Deconstructing  Motivation  Deficits  

in  Schizophrenia  and  Beyond  

by

Susana Da Silva

A thesis submitted in conformity with the requirements for the degree of Master of Science

Institute of Medical Science University of Toronto

© Copyright by Susana Da Silva 2017

ii    

Deconstructing  Motivation  Deficits  in  Schizophrenia  and  

Beyond  

Susana Da Silva

Master of Science

Institute of Medical Science University of Toronto

2017

Abstract

Motivation deficits are a prevalent and pervasive symptom in schizophrenia, and are

inextricably linked to poor functional outcomes in affected individuals. The primary

objective of this thesis was to systematically deconstruct the multiple facets of the motivation

system in schizophrenia and beyond. Our results revealed that reductions in self-reported

pleasure in schizophrenia were primarily driven by patients with severe amotivation. Self-

reported motivation deficits also predicted objective motivation task performance in a non-

clinical sample, with no effect of schizotypal or depressive symptoms. Moreover, in a clinical

sample of schizophrenia and major depressive disorder, our results identified a multi-faceted

motivation framework consisting of five components, and two clusters of individuals

characterized by differential behavioural motivation deficits. Taken together, our findings

highlight the centrality of amotivation in schizophrenia and beyond, as well as the need to

shift away from singular approaches, towards more comprehensive and dimensional

investigations of the motivation system across disorders.

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Acknowledgements  

The past two years of my Master’s would not have been possible without the

numerous people who have helped me along the way. First and foremost, I would like to

thank my supervisor and mentor, Dr. George Foussias for providing me with this opportunity

and entrusting me to complete my Master’s degree in his lab. Thank you for your unwavering

support and guidance over the past two years; your optimism and enthusiasm has been

instrumental throughout this journey. Thank you for your involvement and genuine interest

in all aspects of my graduate experience, and for encouraging and motivating me to learn

new things every day. I can never thank you enough for all that you have done for me. I

would also like to thank my PAC members Dr. Aristotle Voineskos, Dr. Gary Remington,

and Dr. Jeff Daskalakis for their time, support and invaluable contributions.  

I would like to thank all of the current and past members of the VRBN lab for their

encouragement along the way. To Sarah Saperia, thank you for being such a constant source

of love and support. Thank you for teaching me everything I know about clinical research

and statistics, and most importantly for being such an amazing friend. I could not have done

this without you. To Ishraq Siddiqui, thank you for our endless talks about R and statistics,

for keeping me grounded during stressful times, and for our much needed walks to Starbucks.

To my IMSSA family, I am so honoured to have been a part of such an amazing

student association. Over the past two years, I have made innumerable new friendships – in

particular with a group of intelligent and beautiful ladies who support, believe in, and

celebrate each other’s accomplishments. Thank you for the laughs, the tears and for

everything in between. So thankful that medical research brought us together.  

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I would also like to acknowledge my sister, Tania Da Silva and my best friend, Dunja

Knezevic for their undying love and support. Thank you for always being there for me;

words cannot express how grateful I am for the both of you. To my love, I am so lucky to

have such a sweet soul in my life – thank you for always supporting my dreams. To my

family and friends, thank you for believing in me. To my parents, thank you for your

unconditional support and encouragement, and for always reminding me to follow my

dreams. Your love, generosity, and inspiration have been the foundation for my success.

My heart is so full. Love you all, endlessly.

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Contributions

Chapter 3 (Study 1): Susana Da Silva and George Foussias designed the study, and Susana

Da Silva led the statistical analyses and preparation of the first draft of the manuscript. Sarah

Saperia and Ishraq Siddiqui contributed to the data collection and statistical analyses. Gagan

Fervaha, Ofer Agid, Jeff Daskalakis, Arun Ravindran, Aristotle Voineskos, Konstantine

Zakzanis, Gary Remington, and George Foussias reviewed and edited the manuscript, and

provided feedback.

Chapter 4 (Study 2): George Foussias and Areti Apatsidou designed the study, and Susana

Da Silva led the statistical analyses and preparation of the first draft of the manuscript. Areti

Apatsidou, Sarah Saperia, and Ishraq Siddiqui contributed to the data collection and

statistical analyses. Eliyas Jeffay, Aristotle Voineskos, Jeff Daskalakis, Gary Remington,

Konstantine Zakzanis, and George Foussias reviewed and edited the manuscript, and

provided feedback.

Chapter 5 (Study 3): Susana Da Silva and George Foussias designed the study, and Susana

Da Silva led the statistical analyses and preparation of the first draft of the manuscript. Sarah

Saperia and Ishraq Siddiqui contributed to the data collection and statistical analyses. Gagan

Fervaha, Aristotle Voineskos, Jeff Daskalakis, Arun Ravindran, Konstantine Zakzanis, Gary

Remington, and George Foussias reviewed and edited the manuscript, and provided

feedback.

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Table of Contents

Abstract.....................................................................................................................................ii Acknowledgements..................................................................................................................iii Contributions.............................................................................................................................v List of Tables..........................................................................................................................viii List of Figures...........................................................................................................................ix List of Abbreviations................................................................................................................xi  

Chapter 1 1 Introduction and Literature Review........................................................................................1 1.1 Schizophrenia.................................................................................................................1

1.1.1 History...................................................................................................................1 1.1.2 Diagnosis, Epidemiology and Etiology................................................................2 1.1.3 Symptomatology and Course of Illness................................................................7 1.1.4 Outcomes............................................................................................................11 1.1.5 Treatment............................................................................................................13

1.2 Negative Symptoms.....................................................................................................14 1.2.1 History.................................................................................................................14 1.2.2 The Negative Symptoms of Schizophrenia.........................................................15 1.2.3 Treatment of Negative Symptoms......................................................................18 1.2.4 Negative Symptoms and Functional Outcome...................................................20

1.3 Motivation....................................................................................................................22 1.3.1 Motivation – a Definition....................................................................................22 1.3.2 Motivation Deficits across Disorders..................................................................23 1.3.3 Motivation Deficits in Major Depressive Disorder............................................24 1.3.4 Facets of Motivation in Schizophrenia and Major Depressive Disorder............26

 

Chapter 2 2 Overview of experiments and hypotheses............................................................................34 2.1 Study 1: Investigating consummatory and anticipatory pleasure in schizophrenia and healthy controls..................................................................................................................34

2.1.1 Background.........................................................................................................34 2.1.2 Hypotheses..........................................................................................................35

2.2 Study 2: An examination of the multi-faceted motivation system in healthy young adults: the impact of schizotypal, depressive and amotivation symptoms........................35

2.2.1 Background.........................................................................................................35 2.2.2 Hypotheses..........................................................................................................35

2.3 Study 3: A dimensional examination of motivation system impairments in schizophrenia and major depressive disorder....................................................................36

2.3.1 Background.........................................................................................................36 2.3.2 Hypotheses..........................................................................................................37

 

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Chapter 3 3 Investigating consummatory and anticipatory pleasure in schizophrenia and healthy controls.....................................................................................................................................38 3.1 Abstract........................................................................................................................39 3.2 Introduction..................................................................................................................39 3.3 Materials and Methods.................................................................................................42 3.4 Results..........................................................................................................................45 3.5 Discussion....................................................................................................................55  

Chapter 4 4 An examination of the multi-faceted motivation system in healthy young adults: the impact of schizotypal, depressive and amotivation symptoms............................................................60 4.1 Abstract........................................................................................................................60 4.2 Introduction..................................................................................................................62 4.3 Materials and Methods.................................................................................................64 4.4 Results..........................................................................................................................69 4.5 Discussion....................................................................................................................75  

Chapter 5 5 A dimensional examination of motivation system impairments in schizophrenia and major depressive disorder...................................................................................................................80 5.1 Abstract........................................................................................................................80 5.2 Introduction..................................................................................................................81 5.3 Materials and Methods.................................................................................................83 5.4 Results..........................................................................................................................89 5.5 Discussion....................................................................................................................98 5.6 Supplementary Materials...........................................................................................103

5.6.1 Objective Computerized Measures...................................................................103 5.6.2 Analyses............................................................................................................108 5.6.3 Results...............................................................................................................112

 

Chapter 6 6 Overall Discussion..............................................................................................................128 6.1 Summary of Results...................................................................................................128 6.2 Limitations.................................................................................................................134 6.3 Future Directions.......................................................................................................138 References..............................................................................................................................145  

 

 

 

viii    

List of Tables

Table 1-1. DSM-IV-TR Diagnostic Criteria for Schizophrenia. Table 3-1. Sample demographics and clinical characteristics of HC and SZ participants. Table 3-2. Demographic and clinical characterization of SZ patients across levels of amotivation severity. Table 3-3. Correlations between consummatory and anticipatory pleasure, and clinical and cognitive measures in participants with SZ. Table 4-1. Sample demographics and symptom characterization. Table 4-2. Bivariate correlations between clinical measures and motivation tasks. Table 4-3. Inter-task correlations between objective measures of motivation. Table 5-1. Demographic and clinical characterization of sample. Table 5-2. Factor loadings for motivation task variables. Table 5-3. Clinical characterization and objective motivation performance across clusters.

Table 5-4. Clinical characterization and objective motivation performance across age-restricted clusters.

ix    

List of Figures    

Figure 1-1. Course of illness in schizophrenia.

Figure 1-2. The multi-faceted motivation framework.

Figure 3-1. Mean TEPS-Con and TEPS-Ant pleasure ratings for HCs and SZ patients across amotivation severity levels.

Figure 3-2. Scatterplots and regression lines for the significant relationships between AES and A) TEPS-Con and B) TEPS-Ant.

Figure 5-1. Motivation performance profiles across clusters.

Figure 5-2. Proportion of diagnostic groups across clusters.

Figure 5-3. Motivation performance profiles across age-restricted clusters.

Figure 5-4. Mean pleasantness and arousal ratings across valences for SZ, MDD, and HC groups.

Figure 5-5. Average click rate across valences, and diagnostic groups for the A) representational and B) evoked conditions.

Figure 5-6. Median reaction time across reinforcement cues for SZ, MDD, and HC groups. Figure 5-7. Proportion of participants demonstrating reinforcement related speeding across diagnostic groups.

Figure 5-8. The geometric mean of the natural logarithm of the discounting parameter (k) across reward size, and diagnostic groups.

Figure 5-9. Training score across diagnostic groups.

Figure 5-10. Proportion correct for A vs. Novel and B vs. Novel conditions across groups.

Figure 5-11: Mean proportion of hard trials across differing probability conditions for A) low reward and B) high reward trials for SZ, MDD, and HC groups.

Figure 5-12. Completion rate (breakpoint / valid clicks) across diagnostic groups

Figure 5-13. Net score (advantageous minus disadvantageous choices) across diagnostic groups.

Figure 5-14. Cumulative net score (advantageous minus disadvantageous choices) across bins, and diagnostic group.

x      

Figure 5-15. Distance travelled (in virtual environment units) across groups. Figure 5-16. Performance score (total tasks completed – omission and commission errors) across groups.

Figure 5-17. Antipsychotic group differences across facets of motivation.

Figure 5-18. Antidepressant group differences across facets of motivation.

Figure 6-1. Schematic of updated motivation framework.

 

 

 

 

xi    

List of Abbreviations

ACC

AES-C

Anterior Cingulate Cortex

Apathy Evaluation Scale- Clinician version

AES-S Apathy Evaluation Scale- Self-report

ANOVA Analysis of Variance

BACS Brief Assessment of Cognition in Schizophrenia

BARS Barnes Akathisia Rating Scale

BNA Brief Neurocognitive Assessment

BSMSS Barratt Simplified Measure of Social Status

CAMH Centre for Addiction and Mental Health

CDSS Calgary Depression Scale for Schizophrenia

CES-D Centre for Epidemiologic Studies Depression Scale

CPZ Chlorpromazine

CRRT Cued-Reinforcement Reaction Time Task

DCT

DLPFC

Dot Counting Test

Dorsolateral Prefrontal Cortex

DSM Diagnostic and Statistical Manual of Mental Disorders

DSM-IV-TR

EEfRT

DSM-IV-Text Revision

Effort Expenditure for Rewards Task

ERRT Evoked and Representational Responding Task

HC Healthy Control

HDRS Hamilton Depression Rating Scale

IAPS International Affective Picture System

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IGT Iowa Gambling Task

Kirby DD Kirby Delay Discounting Task

MANOVA Multivariate Analysis of Variance

MCT Multitasking in the City Test

MDD Major Depressive Disorder

NIMH

OFC

National Institute of Mental Health

Orbital Frontal Cortex

PSS Probabilistic Stimulus Selection Task

RDoC Research Domain Criteria

SA Schizoaffective Disorder

SANS Scale for the Assessment of Negative Symptoms

SAPS Scale for the Assessment of Positive Symptoms

SAS Simpson-Angus Scale

SCID Structured Clinical Interview for DSM-IV

SPQ Schizotypal Personality Questionnaire

SSRI Selective Serotonin Reuptake Inhibitor

SZ Schizophrenia

TEPS Temporal Experience of Pleasure Scale

TEPS-Ant Temporal Experience of Pleasure Scale-Anticipatory pleasure

TEPS-Con Temporal Experience of Pleasure Scale-Consummatory pleasure

ViPR Virtual Reality Progressive Ratio Task

 

1

Chapter 1

1. Introduction and Literature Review

1.1 Schizophrenia

1.1.1 History

Although the term “schizophrenia” has only been around for approximately 100

years, the clinical picture of the disease dates back to the 19thth century as psychiatrists began

documenting their descriptions of young patients presenting with various, progressively

deteriorating symptoms with an unknown cause. Benedict Morel was one of the earliest

physicians to use the term demence precoce (i.e. premature dementia) to describe these

patients, while Arnold Pick used the Latin term dementia praecox. Other notable

psychiatrists such as Karl Ludwig Kahlbaum, Ewald Hecker, and Thomas Clouston

described similar clinical presentations using the terms “catatonia”, “hebephrenia”, and

“adolescent insanity”, respectively (Clouston, 1904; Hecker, 1871; Jablensky, 2010;

Kahlbaum, 1863; Morel, 1860). Informed by this work, it was Emil Kraepelin who first used

the term dementia praecox (i.e. early dementia) to integrate these various clinical

descriptions and define a single nosological entity caused by the deterioration of the psychic

mind’s emotional, intellectual, and volitional processes in young adults, that was distinct

from “manic-depressive insanity” (Berrios et al., 2003; Kraepelin, 1919). He further

described the clinical presentation of the disease in terms of commonly observed symptoms

such as attention and memory deficits, sensory hallucinations (e.g. auditory, visual),

delusions (e.g. paranoia, ideas of reference), emotional dullness (e.g. indifference towards

life and others) and volitional deterioration (e.g. lack of occupation/activity). Of note,

2

Kraepelin described a ubiquitous course of illness characterized by progressive deterioration,

such that full recovery was unattainable for affected individuals. Further descriptions of this

illness came from the works of Swiss psychiatrist, Eugen Bleuler. In 1911, Bleuler first

coined the term schizophrenia (i.e. split mind), in reference to the distortions of reality

associated with the disease (Bleuler, 1950). Recognizing the heterogeneity of the disease

first noted by Kraepelin, Bleuler went on to describe a “group of schizophrenias” with

slightly different prognoses and clinical manifestations. Ensuing work by Kurt Schneider led

to the identification of 11 pathognomonic “first-rank symptoms” of schizophrenia which

included audible thoughts, voices arguing or discussing, voices commenting on the patient’s

actions, somatic passivity, thought withdrawal, thought insertion, thought broadcast, made

feelings, impulses, and acts, and delusional perceptions (Carpenter and Strauss, 1974;

Schneider, 1959). Along with the work of many other important figures, these contributions

were pivotal in laying the foundation for a century of clinical research that continues to

inform our understanding and conceptualization of schizophrenia.

 

1.1.2 Diagnosis, Epidemiology and Etiology

There are currently two established systems for classifying psychiatric illnesses: the

Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International

Classification of Diseases (ICD). The DSM-IV-TR diagnostic criteria for schizophrenia are

outlined below (American Psychiatric Association, 2000), as these were the diagnostic

criteria used in the studies that follow in this thesis. Of note, DSM-IV-TR has been recently

updated to DSM-V, with only minimal changes to the schizophrenia module. Specifically, in

3

DSM-IV only one of the five key symptoms (i.e. delusions, hallucinations, disorganized

speech, grossly disorganized behaviour, or negative symptoms) was required if bizarre

delusions, or auditory hallucinations with running commentary or conversing voices were

present. In DSM-V, however, this exception has been removed, and 2 of the 5 symptoms are

required, one of which must be delusions, hallucinations or disorganized speech. Further,

whereas in DSM-IV, negative symptoms included affective flattening, alogia, and avolition,

in DSM-V, this was revised to avolition and diminished expression which reflects the current

two-factor conceptualization of negative symptoms (American Psychiatric Association,

2013).

Table 1-1. DSM-IV-TR Diagnostic Criteria for Schizophrenia (American Psychiatric

Association, 2000).

A. Characteristic symptoms: Two (or more) of the following, each present for a

significant portion of time during a 1-month period (or less if successfully treated):

(1) delusions

(2) hallucinations

(3) disorganized speech (e.g., frequent derailment or incoherence)

(4) grossly disorganized or catatonic behavior

(5) negative symptoms, i.e., affective flattening, alogia, or avolition

Note: Only one Criterion A symptom is required if delusions are bizarre or

hallucinations consist of a voice keeping up a running commentary on the person’s

4

behavior or thoughts, or two or more voices conversing with each other.

B. Social/occupational dysfunction: For a significant portion of the time since the onset

of the disturbance, one or more major areas of functioning such as work,

interpersonal relations, or self-care are markedly below the level achieved prior to the

onset (or when the onset is in childhood or adolescence, failure to achieve expected

level of interpersonal academic, or occupational achievement).

C. Duration: Continuous signs of the disturbance persist for at least 6 months. This 6-

month period must include at least 1 month of symptoms (or less if successfully

treated) that meet Criterion A (i.e., active-phase symptoms) and may include periods

of prodromal or residual symptoms. During these prodromal or residual periods, the

signs of the disturbance may be manifested by only negative symptoms or two or

more symptoms listed in Criterion A present in an attenuated form (e.g., odd beliefs,

unusual perceptual experiences).

D. Schizoaffective and Mood Disorder exclusion: Schizoaffective Disorder and Mood

Disorder With Psychotic Features have been ruled out because either (1) no Major

Depressive, Manic, or Mixed Episodes have occurred concurrently with the active-

phase symptoms; or (2) if mood episodes have occurred concurrently with the active-

phase symptoms, their total duration has been brief relative to the duration of the

active and residual periods.

E. Substance/general medical condition exclusion: The disturbance is not due to the

direct physiological effects of a substance (e.g., a drug of abuse, a medication) or a

general medical condition.

5

F. Relationship to a Pervasive Developmental Disorder: If there is a history of Autistic

Disorder or another Pervasive Developmental Disorder, the additional diagnosis of

Schizophrenia is made only if prominent delusions or hallucinations are also present

for at least a month (or less if successfully treated).

Schizophrenia affects approximately 1% of the global population, with average

incidence rates of 15 per 100,000 per year (Saha et al., 2005; Tandon et al., 2008). Incidence

rates are higher for males, individuals living in urban areas, individuals with a history of

migration, and those with lower socio-economic status (McGrath et al., 2004; Tandon et al.,

2008). Despite its relatively low prevalence compared to other mental illnesses, the personal,

social, and economic burden of schizophrenia is among the highest, and has a tremendous

impact on the individual, their family, and society as a whole (Chong et al., 2016; Goeree et

al., 2005). The economic burden of schizophrenia can be attributed to both direct health care

costs (i.e. hospitalizations), as well as unemployment and loss of productivity due to

absenteeism and presenteeism (Chong et al., 2016). Schizophrenia is also linked to mortality

rates that are two to three times higher than in the general population which is driven by

greater premature deaths related to cardiovascular and metabolic problems, as well as

elevated suicide rates (Saha et al., 2007). Moreover, the prevalence of concurrent disorders

among individuals with schizophrenia is also higher than that of the general population.

Specifically, patients with schizophrenia are three times more likely to develop an alcohol

use disorder and five times more likely to develop a substance use disorder compared to the

general population, further exacerbating poor health outcomes and mortality rates (Kessler et

al., 1996). Psychiatric comorbidities are also common in schizophrenia patients, with

6

estimated rates of 15% for panic disorder, 29% for posttraumatic stress, 23% for obsessive

compulsive disorder, and 50% for comorbid depression (Buckley et al., 2009). In addition to

economic costs, and increased morbidity and mortality, patients often endure significant

personal suffering, distress and poor quality of life, a personal burden that often extends to

the family, as well (Knapp et al., 2004).

To date, there is no single known cause of schizophrenia. Instead, the combinations

of genetic, environmental, and neurobiological mechanisms are thought to increase the risk

of developing the disorder. Specifically, higher risks occur for individuals with a familial

history of schizophrenia, particularly those with a first-degree relative with the disorder.

Moreover, although combinations of specific genes and alleles that increase the risk for

developing schizophrenia have been identified, there is no single gene whose expression

causes the manifestation of the disorder. Additionally, environmental triggers such as stress

and trauma may increase the likelihood of developing schizophrenia, especially for those

with a genetic predisposition for the disorder. Other environmental factors include maternal

infections or stress during pregnancy, birth during winter months or in urban areas, poor

nutrition, and exposure to toxins or radiation (Tandon et al., 2008). Further, early and heavy

cannabis use has been associated with a two-fold increase in the risk of developing

schizophrenia, particularly in predisposed individuals (Arseneault et al., 2004).

One of the most enduring neurobiological explanations for the development of the

disorder is the dopamine hypothesis, which suggests that schizophrenia occurs as a result of

excess dopaminergic neurotransmission (Howes and Kapur, 2009). This hypothesis emerged

with the discovery that antipsychotic medications exert their action through selective

antagonism of dopamine D2 receptors. Advances in the field, however, led to a

7

reconceptualization of this hypothesis to account for a region-specific cortical/subcortical

dopaminergic dysfunction (Davis et al., 1991). Differential dopaminergic imbalances have

also been linked to specific symptoms of schizophrenia, with positive symptoms related to an

underlying cortical hyperdopaminergic state and negative symptoms associated with

subcortical hypodopaminergia. Building on this work, Howes and Kapur (2009) broadened

the scope of the dopamine hypothesis to highlight the importance of genetic and

environmental interactions in dopamine dysregulation (Howes and Kapur, 2009).

Neuroimaging studies have also identified structural abnormalities associated with the

disorder, such as enlarged ventricles, loss of gray matter, and reductions in temporal lobe

structures including the hippocampus, amygdala, the superior temporal gyri, the prefrontal

cortex, the thalamus, the anterior cingulate as well as white matter such as the corpus

callosum (Keshavan et al., 2008). To date, however, there have not emerged specific

neuroimaging-based abnormalities that are pathognomonic for schizophrenia.

1.1.3 Symptomatology and Course of Illness

Schizophrenia is a heterogeneous disorder characterized by prominent positive,

negative, and cognitive symptoms. It should be noted, however, that not all symptoms are

experienced by every individual, nor do they occur across all phases of the illness. For

example, for many individuals, positive symptoms begin at first episode and resolve with

treatment; however, many of the negative and cognitive symptoms predate the onset of frank

psychosis, and persist throughout the course of the illness.

8

1.1.3.1 Positive Symptoms

The positive symptoms of schizophrenia are described as an excess or distortion of

normal behavioural functions and perceptual processes. The development of frank psychotic

symptoms marks formal onset of the first-episode of the disorder (Tandon et al., 2008).

Specific examples of positive symptoms include sensory hallucinations (e.g. auditory, visual,

tactile) and delusions (e.g. delusions of reference, grandiosity, thought broadcasting).

Disorganized symptoms such as disorganized speech (e.g. derailment or loose associations)

and disorganized or inappropriate behaviour are also common in individuals with

schizophrenia, and often included within the clinical measures of positive symptoms. This

constellation of symptoms is routinely evaluated in the commonly used clinical measures of

positive and disorganized symptoms in schizophrenia, such as the Scale for the Assessment

of Positive Symptoms (SAPS) (Andreasen, 1984), the Positive and Negative Syndrome Scale

(PANSS) (Kay et al., 1987), and the Brief Psychiatric Rating Scale (BPRS) (Overall and

Gorham, 1962).

1.1.3.2 Negative symptoms

Negative symptoms of schizophrenia are described as a reduction or absence of

normal behaviour or experiences. Negative symptoms are often present in the prodromal

phase of the disorder and persist throughout the course of the illness including remission, and

cause substantial impairments to functioning in affected individuals (Foussias and

Remington, 2010; Tandon et al., 2008). Examples of negative symptoms include anhedonia

9

(i.e. lack of interest or pleasure), affective flattening, apathy or amotivation, and alogia.

Negative symptoms will be discussed in greater detail in Section 2 of this chapter.

One of the most widely used clinical measures of negative symptoms is the Scale for

the Assessment of Negative Symptoms (SANS), which is comprised of 5 subscales: affective

flattening or blunting, alogia, avolition-apathy, anhedonia-asociality, and inattention

(Andreasen, 1982). Other measures include the Positive and Negative Syndrome Scale

(PANSS), the Clinical Assessment Interview for Negative Symptoms (CAINS) (Kring et al.,

2013), the Brief Negative Symptoms Scale (BNSS)(Kirkpatrick et al., 2011) and the Brief

Psychiatric Rating Scale (BPRS) (Overall and Gorham, 1962).

1.1.3.3 Cognitive symptoms

Cognitive impairment, another common feature of schizophrenia, is typically evident

before the onset of the disorder and spans the course of the illness. Cognition has been

classified into two domains: neurocognition and social cognition. Patients with schizophrenia

demonstrate deficits in a wide range of neurocognitive domains including verbal memory,

working memory, verbal fluency and executive functioning (Heinrichs and Zakzanis, 1998).

Neurocognition is often assessed using neuropsychological tests such as the Brief

Assessment of Cognition in Schizophrenia (BACS) (Keefe et al., 2004), and more recently,

the MATRICS Consensus Cognitive Battery (MCCB) (Nuechterlein et al., 2008).

Individuals with schizophrenia also demonstrate cognitive deficits in a variety of

social domains including emotion perception, social perception, and theory of mind.

Commonly used social cognition measures in schizophrenia include the Penn Emotion

10

Recognition Task (ER-40) to assess for emotion recognition, The Awareness of Social

Inference Test (TASIT) for social perception, and the Reading the Mind in the Eyes Task

(Baron-Cohen et al., 2001) for theory of mind.

1.1.3.4 Course of Illness

Schizophrenia is a disorder characterized by several distinct phases (An Der Heiden

and Häfner, 2000; Tandon et al., 2009). During the prodromal phase of illness, patients often

present with non-psychotic symptoms such as social withdrawal, diminished motivation, and

cognitive impairments, followed by an exacerbation of positive symptoms (hallucinations,

delusions, disorganized thinking/behaviour) in the acute phase, and eventually fall into the

remission phase of the illness typically following treatment with antipsychotics, though

residual symptoms (including positive, negative, and cognitive symptoms) remain for a

sizeable number of patients (Tandon et al., 2009). Although positive symptoms are present to

varying degrees of severity across phases, negative symptoms and poor functional outcomes

persist for most individuals throughout the illness (Robinson et al., 2004).

11

Figure 1-1. Illustration of the course of schizophrenia with distinct phases, symptoms,

and level of functioning outlined (Tandon et al., 2008).

1.1.4 Outcomes

Schizophrenia has long been characterized as a disorder with poor outcomes. Dating

back to the early writings of Kraepelin, adequate long-term recovery, which he described as

the ability to “live in freedom without difficulty, and to earn their living”, was only observed

in approximately 13% of patients (Kraepelin, 1919). Over the past several decades, the

definitions for recovery and remission have varied, with criteria ranging from symptom

reduction to improvements in functional capabilities (Leucht and Lasser, 2006). One of the

challenges in developing a formal definition for remission has been identifying criteria that

conform to the heterogeneity of the disorder. The first consensus-based criteria for remission

was proposed by the Remission in Schizophrenia Working Group (RSWG), and defined as a

12

severity score of mild or less on all psychotic, negative, and disorganized symptoms for a

sustained period of 6 months (Andreasen et al., 2005). Longitudinal evaluations based on the

RSWG criteria suggest that remission is attainable for a sizeable proportion of patients, with

rates ranging between 17%-78% for first-episode patients and 16%-62% in chronic

schizophrenia (AlAqeel and Margolese, 2012). Though considerably more optimistic than

Kraepelin’s estimates, these rates are based on a definition of symptomatic remission that

does not encompass functional recovery. This is a particularly important point to consider

given the profound occupational, social, and interpersonal function impairments that persist

beyond symptom remission (Abdallah et al., 2009; Bowie et al., 2008; Green et al., 2004).

According to a World Health Organization study, for instance, only a small subset of

individuals experienced favourable social adjustment outcomes across a 13 year period, with

the majority demonstrating poor to fair outcomes (Mason et al., 1996). Thus, definitions of

remission that rely on symptom amelioration alone may not reflect the reality of the enduring

impairments experienced by individuals with schizophrenia. Indeed, longitudinal studies

examining both symptom remission and functional recovery have reported substantially

lower rates, with the majority of patients experiencing poor long-term outcomes (AlAqeel

and Margolese, 2012; Kurihara et al., 2011; Mason et al., 1996; San et al., 2007; Valencia et

al., 2015). For instance, in a study conducted by Robinson et al. (2004), only 13.7% of

patients met criteria for full recovery lasting for a period of at least 2 years (Robinson et al.,

2004), a number remarkably similar to Kraepelin’s observations in 1919. This has also been

the case in the context of the recent large Danish OPUS early psychosis intervention trial,

where only 14-17% of patients met criteria for recovery over the course of the first 10 years

of illness, with approximately 60% of patients remaining symptomatic or institutionalized

13

(Austin et al., 2013). Thus, despite significant advances in symptom treatment, recovery rates

for schizophrenia have not improved considerably over the past 100 years.

1.1.5 Treatment

Since the fortuitous discovery of chlorpromazine in the 1950s, antipsychotic

medications have represented the cornerstone of treatment for schizophrenia. Advances in

psychopharmacology have since led to the modification and improvement of older generation

drugs in an attempt to minimize the burden of side-effects. For instance, first generation or

typical antipsychotics such as haloperidol and fluphenazine are associated with high

incidence of extrapyramidal symptoms (EPS), tardive dyskinesia, and affective flattening.

Though the mechanism of action remains dopaminergic in nature, specifically through

antagonism at the D2 receptor, atypical or second generation antipsychotics such as clozapine

and olanzapine have a significantly lower risk of EPS. That being said, these newer

generation drugs have been linked to metabolic side-effects including weight gain and

diabetes (Newcomer, 2005). While both generations of antipsychotic medications have been

equally effective at treating the positive symptoms of schizophrenia (with the exception of

clozapine that has shown superiority to all other antipsychotic medications, particularly in

treatment resistant populations), they have been less successful at treating the cognitive and

negative symptoms of the illness (Jones et al., 2006; Keefe et al., 2007; Lieberman et al.,

2005; McEvoy et al., 2006).

14

1.2 Negative Symptoms

1.2.1 History

The recognition of negative symptoms dates back to the early works of Kraepelin and

Bleuler who observed a marked deterioration of volitional processes in individuals with

schizophrenia (Bleuler, 1950; Kraepelin, 1919). The term “negative symptoms”, however, as

well as its distinction from positive symptoms, was introduced in 1861 by John Russell

Reynolds in the context of epilepsy (Berrios, 1985). Building on this work, and the ideas of

Herbert Spencer regarding dissolution and evolution of the nervous system, John Hughlings

Jackson introduced this concept into the field of psychiatry, describing negative symptoms as

the dissolution of “neural arrangements”, and positive symptoms as the “release of lower

levels from higher inhibitory control” (Jackson, 1958; Pearce, 2004).

The introduction of modern psychopharmacology in the 1950s led to an increase in

emphasis on the treatment and assessment of positive symptoms. Ensuing work in the 1980s,

however, led to the revival of interest in negative symptoms with the classification of

symptom-based subtypes of schizophrenia, and more refined definitions of positive and

negative symptoms (Andreasen and Olsen, 1982; Crow, 1985, 1980). The distinctiveness of

negative symptoms was further elucidated by Carpenter et al. (1988), who differentiated

between primary, idiopathic negative symptoms from the secondary negative symptoms that

result from illness-related factors such as depression and medication side-effects (Carpenter

et al., 1988). More recently, the positive-negative symptom dichotomy has been supported by

a number of factor analytic studies. For instance, examinations of the SAPS and SANS have

revealed 3-factor structures composed of positive, negative, and disorganized symptoms

15

(Andreasen et al., 1995; Arndt et al., 1991; Grube et al., 1998), though not consistently, with

one study identifying 11 factors (Peralta and Cuesta, 1999), and others reporting a five-factor

model utilizing the PANSS (Nakaya et al., 1999; White et al., 1997). A consistent finding

across studies, however, is the distinction of negative symptoms from other symptom

domains of the disorder (Blanchard and Cohen, 2006).

1.2.2 The Negative Symptoms of Schizophrenia

Over the years, there has been considerable disagreement regarding the inclusion of

specific symptoms within the domain of negative symptoms (Fenton and McGlashan, 1992).

To this end, the National Institute of Mental Health (NIMH) released a consensus statement

in 2005, with negative symptoms characterized as affective flattening (i.e. blunted or

restricted affect), alogia (i.e. poverty of speech), avolition (i.e. apathy or amotivation),

asociality, and anhedonia (i.e. diminished interest or pleasure). Moreover, symptoms such as

attentional impairment, inappropriate affect, and poverty of content of speech, though

included in traditional measures of negative symptoms (i.e. SANS), were excluded from the

NIMH definition as they were found to be more closely related to the disorganized and

cognitive symptoms of the disorder.

The underlying structure of negative symptoms has also been examined using factor

and component analyses (Blanchard and Cohen, 2006). For instance, Keefe et al. (1992)

conducted a confirmatory factor analysis using the SANS, and found three factors:

diminished expression, social dysfunction, and disorganization (Keefe et al., 1992). Mueser

et al. (1994) also proposed a 3-factor structure consisting of diminished expression,

16

inattention-alogia, and social amotivation (Mueser et al., 1994). However, in another study

examining both medicated and non-medicated patients, Kelley et al. (1999) found a 2-factor

solution comprised of affective flattening and diminished motivation (Kelley et al., 1999), a

finding that has since been replicated by numerous studies (Kimhy et al., 2006; Malla et al.,

2002; Messinger et al., 2011; Toomey et al., 1997). Accordingly, negative symptoms are

currently conceptualized as two separate, but inter-related subdomains consisting of: 1)

diminished expression (i.e. affective flattening, poverty of speech); and 2) amotivation (i.e.

avolition/apathy and anhedonia/asociality) (American Psychiatric Association, 2013;

Foussias and Remington, 2010; Kirkpatrick et al., 2006). Moreover, the two subdomains

have been found to be differentially related to a number of clinical factors, with amotivation

more consistently linked to poorer treatment, illness, and functional outcomes (Strauss et al.,

2013). These findings have spurred interest into the specific symptoms of this subdomain,

with a particular emphasis on anhedonia and amotivation.

Anhedonia has traditionally been considered a core symptom of schizophrenia, rooted

in the findings from self-report measures such as the Chapman anhedonia scales which

suggest a reduction in the ability to experience pleasure in these individuals (Chapman and

Chapman, 1978; Horan et al., 2006a). However, the increasing concern regarding the

construct validity of these measures, coupled with emerging objective investigations of

pleasure, have called into question the true nature of the hedonic deficit in schizophrenia.

Specifically, behavioural studies examining in-the-moment experience of pleasure across a

number of sensory modalities have revealed comparable levels of pleasure between

schizophrenia and healthy participants (Heerey and Gold, 2007; Trémeau et al., 2010). In line

with the aforementioned studies, systematic reviews examining anhedonia in schizophrenia

17

have also revealed intact hedonic capacity in these individuals (Cohen and Minor, 2010;

Llerena et al., 2012). Reflecting the discrepancies between subjective and objective measures

of pleasure, the term “emotion paradox” has been used to describe anhedonia in

schizophrenia. In an attempt to reconcile this paradox, anhedonia has been re-conceptualized

as consummatory (“liking”) and anticipatory (“wanting”) pleasure. To this end, the Temporal

Experience of Pleasure Scale (TEPS) was developed to distinguish between these two

constructs. Studies utilizing the TEPS have revealed intact consummatory pleasure in

schizophrenia but reductions in anticipatory pleasure (Favrod et al., 2009; Gard et al., 2007;

Loas et al., 2009), though not consistently (Strauss et al., 2011). Taken together, these

findings suggest that anhedonia, in its strictest definition, is not a core feature of

schizophrenia. Indeed, a more recent review by Strauss and Gold (2012) has proposed that

anhedonia, as it manifests itself in schizophrenia, should no longer be defined as a reduction

in the capacity to experience pleasure, but rather as a “reduction in pleasure-seeking

activities” and “beliefs of low pleasure” (Strauss and Gold, 2012).

In light of these findings, there has been a renewed interest in examining motivation

deficits in schizophrenia. Amotivation has been consistently reported in individuals with

schizophrenia using traditional measures of negative symptoms (i.e. the SANS), as well as

the Apathy Evaluation Scale (AES) (Faerden et al., 2009; Foussias et al., 2011, 2009; Kiang

et al., 2003). Moreover, motivation deficits are highly prevalent in schizophrenia, with

estimated rates of approximately 30-50% (Evensen et al., 2012; Faerden et al., 2009). Closer

examinations into the separate facets of motivation in schizophrenia have reaffirmed these

findings, with studies revealing impairments in reward expectancy, reward valuation, reward

learning, effort valuation and goal-directed decision making (Barch and Dowd, 2010; Kring

18

and Barch, 2014). The multi-faceted motivation system will be discussed in greater detail in

section 1.3.4.

1.2.3 Treatment of Negative Symptoms

Despite significant advances in treatment, negative symptoms have remained resistant

to pharmacological interventions. While the introduction of second-generation antipsychotics

appeared to offer promising results for treating negative symptoms, large clinical trials and

meta-analyses have revealed inconsistent and modest effects, unlikely to be clinically

significant (Harvey et al., 2016; Murphy et al., 2006; Remington et al., 2016). Given the

clinical resemblance of negative symptoms and depression, the efficacy of anti-depressants

has also been examined as potential treatment adjuncts. The results from these studies have

been mixed, with one meta-analysis finding a positive effect (Helfer et al., 2016; Singh et al.,

2010), but most lacking evidence for the efficacy of anti-depressant augmentation in the

treatment of negative symptoms (Fusar-Poli et al., 2015; Remington et al., 2016; Rummel et

al., 2006; Sepehry et al., 2007). Further, stimulants, glutamatergic, and cholinergic agents

have also been evaluated as adjunct to antipsychotics, with similar modest and inconsistent

benefits (Arango et al., 2013; Buchanan et al., 2007; Singh and Singh, 2011).

Beyond pharmacological interventions, more recent investigations have explored

brain stimulation therapies for negative symptoms. Repetitive transcranial magnetic

stimulation (rTMS), for instance, has demonstrated some efficacy at high frequencies (Goyal

et al., 2007; Hajak et al., 2004; Prikryl et al., 2007; Schneider et al., 2008), though not

consistently (Holi et al., 2004; Mogg et al., 2007; Novák et al., 2006). Further, evidence from

19

a more recent multicenter randomized controlled trial revealed no significant benefit of rTMS

for negative symptoms (Wobrock et al., 2015). Additionally, the use of transcranial direct

current stimulation (tDCS) has revealed some promising findings, with a few studies

demonstrating beneficial effects (Agarwal et al., 2013; Brunelin et al., 2012; Gomes et al.,

2015).

Psychosocial strategies have also been examined as potential treatments for negative

symptoms in schizophrenia, with the focus primarily on cognitive behavioural therapy.

Though there have been some positive findings (Sensky et al., 2000), recent meta-analyses

have again revealed only modest benefits for negative symptoms (Jauhar et al., 2014; Jones

et al., 2012; Klingberg et al., 2011). Moreover, other psychosocial treatments such as family

therapy, assertive community treatment, social skills training, and cognitive remediation have

also offered minimal positive benefits beyond the primary outcomes that they were

developed to target (Bustillo et al., 2001; Pilling et al., 2002).

In the largest and most extensive meta-analysis of existing treatments for negative

symptoms to date, Fusar-Poli et al. (2015) examined the efficacy of typical and atypical

antipsychotics, antidepressants, glutamatergic medications, combinations of pharmacological

agents, brain stimulation, and psychological interventions. The results of this study revealed

only small to moderate effect sizes for the majority of these interventions, with none

demonstrating clinically meaningful improvements (Fusar-Poli et al., 2015). Taken together,

these findings highlight that negative symptoms continue to represent an unmet therapeutic

need in the treatment of schizophrenia.

20

1.2.4 Negative Symptoms and Functional Outcome

Schizophrenia has long been characterized as a disorder with poor functional

outcomes. Amongst the multitude of debilitating symptoms, negative symptoms have been

identified as one of the most reliable predictors of functional deficits, above and beyond the

influence of positive, disorganized and depressive symptoms (Fenton and McGlashan, 1991;

Ho et al., 1998; Kiang et al., 2003; Rabinowitz et al., 2012). Indeed, studies have consistently

shown a strong association between severity of negative symptoms and poor social,

community and vocational functioning, as well as low quality of life (Evensen et al., 2012;

Faerden et al., 2009; Fervaha et al., 2014a). Within the broader construct of negative

symptoms, important findings have also emerged regarding the influence of specific

subdomains on functional outcomes. In terms of diminished expression, for instance, some

studies have demonstrated a significant correlation with functioning (Gur et al., 2006; Kring

et al., 2013), however most have failed to replicate these results (Chang et al., 2017; Fervaha

et al., 2013b; Foussias et al., 2009; Green et al., 2012; Salem and Kring, 1999; Sayers et al.,

1996; Strauss et al., 2013). In contrast, the amotivation subdomain has been consistently

shown to predict poor functional outcomes in schizophrenia (Faerden et al., 2009; Foussias et

al., 2009; Konstantakopoulos et al., 2011).

Within the amotivation subdomain, anhedonia has demonstrated a significant

relationship with occupational and social functioning (Blanchard et al., 1998; Herbener et al.,

2005), though not consistently (Katsanis et al., 1992; Strauss et al., 2011). The nature of this

relation is complicated by the recent reconceptualization of anhedonia in schizophrenia as

reductions in pleasure-seeking activities, along with beliefs of low pleasure, rather than a

reduced capacity for pleasure (Strauss and Gold, 2014). Thus, it is difficult to determine the

21

extent to which the relationship between anhedonia and functioning is driven by a true

hedonic impairment.

Motivation deficits have been consistently highlighted as a core feature of

schizophrenia, especially in terms of its link to poor functioning (Fervaha et al., 2013b;

Foussias et al., 2009). Specifically, it has been posited that amotivation represents the most

reliable predictor of poor functional outcomes in first-episode and chronic schizophrenia,

both cross-sectionally and longitudinally (Faerden et al., 2009; Fervaha et al., 2014b;

Foussias et al., 2009). Indeed, studies have revealed that motivation deficits account for up to

70% of the variance in community and psychosocial functioning, with no significant

contribution from other symptoms (Foussias et al., 2009; Kiang et al., 2003;

Konstantakopoulos et al., 2011).

Neurocognition has also been examined in the context of functional outcomes, with

studies revealing significant relationships between functional disability and cognitive

impairments in domains such as verbal memory, working memory, and executive functioning

(Green, 1996; Green et al., 2004; Milev et al., 2005), though not consistently (Addington and

Addington, 1999; Foussias et al., 2009; Konstantakopoulos et al., 2011; Norman et al., 1999).

The relationship between cognition and functioning is also complicated given the overlap in

variance accounted for by negative symptoms and cognition (Milev et al., 2005), with

motivation deficits partially mediating the relationship between cognition and functioning

(Foussias et al., 2015; Gard et al., 2009; Nakagami et al., 2008; Ventura et al., 2009).

22

In summary, the existing literature on negative symptoms highlights the prevalence of

amotivation in schizophrenia, a symptom that is inextricably linked to poor functional

outcomes, all the while representing an unmet therapeutic need.

1.3 Motivation

1.3.1 Motivation – a Definition

Motivation, the ability to initiate and/or sustain goal-directed behaviour, is a multi-

faceted system, consisting of a number of interrelated reward processes. Current

conceptualizations of motivation outline a framework whereby 1) hedonic capacity (i.e.

“liking”) and 2) reward expectancy (i.e. “wanting”, established through appropriate reward

learning) converge to inform both 3) reward valuation and effort valuation (i.e. cost-benefit

analysis), followed by 4) the development and execution of an action plan to achieve the

desired outcome (Figure 1-2) (Barch and Dowd, 2010). This framework aligns well with the

construct of approach motivation outlined by the National Institute of Mental Health (NIMH)

Research Domain Criteria (RDoC) within the Positive Valence Systems, which similarly

outlines a multi-faceted construct consisting of reward valuation, effort valuation/willingness

to work, reward expectancy, action-selection/preference-based directed decision making,

initial and sustained responsiveness to reward, and reward learning (Cuthbert and Insel,

2013).

23

Figure 1-2: The multi-faceted motivation framework (Barch and Dowd, 2010).

1.3.2 Motivation Deficits across Disorders

The inclusion of motivation within the RDoC Positive Valence System highlights the

prevalence and clinical relevance of motivation deficits, and solidifies its designation as a

core aspect of psychopathology that cuts across diagnostic boundaries (Cuthbert and Insel,

2010). Indeed, motivation deficits are prevalent across a number of neuropsychiatric and

neurologic illnesses beyond schizophrenia, including major depressive disorder (MDD),

Parkinson’s disease, Alzheimer’s disease, and Huntington’s disease (Brown and Pluck, 2000;

Levy et al., 1998; Marin et al., 1991; Pluck and Brown, 2002; Robert et al., 2009; Starkstein

24

and Brockman, 2011). In the context of schizophrenia, the motivational deficits associated

with depression are perhaps the most relevant given the phenomenological symptom overlap

between negative symptoms and depression. Indeed, a major challenge in the assessment and

treatment of schizophrenia has been differentiating negative symptoms from depression

(Addington et al., 1994, 1990; Collins et al., 1996). Thus, examining motivation deficits in

major depressive disorder may serve to elucidate common underlying mechanisms driving

these impairments across disorders, and aligns well with emerging dimensional approaches to

understanding psychopathology (Cuthbert and Insel, 2010).

1.3.3 Motivation Deficits in Major Depressive Disorder

Major depressive disorder is one of the most common mental illnesses, with a lifetime

prevalence of approximately 11% in Canada (Patten et al., 2009). Moreover, it is considered

one of the leading causes of disability in the world (World Health Organization, 2017). In

addition to the personal distress and poor quality of life often experienced by affected

individuals, depression is also associated with a tremendous economic burden driven by loss

of productivity in the form of both presenteeism and absenteeism (Lerner et al., 2004; Patten

et al., 2009).

According to the DSM, there are 9 diagnostic symptoms of MDD: depressed mood;

anhedonia (i.e. loss of interest or pleasure); weight/appetite changes; insomnia/hypersomnia;

motor retardation/agitation; loss of energy; concentration impairments;

worthlessness/inappropriate guilt; and suicidality (American Psychiatric Association, 2000).

In order to meet criteria for MDD, an individual must endorse 5 out of the 9 symptoms, one

25

of which being depressed mood or anhedonia. However, the diagnostic criterion does not

distinguish between the hedonic (i.e. loss of pleasure) and motivational (i.e. loss of

interest/drive) components of anhedonia. Although anhedonia in MDD is traditionally

viewed as a reduction in pleasure, recent examinations have revealed important distinctions

between consummatory (“liking”) and anticipatory (“wanting”) pleasure, with the latter more

closely related to motivational processes (Klein, 1984). Accordingly, anhedonia has been

positioned as a facet within the broader motivational framework (Treadway and Zald, 2011).

This fits well with the results of a study examining the strength of non-diagnostic criteria in

predicting an MDD diagnosis. Specifically, with the exception of depressed mood,

amotivation (i.e. diminished drive and interest) outperformed all other existing criteria in

distinguishing MDD from non-MDD (McGlinchey et al., 2006; Zimmerman et al., 2006).

Motivation deficits have similarly emerged as determinants of poor functional outcomes in

MDD, as well as predictors of treatment and illness outcomes, above and beyond the effects

of depressed mood (Fervaha et al., 2016b; Uher et al., 2012; Wells et al., 1989). Furthermore,

in a study conducted by Lam et al. (2012), lack of motivation was endorsed by 93% of the

sample, and ranked as the symptom most interfering with work functioning (Lam et al.,

2012). Amotivation is also one of the most commonly endorsed residual symptoms of

depression (Fava et al., 2014).

26

1.3.4 The Multiple Facets of Motivation in Schizophrenia and Major Depressive

Disorder

Informed by the work of Barch and Dowd (2010), the ensuing studies presented in

this thesis focus on four major components of the reward and motivation system: hedonics or

liking, reward expectancy and wanting, cost-benefit decision making (i.e. value

representation and cost computation), and goal-directed decision making.

1.3.4.1 Hedonics

Hedonic capacity, the ability to experience pleasure in response to a positive stimulus,

has been extensively examined in both schizophrenia and major depressive disorder. The

impetus for examining hedonic capacity in schizophrenia has been grounded in the long-held

assumption that motivation deficits were driven by reductions in pleasure in these

individuals. However, this assumption has been recently challenged by studies utilizing self-

report questionnaires (i.e. TEPS), as well as objective measures of hedonic capacity which

have revealed intact in-the-moment experience of pleasure (i.e. “liking”) (Cohen and Minor,

2010; Gard et al., 2007; Kring and Moran, 2008; Llerena et al., 2012; Yan et al., 2012). In

line with the aforementioned findings, some neuroimaging studies have revealed intact

ventral striatal activation during reward receipt in patients with schizophrenia (Kirsch et al.,

2007; Mucci et al., 2015; Simon et al., 2010), though not consistently (Barch and Dowd,

2010). Moreover, studies have reported reduced activation in other relevant regions such as

the insula, orbital frontal cortex (OFC), medial frontal cortex and amygdala (Barch and

Dowd, 2010; Plailly et al., 2006; Schneider et al., 2007). Importantly, however, the evidence

27

for intact reward responsiveness in schizophrenia is supported by findings suggesting that

hedonic deficits are not mediated by dopamine hypofunction, but rather by an opiodic or

serotonergic system in the nucleus accumbens, ventral pallidum and OFC (Barch and Dowd,

2010; Berridge, 2007; Smith and Berridge, 2007, 2005).

Research on motivation deficits in depression has been primarily focused on hedonic

capacity, with most studies utilizing traditional self-report questionnaires, such as the

Chapman scales for physical and social anhedonia (Horan et al., 2006b). Though these scales

have consistently revealed a hedonic impairment in depression, they do not distinguish

between the consummatory and anticipatory aspects of anhedonia (Klein, 1984).

Unfortunately, studies utilizing the TEPS in depressed samples are limited, with a single

study suggesting deficits in both consummatory and anticipatory pleasure compared to

healthy controls (Liu et al., 2011). Moreover, behavioural examinations of reward

responsiveness have been inconsistent, with some studies revealing that MDD patients rate

positive stimuli as less pleasant and arousing than healthy controls (Berenbaum and

Oltmanns, 1992; Dunn et al., 2004), but others failing to replicate these findings (Dichter et

al., 2004; Gehricke and Shapiro, 2000; Keedwell et al., 2005). A meta-analysis conducted by

Bylsma et al. (2008) revealed a reduction in emotional responsiveness to both positive and

negative valence stimuli in MDD (Bylsma et al., 2008). Hedonic capacity has also been

examined using a signal detection paradigm, with response bias as a behavioural proxy for

responsiveness to reward. The results from these studies have revealed reduced reward

responsiveness in MDD, such that patients fail to modulate their behaviour towards the more

rewarding stimulus (Henriques et al., 1994; Pizzagalli et al., 2009, 2008). That being said,

these tasks are contingent on the participant’s ability to learn about the most rewarding

28

stimulus, and may therefore be more indicative of impairments in reward learning (Rizvi et

al., 2016). In terms of the neurobiological mechanisms underlying reward responsiveness in

MDD, functional magnetic resonance imaging (fMRI) studies have consistently shown

reduced striatal responses to rewards in depressed individuals, which was correlated with

elevated levels of anhedonia (McCabe et al., 2009; Smoski et al., 2009).

1.3.4.2 Reward Expectancy

Reward expectancy refers to the anticipation and expectation of future rewards, the

processes underlying a sense of “wanting”. To date, there have been limited behavioural

studies examining reward expectancy in schizophrenia. These studies, which have relied on

reinforcement-related speeding tasks, suggest reward anticipation deficits in SZ, such that

patients exhibit reduced speeding in response to rewarding cues compared to healthy controls

(Mann et al., 2013; Murray et al., 2008; Waltz et al., 2010), though not consistently (Roiser

et al., 2009). In another study by Heerey et al. (2007), patients with schizophrenia

demonstrated impairments in generating behaviour in response to an internal representation

of an evocative stimulus, despite intact hedonic capacity. Most research on reward

expectancy in schizophrenia has focused on neuroimaging, with evidence of reduced ventral

striatum activation in response to reward-predicting cues in both medicated and non-

medicated individuals (Barch and Dowd, 2010; Juckel et al., 2006b; Schlagenhauf et al.,

2008, 2014; Simon et al., 2015). Importantly, these reductions were correlated with severity

of negative symptoms, as well as amotivation specifically (Juckel et al., 2006b, 2006a;

Simon et al., 2010). Further, Juckel et al. (2006) revealed differential reward system

29

impairments in patients treated with typical versus atypical antipsychotics, with only the

former demonstrating impairments in reward anticipation (Juckel et al., 2006b). Of note,

there is an important learning component to reward expectancy, such that an individual must

be able to learn the appropriate cues associated with a reward in order to appropriately

predict that it will be rewarding in the future. Behavioural studies examining reward learning

in SZ have revealed impairments in rapid reward learning, but intact gradual or habitual

learning (Strauss et al., 2014; Waltz et al., 2011, 2007). Moreover, there is evidence for

specific reward-driven reinforcement learning deficits, but spared punishment-driven

learning (Gold et al., 2012; Waltz et al., 2007), though some studies have also found

impairments in learning from punishment (Fervaha et al., 2013a). Reinforcement learning

deficits are thought to be mediated by similar abnormal dopamanergic striatial responses

related to reward anticipation (Barch et al., 2016), as well as dysfunctions in the connection

between basal ganglia and orbitofrontal cortex regions of the brain (Barch and Dowd, 2010;

Frank et al., 2004; Frank and Claus, 2006).

Research on reward expectancy in major depressive disorder is limited, though there

is some evidence to suggest that patients with MDD also fail to modulate behaviour in

response to cued rewards (Pizzagalli et al., 2009). Findings from neuroimaging studies in

MDD have been inconsistent, with some studies showing reduced striatal activation in

response to anticipation cues (Forbes et al., 2009; Smoski et al., 2009; Stoy et al., 2012), but

not others (Gorka et al., 2014; Knutson et al., 2008). Reward learning has been extensively

examined in MDD, with consistent findings indicating that patients are impaired in the ability

to utilize feedback or reinforcement to guide behaviour (Henriques and Davidson, 2000;

Pizzagalli et al., 2008; Vrieze et al., 2013). Specifically, these impairments have been linked

30

to abnormal responses to negative feedback as well as a reduction in responsiveness to

reward (Eshel and Roiser, 2010). Reward learning deficits have also been extended to

dysphoric individuals (Henriques et al., 1994; Pizzagalli et al., 2005) and remitted MDD

patients (Pechtel et al., 2013).

1.3.4.3 Cost-Benefit Decision Making

Cost-benefit decision making refers to the process by which reward value (i.e. reward

representation) is integrated and weighed against the effort required (i.e. effort computations)

to pursue and achieve a desired goal.

1.3.4.3.1 Value Representation

Value representation (i.e. reward valuation) refers to the process in which a

prospective reward is appraised according to prior experiences and learning, and

subsequently assigned value. Typically, reward valuation is investigated using discounting

paradigms, which measure participants’ preferences for reward depending on the magnitude,

delay and probability of receiving the reward. On the Kirby Delay Discounting task for

instance, individuals with schizophrenia demonstrate steeper discounting rates compared to

healthy controls, particularly for larger delayed rewards (Ahn et al., 2011; Avsar et al., 2013;

Heerey et al., 2011, 2007; Weller et al., 2014; Yu et al., 2017). Evidence for impaired reward

valuation in schizophrenia has also emerged from probabilistic choice tasks, suggesting that

patients undervalue potential losses when weighting options with differing win-loss

31

probabilities (Heerey et al., 2008). Neurobiological investigations of reward valuation have

been limited in SZ, with suggested links to lateral and medial orbitofrontal cortex functions

(Barch and Dowd, 2010; Gold et al., 2008), as well as to the amygdala, striatum, midbrain,

and ventromedial prefrontal cortex (Lawrence et al., 2011).

MDD participants have also demonstrated impairments in reward valuation, showing

a preference for smaller immediate rewards over the larger and more beneficial, delayed

reward on the Kirby Delay Discounting Task (Dombrovski et al., 2012; Pulcu et al., 2014;

Takahashi et al., 2008). In contrast, a recent study utilizing a probability choice task

independent of learning requirements found that patients with MDD exhibit intact reward

valuation during decision-making (Chung et al., 2017).

1.3.4.4 Effort Computation

Effort computation refers to the process of weighing the cost and benefit of a desired

outcome, which translates into one’s willingness to exert effort. Most behavioural studies

examining effort valuation in schizophrenia have utlized an effort-based decision making

paradigm. Studies utilizing the Effort Expenditure for Rewards Task (EEfRT), for instance,

have consistently revealed reductions in SZ patients’ willingness to exert effort for high

reward-high probability conditions. Interestingly, these findings have suggested that SZ

patients do not demonstrate reductions in their overall expenditure of effort, but rather,

exhibit inefficient allocation of effort across different probability and reward conditions

(Barch et al., 2014; Fervaha et al., 2013d; Gold et al., 2013; Treadway et al., 2015). Of note,

the number of effortful task selections was correlated with severity of negative symptoms

32

(Gold et al., 2013) and amotivation (Fervaha et al., 2013d) in these studies. Effort valuation

has also been assessed using the Virtual Reality Progressive Ratio (ViPR) task, an

ecologically valid measure developed by our own group, which couples a progressive ratio

paradigm with virtual reality. Results from this task similarly reveal that patients with SZ

demonstrate a reduced willingness to work in the face of increasing effort requirements

(Foussias et al., 2013).

Research examining effort valuation in MDD using the EEfRT have also shown a

reduction in patients’ willingness to expend effort for greater monetary gains (Treadway et

al., 2012; Yang et al., 2016), although one study failed to replicate these findings (Sherdell et

al., 2012). Furthermore, a study conducted by Hershenberg et al. (2016) examining effort

using a progressive ratio task revealed reduced motivation in both unipolar and bipolar

depression (Hershenberg et al., 2016).

The neurobiological mechanisms underlying effort valuation have been given much

less attention, though there is evidence for a dopaminergic role in motivated behaviour,

particularly in the nucleus accumbens. This hypothesis is consistent with animal studies

demonstrating effort reductions in dopamine depleted rats (Hauber and Sommer, 2009;

Salamone et al., 2005, 2003). Further, the anterior cingulate cortex (ACC) has also been

implicated in the physical processing of effort-cost computations (Walton et al., 2003).

1.3.4.5 Goal-Directed Decision Making

Goal-directed decision making refers to the process of planning, and optimizing

choice selection in pursuit of a desired outcome. Decision-making has been extensively

33

examined in schizophrenia, with the majority of studies utilizing the Iowa Gambling Task

(IGT) (Bechara et al., 1994; Sevy et al., 2007). These findings have suggested impaired

decision-making, such that SZ patients make significantly more disadvantageous choices

compared to healthy controls (Beninger et al., 2003; Kester et al., 2006; Premkumar et al.,

2008; Ritter et al., 2004; Shurman et al., 2005), though not consistently (Cavallaro et al.,

2003; Evans et al., 2005; Rodríguez-Sánchez et al., 2005; Wilder et al., 1998). Furthermore, a

study examining real-world executive functioning in a natural environment revealed that

patients with schizophrenia committed more ommision and commision errors while

completing activities of daily living (Semkovska et al., 2004). Work from our group has

similarly demonstrated impairments in goal-directed decision making in SZ using the Multi-

tasking in the City Test (MCT), a goal planning and action task in a virtual environment

(Jovanovski et al., 2012; Siddiqui et al., 2015). Neuroimaging studies have consistently

linked decision-making and executive functioning to the DLPFC, with findings of reduced

DLPFC activation in patients with schizophrenia (Glahn et al., 2005; Minzenberg et al.,

2009).

Goal-directed decision making has received considerably less attention in depression

with some studies revealing impairments on the IGT (Cella et al., 2010; Must et al., 2006;

Smoski et al., 2008), but others finding intact performance (Dalgleish, 2004; Gorlyn et al.,

2013; Han et al., 2012). Interestingly, one study noted enhanced performance in MDD

patients, such that they made significantly fewer disadvantageous choices than healthy

controls. Though somewhat unexpected, these findings are consistent with the hypothesis

that depression is associated with a heightened aversion to negative feedback (Smoski et al.,

2008).

34

Chapter 2: Overview of experiments and hypotheses

This thesis contains chapters comprised of three manuscripts, each designed to

advance our understanding of motivation deficits in schizophrenia and beyond. Study 1

(Chapter 3) has been published in Psychiatry Research. Study 2 (Chapter 4) and study 3

(Chapter 5) will be submitted for publication in peer-reviewed journals. The rationale and

hypotheses for each study are outlined below.

2.1 Study 1: Investigating consummatory and anticipatory pleasure across motivation

deficits in schizophrenia and healthy controls

2.1.1 Background

Anhedonia has long been considered a core feature of schizophrenia; however this

notion has recently been challenged. Drawing from the depression literature, anhedonia has

been reconceptualized, and subsequently examined in terms of both consummatory and

anticipatory pleasure. These studies have revealed reduced anticipatory pleasure, but intact

consummatory pleasure, though this finding has not been consistent. Thus, the true nature of

anhedonia in schizophrenia remains elusive. In order to better understand this phenomenon,

the aim of the present study was to examine consummatory and anticipatory pleasure in a

large sample of schizophrenia patients and healthy controls. Further, given the role of

hedonic capacity within the larger motivation framework, we also sought to investigate

consummatory and anticipatory pleasure across a spectrum of motivation deficits.

35

2.1.2 Hypotheses

We hypothesized that patients with schizophrenia would demonstrate a reduction in

anticipatory pleasure, but intact consummatory pleasure. Further, we hypothesized that

anticipatory pleasure deficits would be related to the severity of clinical amotivation.

2.2 Study 2: An examination of the multi-faceted motivation system in healthy young

adults: the impact of schizotypal, depressive and amotivation symptoms

2.2.1 Background

Motivation is conceptualized as a complex multi-faceted system; however, the

existing literature primarily consists of individual studies examining isolated facets of

motivation in schizophrenia and depression. Moreover, our understanding of these deficits is

further complicated by inconsistent findings that have been attributed to illness-related

factors such as medication effects and cognitive dysfunction. Thus, in order to minimize the

effects of these confounds, the present study aimed to comprehensively examine the

motivation framework as it relates to schizotypy, depressive symptoms, and amotivation in a

non-clinical sample.

2.2.3 Hypotheses

We hypothesized that higher levels of amotivation would be related to poorer task

performance across all facets of motivation. We also hypothesized that individuals with

higher schizotypal traits would be impaired in reward expectancy and effort valuation,

36

whereas more severe depressive symptoms would be related to impairments in reward

responsiveness and reward valuation.

2.3 Study 3: A dimensional examination of motivation system impairments in

schizophrenia and major depressive disorder

2.2.1 Background

Amotivation is a highly prevalent symptom in both schizophrenia and major

depressive disorder, and has been linked to poor functional outcomes in affected individuals.

To this end, motivation has been recognized as a fundamental construct of human behaviour,

operationalized as a multi-faceted system comprised of hedonic capacity, reward expectancy,

reward valuation, effort valuation, and goal-directed decision making. The research to date,

however, is limited by studies focusing on discrete facets of motivation within single illness

populations, which ultimately lack the ability to delineate specific processes underlying

clinical amotivation across disorders. Thus, the present study aimed to systematically

deconstruct the multi-faceted motivation framework in schizophrenia, major depressive

disorder, and healthy control participants. In light of emerging dimensional approaches to

examining psychopathology, we also sought to identify clusters of individuals with similar

motivation deficit profiles across diagnostic categories. To our knowledge, this is the first

study to comprehensively examine the motivation framework using objective computerized

tasks concurrently in schizophrenia and major depressive disorder.

37

2.2.2 Hypotheses

We hypothesized that the motivation framework would be comprised of 5 facets:

hedonic capacity, reward expectancy and learning, reward valuation, effort valuation, and

goal-directed decision making. Further, we hypothesized that amotivation would drive

reward processing impairments, regardless of diagnostic status.

38

Chapter 3

Chapter 3: Investigating consummatory and anticipatory pleasure across motivation

deficits in schizophrenia and healthy controls

Contents of this chapter have been published as:

Da Silva S, Saperia S, Siddiqui I, Fervaha G, Agid O, Daskalakis ZJ, Ravindran A,

Voineskos AN, Konstantine KK, Remington G, Foussias G.

Investigating consummatory and anticipatory pleasure across motivation deficits in

schizophrenia and healthy controls

Psychiatry Research 254:112-117, April 2017.

Reprinted with permission from Elsevier

39

3.1 Abstract

Anhedonia has traditionally been considered a characteristic feature of schizophrenia,

but the true nature of this deficit remains elusive. This study sought to investigate

consummatory and anticipatory pleasure as it relates to motivation deficits. Eighty-four

outpatients with schizophrenia and 81 healthy controls were administered the Temporal

Experience of Pleasure Scale (TEPS), as well as a battery of clinical and cognitive

assessments. Multivariate analyses of variance were used to examine the experience of

pleasure as a function of diagnosis, and across levels of motivation deficits (i.e. low vs.

moderate. vs. high) in schizophrenia. Hierarchical regression analyses were also conducted to

evaluate the predictive value of amotivation in relation to the TEPS. There were no

significant differences between schizophrenia and healthy control groups for either

consummatory or anticipatory pleasure. Within the schizophrenia patients, only those with

high levels of amotivation were significantly impaired in consummatory and anticipatory

pleasure compared to low and moderate groups, and compared to healthy controls. Further,

our results revealed that amotivation significantly predicts both consummatory and

anticipatory pleasure, with no independent contribution of group. Utilizing study samples

with a wide range of motivation deficits and incorporating objective paradigms may provide

a more comprehensive understanding of hedonic deficits.

40

3.2 Introduction

Anhedonia, or the diminished capacity to experience pleasure, has traditionally been

considered a core negative symptom in schizophrenia that is linked to poor functional

outcomes in these individuals. The recognition of anhedonia in schizophrenia dates back to

the early works of Kraepelin and Bleuler who observed a marked emotional indifference in

this population (Bleuler, 1950; Kraepelin, 1919). Seeking to replicate this finding

quantitatively, questionnaires such as the Chapman Physical Anhedonia Scale were

developed to measure participants’ self-reported levels of pleasure (Chapman and Chapman,

1978). In line with Kraepelin and Bleuler’s observations, findings from these reports found

that patients with schizophrenia endorsed deficits in hedonic experience.

Rating one’s level of pleasure, however, does not necessarily measure one’s objective

capacity for experiencing pleasure, particularly when this involves the subjective reporting of

non-current feelings and emotional experiences (Strauss and Gold, 2012). For example, when

asked to reflect on, and rate the extent to which a stimulus or event is pleasurable,

participants with schizophrenia report significantly lower levels of pleasure compared to

healthy controls (Blanchard et al., 1998; Herbener and Harrow, 2002; Horan et al., 2006a). In

contrast, when patients are actively exposed to emotionally evocative stimuli, they report

experiencing pleasure to the same degree as healthy controls (Cohen and Minor, 2010;

Llerena et al., 2012; Yan et al., 2012). These contradictory findings have raised questions

regarding the construct validity of previous self-report ratings of anhedonia (Germans and

Kring, 2000; Leventhal et al., 2006).

41

In an attempt to further understand hedonic experience in schizophrenia, the

Temporal Experience of Pleasure Scale (TEPS) was developed (Gard et al., 2006). The TEPS

builds on previous efforts in depression to differentiate between consummatory and

anticipatory experiences of pleasure (Klein, 1984). Consummatory pleasure refers to a state

of liking or enjoying a stimulus at the moment of exposure, while anticipatory pleasure refers

to the experience of wanting a stimulus in anticipation of a future reward or pleasurable

experience (Klein, 1984). Many studies utilizing the TEPS have found comparable levels of

consummatory pleasure between schizophrenia participants and healthy controls, which is in

keeping with experiential laboratory-based findings (Chan et al., 2010; Favrod et al., 2009;

Gard et al., 2007; Heerey and Gold, 2007; Loas et al., 2009; Trémeau et al., 2010). In

contrast, these studies have found that individuals with schizophrenia experience reduced

anticipatory pleasure compared to healthy controls. However, this has not been consistent

across all studies using the TEPS, with a more recent study finding the opposite: differences

in consummatory pleasure, but intact anticipatory pleasure (Strauss et al., 2011). Thus, the

true nature of the hedonic deficit in schizophrenia remains elusive.

Hedonic impairments in schizophrenia comprise one facet of the broader construct of

motivation deficits, and figure prominently within the negative symptoms of the illness,

along with other clinical symptoms such as avolition and apathy (Foussias and Remington,

2010). Recent frameworks for characterizing the human motivation system have positioned

both consummatory pleasure (i.e., liking) and anticipatory pleasure (i.e., wanting) as key

elements that serve to inform goal-directed behaviour (Barch and Dowd, 2010). That is, if an

individual can neither appreciate or enjoy a pleasurable experience in the moment, nor

42

foresee the value of a pleasurable reward in the future, they may be less likely to pursue it,

thus contributing to the clinical manifestation of motivation deficits (Kring and Barch, 2014).

With such clinical motivation deficits being repeatedly linked to poor functional outcomes in

schizophrenia (Fervaha et al., 2014b; Fervaha et al., 2015a; Foussias et al., 2009; Green et

al., 2012; Konstantakopoulos et al., 2011), clarifying the nature and extent of hedonic

impairments in affected individuals is particularly important.

In the present study, we sought to examine consummatory and anticipatory pleasure

in a large sample of schizophrenia and healthy control participants. Given the role of hedonic

capacity within the larger motivation framework, we also sought to investigate differences in

consummatory and anticipatory pleasure across a spectrum of motivation deficits in

schizophrenia, in order to ascertain the influence of amotivation on the experience of

pleasure. We hypothesized that participants with schizophrenia would exhibit significantly

lower levels of anticipatory pleasure compared to healthy participants, but no differences in

consummatory pleasure. We also hypothesized that impaired anticipatory pleasure would be

related to more severe amotivation for patients with schizophrenia.

3.3 Methods & Materials 3.3.1 Participants

Participants recruited for this study consisted of 84 patients with schizophrenia (SZ)

and 81 healthy controls (HC) matched for age and sex. Participants in the SZ group were

between 18 and 55 years of age with a DSM-IV diagnosis of SZ or schizoaffective (SA)

disorder, determined through structured diagnostic interviews (Structured Clinical Interview

43

for DSM-IV Axis I Disorders for 57 participants; Mini International Neuropsychiatric

Interview for 108 participants) (First et al., 1997; Sheehan et al., 1998). All SZ participants

were outpatients on a stable dose of antipsychotic medication for at least four weeks; were

capable to consent to treatment; and were fluent in English. SZ participants were excluded if

they met diagnostic criteria for any other Axis I disorder; had a history of substance abuse or

dependence in the past 6 months (with the exception of nicotine); a history of neurological

disease; or were experiencing significant akathisia (global score of >2 on the Barnes

Akathisia Rating Scale) (Barnes, 1989) or extrapyramidal symptoms (>2 on >2 items of the

Simpson Angus Scale) (Simpson et al., 1970).

HC participants were excluded if they met diagnostic criteria for any Axis I disorder;

or had a family history of a psychotic disorder in a first-degree relative. Participants were

recruited through outpatient clinics and research registries at the Centre for Addiction and

Mental Health (CAMH), as well as through online advertising. The local research ethics

board approved this study, and all participants provided written informed consent.

3.3.2 Measures

To evaluate consummatory and anticipatory pleasure, all participants completed the

self-report Temporal Experience of Pleasure Scale (TEPS; Gard et al., 2006). The TEPS is

comprised of 18 items depicting in-the-moment or anticipated pleasurable experiences, for

which participants rate their agreement on a 6-point Likert scale, providing scores for the

consummatory (TEPS-Con) and anticipatory subscales (TEPS-Ant) of the TEPS. The TEPS

has demonstrated high internal consistency across studies evaluating the experience of

pleasure in patients with schizophrenia (Gard et al., 2006, Strauss et al., 2011). In addition,

44

participants were administered the Scale for the Assessment of Positive Symptoms (SAPS;

Andreasen, 1984), the Scale for the Assessment of Negative Symptoms (SANS; Andreasen,

1982), and the Apathy Evaluation Scale - Clinician Version (AES-C; Marin et al., 1991), to

evaluate the severity of positive symptoms, negative symptoms, and motivation deficits,

respectively. The SANS total score was calculated based on the sum of the items in the

Affective Flattening, Avolition-Apathy, and Anhedonia-Asociality subscales, as well as the

Poverty of Speech item (excluding Inappropriate Affect, Poverty of Content of Speech,

Blocking, and the Attention subscale and all global items) (Foussias and Remington, 2010).

Neurocognition was also assessed in all participants using the Brief Assessment of Cognition

in Schizophrenia (BACS; Keefe et al., 2004). Lastly, in a subsample of 108 participants, the

Calgary Depression Scale for Schizophrenia (CDSS; Addington et al., 1990) was also

administered to assess for depressive symptoms.

3.3.3 Analyses

Group differences in demographic and clinical variables were assessed using chi-

square tests and t-tests, as appropriate. A MANOVA with TEPS-Con and TEPS-Ant as

dependent variables, and diagnostic group (i.e. HC vs. SZ) as the independent variable was

conducted to assess for group differences in consummatory and anticipatory pleasure. In

order to explore the impact of amotivation severity on TEPS-Con and TEPS-Ant specifically

in SZ, this group was categorized according to level of amotivation severity through a tertile

split of AES scores. A second MANOVA, with TEPS-Con and TEPS-Ant as the dependent

variables and amotivation group (HCs vs. low vs. moderate vs. high amotivation SZ) as the

independent variable was then conducted to evaluate differences in TEPS-Con and TEPS-

45

Ant between healthy controls and schizophrenia patients across low, moderate, and high

levels of amotivation.

Examination of the influence of amotivation and diagnostic group (i.e. HC vs. SZ) on

TEPS-Con and TEPS-Ant was conducted through a series of hierarchical multiple

regressions. First, to determine the model of best fit, exploratory regression analyses were

conducted with both linear and quadratic AES terms as predictors. Based on these results,

subsequent hierarchical regressions were then conducted to determine the independent

predictive value of both amotivation and diagnostic group on TEPS-Con and TEPS-Ant.

In addition, Pearson correlations were calculated to determine the relationship

between TEPS-Con, TEPS-Ant and clinical measures. Cronbach’s alpha was also calculated

as a measure of internal consistency within each TEPS subscale, as well as the total scale. SZ

participants’ antipsychotic medication dosages were converted into chlorpromazine (CPZ)

equivalents (Gardner et al., 2010; Leucht et al., 2016) . All analyses were conducted with the

Statistical Package of Social Science (SPSS) version 24.

3.4 Results

3.4.1 Participant demographics and clinical characteristics

The demographic and clinical characteristics of the sample are displayed in Table 3-1.

Groups were well matched for age and sex; however SZ participants had significantly fewer

years of education compared to HCs (t(152)=3.862, p<0.001). Approximately 90% of SZ

participants were treated with a single atypical antipsychotic (i.e. monotherapy). SZ

46

participants had a wide range of illness duration (1 to 39 years), with a mean duration of

illness of 11.8 years.

Table 3-1: Sample demographics and clinical characteristics of HC and SZ participants. Healthy Controls (HC)

n=81 Schizophrenia (SZ) n=84

Age 34.3 (11.3) 35.0 (10.4)

Sex (M:F) 45:36 49:35

Education 15.4 (2.6) 13.9 (2.4)***

Years of Illness - 11.8 (8.7)

Medication -

CPZ Equivalents 531.2 (292.6)

Typical Antipsychotics 5

Atypical Antipsychotics 76

Both 3

Diagnosis (SZ:SA) - 69:15

SAPS Total - 17.3 (14.6)

SANS Total 3.1 (5.2) 18.8 (12.1)***

AES-C 28.1 (5.3) 37.2 (7.4)***

CDSS 0.7 (1.2) 2.4 (2.5)***

BACS 0.2 (1.1) -1.5 (1.2)*** Note: Data are presented as means and standard deviations. Group differences are significant

at p<0.05*, p<0.01**, p<0.001***.

(Abbreviations: SZ: Schizophrenia; SA: Schizoaffective Disorder; SAPS: Scale for the

Assessment of Positive Symptoms; SANS: Scale for the Assessment of Negative Symptoms;

AES-C: Apathy Evaluation Scale – Clinician Version; CDSS: Calgary Depression Scale for

Schizophrenia, BACS: Brief Assessment of Cognition in Schizophrenia)

47

Demographic and clinical characteristics of patients across amotivation severity

groups are displayed in Table 3-2. Low, moderate and high amotivated SZ patients did not

differ in age, years of illness, or depression but did differ in antipsychotic dosage

(F(2,83)=3.673, p=0.030), cognitive functioning (F(2,83)=3.236, p=0.044), positive

symptoms (F(2,83)=11.078, p<0.001) and negative symptoms (F(2,83)=31.409, p<0.001).

48

Table 3-2: Demographic and clinical characterization of SZ patients across levels of

amotivation severity.

Low Amotivation

SZ n=29

Moderate Amotivation SZ

n=31

High Amotivation SZ

n=24

Age 34.3 (9.7) 37.2(11.2) 32.9 (10.0)

Years of Illness 11.3 (7.0) 13.8 (10.6) 9.7 (7.5)

CPZ*a 427.0 (239.6) 625.5 (313.6) 535.3 (291.7)

SAPS Total*** c 8.9 (8.4) 18.5 (15.0) 25.8 (15.0)

SANS Total*** c 9.4 (5.1) 19.4 (10.2) 29.4 (11.4)

AES*** c 29.6 (3.4) 37.5 (2.2) 46.0 (4.6)

CDSS 1.9 (2.5) 2.8 (2.7) 2.8 (1.8)

BACS* b -1.1 (1.1) -1.6 (1.1) -1.9 (1.2)

See Table 3-1 for abbreviations. Group differences are significant at p<0.05*, p<0.01**, or

p<0.001***. In addition, a denotes significant pairwise differences where

High=Low<Moderate; b denotes High=Moderate <Low, c denotes High>Moderate>Low.

49

3.4.2 Consistency of self-report on the TEPS

Cronbach’s alpha for the TEPS total scale (SZ: α=0.87; HC: α=0.79), TEPS-Con (SZ:

α=0.74; HC: α=0.68) and TEPS-Ant (SZ: α=0.80; HC: α=0.72) subscales revealed adequate

reliability, suggestive of consistent responding in both groups.

3.4.3 Group differences in consummatory and anticipatory pleasure

The first MANOVA revealed no significant main effect of diagnosis (F(2,162)=1.889

p=0.155, η2 = 0.023) on either TEPS-Con or TEPS-Ant. The second MANOVA revealed a

significant amotivation group effect (F(6,322)=3.094, p=0.006, η2=0.055) for both TEPS-

Con (F(3,161)=5.341, p=0.002) and TEPS-Ant (F(3,161)=4.933, p=0.003) (Figure 3-1).

Post-hoc comparisons revealed significant differences in both TEPS-Con and TEPS-Ant

between the high and low amotivation SZ groups (TEPS-Con: p<0.001; TEPS-Ant:

p=0.001); between the high and moderate amotivation SZ groups (TEPS-Con: p=0.041;

TEPS-Ant: p=0.026) and between the high amotivation SZ group and HCs (TEPS-Con:

p<0.001; TEPS-Ant: p<0.001), such that TEPS-Con and TEPS-Ant were lower in the high

amotivation SZ group. There were no significant differences between the HC group, the low

amotivation SZ group, and the moderate amotivation SZ group. After Bonferroni correction

for multiple comparisons, differences between the high and moderate amotivation SZ groups

were no longer significant, while other pairwise differences remained unchanged. Controlling

for the effects of cognitive functioning did not significantly change the results. Further,

covarying for level of positive symptoms across the low, moderate and high amotivation SZ

groups did not significantly change the results.

50

Figure 3-1: Mean consummatory (TEPS-Con) and anticipatory (TEPS-Ant) pleasure

ratings for HCs and SZ patients across amotivation severity levels. Group differences

are significant at p<0.05*, or p<0.01**.

51

3.4.4 Relationships between amotivation, diagnostic group, and consummatory and

anticipatory pleasure

The results of the first set of hierarchical multiple regressions revealed that a linear

model provided the best fit for the relationship between consummatory pleasure and

amotivation (F(1,163)=19.811, p<0.001, R2=0.108) (Figure 3-2). For anticipatory pleasure,

however, a quadratic model was found to best fit the relationship with amotivation

(F(2,162)=10.526, p<0.001, R2=0.115), with the addition of the quadratic term significantly

increasing model fit compared to a linear model (F(1,162)=5.995, p=0.015, increase in

R2=0.033), primarily driven by the SZ group (F(2,81)=7.492, p=0.001, R2=0.156).

Accordingly, we utilized a quadratic model (i.e. AES and AES squared term) in subsequent

regression analyses for TEPS-Ant.

52

Figure 3-2: Scatterplots and regression lines for the significant relationships between

amotivation (AES) and A) consummatory pleasure (TEPS-Con) and B) anticipatory

pleasure (TEPS-Ant) across the entire sample. Regression lines represent a linear model

for A) TEPS-Con (y=-0.0341x + 5.6219) and a quadratic model for B) TEPS-Ant (y=-

0.0017x2 + 0.0915x + 3.4047).

53

The second set of hierarchical regressions examining the contributions of amotivation

and diagnostic group to the prediction of pleasure ratings revealed that amotivation is a

significant predictor of both consummatory (F(1,163)=19.811, p<0.001, R2=0.108) and

anticipatory pleasure (F(2,162)=10.526, p<0.001, R2=0.115), with no independent

contribution from diagnostic group.

3.4.5 Clinical correlates of consummatory and anticipatory pleasure

Across the entire sample, both TEPS-Con and TEPS-Ant were correlated with the

AES (TEPS-Con: r=-0.33, p<0.001; TEPS-Ant: r=-0.29, p<0.001) and SANS total scores

(TEPS-Con: r=-0.23, p=0.003; TEPS-Ant: r=-0.29, p<0.001). Neither TEPS-Con nor TEPS-

Ant was correlated with age, education, positive symptoms or depression. Within the SZ

group, our results revealed that TEPS-Con and TEPS-Ant were significantly correlated with

AES and SANS scores (Table 3-3). TEPS-Con and TEPS-Ant were not correlated with age,

education, duration of illness or antipsychotic dosage.

54

Table 3-3: Correlations between consummatory and anticipatory pleasure, and clinical

and cognitive measures in participants with schizophrenia.

See Table 3-1 for abbreviations. Correlations are significant at p<0.05*, p<0.01**, p<0.001***;

ns: not significant (p>0.1).

TEPS-Con TEPS-Ant

SAPS Total ns ns

SANS Total -0.25* -0.35**

AES -0.40*** -0.32**

CDSS ns ns

BACS ns ns

55

3.5 Discussion

Anhedonia has long been considered a core symptom of schizophrenia, but the nature

of this deficit is not well understood. Accordingly, this study sought to evaluate

consummatory and anticipatory pleasure as measured by the TEPS across schizophrenia and

healthy control participants.

3.5.1 Diagnosis

One of the prevailing difficulties in understanding the emotion paradox in

schizophrenia is the inconsistent results across studies using the TEPS. In this study, we did

not find a main effect of diagnosis for either consummatory or anticipatory pleasure. Though

our finding of intact consummatory pleasure between schizophrenia and healthy participants

is consistent with the current literature, our null finding of anticipatory pleasure between

groups is somewhat unexpected (Cassidy et al., 2012; Edwards et al., 2015; Favrod et al.,

2009; Gard et al., 2006; Loas et al., 2009). However, the findings of the present study are in

line with two experimental studies using objective computerized tasks to evaluate

consummatory and anticipatory pleasure, such that schizophrenia patients were shown to

exhibit similar levels of in-the-moment and anticipated pleasure compared to healthy controls

(Choi et al., 2014; Trémeau et al., 2010).

While we did not find a difference in anticipatory pleasure as a function of diagnosis,

it is important to note that mean TEPS-Ant scores in our sample were similar to those

reported by other studies (Edwards et al., 2015; Gard et al., 2007; Strauss et al., 2011), but

notably higher than that found by Gard et al. (2007) for schizophrenia patients. Closer

examination of sample differences across studies, in an effort to ascertain the contributors to

56

the lower anticipatory pleasure in Gard et al. (2007) suggest a potential role of antipsychotic

class. Specifically, there are notable differences in the proportion of patients treated with

typical versus atypical antipsychotics, with 9% of schizophrenia patients treated with typical

antipsychotics in our study, 12% in Strauss et al. (2011), and 31% in Gard et al. (2007). This

pattern has also been observed in other studies that have failed to find differences in

anticipatory pleasure (Cassidy et al., 2012; Edwards et al., 2015). Further, neuroimaging and

behavioural findings have suggested reward system dysfunctions in patients treated with

typical antipsychotics (Juckel et al., 2006b; Schlagenhauf et al., 2008). This raises the

possibility that differences in the TEPS may be subject to the same process.

3.5.2 Amotivation

In light of earlier work by Chan et al, (2010) suggesting differential ratings on

pleasure as a function of negative symptoms, we also examined the relationship between the

experience of pleasure and amotivation. We opted to investigate amotivation specifically,

because of work suggesting that motivational deficits represent the most central feature of

negative symptoms (Foussias and Remington, 2010). Further, consummatory and

anticipatory pleasure capture the hedonic components of the larger motivational framework

which serve to inform goal-directed behaviour. Our results revealed that amotivation

significantly predicted both consummatory and anticipatory pleasure across the entire

sample. Moreover, diagnostic group did not significantly contribute any independent

predictive value above and beyond the effects of amotivation, further supporting our

aforementioned absence of an effect of diagnostic group.

57

Though it is important to acknowledge that difficulties with motivation – or gradients

of them – are ubiquitous, the distribution of amotivation severity is different for patients and

healthy individuals. As expected, there is a broader range of motivation deficits in patients

with schizophrenia compared to healthy controls, hindering comparisons between the two

participant groups at the extremes. Thus, we extended our dimensional examination of

consummatory and anticipatory pleasure across levels of amotivation severity in patients

with schizophrenia only. Our results revealed a significant main effect of amotivation group,

such that schizophrenia patients with high levels of amotivation were significantly impaired

in consummatory and anticipatory pleasure compared to those at both low and moderate

levels, and compared to healthy controls. These results suggest that deficits in the experience

of pleasure are primarily driven by patients with the highest severity of amotivation. This

finding fits well with the quadratic model explaining the curvilinear relationship between

anticipatory pleasure and amotivation, such that low and moderately amotivated

schizophrenia patients cluster around the peak of the curve, with a dramatic decline towards

the more severe end of the amotivation spectrum. Moreover, these results are also in line

with the framework proposed by Strauss and Gold (2012), with reduced pleasure-seeking

behaviour – likely subserved by deficits in motivation – representing a facet of anhedonia in

schizophrenia. Taken together, these results provide further evidence for the suggested link

between impairments in discrete aspects of pleasure and broader motivation deficits (Raffard

et al., 2013). These findings may also shed further light on potential underlying causes for the

discrepant findings across previous studies using the TEPS, with categorical differences

across diagnostic groups potentially driven by different distributions of motivation deficits

between schizophrenia samples in these studies.

58

It is also interesting that schizophrenia patients with moderate levels of amotivation

reported similar levels of consummatory and anticipatory pleasure compared to both the low

amotivation schizophrenia group, and healthy controls. Though the finding of comparable

levels of consummatory and anticipatory pleasure between healthy controls and patients with

low amotivation is not entirely surprising, the findings for moderately amotivated patients are

somewhat unexpected. It is possible that a hedonic impairment does indeed exist for the

moderate group, but with a small effect size that would require a larger sample to detect.

However, another possible explanation for these results is that moderately amotivated

patients experience oscillations between intact and impaired motivation states, rather than

stable moderate impairment. During these periods of intact motivation, patients experience

both interest in, and enjoyment from goal-directed activities. The discrepancy between these

extremes may be poignant enough to lead one to believe that things can get better. Thus, the

sense of hope and optimism gained from these periods of intact motivation – though

temporary – may enable the persistence of current and expected future pleasure (Cummins

and Nistico, 2002). Though speculative, this potential mechanism warrants further

investigation.

3.5.3 Limitations

There are some limitations to this study that should also be noted. Firstly, groups

were not matched by education. Given the underlying cognitive component of emotional

self-report measures such as the TEPS, level of education may influence ratings, though it is

important to note that education was not correlated with either consummatory or anticipatory

pleasure. Further, all patients were being treated with antipsychotic medications, and while

the extent of dopamine antagonism may influence the experience of pleasure along a

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continuum, recent work has suggested this may not in fact be the case (Fervaha et al., 2016a).

We took additional steps to minimize the potential effects of antipsychotic treatment and

other sources of secondary negative symptoms in this study, through exclusion of

schizophrenia participants with clinically significant extrapyramidal symptoms. In addition,

the distribution of our sample with regards to treatment with atypical versus typical

antipsychotics, although consistent with most recent studies in this population, did not permit

a formal investigation of the effects of antipsychotic class on the experience of pleasure.

3.5.4 Conclusions

In summary, the current findings suggest that deficits in consummatory and

anticipatory pleasure are strongly linked to higher levels of amotivation, rather than merely

as a result of having a diagnosis of schizophrenia. Further exploration in patients with

schizophrenia suggests that hedonic deficits are driven specifically by those individuals at the

most severe end of the spectrum. Although we failed to replicate the main findings of the

original study by Gard et al., (2007) of a specific impairment in anticipatory pleasure in

schizophrenia, our results highlight the importance of investigating hedonic deficits as they

relate to a spectrum of amotivation, in addition to diagnostic status. Moving forward, future

studies utilizing enriched samples with a wide range of motivation deficits may offer further

clarification around the factors and processes that contribute to differences in the experience

of pleasure across subpopulations of individuals with schizophrenia. Moreover, replication of

these results is required to confirm the proposed quadratic relationship between anticipatory

pleasure and amotivation. Lastly, the incorporation of objective and experiential paradigms

in this future work may help clarify the relationship between self-reported and experienced

in-the-moment pleasure as a function of both amotivation and a diagnosis of schizophrenia.

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Chapter 4

Chapter 4: An examination of the multi-faceted motivation system in healthy young

adults: the impact of schizotypal, depressive and amotivation symptoms

4.1 Abstract

Background: Amotivation is a significant predictor of poor functional outcomes in

individuals with schizophrenia and depression. Inconsistent findings regarding the precise

reward deficits in these disorders have been attributed to confounding variables such as

medication effects, as well as cognitive impairment. Thus, investigating the motivational

framework in a non-clinical sample may further our understanding of the specific processes

underlying these deficits, while minimizing the effects of illness-specific confounds.

Methods: One hundred seventeen healthy undergraduate students were evaluated for

amotivation, schizotypal traits, depressive symptoms, and cognition, followed by objective

computerized tasks to measure the different facets of motivation. Hierarchical regressions

were conducted to determine the predictive contributions of schizotypy, depression,

cognition, and amotivation on task performance.

Outcomes: Amotivation was a significant predictor of objective in-the-moment experience of

pleasure, as well as subjective reports of both consummatory and anticipatory pleasure in a

non-clinical sample, with little to no independent contribution from schizotypal and

depressive symptoms. Further, correlational analyses revealed that both reward and effort

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valuation may serve to guide effort-cost computations.

Interpretations: We found that in a healthy undergraduate sample, amotivation predicts

reward processing impairments. Further, motivation deficits are related to a reduction in

reward responsiveness. It remains to be seen whether relationships between amotivation and

reward processing are similar or different in clinical populations with more severe

impairment.

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4.2 Introduction

Amotivation, a reduction in the ability to initiate and/or sustain goal-directed

behaviour, is a prevalent symptom in both schizophrenia and major depressive disorder, and

is inextricably linked to poor functional outcomes in these individuals (Fervaha et al., 2016,

2015; Foussias et al., 2009; Wells et al., 1989). Motivation has been conceptualized as a

multi-faceted construct comprised of: 1) hedonic experience (i.e., reward responsiveness or

“liking”); 2) reward expectancy (i.e., “wanting”); 3) reward valuation; 4) effort valuation;

and 5) goal-directed decision-making (Barch and Dowd, 2010). Accordingly, impairments in

any one of these facets can contribute to the manifestation of clinical amotivation. With

schizotypal and depressive traits in non-clinical populations positioned on continua that

extend to their respective clinical populations (Kendler and Gardner, 1998; Linscott and van

Os, 2013; van Os et al., 2000), investigation of the multi-faceted motivation system in non-

clinical samples may afford insights into specific processes that contribute to clinical

amotivation seen in schizophrenia and depression, while minimizing illness-specific

confounds. To our knowledge, no study to date has concurrently examined the multiple

facets that comprise the motivation system in a population of healthy young adults.

To date, most studies examining illness-related motivation deficits have utilized

separate clinical samples of individuals with schizophrenia or depression. Evidence from

imaging and behavioural studies suggests that patients with schizophrenia have impaired

reward valuation, reward expectancy, effort valuation, and goal-directed decision making,

but intact hedonic capacity (Barch et al., 2016; Barch and Dowd, 2010; Kring and Barch,

2014). In contrast, patients with depression are impaired in reward responsiveness, effort

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valuation, and goal-directed decision-making (Barch et al., 2016; Treadway and Zald, 2011).

However, these findings have been inconsistent across studies (Barch et al., 2016), with

discrepancies attributed to differences in cognitive functioning (i.e. memory, recall),

antipsychotic class (i.e. typical vs. atypical) and symptom severity (i.e. anhedonia, negative

symptoms, amotivation) across clinical samples (Beninger et al., 2003; Cassidy et al., 2012;

Gold et al., 2015; Schlagenhauf et al., 2008; Strauss and Gold, 2014). Studies examining

these constructs in non-clinical samples have been limited, with the majority of the existing

literature focusing on isolated facets – particularly anhedonia – in separate subclinical

depressive or schizotypal samples (Chan et al., 2012; Liu et al., 2011; McCarthy et al., 2015;

Yan et al., 2011).

The present study sought to examine the impact of amotivation, schizotypal, and

subsyndromal depressive symptoms on specific motivation system constructs in a non-

clinical sample using objective computerized psychometric tasks. We hypothesized that

poorer performance across tasks would be correlated with more severe amotivation. Further,

we hypothesized that higher schizotypal traits would be linked to deficits in reward

expectancy and effort valuation, with more severe depressive symptoms related to

impairments in reward responsiveness and reward valuation.

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4.3 Materials & Methods

4.3.1 Participants

Participants in this study consisted of 117 undergraduate students at the University of

Toronto Scarborough, who were recruited through an online experiment registry for students

in undergraduate psychology courses, and voluntarily participated for course credit.

Participants were between the ages of 17-38, fluent in English, and capable of providing

informed consent. Participants were excluded if they were taking any psychotropic

medications, diagnosed with a current or past schizophrenia-spectrum illness, had a history of

substance abuse or dependence within the past six months, a history of neurological disease,

or a history of head trauma with loss of consciousness for more than 30 minutes. The local

research ethics board approved this study, and all participants provided written informed

consent.

4.3.2 Measures

4.3.2.1 Self-report Measures

Participants completed a battery of self-report measures of amotivation, hedonic

capacity, schizotypy, and depressive symptoms. Specifically, amotivation was assessed using

the self-report version of the Apathy Evaluation Scale (AES-S; Marin et al., 1991). The

Temporal Experience of Pleasure Scale (TEPS; Gard et al., 2006), an 18-item self-report

questionnaire evaluated consummatory (TEPS-Con) and anticipatory (TEPS-Ant) pleasure.

The Likert-scale version of the Schizotypal Personality Questionnaire (SPQ; Raine, 1991)

was administered to assess for schizotypal traits, from which positive (SPQ-Pos), negative

65

(SPQ-Neg) and disorganized (SPQ-Dis) schizotypy subscores were also calculated.

Depressive symptoms were assessed using the Centre for Epidemiologic Studies –

Depression Scale (CES-D; Radloff, 1977). In addition, global cognitive functioning was

assessed using the Brief Neurocognitive Assessment, consisting of tests of working memory

and processing speed (Fervaha et al., 2014). Lastly, the Dot Counting Test (DCT) was

administered to assess for performance validity (Boone et al., 2002).

4.3.2.2 Objective Computerized Measures

A series of objective computerized psychometric tasks were subsequently

administered to measure each facet of the motivation system. Task administration was

randomized to minimize order effects.

Reward Responsiveness

The International Affective Picture System (IAPS) was used to assess for reward

responsiveness or “liking”, with in-the-moment ratings of pleasantness (IAPS-Pleasant) and

arousal (IAPS-Arousal) on a 9-point Likert scale (Heerey and Gold, 2007). Given our interest

in reward-driven processes we focused our analyses on positive-valence images only, with

higher ratings reflecting greater capacity to experience pleasure in response to positive

stimuli.

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Reward Expectancy

The Cued Reinforcement Reaction Time (CRRT) task was used to assess for reward

expectancy or “wanting” (Cools et al., 2005). Briefly, the CRRT requires rapid “odd-one-

out” judgments involving differentially reinforced cues, such that participants are rewarded

most points when responding fast to high probability reinforcement trials. The variable of

interest is the degree of anticipated reinforcement-related speeding (CRRT-RRS), calculated

by subtracting the median reaction time (RT) for low probability trials from the median RT

for the high probability trials (smaller values indicating greater RRS).

Reward Valuation

Reward valuation was assessed using the Kirby Delay Discounting (Kirby DD) task,

where respondents choose between a small immediate reward or a larger reward delayed over

a varying number of days, and thus measures participants’ differential valuation of monetary

rewards over time (Kirby and Maraković, 1996). The outcome of interest is the rate at which

participants discount the value of future rewards, calculated as the natural logarithm (ln) of

the discounting rate (k) when the delayed reward is small (Kirby-Small), medium (Kirby-

Med), or large (Kirby-Large). Smaller values are indicative of lower discounting rates, or less

impulsivity.

Effort Valuation

Effort valuation was assessed using the Effort Expenditure for Rewards Task

(EEfRT), which measures participants’ willingness to expend effort for monetary gains

(Treadway et al., 2009). Here, participants choose between two effortful tasks according to

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difficulty (easy vs. hard), across trials with differing reward magnitudes (i.e. ranging from

$1.00-$4.85) and reward likelihood (i.e. 12%, 50%, 88%). Easy and hard tasks differed in

effort requirement as determined by number of button presses (i.e. 30 vs. 100). The variables

of interest are total number of hard tasks chosen (EEfRT-Total) and EEfRT-Net, which is

calculated as the number of hard tasks chosen at the high probability level minus the number

of hard tasks chosen at the low probability condition.

Goal-Directed Decision-Making

Lastly, goal-directed decision-making was assessed using the Multitasking in the City

Test (MCT; Jovanovski et al., 2012), a virtual reality task in which participants are asked to

use a joystick to navigate and complete a number of errands in a virtual city (e.g. buy

groceries, attend a doctor’s appointment, etc.). The outcome variables are the total distance

travelled (MCT-Distance; in virtual environment units - VEUs) and performance score

(MCT-Performance), calculated as the total number of tasks completed minus total omission

and commission errors (i.e. tasks failed or repeated, respectively). Shorter distances and

higher performance scores indicate better decision-making.

All self-report scales and objective motivation tasks, except for the Multitasking in

the City Task (MCT), were programmed and administered on Open Sesame v. 2.9.4 (Mathôt

et al., 2012).

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4.4. Analyses

4.4.1 Effort validity

The DCT was used to assess for performance validity. Participants with a score

greater than or equal to 14 (n=12), a well-validated cut-off score for a healthy undergraduate

sample (An et al., 2012; Boone et al., 2002), as well as those with missing DCT scores

(n=16) were deemed invalid. The analyses were first conducted including all participants in

an effort to conserve statistical power (results shown). To ensure validity, however, these

analyses were repeated after excluding participants who exerted non-credible test findings

(not reported unless different from results of entire sample).

4.4.2 Statistical Analyses

Primary analyses consisted of hierarchical multivariate regressions to assess for the

predictive value of illness-related symptoms on the multiple facets of motivation. In the first

regression, CES-D and SPQ total scores were entered into the first step, followed by AES in

the second step. A second regression model was repeated with BNA scores entered as an

additional variable in the first step to evaluate possible confounding effects of cognition,

followed by AES in the second step. To further explore bivariate relationships between

motivation tasks and clinical measures, Spearman correlations were conducted due to non-

normal variable distributions. The false discovery rate (FDR) correction was utilized to

account for multiple comparisons, with q values less than 0.05 considered statistically

significant (Storey and Tibshirani, 2003). In addition, Cronbach’s alpha was calculated for

each self-report measure to ensure consistent responding across the sample. All analyses

were conducted with the Statistical Package of Social Science (SPSS) version 24.

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4.5 Results

4.5.1 Demographics and clinical characterization

Sample demographics and symptom characteristics are shown in Table 4-1.

Participants in this sample had a mean age of 19.9 years, with an average of 13.5 years of

education.

70

Table 4-1: Sample demographics and symptom characterization. Mean (SD)

n=117 Range

Sex (M:F) 45:72 -

Age 19.9 (2.9) 17.0-38.0

Education (yrs) 13.5 (1.4) 11.0-18.0

TEPS-Con 4.6 (0.8) 2.5-6.0

TEPS-Ant 4.6 (0.7) 2.3-5.9

AES-S 30.4 (5.7) 20.0-49.0

CES-D 16.3 (10.3) 0.0-48.0

SPQ Total 117.8 (34.5) 14.0-207.0

SPQ Positive 48.0 (18.2) 0.0-99.0

SPQ Negative 42.9 (16.5) 1.0-81.0

SPQ Disorganization 26.9 (11.3) 0.0-52.0

BNA Global -.19 (0.8) -2.1-2.1

(Abbreviations: TEPS-Con: Temporal Experience of Pleasure-Consummatory subscale;

TEPS-Ant: Temporal Experience of Pleasure-Anticipatory subscale; AES-S: Apathy

Evaluation Scale –Self-report version; CES-D: Centre for Epidemiologic Studies- Depression

Scale; SPQ: Schizotypal Personality Questionnaire; BNA: Brief Neurocognitive

Assessment).

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4.5.2 Consistency of self-report clinical measures

Cronbach’s alpha for the TEPS (TEPS-Con: α=.63; TEPS-Ant: α=.73), AES (α=.78),

CES-D (α=.90) and SPQ (SPQ-Pos: α=.92; SPQ-Neg; α=.91; SPQ-Dis: α=.91) revealed

adequate reliability across all scales, suggestive of consistent responding amongst

participants.

4.5.3 Hierarchical Regressions

The first hierarchical multivariate regression revealed a significant main effect of

CES-D (F(9,90)=2.081, p=.039), specifically in predicting TEPS-Ant (β=-.025, p<.001), as

well as IAPS pleasantness ratings (β=-.020, p=.050) and a trend for TEPS-Con (β=-.014,

p=.059), with no independent contribution from SPQ scores (p=.17) in the first step, though

this main effect of CES-D did not remain significant after excluding invalid responders

(CES-D: p=.10). The addition of amotivation in the second step was significant

(F(9,89)=2.746, p=.007), with AES as a significant predictor of IAPS pleasantness ratings

(β=-.059, p=.001), TEPS-Con (β=-.035, p=.015), and TEPS-Ant (β=-.051, p<.001).

Moreover, after including AES in the model, the effect of CES-D was no longer significant

(p=.31). The second hierarchical regression revealed no significant predictive value of

depression, schizotypy or cognition in the first step. However, the addition of AES into the

second step revealed that both amotivation (F(9,71)=3.103, p=.003) and cognition

(F(9,71)=2.053, p=.046) independently predicted overall motivation task performance,

though the effect of cognition was not significant after excluding invalid responders (p=.16).

In the final model, amotivation significantly predicted IAPS pleasantness ratings (β=-.067,

p=.001), TEPS-Con (β=-.052, p=.003) and TEPS-Ant (β=-.062, p<.001).

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4.5.4 Correlational analyses

Bivariate correlations between clinical measures and motivation task performance are

shown in Table 4-2. Interestingly, the AES was only correlated with IAPS ratings for

pleasantness and arousal. Further, IAPS pleasantness ratings were correlated with both

TEPS-Con and TEPS-Ant, whereas IAPS arousal ratings were only correlated with TEPS-

Ant. Negative schizotypal traits were negatively correlated with IAPS pleasantness and

arousal ratings, but positively correlated with CRRT and EEfRT task performance. Lastly,

cognitive functioning was correlated with the EEfRT task.

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Table 4-2: Bivariate correlations between clinical measures and motivation tasks.

AES CES SPQ-Total

SPQ-Pos

SPQ-Neg

SPQ-Dis

TEPS Con

TEPS Ant BNA

IAPS-Pleasant

-.33**a -.20* - - -.29**a - .34**a .42**a -

IAPS-Arousal

-.24** - - - -.35**a - - .30**a -

CRRT-RRS - - - - - - - - -

Kirby-Small - - - - - - - - -

Kirby-Med - - - - - - - - -

Kirby-Large - - - - - - - - -

EEfRT-Total - - - - - - - - -

EEfRT-Net .21* .20* .32**a .23* .27** .23* - - .22*

MCT-Distance

- - - - - - -.19* - -

MCT-Performance

- - - - - - - - -

Note: Correlations are significant at p <0.05*, or p <0.01**; a denotes q <0.05.

(See table 4-1 for abbreviations; in addition, IAPS: International Affective Picture System;

CRRT-RRS: Cued-Reinforcement Reaction Time Task-Reinforcement Related Speeding;

EEfRT: Effort Expenditure for Rewards Task; MCT: Multitasking in the City Test).

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4.5.5 Inter-task correlations

Correlations were also conducted to examine interrelationships between motivation

task variables. As shown in Table 4-3, Kirby rates, specifically for small delayed rewards,

were negatively correlated with total number of hard choices on the EEfRT.

Table 4-3: Inter-task correlations. 2 3 4 5 6 7 8 9 10

1. IAPS-Pleasant

.44**a - - - - - - - -

2.IAPS-Arousal

- - - - - - - - -

3. CRRT-RRS - - - - - - - -

4. Kirby-Small - .85**a .79**a -.23* -.21* - -

5. Kirby-Med - .85**a - - - -

6. Kirby-Large - - - - -

7. EEfRT-Total

- .37**a - -

8. EEfRT-Net - - .30**a

9. MCT-Distance

- -.46**a

10. MCT-Performance

-

Note: Correlations are significant at p <0.05*, or p <0.01**;a denotes q <0.05. See table 4-2

for abbreviations.

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4.6 Discussion

The present study aimed to ascertain the impact of subclinical levels of schizotypy

and depression, amotivation and cognitive functioning on the multiple facets of the

motivation system. Our regression models revealed that only amotivation emerged as a

significant predictor of objective performance across motivation system tasks. Schizotypal

traits, depressive symptoms, and cognitive performance, particularly after excluding non-

credible participants, did not offer any additional predictive value. Further, amotivation

appeared to specifically predict pleasantness and arousal ratings on the IAPS, as well as self-

reported consummatory and anticipatory pleasure on the TEPS. These results are congruent

with studies suggesting a relationship between anhedonia and amotivation (Raffard et al.,

2013). Indeed, recent work by our own group demonstrated that hedonic deficits are

primarily driven by higher severity of amotivation across a sample of healthy controls and

schizophrenia patients, irrespective of diagnosis (Da Silva et al., 2017). The present results

extend this relationship of amotivation with both current and non-current pleasure in a non-

clinical sample. Taken together, our findings suggest that reduced reward responsiveness

may be the driving force underlying amotivation in a healthy undergraduate sample. That is,

if an individual cannot enjoy a pleasurable experience in the moment, they will be less likely

to pursue it, thus contributing to motivational deficits.

Drawing from the schizophrenia and depression literature where motivation deficits

have been repeatedly linked to impairments across a number of reward-system processes, we

also hypothesized that higher levels of amotivation would be related to poorer performance

on reward expectancy, reward valuation, effort valuation, and goal-directed decision-making

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tasks. Contrary to our hypotheses, however, amotivation did not predict performance on any

of these tasks. Though surprising, the absence of these expected relationships may be a result

of the lower severity of amotivation in a non-clinical sample, which may not translate into

impairments across all facets of the motivation system.

Based on studies in patients with schizophrenia, we hypothesized that participants

with higher levels of subclinical schizotypal traits would demonstrate impaired reward

expectancy and effort valuation. In our regression models, schizotypal traits were not

predictive of motivation task performance. Correlational analyses, however, did reveal a

significant inverse relationship between negative schizotypal traits and reward

responsiveness, such that higher negative schizotypy was associated with lower IAPS ratings

for both pleasantness and arousal. These findings are in line with other studies that have

demonstrated a relationship between overall negative symptoms and pleasure in non-clinical

samples (Chan et al., 2012; Fervaha et al., 2015c; Gard et al., 2006).

The limited number of published studies examining other facets of motivation in

schizotypal populations makes comparisons with our findings challenging. The only study to

our knowledge investigating effort valuation (EEfRT task performance) in a non-clinical

sample found that individuals with elevated social anhedonia (who also reported elevated

negative schizotypal traits) demonstrated increased effort choices particularly in medium

probability trials (McCarthy et al., 2015). Our finding that higher schizotypal traits were

significantly correlated with more effortful choices on the EEfRT are somewhat consistent

with these previous findings, although in our sample this appeared to be driven by increased

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effort choices at the high probability level. Another study using a probabilistic reward task

revealed a trend towards elevated response bias, suggestive of increased propensity towards

rewarding stimuli, in individuals with high schizotypy versus those with low schizotypy (Yan

et al., 2011). While our findings may suggest better performance, taken together they may

also reflect alterations in reward-based decision-making that could be connected to impaired

salience attribution that has been posited to occur in schizophrenia (Kapur, 2003). Here,

however, salience aberrations at the subclinical range of the spectrum may result in

paradoxical increases in reward and effort-based decision-making.

In the current study, we also hypothesized that participants greater in subclinical

depression would demonstrate deficient reward responsiveness and reward valuation, in

keeping with findings in MDD. Our results partially supported these hypotheses, such that

depressive symptoms significantly predicted self-reported anticipatory pleasure scores on the

TEPS. These results align with other studies finding a reduction in anticipatory pleasure in

individuals with subsyndromal depression (Yang et al., 2014). Further, a study by Liu et al.

(2011) found differential reward processing impairments between individuals with

subclinical and clinical depression. Specifically, impaired performance on a probabilistic

reward learning task was driven by deficits in anticipatory pleasure for individuals with

subclinical depression, whereas for clinically depressed patients, this deficit was related to

reductions in consummatory pleasure (Liu et al., 2011). Thus, it may be that deficits in

consummatory pleasure emerge with the more severe depressive symptoms accompanying a

formal diagnosis of major depressive disorder.

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Correlational analyses between objective motivational measures were conducted to

explore inter-task relationships in order to further our understanding of the underlying

mechanisms guiding performance on these tasks. Of note, discounting rates for small delayed

rewards was related to effort valuation on the EEfRT. This finding supports the

conceptualization that both reward and effort valuation serve to inform overall cost-benefit

analyses in the context of rewards and goals. Further, our results revealed that both

consummatory and anticipatory pleasure were correlated with IAPS pleasantness ratings for

positive images, but only anticipatory pleasure was correlated with arousal ratings. Thus, the

capacity for emotional arousal in the context of pleasant stimuli, in addition to in-the-moment

feelings of pleasure, may promote the anticipation of future pleasurable events. Taken

together, these findings lend support to the construct validity of the TEPS and its

differentiation of in-the-moment versus anticipated pleasure. Further, the limited inter-

correlations across other tasks suggest they each tap into different facets of the motivation

system.

There are a few limitations to this study that warrant mention. First, our study sample

consisted entirely of relatively young university students limiting the generalizability of these

results to older populations. In addition, although participants were excluded if they reported

a history of a schizophrenia-spectrum disorder, this was not confirmed with a structured

diagnostic interview. Further, although we utilized objective measures of the motivation

system, and administered the DCT in order to minimize non-credible responding secondary

to poor effort, the subjective nature of symptom test measures serve as another limitation of

this study. Finally, the comprehensive evaluation of discrete facets that comprise the

motivational system would benefit from inclusion of multiple reward-based tasks believed to

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tap into overlapping constructs, although to our knowledge the present investigation

represents the most extensive to date.

The findings of the present study suggest that objective impairments across facets of

the motivation system are driven primarily by variations in amotivation, rather than by

schizotypal or subclinical depressive symptoms more broadly. Moreover, amotivation in this

non-clinical sample appears to be driven by deficits in subjective and objective reward

responsiveness. The absence of impairments in other facets of motivation, however, may be a

reflection of the lower overall amotivation in this sample, with more widespread deficits

emerging in clinical populations that experience more severe amotivation. Overall, our

findings align with recent dimensional approaches to understanding core domains of

psychopathology, and highlight the need for continued comprehensive evaluations of the

motivation system across traditional diagnostic categories.

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Chapter 5

Chapter 5: A dimensional examination of motivation system impairments in

schizophrenia and major depressive disorder

5.1 Abstract

Importance: Motivation deficits are prevalent in schizophrenia and major depressive

disorder. Often associated with poor functional outcomes, their alleviation remains an unmet

therapeutic need. In spite of their transdiagnostic presence, there have been limited

dimensional investigations of motivation system impairments across traditional diagnostic

boundaries.

Objective: To systematically examine the multi-faceted motivation system in schizophrenia,

major depressive disorder, and healthy participants using objective computerized measures.

Design, Setting, and Participants: Cross-sectional study conducted at the Centre for

Addiction and Mental Health in Toronto, Canada. The study sample consisted of 39 patients

with schizophrenia, 38 patients with major depressive disorder, and 39 healthy controls.

Interventions: Clinical amotivation was evaluated using the Apathy Evaluation Scale,

followed by an extensive battery of objective measures of discrete facets of the motivation

system.

Main Outcomes and Measures: Principal components analysis was conducted to examine the

factor structure of motivation task performance, and multivariate hierarchical regression to

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evaluate the roles of clinical amotivation on objective performance across facets of

motivation. Subsequently, K-means clustering was used to identify subgroups of individuals

with similar motivation profiles across the entire sample, with multivariate analyses of

variance to investigate differences across facets of motivation and clinical measures between

clusters.

Results: Principal components analysis revealed five motivation system factors representing:

hedonic capacity; reward expectancy and learning; cost-benefit decision-making; goal-

directed decision-making; and effort expenditure. Hierarchical regression revealed a

significant effect of clinical amotivation (p<.001). Cluster analysis revealed 2 clusters of

motivation performance, with cluster 1 characterized by lower hedonic capacity, and cluster

2 performing worse in cost-benefit and goal-directed decision-making, and effort

expenditure. Each diagnostic group was represented in both clusters, though with

significantly different distributions between clusters.

Conclusions and Relevance: Across SZ, MDD, and HC participants, this dimensional

investigation revealed five underlying facets of the motivation system. Further, two clusters

of motivation system profiles were found, but with diagnostic overlap highlighting the

heterogeneity of clinical amotivation. These profiles of motivation system impairment may

represent unique underlying patterns of neurobiological impairment, and may afford

opportunities for novel treatments that address these deficits across diagnostic boundaries.

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5.2 Introduction

Motivation deficits, or more specifically, the diminished ability to initiate and/or

sustain goal-directed behaviour, are prevalent in schizophrenia (SZ) and major depressive

disorder (MDD), and have been linked to poor functional outcomes in affected individuals.

Amotivation has been shown to be the single most reliable predictor of functional

impairments in schizophrenia (Fervaha et al., 2015a; Foussias et al., 2009; Kiang et al., 2003;

Konstantakopoulos et al., 2011). Similarly, emerging work in major depressive disorder has

also posited that the loss of motivation (which includes anhedonia) drives poor functional

and treatment outcomes in these individuals (Fervaha et al., 2016b; Wells et al., 1989).

The importance of this transdiagnostic symptom has been recognized by the NIMH

Research Domain Criteria (RDoC), which identifies approach motivation as a fundamental

construct within the Positive Valence system. Within this broader construct, separate facets

of motivation are articulated, including reward valuation, effort valuation, reward expectancy

and action-selection/preference-based decision-making (Cuthbert and Insel, 2013). This

definition aligns with the multi-faceted motivation framework proposed by Barch and Dowd

(2010) whereby 1) hedonic capacity (i.e., “liking”) and 2) reward prediction (i.e., “wanting”,

established through appropriate reward learning) converge to inform both 3) reward

valuation and effort valuation (associated with a cost-benefit analysis), followed by the 4)

development and implementation of an action plan to achieve the desired outcome.

To date, most studies examining motivation deficits have focused on isolated, discrete

facets of motivation, often within a single neuropsychiatric disorder (Barch et al., 2016; Der-

Avakian et al., 2015; Kring and Barch, 2014). While providing incremental and valuable

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knowledge on the broader motivational deficits associated with these illnesses, such studies

were not able to comprehensively examine the specific underlying mechanisms driving such

impairments. Moreover, examining these facets across diagnostic boundaries and varying

severity of motivational deficits may enable more precise characterizations of clinical

amotivation, and aligns well with emerging dimensional approaches to understanding

psychopathology.

Accordingly, the aim of this investigation is to objectively and comprehensively

evaluate the facets of the motivational framework in a sample of SZ, MDD, and healthy

participants. Further, we sought to identify subgroups of participants with similar motivation

deficits across diagnostic groups. We hypothesized that the motivational framework would

be comprised of 5 distinct facets: hedonic capacity, reward expectancy and learning, reward

valuation, effort valuation and goal-directed decision making.

5.3 Materials & Methods

5.3.1 Participants

Participants recruited for this study consisted of 51 patients with SZ, 43 patients with

MDD patients and 51 healthy controls (HC) between the ages of 18 and 55, matched for age,

sex, and parental education as measured by the Barratt Simplified Measure of Social Status

(BSMSS) (Barratt, 2006). Participants in the SZ and MDD groups met criteria for a DSM-IV

diagnosis of SZ or schizoaffective (SA) disorder, or current MDD, respectively, as

determined through the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID;

First et al., 1997). All SZ and MDD participants were stable outpatients with no changes in

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treatment for at least 4 weeks. Patients were excluded if they: met diagnostic criteria for any

other Axis I disorder (with the exception of an anxiety disorders for MDD patients); had a

history of substance abuse or dependence in the past 6 months (with the exception of

nicotine); a history of neurological disease; or were experiencing significant akathisia (global

score of >2 on the Barnes Akathisia Rating Scale) (BARS; Barnes, 1989) or extrapyramidal

symptoms (>2 on >2 items of the Simpson Angus Scale) (SAS; Simpson et al., 1970). HC

participants were excluded if they met diagnostic criteria for any Axis I disorder, were taking

psychotropic medication, or had a family history of a psychotic or mood disorder in a first-

degree relative. The local research ethics board approved this study, and all participants

provided written informed consent.

5.3.2 Clinical Assessments

Participants were administered a battery of clinical assessments including the Scale

for the Assessment of Negative Symptoms (SANS; Andreasen, 1982), the Hamilton

Depression Rating Scale (HDRS; Hamilton, 1960), the Temporal Experience of Pleasure

Scale (TEPS; Gard et al., 2006) , as well as the Scale for the Assessment of Positive

Symptoms (SAPS; Andreason, 1984) for SZ patients only. We specifically utilized the

Apathy Evaluation Scale (AES; Marin et al., 1991) as our measure of clinical amotivation as

it captures the behavioural, cognitive and emotional aspects of motivation, and does not rely

solely on behavioural proxies. All participants were administered the Barratt Simplified

Measure of Social Status (BSMSS; Barratt, 2006) , the BARS, and the SAS, Lastly, the Brief

Assessment of Cognition of Schizophrenia (BACS; Keefe et al., 2004) was administered to

assess for cognitive functioning.

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5.3.4 Objective measures of motivation

A battery of objective computerized tasks was also administered to assess for each

facet of the motivation framework. Task administration was counterbalanced to minimize

any order effects. Further, participants were informed that all monetary rewards were

hypothetical, but were instructed to perform as they would of the reward was real. All

computerized tasks were programmed and administered on Open Sesame v. 2.9.4 (Mathôt et

al., 2012), with the exception of the ViPR and MCT which were programmed in a virtual

reality rendering engine. Specific tasks are outlined below, with detailed methodology for

each task provided in the Supplementary Methods.

Hedonic Capacity

The Evoked and Representational Responding Task (ERRT; Heerey and Gold, 2007),

which incorporates images from the International Affective Picture System (IAPS; Lang et

al., 1993) was administered to capture the “liking” and “wanting” components of hedonic

experience. Specifically, the variables of interest for “liking” were pleasantness (IAPS-

Pleasantness) and arousal (IAPS-Arousal) ratings for positive images, and click rate for

positive images in the evoked condition (ERRT-Rate) for “wanting”. The ERRT has been

used in two schizophrenia studies (Heerey and Gold, 2007; Lui et al., 2016), but to our

knowledge has not been examined in the context of depression research. The IAPS, however,

has been used extensively across many neuropsychiatric disorders, and is considered to be

one of the most valid and reliable measures of in-the-moment pleasure (Jayaro et al., 2008).

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Reward Expectancy and Learning

Reward expectancy (i.e. reward anticipation) was assessed using the Cued-

Reinforcement Reaction Time (CRRT) task with the degree of reinforcement-related

speeding (i.e. median reaction time for high probability trials minus median reaction time for

low probability trials) as the variable of interest (Cools et al., 2005). Although the CRRT task

specifically has been limited to schizophrenia samples (Mann et al., 2013; Murray et al.,

2008; Roiser et al., 2009; Waltz et al., 2010), reward-related speeding paradigms more

generally have been used in MDD (Forbes et al., 2009; Pizzagalli et al., 2009), as well as

other disorders to assess for reward anticipation/expectancy. The Probabilistic Stimulus

Selection (PSS) task was used to assess for reward learning, with PSS-Training score

(calculated as percent correct in the last training phase divided by number of training phases),

and proportion of trials correct for the A vs. Novel condition in the test phase (PSS-Test) as

outcome variables (Frank, 2004; Waltz et al., 2007). The PSS has been used in a number of

neuropsychiatric disorders such as schizophrenia and major depressive disorder as a measure

of reinforcement learning (Frank, 2004; Waltz et al., 2007; Fervaha et al., 2013a; Whitmer et

al., 2012).

Reward Valuation

Reward valuation was assessed using the Kirby Delay Discounting (DD) task, with

the natural logarithm of the geometric mean of the discounting rate, k (Kirby-Avg) as the

outcome variable (Kirby and Maraković, 1996). Delay discounting paradigms, including the

Kirby task, is a widely used measure of reward valuation in both SZ (Ahn et al., 2011; Avsar

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et al., 2013; Heerey et al., 2011, 2007) and MDD (Chung et al., 2017; Dombrovski et al.,

2012; Pulcu et al., 2014).

Effort Valuation

Effort valuation was assessed using the Effort Expenditure for Rewards Task

(EEfRT; Treadway et al., 2009) and the Virtual Progressive Ratio Task (ViPR; Foussias et

al., 2013). The variables of interest were the proportion of hard trials chosen at the high

probability level (EEfRT-High) for EEfRT, and task completions (ViPR-Tasks) and task

completion rate (ViPR-Rate) for the ViPR task. The EEfRT task has been extensively used in

SZ (Barch et al., 2014; Fervaha et al., 2013d; Treadway et al., 2015) and MDD (Sherdell et

al., 2012; Treadway et al., 2012; Yang et al., 2016) as a measure of effort valuation (i.e. cost

computation). The ViPR task, which was developed by our group as an ecologically valid

measure of willingness to work, has only been used in schizophrenia (Foussias et al., 2013).

Goal-directed Decision Making

Goal-directed decision making was assessed using the Iowa Gambling Task (IGT)

(Bechara et al., 1994) and the Multitasking in the City Test (MCT; Jovanovski et al., 2012).

For the IGT, the variable of interest was the net score of advantageous minus

disadvantageous card choices (IGT-Net), and for the MCT the distance travelled (MCT-

Distance in virtual environment units) and MCT-Performance score (tasks completed -

omission errors - commission errors). The IGT is one of the most common measures of

reward-driven decision making, and has been extensively used in a number of

neuropsychiatric populations, including SZ (Sevy et al., 2007; Shurman et al., 2005) and

MDD (Cella et al., 2010; Must et al., 2006; Smoski et al., 2008). The MCT has been used by

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our group in schizophrenia (Siddiqui et al., 2015), but to our knowledge, has not been

utilized in a clinically depressed sample.

5.3.5 Analyses

The final sample with complete data across all tasks that could be included in the

subsequent analyses consisted of 39 SZ patients, 38 MDD patients, and 39 HCs. Group

differences in demographic and clinical variables were assessed using chi-square tests and

analyses of variance (ANOVAs), as appropriate. To investigate the factor structure of the

motivation framework, an exploratory principal component analysis (PCA) using an oblique

rotation (direct oblimin) was conducted on IAPS-Pleasantness, IAPS-Arousal, ERRT-Rate,

Kirby-Avg, CRRT-RRS, PSS-Training, PSS-Test, EEfRT-High, ViPR-Rate, ViPR-Tasks,

IGT-Net, MCT-Distance, and MCT-Performance across the entire sample. Factors were

retained based on eigenvalues greater than 1, and regression-based factor scores were then

computed to represent each facet of motivation which were used in all subsequent analyses.

A multivariate hierarchical regression, with factor scores as the dependent variables and AES

total score entered into the first step, followed by diagnostic group into the second step was

conducted in order to determine the predictive value of amotivation and diagnosis on

motivation task performance. To assess the effect of diagnostic group, two contrast codes

were computed to represent SZ and MDD vs. HC, and SZ vs. MDD. In addition, K-means

clustering following MacQueen’s (1967) methodology was conducted on the regression

based factor scores to identify subgroups of individuals with similar motivation performance

profiles across the entire sample (MacQueen, 1967). The optimal number of clusters was

determined using the “nbclust” package which provides 30 indices for determining the

number of clusters across different distance measures and clustering methods (Charrad et al.,

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2014). Further, the consistency of the cluster solution was verified by establishing consensus

with other clustering methods (i.e. hierarchical clustering). Demographic and clinical

differences between clusters were examined using independent samples t-tests. Further, a

multivariate analysis of variance (MANOVA) was conducted to evaluate cluster differences

on motivation task performance. This analysis was repeated to control for the effects of age,

cognition, as well as medication status which was entered as two binary contrasts: treatment

with antipsychotic medication (antipsychotic vs. no antipsychotic), and antidepressant

medication (antidepressant vs. no antidepressant).

All statistical tests were undertaken with the Statistical Package of Social Science

(SPSS) version 24, with the exception of the cluster analysis which was undertaken using the

“fpc” package version 2.1-10 (Henning, 2015) in R version 3.3.3 (R Core Team, 2016).

5.4 Results

5.4.1 Participant demographic and clinical characterization

The demographic and clinical characteristics of participants are shown in Table 5-1.

Groups did not significantly differ in age, sex or parental education. However, the SZ group

had significantly fewer years of education compared to both the MDD and HC groups.

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Table 5-1: Demographic and clinical characterization of SZ, MDD, and HC groups. SZ

n=39 MDD n=38

HC n=39

Group Differences

Age (SD) 33.6 (9.8) 31.8 (11.2) 30.3 (10.9) ns

Sex (M:F) 18:21 16:22 21:18 ns

Education** 15.0 (3.6) 16.7 (2.5) 17.2 (2.7) HC=MDD >SZ

Duration of Illness 11.2 (7.4) 12.3 (9.3) - ns

Parental Education 15.2 (5.6) 15.6 (4.6) 16.8 (4.5) ns

Diagnosis (SZ:SA) : - - -

Medication (Yes:No) 39:0 26:12 - ns

Antipsychotics (n) 31 0 - -

Antidepressants (n) 0 22 - -

Antipsychotics + Antidepressants (n)

8 4 - -

AES*** 39.2 (6.6) 37.2 (4.7) 25.6 (4.8) MDD=SZ >HC

SANS Dim Exp*** 10.6 (9.0) 3.8 (5.5) 0.9 (1.9) HC<MDD<SZ

SAPS 23.5 (16.3) - - -

HDRS*** 10.5 (5.0) 16.9 (4.8) 2.0 (2.0) HC<SZ<MDD

TEPS-Con* 4.4 (0.8) 4.1 (1.0) 4.6 (0.7) HC >MDD

TEPS-Ant*** 4.4 (0.8) 3.6 (0.9) 4.6 (0.6) HC=SZ >MDD

BACS Composite*** -1.7 (1.3) -0.3 (0.9) 0.3 (1.1) HC>MDD>SZ

Note: Group differences are significant at *p<0.05, **p<0.01, or ***p<0.001.

(Abbreviations – AES: Apathy Evaluation Scale; SANS Dim Exp: Scale for the Assessment

of Negative Symptoms - Diminished Expression; SAPS: Scale for the Assessment of Positive

Symptoms; HDRS: Hamilton Depression Rating Scale; TEPS-Con: Temporal Experience of

Pleasure Scale - Consummatory; TEPS-Ant: Temporal Experience of Pleasure Scale -

Anticipatory; BACS: Brief Assessment of Cognition in Schizophrenia).

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5.4.2 Principal Component Analysis

The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis,

KMO=.626, and Bartlett’s test of sphericity χ² (78)=281.73, p <.001, indicated that

correlations between items were sufficiently large for PCA. Based on the scree plot and the

interpretability of factor loadings, five factors were extracted that accounted for 60.93% of

the total variance (Table 5-2). Based on variable loadings, the first factor represented hedonic

capacity, with the second factor representing reward expectancy and learning, the third factor

cost-benefit decision making, the fourth goal-directed decision making, and the fifth factor

representing effort expenditure.

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Table 5-2: Factor loadings for motivation task variables. 1. Hedonic

Capacity 2. Reward Expectancy and Learning

3. Cost- Benefit Decision Making

4. Goal-Directed Decision Making

5. Effort Expenditure

IAPS-Pleasantness .75

IAPS-Arousal .67

CRRT-RRS -.77

PSS-Test .65

Kirby-Avg -.76

EEfRT-High .70

PSS-Training .49

IGT-Net .51

MCT-Distance -.79

MCT-Performance .70

ERRT-Rate .56

ViPR-Rate -.81

ViPR-Tasks .86

Note: Factor loadings less than 0.4 are not shown.

(Abbreviations – IAPS: International Affective Picture System; CRRT-RRS: Cued-

Reinforcement Reaction Time Task-Reinforcement Related Speeding; PSS: Probabilistic

Stimulus Selection task; EEfRT: Effort Expenditure for Rewards Task; IGT: Iowa Gambling

Task; MCT: Multitasking in the City Test; ERRT: Evoked and Representational Responding

Task; ViPR: Virtual Reality Progressive Ratio Task)

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5.4.3 Hierarchical Regression

The hierarchical multivariate regression revealed a significant main effect of

amotivation in the first step (F(5,110)=6.777, p<.001), specifically predicting hedonic

capacity (β=-.041, p<.001), effort expenditure (β=-.027, p=.016) and goal-directed decision

making (β=-.033, p=.003). The addition of diagnosis in the second step revealed a significant

main effect for the SZ vs. MDD contrast (F(5,108)=6.331, p<.001) for hedonic capacity

(β=.051, p=.019), cost-benefit decision making (β=-.521, p=.022) and goal-directed decision

making (β=-.914, p<.001), such that SZ patients performed significantly better than MDD

patients on hedonic capacity, but worse on cost-benefit decision making and goal-directed

decision making. There was no main effect for the SZ and MDD vs. HC contrast. Moreover,

after including diagnostic group in the model, the main effect of AES (F(5,108)=4.583,

p=0.001) remained significant only for hedonic capacity (β=-.063, p<.001).

5.4.4 Cluster Analysis

The k-means clustering algorithm provided a 2 factor solution with 75 participants

assigned to cluster 1 and 41 to cluster 2. The consistency of the cluster solution was verified

using hierarchical clustering which revealed an 85% overlap between the two methods. The

motivation performance profiles of the 2 clusters are shown in Figure 5-1. Significant group

differences suggest that cluster 1 is characterized by impaired hedonic capacity, whereas

cluster 2 is characterized by impaired cost-benefit decision making, goal-directed decision

making, and effort expenditure (Table 5-3). The clusters did not significantly differ on

reward expectancy and learning performance. Furthermore, as shown in table 5-3, clusters

did not significantly differ by sex, duration of illness, consummatory or anticipatory pleasure,

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or severity of depression or clinical amotivation. However, cluster 2 was significantly more

cognitively impaired compared to cluster 1 (Table 5-3). Controlling for the effects of

cognitive functioning and medication status did not significantly change these results.

Moreover, while the distribution of diagnoses was significantly different, all diagnostic

groups were represented in each cluster (Figure 5-2).

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Table 5-3: Demographic, clinical, and motivation performance characterization of

clusters.

Cluster Mean (SD)

1 (n=75)

2 (n=41)

t / χ2 p Cluster Diff.

Group (SZ:MDD:HC) 17 : 29 : 29 22 : 9 : 10 11.4 .003 -

Sex (M:F) 39:36 16:25 1.8 ns -

Age 28.2 (8.4) 38.7 (11.0) -5.7 <.001 1 < 2

Duration of Illness 10.4 (8.0) 13.6 (8.6) -1.7 ns -

Antipsychotics (%) 25.3 58.5 12.5 <.001 1 < 2

Antidepressants (%) 26.7 34.1 0.7 ns -

Education 16.3 (3.0) 16.2 (3.2) .12 ns -

AES 33.2 (7.9) 35.4 (8.3) -1.5 ns -

HDRS 9.3 (7.6) 10.5 (7.0) -0.8 ns -

TEPS-Con 4.4 (0.8) 4.3 (0.8) 0.8 ns -

TEPS-Ant 4.2 (1.0) 4.2 (0.7) -0.3 ns -

BACS Z-score -0.04 (1.1) -1.5 (1.3) 6.4 <.001 1 > 2

Hedonic Capacity -0.2 (1.0) 0.4 (0.9) -3.7 <.001 1 < 2

Reward Expectancy and Learning

0.07 (0.9) -0.1 (1.2) 1.0 ns -

Cost-Benefit Decision Making

0.4 (0.9) -0.7 (0.8) 5.9 <.001 1 > 2

Goal-Directed Decision Making

0.4 (0.7) -0.8 (1.0) 7.3 <.001 1 > 2

Effort Expenditure 0.4 (0.8) -0.7 (1.0) 6.3 <.001 1 > 2

Note: See Table 5-1 for abbreviations.

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Figure 5-1: Motivation performance profiles across clusters. Significant differences are

denoted by *p<0.05, or **p<0.01.

** ** **

**

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Figure 5-2: Proportion of diagnostic groups across clusters.

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Of note, participants in cluster 2 were also significantly older than those in cluster 1.

Moreover, across the entire sample, age was correlated with hedonic capacity, goal-directed

decision making and effort expenditure. Within cluster 1, age was not correlated with any of

the facets of motivation; but in cluster 2, age was correlated with effort expenditure.

Although controlling for the effects of age did not significantly change our results, we took

additional steps to ensure that the impairments seen in cluster 2 were not entirely driven by

the notable age difference between clusters. Specifically, we repeated the cluster analysis in a

subsample of younger participants between the ages of 18 and 35 (n=77). In line with the

aforementioned results, we identified a two cluster solution with cluster 2 characterized by

significant impairments in reward expectancy and learning, goal-directed decision making,

and effort expenditure compared to cluster 1 (See Supplementary Results: Figure 5-3).

Importantly, however, the two clusters did not significantly differ in age, suggesting that the

motivation impairments seen in cluster 2 cannot be exclusively attributed to the effects of age

(See Supplementary Results: Table 5-4).

5.5 Discussion

The present study sought to systematically investigate the multi-faceted motivation

framework across patients with schizophrenia and major depressive disorder, and healthy

individuals. As per the proposed hypotheses and in line with emerging dimensional

approaches to understanding psychopathology, however, we examined the reward and

motivation system beyond diagnostic boundaries. To date, conceptualizations of the

motivational framework have been based on the findings of individual studies investigating

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isolated facets; however, no single study has yet to interrogate the discrete facets of

motivation concurrently in SZ and MDD. Utilizing a comprehensive battery of objective test

measures, our results revealed a five-factor structure of motivation comprised of 1) hedonic

capacity, 2) reward expectancy and learning, 3) cost-benefit decision making, 4) goal-

directed decision making, and 5) effort expenditure. These findings align well with the RDoC

Positive Valence System (Cuthbert and Insel, 2010), as well as with the 4 component

framework outlined by Barch and Dowd (2010), although there are some notable differences.

Specifically, we identified an additional component representing effort expenditure, separate

from both cost-benefit, and goal-directed decision making. Thus, we propose a framework in

which hedonic capacity (“liking”) and reward expectancy (“wanting”) serve to inform cost-

benefit decision-making (the convergence of reward and effort valuation), leading to a goal-

directed decision, or the development and implementation of an action plan to achieve a

desired outcome. The emergence of effort expenditure as a separate facet of motivation

suggests that this may serve as a final step, whereby continuous re-evaluation and updating

of value and cost representations may dynamically inform willingness to continue expending

effort while in pursuit of a goal. In other words, hedonic capacity, reward expectancy and

learning, and cost-benefit decision-making may be more relevant to the initiation of goal-

directed behaviour, with effort expenditure involved in sustaining the motivation to achieve

this final goal.

The results of our regression analyses revealed that clinical amotivation significantly

predicted overall motivation task performance, particularly hedonic capacity. Further, this

effect was independent of the influence of diagnosis, suggesting that differential motivation

impairments may be driven by individual differences in amotivation severity. These results

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also align well with a study by our own group suggesting that deficits in the subjective

experience of pleasure are primarily driven by individuals with more severe amotivation,

with our results extending these findings to objective test measures, as well (Da Silva et al.,

2017). Moreover, the absence of a main effect for the patient vs. healthy control contrast

lends further support for a dimensional approach to examining motivation on a continuum,

rather than exclusively relying on traditional categorical comparisons.

In line with this dimensional approach, we sought to classify individuals – regardless

of diagnosis – according to motivation performance. Our findings revealed two clusters of

individuals with differential motivation profiles. Of note, individuals did not simply cluster

by diagnosis. Rather, there emerged substantial overlap of SZ, MDD, and HC participants

across clusters. While there were significantly more patients with SZ than MDD or HC

participants in cluster 2, the number of SZ, MDD, and HC participants was relatively equal in

cluster 1. Perhaps more interesting was the even distribution of patients with SZ across the

clusters, which underscores the heterogeneous nature of the clinical presentation of

motivational deficits in SZ. Indeed, these findings may shed some light on the source of

discrepant findings across studies (Barch et al., 2016), with categorical diagnostic

comparisons potentially too crude a method to capture individual differences within groups.

In contrast, clustering individuals based on objective motivation performance may provide an

alternative, more nuanced approach to understanding motivational impairments as they

manifest in SZ and MDD. In the current study, we identified a cluster of individuals

characterized by a specific impairment in hedonic capacity, but intact performance in all

other motivation facets, and a second cluster with impaired cost-benefit decision making,

goal-directed decision making, and effort expenditure, despite intact hedonic capacity.

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Despite this clear distinction, however, the two clusters did not significantly differ in severity

of clinical amotivation. This is particularly important as it suggests that differential

impairments in one or more components can equally contribute to the clinical presentation of

motivational deficits. Moreover, our findings align well with the existing literature

suggesting differential neurobiological underpinnings across discrete facets of the motivation

system. Specifically, consummatory anhedonia, the defining feature of the first cluster, has

been linked to serotonergic or opiodic systems in the nucleus accumbens (Berridge, 2007;

Smith and Berridge, 2005), with some evidence for a ventral striatal signal (Dowd and Barch,

2010). Conversely, impairments in higher-order reward processing, as seen in cluster 2, have

been linked to dopaminergic dysfunction in striatal and cortical regions of the brain (Berridge

and Robinson, 1998; Schultz, 2002; Wise, 2002). This distinction has important implications

for the development of treatments targeting specific motivation deficits.

The present study has several strengths worth mentioning. To our knowledge, this

study was the first to systematically examine the multi-faceted motivation system, and

dimensionally investigate these facets concurrently in SZ, MDD, and HCs. Moreover, we

utilized a comprehensive and extensive battery of objective computerized tasks, specifically

intended to tap into the discrete facets of motivation. This being said, the findings of this

study should be interpreted within the context of the following limitations. First, patients

with SZ and MDD were being treated with different medications including antipsychotics,

antidepressants, stimulants or some combination of the three. Unfortunately, there is

currently no standardized method of calculating dose equivalences across different

medication classes, which hinders our ability to directly evaluate the potential impact of

medications across facets of motivation. While controlling for medication status did not

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change the results of our analyses, we recognize the need for more sophisticated methods to

determine the precise effects of medication. Additionally, future studies should aim to

replicate these findings in medicated and non-medicated individuals. Second, there are no

standard guidelines or methods to identifying or defining clusters. Although the present study

took additional steps to verifying the validity and stability of our cluster analyses, replication

in larger and broader clinical samples will be important to enhance the strength of our

findings.

In summary, our findings support a multi-faceted motivation framework comprised of

five components: hedonic capacity; reward expectancy and learning; cost-benefit decision

making; goal-directed decision making; and effort expenditure. Further, across the entire

sample of SZ, MDD, and HC participants, two distinct clusters of individuals emerged, with

differential motivation system impairments. Taken together, the findings of the current study

suggest that the clinical presentation of motivation deficits, irrespective of diagnostic status,

may be determined by unique profiles of impairment across separate facets of the motivation

system. Such impairment profiles may represent unique underlying neurobiological

impairments, and may be differentially responsive to specific interventions. Thus, parsing

motivation deficits into their underlying behavioural and neurobiological profiles may afford

opportunities for the development of novel interventions, thereby fulfilling a key domain of

unmet therapeutic need.

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5.6 Supplementary Methods

5.6.1 Objective computerized measures

Hedonic Experience and Reward Expectancy

1) Evoked and Representational Responding Task

The Evoked and Representational Responding Task (ERRT), developed by Heerey

and Gold (2007) incorporates a subset of positive, neutral, and negative valence images1 (14

each) from the International Affective Picture System (IAPS) (Lang et al., 1993) to evaluate

internal representations of reward coupled with behavioural output (Heerey and Gold, 2007).

In the representational condition of the task, participants are first asked to rate an image for

pleasantness and arousal on 9-point Likert scales. After making the two ratings for each

image, participants are presented with the option to seek or avoid future presentations by

repeatedly pressing the “m” and “n” keys if they wanted to see the image again, or “x” and

“z” if they did not want to see the image again. In the evoked condition of the task,

participants are shown a subset of the images they had previously seen (10 of each valence),

with the opportunity to prolong or reduce the viewing time of each image by repeatedly

pressing the “m” and “n” or “x” and “z” keys on the keyboard.

2) Cued-Reinforcement Reaction Time Task

The Cued Reinforcement Reaction Time (CRRT) task was used to assess for reward

expectancy or “wanting”, whereby participants perform a rapid odd-one-out judgment on

                                                                                                                         1  IAPS stimuli used in the study included: 1052, 1120, 1300, 1390, 1450, 1560, 1602, 1620, 2270, 2304, 2320, 2360, 2370, 2480, 2490, 2495, 2590, 2722, 4598, 5779, 5891, 5920, 5950, 5971, 6260, 6560, 7325, 7640, 8030, 8080, 8160, 8180, 8185, 8370, 8400, 8490, 9001, 9210, 9220, 9250, 9280, 9331.  

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three circles (Cools et al., 2005). A red, blue, or yellow coloured frame was presented along

with the stimulus, which cued the likelihood of receiving points, each with a different

pseudorandom reinforcement schedule (90%, 50%, and 10%, respectively). For each of the

96 trials, participants are instructed press “<”, “>”, or “?” on a computer keyboard to indicate

the odd-one-out as quickly as possible. On reinforced trials, participants are awarded 100

points for correct and fast responses, 1 point for correct and slow responses, and 0 points for

incorrect responses. Fast and slow response times are individually calibrated by calculating

cut-off scores based on mean RT minus standard deviation RT during an initial practice

phase.

Reward Learning

1) Probabilistic Stimulus Selection Task

On the Probabilistic Stimulus Selection (PSS) task, participants are presented with

three different stimulus pairs (AB, CD, and EF) of pictures of common objects (Frank, 2004;

Waltz et al., 2007). During the training phase of the task, participants are informed that there

are no absolute right answers, but that some pictures have a higher chance of being correct,

and to choose the picture that was most correct. Feedback was provided after each trial

according to a predetermined probabilistic schedule. For example, in the AB condition,

choosing “A” was rewarded with correct feedback for 80% of the trials, and choosing “B”

was rewarded for the remaining 20% of the trials. Similarly, choosing “C” during the CD

conditions and “E” from the EF conditions led to correct feedback in 70% and 60% of trials,

respectively. Thus, participants should learn to choose the most rewarded stimuli of the pairs

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(i.e. A, C, and E) during the training phase. Each training phase consisted of 20 randomly

presented trials for each stimulus pair, and in order to proceed to the test phase, participants

must meet learning criteria by selecting the rewarding stimulus 65%, 60% and 50% of the

time for AB, CD and EF stimulus pairs, respectively, or complete 6 training blocks. During

the test phase, the original three stimulus pairs are presented along with novel A and B

pairings (eg. AD, BC). Participants are instructed to continue choosing the most correct

answer; however, no feedback is provided. Thus, the test phase assesses whether participants

are able to transfer previously learned reward association to novel stimulus pairs (i.e.,

choosing A vs. novel, or avoiding B vs. novel).

Reward Valuation

1) Kirby Delay Discounting Task

The Kirby Delay Discounting (Kirby DD) task evaluates preferences for amount of

money over time (Kirby and Maraković, 1996). This monetary choice task consists of 27

questions where participants are asked if they would rather receive a smaller immediate

reward or a larger delayed reward.

Effort Valuation

1) Effort Expenditure for Rewards Task

The Effort Expenditure for Rewards Task (EEfRT) is a measure of willingness to

expend effort for monetary reward (Treadway et al., 2009). In this modified version

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consisting of 51 trials, participants are presented with the choice of completing an easy or

hard task, along with the amount of money they would win for each option, as well as the

probability of receiving the reward upon task completion (12%, 50%, and 88%). Both tasks

involve filling up a bar on the computer screen by pressing a button on the keyboard a certain

number of times, with the easy task requiring significantly fewer button presses than the hard

task. On the easy task, participants were given 7 seconds to fill up the bar by pressing the “B”

button on the keyboard with the index finger of their dominant hand for the possibility of

winning $1.00, whereas the hard task required participants to fill up the bar within 21

seconds using the pinky finger of their non-dominant hand, for rewards ranging from $1.24

to $4.21. Of note, button press criteria for both easy and hard tasks were individually

calibrated at the beginning of the task to account for differences in motor speed. Specifically,

the easy task criterion was calculated as 50% of the maximum practice click rate for the

dominant hand, and the hard task criterion was set at 80% of the maximum practice click rate

for the non-dominant hand.

2) Virtual Reality Progressive Ratio Task

The Virtual Reality Progressive Ratio task (ViPR) is an ecologically-valid measure of

willingness to expend effort in pursuit of a goal. Participants are instructed to complete a

series of common everyday tasks using a joystick at vending machines within stores in a

virtual city to earn as many points as possible (Foussias et al., 2013). Utilizing a

progressively increasing work-to-reward ratio, the required number of tasks increases

exponentially in order to receive more points (i.e. 100) as the task goes on. The task lasts up

to 10 minutes, although participants are free to stop any time they wish.

107

Goal-Directed Decision Making

1) Iowa Gambling Task

The Iowa Gambling (IGT) requires participants to choose a card from 4 decks across

100 trials (Bechara et al., 1994). All decks lead to a reward, but differ according to frequency

and magnitude of losses. Further, decks A and B are advantageous such that they provided

small immediate rewards, but small losses as well. In contrast, C and D are disadvantageous

decks as they offered larger immediate rewards, but greater losses as well. Participants were

instructed to win as much money as possible and were told that some decks were better than

others. Each card selection was followed by feedback indicating how much money was won,

if and how much money was lost, with net wins depicted on a bar at the top of the screen.

2) The Multitasking in the City Test

The Multitasking in the City Test (MCT) is a virtual reality task in which participants

are asked to complete a series of pre-specified errands (e.g. buying stationary, attending a

scheduled doctor’s appointment) within a virtual city using a joystick within a period of 15

minutes (Jovanovski et al., 2012). Further, in order to minimize the cognitive load,

participants have an initial practice run whereby they can navigate freely within the virtual

city to develop familiarity, are provided with a map of the virtual city to which they can refer

throughout the task, and the list of errands remains on the screen throughout the task.

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Analyses:

The subsequent analyses were conducted for ease of comparison to other studies

examining isolated facets of motivation using traditional diagnostic comparisons.

Hedonic Capacity and Reward Expectancy

1) ERRT

To assess for group differences on IAPS pleasantness and arousal ratings, we

conducted a 3 (group: SZ vs. MDD vs. HC) x 3 (valence: positive, neutral, negative) x 2

(rating: pleasantness, arousal) mixed model ANOVA.

To assess for motivational salience, we examined click rate as a behavioural proxy for

wanting. First, slide valence was determined on an individual basis as in Heerey and Gold

(2007), such that pleasantness ratings between 1 and 3 were classified as negative, 4-6

neutral, and 7-9, positive. Click rate per second was then calculated across each valence, and

for both evoked and representational conditions. In the representational condition, click rate

was calculated as total clicks divided by 2 seconds whereas for the evoked condition, total

number of clicks was divided by the response time window per trial. A 3 (group) x 3

(valence: positive, neutral, negative) x 2 (condition: evoked, representational) mixed model

ANOVA was subsequently conducted to assess for group differences.

2) CRRT

To examine reward anticipation, median reaction times (RT) were calculated across

the three different reinforcement probabilities (10% vs. 50% vs. 90%). Inaccurate and invalid

trials in which RT exceeded 2000 milliseconds were excluded from the analysis. A 3 (group)

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x 3 (reinforcement probability) mixed model ANOVA was then conducted to evaluate for

group differences. Reinforcement-related speeding (RRS), calculated by subtracting the

median RT of the 10% probability trials from the median RT of the 90% trials, was

dichotomized in a binary variable with 0 coded for RRS ≥ 0 (i.e. no speeding), and 1 for RRS

< 0 (i.e. speeding). A chi-square was subsequently conducted to evaluate for differences in

the proportion of individuals demonstrating RRS across diagnostic groups.

Reward Learning

1) PSS

Reward learning during the acquisition phase was assessed using a training score,

calculated on an individual basis as the proportion of correct trials on the last training phase

divided by total number of training phases. A one-way ANOVA, with training score as the

dependent variable, and group as the independent variable was conducted to assess for group

differences. Reinforcement learning as a function of reward and punishment was also

examined using the proportion of correct trials for the A vs. Novel and B vs. Novel

conditions of the test phase. Participants who did not meet learning criteria in the practice

phase were excluded from this analysis. A separate MANOVA, with A vs. Novel and B vs.

Novel scores as the dependent variables and group as the independent variable was

conducted to evaluate for group differences.

110

Reward Valuation

1) Kirby DD

The geometric mean of the natural logarithm of the discounting parameter (k) was

calculated as in Kirby (1996) for small, medium, and large delayed rewards. A 3 (group) x 3

(bin: small vs. medium vs. large) mixed model ANOVA was subsequently conducted to

evaluate for group differences in reward valuation.

Effort Valuation

1) EEfRT

Willingness to expend effort was evaluated as the proportion of hard trials chosen

across differing probability and reward conditions. Reward level was dichotomized to

represent high (>$3) and low (<$3) magnitudes. Further, inflexible responders (i.e. those who

never chose the hard option) were excluded from the analysis. A 3(group) x 3 (probability:

12%, 50%, 88%) x 2 (reward: low, high) mixed model ANOVA was then conducted to

assess for group differences in effort valuation.

2) ViPR

Effort valuation was also assessed using task completion rate (calculated as total task

time / total task completions) on the ViPR. An ANOVA with completion rate as the

dependent variable, and group as the independent variable was conducted to evaluate for

group differences.

111

Goal-Directed Decision Making

1) IGT

Goal-directed decision making was assessed using the IGT-Net variable, calculated as

total advantageous minus total disadvantages card choices. A one-way ANOVA was

conducted, with IGT-Net as the dependent variable, and group as the independent variable. A

cumulative net score of total advantageous minus disadvantages choices was also calculated

across 5 bins of 20 trials. A 3 (group) x 5 (bin) mixed model ANOVA was subsequently

conducted to evaluate performance throughout the task.

2) MCT

Completion distance (in virtual environment units-VEU), and performance score

(total tasks completed minus omission and commission errors) on the MCT were also used to

examine goal-directed decision making. A MANOVA with completion distance and

performance score as the dependent variables, and group as the independent variable was

then conducted.

Medication Effects

In order to further explore the effects of medication, we computed a binary

antipsychotic (antipsychotic vs. no antipsychotic), and antidepressant contrast (antidepressant

vs. no antidepressant). A MANOVA was subsequently conducted to evaluate for differences

across the 5 facets of motivation.

112

5.6.3 Results

5.6.3.1 Age-restricted cluster analysis

Table 5-4: Demographic, clinical, and motivation performance characterization of age-

restricted clusters.

Cluster Mean (SD) 1

(n=49) 2

(n=28) t / χ2 p Cluster

Diff. Group (SZ:MDD:HC) 11 : 16 : 22 13 : 7 : 8 4.9 ns -

Sex (M:F) 28:21 11:17 0.1 ns -

Age 24.7 (4.2) 26.0 (5.0) -1.2 ns -

Education 16.4 (3.0) 15.2 (2.8) 1.7 ns -

Duration of Illness 7.5 (4.6) 7.1 (4.1) 0.4 ns -

AES 31.2 (7.7) 35.4 (8.6) -2.2 .035 1 < 2

HDRS 8.3 (7.5) 10.2 (7.2) -1.0 ns -

TEPS-Con 4.4 (0.8) 4.2 (1.0) 1.2 ns -

TEPS-Ant 4.2 (0.8) 4.0 (1.0) 1.0 ns -

BACS Z-score -0.07 (1.1) -1.1 (1.6) 3.4 .004 1 > 2

Hedonic Capacity 0.2 (1.1) -0.3 (0.9) 2.0 .055 -

Reward Expectancy and Learning

0.5 (0.7) -0.8 (0.9) 6.6 <.001 1 > 2

Cost-Benefit Decision Making

0.07 (0.9) -0.1 (1.2) 0.8 ns -

Goal-Directed Decision Making

0.3 (0.9) -0.4 (1.0) 3.2 .002 1 > 2

Effort Expenditure 0.5 (0.7) -0.9 (0.8) 8.1 <.001 1 > 2

See table 5-1 for abbreviations.

113

 

Figure 5-3: Motivation performance profiles across age-restricted (18-35) clusters.

Significant differences are denoted by *p<0.05, or **p<0.01.

**

**

**

114

5.6.3.2 Traditional Diagnostic Comparisons

Hedonic Capacity and Reward Expectancy

1) ERRT

The ANOVA for self-reported liking revealed a significant main effect of valence

(F(2,140)=201.939, p<0.001) and rating (F(1, 141)=14.050, p<0.001) indicating that overall

ratings differed between positive, neutral, and negative valences, and that images were rated

as more arousing than pleasant. Further, there was a significant group by valence interaction

(F(4,282)=4.859, p=0.001), with post hoc analyses revealing a significant effect of arousal

for positive images only (F(2,141)=5.404, p=0.005), such that MDD ratings were

significantly lower than both SZ and HCs. There was no significant rating by group

interaction, or 3-way interaction (Figure 5-4).

115

 

 

 

 

Figure 5-4: Mean A) pleasantness and B) arousal ratings across valences for SZ, MDD,

and HC groups. Group differences are significant at *p<0.05, and **p<0.01. Error bars

represent standard error of the mean.

The ANOVA for motivational salience revealed a significant main effect of valence

(F(2,128)=81.953, p<0.001; negative > positive > neutral) and condition (F(1,129)=72.333,

p<0.001; representational > evoked). Further, our results revealed a significant valence by

group (F(2,128)=81.953, p<0.001), but no condition by group or 3-way interaction. Post-hoc

one-way ANOVAs revealed a significant group difference for neutral images in the

0

1

2

3

4

5

6

7

8

9

Positive Neutral Negative

Mea

n Pl

esan

tnes

s Rat

ings

Valence

SZMDDHC

0

1

2

3

4

5

6

7

8

9

Positive Neutral Negative

Mea

n A

rous

al R

atin

gs

Valence

SZMDDHC*

*

A)

B)

116

representational (F(2,136)=3.484, p=0.033) and evoked (F(2, 135)=9.668, p<0.001)

conditions, and for negative images during the evoked condition (F(2, 135)=3.903, p=0.023).

Specifically, SZ patients had higher click rates for neutral images in both conditions, and

lower rates for negative images in the evoked condition (Figure 5-5).

   

Figure 5-5: Average click rate across valences, and diagnostic groups for the A)

representational and B) evoked conditions. Group differences are significant at *p<0.05,

and **p<0.01. Error bars represent standard error of the mean.

0

1

2

3

4

5

6

Positive Neutral Negative

Aver

age

Clic

k R

ate

Valence

Representational

SZ

MDD

HC

A)

* *

0

1

2

3

4

5

6

Positive Neutral Negative

Aver

age

Clic

k R

ate

Valence

Evoked SZMDDHC

B)

** **

*

117

2) CRRT

The ANOVA revealed no significant main effect, such that reaction times did not

differ across reinforcement probabilities. There were also no significant group by probability

interaction (p=0.8) (Figure 5-6). Similarly, there was no significant difference in the

proportion of participants demonstrating reinforcement-related speeding across diagnoses

(Figure 5-7). Of note, accuracy was not significantly different between groups

Figure 5-6: Median reaction time across reinforcement cues for SZ, MDD, and HC

groups. Error bars represent standard error of the mean.

Figure 5-7: Proportion of participants demonstrating reinforcement-related speeding

across diagnostic groups.

0

10

20

30

40

50

60

Reinforcement-Related Speeding

Prop

ortio

n of

Pa

rtic

ipan

ts

SZMDDHC

500

600

700

800

900

10% 50% 90%Med

ian

Res

pons

e Ti

me

Reinforcement Cue

SZ

MDD

HC

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Reward Valuation

1) Kirby DD

The ANOVA revealed a significant effect of bin, such that participants discount the

value of smaller delayed rewards more rapidly than medium and large rewards. We did not,

however find a significant group by bin interaction (p=.29), suggesting that groups are

discounting the value of future rewards at similar rates (Figure 5-8).

Figure 5-8: The geometric mean of the natural logarithm of the discounting parameter

(k) across reward size, and diagnostic groups. Error bars represent standard error of

the mean.

-6

-5

-4

-3

-2

-1

0

ln K

DD SZ

MDD

HC

Medium LargeSmall

119

Reward Learning

1) PSS

The one-way ANOVA revealed a significant main effect of group (F(2,129)=4.105,

p=0.019), such that HCs had a higher learning score compared to both SZ and MDD groups

(Figure 5-9). In contrast, the MANOVA revealed no significant effect of group, suggesting

that SZ, MDD, and HC participants demonstrated comparable reward- and punishment-

driven reinforcement learning (Figure 5-10).

Figure 5-9: Training score across diagnostic groups. Group differences are significant

at *p<0.05, and **p<0.01. Error bars represent standard error of the mean.

0

10

20

30

40

50

60

SZ MDD HC

Trai

ning

Sco

re

* *

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Figure 5-10: Proportion correct for A vs. Novel and B vs. Novel conditions across

groups. Error bars represent standard error of the mean.

Effort Valuation

1) EEfRT

Groups did not significantly differ in overall effort indexed by total number of hard

choices, or in the proportion of tasks successfully completed. There was, however a

significant main effect of probability (F(2,138)=173.201, p<0.001) and reward level (F(1,

139)=451.348, p<0.001), such that participants chose significantly more hard tasks on the

high probability, and high reward trials. We also found a significant reward by group

interaction (F(2,139)=5.845, p=0.004), and a non-significant trend for a probability by group

interaction (p=0.08). Post-hoc one-way ANOVAs revealed a significant difference for the

high probability-high reward condition (F(2,139)=3.830, p=0.024) such that SZ patients were

choosing significantly fewer hard tasks, compared to both MDD and HC participants (Figure

00.10.20.30.40.50.60.70.80.9

1

A vs. Novel B vs. Novel

Prop

ortio

n of

Cor

rect

R

espo

nses

SZ

MDD

HC

121

5-11). Further, a trend emerged for low probability-low reward trials (F(2,139)=2.991,

p=0.053), such that patients with SZ were choosing significantly more hard tasks compared

to MDD and HC participants. The three-way interaction was not significant.

Figure 5-11: Mean proportion of hard trials across differing probability conditions for

A) low reward and B) high reward trials. Group differences are significant at *p<0.05,

and **p<0.01. Error bars represent standard error of the mean.

0

0.2

0.4

0.6

0.8

1

12% 50% 88%

Prop

ortio

n of

Har

d Tr

ials

Probability

Low Reward Trials SZMDDHC

A)

0

0.2

0.4

0.6

0.8

1

12% 50% 88%

Prop

ortio

n of

Har

d Tr

ials

Probability

High Reward Trials SZMDDHC

* *

B)

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2) ViPR

The ANOVA revealed a significant main effect of group (F(2,141)=3.680, p=0.028),

such that participants with SZ were less willing to work for points in the face of increasing

effort requirements compared to healthy controls only (Figure 5-12).

Figure 5-12: Completion rate (total task time / total task completions) across diagnostic

groups. Group differences are significant at *p<0.05, and **p<0.01. Error bars

represent standard error of the mean.

0

2

4

6

8

10

12

14

16

18

SZ MDD HC

Com

plet

ion

Rat

e

*

123

Goal-Directed Decision Making

1) IGT

The one-way ANOVA revealed a significant main effect of group, such that patients

with SZ chose significantly more disadvantageous card choices compared to MDD and HC

participants (Figure 5-13). The mixed model ANOVA revealed a main effect of bin

(F(4,139)=7.481, p<0.001) and trend for a group by bin interaction (F(8, 280)=1.779,

p=0.081). Post-hoc analyses revealed that patients with SZ chose more disadvantageous card

choices compared to both MDD and HC participants across bins 2-5 (Figure 5-14).

Figure 5-13: Net score (advantageous minus disadvantageous choices) across diagnostic

groups. Group differences are significant at *p<0.05, and **p<0.01. Error bars

represent standard error of the mean.

-5

0

5

10

15

20

25

SZ MDD HC

Net

Sco

re

* *

124

Figure 5-14: Cumulative net score (advantageous minus disadvantageous choices)

across bins, and diagnostic group. Group differences are significant at *p<0.05, and

**p<0.01. Error bars represent standard error of the mean.

2) MCT

The MANOVA revealed a significant main effect of group (F(4, 270)=7.624,

p<0.001) for both distance (F(2,135)=16.151, p<0.001) and performance score

(F(2,135)=6.310, p=0.002). Specifically, patients with SZ travelled a greater distance (Figure

5-15), and completed goal-oriented tasks less efficiently (Figure 5-16) compared to MDD

and HC participants.

   

-10

-5

0

5

10

15

20

1-20 21-40 41-60 61-80 81-100

Cum

ulat

ive

Net

Sco

re

Order of Cards Selected

SZMDDHC

*

* *

*

125

     

 

Figure 5-15: Distance travelled (in virtual environment units) across groups. Optimal

distance represents the shortest possible distance required to complete all tasks. Group

differences are significant at *p<0.05, and **p<0.01. Error bars represent standard

error of the mean.

         

Figure 5-16: Performance score (total tasks completed – omission and commission

errors) across groups. Group differences are significant at *p<0.05, and **p<0.01.

Error bars represent standard error of the mean.

0

1

2

3

4

5

6

7

8

SZ MDD HC

Perf

orm

ance

Sco

re

* *

0

50

100

150

200

250

300

350

400

450

SZ MDD HC

Dist

ance

Tra

velle

d

Optimal Distance

** **

126

Medication Effects

The MANOVA revealed a significant main effect for the antipsychotic contrast

(F(5,108)=8.550, p<0.001) for goal-directed decision making (F(1,112)=40.715, p<0.001)

and effort expenditure (F(1,112)=4.754, p=0.031), such that participants being treated with

antipsychotic medications (AP) performed significantly worse compared to those not being

treated with AP (Figure 5-17). There was no significant main effect for the antidepressant

contrast (Figure 5-18).

Figure 5-17: Antipsychotic group differences across facets of motivation. Group

differences are significant at *p<0.05, and **p<0.01. Error bars represent standard

error of the mean.

**

*

127

Figure 5-18: Antidepressant group differences across facets of motivation. Error bars

represent standard error of the mean.

128

Chapter 6

6. Discussion

6.1 Summary of Results

Anticipatory and consummatory pleasure deficits in schizophrenia – the role of clinical

amotivation

The overarching goal of this thesis was to systematically deconstruct the motivation

and reward system in schizophrenia. Current conceptualizations of motivation outline a

multi-faceted framework comprised of four components: hedonic experience (“liking”),

reward expectancy (“wanting”), cost-benefit decision making (i.e. value representation and

effort computations), and goal-directed decision making (Dowd and Barch, 2010). Though

anhedonia (i.e. hedonic experience deficits) has long been considered a core symptom of

schizophrenia, this notion has recently been called into question. Thus, our first objective was

to clarify the nature and extent of hedonic impairments in schizophrenia by examining

consummatory and anticipatory pleasure in a large sample of schizophrenia and healthy

control participants. Moreover, given that hedonic capacity figures prominently within the

broader motivation framework, we also sought to examine the relationship between

consummatory and anticipatory pleasure, and severity of clinical amotivation. In line with

our hypothesis, patients with schizophrenia and healthy controls reported comparable levels

of in-the-moment consummatory pleasure. In contrast to our hypothesis, however, groups did

not significantly differ in their levels of anticipatory pleasure. In reviewing previous

investigations of the experience of pleasure in schizophrenia, we noted that studies reporting

lower levels of anticipatory pleasure had higher proportions of patients being treated with

129

typical antipsychotics in comparison to our sample (Gard et al., 2007), and to others who

failed to replicate these findings (Edwards et al., 2015; Cassidy et al., 2012 ; Strauss et al.,

2011). Our results also revealed that amotivation severity significantly predicted

consummatory and anticipatory pleasure, with no independent contribution of diagnostic

group. Specifically, significant reductions in consummatory and anticipatory pleasure were

primarily driven by individuals with more severe amotivation. Thus, these results offer some

insight into the potential causes of discrepant findings across studies, with inconsistencies

potentially driven by different distributions of clinical amotivation across diagnostic groups.

Investigation of motivation deficits in a non-clinical population – the roles of amotivation,

depressive, and schizotypal symptoms

Recognizing the multiple facets comprising the motivation framework, we sought to

expand our investigation beyond just hedonic experience. Specifically, our second aim was to

examine the reward system utilizing objective measures of discrete facets of motivation in a

non-clinical sample. The rationale for using a study sample comprised of undergraduate

students, rather than patients, was to examine the role of subclinical depressive and

schizotypal symptoms while minimizing illness-related confounds (i.e. medication class,

illness chronicity) that may potentially affect motivation performance. In line with our

hypothesis, severity of amotivation significantly predicted overall motivation task

performance, and specifically in-the-moment IAPS ratings of pleasure and arousal, as well as

self-reported consummatory and anticipatory pleasure. Surprisingly, however, amotivation

severity did not predict performance across other objective facets of motivation, possibly

130

reflecting a continuum of impairment whereby more widespread impairments emerge with

increasing severity of amotivation typically seen in clinical populations. Interestingly,

EEfRT performance was positively correlated with SPQ scores, such that a higher level of

schizotypy was associated with an increased willingness to expend effort in the 88%

probability condition. Though surprising, we suggest the possibility that these findings may

be a reflection of altered salience attribution related specifically to high probability trials.

Aside from this relationship with EEfRT performance, subclinical depressive and schizotypal

symptoms, as well as neurocognition did not significantly contribute to the prediction of

overall motivation task performance, above and beyond self-reported motivational deficits.

Taken together, these results align well with our first study, and extend the dimensional

relationship between amotivation and subjective and objective hedonic experience to a non-

clinical sample.

Dimensional investigation of the motivation system across schizophrenia and major

depressive disorder

The objective of our final study was to systematically investigate the motivation

framework concurrently across schizophrenia, major depressive disorder, and healthy control

participants. Utilizing a comprehensive battery of objective computerized tasks, the results of

our factor analysis revealed a multi-faceted motivation framework comprised of 5 distinct

components: hedonic capacity, reward expectancy and learning, cost-benefit decision

making, goal-directed decision making, and effort expenditure. These findings are somewhat

in line with our hypothesis, with some notable exceptions. First, reward valuation and effort

131

valuation were subsumed under a single facet representing cost-benefit decision making,

rather than emerging as separate components. Further, we identified an additional component

representing the expenditure of effort that is independent of cost-benefit decision making and

goal-directed action planning. Thus, we proposed a revised framework in which liking (i.e.

hedonic capacity) and wanting (i.e. reward expectancy and learning) interact to inform

reward valuations and effort computations (i.e. cost-benefit decision making), leading to the

development and implementation of an action plan (i.e. goal-directed decision making), with

the resultant goal-directed behaviour (i.e. effort expenditure) sustained through a dynamic

process of re-evaluating and updating reward value and effort/cost requirements based on

progress to the desired goal. This schematic is outlined in Figure 6-1.

132

Figure 6-1: Schematic representation of the motivation framework.

Transitioning our examination of motivation deficits beyond traditional diagnostic

comparisons towards a more dimensional approach, we conducted a cluster analysis in order

to classify individuals based on motivation performance. Our results revealed an optimal 2

cluster solution, with unique motivation performance profiles. The first cluster of individuals

exhibited impaired hedonic capacity, with intact performance across all other facets. In

133

contrast, the second cluster represented individuals with intact hedonic capacity, but impaired

cost-benefit decision making, goal-directed decision making and effort expenditure. Drawing

on previous neurobiological findings (Barch et al., 2016; Barch and Dowd, 2010; Der-

Avakian et al., 2015; Treadway and Zald, 2011), the emergent clusters of individuals with

unique motivation performance profiles suggests the possibility of distinct underlying neural

mechanisms, with hedonic impairments seen in cluster 1 linked to altered opiodic or

serotonergic systems, and cluster 2 impairments more closely related to dopaminergic

dysfunction. Furthermore, perhaps most important to our dimensional investigation is that

our cluster analysis did not classify individuals according to diagnostic status alone.

To date, however, most studies examining motivation have relied primarily on

traditional diagnostic comparisons, often focused on isolated facets of motivation within a

single illness population. In contrast, our study comprehensively examined facets of

motivation more broadly across a sample of schizophrenia, major depression, and healthy

control participants. For consistency with previous studies, we also conducted additional

exploratory analyses using this traditional categorical approach. Our results revealed that SZ

patients were significantly impaired in effort valuation, and goal-directed decision making

compared to both MDD and HC participants. In contrast, SZ patients demonstrated

comparable performance on measures of hedonic capacity (IAPS), reward expectancy

(CRRT), and reward valuation (Kirby DD). Both SZ and MDD participants demonstrated

impairments in rapid reward learning as measured by the PSS. Further, individuals in the

MDD group demonstrated impairments on measures of hedonic capacity, compared to both

SZ and HC groups. Though these findings are in line with some studies, our results are not

consistent with all previous investigations. While our study, to our knowledge, is the most

134

comprehensive investigation of motivation deficits within and across diagnoses, the

inconsistent diagnosis-specific findings highlight the challenges inherent to such

investigations in heterogeneous disorders. Traditional diagnostic comparisons may simply be

too crude a method to discover the true nature of motivation deficits across clinical

populations. Indeed, the results of our dimensional analyses in study 3 suggest that

differential proportions of patients sampled across motivation clusters may be driving the

discrepant findings seen in previous studies.

Taken together, the findings of these three studies serve to advance our understanding

of the phenomenology and behavioural underpinnings of motivation deficits in

schizophrenia, major depressive disorder, and healthy controls. Our dimensional analyses

offer important insights into the manifestation of amotivation across these groups, and

highlight the need to move beyond a singular approach towards more comprehensive

examinations of the motivation system. Going forward, parsing motivation deficits into their

underlying behavioural and neurobiological profiles may allow for improved specificity, with

opportunities to advance novel, more targeted treatment interventions.

6.2 Limitations

The studies presented in this thesis have several strengths worth mentioning. To our

knowledge, our studies were the first to systematically examine the multi-faceted motivation

system, and dimensionally investigate these facets across large clinical and non-clinical

samples. Moreover, we utilized a comprehensive and extensive battery of objective

computerized tasks, specifically intended to tap into the discrete facets of motivation. This

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being said, the findings of these studies should be interpreted within the context of the

following limitations. First, all schizophrenia patients were being treated with antipsychotic

medications. Concerns have been raised regarding the influence of dopamine antagonism on

motivation deficits, as well as the possibility that these symptoms are secondary to

antipsychotic medications (Artaloytia et al., 2006; Mas et al., 2013). Further, antipsychotic

class may also have profound effects on the reward and motivation system. For instance,

neuroimaging and behavioural studies have demonstrated altered reward responses to

anticipation cues and effort-cost computations in patients treated with typical antipsychotic

drugs (Gold et al., 2015; Juckel et al., 2006a). Of note, however, a more recent study failed to

show a significant association between antipsychotic medication and amotivation in

schizophrenia (Fervaha et al., 2016a). Nonetheless, we took additional steps to minimize the

potential effects of excessive dopamine antagonism by excluding individuals who exhibited

significant extrapyramidal symptoms. In addition, we examined the relationship between

antipsychotic dose, and clinical and objective measures of motivation, and included

chlorpromazine equivalents as a covariate in our analyses, as appropriate.

Another important limitation to consider is the use of different psychotropic

medications across diagnostic groups. In study three, for instance, patients were being

treated with a variety of medications including antipsychotics, antidepressants, stimulants or

some combination of the three. To date, however, there is no standardized method of

calculating dose equivalences for different classes of drugs, which hinders our ability to

make meaningful trans-diagnostic comparisons. In our analyses, we attempted to account for

the effects of medication status by computing two binary contrasts: one for antipsychotics

(i.e. antipsychotic vs. no antipsychotic), and one for antidepressants (antidepressant vs. no

136

antidepressant), and including these as covariates in our analyses. In the supplementary

section of this thesis, we further examined the specific roles of antipsychotic and

antidepressant medication status on motivation task performance (of note, a similar contrast

for stimulant medication status was not conducted due to the very small number of

individuals using stimulant medication, thus precluding meaningful comparisons). Though

we recognize that this may be a crude method that fails to capture the nuances of precise

medication dosage effects, this approach has been used by others examining reward

processes across diagnostic groups (Hägele et al., 2015). Future studies should aim to

replicate these findings in both medicated and non-medicated samples in order to ascertain

the effects of different medication class on motivation task performance.

Additionally, in study 1, anhedonia was assessed using only the TEPS, a self-rated

scale that reports non-current feelings, and requires respondents to reliably recall and report

on their experiences of pleasure. Recognizing the inherent restrictions of self-report

questionnaires, as well as the discrepancies often noted between subjective and experiential

measures, we sought to mitigate these limitations by incorporating objective measures in our

investigations of the different motivation facets in studies 2 and 3. Moreover, clinical

measures of negative symptoms, such as the SANS, are limited by their heavy reliance on

behavioural proxies to evaluate amotivation. Although the SANS was administered,

particularly to assess for diminished expression, we utilized the AES as our primary measure

of amotivation in all 3 studies as it captures the behavioural, emotional, and cognitive aspects

of this domain, without relying as heavily on functioning. Nonetheless, the subjective nature

of clinical assessment and self-report serves as a limitation not only within these studies, but

for the field of psychiatry more broadly. Moving forward, the development and incorporation

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of objective assessments of clinical amotivation to augment clinical rating scales, and the

inclusion of neuroimaging may enable the most comprehensive understanding of the human

motivation system.

In our examination of the motivational framework in a non-clinical population, we

utilized a sample comprised entirely of undergraduate students, limiting the generalizability

of our results to the general population. Future studies should therefore aim to replicate these

results in older healthy individuals. Moreover, all students received credit for participation,

so it is possible that this method of recruitment biases studies examining motivation. Indeed,

this selection bias can be applied to all research studies examining motivation. That is, the

individuals partaking in these studies are at least motivated enough to voluntarily participate

in these experiments.

Lastly, the third study utilized a clustering algorithm to identify subgroups of

individuals with unique motivation performance profiles. The benefit of this approach is that

it allows for a dimensional examination of the multiple facets of motivation beyond

diagnostic boundaries. However, the drawback of cluster analyses is that results can vary

depending on the choice of algorithm, distance functions and/or input data. Moreover, there

are currently no guidelines for choosing the best clustering method, nor is there a gold

standard for estimating the optimal number of clusters. Though we took additional steps to

validate our results by computing a number of statistics, as well as establishing consensus

with other clustering methods, these results require replication in a larger sample.

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6.3 Future Directions:

Motivation deficits across neuropsychiatric disorders

The results of the studies presented here mark important steps forward in our

understanding of motivational deficits, and guide future dimensional analyses of the multi-

faceted motivation system. Given that this work is the first, to our knowledge, to

comprehensively investigate the multiple facets of motivation in SZ, MDD, and HCs

concurrently, future studies should aim to replicate our findings in a larger sample.

Moreover, motivation deficits are not exclusive to schizophrenia or major depressive

disorder, but exist in a number of other neuropsychiatric and neurological disorders including

Alzheimer’s disease, Huntington’s disease and acquired traumatic brain injury. Thus,

expanding the examination of specific motivational deficits to a broader range of illnesses is

an important future direction that will serve to further our understanding of the differential

manifestations of amotivation, but also enable further refinement of our proposed motivation

framework and the interaction among discrete facets of motivation. An important direction

going forward will be identifying motivation tasks that capture the core deficits of each

cluster, or alternatively, developing a single computerized task that incorporates measures of

both hedonic capacity, as well as higher-order reward system facets such as goal-directed

decision making that will then allow for cluster profiling on the basis of task performance.

The interaction between motivation and cognition

Throughout this thesis, and across most studies of motivation deficits in clinical

populations, neurocognitive functioning is routinely evaluated. This is driven by the

139

recognition that neurocognitive processes are closely related to, and likely support

performance within specific facets of the motivation system. For example, working memory

impairments have been found to contribute to more rapid discounting of delayed rewards in

schizophrenia, with the suggestion that working memory is important for individuals to be

able to maintain “online” representations of values or rewards (Gold et al., 2008; Heerey et

al., 2011). Similarly, the processing of rapid reward learning and the utilization of this

information for goal-directed decision making would be expected to rely on similar cognitive

processes (Collins et al., 2014; Gold et al., 2008; Waltz et al., 2007). Conversely, overall

motivation or effort that individuals exert during cognitive testing has been shown to

contribute to deficits in cognitive test performance, although cognitive capacity for such

individuals may be intact (Fervaha et al., 2014c; Foussias et al., 2015; Schmand et al., 1994).

Although the findings in our studies were not impacted by differences in cognitive

functioning across participants, there emerged relationships between facets of motivation

(and clusters of motivation profiles) and global neurocognitive deficits. Moving forward, a

more thorough investigation of the interaction of specific neurocognitive domains and their

influence on facets of motivation, across clinical and non-clinical samples, would serve to

deepen our understanding of motivation system functioning.

Importantly, while motivation tasks that are typically employed, including the ones

described in this thesis, focus on abstract or inanimate stimuli and rewards, much of human

existence involves the pursuit of goals in a social world. Social goals and rewards, however,

comprise a domain of motivation that is often overlooked in studies of motivation. Further,

while neurocognitive functions may be involved in motivation, an extended examination of

the additional contributions of social cognition (i.e., mental processes underlying the ability

140

to perceive the intentions and feelings of others), would be particularly relevant given the

growing awareness of social cognitive deficits in schizophrenia (Green et al., 2015), and the

complex interplay between neurocognition, social cognition, and amotivation in predicting

community functioning (Gard et al., 2009; Green et al., 2012; Sergi et al., 2007).

In light of the proposed dynamic process of updating reward value and cost

requirements while in pursuit of a goal, it is also important to consider that the appraisal of

“value” and calibration of “cost” is likely to change over time, as well. Such computations

and decisions are likely influenced by the current clinical state of the individual, and also by

the fundamental changes that occur in one’s environment as a result of developing a chronic

or recurrent illness. Thus, an important area that remains underexplored is the longitudinal

course of, and changes within the motivation system that are experienced by individuals in

response to their illness. For example, the presence of dysfunctional attitudes, defeatist

beliefs, and reduced expectations of reward or pleasure have been extensively documented in

schizophrenia (Couture et al., 2011; Gard et al., 2007; Grant and Beck, 2009; Strauss and

Gold, 2014), with the possibility that the motivational impairments observed in chronic

illnesses may be a consequence of long-term disability, and the subsequent development of

maladaptive cognitive biases about performance and ability (Rector et al., 2005). The extent

to which such beliefs and expectations interact with the facets of motivation articulated in

this thesis, as well as their developmental trajectory, are important questions for the future

which may inform novel therapeutic strategies to address motivation impairments across

clinical populations, and the optimal window within which to intervene.

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The impact of psychotropic medications on the motivation system

With pharmacological interventions representing a clinical reality for patients with

schizophrenia, major depressive disorder, and other neuropsychiatric disorders, it is also

important to consider the influence of medication on clinical amotivation. As outlined above,

antipsychotic medications have been linked to motivation deficits in some studies (Artaloytia

et al., 2006; Mas et al., 2013), but not consistently (Fervaha et al., 2015b). Similarly,

antidepressant medications have also been linked to motivation and hedonic impairments,

with such impairments frequently listed as potential side effects from these medications, and

in particular SSRIs (McCabe et al., 2010; Price et al., 2009). Unfortunately however, the

current lack of standardized methods for calculating dose equivalences across different

medication classes poses as a significant barrier to conducting meaningful transdiagnostic

comparisons, and therefore warrants further investigation. One approach to evaluating the

potential impact of psychotropic medications on the motivation and reward system could

involve the examination of non-medicated samples and comparisons with medicated patient.

An alternative strategy could involve pharmacologic challenge studies, whereby healthy

participants receive acute and sub-acute administration of psychotropic medications (e.g.,

typical or atypical antipsychotic medication, serotonergic or noradrenergic antidepressant

medication) with dose varied across participants, and serial evaluations of performance

across facets of motivation.

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Investigations of the neurobiology of the multi-faceted motivation system

To date, no single study has yet to comprehensively examine the neurobiological

correlates of the multi-faceted motivation framework. Thus, our current understanding of the

neurobiology of the reward system relies on individual studies examining isolated facets of

motivation. According to the existing literature, hedonics (i.e. “liking”) is linked to the

opiodic and gamma-aminobutyric acid-ergic (GABAergic) systems in the ventral striatum,

nucleus accumbens and OFC (Dowd and Barch, 2010; Peciña et al., 2006; Smith and

Berridge, 2007), whereas reward expectancy or anticipation (i.e. “wanting”) is associated

with dopaminergic activity in the ventral striatal regions of the basal ganglia (Dowd and

Barch, 2010; Knutson et al., 2008). Reward valuation, or the representation of value has been

linked to lateral and medial OFC functions (Barch and Dowd, 2010; Gold et al., 2008),

whereas the nucleus accumbens and anterior cingulate cortex have been implicated in effort-

cost computations (Dowd and Barch, 2010; Salamone et al., 2003). Lastly, goal-directed

decision making and planning has been linked to dorsolateral prefrontal cortex (DLPFC)

(Barch, 2005; Semkovska et al., 2004). Though highly informative, these individual studies

cannot address the extent to which specific brain regions are unique to each facet, or whether

these are overlapping areas related to other motivation constructs. Thus, a more

comprehensive examination of the neurobiological underpinnings of the motivation

framework will allow for improved specificity, with the opportunity to uncover precise brain-

behaviour relationships and the full range of motivation impairments across diagnoses.

Informed by these findings, we can then explore an avenue for target engagement in clinical

trials that simulate or challenge the neural circuity of these constructs in new clinical trial

subgroups.

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Advancing therapeutic interventions for motivation deficits – addressing a critical unmet

need

Perhaps most pertinent to the discussion of motivation deficits in clinical populations

is its link to poor functional outcomes, and the unmet therapeutic need that it currently

represents. The results of these studies suggest several potential novel therapeutic avenues

worth pursuing. Pharmacologic and brain stimulation interventions for motivation deficits

(or more broadly negative symptoms) have typically been carried out in schizophrenia

populations, and have yielded inconsistent effects. Examination of results from meta-

analyses, however, suggests that some patients do experience a benefit (Fusar-Poli et al.,

2015; Singh et al., 2010). In light of our findings of distinct clusters, the characterization of

individuals based on the underlying motivation profile driving their clinical amotivation may

serve to identify the subgroups of patients who are more likely to benefit from a particular

intervention. Similarly, non-pharmacologic interventions such as computerized cognitive

remediation (Cella et al., 2017; Sánchez et al., 2014; Vita et al., 2011; Wykes et al., 2011),

and early findings from our group on the use of virtual reality-based motivation training

(Foussias et al., 2017), indicate potential benefit for negative symptoms and in particular

motivation deficits. What remains unanswered is the extent to which a particular cluster of

individuals is more responsive to such interventions based on their profile of motivation

impairment. It will therefore be important for neurobiological investigations to uncover the

neural mechanisms that give rise to unique clusters of motivation profiles in order to inform

targeted therapeutic interventions based on region-specific brain stimulation or

neurotransmitter-specific pharmacologic interventions. Finally, early identification of an

individual’s behavioural and neurobiological motivation system profile may inform strategies

144

for combining brain stimulation, pharmacologic intervention, and computerized brain

training, with the ultimate goal of mitigating functional impairment and optimizing their

functional recovery.

Conclusions

The findings across these three studies serve to further our understanding of the

behavioural and neurobiological mechanisms underlying motivation deficits in schizophrenia

and major depressive disorder. Our results suggest that clinical amotivation is a significant

predictor of subjective and objective motivation performance in both clinical and non-clinical

samples. Further, utilizing the most comprehensive and extensive battery of objective

measures to date, our results revealed a motivational framework comprised of hedonic

capacity, reward expectancy and learning, cost-benefit decision making, goal-directed

decision making, and effort expenditure. In line with dimensional approaches to examining

psychopathology, we identified 2 clusters of individuals with similar behavioural motivation

profiles, and potentially distinct underlying neurobiological mechanisms across SZ, MDD,

and healthy control participants. These findings may shed light on novel treatment

opportunities targeting this domain of unmet therapeutic need.

145

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