Felsky contributed to image analysis (specifically tractography for study one).
Anne Wheeler was instrumental in conducting the cortical coupling analysis in study
one.
Jon Pipitone and Joseph Viviano are part of the technical support team in the Kimel
Family TIGR Lab. They manage the computing cluster and were helpful in debugging scripts
for image processing and data organization. Arash Nazeri was also helpful in debugging
scripts for data organization.
Mallar Chakravarty supervised image processing.
Aristotle Voineskos is the principal investigator, and contributed to manuscript
writing, image processing, and statistical analysis.
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Table of Contents
Acknowledgments .......................................................................................................................... iii
Contributors .................................................................................................................................... v
Table of Contents ........................................................................................................................... vi
List of Tables .................................................................................................................................. x
List of Figures ................................................................................................................................ xi
List of Abbreviations .................................................................................................................... xii
1 Introduction .............................................................................................................................. 1
1.1 Schizophrenia .................................................................................................................... 1
1.1.1 Epidemiology & Diagnosis ......................................................................................... 1
1.1.2 Symptomology, Neurocognition, and Social Cognition ............................................. 2
1.1.3 Functional Outcome .................................................................................................... 5
1.1.4 Treatment ..................................................................................................................... 9
1.2 Brain Morphology and Schizophrenia ............................................................................. 11
1.2.1 Grey Matter Volume and Cortical Thickness Changes in Schizophrenia ................. 11
1.2.2 White Matter Microstructural Changes in Schizophrenia ......................................... 13
1.2.3 Neuroimaging Findings and Schizophrenia Symptomatology .................................. 14
1.2.4 Neuropathology of the Deficit Syndrome ................................................................. 15
1.2.5 Neural Circuitry and Functional Outcome ................................................................ 16
1.3 Neural Circuitry of Social Cognition ............................................................................... 18
1.3.1 Neuroimaging Evidence for Lower Level Social Cognitive Circuitry ...................... 18
1.3.2 Neuroimaging Evidence for Higher Level Social Cognitive Circuitry ..................... 20
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1.4 Imaging Modalities and Analysis .................................................................................... 23
1.4.1 Cortical Thickness Analysis Using Magnetic Resonance Imaging ........................... 23
1.4.2 Measuring White Matter Microstructure Using Diffusion Tensor Imaging ............. 24
1.4.3 Partial Least Squares Path Modeling ......................................................................... 27
1.4.4 Clinical and Cognitive Measures of Interest ............................................................. 28
2 Overview of Experiments and Hypothesis ............................................................................. 32
2.1 Study One: Neuroimaging Predictors of Functional Outcome in Schizophrenia at
Baseline and 6-month Follow-up .............................................................................................. 32
2.1.1 Background ............................................................................................................... 32
2.1.2 Hypothesis ................................................................................................................. 32
2.2 Study Two: Social Cognition-Circuitry Relationships in Schizophrenia, Bipolar
Disorder, and Healthy Individuals: A Pilot Study .................................................................... 33
2.2.1 Background ............................................................................................................... 33
2.2.2 Hypothesis ................................................................................................................. 33
3 Neuroimaging Predictors of Functional Outcome in Schizophrenia at Baseline and 6-month
Follow-up ...................................................................................................................................... 34
3.1 Abstract ............................................................................................................................ 34
3.2 Introduction ...................................................................................................................... 35
3.3 Methods ........................................................................................................................... 37
3.3.1 Participants ................................................................................................................ 37
3.3.2 Image Acquisition ..................................................................................................... 38
3.3.3 Image Processing ....................................................................................................... 39
3.3.4 Statistical Analysis .................................................................................................... 40
3.4 Results .............................................................................................................................. 43
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3.4.1 Brain structure and circuitry differences in schizophrenia ........................................ 43
3.4.2 Data Reduction and Prediction of Functional Outcome ............................................ 43
3.4.3 Negative symptoms mediate the relationship between tract FA and longitudinal
functional outcomes .............................................................................................................. 44
3.4.4 Inter-regional cortical coupling is associated with baseline functional outcome ...... 45
3.5 Discussion ........................................................................................................................ 45
3.6 Appendix .......................................................................................................................... 55
4 Social Cognition-Circuitry Relationships in Schizophrenia, Bipolar Disorder, and Healthy
Individuals: A Pilot Study ............................................................................................................. 58
4.1 Abstract ............................................................................................................................ 58
4.2 Introduction ...................................................................................................................... 59
4.3 Methods ........................................................................................................................... 62
4.3.1 Participants ................................................................................................................ 62
4.3.2 Image Acquisition ..................................................................................................... 64
4.3.3 Image Processing ....................................................................................................... 64
4.3.4 Statistical Analysis .................................................................................................... 65
4.4 Results .............................................................................................................................. 66
4.4.1 Participant demographic and clinical data ................................................................. 66
4.4.2 PLS path modeling reliably elucidates social cognition-circuitry relationships ....... 66
4.5 Discussion ........................................................................................................................ 67
5 General Discussion & Future Directions ................................................................................ 80
5.1 Summary of Results ......................................................................................................... 80
5.2 From DSM to RDoC: Addressing the Heterogeneity in Psychiatric Disorders .............. 82
5.3 Recovery in Schizophrenia .............................................................................................. 85
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5.4 Limitations ....................................................................................................................... 87
5.5 Future Directions ............................................................................................................. 88
5.6 Conclusion ....................................................................................................................... 92
References ..................................................................................................................................... 93
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List of Tables
Table 3-1. Sociodemographic and Clinical Characteristics of 30 Schizophrenia and 30 Health
Control Subjects ……………………………………………………………………………… 51
Table 3-2. Multiple Regression Models with White Matter Tract FA ………………………… 52
Table 3-S1A. Principal Component Loadings (Varimax Rotation), baseline sample (n=30) … 57
Table 3-S1B. Principal Component Loadings (Varimax Rotation), 6-month follow-up sample
(n=24) ………………………………………………………………………………………… 57
Table 4-1. Sociodemographic and Clinical Characteristics of Participants …………………… 72
Table 4-2A. Partial Least Squares Path Model in All Participants …………………………… 75
Table 4-2B. Partial Least Squares Path Model in Bipolar Disorder …………………………… 75
Table 4-2C. Partial Least Squares Path Model in Schizophrenia ……………………………… 76
Table 4-2D. Partial Least Squares Path Model in Healthy Controls ………………………… 76
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List of Figures
Figure 3-1: Right inferior longitudinal fasciculus and arcuate fasciculus …………………… 52
Figure 3-2: Mediation Model ………………………………………………………………… 53
Figure 3-3: Inter-regional Cortical Coupling ………………………………………………… 54
Figure 3-S1: Group-wise differences in cortical thickness and white matter tract FA ………… 56
Figure 4-1: Partial least squares path models ………………………………………………… 73
Figure 4-2: PLSPM path coefficients and manifest variable loadings ………………………… 77
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List of Abbreviations
AIMS Abnormal Involuntary Movement Scale
BACS Brief Assessment of Cognition in Schizophrenia
BAS Barnes Akithisia Scale
C/S consumer/survivor
CIRS-G Cumulative Illness Rating Scale for Geriatrics
CDSS Calgary Depression Scale for Schizophrenia
DS Deficit Syndrome
DSM-5 Diagnostic and Statistical Manual of Mental Disorders - 5
DT Diffusion Tensor
DTI Diffusion Tensor Imaging
FA Fractional Anisotropy
IFO Inferior Fronto-Occipital Fasciculus
MATRICS Measurement and Treatment Research to Improve Cognition in
Schizophrenia
MCCB MATRICS Consensus Cognitive Battery
MD Mean Diffusivity
MMSE Mini Mental State Examination
MRI Magnetic Resonance Imaging
MSCEIT Mayer-Salovey-Caruso Emotional Intelligence Test
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NDS Non-Deficit Syndrome
NIMH National Institute of Mental Health
PANSS Positive and Negative Symptom Scale
PLS Partial Least Squares
PLSPM Partial Least Squares Path Model(ing)
QLS Quality of Life Scale
RBANS Repeatable Battery for the Assessment of Neuropsychological Status
RDoC Research Domain Criteria
ROI Region of Interest
SANS Scale for the Assessment of Negative Symptoms
SAS Symptom Angus Scale
SLF Superior Longitudinal Fasciculus
SS Saggital Stratum
TASIT The Awareness of Social Inference Test
TBSS Tract-Based Spatial Statistics
UF Uncinate Fasciculus
WTAR Wechsler Test for Adult Reading
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Chapter 1
1 Introduction
1.1 Schizophrenia
1.1.1 Epidemiology & Diagnosis
Originally termed “dementia praecox” by Emil Kraepelin (Kraepelin, 1971),
schizophrenia was believed to be a form of early dementia, characterized by mental deterioration
and poor longitudinal course and outcome (Tandon, Nasrallah, & Keshavan, 2009). It was later
renamed by Eugen Bleuler (Bleuler, 1950), whose description of schizophrenia focused on
negative symptoms and suggested variability in outcomes. In contrast, Schneider’s first rank
symptoms of schizophrenia describe the positive symptoms (hallucinations, delusions) associated
with the illness (Schneider, 1959). Today, diagnosis of schizophrenia hinges on the criteria
outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), which
incorporate chronicity of illness (signs of disturbance must persist for at least 6 months with at
least 1 month of positive/negative/disorganized symptoms), negative symptoms, positive
symptoms, and social or occupational dysfunction (Tandon et al., 2013). In general,
schizophrenia is more accurately described as a syndrome, with considerable heterogeneity in
symptom presentation, course of illness, and outcome (Carpenter & Kirkpatrick, 1988; Heinrichs
& Zakzanis, 1998).
The lifetime risk of developing schizophrenia ranges from 0.3 – 2.0%, with similar
prevalence in males and females (Saha, Chant, Welham, & McGrath, 2005). Increased risk of
developing schizophrenia is associated with urbanicity, male gender, history of migration, and
higher paternal age (Malaspina et al., 2001). Though schizophrenia is known to run in families
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(having an affected family member substantially increases the risk of developing schizophrenia)
(Kendler et al., 1993), the nature of genetic contribution to disease liability is still being
investigated. Another active area of schizophrenia research involves interactions between genetic
and environmental factors and their relationships to neurobiological mechanisms of disease.
Earlier onset of schizophrenia is associated with poorer prognosis (DeLisi, 1992).
Schizophrenia usually develops between late adolescence and early adulthood, with males
presenting with psychotic symptoms at an earlier age than females. In addition, the distribution
of age at onset is bimodal for females, with a second peak later in life. Females generally tend to
have better premorbid functioning, more severe affective symptoms, less severe negative
symptoms and cognitive impairment, and better overall prognosis (Leung & Chue, 2000).
1.1.2 Symptomology, Neurocognition, and Social Cognition
Positive symptoms of schizophrenia include hallucinations, which can occur in any sense
(auditory hallucinations are the most common), delusions, disorganization (poverty of content of
speech, attentional impairment, and inappropriate affect), and a lack of appreciation that
symptoms are caused by illness (Foussias, Agid, & Remington, 2011; Picchioni & Murray,
2007). Positive symptoms are not specific to schizophrenia and follow an independent course
from negative symptoms over time (Eaton, Thara, Federman, Melton, & Liang, 1995).
Interest in negative symptoms has grown with evidence that they substantially affect
functional outcome, with some claiming that they are the best predictor of functional recovery in
schizophrenia (Blanchard, Horan, & Collins, 2005; Bowie, Reichenberg, Patterson, Heaton, &
Harvey, 2006; Ho, Nopoulos, Flaum, Arndt, & Andreasen, 1998; Milev, Ho, Arndt, &
Andreasen, 2005; Rosenheck et al., 2006). Negative symptoms are refractory to conventional
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treatments (Stahl & Buckley, 2007) and have been categorized into two domains: 1. Diminished
expression, including affective flattening and poverty of speech; and 2. Amotivation, including
avolition/apathy, anhedonia (more accurately, anticipatory pleasure deficit as hedonic capacity is
preserved in schizophrenia), and asociality (Foussias et al., 2011). Affective flattening and
poverty of speech refer to reduced emotional expression and the lack of additional unprompted
speech observed in schizophrenia, respectively. Avolition is the primary symptom domain of
emphasis by Kraepelin, indicating a lack of motivation in schizophrenia. Avolition presents as
apathy and asociality and may be linked to anticipatory pleasure deficits in schizophrenia. In the
past, negative symptoms were thought to include inattention, inappropriate affect, and poverty of
content of speech; however, these symptoms have been appropriately reclassified into the
disorganization symptom domain (Foussias et al., 2011). Primary negative symptoms are a direct
expression of schizophrenia psychopathology, while secondary negative symptoms are a
consequence of medication effects, depressive anhedonia, paranoid social withdrawal, or
preoccupation with psychotic symptoms (Carpenter, Heinrichs, & Wagman, 1988).
It has been claimed that all persons with schizophrenia have some form of neurocognitive
deficit (Kremen, Seidman, Faraone, Toomey, & Tsuang, 2000; Palmer et al., 1997).
Schizophrenia is associated with widespread neurocognitive deficits, including impairments in
attention, verbal memory, executive function (higher order cognitive functions that control
decision making), verbal fluency (semantic and phonemic fluency, related to language abilities),
motor speed, and processing speed (the ability to process new information rapidly and
efficiently) (Harvey, Green, Keefe, & Velligan, 2004; Swerdlow, 2010). The National Institute
of Mental Health (NIMH) Measurement and Treatment Research Initiative to Improve Cognition
in Schizophrenia (MATRICS) substantiates the importance of neurocognition in achieving
functional recovery for individuals with schizophrenia.
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Although negative symptoms are moderately correlated with several domains of
cognition, they only explain 10% of the variance in cognitive impairment in schizophrenia
(Harvey, Koren, Reichenberg, & Bowie, 2006). A recent longitudinal study showed that these
symptom domains do not change in parallel over time, allowing them to be classified as
semiautonomous disease processes (Bell & Mishara, 2006). Thus, negative symptoms have been
validated as a separate domain of psychopathology of schizophrenia (Kirkpatrick, Fenton,
Carpenter, & Marder, 2006).
Social cognition is defined as the mental operations that underlie social interactions,
including perception, interpretation, and response generation to the intentions, dispositions, and
behaviours of others (Kunda, 1999) and is significantly impaired in people with schizophrenia.
Social cognitive impairment is thought to be a trait feature of schizophrenia, as it is stable in first
episode patients over a 12-month follow-up period and is also comparable across phase of illness
(prodromal, first episode, and chronic schizophrenia patients) (Green et al., 2012; Horan et al.,
2012). Social cognitive processes are essential for the successful execution of complex social
behaviours necessary in daily life. As such, social cognition is a key determinant of functional
outcome in schizophrenia (Couture, Penn, & Roberts, 2006; Horan et al., 2012).
Although social cognition and neurocognition are highly related constructs, sophisticated
statistical analyses have shown that these domains are distinct and therefore not redundant (Sergi
et al., 2007). The relationship between these domains is evident as they rely on shared cognitive
processes, such as working memory and perception. However, their distinction is based on the
type of stimulus and judgments being made in response to the stimulus (Green & Horan, 2010).
Similarly, social cognition is distinct from negative symptoms and further analysis has revealed
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that the relationship between these two symptom domains is weaker than the relationship
between social cognition and neurocognition (Sergi et al., 2007).
1.1.3 Functional Outcome
As suggested by Bleuler, individual outcomes are variable in those diagnosed with
schizophrenia. Although some individuals experience extended periods of recovery, long-term
disability is common for people with schizophrenia due to impairments in social and
occupational functioning (Jobe & Harrow, 2005).
A recent review concluded that schizophrenia is consistently associated with poorer
course of illness and outcome compared to other psychiatric disorders (Jobe & Harrow, 2005).
Substantial functional impairment has also been reported in schizophrenia (Breier, Schreiber,
Dyer, & Pickar, 1991). Functional recovery, or functional outcome, includes social behaviours
(such as communicating with others and engaging in the community), as well as maintaining
employment and caring for oneself.
Historically, outcomes were assessed in accordance with the medical model, with a
greater focus on the effects of pharmacotherapy and (positive) symptom resolution rather than
social, work, or community functioning (Remington, Foussias, & Agid, 2010). This was based
on the expectation that antipsychotic medication would help to control symptoms and allow for
functional recovery in schizophrenia. In a sense, antipsychotics have met expectations by
reducing the impact of positive symptoms, thereby improving clinical outcomes. However,
reduction of positive symptoms has not been shown to be sufficient for attaining functional
recovery (San, Ciudad, Álvarez, Bobes, & Gilaberte, 2007).
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Over time, the definition of “outcome” has expanded to address other symptom clusters,
such as neurocognition, social cognition, and negative symptoms, which are critical for
functional recovery. Due to the limited efficacy of current treatments in addressing these
symptoms, there has been increased interest in the neurobiological underpinnings of these
symptom domains and functional outcome in schizophrenia. The relationships between these
symptom domains and functional outcome, as well as associated neural circuitry, will be
explored in the following sections. Briefly, the effect of neurocognitive impairment on functional
outcome is mediated by social cognition and functional capacity (the ability to perform every day
tasks in a structured environment) (Bowie et al., 2006; Galderisi et al., 2014). In addition,
negative symptoms, particularly amotivation (Foussias et al., 2011; Foussias, Mann, Zakzanis,
van Reekum, & Remington, 2009), have been associated with poorer social and work
functioning, as well as overall functional outcome (Breier et al., 1991).
1.1.3.1 Negative Symptoms and Functional Outcome
A recent meta-analysis of 73 studies concluded that negative symptoms mediate the
relationship between neurocognition and functional outcome (Ventura, Hellemann, Thames,
Koellner, & Nuechterlein, 2009). Negative symptoms, particularly amotivation, have also been
shown to account for majority of the variance in longitudinal functional outcome (Foussias et al.,
2011, 2009). In these studies, cognitive function did not offer any additional contribution to the
prediction of functional outcome in schizophrenia. Amotivation may also affect the relationship
between neurocognition and functional outcome by contributing to insufficient effort of
schizophrenia participants during neuropsychological testing (Foussias & Remington, 2010).
Lack of effort during neuropsychological testing is correlated with negative symptom severity in
schizophrenia. Although there is a complex relationship between negative symptoms,
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neurocognition, and functional outcome, it appears that neurocognition is related to functional
capacity, while negative symptoms are related to the likelihood of implementation of abilities in
real world scenarios (Bowie et al., 2006; Foussias & Remington, 2010).
1.1.3.2 Neurocognition and Functional Outcome
Green et al (Green, 1996) first demonstrated the relationship of neurocognition,
specifically working memory, to functional outcome in schizophrenia patients. Since then, more
complex models have shown that the relationship between neurocognition and functional
outcome is mediated by functional capacity (Bowie et al., 2006; Galderisi et al., 2014). As such,
neurocognitive performance is strongly associated with functional capacity, which is related to
multiple domains of real-world functioning (social, occupational, and community functioning).
In addition, neurocognitive performance contributes little to the prediction of real-world
performance after accounting for functional capacity (Bowie et al., 2006). Therefore, it appears
that neurocognitive performance is more closely related to a person’s ability to navigate the real
world, rather than the actual implementation of these abilities.
1.1.3.3 Social Cognition and Functional Outcome
The role of social cognition as a mediator between neurocognition and functional
outcome in schizophrenia is well supported (Galderisi et al., 2014; Green & Horan, 2010;
Schmidt, Mueller, & Roder, 2011). In a recent review of the literature, this finding was replicated
by several studies, both in cross-sectional and longitudinal designs (Green & Horan, 2010). As
such, the strength of the relationship between neurocognition and functional outcome is reduced
when social cognition is included in the model. Other variables such as social competence
(analogous to social cognition as functional capacity is to neurocognition) and motivation
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mediate the relationship between social cognition and functional outcome (Gard, Fisher, Garrett,
Genevsky, & Vinogradov, 2009; Schmidt et al., 2011).
Social cognition can be broken down into four main domains (Green, Olivier, Crawley,
Penn, & Silverstein, 2005; Penn, Addington, & Pinkham, 2006): 1. Theory of mind, which is the
ability to attribute mental states (beliefs, feelings, perspectives) to oneself and to others, as well
as recognizing that others’ mental states may not be the same as our own. This allows one to
understand and predict the behaviour of others. 2. Emotion processing, which refers to the ability
to understand and identify different emotions, as well as manage one’s own emotions. 3. Social
perception, which is the ability to use verbal and nonverbal cues to understand a social situation
based on context. 4. Attributional bias, which depends on whether a person typically attributes
the causes of an event to internal, external, or situational factors (Green & Horan, 2010).
Exploratory factor analysis has revealed that social cognitive domains in schizophrenia patients
load onto three separate factors, two of which influence functional outcomes. In this study,
attributional bias was not related to functional outcome and loaded on one factor. Emotion
recognition and social perception loaded onto a second factor representing lower level social
cognitive processes, necessary for understanding the emotions and actions of others, while theory
of mind loaded onto a third factor representing higher level social cognitive processes, necessary
for understanding the mental states of others (Mancuso, Horan, Kern, & Green, 2011). Emotion
recognition and social perception are considered to be lower level processes as they correspond
to the experiential properties of a stimulus, whereas higher level symbolic interpretations of a
stimulus are more accurately conceptualized as theory of mind (Ochsner, 2008). Both these
lower and higher level processes were found to influence functional outcome (Mancuso et al.,
2011) and are believed to correspond to separate neural networks: 1. An “embodied simulation
or low-level mental state inference construct” involving the mirror neuron system and limbic
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system and 2. A “high-level mental state/trait inference construct” involving cortical midline and
lateral temporal structures (Ochsner, 2008).
1.1.4 Treatment
Up until the de-instutionalization movement after World Ward II (Novella, 2010), care
for schizophrenia patients was mainly custodial and involved long-term inpatient admission to
psychiatric hospitals. Effective pharmacotherapies were first introduced in the 1950s, known as
first generation antipsychotics, followed by the production of second generation antipsychotics in
the late 1960s. Antipsychotic medications have robust efficacy against positive and
disorganization symptom domains (Leucht et al., 2009; Mazure, Nelson, Jatlow, & Bowers,
1992). However, they are not effective in reducing primary negative symptoms (Kirkpatrick et
al., 2006; Leucht et al., 2009) or most neurocognitive deficits (Mishara & Goldberg, 2004;
Mortimer, 1997). They are also not shown to improve lifespan or social functioning in
schizophrenia (Lehman et al., 2004; Wunderink, Nieboer, Wiersma, Sytema, & Nienhuis, 2013)
and are associated with severe adverse effects, including metabolic syndrome, extrapyramidal
symptoms, and tardive dyskinesia (Tandon, Nasrallah, & Keshavan, 2010).
In order to target the broad symptom profile of schizophrenia, multimodal care is
required (Kern, Glynn, Horan, & Marder, 2009). Psychosocial treatments recommended by the
Patient Outcomes Research Team include Assertive Community Treatment to prevent
hospitalization and homelessness, supported employment, skills training to improve community
functioning, Cognitive Behavioural Therapy, and family based services, amongst others (Dixon
et al., 2010). In addressing neurocognitive impairment of persons with schizophrenia, cognitive
remediation has proved to be effective (McGurk, Twamley, Sitzer, McHugo, & Mueser, 2007),
while pharmacological augmentation using glycine is being investigated in the treatment of
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negative symptoms (Coyle & Tsai, 2003; Heresco-Levy et al., 1999). Intranasal administration of
oxytocin is also being explored as a possible therapy for social cognitive impairment in
schizophrenia as well as in other psychiatric disorders (Davis et al., 2013). The NIMH Recovery
After an Initial Schizophrenia Episode project combines medication management with
psychosocial treatments to decrease the likelihood of long-term disability experienced by many
people with schizophrenia.
In the future, personalized care will hopefully be available for people with schizophrenia,
in order to address the variability in symptom presentation, course of illness, outcome, and
response to treatments. To this end, neurobiological markers of various domains of emotion,
cognition, and behaviour can be assessed on the macroscopic scale with diagnostic imaging
techniques and can lead to more targeted treatments (Morris & Cuthbert, 2012).
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1.2 Brain Morphology and Schizophrenia
1.2.1 Grey Matter Volume and Cortical Thickness Changes in
Schizophrenia
Schizophrenia has been associated with ventricular enlargement, as well as increased
grey matter volume of the basal ganglia and reduced grey matter volume of medial temporal lobe
structures (amygdala, hippocampus, parahippocampal gyrus), the superior temporal gyrus,
frontal lobe, thalamus, and inferior parietal lobule. It is believed that increased grey matter
volume of the basal ganglia is a consequence of antipsychotic treatment (Shenton, Dickey,
Frumin, & McCarley, 2001); however, antipsychotic treatment has been associated with smaller
grey matter volumes as well (Ho, Andreasen, Ziebell, Pierson, & Magnotta, 2011). Enlargement
of the lateral ventricles is one of the most robust findings in schizophrenia, and may be an
indicator of neurodegeneration or neurodevelopmental pathology of adjacent structures, such as
the amygdala and hippocampus (Shenton et al., 2001). Similarly, enlargement of the third
ventricle may correspond to reduced thalamic volume.
In schizophrenia there are often differences in bilateral symmetry, with findings mainly
lateralized to the left hemisphere of the brain. There is also greater leftward thalamic asymmetry
(Csernansky et al., 2004), reduced laterality of the planum temporale (Ratnanather et al., 2013)
and hippocampus (Kim et al., 2005), and reversal of asymmetry of the inferior parietal lobule
(Buchanan et al., 2004). Overall, structural imaging has revealed abnormalities in multiple brain
regions in schizophrenia (Shenton, Whitford, & Kubicki, 2010; Shenton et al., 2001).
The temporal lobe is the site of the superior temporal gyrus, which is the primary
auditory cortex of the brain and is specialized for language and speech in the left hemisphere
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(Howard et al., 2000). Therefore, abnormalities in temporal lobe structures may correspond to
auditory hallucinations and impairments in language processing (Barta, Pearlson, Powers,
Richards, & Tune, 1990). The temporal pole, also affected in schizophrenia, is connected to the
inferior frontal lobe (via the uncinate fasciculus), both of which are important for emotion
recognition and processing (Frith & Frith, 2003; Iacoboni et al., 1999). Reversal of asymmetry of
the inferior parietal lobule may result in similar emotion recognition impairments in
schizophrenia, as well as impairments in neurocognition as it is a key region for integrating
sensory information for perception and language processes (Catani, Jones, & Ffytche, 2005;
Eidelberg & Galaburda, 1984). The frontal lobe serves a diverse set of higher order behavioural,
cognitive, and emotional functions, and is involved in the complex clinical presentation of
schizophrenia (Miyake et al., 2000). Medial temporal lobe structures such as the amygdala and
hippocampus are part of the limbic system, which is important for emotion processing and
memory, both of which are affected in schizophrenia (Holt et al., 2006; Squire, Stark, & Clark,
2004). Alterations in medial temporal and prefrontal cortical areas has also been consistently
related to functional outcome in schizophrenia (Dazzan et al., 2015).
More recently, correlations of interregional grey matter volume or cortical thickness are
being investigated in schizophrenia. It is suggested that these correlations represent anatomical
connectivity or coupling between brain regions, as anatomically connected regions share trophic
factors and correlate in size (Alexander-Bloch, Giedd, & Bullmore, 2013). Increased correlations
may be indicative of overconnectivity, coordinated grey matter loss, or a lack of developmental
specificity, while reduced correlations may be due to dysconnectivity or degeneration of brain
regions. In schizophrenia, stronger frontoparietal relationships have been demonstrated in
comparison to healthy control participants, which may correspond to deficits in social cognitive
13
processes and social function (Abbs et al., 2011; Buchanan & Pearlson, 2004; Niznikiewicz et
al., 2000).
1.2.2 White Matter Microstructural Changes in Schizophrenia
Schizophrenia was first theorized to be a product of alterations in cerebral connectivity
by Friston and Frith (Friston & Frith, 1995). One way of assessing connectivity is to compute
interregional correlations of grey matter morphology, as explained in section 1.2.1. While grey
matter consists of cell bodies and dendrites, white matter consists of axonal projections and
forms the basis for structural connectivity in the brain. White matter microstructure can be
assessed via diffusion metrics, most commonly fractional anisotropy (FA), calculated from
diffusion-weighted images.
White matter tracts most frequently identified as disrupted in schizophrenia include the
uncinate fasciculus, cingulum bundle, corpus callosum, and internal capsule (Pettersson-Yeo,
Allen, Benetti, McGuire, & Mechelli, 2011; Wheeler & Voineskos, 2014). These findings,
coupled with decreased FA in the prefrontal and temporal lobes in schizophrenia (Kubicki et al.,
2007), point towards frontotemporal dysconnectivity in schizophrenia. A recent meta-analysis
reported that decreased FA was lateralized to the left hemisphere, which is consistent with the
laterality observed in grey matter volumes in schizophrenia (Ellison-Wright & Bullmore, 2009).
Another meta-analysis concluded that decreased FA was specific to interhemispheric fibers,
including the anterior thalamic radiation, inferior longitudinal fasciculus, inferior fronto-occipital
fasciculus, cingulum bundle, and fornix (Bora et al., 2011).
The uncinate fasciculus connects the temporal pole and amygdala to the inferior frontal
lobe, and is therefore a key connection for intact social cognition (both mentalizing and emotion
14
processing) (Heide, Skipper, Klobusicky, & Olson, 2013). The cingulum bundle connects
cortical midline structures, such as the medial prefrontal cortex and the posterior cingulate
cortex, which are important for higher level social cognitive function, to be described in more
detail in the following sections. The corpus callosum is a large interhemispheric connection,
allowing for communication between the left and right hemispheres of the brain, and has been
identified as important for social cognitive performance (Ingalhalikar et al., 2014). The inferior
longitudinal fasciculus connects the occipital cortex to the fusiform gyrus, parahippocampal
gyrus, and amygdala, and is therefore important for visuoemotional processing (Gur et al., 2007).
Finally, the arcuate fasciculus, which connects the inferior frontal lobe to the inferior parietal
lobule, is another disrupted circuit in schizophrenia, associated with auditory hallucinations in
the left hemisphere and impaired emotion processing in the right hemisphere (de Weijer et al.,
2011; Iacoboni et al., 1999).
There are considerable differences in findings across studies, which may be due to
methodological differences, variability in symptom presentation of schizophrenia participants,
and variation in confounding variables (Fitzsimmons, Kubicki, & Shenton, 2013; Melonakos et
al., 2011). One such confounding variable may be antipsychotic treatment, which has been
associated with decreased white matter volumes (Ho et al., 2011) and reductions in parietal and
occipital white matter FA (Szeszko et al., 2014).
1.2.3 Neuroimaging Findings and Schizophrenia Symptomatology
Studies investigating negative symptoms and neural circuitry in schizophrenia report
associations between negative symptom severity and frontotemporal white matter. Decreases in
both volume (Sigmundsson et al., 2001) and FA (Szeszko et al., 2008) of the uncinate fasciculus
have been associated with negative symptoms. Other regions have also been associated with
15
negative symptoms, including inferior frontal white matter, the inferior longitudinal fasciculus,
internal capsule, and corpus callosum (Nakamura et al., 2012; Sigmundsson et al., 2001; Wolkin
et al., 2003). Uncinate fasciculus and inferior longitudinal fasciculus impairment have also been
implicated in deficit syndrome (DS) schizophrenia (Kitis et al., 2012; Rowland et al., 2009;
Voineskos et al., 2013), which is characterized by prominent negative symptoms, thereby
lending further support to the potential identification of these tracts as biomarkers for negative
symptoms.
The neural underpinnings of neurocognitive performance in schizophrenia are still under
investigation. Early studies assessing white matter microstructure-cognition relationships
focused on a limited number of tracts, such as the cingulum bundle (Kubicki et al., 2003; Nestor
et al., 2013; Takei et al., 2009) or the uncinate fasciculus (Kubicki et al., 2002; Nestor et al.,
2013). Other implicated tracts include the inferior longitudinal fasciculus, inferior fronto-
occipital fasciculus, and superior longitudinal fasciculus (Karlsgodt et al., 2008; Liu et al., 2013).
More recently however, neurocognitive deficits have been associated with subtle, widespread
tract impairment in schizophrenia (Lim et al., 2006; Spoletini et al., 2009; Voineskos, Felsky, et
al., 2013).
1.2.4 Neuropathology of the Deficit Syndrome
Schizophrenia was described by Kraepelin in 1919 as a disorder of volition (Kraepelin,
1971). The ‘deficit syndrome’ is a subtype of schizophrenia characterized by primary, enduring
negative symptoms for which no effective treatment exists, as well as poor functional outcomes
(Kirkpatrick, Buchanan, Ross, & Carpenter, 2001). This subtype of schizophrenia was originally
described in 1988, and its construct has been shown to be valid and stable over time (Amador et
al., 1999). Several studies have shown altered white matter connectivity in DS compared to
16
nondeficit syndrome (NDS) schizophrenia. Rowland et al (Rowland et al., 2009) reported altered
frontoparietal connectivity in the DS, with reduced FA in the right superior longitudinal
fasciculus (which includes the arcuate fasciculus) compared to NDS schizophrenia. Kitis et al
(Kitis et al., 2012) demonstrated altered frontotemporal connectivity in the DS, specifically
reduced FA in the left uncinate fasciculus, compared to NDS schizophrenia. Our group’s
findings are in line with these studies as DS participants had altered connectivity in the left
uncinate fasciculus, right arcuate fasciculus, and right inferior longitudinal fasciculus compared
to NDS participants (Voineskos et al., 2013). These findings are further supported by structural
correlation analysis, showing stronger coupling of frontoparietal and frontotemporal regions in
DS participants (Wheeler et al., 2015). Interestingly, these tracts are associated with social
cognitive processes, and so impairments in these tracts may correspond to poor social function in
the DS. Overall, it appears that frontotemporal and frontoparietal connectivity may distinguish
the neuropathology of the DS from NDS schizophrenia.
1.2.5 Neural Circuitry and Functional Outcome
Although there are several studies examining altered connectivity of white matter tracts
in the DS, which is associated with particularly poor functional outcome, there are very few
studies examining the direct relationship between neural circuitry and functional outcome.
Karlsgodt et al (Karlsgodt, Niendam, Bearden, & Cannon, 2009) reported decreased FA in the
left hippocampus and bilateral inferior longitudinal fasciculus as predictors of poor role
functioning in individuals at ultra-high risk for psychosis, while decreased FA in the right
inferior longitudinal fasciculus also predicted poor social functioning in ultra-high risk subjects.
More recently, Kumar et al (Kumar et al., 2014) reported that white matter tract microstructure
of the right inferior fronto-occipital fasciculus, corpus callosum, corona radiata, left cingulate
17
gyrus, and left posterior thalamic radiation predicted social and occupational performance in
individuals with psychosis. However, neither study’s sample was specific to schizophrenia, with
the former focusing on ultra-high risk for psychosis participants and the latter including people
with bipolar disorder (who typically do not have negative symptom burden) with a history of
psychosis.
More recently, macroscale wiring architecture of the human connectome has been studied
in relation to general functioning in schizophrenia (Collin, de Nijs, Hulshoff Pol, Cahn, & van
den Heuvel, 2015). However, in this study, symptom remission was included in assessments of
general functioning in schizophrenia, thereby limiting the extent to which clinical and functional
recovery can be studied as distinct constructs. Furthermore, the interpretation of the results their
imaging analysis is limited to local and hub connectivity or organization of the connectome,
rather than identifying specific white matter tracts as potential biomarkers of functional outcome.
Therefore, the direct relationship between neural circuitry and functional outcome in
schizophrenia is a novel and important area of exploration.
18
1.3 Neural Circuitry of Social Cognition
1.3.1 Neuroimaging Evidence for Lower Level Social Cognitive Circuitry
There is considerable evidence supporting the role of the human mirror neuron system in
simulation (Iacoboni et al., 1999; Iacoboni et al., 2005) i.e. observation/imitation of action and
emotion. Mirror neurons are named to reflect their defining property, which is that they fire
during the execution of an action (including facial/bodily expressions of emotion) as well as
during observation of the action/emotion when performed by another individual. Specifically, the
mirror neuron system is activated not only during action recognition but also during intention
understanding i.e. understanding the goal of the action in a given context (Iacoboni et al., 2005).
It is important to emphasize that identifying goal-oriented actions is a lower level social
cognitive process, while inference of mental states is a higher level process (Ochsner, 2008).
The mirror neuron system is lateralized to the right cerebral hemisphere and includes the
inferior frontal gyrus and inferior parietal lobule (Iacoboni et al., 1999). These regions, along
with the right superior temporal sulcus, form a right frontoparietal circuit connected by the right
arcuate fasciculus. Neurons in the superior temporal sulcus respond to body movements engaged
in goal-oriental actions, which includes dynamic facial expressions. Within the right
frontoparietal circuit, the superior temporal sulcus provides a visual representation of observed
actions, which is propagated to the inferior parietal mirror neurons. The inferior parietal mirror
neurons code the kinesthetic details of the movement and send this information to the inferior
frontal mirror neurons. These neurons code the goal of the action and send efferent copies of
motor plans back to the superior temporal sulcus. Once the visual description of the observed
action and predicted sensory consequences of the planned imitative action are matched, imitation
19
can be initiated (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003). The arcuate fasciculus is
the white matter tract responsible for structural connectivity of these regions, and so
dysconnectivity of this tract may result in impairments in simulation.
The neuronal network for these bottom-up stimulus-driven processes is critical for a
person to be able to have shared emotional experiences and therefore empathize with others.
Together with the limbic system, which is critical for emotion processing, the right frontoparietal
circuit forms the neuronal basis for empathy. Empathic individuals demonstrate unconscious
mimicry of postures, mannerisms, and facial expressions of conspecifics more so than others.
This supports the notion that engagement of the right frontoparietal circuit through the imitation
of facial emotions is a precursor to empathy and shared emotional experience (Tangney,
Stuewig, & Mashek, 2007).
In a recent study (Carr et al., 2003), functional magnetic resonance imaging was used to
compare activation in the inferior frontal cortex, superior temporal cortex, and amygdala when
subjects were either observing or imitating emotional facial expressions. Results showed
increased activity of these regions during imitation compared with observation, indicating
functional connectivity of frontoparietal and limbic regions during simulation. Structurally, the
amygdala and fusiform gyrus are connected to the visual cortex by the inferior longitudinal
fasciculus. This tract has been implicated in facial recognition and visuoemotional processing
(Crespi et al., 2014; Philippi, Mehta, Grabowski, Adolphs, & Rudrauf, 2009). Thus, the inferior
longitudinal fasciculus not only facilitates structural connectivity of limbic regions to other
structures, but is also important for emotion recognition and processing. Together, the inferior
longitudinal and arcuate fasciculi subserve the lower level simulation network and may be
implicated in deficits in corresponding social cognitive processes in schizophrenia.
20
1.3.2 Neuroimaging Evidence for Higher Level Social Cognitive Circuitry
The simulation network is insufficient for representing complex mental states, which
requires the interpretation of contextual information that can drastically affect the meaning of
social actions (e.g. sincerity vs. sarcasm or lying). It has been proposed that a separate network
of cortical midline and lateral temporal structures is responsible for theory of mind, also called
mentalizing. Several studies have concluded that the mirror neuron system and the mentalizing
network are distinct from one another (Van Overwalle & Baetens, 2009; Wheatley, Milleville, &
Martin, 2007).
Meta-analytic data of studies investigating the neural correlates of higher level social
cognitive processes in healthy individuals show that cortical midline structures (the medial
prefrontal cortex, posterior cingulate cortex, and precuneus) and lateral temporal structures (the
temporoparietal junction and temporal pole) are nodes of the mentalizing network (Mar, 2011).
However, impairments in these regions has not been directly associated with social cognitive
performance in schizophrenia (Holt et al., 2011). It is likely that different regions support
different subprocesses required for mentalizing and it would be interesting to explore whether
dysconnectivity between these regions prevents the integration of these subprocesses, resulting in
impaired social cognition.
The medial prefrontal cortex is believed to support several social cognitive processes
including mentalizing, as well as non-social processes such as attention (Amodio & Frith, 2006).
Functional neuroimaging studies consistently point to activation of this region during
mentalizing (Frith & Frith, 2003), as well as when reflecting on one’s own emotional states
(Gusnard, Akbudak, Shulman, & Raichle, 2001; Lane, Fink, Chau, & Dolan, 1997) and character
21
traits (Johnson et al., 2002; Macrae, Moran, Heatherton, Banfield, & Kelley, 2004). Thus, this
region is important for attributing mental states to oneself and to others.
The posterior cingulate cortex is associated with a range of functions, including attention
and mentalizing (Laird et al., 2011; Leech & Sharp, 2014; Mar, 2011). It is closely
interconnected with the precuneus, which is also associated with theory of mind tasks (Laird et
al., 2011). The precuneus is considered to have an anterior region associated with mental
imagery involving the self and a posterior region associated with episodic memory retrieval
(Cavanna & Trimble, 2006). Thus, the anterior precuneus supports imagery or imagination
processes that may be required to infer the mental states of others.
Activation of the temporoparietal junction is associated with and specific to tasks
regarding reasoning about the content of another’s mental states (Saxe & Kanwisher, 2003).
Together with the temporal pole, the temporoparietal junction has been implicated in mentalizing
(Frith & Frith, 2003; Völlm et al., 2006). The temporal pole is part of the limbic system (Heimer
& Van Hoesen, 2006) and has been linked to a variety of social cognitive processes, including
face recognition and theory of mind, along with semantic memory (Olson, Plotzker, & Ezzyat,
2007).
These neural circuits overlap with the default mode network, which is active when
humans are not engaged in any specific task and is associated with processes reliant on internally
focused attention, such as self-reflection and mentalizing (Corbetta, Patel, & Shulman, 2008;
Mars et al., 2012; Schilbach, Eickhoff, Rotarska-Jagiela, Fink, & Vogeley, 2008). It has been
proposed that this is because humans have a predisposition for social cognition as the default
mode of thought (Schilbach et al., 2008), as well as because of the unconstrained nature of
higher order social cognitive processes (Mars et al., 2012). Therefore, structural connectivity of
22
the cortical midline and lateral temporal circuits may be linked to functional connectivity within
the default mode network.
Structurally, the cingulum bundle connects the posterior cingulate cortex to the medial
prefrontal cortex, while the genu of the corpus callosum connects the left and right medial
prefrontal cortex. This is supported by studies showing a direct association between
microstructural organization of the cingulum and the strength of default mode functional
connectivity (Greicius, Supekar, Menon, & Dougherty, 2009; van den Heuvel, Mandl, Luigjes,
& Hulshoff Pol, 2008). The uncinate fasciculus is another white matter tract of interest in higher
level social cognitive processes, as it connects the medial prefrontal cortex to the temporal pole
and amygdala. This is supported by studies combining structural and functional neuroimaging to
show that increased structural connectivity of the uncinate fasciculus is associated with greater
coupling of the amygdala and temporal regions involved in higher order social cognition
(Carlson, Cha, & Mujica-Parodi, 2013).
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1.4 Imaging Modalities and Analysis
1.4.1 Cortical Thickness Analysis Using Magnetic Resonance Imaging
Cortical thickness has been a measure of cortical neuroanatomy since the early work by
Brodmann (Brodmann, 1909). Neuroimaging studies have provided substantial evidence for
widespread cortical and subcortical grey matter abnormalities in schizophrenia (Shenton et al.,
2010; Shenton et al., 2001), while postmortem studies have revealed abnormalities in cortical
cytoarchitecture in schizophrenia (Rajkowska, Selemon, & Goldman-Rakic, 1998; Selemon,
Mrzljak, Kleinman, Herman, & Goldman-Rakic, 2003). In contrast to volumetric studies, cortical
thickness findings are among the most replicable patterns of results in studies of brain
morphology in schizophrenia. Therefore, investigating regional cortical thickness in
schizophrenia may provide valuable insight into the neurobiological markers and
pathophysiology of this disorder.
Cortical thickness may represent cell density and arrangement, with changes in thickness
reflecting atypical neurogenesis, neuronal migration, differentiation, synaptogenesis, and
synaptic pruning, all of which have been implicated in schizophrenia (Jakob & Beckmann,
1986). As such, magnetic resonance imaging (MRI) and cortical thickness analysis can allow us
to model changes in neural processes in the cerebral cortex with regional specificity.
Manual delineation of cortical thickness has been replaced by more reliable and
automated techniques. Specifically, the CIVET pipeline was used to acquire cortical thickness
measures in the experiments detailed in the following sections. Cortical thickness is measured as
the distance between the grey matter/white matter boundary and pial surface of the brain, each of
24
which is modeled by a three-dimensional polygonal mesh with 40 962 vertices per hemisphere
(Lerch & Evans, 2005). Vertex-wise statistical analysis can then be used to visualize group
differences of cortical thickness across the entire cortex. The images can also be segmented to
calculate the average thickness of each cortical region per subject (Shattuck et al., 2008).
Segmenting the cortex using the LONI Probabilistic Brain Atlas allows for the
calculation of average thickness values for 52 cortical regions (Shattuck et al., 2008). It has been
previously demonstrated that the thickness of one region influences the thickness of other
structurally and functionally connected regions (Alexander-Bloch et al., 2013). Therefore,
average cortical thickness values can be used in structural covariance analysis, a measure of
cortical connectivity, to complement white matter findings.
1.4.2 Measuring White Matter Microstructure Using Diffusion Tensor
Imaging
Diffusion tensor imaging (DTI) was developed relatively recently, making it possible for
in vivo visualization and analysis of white matter in the brain. In 1996, Pierpaoli acquired the
first DTI of the human brain, and in 1998 Buchsbaum conducted the first DTI study of
schizophrenia (Buchsbaum et al., 1998; Pierpaoli, Jezzard, Basser, Barnett, & Di Chiro, 1996).
Diffusion weighted images are acquired by applying diffusion-sensitizing gradients in multiple
directions to measure the diffusion of water and infer tissue structure and orientation (Jones,
2008).
The degree of anisotropy depends on how restricted the diffusion of water is in different
directions. In cerebrospinal fluid, water is unrestricted in all directions and diffusion is said to be
isotropic. On the other hand, in white matter, the diffusion of water is restricted along the fiber
25
and so diffusion is anisotropic. Once diffusion weighted images are acquired, a tensor (i.e. an
ellipsoid) can be fitted at each voxel to represent the distance of water diffusion in all directions
at that voxel, thereby providing information about the orientation of the tract as well as the
underlying tissue structure. From this tensor, diffusion metrics can be calculated, with fractional
anisotropy and mean diffusivity (MD) being most commonly used in neuroimaging studies
(Jones, 2008).
FA provides information about the shape of the diffusion tensor and is independent of
fiber orientation, with values ranging from 0 (isotropic diffusion) to 1 (maximum anisotropic
diffusion). MD on the other hand, provides information on the size of the diffusion tensor, or the
average displacement of water molecules within the voxel. Therefore, as MD increases in value,
diffusion of water is less restricted, as in cerebrospinal fluid, as opposed to white matter.
Although a change in these diffusion metrics is not specific to a single biological interpretation,
decreases in FA and increases in MD are associated with axonal damage, reductions in the
number/density/coherence of axons, and demyelination (Mori & Zhang, 2006). Decreased FA
and increased MD have been consistently reported in schizophrenia, supporting the
dysconnectivity hypothesis of schizophrenia (Frith & Frith, 2003). Furthermore, changes in these
measures of white matter microstructure have been associated with various behavioural
measures, reviewed by Johansen-Berg (Johansen-Berg, 2010).
DTI analysis involves preprocessing steps to register the diffusion-weighted images and
correct for distortions due to eddy currents and motion artifacts. This is followed by fitting the
tensor, which can then be used either to calculate diffusion metrics or as the input for
tractography (Soares, Marques, Alves, & Sousa, 2013). Tractography is a method for modeling
the trajectory of white matter tracts in order to assess structural connectivity in the brain (Mori &
26
van Zijl, 2002). It is achieved by constructing streamlines from local estimates of fiber
orientation (i.e. the tensor). Diffusion metrics can then be calculated and averaged along each
white matter tract.
Here, the first experiment makes use of a reliable automated clustering tractography
pipeline (O’Donnell et al., 2006; Voineskos et al., 2009). Rather than rely on manual placement
of regions of interest (ROI) to delineate tracts of interest, white matter fibers are grouped
together based on shape and similarity. White matter tracts can then be visualized and diffusion
metrics can be calculated along each tract. This method has been shown to be reliable and in high
agreement with other tractography methods in both healthy control and schizophrenia
populations (O’Donnell et al., 2006; Voineskos et al., 2009). One of the advantages of this
method over manual ROI placement is reduced possibility for human error, as well as less
opportunity for user bias since the user cannot visualize the diffusion tensors when assigning
fiber clusters to a tract.
Another method for DTI analysis is called Tract-Based Spatial Statistics (TBSS), which
addresses the limitations of traditional voxelwise approaches by: 1. Aligning FA images from
multiple subjects using carefully tuned non-linear registration; and 2. Introducing an alignment-
invariant tract representation so as to avoid the arbitrariness of spatial smoothing (Smith et al.,
2006). Similar to our automated clustering tractography pipeline, the TBSS pipeline can be
combined with ROI placements to calculate average FA values along tracts of interest in each
subject, for use in group comparisons or more sophisticated statistical modeling or regression
analyses (Jahanshad et al., 2013).
27
1.4.3 Partial Least Squares Path Modeling
Partial least squares (PLS) path modeling is a multivariate statistical method that allows
for multiple relationships between blocks of variables to be analyzed in a prespecified model,
thereby taking into account background information on the phenomenon of interest. It exists at
the intersection of regression analysis, structural equation modeling, and multiple table analysis
(Sanchez, 2013). More detailed explanations of PLS and PLS path modeling have been
published (McIntosh & Lobaugh, 2004; McIntosh, Bookstein, Haxby, & Grady, 1996; Sanchez,
2013). PLS has several advantages over univariate statistical methods: 1. It has increased power
to detect relationships between sets of variables in a sample; 2. It can be used to analyze data
where variables in a single block are highly correlated; 3. It uses resampling algorithms to
validate findings (resulting in increased certainty in findings compared to univariate tests of
significance) (McIntosh & Lobaugh, 2004).
In PLS path modeling, each block of variables is reduced to a latent variable representing
a theoretical concept (e.g. several white matter tract FA values can be reduced to a ‘neural
circuitry’ latent variable), which is analogous to a factor in principal component analysis. PLS
path modeling is robust to small sample sizes and does not impose any assumptions with respect
to the distribution of data. In order to maximize the explained variance of the dependent
variables, PLS path modeling looks for a linear combination of independent variables in such a
way that the obtained latent variables take into account the relationships in the outer model
(model of latent variables) and inner models (loadings of manifest variables onto latent
variables). It also allows for causal relationships to be explored.
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1.4.4 Clinical and Cognitive Measures of Interest
The heterogeneity of methods used to assess social cognition in schizophrenia
populations has been shown to contribute to inconsistencies in findings across studies (Bora,
Yucel, & Pantelis, 2009). As such, the Social Cognition Psychometric Evaluation study aims to
achieve consensus of social cognitive domains in schizophrenia and to evaluate psychometric
properties of existing scales (Pinkham et al., 2013). The Awareness of Social Inference Test
(TASIT) is one such measure of social cognition and was originally developed for assessing
theory of mind after traumatic brain injury (McDonald, Flanagan, Rollins, & Kinch, 2003). The
TASIT uses video vignettes to allow for recognition of complex and spontaneous emotional
displays, akin to those encountered in daily life (as opposed to static displays in the form of
photographs). Consistent with this goal, the actors in the vignettes were trained in the “method”
style so as to produce as natural a stimulus as possible. The TASIT has been shown to be suitable
for use in both healthy and schizophrenia populations (Kern et al., 2009) and includes three
subtests. Part 1 of the TASIT is the Emotion Evaluation test, and involves identifying six basic
emotions from 28 short vignettes, with a maximum score of 28. Part 2 of the TASIT is the Social
Inference-Minimal test, which includes 15 vignettes, 5 of which display sincere exchanges while
10 display sarcastic exchanges. To identify the sarcastic exchanges correctly, paralinguistic cues
need to be recognized and interpreted. Part 3 of the TASIT is the Social Inference-Enriched test,
which includes 16 vignettes, half of which display sarcastic exchanges, while the other half
display white lies. In addition to paralinguistic cues, enriched contextual cues are given, usually
via additional scenes showing the true state of affairs. Each vignette in the Social Inference tests
is scored out of 4, with higher scores indicating greater social cognitive performance.
29
The Quality of Life Scale (QLS) is a 21 item semistructured interview, intended for
outpatients and designed with the deficit syndrome in mind (Heinrichs, Hanlon, & Carpenter,
1984). It was the highest rated scale by the Validation of Everyday Real-World Outcomes study
(Leifker, Patterson, Heaton, & Harvey, 2011) and is used to assess functioning in schizophrenia
in four domains: 1. Intrapsychic foundations, which includes a sense of purpose, motivation,
curiosity, empathy, ability to experience pleasure, and emotional interaction. As such, this
domain overlaps with measurements of negative symptoms and is excluded from the QLS total
score in our analysis. 2. Interpersonal relations, including the capacity for intimacy,
active/passive participation in relationships, avoidance and withdrawal tendencies, and frequency
of social contact. 3. Instrumental role i.e. assuming the role of a worker/student/parent/etc. and
judging levels of accomplishment in these roles, degree of underemeployment, and satisfaction
derived from these roles. 4. Common objects and activities, such as participation in the
community, which is assumed to be reflected in the possession of common objects and
engagement in a range of regular activities in modern society. Each item is rated from 0 – 6, with
the maximum score indicating unimpaired functioning.
The Scale for the Assessment of Negative Symptoms (SANS) (Andreasen, 1983)
involves multiple symptom domains (attention, avolition/apathy, anhedonia/asociality, alogia,
affective flattening) and multiple items per domain, which improves its psychometric properties
compared to other measurements of negative symptoms, such as the Positive and Negative
Symptom Scale (PANSS). Thus, the SANS is preferred to the PANSS by the NIMH MATRICS
Consensus Statement on Negative Symptoms (Kirkpatrick et al., 2006). In the following studies,
the attentional impairment subscale is excluded from our analysis since it more closely aligns
with the disorganization symptom domain in schizophrenia (Foussias et al., 2011). Each item in
30
the SANS is scored from 0 – 5, which a higher score indicating greater negative symptom
burden.
Neurocognition was assessed using the MATRICS Consensus Cognitive Battery
(MCCB), excluding the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), which is
a measure of social cognition (Nuechterlein et al., 2004). Within the MCCB, speed of processing
is measured using the Brief Assessment of Cognition in Schizophrenia: Symbol Coding,
Category Fluency: Animal Naming, and Trail Making Test: Part A. The Brief Assessment of
Cognition in Schizophrenia: Symbol Coding test is a paper and pencil test in which participants
match symbols to numerals 1-9 in 90 seconds, with the measure of interest being the number of
correct numerals allowing for a maximum score of 110 (Keefe et al., 2004). Category Fluency:
Animal Naming is an oral test in which the participant names as many animals as possible in 60
seconds. Part A of the Trail Making Test involves connecting dots irregularly spaced on a sheet
of paper in ascending numerical order, with scores corresponding to the time taken to complete
the test (in seconds) (Reitan, 1979). Attention is measured using the Continuous Performance
Test-Identical Pairs, which involves responding to consecutive matching numbers as they are
presented in succession (Cornblatt & Keilp, 1994). Scoring involves calculating a manipulated
ratio (d’) of the number of correct and incorrect responses. Working memory is measured using
the Wechsler Memory Scale 3rd Ed.: Spatial Span and Letter Number Sequencing (Wechsler,
1979). The former involves a board with 10 irregularly spaced cubes and the participant taps
cubes in the same or reverse sequence as the administrator, while the latter is an orally
administered test in which the participant repeats strings of numbers and letters after mentally
reordering them. Verbal learning is measured using the Hopkins Verbal Learning Test-Revised,
which involves recall of 12 words from 3 taxonomic categories and is scored up to 36 (with a
maximum score of 12 per trial) (Brandt, 1991). Visual learning is measured using the Brief
31
Visuospatial Memory Test-Revised, which involves reproducing six geometric shapes from
memory and is also scored up to 36 (Benedict, Schretlen, Groninger, Dobraski, & Shpritz, 1996).
Reasoning and problem solving are measured using the Neuropsychological Assessment Battery:
Mazes, which involves completing seven mazes of gradually increasing difficulty, each of which
is scored out of five, for a total of 35 (Stern & White, 2003).
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Chapter 2
2 Overview of Experiments and Hypothesis This thesis aims to elucidate the relationship between neural circuitry of schizophrenia
and functional outcome, as well as identifying the shared and unique neural circuitry underlying
negative symptoms, neurocognition, and domains of social cognition, all of which have been
shown to be correlated with functional outcome.
2.1 Study One: Neuroimaging Predictors of Functional Outcome in Schizophrenia at Baseline and 6-month Follow-up
2.1.1 Background
The uncinate fasciculus, inferior longitudinal fasciculus, and arcuate fasciculus have
shown greater impairment in the deficit (compared to the nondeficit) syndrome of schizophrenia,
which is characterized by primary enduring negative symptoms and poor functional outcome.
This study aims to directly relate these white matter tracts to functional outcome across a broad
heterogeneous sample of schizophrenia participants, as well as to examine the effects of negative
symptoms and neurocognition on this relationship.
2.1.2 Hypothesis
We hypothesized that the uncinate fasciculus, inferior longitudinal fasciculus, and arcuate
fasciculus would be correlated with baseline and 6-month follow-up functional outcome
measures in schizophrenia participants. In addition, we hypothesized that negative symptom
severity would mediate the relationship between neural circuitry and functional outcome.
33
2.2 Study Two: Social Cognition-Circuitry Relationships in Schizophrenia, Bipolar Disorder, and Healthy Individuals: A Pilot Study
2.2.1 Background
Structural equation models have shown that social cognition is a significant predictor of
functional outcome in schizophrenia. This study aims to elucidate the underlying neural circuitry
of higher level and lower level social cognitive processes in schizophrenia, bipolar disorder, and
healthy control participants. By directly assessing brain-behaviour relationships across healthy
and patient populations along a continuum of social cognitive performance, this study aligns with
the NIMH Research Domain Criteria (RDoC) initiative, which emphasizes a dimensional
approach to studying behavioural and neural features of mental illness.
2.2.2 Hypothesis
We hypothesize that structural connectivity of the arcuate fasciculus and inferior
longitudinal fasciculus will be associated with lower level social cognitive performance in
schizophrenia, bipolar disorder, and healthy control participants. Similarly, we hypothesize that
structural connectivity of the uncinate fasciculus will be associated with higher level social
cognitive performance across all participants.
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Chapter 3
3 Neuroimaging Predictors of Functional Outcome in Schizophrenia at Baseline and 6-month Follow-up
3.1 Abstract
Purpose: Studies show that deficit syndrome schizophrenia patients, characterized by primary
negative symptoms and poor functional outcome, have impairment in specific neural circuits. We
assessed whether these same neural circuits are directly linked to functional outcomes across
schizophrenia patients.
Methods: T1- and diffusion-weighted MR images were obtained for schizophrenia (n=30) and
matched healthy control participants (n=30). Negative symptoms and functional outcome were
assessed at baseline and 6-month follow-up. Cortical thickness and tract-wise fractional
anisotropy (FA) were compared between groups. To assess relationships of neuroimaging
measures with functional outcome, principal component analysis (PCA) was performed on tract-
wise FA values and components were entered into a multiple regression model for schizophrenia
participants.
Results: Consistent with the literature, schizophrenia participants showed frontotemporal
reductions in cortical thickness and tract-wise FA compared to controls. The top two components
from PCA explained 71% of the variance in tract-wise FA values. The second component
(associated with inferior longitudinal and arcuate fasciculus FA) significantly predicted
functional outcome (baseline: β=0.54, p=0.03; follow-up: β=0.74, p=0.047), and further analysis
35
revealed this effect was mediated by negative symptoms. Post-hoc network analysis revealed
increased cortical coupling between right inferior frontal and supramarginal gyri (connected by
the arcuate fasciculus) in schizophrenia participants with poorer functional outcome.
Conclusions: Our findings indicate that impairment in the same neural circuitry identified as
susceptible in deficit syndrome schizophrenia predicts functional outcome in a continuous
manner in schizophrenia participants. This relationship was mediated by negative symptom
burden. Our findings provide novel evidence for brain-based biomarkers of longitudinal
functional outcome in people with schizophrenia.
3.2 Introduction
Schizophrenia is almost certainly a heterogeneous group of disorders for which specific
and reliable neurobiological correlates have yet to be identified (Breier et al., 1991; Milev et al.,
2005). Schizophrenia is associated with a high risk of long-term disability and poor functional
outcome (Breier et al., 1991). In people with schizophrenia, more severe negative symptoms and
cognitive deficits have independently been associated with poorer functional outcome
(Blanchard et al., 2005; Green, Kern, Braff, & Mintz, 2000; Ho et al., 1998; Milev et al., 2005;
Rosenheck et al., 2006). However, negative symptoms may mediate the relationship between
neurocognition and functioning in schizophrenia (Ventura et al., 2009). Furthermore, negative
symptoms predict variance in functional outcome in community dwelling outpatients above and
beyond neurocognitive impairment (Foussias et al., 2011, 2009).
Inter-regional dysconnectivity and white matter impairment in schizophrenia has been
well-established through diffusion tensor imaging (DTI) studies (Kubicki et al., 2007; Wheeler &
Voineskos, 2014). In deficit syndrome schizophrenia, characterized by prominent negative
36
symptoms and poor functional outcome (Carpenter et al., 1988; Kirkpatrick et al., 2001), specific
impairments in white matter tract circuitry have been found in the uncinate fasciculus (Kitis et
al., 2012; Voineskos et al., 2013), inferior longitudinal fasciculus, and arcuate fasciculus
(Voineskos et al., 2013), compared to non-deficit syndrome schizophrenia and healthy control
participants. Early studies assessing white matter microstructure-cognition relationships focused
on a limited number of tracts, such as the cingulum bundle (Kubicki et al., 2003; Nestor et al.,
2013; Takei et al., 2009) or the uncinate fasciculus (Kubicki et al., 2002; Nestor et al., 2013).
More recently, neurocognitive deficits have been associated with subtle, widespread tract
impairment in schizophrenia (Lim et al., 2006; Spoletini et al., 2009; Voineskos et al., 2013).
Despite the large number of DTI studies examining negative symptoms and neurocognitive
performance, a direct link between white matter tract microstructure and functional outcome in
schizophrenia patients is less well-established, particularly over a period of time.
Using fractional anisotropy (FA) as a measure of white matter tract microstructure(Jones,
2008), our objective was to identify the neural circuitry that is related to baseline and
longitudinal functional outcomes in people with schizophrenia. We have previously
demonstrated that deficit syndrome patients differ from non-deficit syndrome patients in white
matter microstructure in the arcuate, inferior longitudinal, and uncinate fasciculi (Voineskos et
al., 2013), as well as in network-level properties of cortical regions connected by these tracts
(Wheeler et al., 2015). Our main hypotheses were: At baseline and 6-month follow-up, 1. FA of
these three tracts in people with schizophrenia would be a significant predictor of functional
outcome; 2. FA of these same tracts would be inversely correlated with negative symptom
burden; and 3. Negative symptom burden would mediate the relationship between white matter
tract FA and functional outcome in schizophrenia.
37
3.3 Methods
3.3.1 Participants
Participants were recruited and underwent clinical assessments at the Centre for
Addiction and Mental Health (CAMH) in Toronto, Canada. After receiving a complete
description of the study, approved by the CAMH ethics review board, participants provided
written, informed consent. Participants with a DSM-IV diagnosis of schizophrenia and
schizoaffective disorder, following administration of the Structured Clinical Interview for DSM-
IV-TR Axis I Disorders and diagnostic confirmation by a trained psychiatrist (GF or ANV),
comprised the schizophrenia sample. Schizophrenia (n=30) and healthy control (n=30)
participants were individually matched on sex and handedness (categorized as left or right-
handed based on the Edinburgh handedness inventory), and group-matched on age and parental
level of education. Exclusion criteria for all subjects in this study included current substance use
(verified by urine toxicology screen), history of substance dependence, head trauma with loss of
consciousness, and neurological disorders. Healthy control subjects were also excluded if there
was a history of primary psychotic disorder in a first-degree relative.
The Positive and Negative Symptom Scale (PANSS) (Kay, Flszbein, & Opfer, 1987) was
administered to further characterize illness symptoms in the schizophrenia group. Negative
symptoms of schizophrenia participants were assessed using the Scale for the Assessment of
Negative Symptoms (SANS) (Andreasen, 1983), cognitive function using the Repeatable Battery
for the Assessment of Neuropsychological Status (RBANS) (Randolph, Tierney, Mohr, & Chase,
1998), and functional outcome using the Quality of Life Scale (QLS) (Heinrichs et al., 1984).
The SANS score was calculated excluding the Attention subscale from the total and
inappropriate affect from the Affective Flattening subscale, as these symptoms are more closely
38
associated with the disorganized symptom domain in schizophrenia (Foussias et al., 2011).
Similarly, the QLS total score excluded the Intrapsychic Foundations subscale, to eliminate
overlap in item content of this subscale with measures of negative symptoms (Foussias et al.,
2011, 2009). A number of other measures were administered to participants to further
characterize the sample and to account for secondary negative symptoms. N=24 individuals also
returned to repeat a number of assessments at 6-month follow-up, including the SANS and QLS
(see Appendix and Table 1).
3.3.2 Image Acquisition
Participants underwent magnetic resonance and diffusion tensor imaging at Toronto
General Hospital. Diffusion images were acquired with a single-shot spin echo planar sequence
with diffusion gradients applied in 23 noncollinear directions (b=1000s/mm2). Diffusion images,
including two baseline (b=0) images, were obtained with the following scan parameters: echo
time=85.5 milliseconds, repetition time=15 000 milliseconds, field of view=330 mm, acquisition
matrix=128 mm x 128 mm. Fifty-seven axial slices were acquired, with a 2.6 mm slice thickness
and isotropic voxels. The entire sequence was repeated 3 times to improve the signal to noise
ratio. Magnetic resonance images were acquired using an 8-channel head coil on a 1.5-Tesla GE
Echospeed System (General Electric Medical Systems) with the following acquisition
parameters: echo time=5.3 ms, repetition time=12.3 ms, time to inversion=300 ms, flip
angle=20°, number of excitations=124 contiguous images with 1.5 mm thickness.
39
3.3.3 Image Processing
3.3.3.1 Tractography
For DTI analysis, the 3 repetitions were coregistered to the first b=0 image in the first
repetition, using the FSL FMRIB Linear Image Registration Tool to concatenate the motion
corrected images. Gradients were reoriented using a weighted least squares approach.
Registration corrected eddy current distortions and subject motion while averaging the three
repetitions to improve the signal to noise ratio. A brain mask was then generated and
deterministic whole brain tractography (Runge-Kutta second order tractography with a fixed step
size of 0.5 mm) was performed at seed points in each voxel of the brain. Threshold parameters
for tractography were based on the linear anisotropy measure CL: Tseed=0.3 mm, Tstop=0.15 mm,
Tlength=20 mm(Westin et al., 2002). Tractography, creation of white matter fiber tracts, and
clustering segmentation were performed using 3D Slicer (version 2) and Matlab (version 7.0) as
previously described (Voineskos et al., 2009). Clusters were then identified to comprise each
fiber tract of interest: bilateral uncinate fasciculus, inferior fronto-occipital fasciculus, inferior
longitudinal fasciculus, arcuate fasciculus, cingulum bundle, and the genu and splenium of the
corpus callosum. For each white matter tract, mean measures of fractional anisotropy were
calculated using Matlab.
3.3.3.2 Cortical thickness
All T1-weighted MR images were submitted to the CIVET pipeline (version 1.1.10;
Montreal Neurological Institute at McGill University) (Lerch & Evans, 2005). T1 images were
linearly registered to the ICBM152 nonlinear sixth-generation template, intensity inhomogeneity
corrected (Sled, Zijdenbos, & Evans, 1998), and tissue classified into gray matter, white matter,
40
cerebrospinal fluid, and background (Tohka, Zijdenbos, & Evans, 2004; Zijdenbos, Forghani, &
Evans, 2002). A surface deformation algorithm was applied to create white and gray matter
surfaces for each hemisphere, resulting in 4 surfaces of 40 962 vertices each (Kim et al., 2005).
Cortical thickness can therefore be defined as the distance between the linked vertices of the two
surfaces within each hemisphere. The tlink metric was derived from each surface to determine the
distance between the white and gray matter surfaces (Lerch & Evans, 2005). Thickness data were
blurred using a 20-mm surface-based diffusion blurring kernel and unnormalized, native-space
thickness values were used in the analysis, due to the weak correlation between cortical thickness
and brain volume (Ad-Dab’bagh, Singh, & Robbins, 2005). The software package
mni.cortical.statistics (Brain Imaging Centre, Montreal Neurological Institute) was used for
vertex-wise cortical thickness analysis in R (version 2.15.1). Vertex-wise cortical thickness maps
were also segmented using the LONI Probabilistic Brain Atlas and average regional thickness
values were calculated (http://www.loni.usc.edu/atlases/) (Shattuck et al., 2008).
3.3.4 Statistical Analysis
3.3.4.1 Between-group differences in brain structure
Although not a primary aim of our study, we compared schizophrenia and healthy control
groups on DTI-based FA measures and cortical thickness to further characterize our sample. For
DTI-based mean FA values of each tract, the general linear model was used with age as a
covariate. All twelve tracts listed above were compared between groups. CIVET outputs for
cortical thickness for each group were also compared using the general linear model, with
“group” as the between-group factor and age as a covariate.
41
3.3.4.2 Principal component analysis of 12 white matter tracts
In the baseline and follow-up schizophrenia samples, dimension reduction of FA values
from the 12 frontotemporal and interhemispheric white matter tracts was performed using a
principal component analysis (PCA) with varimax rotation in SPSS (version 20.0.0, SPSS Inc.).
The threshold for retaining components within each principal component was λ>1. This PCA
was performed in preparation for examination of the relationship between white matter tract FA
and QLS total scores, since there is well-established correlation of FA values among white
matter tracts within each individual, and such dimension reduction reduces the number of
comparisons in our main analyses.
3.3.4.3 Multiple Linear Regression Models
Using R (version 3.0.2), a regression model was investigated with principal component
scores as predictors; age, parental level of education, chlorpromazine equivalent dose, and
duration of illness as covariates; and QLS total score as the outcome variable to test our primary
hypothesis. Chlorpromazine equivalent dose and duration of illness were included as covariates
to evaluate the effect of medication and chronic illness on brain structure. Regression models
were similarly built with the SANS total score and RBANS total score as outcome variables to
test whether to proceed with mediation. Principal component scores calculated using data from
the individuals with 6-month follow-up data (n=24) were entered into similar regression models
with follow-up QLS total score or SANS total score as the outcome variable. Age was not
included as a covariate for the RBANS model, as the total score used was age-normed.
42
Where relationships were found with either the baseline SANS or RBANS total scores,
exploratory analyses were then conducted with subscores. These analyses were not corrected for
multiple comparisons.
3.3.4.4 Mediation Analysis
Mediation models were built, initially with baseline and subsequently with 6-month
follow-up data, using the following specifications: independent variable = the second principal
component; outcome variable = QLS total score; mediator = SANS total score; covariates = age,
parental level of education, chlorpromazine equivalent dose, and duration of illness. The model
was tested in the R program as outlined by Baron and Kenny’s protocol (Baron & Kenny, 1986).
The effect size (ab) of the mediator was calculated from the product of the partial correlations of
each coefficient (a and b). The Sobel test was conducted to test for significance of this effect. To
further substantiate the model, reverse causal effects were assessed by testing a feedback model
(switching the outcome and mediator variables). Moderation was also assessed by including an
interaction term of the independent and mediator variables in the model and re-running the
analysis.
3.3.4.5 Structural Correlation Analysis
Based on the results of the principal component and multiple regression analysis, cortical
regions connected by tracts of interest were selected for post-hoc cortical thickness correlation
analysis, as previous data supports associations between structural correlations and white matter
tracts (Gong, He, Chen, & Evans, 2012; Lerch et al., 2006; Raznahan et al., 2011). A linear
regression was performed for each region to remove the effects of age, parental level of
education, chlorpromazine equivalent dose, and duration of illness on cortical thickness.
43
Schizophrenia subjects were assigned to good vs. poor functional outcome groups via median
split of QLS total scores and correlations in cortical thickness were examined in each group.
Significant differences in individual correlations were determined with permutation testing using
Matlab. Permutation testing involved shuffling subject labels to produce several permuted
combinations of the original two groups of data. Following each permutation, the test statistic
(correlation coefficient difference) was recalculated. The number of events that exceeded the
observed test statistic was determined and a probability of the number of observed events being
greater than expected was assigned.
3.4 Results
3.4.1 Brain structure and circuitry differences in schizophrenia
Schizophrenia participants showed modest reductions in white matter tract FA compared
to healthy controls in the left uncinate fasciculus, left arcuate fasciculus, bilateral cingulum
bundle, and genu of the corpus callosum. Similarly, schizophrenia participants were
characterized by reductions in cortical thickness in a number of frontal and temporal regions
(Supplementary Materials Figure 1A and 1B).
3.4.2 Data Reduction and Prediction of Functional Outcome
The PCA generated two components with λ>1 in both the baseline (n=30) and follow-up
(n=24) samples (in both samples 61% and 11% of the variance was explained by the first and
second principal component, respectively). FA of a number of white matter tracts loaded on the
first component (Supplementary Materials Table 1A and 1B), except for that of the inferior
longitudinal and arcuate fasciculus (Figure 1), which loaded prominently on the second
component (data reported for baseline and follow-up samples, respectively: left inferior
44
longitudinal fasciculus loading=0.81 and 0.78, right inferior longitudinal fasciculus loading=0.85
and 0.82, left arcuate fasciculus loading=0.79 and 0.85, and right arcuate fasciculus loading=0.82
and 0.84). The first principal component was not a significant predictor of the QLS total
(baseline β=-0.25, p=0.3; follow-up β=-0.15, p=0.6) or SANS total score (baseline β=1.78,
p=0.62; follow-up β=5.64, p=0.2, uncorrected). The second principal component was a
significant predictor of the QLS total (baseline β=0.54, p=0.03; follow-up β=0.74, p=0.047) and
SANS total scores (baseline β=-8.38, p=0.03; follow-up β=-11.3, p=0.01, uncorrected). There
was no significant relationship between either principal component and the RBANS total score.
Within the baseline sample, exploratory analyses showed that the second principal component
was related to the SANS Avolition/Apathy (β=-2.05, p=0.05, uncorrected), and SANS
Anhedonia/Asociality subscale scores (β=-2.61, p=0.04, uncorrected) in the baseline sample
(Table 2).
3.4.3 Negative symptoms mediate the relationship between tract FA and
longitudinal functional outcomes
As c’<c, partial mediation of the relationship between baseline tract FA and functional
outcome was observed (c, the total effect=0.59; c’, the direct effect=0.25). Similarly, mediation
of the relationship between baseline tract FA and functional outcome in the follow-up sample
was also observed (c=0.75, c’=0.11). The SANS total score had an effect on the relationship
between the second principal component and the QLS total score (effect size ab=0.3). This effect
was found to be significant by the Sobel test (z=2.0, p=0.046). No reverse causal effects were
seen in the feedback model (b=-8.25, c’=-3.89). The SANS total score was not found to be a
moderator of the relationship between the second principal component and the QLS total score
45
(second principal component and SANS total score interaction term β=-0.007, p=0.65) (Figure
2).
3.4.4 Inter-regional cortical coupling is associated with baseline
functional outcome
Results from the principal component and multiple regression analyses were used to
guide post-hoc analyses of cortical coupling. Correlations in cortical thickness among regions
connected by the arcuate fasciculus (inferior frontal gyrus, angular gyrus, supramarginal gyrus)
and the inferior longitudinal fasciculus (inferior temporal gyrus and fusiform gyrus) were
compared between the good and poor-outcome groups. Of these eight comparisons, one was
significantly different between the two groups; there was increased coupling of the right inferior
frontal gyrus and right supramarginal gyrus in poor (r=0.81) vs. good functional outcome
schizophrenia participants (r=0.37, p=0.024, uncorrected) (Figure 3).
3.5 Discussion
In this study, we evaluated the relationship between white matter tract microstructure and
baseline and 6-month functional outcomes in people with schizophrenia. Our main finding was
that inferior longitudinal fasciculus and arcuate fasciculus FA (represented together as a principal
component) were associated with functional outcome at both baseline and 6-month follow-up.
This relationship was mediated by negative symptom burden. In the context of recent findings of
inferior longitudinal and arcuate fasciculus impairment in deficit syndrome patients, who exhibit
severe functional impairment, our present findings extend our previous work to a clinically
heterogeneous group of schizophrenia participants that may be more representative of
neuroimaging schizophrenia studies. Supporting this contention, comparisons between
46
schizophrenia participants and healthy controls showed differences in brain structure and neural
circuitry reflecting those previously reported (Crespo-Facorro et al., 2000; Kubicki et al., 2007;
Onitsuka, Shenton, Kasai, & et al., 2003; Shenton et al., 2001; Wheeler & Voineskos, 2014).
Therefore, on a preliminary basis, we can conclude that impairment in the inferior longitudinal
and arcuate fasciculus may serve as a biomarker of functional outcome in people with
schizophrenia that is stable over time.
Our study is one of the first to directly examine the relationship between white matter
tract microstructure and functional outcome in a sample of schizophrenia participants.
Supporting evidence for our findings come from an earlier study in prodromal patients.
Karlsgodt et al (Karlsgodt et al., 2009) reported decreased FA in the left hippocampus and
bilateral inferior longitudinal fasciculus as predictors of poor role functioning in individuals at
ultra-high risk for psychosis, while decreased FA in the right inferior longitudinal fasciculus also
predicted poor social functioning in ultra-high risk subjects. This was an important finding as it
demonstrated the association between the functional decline seen in antipsychotic naïve
prodromal patients and FA of the inferior longitudinal fasciculus early in the course of illness.
More recently, Kumar et al (Kumar et al., 2014) reported that white matter tract microstructure
of the right inferior fronto-occipital fasciculus, corpus callosum, corona radiata, left cingulate
gyrus, and left posterior thalamic radiation predicted social and occupational performance in
patients with psychosis. It is likely that these results differ from our findings due to the inclusion
of both bipolar disorder patients (who typically do not have negative symptom burden) and
schizophrenia patients.
To date, the link between white matter tract impairment and poor functional outcome in
schizophrenia has primarily been supported indirectly through findings in deficit syndrome
47
patients (Kitis et al., 2012; Rowland et al., 2009; Voineskos et al., 2013), who are known to have
enduring primary negative symptoms and particularly poor long-term outcomes (Strauss,
Harrow, Grossman, & Rosen, 2010). Neural circuitry of deficit syndrome patients has also been
explored using network analysis of cortical properties in schizophrenia (Wheeler et al., 2015).
Wheeler et al (Wheeler et al., 2015) reported stronger frontoparietal and frontotemporal coupling
in deficit syndrome patients compared to non-deficit syndrome patients and healthy control
subjects. Our study results are consistent with this recent work, since stronger frontoparietal
coupling was found in participants with poorer functional outcome.
Analysis of our mediation model demonstrated that the relationship between white matter
tract FA and functional outcome is mediated by negative symptoms. In schizophrenia, negative
symptoms have been previously correlated with white matter tract impairment (Asami et al.,
2014; Kitis et al., 2012; Lee et al., 2013; Mitelman et al., 2009; Nakamura et al., 2012; Rowland
et al., 2009; Sigmundsson et al., 2001; Szeszko et al., 2008; Voineskos et al., 2013; Wolkin et al.,
2003) and with functional outcome (Foussias et al., 2011) As such, based on our previous
findings in community-dwelling outpatients, we predicted that negative symptoms would
mediate the effect of white matter tract FA on functional outcome. Wolkin et al (Wolkin et al.,
2003) reported that inferior frontal white matter tract FA was negatively correlated with SANS
total, Anhedonia/Asociality subscale, and Affective Flattening subscale scores in schizophrenia.
Sigmundsson et al (Sigmundsson et al., 2001) also reported lower white matter volumes in the
inferior longitudinal fasciculus in schizophrenia patients with enduring negative symptoms,
while Rowland et al (Rowland et al., 2009) reported reduced FA values in the right superior
longitudinal fasciculus (which includes the arcuate fasciculus) of deficit syndrome patients
compared to healthy controls. Studies have also reported that other tracts may be related to
negative symptom severity in schizophrenia, including the uncinate fasciculus. Sigmundsson et
48
al (Sigmundsson et al., 2001) showed reduced volumes of the uncinate fasciculus, internal
capsule, and corpus callosum in schizophrenia patients with enduring negative symptoms, while
Szeszko et al (Szeszko et al., 2008) demonstrated that uncinate fasciculus FA was negatively
correlated with SANS Affective Flattening and Alogia subscale scores. In the present study, the
uncinate fasciculus did not load very heavily on the second principal component, although we
did previously find impairment of this tract in deficit syndrome patients, along with that of the
inferior longitudinal and arcuate fasciculi (Voineskos et al., 2013). Another study by Nakamura
et al (Nakamura et al., 2012) reported that the FA of the anterior corpus callosum was negatively
correlated with the SANS Avolition/Apathy subscale score in schizophrenia.
It is likely that differences in methodologies and sample populations resulted in different
patterns of results in these studies. Our statistical analysis features a dimension reduction method
in order to explore the contribution of all white matter tracts, since there is well-established
correlation of FA values among white matter tracts within each individual. We also used our
group’s validated tractography pipeline (Voineskos et al., 2009), whereas other studies have used
voxel-based DTI, focused on a single tract of interest, or used Tract-Based Spatial Statistics and
correlation analyses in their experimental design. Our study also presents data on an outpatient
sample, while some studies include both inpatients and outpatients (Nakamura et al., 2012) or
solely inpatients (Szeszko et al., 2008), which could account for differences in results.
There was no relationship between either principal component and neurocognitive
impairment, as measured by the RBANS total score, in this schizophrenia sample. We had
hypothesized that negative symptoms, not neurocognitive deficits, would mediate the
relationship between white matter tract FA and functional outcome. Our group has previously
built on existing evidence for the relationship between neurocognition and neural circuitry by
49
demonstrating that widespread impairment of white matter tracts is associated with cognitive
performance in schizophrenia (Voineskos et al., 2013). Structural and functional studies show
similar results, where cognitive performance is associated with a larger compensatory network of
cortical (particularly frontal and temporal) brain regions in schizophrenia participants (Ehrlich et
al., 2012; Tan et al., 2006). It is possible that in the current study we were underpowered to
detect these relationships or that the use of a composite score to represent neurocognitive
impairment resulted in a loss of information that may be necessary to identify the complex
associations between neural circuitry and neurocognition.
There are a number of limitations that should be considered in this study. These include
the effects of medication on white matter tracts in the schizophrenia sample, as cumulative
effects of medication is shown to cause reductions in white matter volumes (Ho et al., 2011) and
decreased white matter FA in the parietal and occipital lobe (Szeszko et al., 2014). However, in
an effort to account for this confounding variable, we included chlorpromazine equivalent dose
as a covariate in our statistical analysis. Antipsychotic medication and duration of illness
concerns are further mitigated given a previous similar finding in a prodromal sample (Karlsgodt
et al., 2009). On another note, it has been shown that experiential circumstances can influence
brain structural measures, including FA (Scholz, Klein, Behrens, & Johansen-Berg, 2009).
Though we limited the effects of substance use in our sample by including participants who met
our strict inclusion criteria, other experiences may still have had an effect on brain structure.
Finally, a future study with a larger sample size may enhance confidence in our findings and
provide further clarification regarding the absence of a relationship with neurocognitive deficits
in the present study.
50
In summary, we found that impairment in the inferior longitudinal and arcuate fasciculus,
both of which were identified as being implicated in deficit syndrome schizophrenia, are
associated with functional outcomes across a more heterogeneous group of schizophrenia
participants. The results of our study provide preliminary evidence for brain-based biomarkers of
functional outcome in people with schizophrenia. If confirmed, our findings may assist in
neuroimaging-guided early detection efforts and in determining long-term prognosis. Targeting
the identified brain circuitry in treatment studies may improve negative symptoms and functional
outcome in people with schizophrenia.
51
52
Figure 3-1: Right inferior longitudinal fasciculus and arcuate fasciculus. Sagittal view of the
right inferior longitudinal fasciculus and right arcuate fasciculus as modeled by the tractography
pipeline used in this study.
53
Figure 3-2: Mediation Model. Model showing the relationship between the second principal
component and functional outcome in schizophrenia participants, which was mediated by
negative symptoms. The direct effect (c’=0.25, p=0.26) was less than the total effect (c=0.59,
p=0.02). Mediation was validated by the Sobel test, demonstrating a significant effect (ab=0.3,
p=0.05) of negative symptoms on the relationship between neural circuitry and functional
outcome. Similarly, mediation of the relationship between baseline tract FA and functional
outcome in the follow-up sample was also observed (c=0.75, p=0.04; c’=0.11, p=0.71).
54
Figure 3-3: Inter-regional Cortical Coupling. Increased cortical coupling of the right inferior
frontal gyrus and right supramarginal gyrus (connected by the arcuate fasciculus) was observed
in schizophrenia participants with poorer functional outcome (low QLS total score, r=0.81 vs.
high QLS total score, r=0.37, p=0.024, uncorrected).
55
3.6 Appendix
Participants’ blood pressure, weight (in kg), and height (in m) were measured. In both the
schizophrenia and healthy control samples, IQ was assessed using the Wechsler Test for Adult
Reading (WTAR) (Wechsler, 2001), global cognitive impairment using the Mini-Mental State
Examination (MMSE) (Folstein, Folstein, & McHugh, 1975), and chronic medical illness burden
using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) (Miller et al., 1992). The
Calgary Depression Scale for Schizophrenia (CDSS) (Addington, Addington, & Maticka-
Tyndale, 1993) was used to measure depression, a potential source of secondary negative
symptoms. The Abnormal Involuntary Movement Scale (AIMS) (Lane, Glazer, Hansen, Berman,
& Kramer, 1985), Symptom Angus Scale (SAS) (Simpson et al., 1970), and Barnes Akathisia
Scale (BAS) (Barnes, 1989) were administered to assess extrapyramidal symptoms in
schizophrenia participants. Chlorpromazine equivalent dose was calculated for schizophrenia
participants taking antipsychotic medication, according to Andreasen et al (Andreasen, Pressler,
Nopoulos, Miller, & Ho, 2010).
A number of measures were also administered at 6-month follow up, including the MINI
to re-affirm diagnosis, the CDSS, AIMS, SAS, SANS, and QLS. Neurocognitive performance
was measured using the Brief Assessment of Cognition in Schizophrenia (BACS).
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Figure 3-S1: Group-wise differences in cortical thickness and white matter tract FA.
Comparison of white matter tract FA and cortical thickness in schizophrenia and healthy control
participants. (A) Decreased white matter FA was seen in schizophrenia participants compared to
healthy controls in the left uncinate fasciculus, left arcuate fasciculus, bilateral cingulum bundle,
and genu of the corpus callosum (p<0.05, uncorrected). (B) Widespread cortical thickness
reductions were seen in schizophrenia participants compared to health controls, with greater
localization in the left hemisphere (1% FDR correction).
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Chapter 4
4 Social Cognition-Circuitry Relationships in Schizophrenia, Bipolar Disorder, and Healthy Individuals: A Pilot Study
4.1 Abstract
Background: Studies have shown that social cognitive processes are impaired in schizophrenia
and significantly affect functional outcomes. Using diffusion tensor imaging (DTI), we
previously found specific neural circuits implicated in patients with deficit syndrome
schizophrenia, who are socially impaired. In the present pilot study, we assessed whether those
same circuits may be related to social cognitive performance (and impairment) cross-
diagnostically in healthy individuals, bipolar disorder participants, and schizophrenia
participants.
Methods: Diffusion-weighted magnetic resonance images were acquired for schizophrenia
(n=15), bipolar disorder (n=15), and healthy control participants (n=22) 18-39 years of age on a
3T GE MR750 scanner at CAMH. Average fractional anisotropy (FA) values were calculated for
white matter tracts of interest using Tract-Based Spatial Statistics and the ENIMGA-DTI ROI
extraction protocol. Neurocognitive and social cognitive performance were measured using the
MATRICS Consensus Cognitive Battery and The Awareness of Social Inference Test,
respectively. A prespecified model was built and tested using partial least squares path modeling
59
(PLSPM) to explore relationships between social cognition and neural circuitry across all
participants and within diagnostic subgroups.
Results: Neurocognition was significantly correlated with social cognition across all subjects.
The relationship between FA of the uncinate fasciculus and higher level social cognition was
observed across all participants as well as in the bipolar disorder and schizophrenia subgroups,
while FA of the arcuate and inferior longitudinal fasciculus was associated with moderate to
higher level social cognition in healthy control participants.
Conclusion: By directly assessing brain-behaviour relationships across several patient
populations, our approach is consistent with the NIMH Research Domain Criteria initiative. Our
results provide further insight into the behavioural correlates of white matter tracts previously
implicated in functional outcome, and on a preliminary basis, the complex relationships between
neural circuitry and social cognition.
4.2 Introduction
Social cognitive impairment is believed to be a trait feature of schizophrenia, as it is
stable in first episode patients over a 12-month follow-up period and is also comparable across
phase of illness (Green et al., 2012; Horan et al., 2012). There are four main domains of social
cognition (Green et al., 2005; Penn et al., 2006): theory of mind, emotion processing, social
perception, and attributional bias (Green & Horan, 2010). Social cognition significantly
contributes to poor functional outcome in schizophrenia (Couture et al., 2006; Horan et al., 2012)
and is related to but distinct from neurocognition (Sergi et al., 2007). Social cognition is
consistently shown to be a mediator between neurocognition and functional outcome in
schizophrenia (Galderisi et al., 2014; Green & Horan, 2010; Schmidt et al., 2011).
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Along with schizophrenia, bipolar disorder is recognized as a major psychotic disorder
with shared genetic vulnerability (Cardno et al., 2012; Cardno, Rijsdijk, Sham, Murray, &
McGuffin, 2002), similar brain morphological abnormalities (Rimol et al., 2010), and symptom
overlap (Curtis, van Os, & Murray, 2000). Individuals with bipolar disorder appear to have less
neurocognitive and social cognitive impairment compared to individuals with schizophrenia (Lee
et al., 2013), which makes this group interesting for investigation in a study designed with the
NIMH Research Domain Criteria principles in mind.
Exploratory factor analysis of social cognition in schizophrenia has shown that social
cognitive domains load onto three factors, two of which are related to functional outcome in
schizophrenia. Specifically, emotion recognition and social perception load onto one factor
representing lower level social cognitive processes, necessary for understanding the emotions
and actions of others, while theory of mind loads onto its own factor representing higher level
social cognitive processes, necessary for understanding the mental states of others. Both lower
and higher level processes were found to influence functional outcome in schizophrenia
(Mancuso et al., 2011).
These social cognitive processes are believed to correspond to separate neural networks:
1. An “embodied simulation or low-level mental state inference construct” involving the mirror
neuron system and limbic system and 2. A “high-level mental state/trait inference construct”
involving cortical midline and lateral temporal structures (Ochsner, 2008). Our group has
previously shown altered connectivity of the inferior longitudinal fasciculus, arcuate fasciculus,
and uncinate fasciculus in patients with deficit syndrome schizophrenia (Voineskos et al., 2013).
These patients are known to have considerable social impairment. We subsequently
demonstrated that fractional anisotropy (FA) of the inferior longitudinal fasciculus and arcuate
fasciculus is directly related to functional outcome in schizophrenia. The inferior longitudinal
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fasciculus connects limbic structures (fusiform gyrus, parahippocampal gyrus, and amygdala) to
the occipital cortex, and is therefore important for visuoemotional processing, while the arcuate
fasciculus connects the inferior frontal lobe to the inferior parietal lobule, which are key nodes of
the human mirror neuron system (Gur et al., 2007; Iacoboni et al., 1999). These tracts are
believed to comprise the lower level simulation network, while the uncinate fasciculus connects
cortical midline structures (such as the medial prefrontal cortex) to the temporal pole and
amygdala and is believed to be one important white matter tract that subserves the higher level
mentalizing network (Heide et al., 2013).
Using FA as a measure of white matter tract microstructure, our objective was to assess
the relationship among FA of these three tracts, neurocognitive performance (which has been
shown to covary with social cognitive performance (Sergi et al., 2007)), and social cognitive
performance across schizophrenia, bipolar disorder, and healthy control participants. Social
cognitive performance is impaired in schizophrenia subjects and slightly impaired in bipolar
disorder (though not to a significant degree) but comparable to performance in healthy control
individuals (Lee et al., 2013). In this way, we will be able to capture brain-behaviour
relationships of participants on a continuum of social cognitive performance. We hypothesize
that structural connectivity of the arcuate fasciculus and inferior longitudinal fasciculus
(measured with FA) will be associated with lower level social cognitive processes across all
participants. Similarly, we hypothesize that structural connectivity of the uncinate fasciculus will
be associated with higher level social cognitive processes across all participants.
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4.3 Methods
4.3.1 Participants
Participants were recruited and underwent clinical assessments at the Centre for
Addiction and Mental Health (CAMH) in Toronto, Canada. After receiving a complete
description of the study, approved by the CAMH ethics review board, participants provided
written, informed consent. Participants with a DSM-IV diagnosis of schizophrenia and
schizoaffective disorder, following administration of the Structured Clinical Interview for DSM-
IV-TR Axis I Disorders and diagnostic confirmation by a trained psychiatrist, comprised the
schizophrenia sample, while those with a DSM-IV diagnosis of bipolar disorder who were also
euthymic comprised the bipolar disorder sample. Schizophrenia (n=15), bipolar disorder (n=15),
and healthy control (n=22) participants between the ages of 18 and 39 were included in the
study. This younger age range mitigates (to some effect) duration of illness and long-term
medication exposure confounds, as well as the effect of age on social cognition (Kern et al.,
2008). Exclusion criteria for all subjects in this study included current substance use (verified by
urine toxicology screen), history of substance dependence, head trauma with loss of
consciousness, and neurological disorders. Healthy control participants were also excluded if
there was a history of primary psychotic disorder in a first-degree relative.
Social cognitive performance of all participants was assessed using The Awareness of
Social Inference Test-Revised (TASIT). Part 1 of the TASIT tests the subject’s ability to
recognize six basic emotions after watching a 15-60 second videotaped vignette, and therefore
represents the lower level social cognitive process of emotion recognition. In part 2 of the
TASIT, participants must distinguish between sincere and sarcastic exchanges. In the sarcastic
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scenes, interpretation of paralinguistic cues is required (tone of voice, body language, gestures,
and facial expressions). Part 2 of the TASIT is called the Social Inference-Minimal Test, and can
be thought of as relying on social cognitive processes of higher order than part 1 of the TASIT,
but of lower order than part 3 of the TASIT. Part 3 of the TASIT represents higher level social
cognitive processing as it not only requires interpretation of paralinguistic cues in order to
distinguish between lies and sarcasm, but also includes additional scenes requiring the observer
to interpret information about the true state of affairs (McDonald et al., 2003). Neurocognition
was assessed using the MATRICS Consensus Cognitive Battery (MCCB), excluding the Mayer-
Salovey-Caruso Emotional Intelligence Test (MSCEIT), which is a measure of social cognition.
Within the MCCB, speed of processing is measured using the Brief Assessment of Cognition in
Schizophrenia: Symbol Coding, Category Fluency: Animal Naming, and Trail Making Test: Part
A; attention is measured using the Continuous Performance Test-Identical Pairs; working
memory is measured using the Wechsler Memory Scale 3rd Ed.: Spatial Span and Letter Number
Sequencing; verbal learning using the Hopkins Verbal Learning Test-Revised; visual learning
using the Brief Visuospatial Memory Test-Revised; and reasoning and problem solving using the
Neuropsychological Assessment Battery: Mazes (Nuechterlein et al., 2004). To further
characterize the sample, IQ was assessed using the Wechsler Test for Adult Reading (WTAR)
(Wechsler, 2001), global cognitive impairment using the Mini-Mental State Examination
(MMSE) (Folstein et al., 1975), and chronic medical illness burden using the Cumulative Illness
Rating Scale for Geriatrics (CIRS-G) (Miller et al., 1992). The Abnormal Involuntary Movement
Scale (AIMS) (Lane et al., 1985), Symptom Angus Scale (SAS) (Simpson et al., 1970), and
Barnes Akathisia Scale (BAS) (Barnes, 1989) were administered to assess extrapyramidal
symptoms in schizophrenia participants. The Young Mania Rating Scale (YMRS) and Hamilton
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Depression Rating Scale (HDRS) were administered in bipolar disorder participants to assess
mood symptoms.
4.3.2 Image Acquisition
Participants underwent diffusion imaging at the Centre for Addiction and Mental Health
in Toronto, Canada. Diffusion images were acquired using a 3 Tesla Discovery MR750 system
(General Electric) equipped with an 8-channel head coil. An echo planar imaging sequence with
dual spin echo was applied with diffusion gradients in 60 noncollinear directions
(b=1000 s/mm2). Diffusion images, including five baseline (b=0) images, were obtained with the
following scan parameters: echo time = 85 ms, repetition time = 8800 ms, field of view= 25.6
cm, acquisition matrix = 128 mm x 128 mm. Axial slices parallel to the AC-PC line were
acquired, with 2.0 mm isotropic voxels.
4.3.3 Image Processing
Diffusion images were preprocessed to correct eddy current induced distortions and
misalignment due to motion using the FMRIB Software Library (FSL, version 5.0.6). Brain
extraction was followed by fitting a diffusion tensor model at each voxel, using the dtifit function
to estimate FA maps.
The Tract-Based Spatial Statistics (TBSS, FSL) pipeline (Smith et al., 2006) was
modified slightly to incorporate the ENIGMA-DTI ROI extraction protocol
(http://enigma.ini.usc.edu). First, images were aligned using non-linear registration to the
ENIGMA-DTI target. They were then averaged to create a mean FA map and skeletonized by
projecting the ENIGMA-DTI skeleton onto each image. Regions of interest were then extracted
65
using the skeletonized image and the JHU-atlas to obtain average FA values (Mori et al., 2008).
Specifically, regions of interest were selected to correspond with the specified white matter tracts
under investigation: bilateral superior longitudinal fasciculus (SLF, which includes the arcuate
fasciculus), bilateral uncinate fasciculus (UF), bilateral inferior fronto-occipital fasciculus (IFO,
including projections from the uncinate and inferior longitudinal fasciculus), and bilateral sagittal
stratum (SS, including the inferior fronto-occipital and inferior longitudinal fasciculus).
4.3.4 Statistical Analysis
Before proceeding with partial least squares path modeling (PLSPM) (Sanchez, 2013),
data were cleaned by: 1. Removing outliers (values more than 3 standard deviations above or
below the mean); 2. Imputation of missing MCCB and TASIT values using the mice package in
R (version 3.0.2, http://cran.r-project.org); 3. Regressing out the effects of sex and age on mean
FA values, and using residuals in the following analysis.
Partial least square path models were then built, using the plspm package in R, to
examine the relationships between social cognition, neurocognition, and neural circuitry in our
sample. In each model, the MCCB (excluding the MSCEIT) total scores were manifest variables
for the neurocognition latent variable. In model 1, total scores of parts 1, 2, and 3 of the TASIT
were treated as manifest variables for the social cognition latent variable, while mean FA values
of the white matter regions of interest (SLF, UF, IFO, SS) were manifest variables for the neural
circuitry latent variable. In model 2, the total score of part 3 of the TASIT was treated as the
social cognition manifest variable, while mean FA values of the UF and IFO were neural
circuitry manifest variables. In model 3, the total score of part 1 of the TASIT was treated as the
social cognition manifest variable, while mean FA values of the SLF, IFO, and SS were neural
circuitry manifest variables. These three models were tested across all participants, and further
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exploratory analyses were conducted in diagnostic subgroups. PLSPM allows us to study
multivariate relationships among blocks of variables, and does not impose any assumptions with
respect to distributions of variables (Tenenhaus, Vinzi, Chatelin, & Lauro, 2005). To test these
relationships, path coefficients were calculated and bootstrapping (with 1000 samples) was
performed as a reliability estimate of our findings.
4.4 Results
4.4.1 Participant demographic and clinical data
Participants did not differ in terms of age (F=2.26, p=0.14), sex (F=0.01, p=0.91),
education (F=3.21, p=0.08), or handedness (F=2.47, p=0.12). However, there was a difference
between healthy control and bipolar disorder participants in CIRS-G scores (t=3.24, p=0.003).
4.4.2 PLS path modeling reliably elucidates social cognition-circuitry
relationships
The three prespecified models examine the relationships between neural circuitry, social
cognition, and neurocognition across all participants, as well as in diagnostic subgroups [Figure
1]. Loadings of manifest variables listed below exceeded a threshold magnitude of 0.6. PLSPM
analysis revealed neurocognitive performance to be a significant predictor of social cognitive
performance (β=0.56, p<0.01 for model 1 within all participants) [Table 2]. Across the entire
sample, FA of the bilateral UF and IFO were related to performance on part 3 of the TASIT
(model 2: β=0.32, p<0.01, validated by bootstrapping). In healthy control participants, FA of the
bilateral SLF and SS were related to performance on parts 2 and 3 of the TASIT (model 1:
β=0.47, p=0.04). In bipolar disorder participants, FA of the bilateral UF and IFO were related to
performance on all three parts of the TASIT (model 1: β=0.64, p=0.03). In schizophrenia
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participants, FA of the bilateral UF and IFO were related to performance on all three parts of the
TASIT (model 1: β=0.49, p<0.01) and FA of the bilateral UF and IFO were related to
performance on part 3 of the TASIT (model 2: β=0.41, p=0.03, validated by bootstrapping). In
schizophrenia participants, FA of the left IFO was also related to performance on part 1 of the
TASIT (model 3: β=-0.54, p<0.01); however, in this case the loading of left IFO FA onto the
neural circuitry latent variable was negative (-0.80) [Figure 2].
4.5 Discussion
The objective of this study was to directly relate neural circuitry (specifically the
uncinate, arcuate, and inferior longitudinal fasciculi) to social cognition in participants
(schizophrenia, bipolar disorder, and healthy control) on a continuum of social cognitive
performance. Due to the relationship of the following tracts with functional outcome in
schizophrenia, as well as their role in deficit syndrome schizophrenia, where considerable social
impairment is present, and in structural connectivity of regions involved in social cognition, we
hypothesized that: 1. Structural connectivity of the uncinate fasciculus, which connects the
medial prefrontal cortex to the temporal pole and amygdala, would be correlated with higher
level social cognitive performance; 2. Connectivity of the arcuate fasciculus (connecting the
inferior frontal gyrus to the inferior parietal lobule) and inferior longitudinal fasciculus
(connecting the amygdala and fusiform gyrus to the occipital cortex) would be correlated with
lower level social cognitive performance. Using partial least squares path modeling, we were
able to elucidate several social cognition-circuitry relationships. We consistently observed
association of the uncinate fasciculus (represented by the UF and IFO regions of interest) with
overall and higher level social cognition (represented by performance on the overall TASIT and
part 3 of the TASIT, respectively) in the entire sample, as well as in the bipolar disorder and
68
schizophrenia subgroups. In the healthy control subgroup, we observed relationships between the
arcuate and inferior longitudinal fasciculus (represented by the SLF and SS regions of interest)
and moderate to higher level social cognition (represented by performance on parts 2 and 3 of the
TASIT). We also found neurocognition to be a reliable predictor of social cognitive
performance, which is in line with results of studies using structural equation modeling and path
analysis (Gard et al., 2009; Schmidt et al., 2011).
In this study, we demonstrated the relationship between the arcuate and inferior
longitudinal fasciculi and moderate to higher level social cognitive performance in healthy
control participants. These data suggest that the putative simulation network is also involved in
mentalizing. In order to further investigate this trend, it would be beneficial to combine structural
imaging results with functional neuroimaging in order to visualize circuitry engagement during
social cognitive tasks and identify whether the simulation and mentalizing networks are
dissociated or if there is inclusion of both networks during higher and/or lower level social
cognitive processes. Although the relationship between these networks and social cognition has
not been directly studied in schizophrenia (Holt et al., 2011), preliminary evidence for
impairment in this circuit in schizophrenia has been found during action observation in studies
using brain stimulation techniques (Enticott et al., 2008). There is also evidence in healthy
control individuals linking the inferior longitudinal fasciculus to interpersonal emotional
intelligence (Takeuchi et al., 2013). Our findings are also supported by studies on persons with
diffuse low-grade glioma-a affecting the arcuate fasciculus (Herbet et al., 2014). Furthermore, in
patients with acute ischemic stroke in the right hemisphere, damage to the sagittal stratum was
associated with impaired recognition of sarcasm (Davis et al., 2015).
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We also consistently demonstrated the relationship between the uncinate fasciculus and
overall and higher level social cognition. This may be explained by the use of part 1 of the
TASIT as the measure for lower level social cognitive performance, which may require some
higher level processing as it involves emotion recognition in complex social situations. It is also
possible that the relationship between the uncinate fasciculus and part 3 of the TASIT was
driving the relationship between this tract and the complete TASIT. These results are supported
by preliminary evidence relating default mode network (which overlaps with the mentalizing
network (Heuvel, Mandl, Kahn, & Hulshoff Pol, 2009)) dysfunction to impairments in theory of
mind in schizophrenia (Das, Calhoun, & Malhi, 2012). Structural imaging studies have shown
associations between decreased grey matter volume of the ventromedial prefrontal cortex and
theory of mind deficits (Hooker, Bruce, Lincoln, Fisher, & Vinogradov, 2011). In addition, in
healthy control participants, increased structural connectivity of the uncinate fasciculus has been
associated with greater coupling of the amygdala with temporal regions involved in higher level
social cognition (Carlson et al., 2013).
Although we observed associations between the uncinate fasciculus and social cognition
in all participants, it appears that this relationship was driven by the schizophrenia and bipolar
disorder subgroups. This may suggest that certain individuals in patient populations rely on
different circuitry than healthy control populations during social cognitive performance. Previous
studies have shown lack of cortico-limbic coupling in schizophrenia and bipolar disorder
compared to healthy control participants during emotion regulation (Morris, Sparks, Mitchell,
Weickert, & Green, 2012). Although different activation patterns of these regions is observed in
schizophrenia and bipolar disorder, these findings may point to shared differences in structural
connectivity of cortical and limbic regions, such as those subserved by the uncinate fasciculus.
From a systems neuroscience approach, shared neural abnormalities in the medial prefrontal
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cortex during emotion processing are reported in both schizophrenia and bipolar disorder
(Frangou, 2014). Furthermore, reduced FA of the uncinate fasciculus is observed in
schizophrenia and bipolar disorder compared to healthy control participants (Sussmann et al.,
2009).
Limitations of this study include the effects of medication on white matter tracts in the
schizophrenia and bipolar disorder sample, as cumulative effects of antipsychotic medication is
shown to cause reductions in white matter volumes and decreased white matter FA in the parietal
and occipital lobe (Szeszko et al., 2014). In order to avoid including this confounding variable,
future studies may consider focusing on first episode or medication naïve participants. Future
studies should also consider applying two tensor models to diffusion-weighted images, in order
to account for crossing fibers and partial volume effects. However, by using the TBSS algorithm,
we were able to avoid issues with spatial smoothing of the FA maps (Jones, Symms, Cercignani,
& Howard, 2005). Due to the impact of age on social cognitive performance (Kern et al., 2008),
we decided to restrict our sample to participants between the ages of 18 and 39. A larger sample
size may provide further clarification regarding the relationships between neural circuitry and
social cognitive performance in the present study. A larger sample may also allow for the
division of patient groups into subgroups based on severity of impairment in the domain of
interest. It has been proposed that severity may be a nonlinear phenomenon with “tipping points”
that reflect distinct pathology (Cuthbert & Insel, 2013). Thus, some patients may have
considerably greater impairment in circuitry that is intact in other patient subgroups, and may
consequently be using compensatory circuits during tasks, which increases heterogeneity of the
sample and is less optimal for clearly identifying neurobiological substrates of psychopathology
(Dazzan et al., 2015). We believe that the relationship between the IFO and part 1 of the TASIT
in schizophrenia participants may be a result of heterogeneity in the sample, which can be further
71
examined in a larger group of patients.
In conclusion, this study demonstrates the relationship between the uncinate fasciculus
and higher level social cognitive performance in all participants along a continuum of social
cognitive performance, as well as in the schizophrenia subgroup. We have also shown
relationships between the arcuate and inferior longitudinal fasciculus and moderate to higher
level social cognitive performance in schizophrenia and bipolar disorder participants. This study
therefore highlights and provides preliminary evidence for potential shared and unique
neurobiological correlates of social cognitive processes in both healthy and patient populations,
consistent with modern conceptualizations of psychiatric research and disorders.
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Figure 4-1: Partial least squares path models. (A) Model 1, (B) Model 2, and (C) Model 3
were each tested in all participants and within diagnostic subgroups.
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75
76
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Figure 4-2: PLSPM path coefficients and manifest variable loadings. Models and path
coefficients of neural circuitry-social cognition-neurocognition relationships in schizophrenia,
bipolar disorder, and healthy control participants. Outlined arrows indicate significance of path
coefficients (p<0.05) and loadings of manifest variables onto latent variables is indicated
(threshold=0.6) in (A) model 2 in all participants, (B) model 1 in bipolar disorder, (C) model 1 in
healthy control participants, (D) model 1 in schizophrenia, (E) model 2 in schizophrenia, and (F)
model 3 in schizophrenia.
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Chapter 5
5 General Discussion & Future Directions
5.1 Summary of Results
The first study revealed that impairment in the bilateral arcuate fasciculus and inferior
longitudinal fasciculus is correlated with functional outcome in schizophrenia. The results of this
study supported our hypothesis and previous findings of altered connectivity in these tracts using
diffusion tensor imaging and structural covariance analysis in deficit syndrome schizophrenia
(Voineskos et al., 2013; Wheeler et al., 2015), which is characterized by primary negative
symptoms and poor functional outcomes. Thus, our findings provide preliminary evidence for
brain-based biomarkers of functional outcome in schizophrenia, which, if confirmed, may
become treatment targets and aid in early detection/prognostic efforts in the future. Specifically,
we believe that these white matter tracts may be neural correlates for social cognitive processes
based on their anatomical connectivity to mirror neuron and limbic structures in the brain.
With growing interest in the neural correlates of social cognition, our second study aimed
to elucidate relationships between separate circuits and social cognitive processes across a
sample of participants along a continuum of social cognitive performance. Based on our group’s
previous findings of white matter tracts implicated in deficit syndrome schizophrenia and
determining functional outcomes, we hypothesized that the arcuate fasciculus and inferior
longitudinal fasciculus would be associated with lower level social cognitive processes, while the
uncinate fasciculus would be associated with higher level social cognitive processes. We
consistently observed relationships between the uncinate fasciculus and higher level social
81
cognitive performance. We also observed relationships between the arcuate and inferior
longitudinal fasciculus and moderate to higher level social cognitive performance. Although our
approach was theoretically sound, as partial least squares analysis is not dependent on sample
size or data distribution, our goals would have been better served with a more distributed sample.
Due to the impact of age on social cognitive performance (Kern et al., 2008), we decided to
include participants between the ages of 18 and 39. It is important that these relationships be
examined in a larger sample as well as with different imaging modalities, such as functional
neuroimaging, in order to visualize engagement of neural circuits during social cognitive tasks
and further dissect the underlying neural correlates of social cognition.
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5.2 From DSM to RDoC: Addressing the Heterogeneity in
Psychiatric Disorders
Early forms of documentation of psychiatric disorders include Frederick Wines’ “Report
on the Defective, Dependent, and Delinquent Classes of the Population of the United States” in
1888 and the American Psychiatric Association’s “Statistical Manual for the Use of Institutions
for the Insane” in 1917. The Diagnostic and Statistical Manual of Mental Disorders (DSM),
colloquially known as the “bible of psychiatry”, is the first classification system for psychiatric
disorders to make an impact on how mental illness is diagnosed, studied, and understood.
The purpose of the DSM was to increase standardization of psychiatric diagnosis and
provide a starting point for researching these disorders (Frances, 2013). Despite its many critics,
the DSM has important clinical applications, in terms of diagnosis, guiding appropriate
treatment, and obtaining social supports and services. However, the conceptualization of
diagnostic categories and criteria in the DSM occurred before the age of modern neuroscience,
bringing into question their scientific validity (Morris & Cuthbert, 2012). When the DSM-5 was
released, it fell short of expectations, as our understanding of psychiatric disorders based on
modern neuroscience has not been integrated into their current conceptualization. It is likely that
due to the heterogeneity in each diagnostic category and overlap in symptoms across categories,
a scientific focus on group differences and diagnosis has hindered our progress toward
understanding psychiatric illness. A shift in focus towards the organization of neural circuits and
their associated behaviours is therefore required, in order to dissect the individual presentations
of psychiatric illness and develop targeted treatments.
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The lack of integration of neuroscientific evidence in psychiatric disease classification
has sparked an NIMH initiative called the Research Domain Criteria (RDoC) project (Hyman,
2007). It has been proposed that our current classification system has become reified, thereby
impeding progress in newer conceptualizations of brain-behaviour relationships that may shed
light on etiology and potential treatments (Hyman, 2010). The goal of the RDoC initiative is to
develop new ways of classifying psychiatric disorders according to dimensions of behaviour and
neurobiology, and studying brain-behaviour relationships independently of current classification
systems.
The RDoC is essentially a matrix of functional constructs, grouped into a number of
domains (cognition, social processes, arousal systems, and positive and negative valence
systems) and studied across units of analysis such as genetics, molecular biology, cell biology,
circuitry, physiology, behaviour, and self report (Cuthbert & Insel, 2013; Morris & Cuthbert,
2012). By starting with these basic constructs, the RDoC framework provides a platform for
understanding homogeneous symptom sets that cut across disorders and for dissecting the
heterogeneity in psychiatric diagnosis.
Although the process of selection of these constructs took into account their relevance to
psychopathology, the RDoC framework is sparse so as to allow for a strong focus on these
domains, rather than introduce an overwhelming number of constructs with diminishing utility
(Morris & Cuthbert, 2012). The goal of modern neuroscience, in concert with the RDoC
initiative, is to take a dimensional approach to each construct, studying participants along a
continuum of performance.
One of the shortcomings of a categorical approach to psychiatric disorders is the
expectation that we are able to draw the line between neurotypical and neuroatypical for any
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given diagnosis. It is evident that not all patients with the same diagnosis are impaired to the
same degree or have overlapping areas of impairment (Krueger & Markon, 2006). It is important
to take a more dimensional approach to the study of psychiatric illness in order to cater to the
unique symptom presentations and needs of each patient.
With a focus on neural circuitry, the RDoC will allow for direct assessment of brain-
behaviour relationships to explain phenotypic differences observed in typical and atypical
neurodevelopment, as many psychiatric disorders are postulated to be neurodevelopmental in
origin (Owen, O’Donovan, Thapar, & Craddock, 2011). In this way, we can elucidate the nature
and degree of changes in brain morphology and circuitry, as well as how developmental,
compensatory, environmental, and epigenetic factors modify them (Owen et al., 2011).
It is important to note that the RDoC is not a comprehensive document, and is still in its
early stages of being implemented as a guide for neuroscientific and psychiatric research (Morris
& Cuthbert, 2012). Specifically, the role of the RDoC is one of a research framework, and is not
intended to displace the DSM. In fact, the RDoC and DSM may be thought of as complimentary
to one another. Although the DSM has improved the reliability of classification of psychiatric
disorders, the current conceptualization of these disorders has impeded our understanding of
their etiologies and potential treatment targets. Current diagnostic categories can be thought of as
latent constructs, though they are not necessarily unidimensional (Hartman et al., 2001). Today’s
diagnostic categories do not capture the complex relationships between genetic,
neurophysiological, and behavioural features of mental illness (Borsboom, 2008). Rather than
continue to search for common causes within a heterogeneous group of patients, the RDoC
framework will encourage reconceptualization of psychiatric diagnoses as individual,
multidimensional, brain-behaviour relationships.
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5.3 Recovery in Schizophrenia
The definition of recovery from mental illness varies between the scientific and
psychiatric consumer/survivor (C/S) communities. While scientific definitions of recovery focus
on outcome, C/S definitions conceptualize recovery as an ongoing process (Remington et al.,
2010). The scientific literature has historically considered recovery from a clinical/disease
orientation, involving the elimination or reduction of symptoms and return to premorbid levels of
function (Bellack, 2006). As neuroscience and psychiatric research continues to make advances,
we are getting closer to addressing the heterogeneity in psychiatric illness by identifying
symptom-specific biomarkers and developing personalized treatment plans.
C/S definitions of recovery have developed alongside civil rights movements among
consumer/survivors and sociopolitical shifts in attitudes towards mental illness (Poole & Durbin,
2012). Rather than focusing on recovering “from” a disease, C/S are more interested in being
“in” recovery, which has been supported and described by various mental health consumer
advocates, psychiatric rehabilitation practitioners, and researchers. This notion of recovery refers
to an individualized process that is self-determined and aimed at building meaning into one’s
life. In this way, a person in recovery is able to feel empowered and retain their sense of
autonomy. Although we cannot yet guarantee full remission and a return to premorbid
functioning for everyone with schizophrenia, taking such a perspective of recovery is crucial to
conveying hope and encouraging patients to be an active participant in their own journey towards
wellness.
Patricia Deegan’s definition of recovery claims that individuals must be active
participants in their recovery journey by making use of resources such as medication and
86
institutional care (Deegan, 1997). However, this generalization does not account for diversity in
identity, accessibility, and culture. For instance, describing mental illness in terms of disability
ignores the experiences of those who have found a sense of self and purpose in their lived
experience. In addition, suggesting that individuals must be active participants in their recovery
journey assumes having access to community and health care services and supports. It also
emphasizes Western ideals of individualism and wellbeing, which results in isolation for
culturally diverse populations (Poole & Durbin, 2012). Therefore, it is important to take into
account each person’s individual conceptualization of distress and wellbeing, as each person’s
lived experience will differ.
As shown in a recent comprehensive study of functional outcome in a large cohort of
people with schizophrenia, social supports and access to effective interventions are crucial for
achieving recovery in schizophrenia (Galderisi et al., 2014). Therefore, treatment models for
schizophrenia must incorporate individualized medicine (i.e. brain-based biomarkers) with
patient goals and conceptualizations of wellness as well as access to these interventions.
87
5.4 Limitations
There are a number of limitations to the studies outlined in the chapters above that must
be considered. For the first study, though our results were supported by previous findings in
deficit syndrome participants, they must be replicated in an independent sample. In addition,
since our sample consists of participants with chronic schizophrenia, medication effects on brain
structure are a concern. In an effort to mitigate these effects, we included chlorpromazine
equivalent dose in our analysis. In addition, our images were acquired using a 1.5 Tesla magnet,
which limits the quality of our data. As such, we analyzed images acquired using a 3 Tesla
magnet in the subsequent study. Both studies were primarily cross-sectional, as we did not
include follow-up image acquisitions in our analysis. However, in study one, we analyzed the
effects of brain structure on baseline and 6-month longitudinal functional outcomes. We were
also limited in terms of sample size, with a maximum of 52 participants for any given group
under analysis. In order to maximize our power, we used multivariate approaches, such as partial
least squares path modeling, to allow for more complex relationships in the data to be explored
while limiting the number of multiple comparisons.
More generally, there are limitations to single tensor modeling of diffusion-weighted
images in order to infer fiber orientation and microstructure (Jones, Knösche, & Turner, 2013). A
change in FA may reflect changes in one or more aspects of connectivity (orientational
dispersion, myelination, axonal number and density), though we cannot say which. Furthermore,
FA is affected by partial volume effects, which occur when 1. The dimensions of a voxel exceed
the dimensions of an axonal bundle; or 2. The voxel includes crossing fibers. In these situations,
ambiguity in the microstructure of the tract is introduced, since we cannot discern the cause
88
behind the change in FA (Jones et al., 2013). Thus, a decrease in FA may be related to reductions
in white matter microstructural integrity, or it may be due to limitations in image processing.
5.5 Future Directions
With diffusion tensor imaging, rather than directly tracing axonal fibers, white matter
tract structure and orientation is computationally modeled and therefore limited by image
acquisition and analysis protocols. Therefore, an important issue is the low specificity of current
DTI methods when it comes to describing the pathology associated with changes in DTI indexes
(Jones et al., 2013). Improvements and innovation in computational methods and technology will
significantly increase our ability to identify neural correlates of emotion, cognition, and
behaviour. Acquisition techniques incorporating magnetization transfer imaging, which is
specific to myelination, may address this issue (van Buchem & Tofts, 2000). Another such
technique is Higher Angular Resolution Diffusion Imaging (HARDI), which can be used to infer
more complex information from diffusion of water in white matter tracts, allowing for modeling
of crossing fibers (Tuch et al., 2002). The issue of partial voluming and crossing fibers is also
being addressed with two tensor models of diffusion-weighted images (Peled, Friman, Jolesz, &
Westin, 2006). In the future, higher resolution images may allow us to model individual fibers.
Furthermore, combining DTI with other imaging modalities, such as functional MRI, magnetic
resonance spectroscopy, electroencephalography, magnetoencephalography, and positron
emission tomography, will allow for the integration of anatomical and functional data.
A great deal of the literature focuses on individual white matter tracts and grey matter
regions in psychiatric disorders. However, this trend is shifting towards a focus on neural
networks in the brain. Recent methods such as Brainbow and CLARITY allow researchers to
89
visualize and examine how individual neurons are connected. However, these methods have not
yet been extended to in vivo brain imaging (Chung et al., 2013; Lichtman, Livet, & Sanes, 2008).
Ideally, future studies and initiatives will integrate both macro and micro analyses in order to
understand both individual and broad connections in the brain. The National Institute of Health
BRAIN Initiative is aimed at understanding and mapping neural circuitry, which requires
“identifying and characterizing component cells, defining synaptic connections with one another,
observing patterns of activity in vivo during behaviour…It also requires an understanding of the
algorithms that govern information processing within a circuit and between interacting circuits in
the brain as a whole” (Bargmann & Newsome, 2013). The BRAIN initiative signifies a leap not
only in terms of the conceptualization and approach towards studying brain-behaviour
relationships, but also in terms of developing new methods for analysis and large-scale
collaboration across research institutes.
Similarly, the RDoC is another recent shift in the foundation for studying brain-behaviour
relationships, with the goal of translating identified neural correlates of emotion, cognition, and
behaviour to treatments for psychiatric disorders. Future studies aimed at understanding
psychiatric illness would do well to examine neural processes and corresponding biomarkers,
rather than focus on group differences. Furthermore, studies on neuroplasticity of identified
biomarkers of symptoms (e.g. neurocognition and social cognition) and their response to
psychosocial remediation will be key sources of evidence-based practice (Dodell-Feder, Tully, &
Hooker, 2015).
The RDoC also addresses several other questions in psychiatric research: “How can we
redefine mental illness to capture variability in individual experiences?” and “How do we take
into account a person’s unique biology and experiences in order to best care for them?” The
90
criteria and categories outlined in the current Diagnostic and Statistical Manual are not black and
white – what we have is a formula for heterogeneity in groups and shared symptoms across
psychiatric disorders. By studying brain-behaviour relationships rather than group differences,
we can begin to understand each patient as a unique individual and implement interventions that
take into account the variability in disease presentation.
A major confounding variable in studies of chronically ill patients is medication-induced
changes in brain structure and function (Ho et al., 2011; Szeszko et al., 2014). It is often difficult
to acquire accurate and detailed information on patient medication history, due to errors in
reporting bias. Furthermore, many methods of calculating equivalent antipsychotic dose in
participants, so as to covary for this variable, do not account for medication history or duration of
use. Thus, in order to confirm positive findings in the literature, it is important that studies also
be carried out on first episode or medication-naïve participants.
Lastly, there is some debate around whether the clinical and cognitive scales employed in
studies are measuring what we believe they are measuring (Hayhurst, Massie, Dunn, Lewis, &
Drake, 2014; Khatri, Romney, & Pelletier, 2001). For instance, the Quality of Life Scale, used to
measure functional outcome in schizophrenia, was originally designed with the deficit syndrome
in mind (Heinrichs et al., 1984). As such, it includes items that overlap with negative symptoms,
despite being selected as one of the most representative measures of functional outcome in a
recent RAND panel (Harvey, 2013; Leifker et al., 2011). Some scales of social functioning, like
the QLS and the Birchwood Social Functioning Scale, need to be updated to reduce focus on
activities that are not common in today’s society. Some of the items in these scales also require
one to have the financial means to participate in the specified activities, or focus solely on
heterosexual relationships, which may bias the results. Newer methods of measuring functional
91
outcome are being developed, such as virtual reality simulations, which may be more reliable
and have greater validity than traditional questionnaires (Zawadzki et al., 2013).
92
5.6 Conclusion
The aim of the two studies outlined above was to elucidate the relationships between
neural circuitry impairments in schizophrenia and functional outcome, as well as identifying
shared and unique neural circuitry with negative symptoms and domains of social cognition. We
demonstrated that white matter microstructure of major tracts involved in the putative simulation
network were correlated with functional outcome in schizophrenia; this relationship was
mediated by negative symptom burden. We also demonstrated the relationships of these tracts,
along with the uncinate fasciculus, to social cognitive domains in schizophrenia as well as in
bipolar disorder and healthy populations. Future studies of structural connectivity using DTI and
network analysis can allow us to identify neural circuits involved in different symptom and
behavioural domains of schizophrenia, fostering the identification of potential biomarkers for
future diagnostic purposes and for developing targeted and effective treatments.
93
References
Abbs, B., Liang, L., Makris, N., Tsuang, M., Seidman, L. J., & Goldstein, J. M. (2011).
Covariance modeling of MRI brain volumes in memory circuitry in schizophrenia: Sex
differences are critical. NeuroImage, 56(4), 1865–1874.
http://doi.org/10.1016/j.neuroimage.2011.03.079
Ad-Dab’bagh, Y., Singh, V., & Robbins, S. (2005). Native-space cortical thickness measurement
and the absence of correlation to cerebral volume. Presented at the 11th Annual
Organization of Human Brain Mapping Meeting, Toronto, Ontario, Canada.
Addington, D., Addington, J., & Maticka-Tyndale, E. (1993). Assessing depression in
schizophrenia: The Calgary Depression Scale. The British Journal of Psychiatry,
163(Suppl 22), 39–44.
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013). Imaging structural co-variance
between human brain regions. Nature Reviews Neuroscience, 14(5), 322–336.
http://doi.org/10.1038/nrn3465
Amador, X. F., Kirkpatrick, B., Buchanan, R. W., Carpenter, W. T., Marcinko, L., & Yale, S. A.
(1999). Stability of the diagnosis of deficit syndrome in schizophrenia. The American
Journal of Psychiatry, 156(4), 637–639.
Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: the medial frontal cortex and social
cognition. Nature Reviews Neuroscience, 7(4), 268–277.
http://doi.org/10.1038/nrn1884
94
Andreasen, N. (1983). Scale for the Assessment of Negative Symptoms (SANS). University of
Iowa, Iowa City.
Andreasen, N. C., Pressler, M., Nopoulos, P., Miller, D., & Ho, B. C. (2010). Antipsychotic dose
equivalents and dose-years: a standardized method for comparing exposure to different
drugs. Biological Psychiatry, 67(3), 255–262.
http://doi.org/10.1016/j.biopsych.2009.08.040
Asami, T., Hyuk Lee, S., Bouix, S., Rathi, Y., Whitford, T. J., Niznikiewicz, M., … Kubicki, M.
(2014). Cerebral white matter abnormalities and their associations with negative but not
positive symptoms of schizophrenia. Psychiatry Research: Neuroimaging, 222(1–2), 52–
59.
http://doi.org/10.1016/j.pscychresns.2014.02.007
Bargmann, C., & Newsome, W. (2013). Advisory Committee to the NIH Director INTERIM
REPORT Brain Research through Advancing Innovative Neurotechnologies (BRAIN)
Working Group. National Institutes of Health.
Barnes, T. R. (1989). A rating scale for drug-induced akathisia. The British Journal of
Psychiatry, 154(5), 672–676.
http://doi.org/10.1192/bjp.154.5.672
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 51(6), 1173–1182.
http://doi.org/10.1037/0022-3514.51.6.1173
95
Barta, P. E., Pearlson, G. D., Powers, R. E., Richards, S. S., & Tune, L. E. (1990). Auditory
hallucínations and smaller superior temporal gyral volume in Schizophrenia. American
Journal of Psychiatry, 147(11), 1457–1462.
Bellack, A. S. (2006). Scientific and Consumer Models of Recovery in Schizophrenia:
Concordance, Contrasts, and Implications. Schizophrenia Bulletin, 32(3), 432–442.
http://doi.org/10.1093/schbul/sbj044
Bell, M. D., & Mishara, A. L. (2006). Does negative symptom change relate to neurocognitive
change in schizophrenia? Implications for targeted treatments. Schizophrenia Research,
81(1), 17–27.
http://doi.org/10.1016/j.schres.2005.09.016
Benedict, R. H., Schretlen, D., Groninger, L., Dobraski, M., & Shpritz, B. (1996). Revision of
the Brief Visuospatial Memory Test: Studies of normal performance, reliability, and
validity. Psychological Assessment, 8(2), 145.
Blanchard, J. J., Horan, W. P., & Collins, L. M. (2005). Examining the latent structure of
negative symptoms: is there a distinct subtype of negative symptom schizophrenia?
Schizophrenia Research, 77(2-3), 151–165.
http://doi.org/10.1016/j.schres.2005.03.022
Bleuler, E. (1950). Dementia Praecox, or the group of Schizophrenias. (J. Zinkin, Trans.). New
York: International University Press.
Bora, E., Fornito, A., Radua, J., Walterfang, M., Seal, M., Wood, S. J., … Pantelis, C. (2011).
Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis
96
and meta-regression analysis. Schizophrenia Research, 127(1-3), 46–57.
http://doi.org/10.1016/j.schres.2010.12.020
Bora, E., Yucel, M., & Pantelis, C. (2009). Theory of mind impairment in schizophrenia: meta-
analysis. Schizophrenia Research, 109(1-3), 1–9.
http://doi.org/10.1016/j.schres.2008.12.020
Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical
Psychology, 64(9), 1089–1108.
http://doi.org/10.1002/jclp.20503
Bowie, C. R., Reichenberg, A., Patterson, T. L., Heaton, R. K., & Harvey, P. D. (2006).
Determinants of real-world functional performance in schizophrenia subjects:
correlations with cognition, functional capacity, and symptoms. The American Journal of
Psychiatry, 163(3), 418–425.
http://doi.org/10.1176/appi.ajp.163.3.418
Brandt, J. (1991). The hopkins verbal learning test: Development of a new memory test with six
equivalent forms. Clinical Neuropsychologist, 5(2), 125–142.
http://doi.org/10.1080/13854049108403297
Breier A., Schreiber J. L., Dyer J., & Pickar D. (1991). National institute of mental health
longitudinal study of chronic schizophrenia: Prognosis and predictors of outcome.
Archives of General Psychiatry, 48(3), 239–246.
http://doi.org/10.1001/archpsyc.1991.01810270051007
97
Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien
dargestellt aufgrund des Zellenbaues. Barth.
Buchanan, R. W., Francis, A., Arango, C., Miller, K., Lefkowitz, D. M., McMahon, R. P., …
Pearlson, G. D. (2004). Morphometric assessment of the heteromodal association cortex
in schizophrenia. The American Journal of Psychiatry, 161(2), 322–331.
Buchanan, R. W., & Pearlson, G. (2004). Prefrontal cortex, structural analysis: segmenting the
prefrontal cortex. The American Journal of Psychiatry, 161(11), 1978.
http://doi.org/10.1176/appi.ajp.161.11.1978
Buchsbaum, M. S., Tang, C. Y., Peled, S., Gudbjartsson, H., Lu, D., Hazlett, E. A., … Atlas, S.
W. (1998). MRI white matter diffusion anisotropy and PET metabolic rate in
schizophrenia. Neuroreport, 9(3), 425–430.
Cardno, A. G., Rijsdijk, F. V., Sham, P. C., Murray, R. M., & McGuffin, P. (2002). A twin study
of genetic relationships between psychotic symptoms. The American Journal of
Psychiatry, 159(4), 539–545.
Cardno, A. G., Rijsdijk, F. V., West, R. M., Gottesman, I. I., Craddock, N., Murray, R. M., &
McGuffin, P. (2012). A twin study of schizoaffective-mania, schizoaffective-depression,
and other psychotic syndromes. American Journal of Medical Genetics. Part B,
Neuropsychiatric Genetics: The Official Publication of the International Society of
Psychiatric Genetics, 159B(2), 172–182.
http://doi.org/10.1002/ajmg.b.32011
98
Carlson, J. M., Cha, J., & Mujica-Parodi, L. R. (2013). Functional and structural amygdala –
Anterior cingulate connectivity correlates with attentional bias to masked fearful faces.
Cortex, 49(9), 2595–2600.
http://doi.org/10.1016/j.cortex.2013.07.008
Carpenter Jr., W. T., & Kirkpatrick, B. (1988). The Heterogeneity of the Long-Term Course of
Schizophrenia. Schizophrenia Bulletin, 14(4), 645–652.
Carpenter Jr., W. T., Heinrichs, D. W., & Wagman, A. M. (1988). Deficit and nondeficit forms
of schizophrenia: the concept. The American Journal of Psychiatry, 145(5), 578–583.
Carr, L., Iacoboni, M., Dubeau, M. C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural
mechanisms of empathy in humans: a relay from neural systems for imitation to limbic
areas. Proceedings of the National Academy of Sciences of the United States of America,
100(9), 5497–5502.
http://doi.org/10.1073/pnas.0935845100
Catani, M., Jones, D. K., & Ffytche, D. H. (2005). Perisylvian language networks of the human
brain. Annals of Neurology, 57(1), 8–16.
http://doi.org/10.1002/ana.20319
Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: a review of its functional anatomy and
behavioural correlates. Brain, 129(3), 564–583.
http://doi.org/10.1093/brain/awl004
Chung, K., Wallace, J., Kim, S. Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., …
Deisseroth, K. (2013). Structural and molecular interrogation of intact biological systems.
99
Nature, 497(7449), 332–337.
http://doi.org/10.1038/nature12107
Collin, G., de Nijs, J., Hulshoff Pol, H. E., Cahn, W., & van den Heuvel, M. P. (2015).
Connectome organization is related to longitudinal changes in general functioning,
symptoms and IQ in chronic schizophrenia. Schizophrenia Research.
http://doi.org/10.1016/j.schres.2015.03.012
Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain:
from environment to theory of mind. Neuron, 58(3), 306–324.
http://doi.org/10.1016/j.neuron.2008.04.017
Cornblatt, B. A., & Keilp, J. G. (1994). Impaired Attention, Genetics, and the Pathophysiology
of Schizophrenia. Schizophrenia Bulletin, 20(1), 31–46.
http://doi.org/10.1093/schbul/20.1.31
Couture, S. M., Penn, D. L., & Roberts, D. L. (2006). The functional significance of social
cognition in schizophrenia: a review. Schizophrenia Bulletin, 32(Suppl 1), S44–63.
http://doi.org/10.1093/schbul/sbl029
Coyle, J. T., & Tsai, G. (2003). The NMDA receptor glycine modulatory site: a therapeutic
target for improving cognition and reducing negative symptoms in schizophrenia.
Psychopharmacology, 174(1), 32–38.
http://doi.org/10.1007/s00213-003-1709-2
Crespi, C., Cerami, C., Dodich, A., Canessa, N., Arpone, M., Iannaccone, S., … Cappa, S. F.
(2014). Microstructural white matter correlates of emotion recognition impairment in
100
Amyotrophic Lateral Sclerosis. Cortex; a Journal Devoted to the Study of the Nervous
System and Behavior, 53, 1–8.
http://doi.org/10.1016/j.cortex.2014.01.002
Crespo-Facorro, B., Kim, J., Andreasen, N. C., O’Leary, D. S., Bockholt, H. J., & Magnotta, V.
(2000). Insular cortex abnormalities in schizophrenia: a structural magnetic resonance
imaging study of first-episode patients. Schizophrenia Research, 46(1), 35–43.
Csernansky, J. G., Schindler, M. K., Splinter, N. R., Wang, L., Gado, M., Selemon, L. D., …
Miller, M. I. (2004). Abnormalities of thalamic volume and shape in schizophrenia. The
American Journal of Psychiatry, 161(5), 896–902.
Curtis, V. A., van Os, J., & Murray, R. M. (2000). The Kraepelinian dichotomy: evidence from
developmental and neuroimaging studies. The Journal of Neuropsychiatry and Clinical
Neurosciences, 12(3), 398–405.
Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: the seven
pillars of RDoC. BMC Medicine, 11(1), 126.
http://doi.org/10.1186/1741-7015-11-126
Das, P., Calhoun, V., & Malhi, G. S. (2012). Mentalizing in male schizophrenia patients is
compromised by virtue of dysfunctional connectivity between task-positive and task-
negative networks. Schizophrenia Research, 140(1–3), 51–58.
http://doi.org/10.1016/j.schres.2012.06.023
101
Davis, C. L., Oishi, K., Faria, A. V., Hsu, J., Gomez, Y., Mori, S., & Hillis, A. E. (2015). White
matter tracts critical for recognition of sarcasm. Neurocase, 0(0), 1–8.
http://doi.org/10.1080/13554794.2015.1024137
Davis, M. C., Lee, J., Horan, W. P., Clarke, A. D., McGee, M. R., Green, M. F., & Marder, S. R.
(2013). Effects of single dose intranasal oxytocin on social cognition in schizophrenia.
Schizophrenia Research, 147(2–3), 393–397.
http://doi.org/10.1016/j.schres.2013.04.023
Dazzan, P., Arango, C., Fleischacker, W., Galderisi, S., Glenthøj, B., Leucht, S., … McGuire, P.
(2015). Magnetic resonance imaging and the prediction of outcome in first-episode
schizophrenia: a review of current evidence and directions for future research.
Schizophrenia Bulletin, 41(3), 574–583.
http://doi.org/10.1093/schbul/sbv024
Deegan, P. E. (1997). Recovery and Empowerment for People with Psychiatric Disabilities.
Social Work in Health Care, 25(3), 11–24.
http://doi.org/10.1300/J010v25n03_02
DeLisi, L. E. (1992). The Significance of Age of Onset for Schizophrenia. Schizophrenia
Bulletin, 18(2), 209–215.
http://doi.org/10.1093/schbul/18.2.209
De Weijer, A. D., Mandl, R. C. W., Diederen, K. M. J., Neggers, S. F. W., Kahn, R. S., Pol, H.
E. H., & Sommer, I. E. C. (2011). Microstructural alterations of the arcuate fasciculus in
schizophrenia patients with frequent auditory verbal hallucinations. Schizophrenia
102
Research, 130(1–3), 68–77.
http://doi.org/10.1016/j.schres.2011.05.010
Dixon, L. B., Dickerson, F., Bellack, A. S., Bennett, M., Dickinson, D., Goldberg, R. W., …
Kreyenbuhl, J. (2010). The 2009 Schizophrenia PORT Psychosocial Treatment
Recommendations and Summary Statements. Schizophrenia Bulletin, 36(1), 48–70.
http://doi.org/10.1093/schbul/sbp115
Dodell-Feder, D., Tully, L. M., & Hooker, C. I. (2015). Social impairment in schizophrenia: new
approaches for treating a persistent problem. Current Opinion in Psychiatry, 28(3), 236–
242.
http://doi.org/10.1097/YCO.0000000000000154
Eaton, W. W., Thara, R., Federman, B., Melton, B., & Liang, K. Y. (1995). Structure and course
of positive and negative symptoms in schizophrenia. Archives of General Psychiatry,
52(2), 127–134.
Ehrlich, S., Brauns, S., Yendiki, A., Ho, B.-C., Calhoun, V., Schulz, S. C., … Sponheim, S. R.
(2012). Associations of cortical thickness and cognition in patients with schizophrenia
and healthy controls. Schizophrenia Bulletin, 38(5), 1050–1062.
http://doi.org/10.1093/schbul/sbr018
Eidelberg D., & Galaburda A. M. (1984). Inferior parietal lobule: Divergent architectonic
asymmetries in the human brain. Archives of Neurology, 41(8), 843–852.
http://doi.org/10.1001/archneur.1984.04050190049013
103
Ellison-Wright, I., & Bullmore, E. (2009). Meta-analysis of diffusion tensor imaging studies in
schizophrenia. Schizophrenia Research, 108(1-3), 3–10.
http://doi.org/10.1016/j.schres.2008.11.021
Enticott, P. G., Hoy, K. E., Herring, S. E., Johnston, P. J., Daskalakis, Z. J., & Fitzgerald, P. B.
(2008). Reduced motor facilitation during action observation in schizophrenia: A mirror
neuron deficit? Schizophrenia Research, 102(1–3), 116–121.
http://doi.org/10.1016/j.schres.2008.04.001
Fitzsimmons, J., Kubicki, M., & Shenton, M. E. (2013). Review of functional and anatomical
brain connectivity findings in schizophrenia. Current Opinion in Psychiatry, 26(2), 172–
187. http://doi.org/10.1097/YCO.0b013e32835d9e6a
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: A practical
method for grading the cognitive state of patients for the clinician. Journal of Psychiatric
Research, 12(3), 189–198.
http://doi.org/10.1016/0022-3956(75)90026-6
Foussias, G., Agid, O., & Remington, G. (2011). Negative Symptoms Across the Schizophrenia
Spectrum: Phenomenological and Neurobiological Perspectives. In M. Ritsner (Ed.),
Handbook of Schizophrenia Spectrum Disorders, Volume II (pp. 1–32). Springer
Netherlands.
Foussias, G., Mann, S., Zakzanis, K. K., van Reekum, R., Agid, O., & Remington, G. (2011).
Prediction of longitudinal functional outcomes in schizophrenia: the impact of baseline
motivational deficits. Schizophrenia Research, 132(1), 24–27.
http://doi.org/10.1016/j.schres.2011.06.026
104
Foussias, G., Mann, S., Zakzanis, K. K., van Reekum, R., & Remington, G. (2009). Motivational
deficits as the central link to functioning in schizophrenia: a pilot study. Schizophrenia
Research, 115(2-3), 333–337.
http://doi.org/10.1016/j.schres.2009.09.020
Foussias, G., & Remington, G. (2010). Negative symptoms in schizophrenia: avolition and
Occam’s razor. Schizophrenia Bulletin, 36(2), 359–369.
http://doi.org/10.1093/schbul/sbn094
Frances, A. (2013). Saving Normal. New York, NY: HarperCollins Publishers.
Frangou, S. (2014). A Systems Neuroscience Perspective of Schizophrenia and Bipolar Disorder.
Schizophrenia Bulletin, sbu017.
http://doi.org/10.1093/schbul/sbu017
Friston, K. J., & Frith, C. D. (1995). Schizophrenia: a disconnection syndrome? Clinical
Neuroscience (New York, N.Y.), 3(2), 89–97.
Frith, U., & Frith, C. D. (2003). Development and neurophysiology of mentalizing.
Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences,
358(1431), 459–473.
http://doi.org/10.1098/rstb.2002.1218
Galderisi, S., Rossi, A., Rocca, P., Bertolino, A., Mucci, A., Bucci, P., … Italian Network For
Research on Psychoses. (2014). The influence of illness-related variables, personal
resources and context-related factors on real-life functioning of people with
schizophrenia. World Psychiatry: Official Journal of the World Psychiatric Association,
105
13(3), 275–287.
http://doi.org/10.1002/wps.20167
Gard, D. E., Fisher, M., Garrett, C., Genevsky, A., & Vinogradov, S. (2009). Motivation and its
relationship to neurocognition, social cognition, and functional outcome in schizophrenia.
Schizophrenia Research, 115(1), 74–81.
http://doi.org/10.1016/j.schres.2009.08.015
Gong, G., He, Y., Chen, Z. J., & Evans, A. C. (2012). Convergence and divergence of thickness
correlations with diffusion connections across the human cerebral cortex. NeuroImage,
59(2), 1239–1248.
http://doi.org/10.1016/j.neuroimage.2011.08.017
Green, M. F. (1996). What are the functional consequences of neurocognitive deficits in
schizophrenia? The American Journal of Psychiatry, 153(3), 321–330.
http://doi.org/10.1176/ajp.153.3.321
Green, M. F., Bearden, C. E., Cannon, T. D., Fiske, A. P., Hellemann, G. S., Horan, W. P., …
Nuechterlein, K. H. (2012). Social Cognition in Schizophrenia, Part 1: Performance
Across Phase of Illness. Schizophrenia Bulletin, 38(4), 854–864.
http://doi.org/10.1093/schbul/sbq171
Green, M. F., & Horan, W. P. (2010). Social Cognition in Schizophrenia. Current Directions in
Psychological Science, 19(4), 243–248.
http://doi.org/10.1177/0963721410377600
106
Green, M. F., Kern, R. S., Braff, D. L., & Mintz, J. (2000). Neurocognitive deficits and
functional outcome in schizophrenia: are we measuring the “right stuff”? Schizophrenia
Bulletin, 26(1), 119–136.
Green, M. F., Olivier, B., Crawley, J. N., Penn, D. L., & Silverstein, S. (2005). Social Cognition
in Schizophrenia: Recommendations from the Measurement and Treatment Research to
Improve Cognition in Schizophrenia New Approaches Conference. Schizophrenia
Bulletin, 31(4), 882–887.
http://doi.org/10.1093/schbul/sbi049
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009). Resting-state functional
connectivity reflects structural connectivity in the default mode network. Cerebral
Cortex, 19(1), 72–78.
http://doi.org/10.1093/cercor/bhn059
Gur R. E, Loughead J., Kohler C. G., & et al. (2007). Limbic activation associated with
misidentification of fearful faces and flat affect in schizophrenia. Archives of General
Psychiatry, 64(12), 1356–1366.
http://doi.org/10.1001/archpsyc.64.12.1356
Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001). Medial prefrontal cortex
and self-referential mental activity: relation to a default mode of brain function.
Proceedings of the National Academy of Sciences of the United States of America, 98(7),
4259–4264.
http://doi.org/10.1073/pnas.071043098
107
Hartman, C. A., Hox, J., Mellenbergh, G. J., Boyle, M. H., Offord, D. R., Racine, Y., …
Sergeant, J. A. (2001). DSM-IV Internal Construct Validity: When a Taxonomy Meets
Data. Journal of Child Psychology and Psychiatry, 42(6), 817–836.
http://doi.org/10.1111/1469-7610.00778
Harvey, P. D. (2013). Assessment of everyday functioning in schizophrenia: implications for
treatments aimed at negative symptoms. Schizophrenia Research, 150(2-3), 353–355.
http://doi.org/10.1016/j.schres.2013.04.022
Harvey, P. D., Green, M. F., Keefe, R. S. E., & Velligan, D. I. (2004). Cognitive functioning in
schizophrenia: a consensus statement on its role in the definition and evaluation of
effective treatments for the illness. The Journal of Clinical Psychiatry, 65(3), 361–372.
Harvey, P. D., Koren, D., Reichenberg, A., & Bowie, C. R. (2006). Negative symptoms and
cognitive deficits: what is the nature of their relationship? Schizophrenia Bulletin, 32(2),
250–258.
http://doi.org/10.1093/schbul/sbj011
Hayhurst, K. P., Massie, J. A., Dunn, G., Lewis, S. W., & Drake, R. J. (2014). Validity of
subjective versus objective quality of life assessment in people with schizophrenia. BMC
Psychiatry, 14(1), 365.
http://doi.org/10.1186/s12888-014-0365-x
Heide, R. J. V. D., Skipper, L. M., Klobusicky, E., & Olson, I. R. (2013). Dissecting the uncinate
fasciculus: disorders, controversies and a hypothesis. Brain, awt094.
http://doi.org/10.1093/brain/awt094
108
Heimer, L., & Van Hoesen, G. W. (2006). The limbic lobe and its output channels: Implications
for emotional functions and adaptive behavior. Neuroscience & Biobehavioral Reviews,
30(2), 126–147.
http://doi.org/10.1016/j.neubiorev.2005.06.006
Heinrichs, D. W., Hanlon, T. E., & Carpenter, W. T. (1984). The Quality of Life Scale: an
instrument for rating the schizophrenic deficit syndrome. Schizophrenia Bulletin, 10(3),
388–398.
Heinrichs, W. R., & Zakzanis, K. K. (1998). Neurocognitive deficit in schizophrenia: A
quantitative review of the evidence. Neuropsychology, 12(3), 426–445.
http://doi.org/10.1037/0894-4105.12.3.426
Herbet, G., Lafargue, G., Bonnetblanc, F., Moritz-Gasser, S., Champfleur, N. M. de, & Duffau,
H. (2014). Inferring a dual-stream model of mentalizing from associative white matter
fibres disconnection. Brain, 137(3), 944–959.
http://doi.org/10.1093/brain/awt370
Heresco-Levy U., Javitt D. C., Ermilov M., Mordel C., Silipo G., & Lichtenstein M. (1999).
Efficacy of high-dose glycine in the treatment of enduring negative symptoms of
schizophrenia. Archives of General Psychiatry, 56(1), 29–36.
http://doi.org/10.1001/archpsyc.56.1.29
Ho, B. C., Andreasen, N. C., Ziebell, S., Pierson, R., & Magnotta, V. (2011). Long-term
antipsychotic treatment and brain volumes: a longitudinal study of first-episode
schizophrenia. Archives of General Psychiatry, 68(2), 128–137.
http://doi.org/10.1001/archgenpsychiatry.2010.199
109
Ho, B. C., Nopoulos, P., Flaum, M., Arndt, S., & Andreasen, N. C. (1998). Two-year outcome in
first-episode schizophrenia: predictive value of symptoms for quality of life. The
American Journal of Psychiatry, 155(9), 1196–1201.
Holt, D. J., Cassidy, B. S., Andrews-Hanna, J. R., Lee, S. M., Coombs, G., Goff, D. C., …
Moran, J. M. (2011). An Anterior-to-Posterior Shift in Midline Cortical Activity in
Schizophrenia During Self-Reflection. Biological Psychiatry, 69(5), 415–423.
http://doi.org/10.1016/j.biopsych.2010.10.003
Holt, D. J., Kunkel, L., Weiss, A. P., Goff, D. C., Wright, C. I., Shin, L. M., … Heckers, S.
(2006). Increased medial temporal lobe activation during the passive viewing of
emotional and neutral facial expressions in schizophrenia. Schizophrenia Research, 82(2–
3), 153–162.
http://doi.org/10.1016/j.schres.2005.09.021
Hooker, C. I., Bruce, L., Lincoln, S. H., Fisher, M., & Vinogradov, S. (2011). Theory of Mind
Skills Are Related to Gray Matter Volume in the Ventromedial Prefrontal Cortex in
Schizophrenia. Biological Psychiatry, 70(12), 1169–1178.
http://doi.org/10.1016/j.biopsych.2011.07.027
Horan, W. P., Green, M. F., DeGroot, M., Fiske, A., Hellemann, G., Kee, K., … Nuechterlein, K.
H. (2012). Social cognition in schizophrenia, Part 2: 12-month stability and prediction of
functional outcome in first-episode patients. Schizophrenia Bulletin, 38(4), 865–872.
http://doi.org/10.1093/schbul/sbr001
110
Howard, M. A., Volkov, I. O., Mirsky, R., Garell, P. C., Noh, M. D., Granner, M., … Brugge, J.
F. (2000). Auditory cortex on the human posterior superior temporal gyrus. The Journal
of Comparative Neurology, 416(1), 79–92.
Hyman, S. E. (2007). Can neuroscience be integrated into the DSM-V? Nature Reviews
Neuroscience, 8(9), 725–732.
http://doi.org/10.1038/nrn2218
Hyman, S. E. (2010). The Diagnosis of Mental Disorders: The Problem of Reification. Annual
Review of Clinical Psychology, 6(1), 155–179.
http://doi.org/10.1146/annurev.clinpsy.3.022806.091532
Iacoboni, M., Molnar-Szakacs, I., Gallese, V., Buccino, G., Mazziotta, J. C., & Rizzolatti, G.
(2005). Grasping the intentions of others with one’s own mirror neuron system. PLoS
Biology, 3(3), e79.
http://doi.org/10.1371/journal.pbio.0030079
Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., & Rizzolatti, G. (1999).
Cortical mechanisms of human imitation. Science, 286(5449), 2526–2528.
Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., …
Verma, R. (2014). Sex differences in the structural connectome of the human brain.
Proceedings of the National Academy of Sciences of the United States of America,
111(2), 823–828.
http://doi.org/10.1073/pnas.1316909110
111
Jahanshad, N., Kochunov, P. V., Sprooten, E., Mandl, R. C., Nichols, T. E., Almasy, L., …
Glahn, D. C. (2013). Multi-site genetic analysis of diffusion images and voxelwise
heritability analysis: A pilot project of the ENIGMA–DTI working group. NeuroImage,
81, 455–469.
http://doi.org/10.1016/j.neuroimage.2013.04.061
Jakob, D. H., & Beckmann, H. (1986). Prenatal developmental disturbances in the limbic
allocortex in schizophrenics. Journal of Neural Transmission, 65(3-4), 303–326.
http://doi.org/10.1007/BF01249090
Jobe, T. H., & Harrow, M. (2005). Long-term outcome of patients with schizophrenia: a review.
Canadian Journal of Psychiatry. Revue Canadienne De Psychiatrie, 50(14), 892–900.
Johansen-Berg, H. (2010). Behavioural relevance of variation in white matter microstructure.
Current Opinion in Neurology, 23(4), 351–358.
http://doi.org/10.1097/WCO.0b013e32833b7631
Johnson, S. C., Baxter, L. C., Wilder, L. S., Pipe, J. G., Heiserman, J. E., & Prigatano, G. P.
(2002). Neural correlates of self-reflection. Brain: A Journal of Neurology, 125(8), 1808–
1814.
Jones, D. K. (2008). Studying connections in the living human brain with diffusion MRI. Cortex;
a Journal Devoted to the Study of the Nervous System and Behavior, 44(8), 936–952.
http://doi.org/10.1016/j.cortex.2008.05.002
112
Jones, D. K., Knösche, T. R., & Turner, R. (2013). White matter integrity, fiber count, and other
fallacies: The do’s and don’ts of diffusion MRI. NeuroImage, 73, 239–254.
http://doi.org/10.1016/j.neuroimage.2012.06.081
Jones, D. K., Symms, M. R., Cercignani, M., & Howard, R. J. (2005). The effect of filter size on
VBM analyses of DT-MRI data. NeuroImage, 26(2), 546–554.
http://doi.org/10.1016/j.neuroimage.2005.02.013
Karlsgodt, K. H., Niendam, T. A., Bearden, C. E., & Cannon, T. D. (2009). White Matter
Integrity and Prediction of Social and Role Functioning in Subjects at Ultra-High Risk
for Psychosis. Biological Psychiatry, 66(6), 562–569.
http://doi.org/10.1016/j.biopsych.2009.03.013
Karlsgodt, K. H., van Erp, T. G. M., Poldrack, R. A., Bearden, C. E., Nuechterlein, K. H., &
Cannon, T. D. (2008). Diffusion Tensor Imaging of the Superior Longitudinal Fasciculus
and Working Memory in Recent-Onset Schizophrenia. Biological Psychiatry, 63(5), 512–
518. http://doi.org/10.1016/j.biopsych.2007.06.017
Kay, S. R., Flszbein, A., & Opfer, L. A. (1987). The Positive and Negative Syndrome Scale
(PANSS) for Schizophrenia. Schizophrenia Bulletin, 13(2), 261–276.
Keefe, R. S. E., Goldberg, T. E., Harvey, P. D., Gold, J. M., Poe, M. P., & Coughenour, L.
(2004). The Brief Assessment of Cognition in Schizophrenia: reliability, sensitivity, and
comparison with a standard neurocognitive battery. Schizophrenia Research, 68(2-3),
283–97. http://doi.org/10.1016/j.schres.2003.09.011
113
Kendler, K. S., McGuire, M., Gruenberg, A. M., O’Hare, A., Spellman, M., & Walsh, D. (1993).
The Roscommon Family Study. I. Methods, diagnosis of probands, and risk of
schizophrenia in relatives. Archives of General Psychiatry, 50(7), 527–540.
Kern, R. S., Glynn, S. M., Horan, W. P., & Marder, S. R. (2009). Psychosocial Treatments to
Promote Functional Recovery in Schizophrenia. Schizophrenia Bulletin, 35(2), 347–361.
http://doi.org/10.1093/schbul/sbn177
Kern, R. S., Green, M. F., Fiske, A. P., Kee, K. S., Lee, J., Sergi, M. J., … Nuechterlein, K. H.
(2009). Theory of mind deficits for processing counterfactual information in persons with
chronic schizophrenia. Psychological Medicine, 39(04), 645–654.
http://doi.org/10.1017/S0033291708003966
Kern, R. S., Nuechterlein, K. H., Green, M. F., Baade, L. E., Fenton, W. S., Gold, J. M., …
Marder, S. R. (2008). The MATRICS Consensus Cognitive Battery, Part 2: Co-Norming
and Standardization. American Journal of Psychiatry, 165(2), 214–220.
http://doi.org/10.1176/appi.ajp.2007.07010043
Khatri, N., Romney, D. M., & Pelletier, G. (2001). Validity of self-reports about quality of life
among patients with schizophrenia. Psychiatric Services (Washington, D.C.), 52(4), 534–
535.
Kim, J. S., Singh, V., Lee, J. K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., … Evans, A. C.
(2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces
using a Laplacian map and partial volume effect classification. NeuroImage, 27(1), 210–
221. http://doi.org/10.1016/j.neuroimage.2005.03.036
114
Kim, S. H., Lee, J. M., Kim, H. P., Jang, D. P., Shin, Y. W., Ha, T. H., … Kim, S. I. (2005).
Asymmetry analysis of deformable hippocampal model using the principal component in
schizophrenia. Human Brain Mapping, 25(4), 361–369.
http://doi.org/10.1002/hbm.20106
Kirkpatrick, B., Buchanan, R. W., Ross, D. E., & Carpenter, W. T. (2001). A separate disease
within the syndrome of schizophrenia. Archives of General Psychiatry, 58(2), 165–171.
http://doi.org/10.1001/archpsyc.58.2.165
Kirkpatrick, B., Fenton, W. S., Carpenter, W. T., & Marder, S. R. (2006). The NIMH-MATRICS
Consensus Statement on Negative Symptoms. Schizophrenia Bulletin, 32(2), 214–219.
http://doi.org/10.1093/schbul/sbj053
Kitis, O., Ozalay, O., Zengin, E. B., Haznedaroglu, D., Eker, M. C., Yalvac, D., … Gonul, A. S.
(2012). Reduced left uncinate fasciculus fractional anisotropy in deficit schizophrenia but
not in non-deficit schizophrenia. Psychiatry and Clinical Neurosciences, 66(1), 34–43.
http://doi.org/10.1111/j.1440-1819.2011.02293.x
Kraepelin, E. (1971). Dementia Praecox and Paraphrenia. (R. G. Krieger, Ed.). New York, NY,
US.
Kremen, W. S., Seidman, L. J., Faraone, S. V., Toomey, R., & Tsuang, M. T. (2000). The
paradox of normal neuropsychological function in schizophrenia. Journal of Abnormal
Psychology, 109(4), 743–752.
Krueger, R. F., & Markon, K. E. (2006). Reinterpreting Comorbidity: A Model-Based Approach
to Understanding and Classifying Psychopathology. Annual Review of Clinical
115
Psychology, 2, 111–133.
http://doi.org/10.1146/annurev.clinpsy.2.022305.095213
Kubicki, M., McCarley, R., Westin, C. F., Park, H. J., Maier, S., Kikinis, R., … Shenton, M. E.
(2007). A review of diffusion tensor imaging studies in schizophrenia. Journal of
Psychiatric Research, 41(1–2), 15–30.
http://doi.org/10.1016/j.jpsychires.2005.05.005
Kubicki, M., Westin, C. F., Maier, S. E., Frumin, M., Nestor, P. G., Salisbury, D. F., … Shenton,
M. E. (2002). Uncinate Fasciculus Findings in Schizophrenia: A Magnetic Resonance
Diffusion Tensor Imaging Study. American Journal of Psychiatry, 159(5), 813–820.
http://doi.org/10.1176/appi.ajp.159.5.813
Kubicki, M., Westin, C. F., Nestor, P. G., Wible, C. G., Frumin, M., Maier, S. E., … Shenton, M.
E. (2003). Cingulate fasciculus integrity disruption in schizophrenia: a magnetic
resonance diffusion tensor imaging study. Biological Psychiatry, 54(11), 1171–1180.
http://doi.org/10.1016/S0006-3223(03)00419-0
Kumar, J., Iwabuchi, S., Oowise, S., Balain, V., Palaniyappan, L., & Liddle, P. F. (2014). Shared
white-matter dysconnectivity in schizophrenia and bipolar disorder with psychosis.
Psychological Medicine, FirstView, 1–12.
http://doi.org/10.1017/S0033291714001810
Kunda, Z. (n.d.). Social Cognition: Making Sense of People. MIT Press.
Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., … Fox, P. T.
(2011). Behavioral interpretations of intrinsic connectivity networks. Journal of
116
Cognitive Neuroscience, 23(12), 4022–4037.
http://doi.org/10.1162/jocn_a_00077
Lane, R. D., Fink, G. R., Chau, P. M., & Dolan, R. J. (1997). Neural activation during selective
attention to subjective emotional responses. Neuroreport, 8(18), 3969–3972.
Lane, R. D., Glazer, W. M., Hansen, T. E., Berman, W. H., & Kramer, S. I. (1985). Assessment
of tardive dyskinesia using the Abnormal Involuntary Movement Scale. The Journal of
Nervous and Mental Disease, 173(6), 353–357.
Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and
disease. Brain: A Journal of Neurology, 137(1), 12–32.
http://doi.org/10.1093/brain/awt162
Lee, J., Altshuler, L., Glahn, D. C., Miklowitz, D. J., Ochsner, K., & Green, M. F. (2013). Social
and nonsocial cognition in bipolar disorder and schizophrenia: relative levels of
impairment. The American Journal of Psychiatry, 170(3), 334–341.
http://doi.org/10.1176/appi.ajp.2012.12040490
Lee, S. H., Kubicki, M., Asami, T., Seidman, L. J., Goldstein, J. M., Mesholam-Gately, R. I., …
Shenton, M. E. (2013). Extensive white matter abnormalities in patients with first-
episode schizophrenia: A diffusion tensor imaging (DTI) study. Schizophrenia Research,
143(2–3), 231–238. http://doi.org/10.1016/j.schres.2012.11.029
Lehman, A. F., Lieberman, J. A., Dixon, L. B., McGlashan, T. H., Miller, A. L., Perkins, D. O.,
… Steering Committee on Practice Guidelines. (2004). Practice guideline for the
117
treatment of patients with schizophrenia, second edition. The American Journal of
Psychiatry, 161(Suppl 2), 1–56.
Leifker, F. R., Patterson, T. L., Heaton, R. K., & Harvey, P. D. (2011). Validating measures of
real-world outcome: the results of the VALERO expert survey and RAND panel.
Schizophrenia Bulletin, 37(2), 334–343. http://doi.org/10.1093/schbul/sbp044
Lerch, J. P., & Evans, A. C. (2005). Cortical thickness analysis examined through power analysis
and a population simulation. NeuroImage, 24(1), 163–173.
http://doi.org/10.1016/j.neuroimage.2004.07.045
Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R. K., Giedd, J., & Evans, A.
C. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using
cortical thickness from MRI. NeuroImage, 31(3), 993–1003.
http://doi.org/10.1016/j.neuroimage.2006.01.042
Leucht, S., Corves, C., Arbter, D., Engel, R. R., Li, C., & Davis, J. M. (2009). Second-generation
versus first-generation antipsychotic drugs for schizophrenia: a meta-analysis. The
Lancet, 373(9657), 31–41. http://doi.org/10.1016/S0140-6736(08)61764-X
Leung, A., & Chue, P. (2000). Sex differences in schizophrenia, a review of the literature. Acta
Psychiatrica Scandinavica. Supplementum, 401, 3–38.
Lichtman, J. W., Livet, J., & Sanes, J. R. (2008). A technicolour approach to the connectome.
Nature Reviews Neuroscience, 9(6), 417–422. http://doi.org/10.1038/nrn2391
Lim, M. D., Kelvin, Ardekani, P. D., Babak, Nierenberg, M. D., Ph.D. ,Jay, Butler, P. D.,
Pamela, Javitt, M. D., Ph.D. ,Daniel, & Hoptman, P. D., Matthew. (2006). Voxelwise
118
Correlational Analyses of White Matter Integrity in Multiple Cognitive Domains in
Schizophrenia. American Journal of Psychiatry, 163(11), 2008–2010.
http://doi.org/10.1176/appi.ajp.163.11.2008
Liu, X., Lai, Y., Wang, X., Hao, C., Chen, L., Zhou, Z., … Hong, N. (2013). Reduced white
matter integrity and cognitive deficit in never-medicated chronic schizophrenia: A
diffusion tensor study using TBSS. Behavioural Brain Research, 252, 157–163.
http://doi.org/10.1016/j.bbr.2013.05.061
Macrae, C. N., Moran, J. M., Heatherton, T. F., Banfield, J. F., & Kelley, W. M. (2004). Medial
prefrontal activity predicts memory for self. Cerebral Cortex (New York, N.Y.: 1991),
14(6), 647–654. http://doi.org/10.1093/cercor/bhh025
Malaspina, D., Harlap, S., Fennig, S., Heiman, D., Nahon, D., Feldman, D., & Susser, E. S.
(2001). Advancing paternal age and the risk of schizophrenia. Archives of General
Psychiatry, 58(4), 361–367.
Mancuso, F., Horan, W. P., Kern, R. S., & Green, M. F. (2011). Social cognition in psychosis:
multidimensional structure, clinical correlates, and relationship with functional outcome.
Schizophrenia Research, 125(2-3), 143–151.
http://doi.org/10.1016/j.schres.2010.11.007
Mar, R. A. (2011). The neural bases of social cognition and story comprehension. Annual Review
of Psychology, 62, 103–134.
http://doi.org/10.1146/annurev-psych-120709-145406
119
Mars, R. B., Neubert, F. X., Noonan, M. P., Sallet, J., Toni, I., & Rushworth, M. F. S. (2012). On
the relationship between the “default mode network” and the “social brain.” Frontiers in
Human Neuroscience, 6, 189.
http://doi.org/10.3389/fnhum.2012.00189
Mazure, C. M., Nelson, J. C., Jatlow, P. I., & Bowers, M. B. (1992). Drug-responsive symptoms
during early neuroleptic treatment. Psychiatry Research, 41(2), 147–154.
McDonald, S., Flanagan, S., Rollins, J., & Kinch, J. (2003). TASIT: A new clinical tool for
assessing social perception after traumatic brain injury. The Journal of Head Trauma
Rehabilitation, 18(3), 219–238.
McGurk, S. R., Twamley, E. W., Sitzer, D. I., McHugo, G. J., & Mueser, K. T. (2007). A Meta-
Analysis of Cognitive Remediation in Schizophrenia. The American Journal of
Psychiatry, 164(12), 1791–1802.
http://doi.org/10.1176/appi.ajp.2007.07060906
McIntosh, A. R., Bookstein, F. L., Haxby, J. V., & Grady, C. L. (1996). Spatial Pattern Analysis
of Functional Brain Images Using Partial Least Squares. NeuroImage, 3(3), 143–157.
http://doi.org/10.1006/nimg.1996.0016
McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data:
applications and advances. NeuroImage, 23(Suppl 1), S250–S263.
http://doi.org/10.1016/j.neuroimage.2004.07.020
Melonakos, E. D., Shenton, M. E., Rathi, Y., Terry, D. P., Bouix, S., & Kubicki, M. (2011).
Voxel-based morphometry (VBM) studies in schizophrenia-can white matter changes be
120
reliably detected with VBM? Psychiatry Research, 193(2), 65–70.
http://doi.org/10.1016/j.pscychresns.2011.01.009
Milev, P., Ho, B. C., Arndt, S., & Andreasen, N. C. (2005). Predictive values of neurocognition
and negative symptoms on functional outcome in schizophrenia: a longitudinal first-
episode study with 7-year follow-up. The American Journal of Psychiatry, 162(3), 495–
506. http://doi.org/10.1176/appi.ajp.162.3.495
Miller, M. D., Paradis, C. F., Houck, P. R., Mazumdar, S., Stack, J. A., Rifai, A. H., … Reynolds
III, C. F. (1992). Rating chronic medical illness burden in geropsychiatric practice and
research: Application of the Cumulative Illness Rating Scale. Psychiatry Research, 41(3),
237–248. http://doi.org/10.1016/0165-1781(92)90005-N
Mishara, A. L., & Goldberg, T. E. (2004). A meta-analysis and critical review of the effects of
conventional neuroleptic treatment on cognition in schizophrenia: opening a closed book.
Biological Psychiatry, 55(10), 1013–1022.
http://doi.org/10.1016/j.biopsych.2004.01.027
Mitelman, S. A., Canfield, E. L., Newmark, R. E., Brickman, A. M., Torosjan, Y., Chu, K. W.,
… Buchsbaum, M. S. (2009). Longitudinal Assessment of Gray and White Matter in
Chronic Schizophrenia: A Combined Diffusion-Tensor and Structural Magnetic
Resonance Imaging Study. The Open Neuroimaging Journal, 3, 31–47.
http://doi.org/10.2174/1874440000903010031
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D.
(2000). The Unity and Diversity of Executive Functions and Their Contributions to
121
Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cognitive Psychology,
41(1), 49–100. http://doi.org/10.1006/cogp.1999.0734
Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., … Mazziotta, J. (2008). Stereotaxic
white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage,
40(2), 570–582. http://doi.org/10.1016/j.neuroimage.2007.12.035
Mori, S., & van Zijl, P. C. M. (2002). Fiber tracking: principles and strategies – a technical
review. NMR in Biomedicine, 15(7-8), 468–480.
http://doi.org/10.1002/nbm.781
Mori, S., & Zhang, J. (2006). Principles of Diffusion Tensor Imaging and Its Applications to
Basic Neuroscience Research. Neuron, 51(5), 527–539.
http://doi.org/10.1016/j.neuron.2006.08.012
Morris, R. W., Sparks, A., Mitchell, P. B., Weickert, C. S., & Green, M. J. (2012). Lack of
cortico-limbic coupling in bipolar disorder and schizophrenia during emotion regulation.
Translational Psychiatry, 2, e90.
http://doi.org/10.1038/tp.2012.16
Morris, S. E., & Cuthbert, B. N. (2012). Research Domain Criteria: cognitive systems, neural
circuits, and dimensions of behavior. Dialogues in Clinical Neuroscience, 14(1), 29–37.
Mortimer, A. M. (1997). Cognitive function in schizophrenia-do neuroleptics make a difference?
Pharmacology, Biochemistry, and Behavior, 56(4), 789–795.
Nakamura, K., Kawasaki, Y., Takahashi, T., Furuichi, A., Noguchi, K., Seto, H., & Suzuki, M.
(2012). Reduced white matter fractional anisotropy and clinical symptoms in
122
schizophrenia: a voxel-based diffusion tensor imaging study. Psychiatry Research,
202(3), 233–238. http://doi.org/10.1016/j.pscychresns.2011.09.006
Nestor, P. G., Kubicki, M., Nakamura, M., Niznikiewicz, M., Levitt, J. J., Shenton, M. E., &
McCarley, R. W. (2013). Neuropsychological variability, symptoms, and brain imaging
in chronic schizophrenia. Brain Imaging and Behavior, 7(1), 68–76.
http://doi.org/10.1007/s11682-012-9193-0
Niznikiewicz, M., Donnino, R., McCarley, R. W., Nestor, P. G., Iosifescu, D. V., O’Donnell, B.,
… Shenton, M. E. (2000). Abnormal angular gyrus asymmetry in schizophrenia. The
American Journal of Psychiatry, 157(3), 428–437.
Novella, E. J. (2010). Mental health care in the aftermath of deinstitutionalization: a
retrospective and prospective view. Health Care Analysis: Journal of Health Philosophy
and Policy, 18(3), 222–238.
http://doi.org/10.1007/s10728-009-0138-8
Nuechterlein, K. H., Barch, D. M., Gold, J. M., Goldberg, T. E., Green, M. F., & Heaton, R. K.
(2004). Identification of separable cognitive factors in schizophrenia. Schizophrenia
Research, 72(1), 29–39.
http://doi.org/10.1016/j.schres.2004.09.007
Ochsner, K. N. (2008). The social-emotional processing stream: five core constructs and their
translational potential for schizophrenia and beyond. Biological Psychiatry, 64(1), 48–61.
http://doi.org/10.1016/j.biopsych.2008.04.024
123
O’Donnell, L. J., Kubicki, M., Shenton, M. E., Dreusicke, M. H., Grimson, W. E. L., & Westin,
C. F. (2006). A Method for Clustering White Matter Fiber Tracts. American Journal of
Neuroradiology, 27(5), 1032–1036.
Olson, I. R., Plotzker, A., & Ezzyat, Y. (2007). The Enigmatic temporal pole: a review of
findings on social and emotional processing. Brain: A Journal of Neurology, 130(Pt 7),
1718–1731. http://doi.org/10.1093/brain/awm052
Onitsuka, T., Shenton, M. E., Kasai, K., & et al. (2003). Fusiform gyrus volume reduction and
facial recognition in chronic schizophrenia. Archives of General Psychiatry, 60(4), 349–
355. http://doi.org/10.1001/archpsyc.60.4.349
Owen, M. J., O’Donovan, M. C., Thapar, A., & Craddock, N. (2011). Neurodevelopmental
hypothesis of schizophrenia. The British Journal of Psychiatry, 198(3), 173–175.
http://doi.org/10.1192/bjp.bp.110.084384
Palmer, B. W., Heaton, R. K., Paulsen, J. S., Kuck, J., Braff, D., Harris, M. J., … Jeste, D. V.
(1997). Is it possible to be schizophrenic yet neuropsychologically normal?
Neuropsychology, 11(3), 437–446.
Peled, S., Friman, O., Jolesz, F., & Westin, C. F. (2006). Geometrically constrained two-tensor
model for crossing tracts in DWI. Magnetic Resonance Imaging, 24(9), 1263–1270.
http://doi.org/10.1016/j.mri.2006.07.009
Penn, D. L., Addington, J., & Pinkham, A. (2006). Social Cognitive Impairments. In J. A.
Lieberman, T. S. Stroup, & D. O. Perkins (Eds.), The American Psychiatric Publishing
124
Textbook of Schizophrenia (pp. 261–274). Arlington, VA, US: American Psychiatric
Publishing, Inc.
Pettersson-Yeo, W., Allen, P., Benetti, S., McGuire, P., & Mechelli, A. (2011). Dysconnectivity
in schizophrenia: Where are we now? Neuroscience & Biobehavioral Reviews, 35(5),
1110–1124. http://doi.org/10.1016/j.neubiorev.2010.11.004
Philippi, C. L., Mehta, S., Grabowski, T., Adolphs, R., & Rudrauf, D. (2009). Damage to
association fiber tracts impairs recognition of the facial expression of emotion. The
Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 29(48),
15089–15099. http://doi.org/10.1523/JNEUROSCI.0796-09.2009
Picchioni, M. M., & Murray, R. M. (2007). Schizophrenia. BMJ (Clinical Research Ed.),
335(7610), 91–95. http://doi.org/10.1136/bmj.39227.616447.BE
Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A., & Di Chiro, G. (1996). Diffusion tensor MR
imaging of the human brain. Radiology, 201(3), 637–648.
http://doi.org/10.1148/radiology.201.3.8939209
Pinkham, A. E., Penn, D. L., Green, M. F., Buck, B., Healey, K., & Harvey, P. D. (2013). The
Social Cognition Psychometric Evaluation Study: Results of the Expert Survey and
RAND Panel. Schizophrenia Bulletin, 0, 81.
http://doi.org/10.1093/schbul/sbt081
Poole, J., & Durbin, J. (2012). Behind the Rhetoric: Mental Health Recovery in Ontario.
Canadian Journal of Community Mental Health, 31(2), 119–121.
http://doi.org/10.7870/cjcmh-2012-0017
125
Rajkowska, G., Selemon, L. D., & Goldman-Rakic, P. S. (1998). Neuronal and glial somal size
in the prefrontal cortex: a postmortem morphometric study of schizophrenia and
Huntington disease. Archives of General Psychiatry, 55(3), 215–224.
Randolph, C., Tierney, M. C., Mohr, E., & Chase, T. N. (1998). The Repeatable Battery for the
Assessment of Neuropsychological Status (RBANS): Preliminary Clinical Validity.
Journal of Clinical and Experimental Neuropsychology, 20(3), 310–319.
http://doi.org/10.1076/jcen.20.3.310.823
Ratnanather, J. T., Poynton, C. B., Pisano, D. V., Crocker, B., Postell, E., Cebron, S., … Barta,
P. E. (2013). Morphometry of superior temporal gyrus and planum temporale in
schizophrenia and psychotic bipolar disorder. Schizophrenia Research, 150(2-3), 476–
483. http://doi.org/10.1016/j.schres.2013.08.014
Raznahan, A., Lerch, J. P., Lee, N., Greenstein, D., Wallace, G. L., Stockman, M., … Giedd, J.
N. (2011). Patterns of coordinated anatomical change in human cortical development: a
longitudinal neuroimaging study of maturational coupling. Neuron, 72(5), 873–884.
http://doi.org/10.1016/j.neuron.2011.09.028
Reitan, R. M. (1979). Manual for the Administration of Neuropsychological Test Batteries for
Adults and Children. Tucson, AZ: Ralph M. Reitan.
Remington, G., Foussias, G., & Agid, O. (2010). Progress in defining optimal treatment outcome
in schizophrenia. CNS Drugs, 24(1), 9–20. http://doi.org/10.2165/11530250-000000000-
00000
126
Rimol, L. M., Hartberg, C. B., Nesvåg, R., Fennema-Notestine, C., Hagler, D. J., Pung, C. J., …
Agartz, I. (2010). Cortical thickness and subcortical volumes in schizophrenia and bipolar
disorder. Biological Psychiatry, 68(1), 41–50.
http://doi.org/10.1016/j.biopsych.2010.03.036
Rosenheck, R., Leslie, D., Keefe, R., McEvoy, J., Swartz, M., Perkins, D., … CATIE Study
Investigators Group. (2006). Barriers to employment for people with schizophrenia. The
American Journal of Psychiatry, 163(3), 411–417.
http://doi.org/10.1176/appi.ajp.163.3.411
Rowland, L. M., Spieker, E. A., Francis, A., Barker, P. B., Carpenter, W. T., & Buchanan, R. W.
(2009). White matter alterations in deficit schizophrenia. Neuropsychopharmacology:
Official Publication of the American College of Neuropsychopharmacology, 34(6), 1514–
1522. http://doi.org/10.1038/npp.2008.207
Saha, S., Chant, D., Welham, J., & McGrath, J. (2005). A Systematic Review of the Prevalence
of Schizophrenia. PLoS Med, 2(5), e141. http://doi.org/10.1371/journal.pmed.0020141
Sanchez, G. (2013). PLS Path Modeling with R. Retrieved May 11, 2015, from
www.gastonsanchez.com
San, L., Ciudad, A., Álvarez, E., Bobes, J., & Gilaberte, I. (2007). Symptomatic remission and
social/vocational functioning in outpatients with schizophrenia: Prevalence and
associations in a cross-sectional study. European Psychiatry, 22(8), 490–498.
http://doi.org/10.1016/j.eurpsy.2007.06.005
127
Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people. The role of the
temporo-parietal junction in “theory of mind.” NeuroImage, 19(4), 1835–1842.
Schilbach, L., Eickhoff, S. B., Rotarska-Jagiela, A., Fink, G. R., & Vogeley, K. (2008). Minds at
rest? Social cognition as the default mode of cognizing and its putative relationship to the
“default system” of the brain. Consciousness and Cognition, 17(2), 457–467.
http://doi.org/10.1016/j.concog.2008.03.013
Schmidt, S. J., Mueller, D. R., & Roder, V. (2011). Social cognition as a mediator variable
between neurocognition and functional outcome in schizophrenia: empirical review and
new results by structural equation modeling. Schizophrenia Bulletin, 37(Suppl 2), S41–
54. http://doi.org/10.1093/schbul/sbr079
Schneider, K. (1959). Clinical Psychopathology. (M. W. Hamilton, Trans.). Grune and Stratton,
New York.
Scholz, J., Klein, M. C., Behrens, T. E. J., & Johansen-Berg, H. (2009). Training induces
changes in white-matter architecture. Nature Neuroscience, 12(11), 1370–1371.
http://doi.org/10.1038/nn.2412
Selemon, L. D., Mrzljak, J., Kleinman, J. E., Herman, M. M., & Goldman-Rakic, P. S. (2003).
Regional specificity in the neuropathologic substrates of schizophrenia: a morphometric
analysis of Broca’s area 44 and area 9. Archives of General Psychiatry, 60(1), 69–77.
Sergi, M. J., Rassovsky, Y., Widmark, C., Reist, C., Erhart, S., Braff, D. L., … Green, M. F.
(2007). Social cognition in schizophrenia: relationships with neurocognition and negative
128
symptoms. Schizophrenia Research, 90(1-3), 316–324.
http://doi.org/10.1016/j.schres.2006.09.028
Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., … Toga,
A. W. (2008). Construction of a 3D probabilistic atlas of human cortical structures.
NeuroImage, 39(3), 1064–1080. http://doi.org/10.1016/j.neuroimage.2007.09.031
Shenton, M. E., Dickey, C. C., Frumin, M., & McCarley, R. W. (2001). A review of MRI
findings in schizophrenia. Schizophrenia Research, 49(1-2), 1–52.
Shenton, M. E., Whitford, T. J., & Kubicki, M. (2010). Structural neuroimaging in
schizophrenia: from methods to insights to treatments. Dialogues in Clinical
Neuroscience, 12(3), 317–332.
Sigmundsson, T., Suckling, J., Maier, M., Williams, S., Bullmore, E., Greenwood, K., … Toone,
B. (2001). Structural abnormalities in frontal, temporal, and limbic regions and
interconnecting white matter tracts in schizophrenic patients with prominent negative
symptoms. The American Journal of Psychiatry, 158(2), 234–243.
Simpson, G. M., Angus, J. W. (1970). A Rating Scale for Extrapyramidal Side Effects. Acta
Psychiatrica Scandinavica, 45(Suppl 212), 11–19.
http://doi.org/10.1111/j.1600-0447.1970.tb02066.x
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic
correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical
Imaging, 17(1), 87–97. http://doi.org/10.1109/42.668698
129
Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., …
Behrens, T. E. J. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-
subject diffusion data. NeuroImage, 31(4), 1487–1505.
http://doi.org/10.1016/j.neuroimage.2006.02.024
Soares, J. M., Marques, P., Alves, V., & Sousa, N. (2013). A hitchhiker’s guide to diffusion
tensor imaging. Frontiers in Neuroscience, 7.
http://doi.org/10.3389/fnins.2013.00031
Spoletini, I., Cherubini, A., Di Paola, M., Banfi, G., Rüsch, N., Martinotti, G., … Spalletta, G.
(2009). Reduced fronto-temporal connectivity is associated with frontal gray matter
density reduction and neuropsychological deficit in schizophrenia. Schizophrenia
Research, 108(1–3), 57–68. http://doi.org/10.1016/j.schres.2008.11.011
Squire, L. R., Stark, C. E. L., & Clark, R. E. (2004). The Medial Temporal Lobe*. Annual
Review of Neuroscience, 27(1), 279–306.
http://doi.org/10.1146/annurev.neuro.27.070203.144130
Stahl, S. M., & Buckley, P. F. (2007). Negative symptoms of schizophrenia: a problem that will
not go away. Acta Psychiatrica Scandinavica, 115(1), 4–11.
http://doi.org/10.1111/j.1600-0447.2006.00947.x
Stern, R. A., & White, T. (2003). Neuropsychological Assessment Battery. Lutz, FL:
Psychological Assessment Resources, Inc.
130
Strauss, G. P., Harrow, M., Grossman, L. S., & Rosen, C. (2010). Periods of recovery in deficit
syndrome schizophrenia: a 20-year multi-follow-up longitudinal study. Schizophrenia
Bulletin, 36(4), 788–799. http://doi.org/10.1093/schbul/sbn167
Sussmann, J. E., Lymer, G. K. S., McKirdy, J., Moorhead, T. W. J., Maniega, S. M., Job, D., …
McIntosh, A. M. (2009). White matter abnormalities in bipolar disorder and
schizophrenia detected using diffusion tensor magnetic resonance imaging. Bipolar
Disorders, 11(1), 11–18. http://doi.org/10.1111/j.1399-5618.2008.00646.x
Swerdlow, N. R. (2010). Behavioral Neurobiology of Schizophrenia and Its Treatment. Springer
Science & Business Media.
Szeszko, P. R., Robinson, D. G., Ashtari, M., Vogel, J., Betensky, J., Sevy, S., … Bilder, R. M.
(2008). Clinical and neuropsychological correlates of white matter abnormalities in
recent onset schizophrenia. Neuropsychopharmacology: Official Publication of the
American College of Neuropsychopharmacology, 33(5), 976–984.
http://doi.org/10.1038/sj.npp.1301480
Szeszko, P. R., Robinson, D. G., Ikuta, T., Peters, B. D., Gallego, J. A., Kane, J., & Malhotra, A.
K. (2014). White matter changes associated with antipsychotic treatment in first-episode
psychosis. Neuropsychopharmacology: Official Publication of the American College of
Neuropsychopharmacology, 39(6), 1324–1331. http://doi.org/10.1038/npp.2013.288
Takei, K., Yamasue, H., Abe, O., Yamada, H., Inoue, H., Suga, M., … Kasai, K. (2009).
Structural disruption of the dorsal cingulum bundle is associated with impaired Stroop
performance in patients with schizophrenia. Schizophrenia Research, 114(1–3), 119–127.
http://doi.org/10.1016/j.schres.2009.05.012
131
Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Nagase, T., … Kawashima, R.
(2013). White matter structures associated with emotional intelligence: Evidence from
diffusion tensor imaging. Human Brain Mapping, 34(5), 1025–1034.
http://doi.org/10.1002/hbm.21492
Tandon, R., Gaebel, W., Barch, D. M., Bustillo, J., Gur, R. E., Heckers, S., … Carpenter, W. T.
(2013). Definition and description of schizophrenia in the DSM-5. Schizophrenia
Research, 150(1), 3–10. http://doi.org/10.1016/j.schres.2013.05.028
Tandon, R., Nasrallah, H. A., & Keshavan, M. S. (2009). Schizophrenia, “just the facts” 4.
Clinical features and conceptualization. Schizophrenia Research, 110(1–3), 1–23.
http://doi.org/10.1016/j.schres.2009.03.005
Tandon, R., Nasrallah, H. A., & Keshavan, M. S. (2010). Schizophrenia, “Just the Facts” 5.
Treatment and prevention Past, present, and future. Schizophrenia Research, 122(1–3),
1–23. http://doi.org/10.1016/j.schres.2010.05.025
Tangney, J. P., Stuewig, J., & Mashek, D. J. (2007). Moral emotions and moral behavior. Annual
Review of Psychology, 58, 345–372.
http://doi.org/10.1146/annurev.psych.56.091103.070145
Tan, H. Y., Sust, S., Buckholtz, J. W., Mattay, V. S., Meyer-Lindenberg, A., Egan, M. F., …
Callicott, J. H. (2006). Dysfunctional prefrontal regional specialization and compensation
in schizophrenia. The American Journal of Psychiatry, 163(11), 1969–1977.
http://doi.org/10.1176/appi.ajp.163.11.1969
132
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling.
Computational Statistics & Data Analysis, 48(1), 159–205.
http://doi.org/10.1016/j.csda.2004.03.005
Tohka, J., Zijdenbos, A., & Evans, A. (2004). Fast and robust parameter estimation for statistical
partial volume models in brain MRI. NeuroImage, 23(1), 84–97.
http://doi.org/10.1016/j.neuroimage.2004.05.007
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002).
High angular resolution diffusion imaging reveals intravoxel white matter fiber
heterogeneity. Magnetic Resonance in Medicine, 48(4), 577–582.
http://doi.org/10.1002/mrm.10268
Van Buchem, M. A., & Tofts, P. S. (2000). Magnetization transfer imaging. Neuroimaging
Clinics of North America, 10(4), 771–88 ,ix.
Van den Heuvel, M., Mandl, R., Luigjes, J., & Hulshoff Pol, H. (2008). Microstructural
organization of the cingulum tract and the level of default mode functional connectivity.
The Journal of Neuroscience: The Official Journal of the Society for Neuroscience,
28(43), 10844–10851. http://doi.org/10.1523/JNEUROSCI.2964-08.2008
Van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S., & Hulshoff Pol, H. E. (2009).
Functionally linked resting-state networks reflect the underlying structural connectivity
architecture of the human brain. Human Brain Mapping, 30(10), 3127–3141.
http://doi.org/10.1002/hbm.20737
133
Van Overwalle, F., & Baetens, K. (2009). Understanding others’ actions and goals by mirror and
mentalizing systems: a meta-analysis. NeuroImage, 48(3), 564–584.
http://doi.org/10.1016/j.neuroimage.2009.06.009
Ventura, J., Hellemann, G. S., Thames, A. D., Koellner, V., & Nuechterlein, K. H. (2009).
Symptoms as mediators of the relationship between neurocognition and functional
outcome in schizophrenia: A meta-analysis. Schizophrenia Research, 113(2–3), 189–199.
http://doi.org/10.1016/j.schres.2009.03.035
Voineskos, A. N., Felsky, D., Kovacevic, N., Tiwari, A. K., Zai, C., Chakravarty, M. M., …
Kennedy, J. L. (2013). Oligodendrocyte Genes, White Matter Tract Integrity, and
Cognition in Schizophrenia. Cerebral Cortex, 23(9), 2044–2057.
http://doi.org/10.1093/cercor/bhs188
Voineskos, A. N., Foussias, G., Lerch, J., Felsky, D., Remington, G., Rajji, T. K., … Mulsant, B.
H. (2013). Neuroimaging evidence for the deficit subtype of schizophrenia. JAMA
Psychiatry, 70(5), 472–480. http://doi.org/10.1001/jamapsychiatry.2013.786
Voineskos, A. N., O’Donnell, L. J., Lobaugh, N. J., Markant, D., Ameis, S. H., Niethammer, M.,
… Shenton, M. E. (2009). Quantitative examination of a novel clustering method using
magnetic resonance diffusion tensor tractography. NeuroImage, 45(2), 370–376.
http://doi.org/10.1016/j.neuroimage.2008.12.028
Völlm, B. A., Taylor, A. N. W., Richardson, P., Corcoran, R., Stirling, J., McKie, S., … Elliott,
R. (2006). Neuronal correlates of theory of mind and empathy: a functional magnetic
resonance imaging study in a nonverbal task. NeuroImage, 29(1), 90–98.
http://doi.org/10.1016/j.neuroimage.2005.07.022
134
Wechsler, D. (1979). Wechsler Memory Scale (3rd ed.). San Antonio, TX: The Psychological.
Wechsler, D. (2001). Wechsler Test of Adult Reading: WTAR. Psychological Corporation.
Westin, C. F., Maier, S. E., Mamata, H., Nabavi, A., Jolesz, F. A., & Kikinis, R. (2002).
Processing and visualization for diffusion tensor MRI. Medical Image Analysis, 6(2), 93–
108.
Wheatley, T., Milleville, S. C., & Martin, A. (2007). Understanding animate agents: distinct
roles for the social network and mirror system. Psychological Science, 18(6), 469–474.
http://doi.org/10.1111/j.1467-9280.2007.01923.x
Wheeler, A. L., & Voineskos, A. N. (2014). A review of structural neuroimaging in
schizophrenia: from connectivity to connectomics. Frontiers in Human Neuroscience, 8,
653. http://doi.org/10.3389/fnhum.2014.00653
Wheeler, A. L., Wessa, M., Szeszko, P. R., Foussias, G., Chakravarty, M. M., Lerch, J. P., …
Voineskos, A. N. (2015). Brain network investigation in schizophrenia, bipolar disorder
and healthy controls reveals prominent network differences in deficit-subtype
schizophrenia. JAMA Psychiatry.
Wheeler, A. L., Wessa, M., Szeszko, P. R., Foussias, G., Chakravarty, M. M., Lerch, J. P., …
Voineskos, A. N. (2015). Further neuroimaging evidence for the deficit subtype of
schizophrenia: a cortical connectomics analysis. JAMA Psychiatry, 72(5), 446–455.
http://doi.org/10.1001/jamapsychiatry.2014.3020
135
Wolkin, A., Choi, S. J., Szilagyi, S., Sanfilipo, M., Rotrosen, J. P., & Lim, K. O. (2003). Inferior
frontal white matter anisotropy and negative symptoms of schizophrenia: a diffusion
tensor imaging study. The American Journal of Psychiatry, 160(3), 572–574.
Wunderink, L., Nieboer, R. M., Wiersma, D., Sytema, S., & Nienhuis, F. J. (2013). Recovery in
remitted first-episode psychosis at 7 years of follow-up of an early dose
reduction/discontinuation or maintenance treatment strategy: long-term follow-up of a 2-
year randomized clinical trial. JAMA Psychiatry, 70(9), 913–920.
http://doi.org/10.1001/jamapsychiatry.2013.19
Zawadzki, J. A., Girard, T. A., Foussias, G., Rodrigues, A., Siddiqui, I., Lerch, J. P., … Wong,
A. H. C. (2013). Simulating real world functioning in schizophrenia using a naturalistic
city environment and single-trial, goal-directed navigation. Frontiers in Behavioral
Neuroscience, 7. http://doi.org/10.3389/fnbeh.2013.00180
Zijdenbos, A. P., Forghani, R., & Evans, A. C. (2002). Automatic “pipeline” analysis of 3-D
MRI data for clinical trials: application to multiple sclerosis. IEEE Transactions on
Medical Imaging, 21(10), 1280–1291.
http://doi.org/10.1109/TMI.2002.806283
