Anhedonia in Major Depressive Disorder: Exploration of a Predictive Clinical Phenotype

Transcription

1 Anhedonia in Major Depressive Disorder: Exploration of a Predictive Clinical Phenotype by Sakina Rizvi A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Pharmaceutical Sciences University of Toronto Copyright by Sakina Rizvi 2015

2 Anhedonia in Major Depressive Disorder: Exploration of a Clinical Phenotype Sakina Rizvi Doctor of Philosophy Pharmaceutical Sciences University of Toronto 2015 Abstract Major Depressive Disorder (MDD) is a debilitating psychiatric illness that involves a complex interplay of neurobiological dysfunction. At present, the majority of MDD patients fail to remit with current antidepressants that target the serotonin and norepinephrine systems, resulting in a high prevalence of treatment resistant depression (TRD). In the last several years, emerging evidence has pointed to the role of anhedonia in predicting non-response to pharmacologic treatment. However, anhedonia scales, as measured in MDD, reflect consummatory pleasure, despite findings supporting a broader definition including interest, motivation and pleasure. Furthermore, the link between anhedonia and dopamine has not been clearly elucidated in a human MDD sample, nor has either been evaluated as a predictor of therapy in TRD. The goal of the present studies was to refine the measurement of anhedonia, explore its association with dopamine, and evaluate anhedonia and dopamine as potential biomarkers of response to Deep Brain Stimulation (DBS), a neurosurgery for TRD with putative dopaminergic effects. From the present studies it has been shown that refinement in anhedonia measurement in the Dimensional Anhedonia Rating Scale (DARS) by including interest, motivation, effort and consummatory pleasure provides additional utility over the gold standard scale, and may be able to identify MDD subtypes (i.e. TRD). Furthermore, the first preliminary evidence was provided for a direct link between self-reported anhedonia in MDD and dopamine D2/D3 receptor binding in the anterior cingulate cortex and dorsolateral prefrontal cortex, two regions implicated in reward response and depression. Finally, preliminary evidence suggests that D2/D3 binding potential in the orbitofrontal cortex, additional prefrontal regions, insula and temporal cortex can predict outcome to antidepressant therapy with DBS, representing a potential biomarker of response. ii

3 Acknowledgments Research is not a solitary process. With that in mind, I am greatly indebted to the team that helped to make the culmination of this work possible. In particular, my mentors Dr. Sidney Kennedy and Dr. Beth Sproule provided incredible guidance and support. Under their tutelage, I have developed a keen understanding of not only what good research entails, but how to conduct it with ethics and style! The confidence they have shown in my abilities over the years has no doubt inspired my own confidence stepping out of my PhD nest into the great big world. I was privileged and fortunate to have these coaches on my side. I am also very grateful for the help of Dr. Antonio Strafella, who brought me into his PET laboratory and provided me with the tools and support from his own team to enable my success as a PET imager. Dr. Lena Quilty has also been a great mentor in scale development and whose expertise was instrumental to the creation of the Dimensional Anhedonia Rating Scale (DARS). Dr. Usoa Busto and Dr. Jeffrey Henderson were also valuable members on my committee, who were thought provoking and challenged me to dig deeper. I have to specially acknowledge Anna Cyriac for her remarkable research support over the years, without which I wouldn t have accomplished nearly as much. Numerous members of the University Health Network team were essential to the success of these projects and overall PhD program including Dr. Peter Giacobbe, Dr. Andres Lozano, Dr. Shane McInerney, Dr. Jonathan Downar, Dr. Roger McIntyre, Dr. Joseph Geraci, Dr. Franca Placenza, Dr. Susan Rotzinger, Joanna Soczynska, Wilma Aranha, Madelin Donovan and Dr. Tim Salomons. Researchers at the Centre for Addiction and Mental Health (CAMH) were also instrumental to these projects, including the staff at the CAMH PET centre. In particular, the analysis of PET data was greatly facilitated by Dr. Sangsoo Choo, Dr. Pablo Rusjan, Dr. Nicola Ray, and Ms. Leigh Christopher. I would also like to acknowledge Dr. Alexander Elkader, Dr. Xavier Balducci, and Dr. Helen Mayberg for advice during the early stages of project development. On a personal note, the unfailing support of my cheerleaders in life, especially towards the end of this degree, inspired and motivated me just when I needed it most. Finally, funding of the PET data was provided by St. Jude Medical, and the DARS development was supported by funds from the Ontario Problem Gambling Research Centre (grant holders: Dr. Michael Bagby and Dr. Lena Quilty), and the Department of Pharmaceutical Sciences, University of Toronto. iii

4 Table of Contents Abstract... ii Acknowledgments... iii Table of Contents... iv List of Tables... ix List of Figures... x List of Appendices... xi List of Abbreviations... xii Chapter 1 Literature Review Major Depressive Disorder: Burden, Diagnosis and Neurobiology Burden of Depression Diagnosis of MDD Neurobiology of Depression Brain structure Brain function Neurotransmitter function and neural processes Monoamines and Depression Neuropeptides and the HPA-axis Amino acids and Depression Acetylcholine Opioids Inflammation Summary of MDD pathophysiology Overview of Treatments for MDD Antidepressant Monotherapy iv

5 2.2 Antidepressant Switching and Augmentation Neuromodulation Therapies Poor Antidepressant Outcomes and Treatment Resistant Depression Deep Brain Stimulation Prediction of Treatment Outcome Anhedonia as a Clinical Predictor Anhedonia Construct Prediction of MDD Status and Treatment Outcome Neurobiology of Anhedonia Anhedonia and antidepressant mechanism of action Anhedonia Measurement Scale development Reliability and Validity Factor Analysis Neuroimaging PET imaging Imaging Dopamine using PET Analysis of binding potential data Sumary and Identified Needs Study Objectives and Hypotheses Chapter 2 Methods Anhedonia Rating Scale Development Design Phase 1: Content Validity Scale Development Expert Review v

6 8.2.3 Analysis Plan Phase 2: Item Selection Subjects Procedure Analysis Plan Phase 3: Cross-Validation Subjects Procedure Measures Analysis Plan Phase 4: Validation in MDD Subjects Procedure Analysis Plan Anhedonia and Dopamine D2 Receptor Association and Prediction of DBS Outcome Design Subjects Procedure Measures Imaging Methods Statistical Tests Baseline D2 binding and relationship to anhedonia Prediction of DBS outcome based on D2 binding and anhedonia Risks and Safety Issues MRI PET vi

7 Chapter 3 Results Anhedonia Scale Development Phase 1: Content Validity Phase 2: Item Selection Phase 3: Cross-validation Phase 4: Validation in MDD Dopamine D2 Binding D2 Binding and relationship to anhedonia Prediction of DBS outcome at 1 year based on D2 binding and anhedonia Chapter 4 Discussion Executive Summary DARS Scale Development Self-report themes Item selection and factor solution Reliability Validity Group differences Hierarchical regression analysis Limitations D2 association with anhedonia and DBS outcome Anhedonia and dopamine in depression Predictors of depression outcome Limitations Conclusions and Future Directions References Appendix vii

8 Appendix Appendix Appendix Appendix Copyright Acknowledgement viii

9 List of Tables Table 1. Criteria for a Major Depressive Episode and severity indicators Table 2. CANMAT Guidelines for Antidepressant Monotherapy Table 3. Content validity based on expert comments Table 4. Demographic information of Phase 2 participants Table 5. Self-report examples of hobbies Table 6. Self-report examples of social activities Table 7. Self-report examples of food/drink Table 8. Self-report examples of sensory experience Table 9. Cronbach s alpha and inter-item correlations for the DARS item selection study Table 10. Demographic data for cross-validation study sample Table 11. Reliability and validity of the DARS total score and subscales in online sample Table 12. Demographic information in Depressed Patients and Healthy Controls Table 13. Reliability and validity of the DARS total score and subscales in MDD sample Table 14. Differences in anhedonia/reward scale scores between TRD and non-trd patients Table 15. Demographic and clinical information for TRD sample Table 16. Demographic and clinical information for TRD DBS sample ix

10 List of Figures Figure 1. Structural overlap of dopamine, serotonin and norepinephrine systems Figure 2. Neurocircuitry of MDD Figure 3. Neurobiology of reward circuitry Figure 4. Model of anhedonic function in major depression, and areas of thesis focus Figure 5. DARS feedback questionnaire Figure 6. Deep Brain Stimulation study design Figure 7. DARS 17-item scale Figure 8. Difference in DARS-17 score between MDD and Healthy Controls in pivotal validation trial Figure 9. DARS and SHAPS scores in TRD and non-trd patients in pivotal validation trial Figure 10. Participants rating 4 or 5 on DARS feedback questions Figure 11. Scatterplot of D2/D3 binding in relation to DARS total score Figure 12. Association between high and low anhedonia scores with D2/D3 binding Figure 13. Differences in regional D2 binding among responders and non-responders to DBS x

11 List of Appendices Appendix 1. Initial version of the DARS 34-item scale Appendix 2. Phase 2 factor analysis data Appendix 3. Comparison of SHAPS and DARS correlations with the BIS/BAS Appendix 4. Distribution of DARS items between MDD and Healthy Controls Appendix 5. Average DARS scores across studies xi

12 List of Abbreviations ACC: AIC: AMPA: BIS/BAS: Bmax: BP: CAMH CBF: CESD: CMT CPAS: CRF: CSAS: DARS DAT: DBS: DLPFC: DMN: DSM-5: DSM-IV: DSM-IV-TR: DTI: ECT: EFA: FCPS: fmri: GABA: GENDEP GR: HAMD-17: HPA: Anterior cingulate cortex Average inter-item correlation α-amino-3-hydroxy-5-methyl-4- isoxazole propionic acid Behavioral Inhibition System/ Behavioral Activation System Total number of receptors in a brain region Binding potential Centre for Addiction and Mental Health Cerebral blood flow Centre for Epidemiologic Studies in Depression: Classical measurement theory Revised Chapman Physical Anhedonia Scale Corticotropin-releasing factor Chapman Social Anhedonia Scale Dimensional Anhedonia Rating Scale Dopamine transporter Deep Brain Stimulation Dorsolateral prefrontal cortex Default Mode Network Diagnostic and Statistical Manual of Mental Disorders, 5 th edition Diagnostic and Statistical Manual of Mental Disorders, 4 th edition Diagnostic and Statistical Manual of Mental Disorders, 4 th edition, text revision Diffusion tensor imaging Electroconvulsive therapy Exploratory factor analysis Fawcett-Clark Pleasure Capacity Scale Functional magnetic resonance imaging Gamma-aminobutyric acid Genome Based Therapeutic Drugs for Depression Glucocorticoid receptors Hamilton Depression Rating Scale 17 item Hypothalamic-pituitary-adrenal ICD-10: IL-6: Kd: MADRS: MAOIs: MDD: MDE: MFB MRI: MRS: nachr: NASA: NMDA: NRIs: OFC: PCA: PCC: PET: PFC: rtms: SCG: SCID SERT: SHAPS: SNRI: SPECT: SSRI: STAR*D TCAs: TNFα: TRD: UHN VLPFC: VNS: International Classification of Diseases and Related Health Problems, 10 th revision Interleukin-6 Dissociation constant of the ligand binding to the receptor Montgomery Asberg Depression Rating Scale Monoamine oxidase inhibitors Major Depressive Disorder Major Depressive Episode Medial forebrain bundle Magnetic resonance imaging Magnetic resonance spectroscopy Nicotinic acetylcholine receptor NASA Physical Activity Scale N-methyl-D-aspartate Norepinephrine reuptake inhibitors Orbitofrontal cortex Principal components analysis Posterior cingulate cortex Positron emission tomography Prefrontal cortex Repetitive transcranial magnetic stimulation Subcallosal cingulate gyrus Structured Clinical Interview for DSM-IV Disorders Serotonin transporter Snaith-Hamilton Pleasure Scale Serotonin and norepinephrine reuptake inhibitor Single photon emission tomography Selective serotonin reuptake inhibitor Sequenced Treatment Alternatives to Relieve Depression Tricyclic antidepressants Tumor necrosis factor alpha Treatment resistant depression University Health Network Ventrolateral prefrontal cortex Vagus Nerve Stimulation xii

13 1 Chapter 1 Literature Review 1 Major Depressive Disorder: Burden, Diagnosis and Neurobiology 1.1 Burden of Depression Depression is not only an experience in the mind; it is also an affliction of the body. - Philip Martin, The Zen Way through Depression Major Depressive Disorder (MDD) is a complex illness that involves an interplay among physiological systems. As a highly prevalent disorder, it can render one s functioning impaired, causing an adverse domino effect on an individual s self-perceptions, relationships, and employment, ultimately leading to broader societal economic consequences. In Canada alone, the direct and indirect costs of mental illness were estimated to be $14 billion annually (Stephen & Joubert, 2001). Currently MDD is the leading cause of worldwide disability among neurological and mental disorders and accounts for the loss of over 65 million disability-adjusted life years (Collins et al, 2011). 1.2 Diagnosis of MDD Psychiatric diagnoses are symptom based, for which there are two diagnostic systems: (1) the Diagnostic and Statistical Manual of Mental Disorders, 5 th Edition (DSM-5; APA, 2013) developed by the American Psychiatric Association and (2) the International Statistical Classification of Diseases and Related Health Problems, 10 th revision (ICD-10, 2000) developed by the World Health Organization. The ICD system is more frequently used internationally for clinical and training purposes, the DSM remains the most widely used classification system for research and clinical practice in North America. Importantly, there are no major differences between the two systems for psychiatric diagnoses and the DSM attempts to conform to ICD. The first edition of DSM appeared in 1952 with a revision approximately every years until 1994 when the DSM-IV was published (a text revision was released in 2000 DSM-IV-TR).

14 Consequently, most psychiatric research in the last 20 years uses DSM-IV criteria. The studies in this thesis also utilize DSM-IV. Broadly, Axis I disorders, as defined by the DSM-IV, include mood disorders (MDD and Bipolar Disorder), anxiety disorders, psychotic disorders (e.g. schizophrenia), and eating disorders. There is some degree of symptom overlap across disorders (e.g. insomnia is a symptom of depression as well as post-traumatic disorder), although the driving disturbance is distinct. There are also high rates of comorbidity across disorders with the presence of depression, in particular, predisposing an individual to other mental illness (Kessler et al, 2003). A major depressive episode (MDE) can be a single episode or be recurrent. Core features of an MDE are the experience of sadness and/or anhedonia (loss of interest). Without the presence of at least one of these symptoms and a minimum of 5 symptoms in total, a clinical diagnosis cannot be made. No other psychiatric diagnosis specifies these symptoms as essential diagnostic criteria. While it may be a common public perception to view depression as a disorder of sadness, some patients do not experience sadness at all and instead primarily report anhedonia. In addition, individuals may also experience the following: sleep disruption, appetite disturbance, fatigue, psychomotor retardation or agitation, feelings of worthlessness, decreased concentration or decision making capacity, and suicidal ideation (Table 1). The DSM-IV does not have a metric to evaluate severity of symptoms, therefore, clinical scales are used; the most common of which are the Hamilton Rating Scale for Depression 17 item (HAMD-17) (Hamilton, 1960) and 2 Table 1. Criteria for a Major Depressive Episode and severity indicators * At least either criterion # 1 or 2 have to be present. 1 Hamilton, 1960; 2 Montgomery & Asberg, 1979

15 the Montgomery-Asberg Depression Rating Scale (MADRS) (Montgomery & Asberg, 1979). The cutoff scores for severity based on these clinician administered scales can be found in Table 1. Briefly a score less than or equal to 7 on the HAMD-17 or less than or equal to 10 on the MADRS reflects an absence of or clinically non-significant depressive symptoms, while greater than 20 on the HAMD-17 or greater than 34 on the MADRS is indicative of severe depressive symptomology. In the recently published DSM-5, there were few significant changes to the diagnostic criteria for an MDE except the addition of hopelessness as a separate symptom (DSM-5, 2013). With the requirement that either sadness or anhedonia must be included as one of the 5 or more symptoms that are present for at least 2 weeks in order to reach a diagnosis of an MDE, it is evident that many permutations exist to meet criteria and there may be no overlap across individuals with the exception of the core symptoms. This leads to a significant degree of clinical heterogeneity and likely biological heterogeneity (Ostergaard et al, 2011). Criteria within the ICD-10 are similar, with the exception of fatigue/low energy as an additional core symptom along with sadness/anhedonia. MDEs are determined by the severity and number of symptoms present (e.g. mild MDE: at least 2 of the key symptoms, and at least 2 secondary symptoms of mild severity; severe MDE: all 3 of the key symptoms, and at least 4 secondary symptoms of severe intensity) (ICD-10, 2000). There are also several MDE specifiers that categorize the clinical phenotype of depression of which the two main ones in DSM-IV are: (1) melancholia: characterized by the presence of anhedonia, insomnia, anorexia, depression quality that is different from grief/loss, feeling worse in the morning, slowness of thought/movement, and excessive guilt; (2) atypical: characterized by emotional reactivity, hypersomnia, overeating, and leaden paralysis. Other specifiers include seasonal pattern and post-partum onset. DSM-5 has additional specifiers for anxiety (with Anxious Distress) and for mixed features requiring the presence of 3 or more hypo(manic) symptoms but without sufficient symptoms to meet criteria for mania. Depression can be conceptualized broadly as a stress disorder, although there does not need to be a life event trigger for an MDE. In contrast, an accumulation of smaller stressors in a genetically vulnerable individual could predispose one to depression. Stressors are not taken into account in the diagnosis of MDD, if the symptoms are not considered to have directly arisen from a life event, with the exception of bereavement or a general medical condition. 3

16 4 Importantly, an MDE can also occur in the context of bipolar disorder as well as schizoaffective disorder (DSM-IV, 2000). In bipolar disorder, individuals cycle between mania and depression. The diagnosis of Bipolar type I requires the presence of at least one manic episode, while the diagnosis of Bipolar type II includes less severe mania (hypomania) and invariable episodes of depression. It is therefore possible for a bipolar patient to spend most active ill time in an MDE. There have been few studies conducted to differentiate unipolar and bipolar MDEs on the basis of neurocircuitry with preliminary evidence of greater white matter abnormalities and differences in emotional processing in the amygdala in bipolar disorder (de Almeida Jr & Phillips, 2013). Schizoaffective disorder is a psychotic disorder where a patient experiences a mood episode (MDE or mania) during and outside of a psychotic episode. 1.3 Neurobiology of Depression Brain structure Experiencing the symptoms of an MDE is generally believed to reflect an interaction between environmental factors and genetics. Consequently, environmental or social stressors (e.g. overcrowding in living conditions, parental divorce) can cause a host of downstream neurobiological effects including overactivity of the hypothalamic-pituitary-adrenal (HPA) axis, dysfunction in neurotransmitter signaling, reduced neurogenesis and increased neuroinflammation (Miller et al, 2009). During a depressive episode and with successive episodes, an individual may also experience brain atrophy (Yucel et al, 2008; MacQueen et al, 2008). Meta-analyses of magnetic resonance imaging (MRI) data have shown that the most consistent findings are large volumetric decreases in the prefrontal cortex (PFC), including the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC) (Koolschijn et al, 2009; Sacher et al, 2012), areas important for reward learning, executive function, and emotion regulation. Moderate decreases in the hippocampus and striatum are also observed, which are also involved in memory and reward learning. Reductions in the hippocampus have even been observed prior to depression onset in a group at high risk for familial depression (Chen et al, 2010). While white matter abnormalities have also been observed in depression (Wang et al, 2014), results have been variable with other authors suggesting white matter changes may not be a core feature of depression pathophysiology (Choi et al, 2014). However, decreased structural integrity of the

17 anterior corpus callosum may contribute to the interhemispheric differences in activity observed in depression (Xu et al, 2013) Brain function Through the use of positron emission tomography (PET), and functional MRI (fmri), brain activity changes at rest and in response to tasks (primarily affective challenges) provide important information regarding the depressed brain. At rest, individuals with MDD demonstrate hyperactivity in medial subcortical and cortical structures including the posterior cingulate cortex (PCC), ventromedial PFC, thalamus, striatum ventral tegmental area, and periaqueductal gray along with the hippocampus and amygdala (Alcaro et al, 2010; Price & Drevets, 2010). In contrast, hypoactivity has been observed in more lateral structures such as the dorsolateral PFC (Alcaro et al, 2010; Price & Drevets, 2010). Not only do some of these areas map onto the structural deficits observed in MDD (see Section above), but they also map onto areas in the default mode network (DMN) (Raichle et al, 2001). This network of midline and lateral cortical brain areas is thought to mediate emotional and self-referential processing, memory, internal mentation (i.e. introspection and internal attention), and allocation of attention to cognitive tasks (Broyd et al, 2009; Andrews-Hanna et al, 2010). The DMN has greater activity at-rest (during no goal-directed behavior) compared to during goal-directed or stimulusinduced tasks. Considering the negative bias, rumination, memory and attentional deficits observed in MDD, the DMN is of particular interest in elucidating the pathophysiology of depression. In particular, fmri studies have been used to define the brain networks associated with different emotions (Fu et al., 2004; 2007; Hariri et al., 2002; Kalin et al., 1997; McCabe et al, 2009; Northoff et al, 2000; Phillips et al, 2001; Siegle et al., 2002; Wang et al, 2012). A replicated outcome when employing emotional provocation techniques evaluated with fmri in depressed states is overactivity in the subgenual ACC, amygdala, insula, and PFC (Anand et al., 2005; Harmer et al., 2009; Keedwell et al., 2009; Kalin et al., 1997; Sheline et al., 2001). These abnormalities have been found to normalize following successful treatment with selective serotonin reuptake inhibitors (SSRI) or serotonin and norepinephrine reuptake inhibitors (SNRIs) (Rosenblau et al., 2012; Davidson et al., 2003). Furthermore, following 6 weeks of

18 olanzapine/fluoxetine combination therapy, Rizvi and colleagues (2013a) demonstrated increases in baseline insula activity during exposure to negative images in antidepressant nonresponders, compared to responders and healthy controls. Higher baseline activity in the PCC during exposure to positive images was also predictive of greater percent change in depression scores at 6 weeks. Importantly, the field of biological psychiatry has moved away from identifying differences in regional areas of activation to evaluating differences in functional connectivity between groups. By extracting the timelines of activation, networks of activity can be identified. Given the temporal nature of the modeling, a causal direction can be implied (e.g. amygdala is activated before the dorsomedial prefrontal cortex). The analysis may employ a whole-brain approach or, preferably, a seed brain region is identified from which to distinguish connectivity to other areas. The whole brain approach is useful to understand which brain areas are activated together, while using a seed region is equivalent to a hypothesis driven region of interest analysis. Considering the multitude of possible connections in the brain, results of functional connectivity studies depend on the method of analysis, and the seed region used. Whole brain analyses have found decreased connectivity among the amygdala, hippocampus, ACC, and PFC regions (Lai & Wu, 2014; Wang et al, 2014; Cullen et al, 2014). In contrast, seed analysis studies show a somewhat different picture with increased and decreased connectivity occurring from the same region depending on the network. For example, the subgenual ACC has increased functional connectivity to the insula and amygdala, but decreased connectivity to the precuneus (Connolly et al, 2013). Given the prominence of the ACC in depression, other reports have focused on connectivity of this area. One report demonstrated increased connectivity between the subgenual ACC and the thalamus, which was positively correlated with depression scores (Greicius et al, 2007). In a youth MDD sample, increased connectivity between the subgenual ACC and dorsomedial PFC was positively correlated with depression scores (Davey et al, 2012). Within this same group, subgenual ACC connectivity was also increased to the left dorsolateral PFC, while decreased to the bilateral caudate. Functional connectivity does not imply structural connectivity and vice versa, and few studies have addressed this issue. In MDD patients, De Kwaasteniet and colleagues (2013) demonstrated a negative correlation between the functional connectivity of the subgenual ACC and the medial temporal lobe with white matter integrity of the uncinate fasciculus, a tract that 6

19 connects these two areas. While the findings of a negative correlation between structure and function are corroborated by two preliminary studies in MDD (Wu et al, 2011; Steffens et al, 2011), further empirical validation is needed. In addition, the stability of this relationship may be network dependent, where some manifest positive correlations and others negative Neurotransmitter function and neural processes Many neurotransmitter systems are involved in depression, however the main neurotransmitter dysfunctions implicated in depression are decreased monoaminergic (serotonin, norepinephrine, dopamine) and GABA activity, as well as increased glutamate and glucocorticoid activity. The cholinergic and neuropeptide systems are also disrupted in depression, although they have received less attention, likely due to the failure of antidepressant development programs in these areas (Rizvi & Kennedy, 2012). Finally, aberrant opioid signaling in depression is more recently being evaluated. The effects of these systems in depression have been reviewed (Kennedy & Rizvi, 2009; Hamon & Blier, 2013). For the purposes of the thesis objectives, dopamine will be discussed in more detail than the other systems Monoamines and Depression The classic hypothesis on the pathophysiology of depression asserts that decreased monoaminergic function, primarily serotonin and norepinephrine, is responsible for the manifestation of MDD (Schildkraut, 1965; Maas, 1975). This idea was borne out of findings that both imipramine and amitriptyline, which are tricyclic antidepressants (TCAs), inhibit reuptake of monoamines. This hypothesis has driven psychopharmacological development for over 50 years, with virtually all antidepressants available today targeting serotonin or norepinephrine. A multitude of studies have consistently demonstrated decreased serotonergic function in preclinical models of depression and in humans (Dominguez-Lopez et al, 2012; Artigas, 2013; Savitz & Drevets, 2013). Prior to the advancement of neuroimaging methods, this dysfunction was evaluated using tryptophan depletion, as tryptophan is a precursor of serotonin. In these

20 studies, administration of a tryptophan hydroxylase inhibitor, or dietary depletion of tryptophan reversed the antidepressant effects of TCAs, monoamine oxidase inhibitors (MAOIs) and SSRIs in individuals with MDD (Shopsin et al, 1975; Shopsin et al, 1976; Delgado et al, 1999). Positron emission tomography (PET) imaging has provided important insight into the role of the serotonin transporter (SERT) as well as receptor subtypes in depression. The focus of research has been on SERT, with PET studies showing increased binding in MDD patients compared to healthy controls, and decreased binding with successful SSRI treatment (Savitz & Drevets, 2013). 8 Norepinephrine deficits in depression are more apparent through preclinical models, partly because of the difficulties in developing high quality ligands that can be used for PET (Schou et al, 2007). In animal studies, mice lacking norepinephrine transporters have increased norepinephrine concentrations and reduced depressive behavior (e.g. reduced immobility time in the forced swim test) (Dziedzicka-Wasylewska et al, 2006). Analogous to the tryptophan depletion studies described above, catecholamine depletion has been used to evaluate the role of norepinephrine and dopamine in preclinical and clinical trials. Catecholamine depletion reduces the effects of norepinephrine reuptake inhibitors (NRIs), but not SSRIs in mice (O Leary et al, 2007). Human studies in MDD demonstrate parallel findings whereby blocking catecholamine synthesis induced relapse in MDD patients treated successfully with an NRI (Miller et al, 1996). The role of dopamine in depression is suggested in human populations, based on results from metabolite studies, dopamine challenges and neuroimaging studies. Several investigators suggest that dopamine metabolites are significantly decreased in depressed patients (Hideaki et al, 2006; Kishida et al, 2007; Roy et al, 1985; Traskman et al, 1981). In particular, decreased levels of plasma homovanillic acid, a dopamine metabolite, have been reported in patients with treatment resistant depression (Lambert et al, 2000; Kishida et al, 2007), and have been linked to severity (Lambert et al, 2000). Preliminary evidence also suggests that MDD patients exhibit greater rewarding effects from amphetamine, a dopamine D2 agonist, compared to controls; an effect more pronounced in patients with greater depression severity and anhedonia (Tremblay et al, 2002; 2005). Furthermore following amphetamine administration, MDD patients had decreased activity in the ventrolateral PFC and orbitofrontal cortex (Tremblay et al, 2005). The increased sensitivity to dopamine in severely depressed and anhedonic patients could be explained by an upregulation of D2 receptors, potentially in frontal areas.

21 9 Several investigators have also demonstrated a correlation between depression and increased dopamine D2 receptor binding potential (D haenen et al, 1994; Meyer et al, 2006; Shah et al, 1997). Similar to the data showing increased amphetamine reward response with greater severity, positive correlations between D2 binding and symptom severity have been reported (Larisch et al, 1997; Lehto et al, 2008). This suggests greater symptom burden must be present before deficits in D2 regulation are detectable. It is important to note that multidirectional structural and functional relationships exist among serotonin, norepinephrine, and dopamine systems (Arencibia-Arite et al, 2007; Guiard et al, 2008). The primary sources of serotonin, norepinephrine, and dopamine are, respectively, the raphe nucleus, locus coeruleus, and ventral tegmentum, located in the midbrain/hindbrain. The respective monoaminergic neurons project from these areas to limbic structures including the amygdala, hippocampus, cingulate cortex, and hypothalamus, in addition to the striatum and cortical areas. Both the serotonergic and noradrenergic systems have more diffuse connections through the cortex and hindbrain compared to the dopamine system (Figure 1). Figure 1. Structural overlap of dopamine, serotonin and norepinephrine systems (modified from Black & Andreasen, 2006 with permission)

22 Due to their structural overlap, these systems are also well positioned for functional overlap, where affecting one network will consequently impact the others. The direction of effects also depends on receptor subtypes. For example, in the nucleus accumbens serotonin 5HT2C receptor agonism exerts an inhibitory effect on dopamine (Dremencov et al, 2005), whereas agonists of 5HT1A, 5HT2A and 5HT3 induce dopamine release (Yan, 2000; Parsons et al, 1993, Campbell et al, 1995). 10 While the monoamine hypothesis has served an important utility in depression research, it does not explain the occurrence of MDD in its entirety. Observations that (1) antidepressants have a delayed onset of action of approximately 2 weeks (Katz et al, 1997), (2) drugs that increase monoamines are not necessarily effective antidepressants (e.g. amphetamine, methylphenidate primarily dopamine agonists) (Madhoo et al, 2014; Ravindran et al, 2008), and (3) drugs that do not primarily target monoamines may have antidepressant utility (e.g. lithium) (Lam et al, 2009), all undermine the monoamine hypothesis as a unified theory. Consequently, understanding MDD requires the evaluation of neural processes beyond the monoamines Neuropeptides and the HPA-axis Stress has long been considered a key pathway to depression due to its physiological and behavioral effects through the hypothalamic-pituitary-adrenal (HPA) axis (Holsboer, 2000). It can trigger disruptions in sleep/wake cycles, immune function, dopamine levels, and neurogenesis and may ultimately be responsible for altered brain structures (Licinio & Wong, 1999; McEwen, 2003; Mizoguchi et al, 2000), in part via increased glutamate release (Musazzi et al, 2013). Arguably, all of the neurotransmitter pathways involved in MDD are affected directly or indirectly by stress. Although HPA-axis dysfunction has been consistently found and is well documented in MDD, it is not observed in all patients (Gillespie & Nemeroff, 2005), but may be disproportionately affected in individuals with a history of childhood trauma (Nemeroff, 2004). Once the HPA-axis is activated, the neuropeptide corticotropin-releasing factor (CRF) binds to glucocorticoid receptors (GR), and ultimately acts to release cortisol into the system (Pariante & Miller, 2001). Consequently, CRF mediates neuroendocrine, immune, behavioral,

23 and autonomic responses to stress and plays a role in the extent and duration of the stress response (Holsboer et al, 2008). It follows then increased activity of CRF and excessive stimulation of GR receptors are reported in preclinical models of depression as well as clinical studies (Holsboer et al, 2008; Belanoff et al, 2002). There is an important functional connection among stress, CRF and dopamine, whereby stress increases dopamine via CRF (Burke & Miczek, 2014); however, extreme stress abolishes this effect and renders CRF unable to regulate stress-related dopamine activity (Lemos et al, 2012) Amino acids and Depression Glutamate and GABA are the most ubiquitous neurotransmitters in the brain and represent the primary excitatory and inhibitory messengers, respectively. GABAergic neurons represent 80% of all neurons in the brain. Although glutamate and GABA work synergistically in the brain, they have been investigated independently. The following review will focus on these studies with a brief discussion on hypotheses related to complementary function. There is evidence that GABA directly modulates HPA axis activity, neurogenesis, dopamine and glutamate function, and indirectly has downstream effects from serotonin and norepinephrine transmission, all systems implicated in the etiopathology of MDD. Specifically in MDD, there are consistent reports of reduced GABA metabolites in plasma and cerebrospinal fluid compared to healthy controls (Gerner & Hare, 1981; Petty et al, 1992). Neuroimaging studies of GABA using single photon emission tomography (SPECT) and PET are limited by the availability of adequate tracers. As a result, there are few trials using these methods in MDD. One SPECT study failed to find any differences between MDD patients and healthy controls (Kugaya et al, 2003), while an 11C-flumazenil PET study showed decreased GABA binding in the parahippocampal gyrus and lateral superior temporal lobe in depressed patients (Klumpers et al, 2010). The majority of neuroimaging studies evaluating GABA use magnetic resonance spectroscopy (MRS), replicating previous findings of consistently lower concentrations of brain GABA in depression from adolescence into adulthood, particularly in the anterior cingulate and occipital cortices (Gabbay et al, 2012; Sanacora et al, 1999; Kugaya et al, 2003; Hasler et al, 2007). However, there is conflicting evidence of normal and lower GABA levels in remitted depression, although most report lower concentrations (Bhagwagar et al, 2008; Hasler et al,

24 2005). This suggests there is a subgroup of patients with GABA dysfunction, which may be indicative of treatment resistance as this subgroup demonstrates reduced GABA levels compared to non-resistant patients as well as healthy controls (Price et al, 2009). Glutamatergic dysfunction in depression is associated with ionotropic receptors (NMDA and AMPA) as well as metabotropic receptors. The ionotropic receptors regulate ion channel activity, while the metabotropic effects are modulated through second messenger systems. Among the NMDA receptor subunits, NR2 is most involved in glutamate binding, while AMPA receptors are responsible for the rapid desensitization of glutamate s excitatory effects (Paul et al, 1994; Bartanusz et al, 1995). The metabotropic receptors (mglurs) are divided into 3 groups: group I (postsynaptic excitatory receptors), group II (autoreceptors), group III (act in both capacities). However, group II mglu2 and mglu3 receptors have attracted the most interest as antidepressant and anxiolytic targets (Palucha et al, 2007; Witkin et al, 2007). Several MDD studies have demonstrated reduced binding and expression of NMDA receptors in various brain regions (Feyissa et al, 2009; Benyeto & Meador-Woodruff, 2008; Nowak et al, 1995). MRS studies have also revealed increased concentrations of glutamate in the cingulate cortex and occipital cortex of depressed patients (Abdallah et al, 2014; Salvadore et al, 2012; McEwen et al, 2012), however decreased glutamate has also been reported (Auer et al, 2000), an inconsistency that may be due to MRS methods or subtypes of MDD. In the last decade, the NMDA antagonist, ketamine, has demonstrated efficacy in treatment resistant depression (TRD) groups, with amelioration of depressive symptoms within hours when administered intravenously (Zarate et al, 2006). Psychotomimetic effects limit its use, and research is underway to develop similar formulations without dissociation effects. In addition to the substantial evidence of increased glutamate transmission in depressive states, its contribution to stress-related neurotoxicity, provides further evidence for the role of glutamate in depression and as a potential target for antidepressant action (Sapolsky et al, 2000; Zarate et al, 2004). The interaction of GABA and glutamate in depression has been evaluated using MRS. Results from MRS studies linked with fmri have shown disparate activity of the GABA and glutamate system. In healthy controls, higher GABA concentrations were associated with decreased activity in the posterior ACC, while no such effect was observed for glutamate (Northoff et al, 2007). MDD patients have the opposite effect, where higher glutamate concentrations relate to decreased activity in the posterior ACC (Walter et al, 2009). This 12

25 13 suggests depression, in a subset of patients, may be characterized by increased glutamate in parallel with decreases in GABA. The relationship between amino acids and dopamine is complex given the many receptor subtypes that have different functional properties (e.g. agonism vs. antagonism). At a basic level it is understood that: (1) GABA agonists inhibit dopamine D2 neurons in the ventral tegmental area as well as the nucleus accumbens (Wong et al, 1991; van Zessen et al, 2012) (2) dopamine D1 receptor agonists increase GABA transmission, while the opposite is the case for D2 (Seamans et al, 2001; Acosta-Garcia et al, 2009; Jung et al, 2011). In addition, glutamate action on dopamine within the striatum remains controversial with some research suggesting glutamate inhibits dopamine via AMPA receptors (Avshalumov et al, 2003; Imperato et al, 1990) Acetylcholine There is also evidence that hyperactivity in the cholinergic system in MDD (Janowsky et al, 1972), results in symptoms of melancholic depression (Dilsaver et al, 1986; Janowsky et al, 1994). Currently approved antidepressant agents antagonize the nicotinic acetylcholine receptor (nachr) subtype, supporting the hypothesis that antagonism may result in antidepressant effects (Shytle et al, 2002). These receptors are widely distributed in areas implicated in depression, including the striatum, amygdala, ventral tegmental area, locus coeruleus and raphe nucleus, and are thought to regulate monoamine release (Albuquerque et al, 2009), particularly dopamine. The effects of acetylcholine on dopamine can be direct or indirect (via glutamatergic and GABAergic effects on dopamine neurons). In addition to dopamine, nachrs are also located on CRF neurons in the HPA-axis (Okuda et al, 1993). Unfortunately, nachr antagonists have been largely unsuccessful as antidepressants (Rizvi & Kennedy, 2012). However, intravenous scopolamine has demonstrated efficacy in MDD compared to placebo (Drevets & Furey, 2010). In this study, the sample consisted of treatment resistant MDD participants, suggesting the cholinergic system may play an important role in the pathophysiology of resistant depression.

26 Opioids Opioid receptors have been sparsely studied in the context of depression; nevertheless, hypotheses based on receptor signaling have been posited (Jutkiewicz & Roques, 2012). In the nucleus accumbens enkephalins and dynorphins have an antagonistic role, where enkephalins increase dopamine via δ-opioid receptors and dynorphins inhibit dopamine via κ-opioid receptors (Skoubis et al, 2005; Bals-Kubik et al, 1993). Agonism of morphine like μ-opioid receptors may decrease GABAergic inhibition of dopamine (Johnson & North, 1992). The few studies conducted in MDD focus on the μ-opioid receptor, the agonism of which results in analgesic effects. An increase in μ-opioid binding potential in the subgenual ACC and amygdala during induced sadness in MDD (Prossin et al, 2011) and healthy controls (Zubieta et al, 2003) has been observed. These effects also correlated with affect ratings. Furthermore, increased frontal μ- opioid receptors in MDD suicide victims have been reported (Gross-Iseroff et al, 1990) Inflammation The effect of immune system dysfunction on mood has been well documented and linked to alterations in cytokines (Connor et al, 1998; Maes et al, 1992; Miller et al, 2009). Cytokines are proteins, which are necessary for regulating immune response, and are classified into proinflammatory and anti-inflammatory subgroups. Pro-inflammatory cytokines such as interleukin- 6 (IL-6) and tumor necrosis factor alpha (TNFα) stimulate CRF, activating the HPA axis and glutamate to cause neurodegeneration. There are reports that MDD is associated with higher levels of pro-inflammatory cytokines and these levels decrease with antidepressant response (Capuron et al, 2003; Hernández et al, 2008; O Brien et al, 2007). While elevated levels of proinflammatory cytokines have also been linked to medical illness such as cancer, comorbidity with depression induces higher than normal levels of IL-6 compared with cancer patients without depression (Musselman et al, 2001). There is also neuroimaging evidence that healthy volunteers who have IL-6-induced mood deterioration following tetanus toxoid display higher activity in the subgenual cingulate gyrus (Harrison et al, 2009), an effect which is attenuated by antidepressant treatment (Capuron et al, 2002; 2004). Interestingly, the administration of adjunctive infliximab, a TNFα antagonist, to treatment resistant MDD patients resulted in a significant decrease of depressive symptoms, particularly anhedonia, psychomotor retardation, mood, suicidality, and

27 15 anxiety. However, this effect was only observed in patients with a pretreatment C-reactive protein concentration >5mg/L, suggesting the presence of an inflammatory MDD subgroup that would respond to anti-inflammatory therapy (Raison et al, 2013) Summary of MDD pathophysiology It is clear that depression is not caused by a single factor. In all likelihood, the heterogeneous nature of MDD reflects a multi-faceted pathophysiology where systems at the molecular, neuronal, and environmental level interact. To this effect Miller and colleagues (2009) proposed a model where stress in susceptible individuals (e.g. early adversity, social isolation) activates the HPA-axis, which has downstream effects on neuroinflammation, and results in decreased monoaminergic and GABAergic activity along with increased glutamatergic activity in limbic and cortical regions (Figure 2). Although this model presents a linear pathway, which may or may not be the case, it is a useful heuristic to understand the associations among systems and brain regions. Indeed, dysfunction stemming from any part of this system could result in the experience of depression. Furthermore, other neurotransmitters within the same brain regions, including acetylcholine and opioids, are linked to MDD. Figure 2. Neurocircuitry of MDD Miller et al, 2009 with permission

28 16 2 Overview of Treatments for MDD 2.1 Antidepressant Monotherapy All first-line antidepressants approved to treat depressive episodes in MDD target monoamine systems as their primary mechanism of action (Lam et al, 2009). The mainstay of treatment for MDD remains SSRIs or serotonin and norepinephrine reuptake inhibitors (SNRIs) (Table 2), which exert their effects via serotonin, as well as serotonin and norepinephrine transporter blockade, respectively. Other first-line agents act on specific receptor subtypes of norepinephrine or serotonin or (e.g. mirtazapine and agomelatine) or are norepinephrine-specific (reboxetine). Bupropion is the only first-line agent that is considered to be dopaminergic, although current evidence suggests that the level of dopamine transporter binding is relatively low (Meyer et al, 2002) and dopamine firing in the ventral tegmentum is unaffected with sustained treatment (El-Mansari et al, 2008). First generation antidepressants, tricyclic antidepressants (TCAs) and monoamine oxidase inhibitors (MAOIs) affect multiple monoamine systems, although they are generally second- and third-line interventions, for TRD, due to the high associated adverse effects (Lam et al. 2009). Table 2. CANMAT Guidelines for Antidepressant Monotherapy

29 There is still considerable debate over which antidepressants are the most efficacious. In meta-analytic studies no difference between TCAs and SSRIs were observed, except in severe and inpatient populations where TCAs were superior to SSRIs (Anderson 2000; Geddes et al. 2000). There are not many direct comparisons between MAOIs and SSRIs, however evidence suggests that moclobemide has similar efficacy rates and greater tolerability (Papakostas & Fava, 2006). Although meta-analyses of remission with SNRIs compared to SSRIs and TCAs have shown an advantage for venlafaxine (Bauer et al. 2009; Nemeroff et al. 2008), subsequent metaanalyses demonstrated superiority of escitalopram compared to other SSRIs and SNRIs (Kennedy et al. 2009; Kornstein et al. 2009). In a comprehensive network meta-analysis involving comparisons of 12 new-generation antidepressants, Cipriani and colleagues (2009) concluded that escitalopram and sertraline were deemed to have the best balance between efficacy and tolerability Antidepressant Switching and Augmentation A common treatment strategy for inadequate antidepressant monotherapy is to either switch or combine/augment with another pharmacological agent. Switching to another antidepressant may be within class (i.e. another SSRI) or to another antidepressant class (i.e. switch from SSRI to SNRI). There is no evidence to suggest that switching to an SNRI after SSRI failure confers greater benefit than substituting with another SSRI (Lam et al, 2009). Although not extensively validated in clinical studies, combination or augmentation strategies are common (Mojtabai & Olfson, 2010), in order to improve response or target specific symptoms, such as fatigue, sexual dysfunction, insomnia, or anxiety. Some evidence has demonstrated that initiating two antidepressants from the start of therapy may improve MDD outcomes. For example, combination of mirtazapine with fluoxetine, venlafaxine, or bupropion for 6 weeks compared with fluoxetine monotherapy resulted in significantly higher rates of remission (Blier et al, 2010). However, the combination of escitalopram with bupropion or mirtazapine with venlafaxine were not more effective than escitalopram monotherapy (Rush et al, 2011). Atypical antipsychotics, dopaminergic drugs and benzodiazepines are common augmentation agents. Dopamine agonists (pramipexole, bromocriptine) and dopamine transporter

30 (DAT) antagonists (nomifensine, amineptine and methylphenidate) all have antidepressant effects (Papakostas, 2006). However, nomifensine and amineptine are no longer available due to abuse potential and concerns of kidney and liver toxicity. Pramipexole, as an adjunctive therapy has efficacy in TRD based on small randomized controlled pilot studies (Corrigan et al, 2000, Lattanzi et al, 2002) and methylphenidate has also shown effectiveness as an augmentation to antidepressant therapy in TRD (Patkar et al, 2006 ; Stoll et al, 1996). Furthermore, dopaminergic agents are a common strategy to alleviate sexual dysfunction, or improve attention in MDD (Balon & Segraves, 2008; Madhoo et al, 2014). Atypical antipsychotics, which have putative dopaminergic mechanisms, are an increasingly utilized augmentation strategy (Kennedy & Lam, 2003; Lam et al, 2009). Augmenting SSRIs with risperidone, olanzapine, ziprasidone, and quetiapine, have demonstrated improved remission rates (Shelton and Papakostas, 2008), and preliminary findings indicate that quetiapine in combination with cognitive behavioural therapy may increase alleviation of symptoms (Chaput et al, 2008). Aripiprazole is the only atypical agent approved as an adjunctive treatment after failure of an initial antidepressant. Although, atypical antipsychotics provide an additional treatment avenue for TRD, the lower tolerability compared to first-line antidepressants and potential for serious side effects (e.g. tardive dyskinesia, diabetes) limit their use in MDD patients (Kennedy & Lam, 2003). Benzodiazepines are sedative hypnotics with GABAergic effects. They are prescribed for the purpose of alleviating anxiety (alone or in the context of other mental disorders), insomnia, pain, alcohol withdrawal, or as an anaesthetic or anticonvulsant agent. Importantly, for the treatment of MDD, international guidelines agree that there is a limited role for benzodiazepine use and should not be used beyond 4 weeks (Davidson, 2010; Higuchi, 2010), for reasons associated with risks of dependence, and lack of proven antidepressant benefit. Despite the availability of guidelines, this has not significantly affected benzodiazepine prescribing practices (Lai et al, 2011; Schneider et al, 2005), and many individuals continue to be prescribed chronic benzodiazepine treatment in clinical practice. Current estimates in Canada, the US and Europe place benzodiazepine use at 36-50% of depressed patients (Sanyal et al, 2011; Demyttenaere et al, 2008; Valenstein et al, 2004). Another Canadian study revealed that of the 3% of the general population receiving a benzodiazepine, 80% had been users for more than one year (Esposito et al, 2009). 18

31 Neuromodulation Therapies The development of non-pharmacological neuromodulatory techniques over the past several decades has offered alternative therapies for patients with TRD. Electroconvulsive therapy (ECT), and more recently, repetitive transcranial magnetic stimulation (rtms), Vagus Nerve Stimulation (VNS) and Deep Brain Stimulation (DBS), all demonstrate effectiveness in TRD (Daskalakis et al, 2008; Kennedy & Giacobbe, 2007; Lozano et al, 2008; Rush et al, 2005). Electroconvulsive therapy (ECT), in particular, is a common treatment for severe, suicidal, and/or psychotic depression. Evidence suggests that ECT may increase dopamine function (Nikisch et al, 2008; Thomas et al, 1992; Yoshida et al, 1997). In addition, preclinical evidence shows that the use of rtms applied to the rat frontal cortex increases extracellular dopamine concentrations in the striatum (Kanno et al, 2004). This effect has also been reported in humans (Pogarell et al, 2007; Strafella et al, 2003). 2.4 Poor Antidepressant Outcomes and Treatment Resistant Depression The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study involving over 3000 patients in both primary care and psychiatry clinic settings, was a naturalistic trial to evaluate antidepressant effectiveness. If patients did not achieve remission with the first treatment, they received additional treatment (medication switch or augmentation), and so on for a total of four treatment steps. At the first level, approximately 30% of patients treated with a first-line selective serotonin reuptake inhibitor (SSRI), citalopram, achieved remission (Trivedi et al, 2006). Notably, at the end of step four, 30% of patients had still not attained remission after a further three interventions, and the chances of achieving remission declined markedly with each successive level (Warden et al, 2007). This study was instrumental in highlighting the poor treatment outcomes with currently available antidepressants in real world settings and the consequent high level of TRD. Within primary care centres in Canada, the prevalence of TRD based on a minimum of two failed antidepressant trials was approximately 20% (Rizvi et al, 2014a). Even among neuromodulation therapies such as VNS and DBS, the 6-month remission rates are approximately 30% (Rizvi et al, 2011), indicating a need for diverse treatment options.

32 There is a lack of consensus as to what constitutes TRD, however, at least two failed antidepressant trials for the current depressive are the minimum criteria (Souery et al, 1999). Other methods to quantify and define TRD have been proposed, including a staging definition that incorporates failure to different antidepressant classes, including SSRIs, TCAs and ECT (Thase & Rush, 1997). The Massachusetts General Hospital Staging method assigns a weighting algorithm to the number of treatments and modalities used, with ECT receiving a higher score (Fava et al, 2003). However, none of these methods are representative of the sequential and combination treatment approaches that are clinically utilized. A multi-faceted approach to evaluating TRD has also been outlined that reflects severity, duration of illness, as well as antidepressant, augmentation, and ECT failures (Fekadu et al, 2009). The impact of TRD on personal, social and economic variables is substantial. Several studies have demonstrated that TRD is associated with doubled health-related costs compared to non-resistant MDD as well as additional costs due to lost productivity (Gibson et al, 2010; Ivanovna et al, 2010; Mrazek et al, 2014). Treatment resistant patients also report poorer quality of life and increased suicidal ideation (Mrazek et al, 2014). Interestingly, TRD has not been a large focus in research with respect to understanding the pathophysiology and interacting factors that result in resistance. A problematic approach consistently observed in the literature is the lack of a non-trd comparison group, which limits the ability to draw conclusions about the additional effects of TRD over and above a non-resistant group. Symptomatically, Malhi and colleagues (2007) report a disproportionate presence of anhedonia and psychomotor retardation in TRD patients, symptoms that are associated with dopaminergic dysfunction. Indeed, dopamine metabolites in TRD patients are reduced (Lambert et al, 2000). Reduced levels of GABA in the occipital cortex and ACC have also been reported in TRD vs. non-trd (Price et al, 2009). Other neurobiological findings, such as volume decreases in the right OFC and the right inferior temporal gyrus as well as decreased white-matter volume in the left anterior limb of the internal capsule (Phillips et al, 2012) and bilateral hippocampus have been reported (Zhou et al, 2011). A recent analysis of patients who underwent DBS to the subgenual ACC revealed that all DBS responders at 6 months and 2 years demonstrated bilateral white matter pathways from the target site to the medial frontal cortex (via forceps minor and uncinate fasciculus), rostral and dorsal cingulate cortex (via the cingulum bundle) and to 20

33 21 subcortical regions, while non-responders had reduced structural integrity of these pathways (Riva-Posse et al, 2014). Common pharmacologic treatment strategies for TRD are augmentation with atypical antipsychotics (Lam et al, 2009; Shelton et al, 2010). Dopamine agonists, such as pramipexole, are also frequently prescribed (Lattanzi et al, 2002). Neuromodulation with ECT, rtms, and VNS is also indicated (Kennedy et al, 2009). Importantly, access to and side effects of ECT limits it as a viable ongoing treatment option. Both VNS and rtms demonstrate effectiveness in TRD, although effect sizes are not as high as with ECT (Rizvi et al, 2011; Lee et al, 2012). DBS is still an investigational therapy for TRD. 2.5 Deep Brain Stimulation DBS is a neurosurgical procedure that was developed for the treatment of pain and movement disorders. It involves the implantation of two electrodes bilaterally to a target brain region with leads terminating in a chest pacemaker that delivers remote electrical stimulation to the target (Kennedy & Giacobbe, 2007). The mechanism of action for DBS remains unclear, however, DBS is thought to interrupt an abnormal pattern of firing in the brain due to the overstimulation or understimulation of a target site and its surrounding areas (Liu et al, 2008). Based on evidence to support the role of the ACC in the regulation of emotion (Mayberg et al, 1999), DBS to a region of the ACC, the subcallosal cingulate gyrus (SCG), was first investigated in Toronto as a novel target for patients with TRD (Mayberg et al, 2005). In this pilot sample of 6 patients, 4 patients (66.6%) responded after 6 months (response defined as > 50% decrease from baseline HAMD-17 score). This sample was then expanded to 20 patients who received open-label stimulation over the course of one year, including the previous 6 patients (Lozano et al, 2008). After 1 month of active DBS, 7 patients (35%) were considered responders and 2 (10%) were in remission (HAMD-17 score < 7). At 6 months, 12 subjects (60%) had responded and 7 (35%) had remitted. This pattern was largely sustained at 12 months, when 11 patients (55%) met criteria for a response and 7 (35%) were in remission. In a long-term follow-up study in the same population evaluated 3-6 years post-dbs, 46% of patients had responded at 2 years and 15% had remitted. After three years, 75% were responders and 50% were in remission, while at the time of the last follow-up visit (3-6 years), the response rate was

34 64% and the remission rate was 43% (Kennedy et al., 2011). Importantly, similar findings were reported in three other open-label studies involving the same anatomical targets where 6-month response rates were and 48% and 63%, and 41%, respectively (Lozano et al, 2012; Puigdemont et al, 2011; Holtzheimer et al, 2012). Bewernick and colleagues (2008) provided preliminary evidence that DBS to the nucleus accumbens (a key structure in reward learning) alleviates TRD in a small study. In parallel, PET scans measuring brain glucose metabolism revealed significant brain activity decreases in the SCG, OFC and within the striatum following 6 months of stimulation. All patients improved when the stimulator was on, and worsened when the stimulation was turned off. Given dopamine is the primary receptor expressed in the nucleus accumbens, the implication of these findings is that DBS to this area impacts dopamine function. However, the small sample size of this group necessitates confirmation of these findings in a larger group. More recently, the same group evaluated the medial forebrain bundle (MFB) as a DBS target (Schlaepfer et al, 2014). The MFB is a tract that as part of its pathway connects the ventral tegmental area to the nucleus accumbens. In a small open label study of 7 TRD patients, DBS to the MFB demonstrated response in 6 patients within 7 days and remission in 4 patients within weeks (Schlaepfer et al, 2013). This rapid effect of DBS is promising but requires further evaluation. In summary, DBS has demonstrated effectiveness in TRD patients, however, it is still unclear which patients represent the best candidates for treatment. Importantly, the heterogeneity of depression and the high rate of treatment resistance have prompted a need to define subtypes of depression, along with their associated phenotypic symptom clusters and pathophysiology, in order to: (1) improve treatment outcomes through the development of targeted therapies, and (2) optimize treatment strategies by identifying predictors of antidepressant response Prediction of Treatment Outcome Prediction of treatment response (defined as a 50% drop in depression score) has been evaluated based on clinical profile, genetics, neuroimaging and molecular data. Clinical

35 predictors, in particular, would be the most useful since expensive testing or access to technology would not be required. As a result much effort has been given to evaluating predictors of response based on clinical profile (e.g. gender, family history, childhood abuse, personality factors) as well as symptom presence (e.g. anxiety, psychomotor retardation) and severity. Unfortunately, clinical variables thus far have been poor prognostic indicators. For example, lack of an improvement following 2 weeks of antidepressant therapy, defined as a 20% reduction in depression symptoms, was a demonstrated predictor of non-response (but not response) to pharmacotherapy (Szegedi et al, 2009). This finding has been incorporated into current MDD treatment guidelines (Lam et al, 2009). However it should be noted that this process of predicting outcome based on symptom severity is statistically problematic considering scores on a questionnaire at two time points will have a degree of correlation between them since the same measurement methods are used (Rizvi et al, 2014b). Furthermore, in a large treatment trial, early improvement at 2 weeks correctly classified only 65% of subjects at 12 weeks, 6% higher than chance (Uher et al., 2011) Anhedonia as a Clinical Predictor 4.1 Anhedonia Construct Though anhedonia is a core feature of an MDE and is a key diagnostic symptom for the depressive subtype melancholia, assessment of this construct has received disproportionately less attention in the context of MDD. This may be partly due to the broad conceptualization of anhedonia that has resulted in a paucity of adequate measures and tasks that tap into the different facets of a pleasure response. Despite increasing research in this domain, there remain several key limitations in anhedonia research. Traditionally anhedonia has been defined as a loss of pleasure (Ribot, 1896). However, the assessment of anhedonia according to the DSM-5 reflects a broader conceptualization that includes interest as well as consummatory pleasure. Even within the HAMD-17, one of the most widely used scales to assess severity of depression, the single anhedonia item is measured as a dimensional construct representing desire, effort and consummatory pleasure ( loss of interest in activities, decrease in actual time spent on activities, experiencing pleasure ) (Hamilton, 1960). This broad conceptualization makes anhedonia measurement difficult and imprecise. As

36 24 Treadway & Zald (2011) assert heterogeneity at the level of symptom definition is at least as problematic as issues of comorbidity, and that refining the construct is imperative if we hope to understand the neurobiological underpinnings of anhedonia. Neuroscientific evidence also supports moving anhedonia out of an exclusive pleasure domain and incorporating the unique aspects of reward processing that can give rise to loss of pleasure, or perhaps that even act independently of pleasure. This was evaluated in a reward task whereby MDD patients viewed cartoons for which they provided liking scores. The cartoons were subsequently associated with a specified effort expenditure (number of clicks on a moving square), in order to view the liked cartoons once again. While MDD patients experienced similar levels of consummatory pleasure to healthy controls, lower levels of reward anticipation were associated with motivation for effort expenditure in the MDD group (Sherdell et al, 2012). This suggests anhedonia is a more complex construct, whereby anticipatory anhedonia may be particularly affected in depression. Furthermore, Henriques and Davidson (2000) demonstrated that depressed patients are less motivated to maximize their earnings than healthy controls, in a monetary reward paradigm. There is also evidence that MDD patients have reduced ability to detect reward and incorporate experience of reward into their reward-learning schemas (reviewed in Pizzagalli, 2014). Consistent with this work are data showing reduced positive prediction error in depression when a positive reward has occurred, suggesting patients do not experience the same level of feedback from rewards as healthy controls (Gradin et al, 2011; Kumar et al 2008). Taken together, it is clear that anhedonia is much more than an impairment in consummatory pleasure, and available tasks and tools need to reflect this refinement in conceptualization in order to yield an adequate assessment of this cardinal symptom of depression. 4.2 Prediction of MDD Status and Treatment Outcome Although anhedonia has been recognized as a core symptom of depression for decades (Ribot, 1896), there has been a recent resurgence in its evaluation, which has been prompted by findings that it may be able to predict MDD status, treatment outcome and its association with treatment resistance.

37 A longitudinal study of 197 adolescents at familial risk for MDD compared to those with no history evaluated the predictive value of reward seeking using a gambling task. At one year, low reward seeking predicted a diagnosis of MDD, independent of baseline depression symptoms (Rawal et al, 2012). These data provide preliminary support for reward function as a mechanism of depression risk, and suggest that aspects of this functioning may be trait related. Several large scale studies, including STAR*D and the Genome Based Therapeutic Drugs for Depression (GENDEP), have demonstrated that loss of interest is a predictor of nonresponse to SSRIs (Uher et al, 2008, 2012). Out of 6 symptom dimensions that describe the structure of depressive symptoms (mood, anxiety, sleep, appetite, pessimism and interestactivity), low interest-activity strongly predicted poor antidepressant outcome in both GENDEP and STAR*D (Uher et al, 2012). This was also observed in an adolescent MDD sample, which found anhedonia to be the only unique negative predictor of time to remission and depression free days with SSRI use (McMakin et al, 2012). Providing further generalizability across treatment modalities, anhedonia was a predictor of non-response to rtms of the dorsomedial PFC in MDD (Downar et al, 2014). As discussed, anhedonia is a prominent symptom in patients with treatment resistance (Malhi et al, 2007). It follows then that melancholia, which is characterized by high levels of anhedonia, is an important clinical predictor of TRD (Souery et al, 2007). An inherent issue with research into response prediction using clinical symptoms is the lack of corresponding data grounding them in the neurobiology affected in depression. Directly linking biomarkers to physical disease state has the added value of providing greater face validity, and could lead to higher specificity when predicting treatment outcomes. Consequently, a better understanding of anhedonia and its neurobiological underpinnings are necessary in order to determine whether the presence of this symptom represents a unique subtype and consequently could truly be an effective clinical predictor of antidepressant outcome Neurobiology of Anhedonia As discussed, a rewarding experience encompasses several aspects: anticipation of reward, judgment of the reward value and size, acting on the reward, as well as the consummatory pleasure response. Evidence suggests that there are specific neuroanatomical

38 26 areas that underlie each aspect of reward learning which include the nucleus accumbens, the prefrontal cortex (orbitofrontal cortex, ventromedial PFC and anterior cingulate cortex), the striatum (caudate and putamen), and the amygdala (Figure 3) (O Doherty, 2004; Haber & Knutson, 2010; Treadway & Zald, 2011). Figure 3. Neurobiology of reward circuitry The nucleus accumbens has historically been characterized as the brain s pleasure centre, derived from the seminal findings that rats bar press for pleasurable electrical stimulation to the nucleus accumbens (Olds and Milner, 1954). Subsequently there has been a wealth of literature to implicate the nucleus accumbens in pleasurable responses to music, attractive facial expressions, food reward and monetary gain (Blood and Zatorre, 2001; Di Chiara et al, 1999; Pizzagalli et al, 2009; Senior, 2003). In addition, Hasler and colleagues (2008) demonstrated increased depressive and anhedonic symptoms during catecholamine depletion in 15 remitted MDD patients, which was in turn related to decreased activity in the nucleus accumbens. From Treadway & Zald, 2011 with permission With advancements in the understanding of the reward process and neuroimaging capabilities, there has been a development in linking the function of the nucleus accumbens to the anticipatory and value judgment aspects of reward response (Breiter et al, 2001; O Doherty et al, 2002; Khamassi et al, 2008; Knutson et al, 2003). These aspects of reward reflect an ability to predict whether a reward will occur (reward prediction). Findings from neuroimaging studies demonstrate that in addition to the nucleus accumbens, the orbitofrontal cortex, striatum and amygdala are also activated according to the specific value of a reward or reward prediction (Gottfried et al, 2003; Knutson et al, 2001). However, while amygdala activity occurs with potential reward, it is also activated with potential punishment, indicating that this structure may be important for general arousal (positive or negative) as opposed to a specific reward function

39 (Anderson et al, 2003). Consistent with this hypothesis are findings that demonstrated a relationship between decreased amygdala activity and reward devaluation (Gottfried et al, 2003). The ventromedial PFC and anterior cingulate cortex have a different role in that they help to add context to the representation of a rewarding stimulus, as well as learning contingencies based on the outcome of a rewarding event (Elliot et al, 2000; Knutson et al, 2001). These cortical areas are also necessary when working memory is required to monitor reward properties of a stimulus. This is of particular importance when several options need to be evaluated and compared, in order to select the most preferable option (MacDonald et al, 2000). The structural network underlying reward responses comprises sites of high dopaminergic activity relative to other transmitters (Volkow et al, 2008; Shirayama & Chaki, 2006). Consequently for the last few decades, dopamine has been considered the key neurotransmitter in reward behaviours. Specifically, Dopamine D1/D2 receptors participate in the rewarding properties of self-stimulation in rats (Nakajima et al, 1993). Moreover, negative environmental and social stimuli result in decreased dopamine transmission in the nucleus accumbens and caudate putamen in rats (Kabbaj and Isgor, 2007), which suggests that adverse social conditions affect reward circuitry. Such decreases in dopamine transmission can result in reduced ability to respond to reward cues (Berridge, 1989; Munafo et al, 2007) and more specifically, rats with nucleus accumbens dopamine depletion are less active, less likely to work for significant stimuli (e.g. food) and exhibit psychomotor slowing (Salamone et al, 2003). In MDD, there is little direct evidence linking dopamine activity and the symptom of anhedonia. However, one report demonstrated that high levels of anhedonia, as measured by the Snaith Hamilton Pleasure Scale (SHAPS) (Snaith et al, 1995), were correlated with lower dopamine transporter binding in severely depressed patients (Sarchiapone et al, 2006), while another linked high D2 binding in the left ventromedial PFC and bilateral dorsal ACC with higher anhedonia in healthy controls following a reward task (Vrieze et al, 2013). There is also mounting evidence that systems other than dopamine may be critically involved in the reward process. Specifically, opioids, glutamate, GABA and serotonin also play a significant role. Functionally, the nucleus accumbens receives dopaminergic input from the ventral tegmental area as well as the PFC (Shirayama & Chaki, 2006). In addition, serotonergic afferents from the raphe nuclei and noradrenergic afferents from the locus coeruleus also feed into the nucleus accumbens. Glutamatergic neurons reach the nucleus accumbens from the PFC, 27

40 hippocampus, and amygdala, while small amounts of GABA project outwards from this site. The μ-opioid receptors in the ventral tegmentum have been shown to disinhibit dopamine projecting to the nucleus accumbens (Johnson & North, 1992). Furthermore, μ-opioid receptors in the amygdala may also mediate the incentive properties of reward (Wassum et al, 2009). The interaction of these systems has yet to be clearly defined, and most research is among monoamine system connections. Decreasing levels of dopamine and serotonin have both been found to affect reward response. Administration of dopamine antagonists directly into the nucleus accumbens attenuates the reinforcing properties of reward stimuli (Taylor & Robbins, 1986). However, dopamine D2 receptor function plays a critical role in reward (Caine et al, 2000), while reduced serotonin levels may be more associated with increased impulsivity and preference for immediate reward (Schweighofer et al, 2008). Indeed, there are also reports indicating that dopamine-independent reward activity is possible. Self-administration of phencyclidine, a dopamine antagonist and glutamate NMDA antagonist occurs in rats, and rats will also self-administer other NMDA antagonists, which act to increase glutamate levels (Carlezon & Wise, 1996). More recently, the functional interaction between GABA and dopamine GABA in reward has been evaluated. Gabbay and colleagues (2012) conducted an MRS study assessing GABA concentration in depressed adolescents compared to healthy controls. Adolescents had significantly reduced GABA in the anterior ACC. Furthermore, when participants were clinically categorized according to the presence or absence of anhedonia, only those with anhedonia had reduced GABA concentrations. Interestingly, there is preclinical evidence that activation of GABA neurons in the ventral tegmentum, an area that primarily expresses dopamine neurons, decreases sucrose consummatory reward, but not cue-induced anticipatory reward behaviour (van Zessen et al, 2012). In this study, ventral tegmentum GABA significantly reduced dopamine transmission in surrounding regions as well, including the nucleus accumbens. In summary, there are intricate interactions among key neurotransmitters (dopamine, opioids, glutamate, GABA) in cortical (orbitofrontal cortex, ventromedial PFC, dorsolateral PFC and ACC) and subcortical structures (nucleus accumbens, striatum, amygdala, ventral tegmental area) to produce the experience of reward. Given serotonin is the key system impacted by pharmacotherapy, it follows that conventional antidepressants have not been successful in treating anhedonia (McMakin et al, 2012, Uher et al, 2012). 28

41 Anhedonia and antidepressant mechanism of action The manifestation of anhedonia and its related neurocircuitry may also play an important role in antidepressant mechanism of action. For example, in a recent secondary analysis of clinical trial data of sustained release methylphenidate (OROS methylphenidate) as an adjunctive treatment in MDD, responders had decreases in anhedonia evident by week 2; an effect that also distinguished responders to active drug and to placebo (Rizvi et al, 2014b). Considering the role of dopamine in reward and the dopamine transporter antagonism action of methylphenidate, these data suggest the resolution of reward-related symptoms could be a marker of response for treatments that target this neurotransmitter system. Predictors of DBS for TRD have not been identified, but anhedonia may similarly function as a marker of response, suggestive of a dopaminergic mechanism of action. Firstly, the SCG DBS target has direct connections to the ventral striatum, nucleus accumbens, central nuclei of the amygdala and rostral portions of the PFC (Johansenberg et al, 2008; Room et al, 1985), all areas that are important in reward circuitry (Der-Avakian & Markou, 2012). Secondly, preliminary evidence suggests that stimulation to this area may have an effect on both mood and anhedonia. In a pooled analysis of open-label data encompassing 56 patients (studies: Lozano et al, 2008; Lozano et al, 2011; Holtzheimer et al, 2012), the greatest contributors to 6-month HAMD-17 score change (corrected for multiple comparisons, p<.002) were: mood (r=.71), interest (r=.66), psychic anxiety (r=.54), middle insomnia (r=.51), and suicidality (r=.50). The linear regression revealed a model including mood, interest, psychic anxiety and suicidality, which accounted for 83.6% of total variance (p<.001) (Rizvi et al, 2013b). Furthermore, Witt and colleagues (2006) used a single L-dopa challenge after DBS to the STN in Parkinson s patients with mild to moderate depression to assess the effects on symptoms of depression and hedonic tone. Overall depressive symptoms improved with both stimulation and L-dopa, although hedonic tone only improved with L-dopa. These results further support the role of dopamine in anhedonia, and suggest that it may be a useful behavioural marker of treatment response for therapies that modulate the dopamine system.

42 Anhedonia Measurement The four main validated self-report measures for anhedonia used in clinical research are the SHAPS (Snaith et al, 1995), the Fawcett-Clark Pleasure Capacity Scale (FCPS) (Fawcett et al, 1983) and the Revised Chapman Physical Anhedonia Scale (CPAS) along with the Chapman Social Anhedonia Scale (CSAS) (Chapman et al, 1976). Ideally, a scale quantifying anhedonia in the context of MDD should be able to detect state versus trait differences, measure different aspects of anhedonia, distinguish between anhedonia and related constructs, and take into account varying cultural beliefs and preferences (generalizability). Although all of these scales have been validated in clinical populations, they differ in their ability to address these factors. It is still unclear whether anhedonia or its subcomponents are stable constructs over time in depressed patients (trait) or whether it is a symptom that fluctuates depending on severity or even antidepressant mechanism (state). Furthermore, there may be subgroups of patients who have persistent anhedonia as opposed to acute changes over an episode. Therefore, a scale that is able to assess responses right now versus over time or in general would be ideal for measurement in MDD. The CPAS and CSAS measure anhedonia in general and the items reflect anhedonia as a personality trait instead of specific aspects of hedonic function (e.g. when I move to a new city, I feel a strong need to make new friends on the CSAS). Both the FCPS and SHAPS measure state anhedonia (FCPS right now, SHAPS last two days ), which may be more beneficial in capturing information in the context of a depressive episode. For example, in a sample of inpatients, anhedonia scores based on the FCPS were stable over 7 months despite recovery in two thirds of patients (Clark et al, 1984); however, as inpatients this group may reflect a more chronically ill group. In another naturalistic study in chronic MDD patients, anhedonia (based on the FCPS) did not change over a 1 year follow-up despite reductions in depressive symptoms (Schrader, 1997). It is unclear whether the same effect would be observed in other samples and to what extent anhedonia is related to the failure of antidepressants in targeting or exacerbating this symptom. Importantly, measuring state and trait anhedonia has implications for assessing changes with antidepressant response. A trait measure will not be as sensitive to the acute and perhaps early changes that can occur with treatment. Both the SHAPS and FCPS have demonstrated ability to measure acute changes in anhedonia following treatment (Martinotti et al, 2012; Willner et al, 2005).

43 The scales also differ in the aspects of anhedonia measured. Whereas the CPAS and CSAS measure various aspects of anhedonia (motivation, effort, and pleasure) in addition to personality traits, both the FCPS and SHAPS focus exclusively on consummatory pleasure. Factor analysis of the SHAPS and FCPS revealed a unitary structure that primarily loaded onto hedonic capacity (Nakonezny et al, 2010; Leventhal et al, 2006), whereas the CPAS did not significantly relate to hedonic capacity (Leventhal et al, 2006). In this aspect, the CPAS and CSAS may be more likely to detect individual differences where consummatory pleasure is not the main variable of interest. In one study, high scores on the Chapman scales were correlated with low willingness to expend effort for a reward task, whereas the SHAPS did not (Treadway et al, 2009). All four scales incorporate anhedonia questions relating to both primary and secondary reward. While food or sex represent primary rewards (inherent rewards), photography or money represent secondary rewards (no inherent reward in itself and for which reward value must be learned). The SHAPS, in particular, has items based on the domains of pastimes, social interaction, food/drink, achievement and sensory experience. This is in contrast to the CPAS and CSAS which separate physical and social anhedonia into different scales. While they may be separate subconstructs of anhedonia, it would be more feasible for clinical use if a questionnaire incorporated different domains of anhedonia in one short scale. However, as discussed the SHAPS encompasses a unitary construct of consummatory anhedonia and does not have subscales based on reward type. In order for a scale to effectively evaluate a construct, it needs to avoid unnecessary measurement of other overlapping but different features (e.g., mood and anxiety in the context of MDD; divergent validity), while retaining the ability to demonstrate a correlation with similar features (convergent validity). Both the SHAPS and FCPS exhibit good convergent and discriminant validity: they moderately correlate with depression severity as would be expected, but do not correlate with measures of anxiety (Leventhal et al, 2006; Nakonezny et al, 2010). In addition, the SHAPS is positively correlated with quality of life and functioning (Nakonezny et al 2010). Contrary to this, the CPAS has a weak correlation with depression severity, while both the CPAS and CSAS have strong associations with non-affective aspects of personality and psychotic disorders (Leventhal et al, 2006). This is likely due to the development of these scales for the assessment of anhedonia in schizophrenia. In addition, the Chapman scales include items 31

44 32 that are not clearly related to anhedonia (CPAS: I have often felt uncomfortable when my friends touch me; CSAS: My emotional responses seem very different from those of other people ). The CPAS, CSAS as well as the FCPS lack generalizability due to the high degree of cultural bias demonstrated from questions such as I always find organ music dull and unexciting (CPAS) and you are skiing down a mountain very fast while still in good control of yourself (FCPS). In contrast, the SHAPS was constructed to avoid cultural bias and so contains items with wider applicability ( I would be able to enjoy my favourite meal ). As a result, one could argue that it does not capture the events or activities that are likely to elicit strong hedonic responses. The ability to accurately tap into the subjective nature of what individuals find pleasurable or interesting is the greatest challenge in measuring anhedonia. 4.6 Scale development The majority of scales developed follow Classical Measurement Theory (CMT) (reviewed in Devellis, 2012). This theory has several key assumptions of a latent variable (i.e. construct being measured): 1. Items in a scale reflect a unidimensional latent variable. 2. The latent variable has an equal influence on all items 3. Error associated with items is random 4. Error across items are not correlated 5. The amount of error across items is equal 6. Error does not correlate with the latent variable The strict assumptions with regards to the latent variable contributing equally to all items and to error being equal across items are relaxed in alternative models of CMT. While these assumptions may be restrictive and may not reflect real-world observations, the scale development procedures that follow from a classical model generally yield satisfactory scales

45 33 (Devellis, 2012). The focus on error in CMT can be understood by the statistical tests used to evaluate a scale under this theory, which all evaluate the variance of items for a latent variable and covariance among items. The implication of unequal error and correlations among error would yield inaccurate estimates and would require a more complicated and less easily interpretable model Reliability and Validity Reliability and validity are the primary methods used to evaluate a scale s utility. Where reliability is an estimation of a scale s ability to produce consistent results under the same conditions, and can also reflect item unidimensionality, validity tests provide evidence that a scale is actually measuring the intended construct. The most common method for testing a scale s reliability is the Cronbach s α. This tests the ratio of common variance across items against the total variance, the idea being that item scores should reflect the common shared variance attributable to the latent variable and not the unique error. If this was the case, scale scores could be different even under the same conditions due to chance extraneous factors. This calculation is done using data extracted from the covariance matrix of all the items. Covariance of items with themselves represents the amount of unique error associated with that item. All of the other correlations represent the shared variance. Alternatively, the average inter-item correlations can be used to calculate α, which produces a standardized score since correlations of items with themselves are set to 1.0. It is preferred to present unstandardized α s since it takes advantage of the original raw data. A scale is also considered to demonstrate internal consistency reliability if the items correlate with one another, which reflects the unidimensional nature of a scale (Briggs et al, 1986). Since according to CMT, all items are influenced by the latent variable to the same extent, they should strongly correlate with one another. The closer inter-item correlations are to 1.0, the more unidimensional the scale. Validity tests attempt to discern what the construct being measured is and also provide evidence relating to the additional variables that could affect scores. Content validity, construct (convergent) validity, and divergent validity are the main methods to assess validity. Content

46 validity is often more qualitative, where experts in the field are asked to evaluate a scale based on their own theoretical understanding of the construct of interest, as well as item clarity and coherence. These data should be collected prior to the initiation of a validation in order to address pertinent item issues. Convergent and divergent validity involve testing the scale against others that are theoretically related or unrelated to the scale construct. For example, in testing a new depression scale, the HAMD-17 was used as a gold standard (Rush et al, 2006). A high level of correlation between the scale and a current gold standard would suggest similar underlying constructs. In contrast, while depression and anxiety are related, one would want to ensure that the new depression scale does not contain items that overlap with anxiety (divergent validity), otherwise there is no use for a new scale. If there was a high correlation between the depression and anxiety scales it would suggest that the depression scale items are, in fact, tapping into anxiety and are not distinct enough. Divergent validity can also be used to ensure that a scale is not related to a theoretically unrelated construct (e.g. depression should not have a high correlation to general levels of fatigue) Factor Analysis Importantly, a scale may not be unidimensional and may have several latent variables that account for the variance. In order to evaluate this, factor analytic techniques are employed. The goal of factor analysis is (1) to reduce information into fewer components that reflect the full range of information, (2) to determine the number of underlying latent variables, (3) provide evidence for what the latent variables are, and finally (4) to determine what items are performing well and are related or unrelated to the latent variables identified. The main methods to reduce data are common factor analysis or principal components analysis (PCA). Within a common factor analysis, the technique can be exploratory (atheoretical) or confirmatory (based on previous observation or theory). There is considerable debate as to whether PCA or exploratory factor analysis (EFA) should be conducted, although they can provide similar results. PCA is the default technique in statistical software packages and so is more often used, although technically it is not a factor analytic technique and does not make any causal claims of factors on item response whereas factor analysis does. The main difference between the two methods is that PCA uses observed values from the data, whereas EFA uses

47 predicted values. Ultimately, the goal of both is to maximize the variance accounted for by a latent variable. If there is additional variance left over, a second variable is evaluated, and so on. In PCA, after the first factor is defined, consecutive factors are extracted from the remaining variance until all of the variance is accounted for, while in EFA the maximum number of factors equals the number of items in the scale. In order to determine the number of factors to extract, several methods are used. The most common are the nonstatistical techniques based on eigenvalues and scree plots. Eigenvalues represent the total amount of information a factor has. They can be calculated in different ways. In PCA they represent the total number of items attributable to a factor. It is a general rule that any factor with an eigenvalue less than 1.0 should be excluded and any factor above 1.0 can be considered to be part of the latent factor structure (Kaiser, 1960). An additional tool to evaluate factor structure is the Screeplot of eigenvalues, which is a line graph plotting all of the factors by their eigenvalue. The initial phase of the u-shaped curve is a steep drop, representing the factor extracted that accounts for the most variance. After this initial drop, there are additional smaller drops, accounted for by the subsequent factors (Zaslavsky et al, 2002). The cut-off point where factors are retained is where the slope of the curve levels off and approaches zero. Factors with values above this point are retained while those below it are deleted as they reflect factors that are not significantly contributing to the variance (Floyd & Widaman, 1995; Grimshaw et al, 2003). Finally, parallel analysis is a statistical method gaining traction in the field since it is supported by statistical data as opposed to subjective interpretation of factor plots (e.g. scree plot) (Hayton et al, 2004). This method involves resampling the data to determine the stability of potential factors. Of crucial importance to both PCA and EFA is the method of rotation used to evaluate the relationship among factors. Conceptually, rotation is essentially adjusting the geometric space so that the patterns among items can be viewed more easily. For example, in a corn field there is a vantage point where one may not be able to see the rows (e.g. on the ground). However, if one moves to a higher space (e.g. roof of a 3-storey house), the rows will be more apparent. It is important to note that applying a rotation does not change the relationship among items, but instead, locates the ideal perspective to view the patterns within the data. The type of rotation used will be dependent on whether it is theoretically anticipated that factors within a scale are correlated. In social sciences, it is more often the case that factors will be related 35

48 therefore, oblique rotations are mostly used. If there is certainty that no correlation exists among factors an orthogonal rotation is applied Neuroimaging Brain imaging techniques have significantly advanced in the last three decades, particularly in the quality of imaging data and safety of subjects undergoing scanning. Current methods to image the brain are either structural, where gross morphology is captured or functional, where dynamic activity is captured. Three dimensional methods to image brain structure depend on the cell type. Gray matter and white matter can be imaged using MRI. White matter tracts can be evaluated with diffusion tensor imaging (DTI), which is performed using an MRI scanner but tracks the water flow in white matter tracts. Both of these methods rely on the magnetic resonance of brain tissue and their composites. The level of resolution with MRI is continually increasing with the use of high power magnets capable of resolving brain tissue within a millimeter. Brain function can be evaluated using magnetic resonance methods (fmri) or via localization of radioactive nuclides (single photon emission computed tomography, SPECT, and PET). Due to the limited spatial and temporal resolution, SPECT has been supplanted by PET over the course of the last three decades. For the purposes of the methodologies presented herein, PET imaging will be focused on. 5.1 PET imaging Using PET, cerebral blood flow (CBF), glucose metabolism, receptors/transporters, enzymes and various substrates can be imaged. The driving force of PET imaging is radioactive tracers that rapidly decay. Common isotopes used for radiolabelling are 11 C, 18 F, 15 O, 13 N. Radioactive tracers injected into the bloodstream cross the blood brain barrier where, upon collision with brain tissue/matter, the radioactive nuclei emit two gamma rays captured via a detector (scintillator) that localizes the event in space. The scintillator also converts the photon light energy into electrical impulses that are used to produce a two-dimensional brain slice image. This process is repeated, and slices are later compiled into a three-dimensional functional

49 image. In order to view where the activity is occurring, the functional image is superimposed on a structural image (either MRI or computed tomography scan). Radioactively labeled water (15O-H2O) and fluorine labeled glucose allow for evaluation of CBF and glucose metabolism, respectively. The short half-life of 15O (~2 min) allows for the acquisition of multiple scans during a single session, providing an opportunity to perform tasks in the scanner. Glucose scans have lower temporal resolution and are not ideal to measure task based differences in regional activation. Since fmri use in psychiatry began in the 1990s there has been a progressive decrease in PET studies imaging cerebral blood flow and glucose metabolism due to the superior temporal resolution of fmri as well as significantly lower cost. The primary advantage of PET over fmri is the ability to perform ligand-binding studies. A ligand is a molecule with an affinity for a biological target (e.g. receptor or transporter). As a result, this provides a unique opportunity to examine neuronal functioning in vivo, which can lead to discoveries regarding disorder pathophysiology as well as the pharmacodynamics of psychotropic medications (Eckelman et al., 1984; Wagner, Jr. et al., 1983; Smith et al., 2003). Importantly, a ligand should have high specificity for the biological target and low specificity for other proteins/receptors. Many radiotracers targeting neurotransmitter receptors/transporters have been developed for human imaging studies, including serotonin transporter/receptor subtypes, glutamate, GABA, and dopamine transporter/receptors (Erlandsson et al., 2003; Farde et al., 1986;Costa et al., 1990; Wong et al., 1984;Passchier et al., 2000;Szabo et al., 1995;Houle et al., 2000;Meyer et al., 2001;Tauscher et al., 2001; Savitz & Drevets, 2013) Imaging Dopamine using PET Dopamine receptors are categorized as excitatory (D1, D5) or inhibitory (D2, D3, D4), with varying concentrations of the receptors across brain regions. D1 and D2 receptors are the most prevalent subtypes and while the striatum expresses the greatest concentrations of both types, D1 is more prominently found in the cortex than D2 (Hurd et al, 2001). It is important to note that there is a significant level of D2/D3 co-expression (Gurevich & Joyce, 1999), although D3 is expressed in lower concentrations, particularly in the cortex (Vallone et al, 2000). Imaging of dopamine receptors is limited by the lack of appropriate radiotracers and currently only

50 ligands to evaluate D1, D2/D3 receptors are readily available and clinically utilized (Savitz & Drevets, 2013). Although D1 ligands have been developed, they have been evaluated in the context of psychiatric disorders much less compared to the D2 ligands. This is largely due to the limited selectivity of ligands for D1. For example, the most frequently used D1 receptor ligand [ 11 C]NNC-112 has affinity for D1 as well as 5HT2A receptors, and it has been demonstrated that administration of a 5HT2A antagonist results in decreased D1 binding potential in cortical regions by 30% (Catafau et al, 2009; Silfstein et al, 2007). The D2 antagonist [ 11 C]raclopride is the most widely used dopamine tracer in PET studies and has been useful in the assessment of differences in binding potential in psychiatric patients compared to healthy controls as well as the determination of dopamine receptor occupancy with pharmacotherapy (Meyer et al, 2002; Meyer et al, 2006; Mizrahi et al, 2007; Vernaleken et al, 2008). The significant disadvantage of [ 11 C]raclopride is its specificity for areas of high D2/D3 concentration, which occur primarily in the striatum. Since extrastriatal areas have lower D2/D3 concentration, the use of [ 11 C]raclopride results in poor signal to noise ratios (Arakawa et al, 2008; Farde et al, 1995; Suhara et al, 1999). Given the importance of extrastriatal regions in the neurobiology of psychiatric disorders, there has been an impetus to develop ligands that can measure D2 receptors in these areas. In the last decade, two high affinity D2/D3 ligands, [ 11 C]FLB 457 and [ 18 F]fallypride, have demonstrated the ability to bind to extrastriatal receptors (Mukherjee et al, 1995; Olsson et al, 2004). Since fallypride is labelled with the slow decaying F-18 isotope, this may allow evaluation of both striatal and extrastriatal regions compared to FLB 457 (Cropley et al, 2008). However, one report directly comparing [ 11 C]FLB 457 and 11-C labeled fallypride in healthy controls demonstrated that FLB 457 had a greater signal to noise ratio and resulted in significantly greater cortical binding potential than fallypride (Narendran et al, 2009). While fallypride has not been utilized in any published MDD studies, the binding of [ 11 C]FLB 457 has been investigated in two depression studies (Montgomery et al, 2007; Saijo et al, 2010). In the first study using [ 11 C]FLB 457, no significant group differences between MDD and healthy controls were noted (Montgomery et al, 2007), although patients were mild to moderately depressed in these samples, suggesting dopaminergic dysfunction may only be detected in more severe or treatment resistant individuals. The second study using FLB 457 only included 7 MDD patients who underwent ECT and 11 healthy controls. While there were no between group differences, MDD patients 38

51 39 experienced a 25% reduction in rostral ACC D2/D3 binding following 6-7 bilateral ECT treatments (Saijo et al, 2010). 5.3 Analysis of binding potential data Nerve signals are transmitted through the synapse via neurotransmitters released from the presynaptic neuronal terminal. From the synapse, neurotransmitters act on postsynaptic receptors to either excite or inhibit activity of the neuron. Neurotransmission is terminated when excess neurotransmitter is removed from the synapse via reuptake transporters on the presynaptic terminal. Radioligands, as mentioned, can be designed to bind with transporters or receptors. The binding potential then, is a measure of the number of receptor or transporter sites the radioligand occupies in a brain region (Ichise et al, 2001). The timecourse of a radioligand in tissue is affected by the tracer s affinity for the target and number of receptor binding sites available (Schmidt & Turkheimer, 2002). However, additional factors will affect this timecourse including blood flow and radioligand clearance rates. In addition, receptor binding follows a 3-compartment kinetic model representing free ligand in tissue, non-specifically bound ligand in tissue and specifically bound ligand. Consequently, analysis of receptor imaging data requires pharmacokinetic models in order to estimate the binding potential from PET data. Essentially, models used to estimate binding potential, BP, are based on the following association: BP = Bmax/Kd Where Bmax reflects the total number of receptors in a brain region, and Kd is the dissociation constant of the ligand binding to the receptor (Ichise et al, 2001). The specific parameters and assumptions used to estimate binding potential vary depending on the model. The derivation of these parameters is based on either invasive or noninvasive methods. Invasive methods utilize arterial input where blood samples are collected throughout the scan and subsequently analyzed in order to estimate binding potential equation parameters. However, this is a time consuming and invasive method that can be imprecise due to the number of parameters that need to be estimated from blood (Scmidt & Turkheimer, 2002). In non-invasive models, parameters are extracted from the time-activity curve of the radioligand in

52 a region of interest. This requires an assumption of rapid equilibrium between free and nonspecifically bound ligand, which then form one compartment, thereby producing a two compartment model. Consequently, binding potential can be calculated from the ratio of the volume of distribution in a region of interest to that of a reference region devoid of the receptor. However, this method can produce large standard errors and thus the simplified reference tissue model was developed to address this issue and further reduce the number of parameters that need to be estimated. This method is based on the assumption that the kinetics of the free tracer and specifically bound tracer are indistinguishable. It is frequently used due to its simplicity and greater likelihood of reproducibility across studies compared to arterial inputs that can have a large degree of inter-subject variation in parameter estimation (e.g. metabolites in plasma). Ultimately, the fit of a quantification model for a radiotracer is evaluated through pharmacokinetic studies done to determine whether a given model s assumptions are appropriate for the ligand Summary and Identified Needs Anhedonia is a core symptom in depression that has been linked to treatment nonresponse with SSRIs. Importantly, current measurement of anhedonia in MDD largely reflects consummatory pleasure and does not capitalize on the neurobiological and neuropsychological findings of different (but possibly overlapping) facets of desire, motivation, effort, and consummatory pleasure in reward response. Current evidence suggests anhedonia is associated with increased activity in the ACC and prefrontal regions. In particular, the ACC is involved in evaluating rewarding properties and relaying this information to the prefrontal regions as well as the nucleus accumbens. It is clear that dopamine plays a central role in reward and, despite the role of other systems, has been the main neurotransmitter system studied in reward. The majority of research is derived from preclinical studies with a paucity of direct evaluation in MDD. Due to the consistent findings of aberrant activity in the subgenual ACC in depression and during mood induction of sadness, this became a target for DBS in TRD groups and has demonstrated effectiveness. The subgenual ACC, as mentioned, also has direct connections to key reward areas including the orbitofrontal cortex, nucleus accumbens and amygdala, which also receive dopaminergic inputs, indicating a potential anhedonia x dopaminergic role in DBS response. Therefore in order to advance the field, the following needs have been identified:

53 1. An anhedonia measure that encompasses a more refined conceptualization incorporating the current understanding of reward responses. 2. A better understanding of the region-specific correlations of anhedonia with dopamine activity in MDD in order to more accurately advance a neurobiological model of anhedonia, and develop a potential clinical proxy of dopaminergic dysfunction. 3. Identification of anhedonia and dopamine receptor availability as predictors of outcome with antidepressant treatment to highlight potential mechanisms of action and to provide evidence for the pathophysiology of treatment resistance. 41

54 42 7 Study Objectives and Hypotheses The overall aims of this body of research are: to refine anhedonia as a measureable construct, to elucidate the association of anhedonia with dopamine in cortical regions in MDD, and to determine the predictive potential of dopamine function and anhedonia in identifying treatment responders to DBS (Figure 4). Below are the specific objectives and hypotheses. Figure 4: Model of anhedonic function in MDD, and areas of thesis focus Objective #1: To develop and establish the reliability of an anhedonia scale for use in MDD, the Dimensional Anhedonia Rating Sale (DARS), that measures interest, motivation, effort and consummatory pleasure of activities/events. An additional aim is to determine the validity of the DARS against the current gold standard, SHAPS, and to discern whether the DARS has utility above and beyond the SHAPS. Hypothesis: The DARS will demonstrate high internal consistency reliability and validity tests will demonstrate a strong correlation with anhedonia based on the SHAPS and moderate

55 43 correlations with depression severity, demonstrating anhedonia is partially independent of depression symptom severity. Based on the role of desire, motivation and effort as well as consummatory pleasure in a reward response, the inclusion of these item types in the DARS will result in a scale that is better able to predict depression status (MDD vs. healthy controls) as well as TRD status within the MDD group compared to the SHAPS. The DARS will also demonstrate generalizability across age, gender and ethnicity. Objective #2: To determine whether there is a direct link between anhedonia, based on DARS and SHAPS scores, and extrastriatal dopamine D2/D3 receptor binding potential in MDD. Hypothesis: Higher levels of anhedonia will be correlated with higher D2/D3 binding potential in the ACC, orbitofrontal cortex, and dorsolateral PFC. Specifically, a high negative correlation between these regions and the DARS will be present, in addition to a positive correlation using the SHAPS. Objective #3: To evaluate whether D2/D3 binding potential and DARS scores prior to DBS can predict DBS treatment outcome at 1-year. Hypothesis: DBS non-responders will have lower D2/D3 binding potential than responders at baseline in the ACC, orbitofrontal cortex, and dorsolateral PFC. Non-responders will also have lower DARS scores, demonstrating less anhedonia. In turn these baseline D2/D3 receptor binding and anhedonia values will predict DBS response at 1 year.

56 44 Chapter 2 Methods 8 Anhedonia Rating Scale Development Objective: To develop and establish the reliability of an anhedonia scale for use in MDD, the DARS, that measures interest, motivation, effort and consummatory pleasure of activities/events. An additional aim is to determine the validity of the DARS against the current gold standard, SHAPS, and to discern whether the DARS has utility above and beyond the SHAPS. 8.1 Design The development of the DARS was driven by the limitations of existing scales and a need to capture the three-dimensional construct of anhedonia that involves interest, effort and pleasure. All three dimensions may be necessary to elicit a complete hedonic response, and a deficiency in either interest or effort could consequently reduce the experience of pleasure. In the DARS, individuals list their favourite activities/events in the dimensions of hobbies/past-times, food/drink, social activities, and sensory experience, then respond to a set of standardized questions on a Likert scale according to how they are feeling right now. Consequently, the DARS is a dynamic scale where examples change based on the respondent. Inclusion of these elements in the DARS is expected to capture a broader representation of the individual anhedonic experience and as a result be able to detect group differences where the SHAPS is not. Piloting of the DARS was be performed in four phases: (1) establishing content validity of the pilot scale by experts in the field (Phase 1), (2) first validation of the scale to determine final item-selection using community participants (Phase 2), (3) cross-validation of Phase 2 in an online study using community participants (Phase 3), and (4) validation of the DARS using unipolar or bipolar depressed patients and healthy controls (Phase 4).

57 Phase 1: Content Validity Scale Development A scale comprised of 34 items was created across the following 4 domains: hobbies/pasttimes, food/drinks, social activities, and sensory experiences (Appendix 1). These domains were based on the factor analysis of focus group data obtained during the SHAPS development (Snaith et al, 1991). Items within domains were created to reflect desire, motivation, effort and consummatory pleasure. The goal was to develop a scale that was as short as possible, items long. Many items were included in the initial version of the scale in order to test which items performed the best statistically and were easily understood by participants. Participants were required to provide their own examples within each domain. Examples of the specificity of examples was provided at the top of each domain. Subsequently, a set of standardized questions were answered with responses based on a 5-point Likert scale (Not at all = 0; Slightly = 1; Moderately = 2; Mostly = 3; Very Much = 4). Scoring was based on summation of all items. Items 2, 5, 6, 7, 18, 22, 28, and 32 were reverse keyed). A high score reflects less anhedonia (i.e. high score is good) Expert Review The initial DARS scale of 34 items was sent to 5 experts in the field of psychology who were identified by DARS collaborator, Dr. Lena Quilty, to review the content. All reviewers had approximately 5 years of experience in their respective fields. They were asked to comment on the scale design, the dimensional structure, and the wording of items. All feedback was qualitative (i.e. free-form responses, and a quantitative questionnaire was not utilized). The interviewer responses were subsequently de-identified before review. The scale was then revised according to the suggestions Analysis Plan No formal statistical analysis was required for this phase. The comments were qualitatively reviewed and scale items amended based on the suggestions.

58 Phase 2: Item Selection As a collaborator on the DARS development, Dr. Lena Quilty included the DARS within a study she conducted as a Co-Investigator at the Centre for Addiction and Mental Health (CAMH) (Principal Investigator: Dr. R. Michael Bagby). The aim of the study was to evaluate personality variables associated with gambling in the community. As this was a community study, the inclusion criteria were minimal and participants were not required to meet minimum criteria for gambling abuse. Consequently, this community study provided a good sample to test the items of the DARS since it would be more likely to include people with a range of anhedonia severity Subjects Male and female community participants were recruited with the following inclusion criteria: 1. Ages between 18 and 65 years. 2. At least 8 years of education. 3. Capacity to provide written informed consent Procedure Recruitment for this study in a community sample was from newspaper advertisements. Participants were required to attend a single research visit at CAMH. Completion of the DARS was embedded into the study and so did not require a separate consent form. Following written informed consent, the Structured Clinical Interview for Axis I DSM-IV Disorders (SCID-I) (First, 1997). Demographic information was obtained and the participants subsequently completed the DARS. All DARS data were entered into Statistical Packages for the Social Sciences (SPSS) 17.0.

59 Analysis Plan All analyses were performed using SPSS The first step was to determine the factor structure of the DARS using factor analytic procedures. Although no formal sample size calculation was done, factor analysis methods require a minimum of 200 participants to avoid an unstable factor solution (Comfrey & Lee, 1992). To do this a parallel analysis was conducted, which is the preferred method for determining the number of components or factors to retain since it is supported by statistical data as opposed to subjective interpretation of factor plots (e.g. scree plot) (Hayton et al, 2004). This method involves resampling the data to determine the stability of potential factors. The factor number that arose from this analysis was then entered into a PCA (see section 4.5.2). PCA was used to identify the factors and items which account for the most variability in the scale. After the first factor is defined, consecutive factors are extracted from the remaining variance until all the variance attributable to the items is explained. A table of eigenvalues is presented to this effect. Any factor with an eigenvalue greater than 1 can be considered to be part of the latent factor structure. The factors represent the latent subscales. An additional tool used to evaluate factor structure was the Screeplot of eigenvalues, which is a line graph plotting all of the factors by their eigenvalue. The cut-off point where factors were retained was where the slope of the curve levels off and approaches zero. Factors with values above this point were retained while those below it were deleted as they reflected factors that were not significantly contributing to the variance (Floyd & Widaman, 1995; Grimshaw et al, 2003). An oblique rotation was used to evaluate the relationship among factors, since it was theoretically anticipated that factors within the DARS would be correlated. If a relationship is expected a promax or oblimin rotation is applied. The resulting data demonstrated the extent to which items correlated across factors. A promax rotation was used in this analysis since it produces more accurate results (Dien et al, 2005). The maximum number of iterations for the rotation (i.e. the number of searches for an optimal solution) was set at the SPSS default of 25. Communalities reveal the variance of each item explained by the extracted factors. Values that were less than 0.5 were removed (Field, 2005). Furthermore, factor loadings with absolute

60 values 0.5 were considered to contribute sufficiently to the overall variability accounted for by the factor (Stevens, 1992; Costello & Osborne, 2005). Cross loading items with values >0.3 were removed to improve consistency. The next step for item reduction was to ensure that an item coding for desire, motivation, effort and pleasure facets were included within each domain. For each domain, if there was only one question remaining representing a facet (e.g. effort), this item was retained. To minimize overrepresentation, no more than two items per facet were included in any domain; however, where possible, only one of each facet was kept. Within each facet if there was ambiguity as to which item to retain, the decision was based on a combination of factor loadings, clarity and coherence of wording. The second step was to evaluate the internal consistency reliability for the items in the full scale and any subscales retained in the DARS following the factor analysis. Reliability was assessed using Cronbach s and the average inter-item correlation (AIC). The third step was to ascertain whether there were any differences in DARS scoring based on ethnicity, gender, age or depression treatment. These variables were specifically evaluated to test the generalizability of the DARS across cultures, gender and age, as well as to ascertain preliminary data as to whether the DARS was able to distinguish between individuals who are depressed and not depressed. Normality of the data were evaluated using Shapiro-Wilkes tests, where statistical significance above 0.05 is indicative of normally distributed data. Data not following the normal distribution were evaluated using non-parametric statistics. Specifically, assessment of mean differences were tested using the Mann Whitney U test. Where there was more than one test of the same type performed for a given hypothesis, Bonferroni correction for multiple comparisons was utilized. Self-report themes were also screened to ensure appropriate examples were being specified. This entailed compiling all of the examples within a domain and identifying overarching themes. Subsequently, activities/interests were categorized according to theme and the extent to which they reflected the domain was captured (yes/no). For example, in the hobbies domain which is not supposed to be primarily social, if an individual reported a social activity, this was deemed an inadequate example (i.e. no). The aim was to determine the percentage of examples that were not suitable for a given domain. 48

61 Phase 3: Cross-Validation The purpose of this study was to determine the stability of the DARS psychometric properties in an independent community sample Subjects Community participants between the ages of were enrolled. Target enrollment was 150 participants, which yields an adequate subject to item ration of 9:1. No other inclusion criteria were added in order to obtain a broad sample of subjects with a range of anhedonia for the purpose of confirming item selection in Phase Procedure Recruitment for this study was through the online laboratory of the Hanover College Psychology Department website ( that lists active online studies in the area of Psychology. Evidence indicates that the psychometric properties of scales performed in a web-based vs. laboratory-based environment may be comparable (Risko et al, 2006). Although the means acquired through the online pilot of a scale on a website may be variable, the data are sufficient to approximate tests of reliability. Therefore, web-based scale administration provides an opportunity to easily assess a scale s internal characteristics. Participation was anonymous. Upon clicking the website link for the DARS, participants were directed to a secure external website affiliated with the Department of Psychiatry, University Health Network (UHN), which hosted the online DARS study: Following online informed consent (done through clicking I agree at the bottom of the consent form), participants were asked to complete an online package of questionnaires, including a demographic form, the DARS and SHAPS, a depression symptom scale (Centre for Epidemiological Studies in Depression), reward and motivation scale (Behavioral Inhibition System/ Behavioral Activation System), and a physical activity scale (NASA Physical Activity Scale). Upon completion of the battery by a participant, data were then sent from the website to a UHN , after which the responses were compiled into SPSS 17.0, and original data stored offline on the UHN secured server.

62 Measures The following measures were included to evaluate the validity of the DARS; specifically to determine the extent to which the DARS demonstrated divergent validity with respect to depression severity, physical activity, and behavioral inhibition, and convergent validity with the current gold standard anhedonia scale, SHAPS, as well as measures of drive, motivation and reward responsivity. This set of measures will herein be called the DARS battery. Convergent Validity: Snaith Hamilton Pleasure Scale (SHAPS) - A validated 14-item self-assessment scale estimating the degree to which a person is able to experience pleasure or the anticipation of a pleasurable event (i.e. hedonic tone) (Snaith et al, 1995). A score of 2 or more "disagree/definitely disagree" is considered to be indicative of an anhedonic state (i.e. higher score reflects greater anhedonia). The SHAPS was developed specifically for use in MDD and is the current gold standard in the field. This is the current gold standard scale to measure MDD and, therefore, was the primary scale of interest for the convergent validity tests with the DARS. Dimensional Anhedonia Rating Scale (DARS) 17-item scale undergoing validation that measures levels of anhedonia across domains (hobbies, social activities, food/drink, sensory experience) and facets (desire, motivation, effort, consummatory pleasure). A high score is reflective of less anhedonia. Behavioral Inhibition System and the Behavioral Activation System (BIS/BAS) - Developed to measure personality traits of behavioral approach and inhibition based on Gray s theory of behavior; the concept being there are two distinct neurological systems for aversive motivation and appetitive motivation (Carver & White, 1994). Accordingly, the BIS system is activated in response to non-rewarding, novel stimuli that can lead to negative outcomes and inhibits behavior towards that stimulus. The BAS system, in contrast, is activated by rewarding stimuli. The BIS/BAS is a 24-item self-report scale with 4 factors: one for the BIS and 3 subscales for the BAS (drive, reward response and fun seeking). Psychometric properties of the BIS/BAS reflect convergent validity with depression and anxiety measures (Campbell et al, 2004). The subscales of the BAS were used to establish convergent validity with facets of reward (novelty seeking, drive, reward responsivity). Currently, there are no other scales to measure facets of appetitive motivation.

63 51 Divergent Validity: Centre for Epidemiological Studies in Depression (CESD) - The CESD is a validated 20-item scale that was developed for use in studies of the epidemiology of depressive symptomatology in the general population, as a basic screening tool for depression (Radloff, 1977). This scale has been frequently used as a self-report measure. It also has established diagnostic cutoff scores, therefore, was preferred over other questionnaires. Depression was evaluated to determine the extent to which anhedonia represents a distinct construct. NASA Activity Scale (NASA) - A 1 item physical activity scale to assess the degree of physical activity over the preceding month. It was developed by (Wier et al, 2001). This scale was used to determine the effects of physical activity levels on anhedonia and the degree of correlation. It was chosen due to its ability to broadly capture levels of activity in a single item. Behavioral Inhibition System (BIS) subscale of the BIS/BAS - (see ): The BIS system reflects punishment and harm avoidance, and inhibition of reward seeking. As behavioral inhibition is a distinct construct from behavioral activation, this subscale was used to confirm this distinction against the DARS Analysis Plan A sample size of 150 participants was sufficient to conduct tests of reliability and correlations; however, this sample size was insufficient to perform a factor analysis. Internal consistency reliability for DARS total scale and subscales were assessed using Cronbach s-α and AICs. Convergent and divergent validity were established using correlations between totals scores of the DARS and the SHAPS, BIS/BAS, CESD and NASA physical activity scale. Either Pearson s correlation coefficients or Spearman s Rank correlations were conducted, based on whether the variables followed a normal distribution. Group differences based on age, gender, ethnicity, and depression treatment were evaluated with Student s t-tests or Mann-Whitney U tests depending on the normality of the data. Where there was more than one test of the same type performed for a given hypothesis, Bonferroni correction for multiple comparisons was utilized. Self-report themes were also analyzed as per section

64 Phase 4: Validation in MDD Subjects MDD Inclusion Criteria: 1. Between the ages of Patients with an Axis I diagnosis of MDD or Bipolar Disorder, meeting DSM-IV criteria for a current Major Depressive Episode (MDE). 3. No other primary Axis I or II disorders. 4. No substance abuse or dependence in the last 6 months. 5. No significant/unstable medical illness. Healthy Control Inclusion Criteria: 1. Between the ages of No psychiatric history, including alcohol or substance abuse/dependence. 3. No lifetime use of psychotropic medication. 4. No significant/unstable medical illness Procedure Recruitment for this study was done through referral from the Department of Psychiatry at UHN, participant flyers at UHN and CAMH, as well as the research participant database at CAMH. One research visit to the Toronto General Hospital or Toronto Western Hospital was required that lasted between minutes. Following written informed consent, demographic information (age, gender, ethnicity, family history of MDD, current Axis III physical condition) was collected and all patients and healthy controls underwent the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al, 1998) to determine the presence of Axis I psychiatric disorders according to DSM-IV criteria. Subsequently, the DARS battery was administered. In addition to the battery, a feedback questionnaire was administered to all participants with 4 quantitative questions answered on a Likert scale (#1,2,4,5) and a qualitative question (#3) (Figure 5).

65 53 Figure 5: DARS feedback questionnaire Very Not at all clear 1. Were the instructions at the beginning on how to complete the scale clear? If not a 5, ask: what was unclear? Very Not at all clear 2. Were the questions understandable and clearly worded? If not a 5, ask: what questions were difficult to answer and why? 3. Was there anything else about completing the scale that you did not understand? Difficult 4. Overall, how easy was the scale to complete? Very Very long short 5. What do you think about how long it takes to complete the scale Very easy Analysis Plan Recommendations of sample size for initial scale development state a minimum of 30 representative individuals is sufficient (Johanson & Brooks, 2010). Internal consistency reliability for DARS items was assessed using Cronbach s-α and AICs within the depressed group. This was done since the variability in anhedonia among healthy controls was expected to be minimal based on data from the SHAPS validation (Snaith et al, 1995). Thus, reliability assessments which are dependent on variation within data sets would not be interpretable. The SHAPS validation followed a similar procedure of only assessing internal consistency in the MDD group (Snaith et al, 1995). Convergent and divergent validity were established using Pearson s or Spearman s Rank correlations between total scores of the DARS and the SHAPS, BIS/BAS, CESD and NASA physical activity scale. The effects of other variables on DARS scores (e.g. age, gender, ethnicity, depression status, treatment resistant status 1 ) were evaluated using parametric or non-parametric tests, based on normality. For non-normal data, Mann Whitney U tests or Kruskal-Wallis tests were utilized depending on the number of groups in the independent variable. Parametric tests included Student s t-tests or analysis of variance 1 Treatment resistant depression was defined as two or more failed antidepressant trials of adequate dose and duration for the current episode.

66 (ANOVA) tests. Where there was more than one test of the same type performed for a given hypothesis, Bonferroni correction for multiple comparisons was utilized. Self-report themes were also analyzed as per section To ascertain the additional value of the DARS over the SHAPS, a hierarchical regression analysis was conducted with the SHAPS entered as the first independent variable, followed by the DARS in the second step. Dependent variables in the regressions included depression severity (based on the CESD), behavioral activation (BAS subscales drive, reward, and fun), depression status (MDD vs. HC) as well as treatment resistant status within the depressed group. Ultimately, the goal was to validate the DARS for use in MDD. Therefore, the extent to which the DARS predicted depression severity and, particularly, clinical status is important. The ability of the DARS to predict facets of reward would provide further validity of the constructs being measured within the scale. 54

67 55 9 Anhedonia and Dopamine D2 Receptor Association and Prediction of DBS Outcome Objectives: (1) To determine whether there is a direct link between anhedonia, based on DARS and SHAPS scores, and extrastriatal dopamine D2/D3 receptor binding potential in MDD; (2) To evaluate whether D2/D3 binding potential and DARS scores prior to DBS can predict DBS treatment outcome at 1-year. 9.1 Design Given the paucity of research linking dopamine to anhedonia and treatment outcome in humans, the purpose of these studies was to evaluate baseline extrastriatal D2 binding and its relationship to anhedonia, and to subsequently assess their utility as predictors of response to open-label DBS. These studies were embedded into a double blind, randomized, placebocontrolled 6-month DBS trial in TRD patients. Following the end of the 6-month RCT all patients receive ongoing open label stimulation indefinitely (Figure 6). The timepoint of interest for the prediction portion of these studies was after 6 months of open-label stimulation (i.e. 1 year from baseline). Figure 6. Deep Brain Stimulation study design

68 Subjects Treatment resistant MDD patients recruited from physician referrals were enrolled into the 6-month Deep Brain Stimulation randomized controlled trial. Inclusion criteria: 1. Ages between 21 and 70 years 2. DSM-IV criteria for MDD confirmed through SCID diagnosis 3. Current MDE duration greater than 2 years 4. Demonstrated treatment resistance (a minimum of 4 failed adequate antidepressant trials from each of the different pharmacotherapy classes and/or electroconvulsive therapy and psychotherapy 5. Hamilton Depression Rating Scale-17 item (HAMD-17; Hamilton, 1960) > 20 (moderate to severe depression) 6. On a stable medication regimen for at least 4 weeks prior to enrollment 7. Capable of giving informed consent Exclusion criteria: 1. Current suicidal risk, posing immediate threat to the subject's life 2. Recent (< 1 year)/current history of drug abuse or dependence on a substance other than caffeine, or nicotine 3. Comorbid DSM-IV Axis I or Axis II disorder 4. Lifetime history of psychosis 5. Pregnancy/lactation 6. Current or past history of a cardiovascular disorder 7. Medical condition requiring immediate investigation or treatment 9.3 Procedure Entry into the DBS program was through referral by psychiatrist or primary care physician only. All referred patients entered a screening phase, and if considered a candidate, underwent consent, followed by more detailed eligibility assessments. Screening involved a phone interview followed by consultation with two study psychiatrists and the neurosurgeon. If

69 all doctors agreed on a participant s potential eligibility, she/he underwent the consent process. After patients signed informed consent, they underwent 3 monthly baseline evaluations prior to surgery to confirm eligibility and collect additional demographic, clinical, and neuroimaging data. During the first baseline assessment demographic information, including age, gender, education, and medication history was obtained, as well as the administration of a structured clinical interview to confirm Axis I and II diagnoses (SCID-I and SCID-II). The patient also completed a battery of self-report measures in addition to undergoing measures to determine medical history, depression severity, functional status, and suicide risk by the study psychiatrist. The two subsequent baseline visits entailed confirmation of depression severity and study eligibility by the study psychiatrist and research assistant/coordinator, including a review of pharmacy records in order to confirm the classes of medications failed for the current episode. At the second baseline visit, the PET scan was conducted at CAMH. Either the research assistant or coordinator was in attendance at all of the PET scans, as per CAMH regulations. The PET scans are a component of the main DBS study, and thus, the main DBS consent form. The completion of the DARS at baseline in this sample constituted a separate study that required a different informed consent form. The day of the baseline PET scan, patients who provided written informed consent completed the DARS battery (see Section 8.4.3). The day of surgery, all patients were fitted with a lexical steel frame around their head and underwent a 1.5 Tesla MRI scan for DBS localization of the subcallosal cingulate gyrus as well as for co-registration of PET data. The surgery, performed by Dr. Andres Lozano, was conducted at the Toronto Western Hospital. Two weeks after surgery, patients were randomized to receive active vs. sham stimulation (Figure 6). All researchers were blinded to treatment allocation, except for the unblinded psychiatrist who managed stimulation settings. Following randomization, they attended weekly to biweekly clinic visits for 6 months where the research assistant/coordinator documented any medication changes, adverse events and ensured the completion of self-report measures, while the study psychiatrist evaluated depression severity, suicidal risk, and patient safety. Following the 6-month RCT, all patients entered into ongoing long-term follow-up and began receiving open-label stimulation. Frequency of visits varied depending on whether a 57

70 58 patient s depression had resolved or not, however, every 6 months standardized depression severity scales were administered. Adverse events were evaluated at every visit Measures Depression severity was evaluated at all visits using the HAMD-17 and MADRS. The MADRS was designed to be more sensitive to changes with antidepressant therapy; therefore the MADRS was used as the primary depression outcome measure. The DARS battery conducted at baseline is described in Imaging Methods The baseline PET scan was completed at CAMH between 2010 and All scans were acquired with a high resolution PET CT, Siemens-Biograph HiRez XVI (Siemens Molecular Imaging, Knoxville, TN, U.S.A.) operating in 3D mode with an in-plane resolution of approximately 4.6 mm full width at half-maximum. To minimize head movements in the PET scanner, a custom-made thermoplastic facemask together with a head-fixation system (Tru-Scan Imaging, Annapolis) was used. Before each emission scan, following the acquisition of a scout view for accurate positioning of the subject, a low dose (0.2 msv) CT scan was conducted and used for attenuation correction. [ 11 C]FLB 457 (10 mci) was injected into the left antecubital vein over 60 seconds and emission data were acquired over a period of 90 minutes in 15 one-minute frames and 15 five-minute frames. A high-resolution MRI (GE Signa 1.5 T, T1-weighted images, 1 mm slice thickness) of each subject s brain was also attained and transformed into a standardized stereotaxic space (Talairach & Tournoux, 1998) using a nonlinear automated feature-matching to the Montreal Neurological Institute (MNI) template (Collins et al, 1994; Robbins et al, 2004). Regions of interest (ROIs) were delineated on the MRIs using a semiautomated method based on linear and nonlinear transformations of an ROI template in standard space to the individual MRI, followed by a refinement process based upon the gray matter probability (Rusjan et al, 2006; Ashburner et al, 1997). While ROIs for frontal and limbic areas were delineated, the ones of primary interest were the frontal brain regions: OFC, dorsolateral PFC, and ACC. PET frames were summed and

71 59 registered to the corresponding MRI using a mutual information algorithm (Studholme et al, 1997). The resulting transformation was applied to discern the ROIs from the PET image. The location of the ROI was verified by visual assessment on the summated PET image. [ 11 C]FLB 457 binding potential was calculated in MATLAB 10.0 using a simplified reference tissue (cerebellum) method (Lammertsma & Hume, 1996; Sudo et al, 2001) for each ROI. 9.4 Statistical Tests Baseline D2 binding and relationship to anhedonia Sample size for PET studies is partially based on feasibility due to the high cost. Formal calculation of sample size is not conducted due to the complex multivariate nature of the data; however, pilot data often include approximately 10 participants per group (Montgomery et al, 2007; Saijo et al, 2010). Pearson s or Spearman s Rank correlations were conducted between DARS total scores and D2 binding potential, depending on the data normality. Secondarily, correlations of D2 binding with the SHAPS, BIS/BAS, CESD, HAMD-17 and MADRS were also conducted. To identify high vs. low anhedonia groups, the median split of the DARS and SHAPS total score, respectively, was used as a cutoff to establish these groups. Student s t-tests or Mann Whitney U tests were performed to evaluate group differences in D2 binding potential Prediction of DBS outcome based on D2 binding and anhedonia To explore baseline differences between responders and nonresponders at 1 year post-dbs (response defined as 50% drop in MADRS total score), Student s t-tests were conducted for normal data, Mann Whitney U tests were conducted for non-normal data, and Likelihood Ratio tests were conducted for categorical data due to their preferred use with small sample sizes (Xing et al, 2013). Where there was more than one test of the same type performed for a given hypothesis, Bonferroni correction for multiple comparisons was utilized. To test the hypothesis that baseline D2 binding or anhedonia are associated with change in depression score at one year (based on the MADRS), Pearson s or Spearman s Rank correlations between the percent change MADRS total score at 1 year and D2 binding potentials for the ROIs

72 and anhedonia total scores were conducted. Based on these findings, a linear or logistic regression using percent MADRS change or responder status, respectively, as the dependent variable was performed for regions of interest (OFC, dorsolateral PFC, and ACC) and anhedonia scores (DARS and SHAPS) Risks and Safety Issues MRI MRI scanning is not associated with any known risks and there is no evidence of short-term or long-term side effects. Prior to the MRI, patients were required to fill out a questionnaire to ensure that it is safe for them to have an MRI. As the MRI scanner uses a magnetic field to generate images, it was absolutely necessary that patients do not have any metal implants in their body or a cardiac peacemaker. The major discomfort with MRI scanning is the knocking sound that the machine makes and patients were given earplugs to reduce this effect. Some people have found the closed-space of the MRI scanner uncomfortable PET Patients were exposed to a small amount of radiation from a brief transmission scan to measure how much radiation is absorbed by the head. They received 10 mci of [ 11 C] FLB 457 (specific radioactivity, 418.5MBq/mmol) for the receptor binding scan. The radiation dose during a PET scan is comparable to other nuclear medicine scans and represents a very low risk. The potential long-term risk from the radiation dose is uncertain but these doses have never been associated with any definite adverse effects. Thus the risk, if any, was estimated to be slight.

73 61 Chapter 3 Results 10 Anhedonia Scale Development 10.1 Phase 1: Content Validity All 5 reviewers endorsed good face validity. Comments were provided to clarify the concepts addressed and wording of questions. The scale was revised according to the comments in Table 3. Changes based on reviewer suggestions were as follows: additional instructions and examples were provided for each of the domains (Reviewer #1), items with a double negative were removed (Reviewer #2), and item questions were altered to reflect all of the domain examples instead of at least one (Reviewer #3). Scales to measure depression, overall physical activity and motivation were identified for inclusion in Phase 3 based on comments from Reviewer #4. Table 3: Content validity based on expert comments Reviewer # Comments Useful scale For domain of pastimes, some people might list hobbies/pastimes that are social in nature, so instructions to clarify that the examples should be nonsocial activities would be beneficial Content domain is very good Try to avoid double negatives in the wording of questions Consider having items precise in terms of whether difficulty getting started, amotivation in general or difficulty staying on task is being tapped into Some items refer to ALL activities where others refer to AT LEAST ONE. Questions may be difficult to answer if both are included. Consider revising. Good scale Consider including measures of depression, boredom and amotivation in validation phase Comprehensive but a bit long Questions straightforward and understandable Question 8 refers to needing help to do activities, which seems broad and does not necessarily tap anhedonia, but perhaps more obstacles to hedonic experience

74 Phase 2: Item Selection Subject Characteristics A total of 229 participants recruited from the community were enrolled in this study. The demographic data represent a middle- aged sample, 40% of whom were not born in Canada (Table 4). This allowed a preliminary evaluation of generalizability of the DARS based on cultural differences. Overall, a third of the sample had a current psychiatric diagnosis of which 21% met criteria for MDD in the past month. Illness severity across disorders was unknown, as this was not quantified. Table 4. Demographic information of Phase 2 participants Enrolled 229 % Female 45.4 Age (SD) 41.7 (12.7) Ethnicity (%) Caucasian African Hispanic Native Asian East Indian Pacific Islander Bi-racial Other Education % Born in Canada % > High School % Employed 58.1 Family History- Mental Illness (%) Current Axis I Diagnosis (%) Major Depression Bipolar Disorder Schizophrenia OCD Panic Disorder PTSD GAD Alcohol/Substance Use Disorder Eating Disorder

75 63 Factor Analysis The initial parallel analysis of the 34-item DARS yielded a 5-factor solution based on eigenvalues>1 and examining the scree plot (Appendix 2). Specifying 5 factors in the PCA yielded communalities ranging from The rotated factor loadings revealed that items were grouped according to social activities (35.4% variance explained), food/drinks (10.2% variance explained), hobbies/pastimes (8.4% variance explained), sensory experience (5.2% variance explained), and a factor that was comprised of only the negatively keyed items (4.5% variance explained). The structure matrix, which presents correlations of each item on the different factors revealed a similar pattern of grouping, but also demonstrated low to moderate correlation of items across factors (Appendix 2). Based on the criteria set out for item reduction, all items with communalities below 0.5 were removed (items 3,4,5,6,7,18, 32), which left 27 items. Since the reverse key items across domains strongly loaded onto only one factor with little or no correlation with the other factors, the remainder of these questions was removed (items 2, 22,28) producing a 24 item scale. The remaining 24 items all had high loadings (>0.5) and correlations (>0.6) with their respective factor. The remaining items were screened to determine an item for desire, motivation, effort and pleasure was represented within each domain. In some cases, there was more than one item representing the same facet. As previously stated, the decision on which items within a facet to retain was based on a combination of factor loadings, clarity and coherence of wording. For example, in the foods/drinks domain there was one question for each of the desire, motivation and effort facets, but three evaluating pleasure (item 12: having these foods/drinks would satisfy me, item 13: the thought of having my favourite food/drink at the end of the day pleases me, and item 15: I would enjoy these foods/drinks ). Item 15 had the highest factor loading (0.85 vs and 0.78, respectively) and was also the most clearly worded, thus it was kept in the domain and the other two removed. Where it was unclear which item should be removed, the scale factor structure was evaluated with and without the item. If the item eliminated the 4-factor structure it was retained. Within the hobbies domain, the motivation item was the reverse key item which was removed; therefore, this was the only domain that did not have an item to assess this.

76 This process yielded a 17-item scale with a 4-factor structure mapping onto the different domains of anhedonia: hobbies, foods/drinks, social activities and sensory experience (Figure 7). The highest score attainable is 68, where a high score is reflective of less anhedonia. This 17- item scale was used for validation in the subsequent studies. 64 Themes of self-report examples Self-report examples were reflective of their respective domain, indicating an understanding of the categories. Overall themes for hobbies and social activities were: arts & crafts, lifestyle/culture, leisure, fitness/wellness/sports, multimedia/technology, education/training, food/cuisine, home & garden (Tables 5 & 6). The most frequent hobbies reported were reading and a form of exercise, while for social activities meeting friends/family for dinner or watching movies/tv with others were the most common examples. There was a conceptual overlap in examples cited by participants within the hobby and social domains, however, within the social domain it was specified the activity was with other people and not solitary (e.g. shopping in hobbies vs. shopping with friends in social activities). In the food/drinks domain, examples were straightforward and represented different cuisines and types of drinks (Table 7). Examples in the sensory domain were categorized based on sense (smell, touch, taste, hearing, sight, and other if examples included more than one sense) (Table 8). There was an emphasis on the frequency of participants citing touch examples. Overall out of over 2,000 examples, only 10 were not reflective of the category (e.g. social activity listed in the hobby domain).

77 65 Table 5. Self-report examples of hobbies Arts & crafts Beading Acting Sketching Soap making Knitting Play guitar Writing stories Lifestyle/ Culture Concerts Museums Theatre Dance shows Comedy clubs Listen to music Leisure/Games Fitness/Wellness/ Sports Reading Gym workout Boating Spinning Shopping Biking Travel Martial arts Play with Running pet Swimming Nature Yoga walks Meditation Puzzles Skiing Skating Multimedia/ Technology Watch movies/tv Videogames Internet surfing Download music Programming Education/Training Food/cuisine Home/garden Dog training Equine therapy Science research Cooking Baking Eating out Gardening Yard work House repair Cleaning Table 6. Self-report examples of social activities Arts & crafts Play in band Make costumes Lifestyle/ Culture Going dancing Going to concerts House party Going to plays Dance classes Art exhibits Meet new people Leisure/Games Bowling Cards Chess Shopping Playing pool Cottage trips Camping Karaoke Play with grandkids Fitness/Wellness/ Sports Biking Going to gym Going to sporting event Hockey Multimedia/ Technology Watch movies/tv ing Chatroom Facebook Education/ Training Teaching genealogy Volunteering Food/cuisine Cooking with partner Dinner with friends/family Coffee with friends/family Cooking classes

78 66 Table 7. Self-report examples of food/drink Foods Bagels Brownies Butter chicken Dumplings Chicken noodle soup Lasagna NY strip steak Perogies Sushi Drinks Bubble tea Coffee Gatorade Fruit smoothies Margaritas Red wine Coca-cola Chocolate milk Orange juice Table 8. Self-report examples of sensory experience Smell Touch Hearing Sight Taste Other Bread Nature Home cooked food Partner s fragrance Candles Sea Coffee Flowers Hawaiian pizza Massage Sex Feel sun on skin Cuddling Back scratch Petting dog Hot bath Hugs Listening to music Comedy podcasts Waterfalls Sports on radio Grandson Cello music in garden Sunrise Sunset Viewing art People watching Star gazing Daughter sleeping Fireworks Waves Wine Favourite foods Tea Pasta Being by water Laying on beach Waves crashing on rocks Drive up north Hot beverage Running in the sun Swimming in sea Mindfulness meditation

79 Figure 7. DARS 17-item scale 67

80 68 Reliability Analysis Cronbach s was 0.92 for the DARS total score. The AIC was in the moderate range: The Cronbach s for each of the domains was also high: pastimes/hobbies (0.91), foods/drinks (0.86), social activities (0.83), and sensory experiences (0.89). The AICs for the subscales ranged from (Table 9). Table 9. Cronbach s alpha and inter-item correlations for the DARS item selection study Cronbach s alpha (α) Inter-item Correlation Total Scale Pastimes/Hobbies Foods/Drinks Social Activities Sensory Experiences Effects of Other Variables on DARS scores The mean DARS total score (17 items) for the whole sample was with the subscale means as follows: hobbies ( ), food/drink ( ), social ( ), and sensory ( ). These values did not vary by ethnicity or gender. However, those with current MDD had lower scores than participants with no psychiatric history (51.2 vs. 57.5, p<0.012). Among the domains, this group difference was observed within the social subscale with a trend toward significance in the sensory subscale (social: 10.0 vs. 12.9, p=0.001; sensory: 15.3 vs. 17.4, p=0.016, respectively, corrected for multiple comparisons).

81 Phase 3: Cross-validation Subject Characteristics A total of 150 participants completed the online study. Compared to Phase 2, subjects were more likely to be female, younger in age, and had less ethnic diversity (Table 10). There was a greater percentage of participants reporting treatment for MDD compared to Phase 2. Those who reported treatment for MDD had higher CESD scores than participants with no psychiatric history (25.2 vs. 18.4, p=0.001). Self-report themes were similar to Phase 2 as well (Tables 5-8). Table 10. Demographic data for cross-validation study sample Enrolled 150 % Female 70.6% Age (SD) 27.5 (11.9) Ethnicity (%) Caucasian African Hispanic Asian East Indian Native Other Education % > High School Current/Past Treatment for MDD 31% Reliability Analysis Cronbach s was 0.92 for the DARS total score. The AIC was 0.41, in the moderate range. The Cronbach s for each domain was high, similar to Phase 2 findings:

82 70 pastimes/hobbies (0.90), foods/drinks (0.75), social activities (0.85), and sensory experiences (0.89). The AICs for the subscales ranged from (Table 11). For comparison, the SHAPS Cronbach s was 0.88 and the AIC was Table 11. Reliability and validity of the DARS total score and subscales in online sample Reliability Convergent and Divergent Validity* Cronbach s alpha (α) Inter-item Correlation SHAPS Validity Analysis The DARS total score demonstrated moderate convergent validity with the SHAPS (rs=- 0.58, p<0.001), BAS-reward (rs=0.53) and BAS-drive (rs=0.35, p<0.001), as well as a small correlation with BAS-fun (rs=0.18, p=0.034). The DARS subscales all had moderate correlations with the SHAPS and BAS-reward, and small to moderate correlations with BAS-drive (Table 11). The DARS-social subscale was the only domain to have a correlation with BAS-fun (rs=0.35, p<0.001) Divergent validity was demonstrated by the lack of correlation between the DARS total score and subscales with physical activity and the BIS. In addition there was only a moderate correlation with the CESD total score (rs=-0.47, p<0.001). Similar correlations between the CESD and hobbies (rs=0.47, p<0.001), social subscale (both rs=-0.47, p<0.001) were observed and weak correlations were demonstrated for the foods/drinks (rs=-0.29, p<0.001) and sensory experience subscale (rs=-0.31, p<0.001) BASreward BAS- Drive CESD Total Scale Pastimes/Hobbies Foods/Drinks Social Activities Sensory Experiences * All SHAPS, BAS-reward and CESD correlations are significant at p< All BAS-Drive correlations are significant at p<0.05.

83 71 Effects of Other Variables on DARS scores Similar to Phase 2, the mean DARS score was , with the subscale means as follows: hobbies ( ), food/drink ( ), social ( ), and sensory ( ). In addition, the total score did not vary by age, ethnicity or gender. Those who reported treatment for MDD had lower DARS total scores (46.1 vs. 51.2, p<0.01). Among the subscales, a group difference was only observed within the DARS social domain, where the MDD group had lower scores (9.3 vs. 11.6, p=0.005) Phase 4: Validation in MDD Subject Characteristics A total of 102 MDD (n=52) and healthy control (n=50) subjects were enrolled in this study. Age and gender were not different between groups, although the depressed group was less ethnically diverse (Table 12). While both groups reported high years of education, reflecting the completion of post-secondary education, the healthy controls had significantly more years (18.1 vs. 16.1, p=0.004). As expected based on previous literature, Axis III 2 comorbidity was higher in the MDD group compared to the control group. Self-report themes were similar to Phase 2 (Tables 5-8). Reliability Analysis Cronbach s was 0.96 for the DARS total score. The AIC was 0.60, in the high range. The Cronbach s for the domains were high: pastimes/hobbies (0.91), foods/drinks (0.90), 2 Axis III reflects acute medical conditions and physical disorders based on the Diagnostic and Statistical Manual of Mental Disorders

84 72 social activities (0.88), and sensory experiences (0.99). The AICs for the subscales ranged from (Table 12). For comparison, the SHAPS Cronbach s was 0.85 and the AIC was Table 12: Demographic information in Depressed Patients and Healthy Controls Variable Depressed (n=52) Healthy Controls (n=50) p Age (years) 43.4 (14.3) 37.8 (13.1) ns Gender (% female) 61.5% 74.0% ns Education (years) 16.1 (2.8) 18.1 (3.1).004 Ethnicity (%) Caucasian African Hispanic East Indian Asian Pacific Islander Other Axis III diagnosis 3 (%) Hypertension Diabetes Thyroid dysfunction High cholesterol Arthritis Chronic pain Osteopoenia CESD total score 39.5 (9.9) 6.3 (7.1) <.001 Duration of MDE (months) 60.8 (73.6) n/a n/a 3 Diagnoses were self-reported.

85 73 Validity Analysis The DARS total score demonstrated high convergent validity with the SHAPS (rs=0.79, p<0.001) as well as moderate correlations with the BAS-Drive (rs=0.42, p=0.002), BAS-Reward (rs=0.48, p<0.001), and BAS-Fun (rs=0.52, p<0.001). Anhedonia based on the DARS was not related to behavioral inhibition (based on the BIS), and demonstrated a moderate correlation with depression severity (rs=0.37, p=0.036) and physical activity (rs=0.4, p=.004). All subscales had moderate correlations with the SHAPS, BAS and small to moderate correlations with the CESD (Table 13). For comparison of SHAPS vs. DARS correlations with the BIS/BAS see Appendix 3. To evaluate the link between physical activity and anhedonia, post-hoc tests were done to assess the correlation with the SHAPS, BIS/BAS, group differences in CESD in the depressed group and prediction of groups (MDD vs. healthy control; TRD vs. non-trd). The correlation of physical activity on the NASA with the SHAPS was similar (rs=-0.38, p=.006), and the BIS/BAS did not correlate with physical activity. NASA scores were not significantly different between TRD and non-trd groups. It follows then that physical activity was not a predictor of TRD status. However, NASA scores were able to predict depression status (R 2 =0.30, p<0.001). Table 13: Reliability and validity of the DARS total score and subscales in MDD sample Cronbach s alpha (α) Reliability Inter-item Correlation Convergent and Divergent Validity* SHAPS BASreward BASfun BAS- Drive CESD Total Scale Pastimes/Hobbies Foods/Drinks Social Activities Sensory Experiences *All convergent and divergent correlations significant at p<

86 74 Group Differences The distribution of DARS scores for the MDD group was 1-44, while in the healthy control group it was (Appendix 4). No differences based on gender, or ethnicity, were observed for the DARS total score. In addition, there was no correlation between age and DARS scores. However, the MDD group had a lower DARS total score than healthy controls ( vs , p<0.001) (Figure 8). Similarly, the MDD group had lower scores for all of the subscales: hobbies ( vs , p<0.001), foods/drinks ( vs , p<0.001), social activities ( vs , p<0.001), and sensory experience ( vs , p<0.001). A summary of the average DARS scores across studies is presented in Appendix 5. In the TRD subanalysis, TRD patients reported lower levels of anhedonia based on the DARS (Figure 9) and SHAPS. Behavioral approach, as demonstrated by the BAS subscales, was also lower than in non-resistant MDD patients, while behavioral inhibition was not significantly different (Table 14). There were no correlations between CESD scores and DARS or SHAPS scores in the TRD subset. Table 14. Differences in anhedonia/reward scale scores between TRD and non-trd patients DARS Total Hobbies Food/drink Social Sensory Scale Non-TRD (n=37) TRD (n=15) p-value 36.3 (15.5) 8.6 (4.4) 10.0 (4.2) 7.1 (3.9) 10.7 (5.6) 15.1 (8.8) 3.7 (2.7) 4.7 (2.9) 2.7 (2.3) 3.9 (2.4) <0.001 SHAPS 10.2 (2.7) 5.3 (3.6) <0.001 BAS-reward 14.8 (2.6) 11.2 (3.8).001 BAS-drive 6.9 (2.6) 9.4 (2.7).003 BAS-fun 10.8 (2.3) 7.1 (2.7) <0.001 BIS 25.1 (7.5) 25.1 (3.3) ns

87 75 Figure 8: Difference in DARS score between MDD and Healthy Controls in pivotal validation trial * p<0.001 * n=52 n=50 Figure 9. DARS and SHAPS scores in TRD and non-trd patients in pivotal validation trial * p<0.001 * n=37 n=15

88 76 Hierarchical Regression Analysis The hierarchical analysis was done using scale variables (e.g. CESD and BIS/BAS) as well as diagnostic variables (MDD vs. healthy controls; TRD vs. healthy controls). Depression severity was predicted by the SHAPS (R2=.617, p<0.0001), with only an additional 2.6% of the variance explained when the DARS was entered into the model (R2=.643, p=.011). For all of the BAS subscales (drive, reward, fun), the SHAPS was significant as a single predictor, but was no longer significant when the DARS was entered into the model For all the subscales, the additional variance explained by the DARS was 14.1% (p<0.001), 5.7% (p=0.001), and 7.1% (p=0.001), respectively. Using logistic regression to predict group status (MDD vs. healthy control, both the SHAPS and DARS performed similarly, with the SHAPS explaining 51.9% of the variance (p<0.001). The model improved to 56.2% when the DARS was included (p<0.001). However, when prediction of TRD vs. healthy control was estimated, a forced hierarchical regression model demonstrated the superiority of DARS score to predict TRD. The SHAPS on its own accounted for 31.1% of the variance (p<0.0001). When both terms were entered the model explained 38.8% of the variance (p<0.001), however the SHAPS was no longer a significant predictor when the DARS was entered into the model. Participant Feedback Over 90% of participants rated the DARS feedback questions with at least a 4, indicating excellent clarity of the DARS instructions, questions, and ease of use (Figure 10). Over 70% of participants felt the time to complete the scale was short. MDD patients were statistically more likely to rate the clarity of instructions lower, however the means were almost the same (4.6 vs. 5.0, p=0.003). There were no other group differences with respect to feedback. In total, there were 16 qualitative comments offered by participants, which fell into four themes related to the domains, self-report examples, question content, and Likert scale content. Specifically, 38% of the responses were related to the difficulty in coming up with examples.

89 77 Another 25% of comments were regarding questions being repetitive or too wordy, with another 19% reporting confusion about what the categories meant. The remainder of responses reflected the Likert scale: suggested addition of neutral anchor, and rewording of very much ). Figure 10: Percent of participants rating 4 or 5 on DARS feedback questions 11 Dopamine D2 Binding 11.1 D2 Binding and relationship to anhedonia Subjects Participants in this study were recruited from a DBS for TRD trial carried out as a joint initiative between investigators in the Department of Psychiatry and Division of Neurosurgery at UHN (see section 9). A total of 15 patients participated. Demographic variables for the sample are listed in Table 15, and are reflective of a TRD sample (i.e. high depression severity, and duration of episode). All participants were on a stable antidepressant regimen at for least four months prior to surgery, managed by their local psychiatrist or family physician. The most common antidepressants used were SSRIs or SNRIs. Half of the patients were receiving an adjunctive atypical antipsychotic.

90 78 Table 15. Demographic and clinical information for TRD sample Variable Mean (N=15) Age (years) 47.4 (10.3) Gender (% female) 66.7% Education (years) 16.5 (2.3) Age at onset 22.9 (7.6) Duration of current MDE (months) 108 (82) CESD 44.8 (6.5) NASA Physical Act. 1.4 (2.4) SHAPS 10.2 (2.7) DARS 15.3 (8.7) D2 Binding and Anhedonia Upon evaluating the reliability of the D2 binding data for each brain region, several patients demonstrated high variability in their binding values. All covariance values that were greater than 10 were removed. This resulted in a reduced sample size of 8-14 patients depending on the brain region. The only brain area that correlated with DARS total score (n=8) was the right anterior cingulate cortex (ACC), where high D2/D3 binding was associated with greater levels of anhedonia (r= , p=.047; DARS low score reflects increased anhedonia) (Figure 11). The SHAPS demonstrated a similar correlation Figure 11. Scatterplot of D2/D3 binding in relation to DARS total score

91 79 with the right ACC (r=0.74, p=0.036). There were no significant correlations between D2 binding and any of the DARS subscales, BAS subscales or the BIS. D2/D3 function also did not correlate with depression severity based on the CESD, HAMD-17 or the MADRS. Furthermore, TRD patients with high anhedonia had greater D2/D3 binding in the right dorsolateral PFC (U=9.0, p=0.048) (Figure 12) as well as the ACC, specifically within Brodmann Area 25 (U=6.0, p=0.034). There was no differentiation in brain regions using low vs. high anhedonia based on the SHAPS (>10). Figure 12. Association between high and low anhedonia scores with D2/D3 binding Sanfey, 2007 with permission

92 Prediction of DBS outcome at 1 year based on D2 binding and anhedonia Subjects A total of 19 patients with TRD who underwent DBS and received follow-up to 1 year were evaluated. Within this group, 11 were classified as responders at 1 year and 8 were nonresponders. There were no group baseline differences in key demographic or clinical variables such as age, gender, depression severity, or anhedonia (Table 16). Table 16. Demographic and clinical information for TRD DBS sample Variable Non-responders (n=8) Responders (n=11) Age (years) 41.1 (8.3) 48.5 (9.0) ns Gender (% female) 87.5% 63.6% ns Education (yrs) 16.0 (1.9) 15.9 (2.7) ns Age of onset 19.9 (5.8) 25.6 (8.8) ns No. lifetime MDEs 5.3 (5.0) 4.8 (5.9) ns Duration of MDE (60.8) (82.7) ns Baseline HAMD (2.6) 25.6 (2.5) ns Baseline HAMA 27.4 (4.6) 23.7 (4.6) ns Baseline DARS* 16.0 (7.7) 14.3 (7.7) ns *Sample size based on n=15; see section 10.1 for details. p With respect to dopamine binding, non-responders consistently had higher D2/D3 binding potential than responders in cortical and subcortical regions, including the bilateral OFC, dorsolateral PFC and insula (Figure 13). Adjusted sample sizes are presented in Figure 13.

93 81 Figure 13. Differences in regional D2 binding among responders and non-responders to DBS DLPFC: dorsolateral prefrontal cortex; OFC: orbitofrontal cortex; VLPFC: ventrolateral prefrontal cortex; Temp. Cor.: temporal cortex High baseline D2/D3 binding potential, indicative of lower dopaminergic tone, correlated with lower percent change on the MADRS at 1 year in the following areas: OFC (rs=-.683, p=0.042), dorsolateral PFC (rs=-.685, p=0.029), PFC (rs=-.648, p=0.043), insula (rs=-.673, p=0.033), and temporal cortex (rs=-.648, p=0.043). Logistic regressions using MADRS response status as the dependent variable were not significant using these brain areas as independent variables. Baseline clinical variables including anhedonia based on the DARS or SHAPS did not predict outcome at 1 year. However, there was a trend for patients with higher anhedonia based on the DARS (<16) to be classified as non-responders (LR=3.56, p=.059). These effects were not observed for the SHAPS.

94 82 12 Executive Summary Chapter 4 Discussion From the present studies it has been shown that anhedonia can be linked to dopaminergic function in MDD, both of which can be used as predictors of response to DBS. In particular, the DARS demonstrated excellent psychometric properties and the refined measurement of anhedonia through the inclusion of interest, motivation, effort and consummatory pleasure provided additional utility over the SHAPS in identifying MDD subtypes (i.e. TRD). Furthermore, the first evidence has been provided in an MDD sample for a direct link between anhedonia and dopamine function in the ACC as well as the dorsolateral PFC, two regions implicated in reward response and impaired activity in depression (Mayberg et al, 1999; Der- Avakian & Markou, 2012). Finally, preliminary evidence suggests that D2/D3 binding potential in the OFC, additional prefrontal regions, insula and temporal cortex can predict outcome to antidepressant therapy with DBS, representing a potential biomarker of response. While baseline anhedonia as a continuous measure did not predict DBS response, the trend for patients with higher anhedonia to be categorized as non-responders needs further exploration. 13 DARS Scale Development 13.1 Self-report themes At a fundamental level, the correct conceptualization of the DARS dimensions and appropriate examples within them is paramount to the success of the dynamic nature of the scale. If respondents do not provide adequate examples that reflect the categories, this has an impact on the validity of the scale (i.e. uncertainty with regards to the construct being assessed) as well as the factor solution (i.e. erroneous latent variables that are derived by chance depending on how the category definitions are understood). This scale design has been employed in one other measure. The Multidimensional Health Locus of Control Form C developed by Wallston and colleagues (1978) is condition specific, where respondents specify their most problematic health condition and subsequently answer a set of standardized questions. This scale has demonstrated

95 83 good reliability and validity. However, health conditions are more tangible than interest examples. Therefore, additional screening of the DARS activities/interests was necessary to ascertain whether the categories were being accurately depicted. In the item selection study, the majority of examples provided accurately reflected the category definitions; less than 1% (N=1,832 across categories) were not reflective of the domain. This improved with subsequent studies, where self-report themes reflected an accurate conceptualization of the categories. However, in order to minimize this issue and aid in improving dimension conceptualization, a reference activity list derived from the data captured will be developed and provided with more interests/activities than given in the instructions. A secondary issue is the extent to which examples could overlap across dimensions. Across all of the validation studies there was some conceptual overlap in themes, although a different aspect was focused on. For example, watching hockey may have been selected in the hobbies category, while under the social activities, playing hockey was reported. Since these examples both relate to hockey but reflect a different feature of its enjoyment, this is not inherently problematic. Even if different aspects of the same example are used, the 4-factor solution of the scale that maps onto the domains provides evidence that the DARS is tapping into discriminate aspects of anhedonia. However, this could result in increased cross-factor correlations. To this effect, since the DARS measures different facets of anhedonia, it is theoretically reasonable that there will be a moderate degree of correlation among the different domains, which was observed; correlations of items across factors ranged from small to moderate ( ). It will be pertinent in future DARS studies to confirm the factor solution in order to demonstrate that any overlap in examples is not affecting the factor stability Item selection and factor solution It is recommended in the initial stages of validating a scale, to generate an item pool that is at least double that of the final desired scale width (Devellis, 2012). As such the initial 34-item DARS was an adequate item pool size for a 17-item scale. The item pool will ultimately include similar questions that are asked somewhat differently. However, this increased number of similar items also introduces a degree of redundancy. In the initial phases of scale development, this is

96 84 not so problematic since it is expected that the number of items will be reduced. Even if two items are very similarly worded, inclusion of both during item selection will allow a scale developer to determine which item psychometrically performs better and is more clearly understood by respondents. Furthermore, the sample selected for item selection is often not based on the target population, where a limited range in response is expected. This is because at this critical stage variance is ideal because it is important to know which items are the most discriminating between those that have the attribute and those that do not. For this reason, the first two DARS studies (item selection and cross-validation) were conducted in a community sample. The initial 5-factor solution of the DARS reflected the domains of hobbies, foods/drinks, social activities, sensory experience, as well as a factor that included all of the reverse-keyed items, which had negatively valenced wording. Scale development techniques for item selection recommend the inclusion of items that are reversed in order to avoid agreement bias the tendency to agree with items irrespective of their content (Devellis, 2012). For example, a person who is severely depressed would respond strongly to items that reflect high depression ( I feel very sad ) and not strongly to items that represent the absence of depression ( I am happy most of the time ). Agreement bias would be indicated if the person reported strongly to both positively and negatively valenced items. While inclusion of opposingly valenced items is an ideal method to ensure appropriate responding, experts in scale development assert that the disadvantages of including items worded in an opposite direction outweigh potential benefits (Devellis, 2012). For example, reversed items may confuse respondents, who may not recognize the difference between what constitutes agreement with the attribute being probed. Furthermore, reverse-keyed items tend to perform poorly psychometrically (Currey et al, 2002). The issue of reverse polarity items poorly performing in the present study is consistent with this observation. The fifth factor accounted for less than 5% of the variance attributed to the full factor solution and the pattern and structure matrix revealed small correlations within the factor as well as across factors. This demonstrates a lack of contribution of these items to the overall scale. Since the items in this factor were mostly motivation items, it is possible that the factor was reflective of this reward facet instead of the reverse polarity. In either case, the poor item performance necessitated their exclusion.

97 85 The final 4-factor solution reflected the domains of anhedonia as opposed to the components (interest, motivation, effort, consummatory pleasure). This suggests that reward type is a significant factor when measuring anhedonia. The anhedonia categories in the DARS were based on factor analytic findings from the initial SHAPS publication (Snaith et al, 1995), where focus group generation of reward examples fell into those themes. However, the SHAPS is a unitary construct of consummatory pleasure, whereas the DARS is a multifactorial measure of reward category. The increased items within a category or the use of personal examples could have led to the multi-factorial solution of reward-type in the DARS. The distinction of reward-type in the DARS is also consistent with the notion of primary versus secondary reward. The experience of pleasure can pertain to many things that are instinctual (e.g. food, sex) versus non-instinctual (e.g. photography, reading). It could be argued that food and sex represent primary rewards (inherent rewards), whereas photography or money are secondary rewards (no inherent reward in itself and for which reward value must be learned). This distinction of reward type is supported by overlapping and distinct neurobiological response to primary and secondary reward. In a meta-analysis of 87 studies to determine the overlapping and distinct brain areas activated in response to monetary, erotic and food reward, it was found that there were some differences in activation within a common network that was engaged, which included the ventromedial PFC, ventral striatum, amygdala, anterior insula and mediodorsal thalamus (Sescousse et al, 2013). In particular, the primary rewards (food and erotic reward) resulted in activation of the anterior insula and amygdala (in the case of erotic reward), while the monetary secondary reward activated the orbitofrontal cortex. The authors suggested this distinction supports the hypothesis that secondary rewards preferentially activate areas within the more evolved neocortex. Furthermore, the authors propose that even within the neocortex there may be a difference in how primary and secondary reward is encoded. Accordingly, the more phylogenetically recent anterior portion of the OFC was more likely to respond to monetary reward. While the same group previously demonstrated that the posterior OFC is activated in response to more primary stimuli (e.g. erotic) (Sescousse et al, 2010), this needs to be further replicated due to some inconsistencies in findings (Sescousse et al, 2013). Taken together, these data support the idea of a common network of reward processing along with incentive-type specific activation. Consequently, the 4-factor solution of the DARS is

98 86 uniquely positioned to test the neurobiological correlates of subjective hedonic responses to different reward types. Finally, the anhedonia components of interest, motivation, effort, and pleasure, may not have been distinct enough from each other to be distinguished as separate factors. In other words, the conceptual nuance among these facets may be too subtle for a subjective measure. A larger item set size may be necessary to capture components of anhedonia as opposed to type. Introducing redundancy within a scale by including multiple items that express a similar idea (e.g. interest) in different ways can be beneficial to this end. This is because what is common across the items will summate, and what is irrelevant will be cancelled out (Devellis, 2012). Alternatively, the item wording may not have been discriminating enough across components Reliability Across the studies, the DARS demonstrated excellent internal consistency reliability, as evidenced by the high alphas overall ( ) and in the subscales ( ). The similarity of alphas across studies also suggests the DARS is a reproducible measure. These findings are similar to the SHAPS with internal consistency values of in the present studies and in reported studies (Snaith et al, 1995; Franken et al, 2007; Nakonezny et al, 2011). The inter-item correlations reflect the extent to which the items are measuring the same variable. Thus, the stronger the correlation, the stronger the evidence for unidimensionality (one factor) (Devellis, 2012). In the first two DARS studies, the average inter-item correlation among all the items was moderate, This is suggestive of non-unidimensionality, which is further supported by the four factor solution reported herein. Inter-item correlations were not reported in either of the validation studies of the SHAPS (Snaith et al, 1995; Nakonezny et al, 2011). However, in the present studies the AICs for SHAPS were 0.35 in the online sample and 0.29 in the MDD sample, which suggests additional variables other than consummatory pleasure are being captured. Considering the factor analysis of the SHAPS revealed only one factor of consummatory pleasure (Nakonezny et al, 2011), these variables may not be discriminating enough to produce a separate factor.

99 Validity As described, the main forms of validity testing for a scale are content validity and construct validity (section 4.6.1). The content validity of the DARS was high based on reviewer comments. Important information was derived at this stage, including revision of item instructions in order to avoid example overlap, and item content changes to avoid double negatives and increase clarity of questions. Convergent validity was demonstrated through correlation of the DARS with the SHAPS. The correlation was lower in the online sample than the MDD sample. The correlation of 0.58 in the online sample indicates that there is shared variance with the DARS, but that there is still a degree of unexplained variance which could be due to the scale construct differences. In the MDD study, there was a high correlation between the DARS and SHAPS, reflective of good convergent validity. However, this could be problematic due to the proposed difference in dimensionality across scales. Similar to the AICs, because the DARS is measuring additional components of the anhedonic experience a very high correlation with the SHAPS would suggest the DARS is not adequately capturing these components in MDD, and is only assessing consummatory pleasure. A high correlation could also be interpreted in another way. MDD impacts hedonic responsivity, however, this could be a general effect across components of anhedonia, resulting in similar responding across reward types, thus driving up the correlation. Support for this hypothesis comes from the DARS correlations with trait reward response, measured by the BAS(Carver & White, 1994). The subscales of fun (sensation seeking), drive (goal-directed reward behavior), and reward (responsivity to reward), measure interest, motivation/effort, and consummatory pleasure. The correlations of the DARS across the BAS subscales were moderate but much stronger in the MDD trial than in the online trial, particularly for the fun and drive subscales. Furthermore, there may be subtypes of MDD that demonstrate trait anhedonia, such as in TRD. This is supported by the higher BAS subscale scores in TRD than in the non-trd group. Alternatively, the high correlation could also mean that in an MDD group the DARS loses its multi-dimensionality and this will have to be evaluated in a larger MDD sample. The correlations with the BAS subscales also provide convergent validity support for the multifaceted structure of the DARS within the domains. In comparison, the SHAPS did

100 88 demonstrate correlations with BAS fun and reward, but not drive in the MDD sample (Appendix 3). Again, this is in contrast to the unidimensional structure reported (Nakonezny et al, 2011), and further confirmation of the factors is warranted. These data highlight the importance of approaching convergent validity (and divergent validity) using multiple scales rather than just one in order to garner more insight into the constructs a scale is tapping into. Divergent validity was assessed using measures of self-report depression severity (CESD), behavioural inhibition (BIS), and physical activity (NASA). Across the online and MDD validation trials, there were significant moderate correlations between the DARS total score and CESD (rs= ) consistent with previous reports of the SHAPS (Snaith et al, 1995; Franken et al, 2007; Leventhal et al, 2006; Nokonezny et al 2011). Among the subscales there were small to moderate correlations, with sensory experiences consistently being a small correlation (rs= ). Interestingly, in the MDD sample, the CESD correlations with the DARS total and subscales were lower than in the online sample, which also lends support to the idea that anhedonia represents an overlapping but distinct construct with depression. The lack of correlation between CESD and DARS scores in the TRD only group also suggests that anhedonia is a distinct construct. The absence of a correlation with the BIS is in line with expectations, as behavioural inhibition is a separate construct from reward and includes aspects of punishment and harm avoidance (Carver & White, 1994). Finally, divergent validity of the DARS with physical activity on the NASA was demonstrated in the online study; however, there was a moderate correlation in the MDD sample (rs=0.4, p=0.004). In one vein, this is not surprising given lack of energy is a diagnostic feature of an MDE (APA, 2013) and associations between anhedonia and low energy have been reported (Brown et al, 1995). This correlation in MDD may reflect a link with state anhedonia, given NASA scores predicted current MDD status, while the BIS/BAS, measuring trait anhedonia, did not correlate with current physical activity levels Group differences The DARS did not differ by age, gender or ethnicity across all studies, which provides support for its use in various populations. Of particular interest, is the lack of effect by ethnicity. The SHAPS was designed, in part, to avoid the generalizability issues in other anhedonia scales,

101 89 which utilize items that are culturally specific (see section 4.4 for review). The SHAPS succeeded in this goal as the validation trials do not demonstrate an effect by ethnicity (Nakonezny et al, 2011; Franken et al, 2007; Nagayama et al 2012; Liu et al, 2012). The issue that arises with increased generalizability is the lack of specificity in items that elicit a strong hedonic response. To this effect, the DARS has demonstrated generalizability while retaining item specificity. The presence of depression was also a discriminating feature among DARS scores across the validation trials. Notably, the means associated with depression varied considerably (51.2 vs vs in Phase 2, 3, and 4 studies, respectively), likely due to the differences in symptom severity and presence of MDD across studies: severity was not characterized in the item selection study, and a diagnostic interview was not conducted in the online sample. As a result, in the item selection study, participants could meet criteria for an MDE, but be mild in severity; and in the online study, participants may report severe depressive symptoms but not meet formal criteria for an MDE (i.e. could be in the context of a life event, such as bereavement, or associated with another comorbidity). Furthermore, in the online study the demographic question referred to past or current treatment for depression, which also introduces a mix of severity. The subscale differences across studies in the depressed groups may also reflect differences in severity. In both the item selection, and online sample social anhedonia was the most prominent dysfunction, while in the MDD sample, all of the domains were affected. However, research suggests that social anhedonia improves with treatment in depression (Blanchard et al 2001). It is important to note that anhedonia is not just a symptom related to depression and overlaps with schizophrenia, bipolar disorder type (more type II than I), and to a lesser extent, anxiety disorders (Abramovitch et al, 2014). In schizophrenia, however, this deficit is considered to be social and physical in nature (Chapman et al, 1976), and is associated with levels of psychosis (Pelizza & Ferrari, 2009). In addition, anhedonia in this group may be primarily representative of a trait, where in MDD it may reflect current state (Blanchard et al, 2001; Katsanis et al, 1992) or trait. Nevertheless, considering that the MDD sample in Phase 4 (pivotal validation trial in MDD) was more severely depressed than in the online study (mean CESD scores of 40 vs. 18, where above 16 is the cutoff reflecting presence of depression), this

102 90 finding suggests that at lower severities, social anhedonia is still present, which may, in fact, reflect a trait aspect or perhaps a residual symptom that is not affected by treatment. Finally, the MDD sample demonstrated a large 27-point difference on the DARS from the healthy controls, and likewise, the TRD group demonstrated a large 17-point difference from the non-trd group (Appendix 4). These findings demonstrate the sensitivity of the DARS in distinguishing groups, including depressive subtypes. It also provides further support for a potentially significant role of anhedonia in TRD. TRD is characterized by high levels of anhedonia, as well as psychomotor retardation (Malhi et al 2007). Both symptoms are strongly affected by dopaminergic fluctuations based on preclinical research (Dunlop & Nemeroff, 2007), which supports the role of a dopaminergic deficit in TRD. The lack of correlation between the CESD and DARS in the TRD-only group in Phase 4 also supports a subtype of depression that is characterized by distinct impairment in reward neurocircuitry (see section 14 below for further discussion on this topic) Hierarchical regression analysis The use of hierarchical regression analysis to determine the added value of a scale over another has been used in other studies (Winer et al, 2014). Based on the regression performed in the MDD validation study, both the SHAPS and the DARS are similarly able to distinguish between healthy controls and a depressed group, with the DARS only contributing an additional 4% improvement in the model. A similar finding was observed for prediction of CESD total scores. Where the two scales differed was in the ability to predict BAS subscales and MDD subtypes. While the SHAPS did have predictive value for all of the BAS subscales, the introduction of the DARS into the model, rendered the SHAPS an insignificant contributor. This was particularly evident for the drive subscale where the DARS contributed an additional 14% of explained variance into the model, which demonstrates the DARS is superior at estimating levels of motivation than the SHAPS. This is to be expected considering the specific items within the DARS to measure this anhedonia facet. A similar occurrence was observed when predicting TRD status, where the SHAPS was no longer significant when the DARS was entered into the

103 91 model. The ability to define MDD subgroups clinically and linking such a clinical discriminator to neurobiology is particularly relevant in order to improve strategies for treatment selection (Kennedy et al 2013). Along with the positive participant feedback of the scale, the findings from the regression analysis provide preliminary evidence that the DARS is superior to the SHAPS in evaluating interest, motivation and reward responsivity as well as in defining subgroups, such as TRD 13.7 Limitations Several limitations across the DARS studies should be noted. In the item-selection study, no validity measures were included. Validity tests are used to evaluate whether the construct being assessed is, in fact, the construct being assessed and is done subsequent to item selection and reliability tests. Therefore, since item selection is not based on validity assessments, this was not considered a crucial component for this step, although it would have strengthened the study. To this effect, the lack of illness severity measures, particularly in depression, prevents conclusions regarding group differences in DARS scores across psychiatric conditions. Importantly, validity in the target population is the most essential since they represent the endusers. The cross-validation study was conducted online instead of in-person. Prior studies have demonstrated that reliability based on this method is comparable to in-person studies (Risko et al, 2006), making this an attractive option for a scale validation study due to ease of recruitment and management. In the present studies, the reliability estimates from the item selection and cross-validation were identical, in some cases. Despite this, some demographic information (e.g. gender, ethnicity, age) cannot be confirmed as a result of the anonymous participation. The sample size of 150 participants was not ideal to confirm the factor structure of the DARS. Psychometric studies recommend a minimum of 200 participants, as sample sizes below this result in unstable factor solutions. In the pivotal validation trial in MDD, self-report depression severity was collected in contrast to a clinician-administered measure. This is a potential issue with regards to convergent

104 92 validity, considering some evidence suggest that as severity increases, patients rate depression severity higher than clinicians (Prusoff et al, 1972; Corruble et al, 1999). Future studies should confirm the correlation between DARS scores and depression based on either the HAMD-17 or MADRS. Again, while the sample size was sufficient for reliability and validity tests, it was inadequate to run a factor analysis to confirm the factor solution. Furthermore, while evaluation of the score distributions between groups suggested a cutoff of 44 could be the most discriminating, the sample size was not sufficient to run a receiver operating characteristic curve (ROC) in order to evaluate the sensitivity and specificity of this assertion. Across studies, test-retest reliability was not evaluated. This precludes any conclusions about the stability of the DARS over time. Importantly, anhedonia in itself may not be a stable construct and this has not been adequately tested in the literature. The SHAPS has demonstrated reasonable test-retest reliability at a 4-week interval of 0.64 (Liu et al, 2012), although a correlation coefficient > 0.70 is considered high. This suggests that anhedonia may be subject to variability in level over time, and reflects a state versus trait. Overall, whether anhedonia is present in the depressive state or as a trait needs to be confirmed. In addition, the range of convergent and divergent measures was sufficient but limited in these studies. Additional measures of state anhedonia, anxiety, personality factors, motivation and energy would be prudent in order to more fully evaluate the validity of the DARS. 14 D2 association with anhedonia and DBS outcome 14.1 Anhedonia and dopamine in depression The present study demonstrated a high correlation between anhedonia, based on both the DARS and the SHAPS, and D2/D3 binding potential in the right ACC. Furthermore, when participants were grouped according to higher versus lower anhedonia, using a median split, the additional effect of increased D2/D3 binding potential in the dorsolateral PFC was observed using the DARS total score, but not the SHAPS. The correlation between anhedonia and the right ACC is consistent with previous findings using electroencephalography (Wacker et al, 2009), and fallypride PET (Vrieze et al, 2013).

105 93 The neurobiology of anhedonia and reward circuitry, especially as it relates to neurotransmitter function, has mostly come from preclinical research (Treadway & Zald, 2011; Rizvi et al, 2014c). With regard to neurotransmitter systems, dopamine has received the most attention and has the most empirical support, although other systems in reward are now being evaluated (see section 4.3). Considering anhedonia is a core feature of MDD, this has led to hypotheses regarding dopaminergic deficits in depression, which are empirically supported in animal models (Dunlop & Nemeroff, 2007). Interestingly, the evidence for a direct dopaminergic link to MDD in human studies has not been adequately investigated, mostly due to the lack of methodology to probe this system in vivo. Initial studies utilized peripheral plasma metabolites of dopamine as a marker for central nervous system activity in depression (Roy et al, 1985; Traskman et al, 1981; Lambert et al, 2000), followed by catecholamine depletion in remitted MDD (Miller et al, 1996; Hasler et al, 2009), and subsequently the use of SPECT and PET imaging was employed with most studies demonstrating increased binding potential in MDD (Savitz & Drevets, 2013) (see section for review of dopamine in MDD). However, the direct link between dopamine and anhedonia in depression has yet to be firmly established. There are only two published reports evaluating the role of dopamine and anhedonia in the context of MDD. The first trial was conducted by Sarchiapone and colleagues (2006) using SPECT imaging of the dopamine transporter, DAT, in depressed patients compared to healthy controls. Decreased DAT binding was observed compared to healthy controls, but this did not correlate with subjective symptom scores. In healthy controls, Vrieze et al (2013) evaluated the effect of anhedonia during a reward task using 11C-fallypride (D2/D3 extrastriatal radioligand). Increased anhedonia based on the SHAPS correlated with increased activation in the left ventromedial PFC and dorsal ACC. Other studies have utilized glucose metabolism (PET) to evaluate the role of anhedonia and brain function. In a seminal study, in MDD and bipolar disorder, anhedonia correlated with decreased activity in the insula, striatum, and temporal cortex, but increased activity in the ACC (Dunn et al, 2002). These studies demonstrate the important role of the ACC in the experience of anhedonia. Importantly, it is clear that dopamine is not necessary for a hedonic liking response in animals (Salamone et al, 2007; Correa et al, 2002; Cousins et al, 1996). Opioids and even glutamate have been linked to consummatory reward as demonstrated using opioid antagonists (Barbano & Cador, 2007) and optogenetic inhibition of glutamatergic projections (Stuber et al, 2011). Notably, in humans

106 94 decreased glutamate metabolism has also been observed in the ACC of depressed individuals presenting with anhedonia (Walter et al, 2009). Much of what is known about dorsolateral PFC associations with anhedonia comes from schizophrenia research. Several studies have linked increased anhedonia ratings to decreased dorsolateral PFC activity (Harvey et al, 2010; Park et al, 2009). Notably, in healthy individuals, trait anhedonia was negatively correlated with rostral ACC (Harvey et al, 2010), and dorsolateral PFC activity (Park et al, 2009). Other studies have demonstrated that the dorsolateral PFC and ventromedial PFC are involved in reward-based decision-making through analysis of reward values and effort calculations (Der-Avakian & Markou, 2012). This finding is in keeping with non-responders to rtms being more anhedonic and also having increased functional connectivity from the dorsolateral PFC to the ventromedial PFC (Downar et al, 2014). Decreased white matter integrity of the dorsolateral PFC has also been reported in MDD (Blood et al, 2010), which strengthens the association among dorsolateral PFC structural integrity, functional connectivity and anhedonia. The lack of association between depression severity and extrastriatal D2/D3 binding potential is consistent with other studies using FLB 457 (Montgomery et al, 2007; Saijo et al, 2010). In addition, these studies did not find a difference between MDD and healthy controls. This is in contrast with trials demonstrating increased binding potential in striatal D2/D3 in MDD (Meyer et al, 2006), which is correlated with severity (Larisch et al, 1997; Lehto et al, 2008). This disparity could be for one of two reasons: either extrastriatal dopamine is not significantly involved in the expression of depressive symptoms overall, or considering dopamine is not as highly expressed extrastriatally, the ligands to measure extrastriatal D2/D3 are not sensitive enough to measure small changes in depression compared to controls. There have only been two published reports utilizing FLB 457 in MDD, as described above. Interestingly, although Saijo and colleagues (2010) did not find a difference between MDD and healthy controls, they did report a 25% decrease in D2/D3 binding within the ACC following treatment with ECT, suggesting a larger difference in D2/D3 binding potential is required in order to be detected between groups. This hypothesis could also underlie the absence of correlation between DARS subscales and binding potential; however, the small sample size or a lack of sensitivity in the DARS cannot be ruled out.

107 Predictors of depression outcome In the present study a correlation between baseline extrastriatal D2/D3 binding potential and DBS outcome at 1 year was observed. This is the first study to evaluate extrastriatal D2/D3 receptor binding as a possible biomarker to treatment response in depression. In addition, baseline DARS or SHAPS scores did not predict 1 year outcome, although there was a statistical trend where TRD patients scoring higher on the DARS were more likely to be categorized as non-responders. Non-responders also had higher baseline D2/D3 binding potential in frontal regions, including the OFC, dorsolateral PFC, ventrolateral PFC, and insula. In particular, high binding in the OFC, dorsolateral PFC, and insula was associated with less change in depressive symptoms after 1 year of DBS. These results are interesting in several ways. Firstly, it is the first data to show that resistant depression is directly linked to dopaminergic dysfunction. Secondly, it shows that impairment of dopamine can predict outcome to an antidepressant therapy. Thirdly, although in need of replication in a larger sample, supports the assertion that anhedonia is a key factor in treatment resistance (Malhi et al, 2007). To address the first point, it was unexpected to observe greater dopaminergic dysfunction in non-responders. In contrast, the hypothesis (Objective #3, Section 7) from the outset was that high dopamine impairment would be predictive of response, since it was hypothesized that DBS may exert its therapeutic effect through dopaminergic mechanisms due to the direct connections from the subgenual ACC to key regions of this system. However, we cannot rule this out until longitudinal data are analyzed. In addition, given the observed association between dopamine and anhedonia (sections 11.1 and 14.1), the extreme state of anhedonia in this TRD sample, in comparison to the non-resistant MDD group in the DARS study (13 vs. 30), presents the possibility that all of these TRD patients had a degree of dopaminergic dysfunction. The question then is, if dopamine and anhedonia are truly connected, why did the patients with the worst anhedonia not respond? One interpretation could be that there are alternate systems that are not affected by DBS, yet have indirect or direct inhibitory connections onto the dopamine system (and other systems), resulting in persisting depression and anhedonia. For example, the link between neuroinflammation and increased anhedonia has been reported (Harrison et al, 2009; Capuron et al, 2012). Inflammation in the brain is also non-specific and will target other systems including dopamine, serotonin, norepinephrine, and glutamate (Muller, 2014), all systems involved in the pathophysiology of depression (See section 1.3.3). Further support for this

108 96 hypothesis comes from findings that treatment with adjunctive anti-inflammatory drugs has demonstrated some effectiveness in TRD, yet only in those with high baseline inflammatory markers (Raison et al, 2013). An alternative theory relates to the demonstrated reduced structural integrity of white matter tracts observed in MDD (de Kwaasteniet et al, 2013) and particularly in TRD (Zhou et al, 2011; Riva-Posse et al, 2014). The DBS target of the subgenual ACC has a white matter tract, called the uncinate fasciculus that connects this region to prefrontal areas (including the OFC), and the temporal cortex (including the amygdala). In MDD, there have been several reports of structural abnormalities in this tract (de Kwaasteniet et al, 2012; Dalby et al, 2010; Murphy & Frodl, 2011). A recent report of TRD patients who underwent DBS to the subgenual ACC, demonstrated the tract through the uncinate fasciculus was a critical pathway for DBS response, for which non-responders had decreased integrity (Riva-Posse et al, 2014). This evidence suggests that if the appropriate connections from the site of DBS stimulation are not intact, the neurochemical and neurocircuitry dysfunction will not improve, and sites of impairment remain hyper or hypoactive. Consequently, it may be the case that the stronger the impairment due to depression, the worse the structural integrity. However, the overlap between structural integrity and receptor function has not been empirically tested to date. If higher dopamine binding does relate to decreased tract integrity, it follows then that non-responders demonstrated higher binding potential in the OFC and prefrontal regions, and why these areas were predictive of 1- year outcomes. This would also explain the trend for TRD patients who were more anhedonic to be less likely to respond to DBS. While this theory could explain why individuals with high anhedonia and dopaminergic impairment did not improve, the reality is that depression is a heterogeneous disorder. As such it is likely a combination of multiple neurobiological systems and decreased structural integrity in non-responders that resulted in poor outcomes, although this needs to be empirically validated. With regards to the specific areas of difference between responders and non-responders (e.g. OFC, dorsolateral PFC, insula), there have been few studies evaluating the neurobiological differences among TRD and non-trd patients. As previously discussed, Downar and colleagues (2014) demonstrated increased functional activity from the ventromedial PFC to the dorsolateral PFC in non-responders to rtms, who were also more anhedonic. The brain areas with higher

109 97 dopamine binding potential in non-responders and that are predictive of outcome also map onto current understanding of reward circuitry and anhedonia (Der-Avakian & Markou, 2012) Limitations Several limitations of the PET studies should be noted. The primary limitation was sample size. Also, the high number of covariance percentages over 10 resulted in the removal of a number of participants, reducing the sample size even further. This can occur in ligand binding studies where a single bolus injection is used. While less invasive, the parameter estimates may be subject to more error than deriving parameter estimates from arterial input. Consequently, given TRD patients with higher anhedonia had greater D2/D3 binding in the right dorsolateral PFC, which was also predictive of poor DBS outcome at 1 year, the functional interaction between anhedonia and D2 binding in the dorsolateral PFC may not have been directly observed due to sample size effects. This may also have been the reason for a lack of association between DARS subscales and D2/D3 binding. Secondly, all participants were maintained on antidepressant medication. Consequently, medication effects on dopamine receptor function in the present studies are unclear. In particular, atypical antipsychotics bind to dopamine D2 receptors (Seemans, 2002). Given DBS is an adjunctive therapy, future trials will likely have the same limitation. However, with larger samples, the pharmacologic effects on receptor binding could be evaluated and potentially statistically controlled for. Thirdly, the use of a median split to define higher and lower anhedonia represents an arbitrary grouping not based on actual cutoff scores. These findings would need to be confirmed in a subsequent study once severity cutoff scores are determined for the DARS. In addition, the lack of an MDD control and healthy control group further limits the interpretations of the association between anhedonia and dopamine as well as for the DBS responder analysis. More specifically, the use of a TRD sample only may have skewed the results linking DARS scores to ACC binding potential, and may not be generalizable to an MDD population overall. As a result it cannot be confirmed from these findings that dopaminergic function is impaired in MDD in general, but rather in TRD. The same reasoning applies to the

110 98 responder analysis of DBS outcome, where it is unclear how responders and non-responders would compare to a non-trd group as well as healthy controls. Furthermore, since the blind was not able to be broken, it is unclear how the varying lengths of stimulation affected the predictor analysis. Finally, analysis of longitudinal D2/D3 binding data is necessary to provide further support for the role of dopamine in DBS mechanism of action Conclusions and Future Directions In summary, this body of work has demonstrated that the measurement of anhedonia through the DARS is reliable and can be used to identify MDD subtypes, as well as linked to dopamine receptor binding in humans, both of which may have predictive potential for treatment outcome. A direct link between anhedonia and dopamine function in the ACC as well as the dorsolateral PFC was observed, highlighting the role of these two regions in reward responsivity and also providing additional support for the ACC as a treatment target in MDD. Finally, these data also suggest baseline dopamine function may be used to predict treatment outcome, highlighting the role of this system, particularly in TRD. Further studies need to confirm the factor structure of the DARS and broaden the assessments to confirm its validity. For example, specific measures of anxiety and fatigue should be used to determine whether anhedonia and these constructs overlap. To evaluate the DARS sensitivity to change (and the stability of anhedonia as a construct), test-retest reliability at frequent time points is necessary in MDD samples undergoing treatment and those stable on treatment. A larger sample of MDD, TRD and healthy control participants should also be evaluated in order to develop cutoff scores for the DARS. The correlation between anhedonia and extrastriatal dopamine should also be confirmed in a larger non-trd sample in addition to non-clinical community participants. The presence of dopaminergic dysfunction in TRD could be established by evaluating the difference in striatal or extrastriatal dopamine receptors between TRD and non-trd groups. Finally, elucidating the relationship between dopamine binding potential with functional connectivity as well as structural integrity of tracts in an MDD and TRD sample would shed light on the neurobiological mechanisms associated with treatment resistance and could also provide a sensitive biomarker of treatment response.

111 99 References Abdallah CG, Niciu MJ, Fenton LR, Fasula MK, Jiang L, Black A, Rothman DL, Mason GF, Sanacora G. Decreased occipital cortical glutamate levels in response to successful cognitive-behavioral therapy and pharmacotherapy for major depressive disorder. Psychother Psychosom. 2014;83: Abramovitch A, Pizzagalli DA, Reuman L, Wilhelm S. Anhedonia in obsessive-compulsive disorder: beyond comorbid depression. Psychiatry Res. 2014;216: Acosta-García J, Hernández-Chan N, Paz-Bermúdez F, Sierra A, Erlij D, Aceves J, Florán B. D4 and D1 dopamine receptors modulate [3H] GABA release in the substantia nigra pars reticulata of the rat. Neuropharmacol. 2009;57: Albuquerque EX, Pereira EF, Alkondon M, Rogers SW. Mammalian nicotinic acetylcholine receptors: from structure to function. Physiol Rev. 2009;89: Alcaro A, Panksepp J, Witczak J, Hayes DJ, Northoff G. Is subcortical-cortical midline activity in depression mediated by glutamate and GABA? A cross-species translational approach. Neurosci Biobehav Rev. 2010;34: American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing, Anand A, Li Y, Wang Y, Wu J, Gao S, Bukhari L, Mathews VP, Kalnin A, Lowe MJ. Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biol Psychiatry. 2005;57: Anderson IM, Richell RA, Bradshaw CM. The effect of acute tryptophan depletion on probabilistic choice. J Psychopharmacol. 2003;17:3-7. Anderson IM. Selective serotonin reuptake inhibitors versus tricyclic antidepressants: a metaanalysis of efficacy and tolerability. J Affect Disord. 2000;58: Andrews-Hanna JR, Reidler JS, Huang C, Buckner RL. Evidence for the default network's role in spontaneous cognition. J Neurophysiol. 2010;104: Anisman H, Merali Z, Poulter MO, Hayley S. Cytokines as a precipitant of depressive illness: animal and human studies. Curr Pharm Des. 2005;11: Arakawa R, Ito H, Takano A, Takahashi H, Morimoto T, Sassa T, Ohta K, Kato M, Okubo Y, Suhara T. Dose-finding study of paliperidone ER based on striatal and extrastriatal dopamine D2 receptor occupancy in patients with schizophrenia. Psychopharmacol 2008;197: Arencibia-Albite F, Paladini C, Williams JT, Jiménez-Rivera CA. Noradrenergic modulation of the hyperpolarization-activated cation current (Ih) in dopamine neurons of the ventral tegmental area. Neurosci. 2007;149: Artigas F. Serotonin receptors involved in antidepressant effects. Pharmacol Ther. 2013;137: Ashburner J, Friston K. Multimodal image coregistration and partitioning--a unified framework. Neuroimage. 1997;6: Auer DP, Pütz B, Kraft E, Lipinski B, Schill J, Holsboer F. Reduced glutamate in the anterior cingulate cortex in depression: an in vivo proton magnetic resonance spectroscopy study. Biol Psychiatry. 2000;47: Avshalumov MV, Chen BT, Marshall SP, Peña DM, Rice ME. Glutamate-dependent inhibition of dopamine release in striatum is mediated by a new diffusible messenger, H2O2. J Neurosci. 2003;23:

112 Bals-Kubik R, Ableitner A, Herz A, Shippenberg TS. Neuroanatomical sites mediating the motivational effects of opioids as mapped by the conditioned place preference paradigm in rats. J Pharmacol Exp Ther. 1993;264: Balon R, Segraves RT. Survey of treatment practices for sexual dysfunction(s) associated with anti-depressants. J Sex Marital Ther. 2008;34: Barbano MF, Cador M. Opioids for hedonic experience and dopamine to get ready for it. Psychopharmacology (Berl). 2007;191: Bartanusz V, Aubry JM, Pagliusi S, Jezova D, Baffi J, Kiss JZ. Stress-induced changes in messenger RNA levels of N-methyl-D-aspartate and AMPA receptor subunits in selected regions of the rat hippocampus and hypothalamus. Neurosci 1995;66: Bauer M, Tharmanathan P, Volz HP, Moeller HJ, Freemantle N. The effect of venlafaxine compared with other antidepressants and placebo in the treatment of major depression: a meta-analysis. Eur Arch Psychiatry Clin Neurosci. 2009;259: Belanoff JK, Rothschild AJ, Cassidy F, DeBattista C, Baulieu EE, Schold C, Schatzberg AF. An open label trial of C-1073 (mifepristone) for psychotic major depression. Biol Psychiatry. 2002;52: Beneyto M, Meador-Woodruff JH. Lamina-specific abnormalities of NMDA receptor-associated postsynaptic protein transcripts in the prefrontal cortex in schizophrenia and bipolar disorder. Neuropsychopharmacol. 2008;33: Berridge KC. Substantia nigra 6-OHDA lesions mimic striatopallidal disruption of syntactic grooming chains: a neural systems analysis of sequence control. Psychobiol. 1989;17: Bewernick BH, Hurlemann R, Matusch A, Kayser S, Grubert C, Hadrysiewicz B, Axmacher N, Lemke M, Cooper-Mahkorn D, Cohen MX, Brockmann H, Lenartz D, Sturm V, Schlaepfer TE. Nucleus accumbens deep brain stimulation decreases ratings of depression and anxiety in treatment-resistant depression. Biol Psychiatry. 2010;67: Bhagwagar Z, Wylezinska M, Jezzard P, Evans J, Boorman E, M Matthews P, J Cowen P. Low GABA concentrations in occipital cortex and anterior cingulate cortex in medication-free, recovered depressed patients. Int J Neuropsychopharmacol 2008;11: Bilkei-Gorzo A, Racz I, Michel K, Zimmer A. Diminished anxiety- and depression-related behaviors in mice with selective deletion of the Tac1 gene. J Neurosci. 2002;22: Black DW, Andreasen NC. Introductory Textbook of Psychiatry, Fourth Edition Arlington, VA: American Psychiatric Publishing, Inc., Blanchard JJ, Horan WP, Brown SA. Diagnostic differences in social anhedonia: a longitudinal study of schizophrenia and major depressive disorder. J Abnorm Psychol. 2001;110: Blier P, Ward HE, Tremblay P, Laberge L, Hébert C, Bergeron R. Combination of antidepressant medications from treatment initiation for major depressive disorder: a double-blind randomized study. Am J Psychiatry. 2010;167: Blood AJ, Zatorre RJ. Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proc Natl Acad Sci U S A. 2001;98: Blood AJ, Iosifescu DV, Makris N, Perlis RH, Kennedy DN, Dougherty DD, Kim BW, Lee MJ, Wu S, Lee S,Calhoun J, Hodge SM, Fava M, Rosen BR, Smoller JW, Gasic GP, Breiter HC; Phenotype Genotype Project on Addiction and Mood Disorders. Microstructural abnormalities in subcortical reward circuitry of subjects with major depressive disorder. PLoS One. 2010;5:e

113 Blumenthal JA, Lett HS, Babyak MA, White W, Smith PK, Mark DB, Jones R, Mathew JP, Newman MF; NORG Investigators. Depression as a risk factor for mortality after coronary artery bypass surgery. Lancet. 2003; 362: Breiter HC, Aharon I, Kahneman D, Dale A, Shizgal P. Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron. 2001;30: Briggs SR, Cheek JM. The role of factor analysis in the development and evaluation of personality scales. J Personality. 1986;54: Brown C, Schulberg HC, Madonia MJ. Assessment depression in primary care practice with the Beck Depression Inventory and the Hamilton Rating Scale for Depression. Psycho Assess. 1995;7: Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev. 2009;33: Burke AR, Miczek KA. Stress in adolescence and drugs of abuse in rodent models: role of dopamine, CRF, and HPA axis. Psychopharmacology (Berl). 2014;231: Caine SB, Negus SS, Mello NK, Bergman J. Effects of dopamine D1-like and D2-like agonists in rats trained to discriminate cocaine from saline: influence of experimental history. Exp Clin Psychopharmacol. 2000;8: Campbell AD, McBride WJ. Serotonin-3 receptor and ethanol-stimulated dopamine release in the nucleus accumbens. Pharmacol Biochem Behav. 1995;51: Campbell-Sills L, Liverant GI, Brown TA. Psychometric evaluation of the behavioral inhibition/behavioral activation scales in a large sample of outpatients with anxiety and mood disorders. Psychol Assess. 2004;16: Capuron L, Neurauter G, Musselman DL, Lawson DH, Nemeroff CB, Fuchs D, Miller AH. Interferon-alpha-induced changes in tryptophan metabolism. relationship to depression and paroxetine treatment. Biol Psychiatry. 2003;54: Capuron L, Gumnick JF, Musselman DL, Lawson DH, Reemsnyder A, Nemeroff CB, Miller AH. Neurobehavioral effects of interferon-alpha in cancer patients: phenomenology and paroxetine responsiveness of symptom dimensions. Neuropsychopharmacol. 2002;26: Capuron L, Miller AH. Cytokines and psychopathology: lessons from interferon-alpha. Biol Psychiatry. 2004;56: Capuron L, Pagnoni G, Drake DF, Woolwine BJ, Spivey JR, Crowe RJ, Votaw JR, Goodman MM, Miller AH. Dopaminergic mechanisms of reduced basal ganglia responses to hedonic reward during interferon alfa administration. Arch Gen Psychiatry. 2012;69: Carver CS, White TL. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. J Person Soc Psychol. 1994;67: Catafau AM, Searle GE, Bullich S, Gunn RN, Rabiner EA, Herance R, Radua J, Farre M, Laruelle M. Imaging cortical dopamine D1 receptors using [11C]NNC112 and ketanserin blockade of the 5-HT 2A receptors. J Cereb Blood Flow Metab. 2010;30: Chapman LJ, Chapman JP, Raulin ML. Scales for physical and social anhedonia. J Abnorm Psychol. 1976;85: Chaput Y, Magnan A, Gendron A. The co-administration of quetiapine or placebo to cognitivebehavior therapy in treatment refractory depression; a preliminary trial. BMC Psychiatry 2008;8:

114 Chen MC, Hamilton JP, Gotlib IH. Decreased hippocampal volume in healthy girls at risk of depression. Arch Gen Psychiatry. 2010;67: Choi KS, Holtzheimer PE, Franco AR, Kelley ME, Dunlop BW, Hu XP, Mayberg HS. Reconciling variable findings of white matter integrity in major depressive disorder. Neuropsychopharmacol. 2014;39: Cipriani A, Furukawa TA, Salanti G, Geddes JR, Higgins JP, Churchill R, Watanabe N, Nakagawa A, Omori IM, McGuire H, Tansella M, Barbui C. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. Lancet. 2009;373: Clark DC, Fawcett J, Salazar-Grueso E, Fawcett E. Seven-month clinical outcome of anhedonic and normally hedonic depressed inpatients. Am J Psychiatry. 1984;141: Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18: Collins PY, Patel V, Joestl SS, March D, Insel TR, Daar AS; Scientific Advisory Board and the Executive Committee of the Grand Challenges on Global Mental Health, Anderson W, Dhansay MA, Phillips A, Shurin S, Walport M, Ewart W, Savill SJ, Bordin IA, Costello EJ, Durkin M, Fairburn C, Glass RI, Hall W, Huang Y, Hyman SE, Jamison K, Kaaya S, Kapur S, Kleinman A, Ogunniyi A, Otero-Ojeda A, Poo MM, Ravindranath V, Sahakian BJ, Saxena S, Singer PA, Stein DJ. Grand challenges in global mental health. Nature. 2011;475: Comfrey AL, Lee HB. A First Course in Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum Associates, Connolly CG, Wu J, Ho TC, Hoeft F, Wolkowitz O, Eisendrath S, Frank G, Hendren R, Max JE, Paulus MP, Tapert SF, Banerjee D, Simmons AN, Yang TT. Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biol Psychiatry. 2013;74: Connor TJ, Leonard BE. Depression, stress and immunological activation: the role of cytokines in depressive disorders. Life Sci. 1998;62: Correa M, Carlson BB, Wisniecki A, Salamone JD. Nucleus accumbens dopamine and work requirements on interval schedules. Behav Brain Res. 2002;137: Corrigan MH, Denahan AQ, Wright CE, Ragual RJ, Evans DL. Comparison of pramipexole, fluoxetine, and placebo in patients with major depression. Depress Anxiety. 2000;11: Corruble E, Legrand JM, Duret C, Charles G, Guelfi JD: IDS-C and IDS-sr: psychometric properties in depressed in-patients. J Affect Dis 1999; 56: Costa DC, Verhoeff NP, Cullum ID, Ell PJ, Syed GM, Barrett J, Palazidou E, Toone B, Van RE, Bobeldijk M. In vivo characterisation of 3-iodo-6-methoxybenzamide 123I in humans. Eur J Nucl Med. 1990;16: Costello AB, Osborne JW. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Prac Assess Res Eval. 2005;10:1-9. Cousins MS, Atherton A, Turner L, Salamone JD. Nucleus accumbens dopamine depletions alter relative response allocation in a T-maze cost/benefit task. Behav Brain Res. 1996;74: Cropley VL, Innis RB, Nathan PJ, Brown AK, Sangare JL, Lerner A, Ryu YH, Sprague KE, Pike VW, Fujita M. Small effect of dopamine release and no effect of dopamine depletion on [18F]fallypride binding in healthy humans. Synapse 2008;62:

115 Cullen KR, Westlund MK, Klimes-Dougan B, Mueller BA, Houri A, Eberly LE, Lim KO. Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry. 2014;71: Currey SS, Callahan LF, DeVellis RF. Five-item rheumatology attitudes index (RAI): Disadvantages of a single positively worded item. Arthritis Rheum. 2002;46:9:S239. Dalby RB, Frandsen J, Chakravarty MM, Ahdidan J, Sørensen L, Rosenberg R, Videbech P, Ostergaard L. Depression severity is correlated to the integrity of white matter fiber tracts in late-onset major depression. Psychiatry Res. 2010;184: Daskalakis ZJ, Levinson AJ, Fitzgerald PB. Repetitive transcranial magnetic stimulation for major depressive disorder: a review. Can J Psychiatry. 2008;53: Davey CG, Harrison BJ, Yücel M, Allen NB. Regionally specific alterations in functional connectivity of the anterior cingulate cortex in major depressive disorder. Psychol Med. 2012;42: Davidson JR. Major depressive disorder treatment guidelines in America and Europe. J Clin Psychiatry. 2010;71 Suppl E1:e04. Davidson RJ, Irwin W, Anderle MJ, Kalin NH. The neural substrates of affective processing in depressed patients treated with venlafaxine. Am J Psychiatry. 2003;160: de Almeida Jr C, Phillips ML. Distinguishing between unipolar depression and bipolar depression: current and future clinical and neuroimaging perspectives. Biol Psychiatry. 2013;73: de Kwaasteniet B, Ruhe E, Caan M, Rive M, Olabarriaga S, Groefsema M, Heesink L, van Wingen G, Denys D. Relation between structural and functional connectivity in major depressive disorder. Biol Psychiatry. 2013;74:40-7. Delgado PL, Miller HL, Salomon RM, Licinio J, Krystal JH, Moreno FA, Heninger GR, Charney DS. Tryptophan-depletion challenge in depressed patients treated with desipramine or fluoxetine: implications for the role of serotonin in the mechanism of antidepressant action. Biol Psychiatry. 1999;46: Demyttenaere K, Bonnewyn A, Bruffaerts R, De Girolamo G, Gasquet I, Kovess V, Haro JM, Alonso J. Clinical factors influencing the prescription of antidepressants and benzodiazepines: results from the European study of the epidemiology of mental disorders (ESEMeD). J Affect Disord. 2008;110: Der-Avakian A, Markou A. The neurobiology of anhedonia and other reward-related deficits. Trends Neurosci. 2012;35: Devellis RF. Scale Development: Theory and Applications, Third Edition. Sage Publications, Inc: Thousand Oaks, California, D'haenen HA, Bossuyt A. Dopamine D2 receptors in depression measured with single photon emission computed tomography. Biol Psychiatry. 1994;35: Di Chiara G, Loddo P, Tanda G. Reciprocal changes in prefrontal and limbic dopamine responsiveness to aversive and rewarding stimuli after chronic mild stress: implications for the psychobiology of depression. Biol Psychiatry. 1999;46: Dien J, Beal DJ, Berg P. Optimizing principal components analysis of eventrelated potentials: matrix type, factor loading weighting, extraction, and rotations. Clin Neurophysiol. 2005;116: Dilsaver SC. Pathophysiology of "cholinoceptor supersensitivity" in affective disorders. Biol Psychiatry. 1986;21: Domínguez-López S, Howell R, Gobbi G. Characterization of serotonin neurotransmission in knockout mice: implications for major depression. Rev Neurosci. 2012;23:

116 Downar J, Geraci J, Salomons TV, Dunlop K, Wheeler S, McAndrews MP, Bakker N, Blumberger DM, Daskalakis ZJ, Kennedy SH, Flint AJ, Giacobbe P. Anhedonia and reward-circuit connectivity distinguish nonresponders from responders to dorsomedial prefrontal repetitive transcranial magnetic stimulation in major depression. Biol Psychiatry. 2014;76: Dremencov E, Newman ME, Kinor N, Blatman-Jan G, Schindler CJ, Overstreet DH, Yadid G. Hyperfunctionality of serotonin-2c receptor-mediated inhibition of accumbal dopamine release in an animal model of depression is reversed by antidepressant treatment. Neuropharmacol. 2005;48: Drevets WC, Savitz J, Trimble M., The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 13, Drevets WC, Furey ML. Replication of scopolamine's antidepressant efficacy in major depressive disorder: a randomized, placebo-controlled clinical trial. Biol Psychiatry. 2010;67: Dunlop BW, Nemeroff CB. The role of dopamine in the pathophysiology of depression. Arch Gen Psychiatry. 2007;64: Dunn RT, Kimbrell TA, Ketter TA, Frye MA, Willis MW, Luckenbaugh DA, Post RM. Principal components of the Beck Depression Inventory and regional cerebral metabolism in unipolar and bipolar depression. Biol Psychiatry. 2002;51: Dziedzicka-Wasylewska M, Faron-Górecka A, Kuśmider M, Drozdowska E, Rogóz Z, Siwanowicz J, Caron MG, Bönisch H. Effect of antidepressant drugs in mice lacking the norepinephrine transporter. Neuropsychopharmacol. 2006;31: Eckelman WC, Reba RC, Rzeszotarski WJ, Gibson RE, Hill T, Holman BL, Budinger T, Conklin JJ, Eng R, Grissom MP. External imaging of cerebral muscarinic acetylcholine receptors. Science. 1984;223: El Mansari M, Ghanbari R, Janssen S, Blier P. Sustained administration of bupropion alters the neuronal activity of serotonin, norepinephrine but not dopamine neurons in the rat brain. Neuropharmacol. 2008;55: Elliott R, Friston KJ, Dolan RJ. Dissociable neural responses in human reward systems. J Neurosci. 2000;20: Erlandsson K, Bressan RA, Mulligan RS, Gunn RN, Cunningham VJ, Owens J, Wyper D, Ell PJ, Pilowsky LS. Kinetic modelling of [123I]CNS a potential SPET tracer for the NMDA receptor. Nucl Med Biol. 2003;30: Esposito E, Barbui C, Patten SB. Patterns of benzodiazepine use in a Canadian population sample. Epidemiol Psichiatr Soc. 2009;18: Farde L, Hall H, Ehrin E, Sedvall G. Quantitative analysis of D2 dopamine receptor binding in the living human brain by PET. Science. 1986;231: Farde L, Hall H, Pauli S, Halldin C. Variability in D2-dopamine receptor density and affinity: a PET study with [11C] raclopride in man. Synapse 1995;20: Fava M. Diagnosis and definition of treatment-resistant depression. Biol Psychiatry. 2003;53: Fawcett J, Clark DC, Scheftner WA, Gibbons RD. Assessing anhedonia in psychiatric patients. Arch Gen Psychiatry. 1983;40: Fekadu A, Wooderson S, Donaldson C, Markopoulou K, Masterson B, Poon L, Cleare AJ. A multidimensional tool to quantify treatment resistance in depression: the Maudsley staging method. J Clin Psychiatry. 2009;70:

117 Feyissa AM, Chandran A, Stockmeier CA, Karolewicz B. Reduced levels of NR2A and NR2B subunits of NMDA receptor and PSD-95 in the prefrontal cortex in major depression. Prog Neuropsychopharmacol Biol Psychiatry. 2009;33:70-5. Field A. Discovering statistics using SPSS. Second edition Sage. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). New York: Biometrics Research, New York State Psychiatric Institute, Floyd FJ, Widaman KF. Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess 1995;7: Franken IH, Rassin E, Muris P. The assessment of anhedonia in clinical and non-clinical populations: further validation of the Snaith-Hamilton Pleasure Scale (SHAPS). J Affect Disord. 2007;99:83-9. Fu CH, Williams SC, Brammer MJ, Suckling J, Kim J, Cleare AJ, Walsh ND, Mitterschiffthaler MT, Andrew CM, Pich EM, Bullmore ET. Neural responses to happy facial expressions in major depression following antidepressant treatment. Am J Psychiatry. 2007;164: Fu CH, Williams SC, Cleare AJ, Brammer MJ, Walsh ND, Kim J, Andrew CM, Pich EM, Williams PM, Reed LJ, Mitterschiffthaler MT, Suckling J, Bullmore ET. Attenuation of the neural response to sad faces in major depression by antidepressant treatment: a prospective, event-related functional magnetic resonance imaging study. Arch Gen Psychiatry. 2004;61: Gabbay V, Mao X, Klein RG, Ely BA, Babb JS, Panzer AM, Alonso CM, Shungu DC. Anterior cingulate cortex γ-aminobutyric acid in depressed adolescents: relationship to anhedonia. Arch Gen Psychiatry. 2012;69: Geddes JR, Freemantle N, Mason J, Eccles MP, Boynton J. SSRIs versus other antidepressants for depressive disorder. Cochrane Database Syst Rev. 2000;(2):CD Gerner RH, Hare TA. CSF GABA in normal subjects and patients with depression, schizophrenia, mania, and anorexia nervosa. Am J Psychiatry 1981;138: Gibson TB, Jing Y, Smith Carls G, Kim E, Bagalman JE, Burton WN, Tran QV, Pikalov A, Goetzel RZ. Cost burden of treatment resistance in patients with depression. Am J Manag Care. 2010;16: Gillespie CF, Nemeroff CB. Hypercortisolemia and depression. Psychosom Med. 2005;67(Suppl 1):S26-8. Gottfried JA, O'Doherty J, Dolan RJ. Encoding predictive reward value in human amygdala and orbitofrontal cortex. Science. 2003;301: Gradin VB, Kumar P, Waiter G, Ahearn T, Stickle C, Milders M, Reid I, Hall J, Steele JD. Expected value and prediction error abnormalities in depression and schizophrenia. Brain. 2011;134: Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Reiss AL, Schatzberg AF. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 2007;62: Grimshaw J, McAuley L, Bero L, Grilli R, Oxman A, Ramsay C, Vale L, Zwarenstein M. Systematic reviews of the effectiveness of quality improvement strategies and programmes. Qual Saf Health Care. 2003;12: Gross-Isseroff R, Dillon KA, Israeli M, Biegon A. Regionally selective increases in mu opioid receptor density in the brains of suicide victims. Brain Res. 1990;530:

118 Guiard BP, El Mansari M, Merali Z, Blier P. Functional interactions between dopamine, serotonin and norepinephrine neurons: an in-vivo electrophysiological study in rats with monoaminergic lesions. Int J Neuropsychopharmacol 2008;11: Gurevich EV, Joyce JN. Distribution of dopamine D3 receptor expressing neurons in the human forebrain: comparison with D2 receptor expressing neurons. Neuropsychopharmacol. 1999;20: Haber SN, Knutson B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacol. 2010;35:4-26. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry 1960;23: Hamon M, Blier P. Monoamine neurocircuitry in depression and strategies for new treatments. Prog Neuropsychopharmacol Biol Psychiatry. 2013;45: Hariri AR, Tessitore A, Mattay VS, Fera F, Weinberger DR. The amygdala response to emotional stimuli: a comparison of faces and scenes. Neuroimage. 2002;17: Harmer CJ, O'Sullivan U, Favaron E, Massey-Chase R, Ayres R, Reinecke A, Goodwin GM, Cowen PJ. Effect of acute antidepressant administration on negative affective bias in depressed patients. Am J Psychiatry. 2009;166: Harrison NA, Brydon L, Walker C, Gray MA, Steptoe A, Critchley HD. Inflammation causes mood changes through alterations in subgenual cingulate activity and mesolimbic connectivity. Biol Psychiatry. 2009;66: Harvey PO, Armony J, Malla A, Lepage M. Functional neural substrates of self-reported physical anhedonia in non-clinical individuals and in patients with schizophrenia. J Psychiatr Res. 2010;44: Hasler G, Neumeister A, van der Veen JW, Tumonis T, Bain EE, Shen J, Drevets WC, Charney DS. Normal prefrontal gamma-aminobutyric acid levels in remitted depressed subjects determined by proton magnetic resonance spectroscopy. Biol Psychiatry. 2005;58: Hasler G, van der Veen JW, Tumonis T, Meyers N, Shen J, Drevets WC. Reduced prefrontal glutamate/glutamine and gamma-aminobutyric acid levels in major depression determined using proton magnetic resonance spectroscopy. Arch Gen Psychiatry. 2007;64: Hasler G, Mondillo K, Drevets WC, Blair JR. Impairments of probabilistic response reversal and passive avoidance following catecholamine depletion. Neuropsychopharmacol. 2009;34: Hayton JC, Allen DG, Scarpello V. Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Org Res Methods. 2004;7: Henriques JB, Davidson RJ. Decreased responsiveness to reward in depression. Cog Emotion. 2000;14: Hernández ME, Mendieta D, Martínez-Fong D, Loría F, Moreno J, Estrada I, Bojalil R, Pavón L. Variations in circulating cytokine levels during 52 week course of treatment with SSRI for major depressive disorder. Eur Neuropsychopharmacol. 2008;18, Hideaki M, Yukihiko S, Takeshi Y, Ryuzou K. Plasma levels of homovanillic acid, 5- hydroxyindoleacetic acid and cortisol, and serotonin turnover in depressed patients. Prog Neuropsychopharmacol Biol Psychiatry. 2006;30: Higuchi T. Major depressive disorder treatment guidelines in Japan. J Clin Psychiatry 2010;71 (Suppl E1):e05. Holsboer F, Ising M.Central CRH system in depression and anxiety--evidence from clinical studies with CRH1 receptor antagonists. Eur J Pharmacol. 2008;583: Holsboer F. The corticosteroid receptor hypothesis of depression. Neuropsychopharmacol. 2000;23:

119 Holtzheimer PE, Kelley ME, Gross RE, Filkowski MM, Garlow SJ, Barrocas A, Wint D, Craighead MC, Kozarsky J, Chismar R, Moreines JL, Mewes K, Posse PR, Gutman DA, Mayberg HS. Subcallosal cingulate deep brain stimulation for treatment-resistant unipolar and bipolar depression. Arch Gen Psychiatry. 2012;69: Houle S, Ginovart N, Hussey D, Meyer JH, Wilson AA. Imaging the serotonin transporter with positron emission tomography: initial human studies with [11C]DAPP and [11C]DASB. Eur J Nucl Med. 2000;27: Hurd YL, Suzuki M, Sedvall GC. D1 and D2 dopamine receptor mrna expression in whole hemisphere sections of the human brain. J Chem Neuroanat. 2001;22: Ichise M, Meyer JH, Yonekura Y. An introduction to PET and SPECT neuroreceptor quantification models. J Nucl Med. 2001;42: Imperato A, Honoré T, Jensen LH. Dopamine release in the nucleus caudatus and in the nucleus accumbens is under glutamatergic control through non-nmda receptors: a study in freelymoving rats. Brain Res. 1990;530: Ivanova JI, Birnbaum HG, Kidolezi Y, Subramanian G, Khan SA, Stensland MD. Direct and indirect costs of employees with treatment-resistant and non-treatment-resistant major depressive disorder. Curr Med Res Opin. 2010;26: Janicki-Deverts D, Cohen S, Doyle WJ, Turner RB, Treanor JJ. Infection-induced proinflammatory cytokines are associated with decreases in positive affect, but not increases in negative affect. Brain Behav Immun. 2007;21: Janowsky DS, Overstreet DH, Nurnberger JI Jr. Is cholinergic sensitivity a genetic marker for the affective disorders? Am J Med Genet. 1994;54: Janowsky DS, el-yousef MK, Davis JM, Sekerke HJ. A cholinergic-adrenergic hypothesis of mania and depression. Lancet. 1972;2: Johanson GA, Brooks GP. Initial Scale Development: Sample Size for Pilot Studies. Educ Psych Meas. 2010;70: Johansen-Berg H, Gutman DA, Behrens TE, Matthews PM, Rushworth MF, Katz E, Lozano AM, Mayberg HS. Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression. Cereb Cortex. 2008;18: Johnson SW, North RA. Opioids excite dopamine neurons by hyperpolarization of local interneurons. J Neurosci. 1992;12: Jung EY, Shim I. Differential DAergic Control of D1 and D2 Receptor Agonist Over Locomotor Activity and GABA Level in the Striatum. Exp Neurobiol. 2011;20: Jutkiewicz EM, Roques BP. Endogenous opioids as physiological antidepressants: complementary role of δ receptors and dopamine. Neuropsychopharmacol. 2012;37: Kabbaj M, Isgor C. Effects of chronic environmental and social stimuli during adolescence on mesolimbic dopaminergic circuitry markers. Neurosci Lett. 2007;422:7-12. Kalin NH, Davidson RJ, Irwin W, Warner G, Orendi JL, Sutton SK, Mock BJ, Sorenson JA, Lowe M, Turski PA. Functional magnetic resonance imaging studies of emotional processing in normal and depressed patients: effects of venlafaxine. J Clin Psychiatry. 1997;58(Suppl 16):32-9. Kanno M, Matsumoto M, Togashi H, Yoshioka M, Mano Y. Effects of acute repetitive transcranial magnetic stimulation on dopamine release in the rat dorsolateral striatum. J Neurol Sci. 2004;217:73-81 Kasch, Karen L.; Rottenberg, Jonathan; Arnow, Bruce A.; Gotlib, Ian H. Behavioral activation and inhibition systems and the severity and course of depression. J Abnorm Psychol. 2002; 111:

120 Katsanis J, Iacono WG, Beiser M, Lacey L. Clinical correlates of anhedonia and perceptual aberration in first-episode patients with schizophrenia and affective disorder. J Abnorm Psychol. 1992;101: Katz MM, Bowden C, Stokes P, Casper R, Frazer A, Koslow SH, Kocsis J, Secunda S, Swann A, Berman N. Can the effects of antidepressants be observed in the first two weeks of treatment? Neuropsychopharmacol. 1997;17: Keedwell P, Drapier D, Surguladze S, Giampietro V, Brammer M, Phillips M. Neural markers of symptomatic improvement during antidepressant therapy in severe depression: subgenual cingulate and visual cortical responses to sad, but not happy, facial stimuli are correlated with changes in symptom score. J Psychopharmacol. 2009;23: Kennedy SH and Lam RW. Enhancing outcomes in the management of treatment resistant depression: a focus on atypical antipsychotics. Bipolar Disord 2003;5 (Suppl 2): Kennedy SH, Giacobbe P. Treatment resistant depression--advances in somatic therapies. Ann Clin Psychiatry. 2007;19: Kennedy SH, Andersen HF, Thase ME. Escitalopram in the treatment of major depressive disorder: a meta-analysis. Curr Med Res Opin. 2009;25: Kennedy SH, Rizvi SJ. Emerging drugs for major depressive disorder. Expert Opin Emerg Drugs. 2009;14: Kennedy SH, Downar J, Evans KR, Feilotter H, Lam RW, MacQueen GM, Milev R, Parikh SV, Rotzinger S, Soares C. The Canadian Biomarker Integration Network in Depression (CAN-BIND): advances in response prediction. Curr Pharm Des. 2012;18: Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush AJ, Walters EE, Wang PS; National Comorbidity Survey Replication. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289: Khamassi M, Mulder AB, Tabuchi E, Douchamps V, Wiener SI. Anticipatory reward signals in ventral striatal neurons of behaving rats. Eur J Neurosci. 2008;28: Kishida I, Aklillu E, Kawanishi C, Bertilsson L, Agren H. Monoamine metabolites level in CSF is related to the 5-HTT gene polymorphism in treatment-resistant depression. Neuropsychopharmacol. 2007;32: Klumpers UM, Veltman DJ, Drent ML, Boellaard R, Comans EF, Meynen G, Lammertsma AA, Hoogendijk WJ. Reduced parahippocampal and lateral temporal GABAA-[11C]flumazenil binding in major depression: preliminary results. Eur J Nucl Med Mol Imaging. 2010;37: Knutson B, Fong GW, Adams CM, Varner JL, Hommer D. Dissociation of reward anticipation and outcome with event-related fmri. Neuroreport. 2001;12: Knutson B, Fong GW, Bennett SM, Adams CM, Hommer D. A region of mesial prefrontal cortex tracks monetarily rewarding outcomes: characterization with rapid event-related fmri. Neuroimage. 2003;18: Koolschijn PC, van Haren NE, Lensvelt-Mulders GJ, Hulshoff Pol HE, Kahn RS. Brain volume abnormalities in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Hum Brain Mapp. 2009;30: Kornstein SG, Li D, Mao Y, Larsson S, Andersen HF, Papakostas GI. Escitalopram versus SNRI antidepressants in the acute treatment of major depressive disorder: integrative analysis of four double-blind, randomized clinical trials. CNS Spectr. 2009;14: Kugaya A, Sanacora G, Verhoeff NP, Fujita M, Mason GF, Seneca NM, Bozkurt A, Khan SA, Anand A, Degen K, Charney DS, Zoghbi SS,Baldwin RM, Seibyl JP, Innis RB. 108

121 Cerebral benzodiazepine receptors in depressed patients measured with [123I]iomazenil SPECT. Biol Psychiatry. 2003;54: Kumar P, Waiter G, Ahearn T, Milders M, Reid I, Steele JD. Abnormal temporal difference reward-learning signals in major depression. Brain. 2008;131: Lai CH, Wu YT. Decreased inter-hemispheric connectivity in anterior sub-network of default mode network and cerebellum: significant findings in majordepressive disorder. Int J Neuropsychopharmacol. 2014;17: Lam RW, Kennedy SH, Grigoriadis S, McIntyre RS, Milev R, Ramasubbu R, Parikh SV, Patten SB, Ravindran AV; Canadian Network for Mood and Anxiety Treatments (CANMAT). Canadian Network for Mood and Anxiety Treatments (CANMAT) clinical guidelines for the management of major depressive disorder in adults. III. Pharmacotherapy. J Affect Disord. 2009;117(Suppl 1):S Lambert G, Johansson M, Agren H, Friberg P. Reduced brain norepinephrine and dopamine release in treatment-refractory depressive illness: evidence in support of the catecholamine hypothesis of mood disorders. Arch Gen Psychiatry. 2000;57: Lammertsma AA and Hume SP. Simplified reference tissue model for PET receptor studies. NeuroImage 1996;4: Larisch R, Klimke A, Vosberg H, Löffler S, Gaebel W, Müller-Gärtner HW. In vivo evidence for the involvement of dopamine-d2 receptors in striatum and anterior cingulate gyrus in major depression. Neuroimage 1997;5: Lattanzi L, Dell'Osso L, Cassano P, Pini S, Rucci P, Houck PR, Gemignani A, Battistini G, Bassi A, Abelli M, Cassano GB. Pramipexole in treatment-resistant depression: a 16-week naturalistic study. Bipolar Disord. 2002;4: Lehto SM, Kuikka J, Tolmunen T, Hintikka J, Viinamäki H, Vanninen R, Haatainen K, Koivumaa-Honkanen H, Honkalampi K, Tiihonen J. Temporal cortex dopamine D2/3 receptor binding in major depression. Psychiatry Clin Neurosci. 2008;62: Lemos JC, Wanat MJ, Smith JS, Reyes BA, Hollon NG, Van Bockstaele EJ, Chavkin C, Phillips PE. Severe stress switches CRF action in the nucleus accumbens from appetitive to aversive. Nature. 2012;490: Leventhal AM, Chasson GS, Tapia E, Miller EK, Pettit JW. Measuring hedonic capacity in depression: a psychometric analysis of three anhedonia scales. J Clin Psychol. 2006;62: Licinio J, Wong ML. The role of inflammatory mediators in the biology of major depression: central nervous system cytokines modulate the biological substrate of depressive symptoms, regulate stress-responsive systems, and contribute to neurotoxicity and neuroprotection. Mol Psychiatry. 1999;4: Liu Y, Postupna N, Falkenberg J, Anderson ME. High frequency deep brain stimulation: what are the therapeutic mechanisms? Neurosci Biobehav Rev. 2008;32: Liu WH, Wang LZ, Zhu YH, Li MH, Chan RC. Clinical utility of the Snaith-Hamilton-Pleasure scale in the Chinese settings. BMC Psychiatry. 2012;12:184. Lozano AM, Giacobbe P, Hamani C, Rizvi S, Kennedy SH, Kolivakis TT, Debonnel G, Sadikot A, Lam RW, Howard A, Ilcewicz M, Honey CR, Mayberg HS. A multicenter pilot study of subcallosal cingulate area deep brain stimulation for treatment resistant depression J Neurosurg. 2012;116: Lozano AM, Mayberg HS, Giacobbe P, Hamani C, Craddock RC, Kennedy SH. Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression. Biol Psychiatry. 2008;64:

122 Maas JW. Biogenic amines and depression. Biochemical and pharmacological separation of two types of depression. Arch Gen Psychiatry. 1975;32: MacDonald AW, Cohen JD, Stenger VA, Carter CS. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science. 2000;288: MacQueen GM, Yucel K, Taylor VH, Macdonald K, Joffe R. Posterior hippocampal volumes are associated with remission rates in patients with major depressive disorder. Biol Psychiatry. 2008;64: Madhoo M, Keefe RS, Roth RM, Sambunaris A, Wu J, Trivedi MH, Anderson CS, Lasser R. Lisdexamfetamine dimesylate augmentation in adults with persistent executive dysfunction after partial or full remission of major depressive disorder. Neuropsychopharmacol. 2014;39: Maes M, Scharpé S, Van Grootel L, Uyttenbroeck W, Cooreman W, Cosyns P, Suy E. Higher alpha 1-antitrypsin, haptoglobin, ceruloplasmin and lower retinol binding protein plasma levels during depression: further evidence for the existence of an inflammatory response during that illness. J Affect Disord. 1992;24: Malhi GS and Berk M. Does dopamine dysfunction drive depression? Acta Psychiatr Scand. 2007;115(Suppl 433): Marin RS, Biedrzycki RC, Firinciogullari S. Reliability and validity of the Apathy Evaluation Scale. Psychiatry Res. 1991;38: Martinotti G, Sepede G, Gambi F, Di Iorio G, De Berardis D, Di Nicola M, Onofrj M, Janiri L, Di Giannantonio M. Agomelatine versus venlafaxine XR in the treatment of anhedonia in major depressive disorder: a pilot study. J Clin Psychopharmacol. 2012;32: Maubach KA, Martin K, Chicchi G, Harrison T, Wheeldon A, Swain CJ, Cumberbatch MJ, Rupniak NM, Seabrook GR. Chronic substance P (NK1) receptor antagonist and conventional antidepressant treatment increases burst firing of monoamine neurones in the locus coeruleus. Neurosci. 2002;109: Mayberg HS, Liotti M, Brannan SK, McGinnis S, Mahurin RK, Jerabek PA, Silva JA, Tekell JL, Martin CC, Lancaster JL, Fox PT. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry. 1999;156: Mayberg HS, Lozano AM, Voon V, McNeely HE, Seminowicz D, Hamani C, Schwalb JM, Kennedy SH. Deep brain stimulation for treatment-resistant depression. Neuron. 2005;45: McCabe C, Cowen PJ, Harmer CJ. Neural representation of reward in recovered depressed patients. Psychopharmacology (Berl). 2009;205: McEwen AM, Burgess DT, Hanstock CC, Seres P, Khalili P, Newman SC, Baker GB, Mitchell ND,Khudabux-Der J, Allen PS, LeMelledo JM. Increased glutamate levels in the medial prefrontal cortex in patients with postpartum depression. Neuropsychopharmacol. 2012;37: McEwen BS., Mood disorders and allostatic load. Biol Psychiatry. 54, McMakin DL, Olino TM, Porta G, Dietz LJ, Emslie G, Clarke G, Wagner KD, Asarnow JR, Ryan ND, Birmaher B, Shamseddeen W, Mayes T, Kennard B, Spirito A, Keller M, Lynch FL, Dickerson JF, Brent DA. Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor treatment-resistant depression. J Am Acad Child Adolesc Psychiatry. 2012;51:

123 Meyer JH, Goulding VS, Wilson AA, Hussey D, Christensen BK, Houle S. Bupropion occupancy of the dopamine transporter is low during clinical treatment. Psychopharmacology (Berl). 2002;163: Meyer JH, McNeely HE, Sagrati S, Boovariwala A, Martin K, Verhoeff NP, Wilson AA, Houle S. Elevated putamen D(2) receptor binding potential in major depression with motor retardation: an [11C]raclopride positron emission tomography study. Am J Psychiatry. 2006;163: Meyer JH, Wilson AA, Ginovart N, Goulding V, Hussey D, Hood K, Houle S. Occupancy of serotonin transporters by paroxetine and citalopram during treatment of depression: a [(11)C]DASB PET imaging study. Am J Psychiatry. 2001;158: Miller AH, Maletic V, Raison CL., Inflammation and Its discontents: The role of cytokines in the pathophysiology of major depression. Biol Psychiatry. 2009;65: Miller HL, Delgado PL, Salomon RM, Berman R, Krystal JH, Heninger GR, Charney DS. Clinical and biochemical effects of catecholamine depletion on antidepressant-induced remission of depression. Arch Gen Psychiatry. 1996;53: Mizoguchi K, Yuzurihara M, Ishige A, Sasaki H, Chui DH, Tabira T. Chronic stress induces impairment of spatial working memory because of prefrontal dopaminergic dysfunction. J Neurosci. 2000;20: Mizrahi R, Rusjan P, Agid O, Graff A, Mamo DC, Zipursky RB, Kapur S. Adverse subjective experience with antipsychotics and its relationship to striatal and extrastriatal D2 receptors: a PET study in schizophrenia. Am J Psychiatry. 2007;164: Mojtabai R, Olfson M. National trends in psychotropic medication polypharmacy in office-based psychiatry. Arch Gen Psychiatry. 2010;67: Montgomery AJ, Stokes P, Kitamura Y, Grasby PM. Extrastriatal D2 and striatal D2 receptors in depressive illness: pilot PET studies using [11C]FLB 457 and [11C]raclopride. J Affect Disord. 2007;101: Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134: Mrazek DA, Hornberger JC, Altar CA, Degtiar I. A review of the clinical, economic, and societal burden of treatment-resistant depression: Psychiatr Serv. 2014;65: Mukherjee J, Yang ZY, Das MK, Brown T. Fluorinated benzamide neuroleptics--iii. Development of (S)-N-[(1-allyl-2-pyrrolidinyl)methyl]-5-(3-[18F]fluoropropyl)-2, 3- dimethoxybenzamide as an improved dopamine D-2 receptor tracer. Nucl Med Biol. 1995;22: Müller N. Immunology of major depression. Neuroimmunomodulation. 2014;21: Munafò MR, Mannie ZN, Cowen PJ, Harmer CJ, McTavish SB. Effects of acute tyrosine depletion on subjective craving and selective processing of smoking-related cues in abstinent cigarette smokers. J Psychopharmacol. 2007;21: Murphy ML, Frodl T. Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression. Biol Mood Anxiety Disord. 2011;1:3. Mussap CJ, Geraghty DP, Burcher E. Tachykinin receptors: a radioligand binding perspective. J Neurochem. 1993;60: Musselman DL, Miller AH, Porter MR, Manatunga A, Gao F, Penna S, Pearce BD, Landry J, Glover S, McDaniel JS, Nemeroff CB. Higher than normal plasma interleukin-6 111

124 concentrations in cancer patients with depression: preliminary findings. Am J Psychiatry. 2001;158: Nagayama H, Kubo S, Hatano T, Hamada S, Maeda T, Hasegawa T, Kadowaki T, Terashi H, Yoshioka M, Nomoto N, Kano O, Inoue M, Shimura H, Takahashi T, Uchiyama T, Watanabe H, Kaneko S, Takahashi T, Baba Y; Young Japanese Expert Group for Parkinson's Disease and Movement Disorders: YJ-EXPANDS. Validity and reliability assessment of a Japanese version of the Snaith-Hamilton pleasure scale. Intern Med. 2012;51: Nakajima S, Liu X, Lau CL. Synergistic interaction of D1 and D2 dopamine receptors in the modulation of the reinforcing effect of brain stimulation. Behav Neurosci. 1993;107: Nakonezny PA, Carmody TJ, Morris DW, Kurian BT, Trivedi MH. Psychometric evaluation of the Snaith-Hamilton pleasure scale in adult outpatients with major depressive disorder. Int Clin Psychopharmacol. 2010;25: Narendran R, Frankle WG, Mason NS, Rabiner EA, Gunn RN, Searle GE, Vora S, Litschge M, Kendro S, Cooper TB, Mathis CA, Laruelle M. Positron emission tomography imaging of amphetamine-induced dopamine release in the human cortex: A comparative evaluation of the high affinity dopamine D(2/3) radiotracers [(11)C]FLB 457 and [(11)C]fallypride. Synapse. 2009;63: Nemeroff CB, Entsuah R, Benattia I, Demitrack M, Sloan DM, Thase ME. Comprehensive analysis of remission (COMPARE) with venlafaxine versus SSRIs. Biol Psychiatry. 2008;63: Nemeroff CB. Neurobiological consequences of childhood trauma. J Clin Psychiatry. 2004;65 (Suppl 1):18-28 Nikisch G, Mathé AA. CSF monoamine metabolites and neuropeptides in depressed patients before and after electroconvulsive therapy. Eur Psychiatry. 2008;23: Northoff G, Richter A, Gessner M, Schlagenhauf F, Fell J, Baumgart F, Kaulisch T, Kötter R, Stephan KE,Leschinger A, Hagner T, Bargel B, Witzel T, Hinrichs H, Bogerts B, Scheich H, Heinze HJ. Functional dissociation between medial and lateral prefrontal cortical spatiotemporal activation in negative and positive emotions: a combinedfmri/meg study. Cereb Cortex. 2000;10: Northoff G, Walter M, Schulte RF, Beck J, Dydak U, Henning A, Boeker H, Grimm S, Boesiger P. GABA concentrations in the human anterior cingulate cortex predict negative BOLD responses in fmri. Nat Neurosci. 2007;10: Nowak G, Ordway GA, Paul IA. Alterations in the N-methyl-D-aspartate (NMDA) receptor complex in the frontal cortex of suicide victims. Brain Res. 1995;675: Nutt DJ. Relationship of neurotransmitters to the symptoms of major depressive disorder. J Clin Psychiatry. 2008;69(Suppl E1):4-7. O'Brien SM, Scully P, Fitzgerald P, Scott LV, Dinan TG. Plasma cytokine profiles in depressed patients who fail to respond to selective serotonin reuptake inhibitor therapy. J Psychiatr Res. 2007;41: O'Doherty JP, Deichmann R, Critchley HD, Dolan RJ. Neural responses during anticipation of a primary taste reward. Neuron. 2002;33: O'Doherty JP. Reward representations and reward-related learning in the human brain: insights from neuroimaging. Curr Opin Neurobiol. 2004;14: Okuda H, Shioda S, Nakai Y, Nakayama H, Okamoto M, Nakashima T. The presence of corticotropin-releasing factor-like immunoreactive synaptic vesicles in axon terminals with 112

125 nicotinic acetylcholine receptor-like immunoreactivity in the median eminence of the rat. Neurosci Lett. 1993;161: Olds J, Milner P. Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. J Comp Physiol Psychol. 1954;47: O'Leary OF, Bechtholt AJ, Crowley JJ, Hill TE, Page ME, Lucki I. Depletion of serotonin and catecholamines block the acute behavioral response to different classes of antidepressant drugs in the mouse tail suspension test. Psychopharmacology (Berl). 2007;192: Olsson H, Halldin C, Farde L. Differentiation of extrastriatal dopamine D2 receptor density and affinity in the human brain using PET. Neuroimage. 2004;22: Ostergaard SD, Jensen SO, Bech P. The heterogeneity of the depressive syndrome: when numbers get serious. Acta Psychiatr Scand. 2011;124: Palucha A, Pilc A. Metabotropic glutamate receptor ligands as possible anxiolytic and antidepressant drugs. Pharmacol Ther. 2007;115: Papakostas GI. Dopaminergic-based pharmacotherapies for depression. Eur Neuropsychopharmacol. 2006;16: Papakostas GI, Fava M. A meta-analysis of clinical trials comparing moclobemide with selective serotonin reuptake inhibitors for the treatment of major depressive disorder. Can J Psychiatry. 2006;51: Pariante CM, Miller AH. Glucocorticoid receptors in major depression: relevance to pathophysiology and treatment. Biol Psychiatry. 2001;49: Park IH, Kim JJ, Chun J, Jung YC, Seok JH, Park HJ, Lee JD. Medial prefrontal default-mode hypoactivity affecting trait physical anhedonia in schizophrenia. Psychiatry Res. 2009;171: Parsons LH, Justice JB Jr. Perfusate serotonin increases extracellular dopamine in the nucleus accumbens as measured by in vivo microdialysis. Brain Res. 1993;606: Passchier J, van WA, Pieterman RM, Elsinga PH, Pruim J, Hendrikse HN, Willemsen AT, Vaalburg W. In vivo delineation of 5-HT1A receptors in human brain with [18F]MPPF. J Nucl Med. 2000;41: Patkar AA, Masand PS, Pae CU, Peindl K, Hooper-Wood C, Mannelli P, Ciccone P. A randomized, double-blind, placebo-controlled trial of augmentation with an extended release formulation of methylphenidate in outpatients with treatment-resistant depression. J Clin Psychopharmacol. 2006;26: Paul IA, Nowak G, Layer RT, Popik P, Skolnick P. Adaptation of the N-methyl-D-aspartate receptor complex following chronic antidepressant treatments. J. Pharmacol. Exp. Ther 1994;269: Pelizza L, Ferrari A. Anhedonia in schizophrenia and major depression: state or trait? Ann Gen Psychiatry. 2009;8:22. Petty F, Kramer GL, Gullion CM, Rush AJ. Low plasma gamma-aminobutyric acid levels in male patients with depression. Biol Psychiatry. 1992;32: Phillips JL, Batten LA, Aldosary F, Tremblay P, Blier P. Brain-volume increase with sustained remission in patients with treatment-resistant unipolar depression. J Clin Psychiatry. 2012;73: Phillips ML, Medford N, Young AW, Williams L, Williams SC, Bullmore ET, Gray JA, Brammer MJ. Time courses of left and right amygdalar responses to fearful facial expressions. Hum Brain Mapp. 2001;12: Pizzagalli DA, Holmes AJ, Dillon DG, Goetz EL, Birk JL, Bogdan R, Dougherty DD, Iosifescu DV, Rauch SL, Fava M. Reduced caudate and nucleus accumbens response to rewards in 113

126 unmedicated individuals with major depressive disorder. Am J Psychiatry. 2009;166: Pizzagalli DA. Depression, stress and anhedonia: Toward a synthesis and integrated model. Ann Rev Clin Psychol. 2014;10: Pogarell O, Koch W, Pöpperl G, Tatsch K, Jakob F, Mulert C, Grossheinrich N, Rupprecht R, Möller HJ, Hegerl U, Padberg F. Acute prefrontal rtms increases striatal dopamine to a similar degree as Dextroamphetamine. Psychiatry Res. 2007;156: Price RB, Shungu DC, Mao X, Nestadt P, Kelly C, Collins KA, Murrough JW, Charney DS, Mathew SJ. Amino acid neurotransmitters assessed by proton magnetic resonance spectroscopy: relationship to treatment resistance in major depressive disorder. Biol Psychiatry. 2009;65: Price JL, Drevets WC. Neurocircuitry of mood disorders. Neuropsychopharmacol. 2010;35: Prossin AR, Koch AE, Campbell PL, McInnis MG, Zalcman SS, Zubieta JK. Association of plasma interleukin-18 levels with emotion regulation and μ-opioid neurotransmitter function in major depression and healthy volunteers. Biol Psychiatry. 2011;69: Prusoff BA, Klerman GL, Paykel ES: Concordance between clinical assessments and patients self-report in depression. Arch Gen Psychiatry. 1972;26: Puigdemont D, Pérez-Egea R, Portella MJ, Molet J, de Diego-Adeliño J, Gironell A, Radua J, Gómez-Anson B, Rodríguez R, Serra M, de Quintana C, Artigas F, Alvarez E, Pérez V. Deep brain stimulation of the subcallosal cingulate gyrus: further evidence in treatmentresistant major depression. Int J Neuropsychopharmacol. 2011;22:1-13. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psych Meas. 1977;1: Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98: Raison CL, Rutherford RE, Woolwine BJ, Shuo C, Schettler P, Drake DF, Haroon E, Miller AH. A randomized controlled trial of the tumor necrosis factor antagonist infliximab for treatment-resistant depression: the role of baseline inflammatory biomarkers. JAMA Psychiatry. 2013;70: Ribot T. La psychologie des sentiment. Paris: Felix Alcan, Risko EF, Quilty LC, Oakman JM. Socially Desirable Responding on the Web: Investigating the Candor Hypothesis. J Pers Assess. 2006;87: Riva-Posse P, Choi KS, Holtzheimer PE, McIntyre CC, Gross RE, Chaturvedi A, Crowell AL, Garlow SJ, Rajendra JK, Mayberg HS. Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression. Biol Psychiatry. 2014;76: Rawal A, Collishaw S, Thapar A, Rice F. 'The risks of playing it safe': a prospective longitudinal study of response to reward in the adolescent offspring of depressed parents. Psychol Med. 2013;43: Rizvi SJ, Donovan M, Giacobbe P, Placenza F, Rotzinger S, Kennedy SH. Neurostimulation therapies for treatment resistant depression: a focus on vagus nerve stimulation and deep brain stimulation. Int Rev Psychiatry. 2011;23: Rizvi SJ, Kennedy SH. Emerging drugs for major depressive disorder: an update. Expert Opin Emerg Drugs. 2012;17:

127 Rizvi SJ, Salomons TV, Konarski JZ, Downar J, Giacobbe P, McIntyre RS, Kennedy SH. Neural response to emotional stimuli associated with successful antidepressant treatment and behavioral activation. J Affect Disord. 2013a;151: Rizvi SJ, Kennedy SH, Holtzheimer PE, Giacobbe P, Lozano A, Lam RW, Kolivakis TT, Mayberg HS. Investigation of the targeted effects of deep brain stimulation on depressive symptom profiles: A pooled analysis. Biol Psychiatry. 2013b;73:68S Rizvi SJ, Grima E, Tan M, Rotzinger S, Lin P, Mcintyre RS, Kennedy SH. Treatment-resistant depression in primary care across Canada. Can J Psychiatry. 2014a;59: Rizvi SJ, Geraci J, Ravindran A, Kennedy SH. Predictors of response to adjunctive osmoticrelease methylphenidate or placebo in patients with major depressive disorder: effects of apathy/anhedonia and fatigue. J Clin Psychopharmacol. 2014b;34: Rizvi SJ, Pizzagalli DA, Sproule BA, Kennedy SH. Assessing anhedonia in depression: potentials and pitfalls. (Accepted Neurosci Biobehav Rev) 2014c. Robbins S, Evans AC, Collins DL, and Whitesides S. Tuning and comparing spatial normalization methods. Med Image Anal. 2004;8: Room P, Russchen FT, Groenewegen HJ, Lohman AH. Efferent connections of the prelimbic (area 32) and the infralimbic (area 25) cortices: an anterograde tracing study in the cat. J Comp Neurol. 1985;242: Rosenblau G, Sterzer P, Stoy M, Park S, Friedel E, Heinz A, Pilhatsch M, Bauer M, Ströhle A. Functional neuroanatomy of emotion processing in major depressive disorder is altered after successful antidepressant therapy. J Psychopharmacol. 2012;26: Roy A, Pickar D, Linnoila M, Doran AR, Ninan P, Paul SM. Cerebrospinal fluid monoamine and monoamine metabolite concentrations in melancholia. Psychiatry Res. 1985;15: Rush AJ, Marangell LB, Sackeim HA, George MS, Brannan SK, Davis SM, Howland R, Kling MA, Rittberg BR, Burke WJ, Rapaport MH,Zajecka J, Nierenberg AA, Husain MM, Ginsberg D, Cooke RG. Vagus nerve stimulation for treatment-resistant depression: a randomized, controlled acute phase trial. Biol Psychiatry. 2005;58: Rush AJ, Bernstein IH, Trivedi MH, Carmody TJ, Wisniewski S, Mundt JC, Shores-Wilson K, Biggs MM, Woo A, Nierenberg AA, Fava M. An evaluation of the Quick Inventory of Depressive Symptomatology and the Hamilton Rating Scale for Depression: a sequenced treatment alternatives to relieve depression trial report. Biol Psychiatry. 2006;59: Rush AJ, Trivedi MH, Stewart JW, Nierenberg AA, Fava M, Kurian BT, Warden D, Morris DW, Luther JF, Husain MM, Cook IA, Shelton RC, Lesser IM, Kornstein SG, Wisniewski SR. Combining Medications to Enhance Depression Outcomes (CO-MED): acute and longterm outcomes of a single-blind randomized study. Am J Psychiatry. 2011;168: Rusjan P, Mamo D, Ginovart N, Hussey D, Vitcu I, Yasuno F, Tetsuya S, Houle S, Kapur S. An automated method for the extraction of regional data from PET images. Psychiatry Res. 2006;147: Sacher J, Neumann J, Fünfstück T, Soliman A, Villringer A, Schroeter ML. Mapping the depressed brain: a meta-analysis of structural and functional alterations in major depressive disorder. J Affect Disord. 2012;140: Saijo T, Takano A, Suhara T, Arakawa R, Okumura M, Ichimiya T, Ito H, Okubo Y. Electroconvulsive therapy decreases dopamine D₂receptor binding in the anterior cingulate in patients with depression: a controlled study using positron emission tomography with radioligand [¹¹C]FLB 457. J Clin Psychiatry. 2010;71:

128 116 Salamone JD, Correa M, Mingote S, Weber SM. Nucleus accumbens dopamine and the regulation of effort in food-seeking behavior: implications for studies of natural motivation, psychiatry, and drug abuse. J Pharmacol Exp Ther. 2003;305:1-8. Salamone JD, Correa M, Farrar A, Mingote SM. Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits. Psychopharmacology (Berl). 2007;191: Salvadore G, van der Veen JW, Zhang Y, Marenco S, Machado-Vieira R, Baumann J, Ibrahim LA,Luckenbaugh DA, Shen J, Drevets WC, Zarate CA Jr. An investigation of aminoacid neurotransmitters as potential predictors of clinical improvement to ketamine in depression. Int J Neuropsychopharmacol. 2012;15: Sanacora G, Mason GF, Rothman DL, Behar KL, Hyder F, Petroff OA, Berman RM, Charney DS, Krystal JH. Reduced cortical gamma-aminobutyric acid levels in depressed patients determined by proton magnetic resonance spectroscopy. Arch Gen Psychiatry. 1999;56: Sanfey AG. Social decision-making: insights from game theory and neuroscience. Science. 2007;318: Sanyal C, Asbridge M, Kisely S, Sketris I, Andreou P. The utilization of antidepressants and benzodiazepines among people with major depression in Canada. Can J Psychiatry. 2011;56: Sapolsky RM. The possibility of neurotoxicity in the hippocampus in major depression: a primer on neuron death. Biol Psychiatry. 2000;48: Sarchiapone M, Carli V, Camardese G, Cuomo C, Di Giuda D, Calcagni ML, Focacci C, De Risio S. Dopamine transporter binding in depressed patients with anhedonia. Psychiatry Res. 2006;147: Savitz JB, Drevets WC. Neuroreceptor imaging in depression. Neurobiol Dis. 2013;52: Schildkraut JJ. The catecholamine hypothesis of affective disorders: a review of supporting evidence. Am J Psychiatry. 1965;122: Schlaepfer TE, Bewernick BH, Kayser S, Mädler B, Coenen VA. Rapid effects of deep brain stimulation for treatment-resistant major depression. Biol Psychiatry. 2013;73: Schlaepfer TE, Bewernick BH, Kayser S, Hurlemann R, Coenen VA. Deep brain stimulation of the human reward system for major depression--rationale, outcomes and outlook. Neuropsychopharmacol. 2014;39: Schou M, Pike VW, Halldin C. Development of radioligands for imaging of brain norepinephrine transporters in vivo with positron emission tomography. Curr Top Med Chem. 2007;7: Schmidt KC, Turkheimer FE. Kinetic modeling in positron emission tomography. Q J Nucl Med. 2002;46: Schrader GD. Does anhedonia correlate with depression severity in chronic depression? Compr Psychiatry. 1997;38: Schweighofer N, Bertin M, Shishida K, Okamoto Y, Tanaka SC, Yamawaki S, Doya K. Lowserotonin levels increase delayed reward discounting in humans. J Neurosci. 2008;28: Seamans JK, Gorelova N, Durstewitz D, Yang CR. Bidirectional dopamine modulation of GABAergic inhibition in prefrontal cortical pyramidal neurons. J Neurosci. 2001;21: Seeman P. Atypical antipsychotics: mechanism of action. Can J Psychiatry. 2002;47: Senior C. Beauty in the brain of the beholder. Neuron. 2003;38:525-8.

129 Sescousse G, Caldú X, Segura B, Dreher JC. Processing of primary and secondary rewards: a quantitative meta-analysis and review of human functional neuroimaging studies. Neurosci Biobehav Rev. 2013;37: Sescousse G, Redouté J, Dreher JC. The architecture of reward value coding in the human orbitofrontal cortex. J Neurosci. 2010;30: Shah PJ, Ogilvie AD, Goodwin GM, Ebmeier KP. Clinical and psychometric correlates of dopamine D2 binding in depression. Psychol Med. 1997;27: Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59(Suppl 20): Sheline YI, Barch DM, Donnelly JM, Ollinger JM, Snyder AZ, Mintun MA. Increased amygdala response to masked emotional faces in depressed subjects resolves with antidepressant treatment: an fmri study. Biol Psychiatry. 2001;50: Shelton RC, Papakostas GI. Augmentation of antidepressants with atypical antipsychotics for treatment-resistant major depressive disorder. Acta Psychiatr Scand 2008;117: Sherdell L, Waugh CE, Gotlib IH. Anticipatory pleasure predicts motivation for reward in major depression. J Abnorm Psychol. 2012;121: Shirayama Y, Chaki S. Neurochemistry of the nucleus accumbens and its relevance to depression and antidepressant action in rodents. Curr Neuropharmacol. 2006;4: Shopsin B, Friedman E, Gershon S. Parachlorophenylalanine reversal of tranylcypromine effects in depressed patients. Arch Gen Psychiatry. 1976;33: Shopsin B, Gershon S, Goldstein M, Friedman E, Wilk S. Use of synthesis inhibitors in defining a role for biogenic amines during imipramine treatment in depressed patients. Psychopharmacol Commun. 1975;1: Shytle RD, Silver AA, Lukas RJ, Newman MB, Sheehan DV, Sanberg PR. Nicotinic acetylcholine receptors as targets for antidepressants. Mol Psychiatry. 2002;7: Siegle GJ, Steinhauer SR, Thase ME, Stenger VA, Carter CS. Can't shake that feeling: eventrelated fmri assessment of sustained amygdala activity in response to emotional information in depressed individuals. Biol Psychiatry. 2002;51: Skoubis PD, Lam HA, Shoblock J, Narayanan S, Maidment NT. Endogenous enkephalins, not endorphins, modulate basal hedonic state in mice. Eur J Neurosci. 2005;21: Slifstein M, Kegeles LS, Gonzales R, Frankle WG, Xu X, Laruelle M, Abi-Dargham A. [11C]NNC 112 selectivity for dopamine D1 and serotonin 5-HT(2A) receptors: a PET study in healthy human subjects. J Cereb Blood Flow Metab. 2007;27: Smith GS, Koppel J, Goldberg S. Applications of neuroreceptor imaging to psychiatry research. Psychopharmacol Bull. 2003;37: Snaith RP, Hamilton M, Morley S, Humayan A, Hargreaves D, Trigwell P. A scale for the assessment of hedonic tone the Snaith-Hamilton Pleasure Scale. Br J Psychiatry. 1995;167: Souery D, Amsterdam J, de Montigny C, Lecrubier Y, Montgomery S, Lipp O, Racagni G, Zohar J, Mendlewicz J. Treatment resistant depression: methodological overview and operational criteria. Eur Neuropsychopharmacol. 1999;9: Souery D, Oswald P, Massat I, Bailer U, Bollen J, Demyttenaere K, Kasper S, Lecrubier Y, Montgomery S, Serretti A, Zohar J, Mendlewicz J; Group for the Study of Resistant Depression. Clinical factors associated with treatment resistance in major depressive disorder: resultsfrom a European multicenter study. J Clin Psychiatry. 2007;68:

130 Steffens DC, Taylor WD, Denny KL, Bergman SR, Wang L. Structural integrity of the uncinate fasciculus and resting state functional connectivity of the ventral prefrontal cortex in late life depression. PLoS One. 2011;6:e Stephens T, Jorbert N. The economic burden of mental health problems in Canada. Chronic Dis Can. 2001;22: Stevens JP. Applied Multivariate Statistics for the Social Sciences (2nd edition). Hillsdale, NJ: Erlbaum, Stoll AL, Pillay SS, Diamond L, Workum SB, Cole JO. Methylphenidate augmentation of serotonin selective reuptake inhibitors: a case series. J Clin Psychiatry. 1996;57:72-6. Strafella AP, Paus T, Fraraccio M, Dagher A. Striatal dopamine release induced by repetitive transcranial magnetic stimulation of the human motor cortex. Brain. 2003;126: Stuber GD, Sparta DR, Stamatakis AM, van Leeuwen WA, Hardjoprajitno JE, Cho S, Tye KM, Kempadoo KA, Zhang F, Deisseroth K, Bonci A. Excitatory transmission from the amygdala to nucleus accumbens facilitates reward seeking. Nature. 2011;475: Studholme C, Hill DL, Hawkes DJ. Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Med Phys. 1997;24: Sudo Y, Suhara T, Inoue M, Ito H, Suzuki K, Saijo T, Halldin C, Farde L. Reproducibility of [11 C]FLB 457 binding in extrastriatal regions. Nucl Med Commun. 2001;22: Suhara T, Sudo Y, Okauchi T, Maeda J, Kawabe K, Suzuki K, Okubo Y, Nakashima Y, Ito H, Tanada S, Halldin C, Farde L. Extrastriatal dopamine D2 receptor density and affinity in the human brain measured by 3D PET. Int J Neuropsychopharmacol. 1999;2: Szabo Z, Kao PF, Scheffel U, Suehiro M, Mathews WB, Ravert HT, Musachio JL, Marenco S, Kim SE, Ricaurte GA. Positron emission tomography imaging of serotonin transporters in the human brain using [11C](+)McN5652. Synapse. 1995;20: Szegedi A, Jansen WT, van Willigenburg AP, van der Meulen E, Stassen HH, Thase ME. Early improvement in the first 2 weeks as a predictor of treatment outcome in patients with major depressive disorder: a meta-analysis including 6562 patients. J Clin Psychiatry. 2009;70: Talairach J and Tournoux P. Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System - an Approach to Cerebral Imaging. Thieme Medical Publishers, New York: NY, Tauscher J, Verhoeff NP, Christensen BK, Hussey D, Meyer JH, Kecojevic A, Javanmard M, Kasper S, Kapur S. Serotonin 5-HT1A receptor binding potential declines with age as measured by [11C]WAY and PET. Neuropsychopharmacol. 2001;24: Taylor JR, Robbins TW. 6-Hydroxydopamine lesions of the nucleus accumbens, but not of the caudate nucleus, attenuate enhanced responding with reward-related stimuli produced by intra-accumbens d-amphetamine. Psychopharmacology (Berl). 1986;90: Thase ME, Rush AJ. When at first you don't succeed: sequential strategies for antidepressant nonresponders. J Clin Psychiatry. 1997;58(Suppl 13):23-9. Thomas DN, Nutt DJ, Holman RB. Effects of acute and chronic electroconvulsive shock on noradrenaline release in the rat hippocampus and frontal cortex. Br J Pharmacol. 1992;106:430-4 Träskman L, Asberg M, Bertilsson L, Sjöstrand L. Monoamine metabolites in CSF and suicidal behavior. Arch Gen Psychiatry. 1981;38: Treadway MT, Zald DH. Reconsidering anhedonia in depression: lessons from translational neuroscience. Neurosci Biobehav Rev. 2011;35:

131 Treadway MT, Buckholtz JW, Schwartzman AN, Lambert WE, Zald DH. Worth the 'EEfRT'? The effort expenditure for rewards task as an objective measure of motivation and anhedonia. PLoS One. 2009;4:e6598. Tremblay LK, Naranjo CA, Cardenas L, Herrmann N, Busto UE. Probing brain reward system function in major depressive disorder: altered response to dextroamphetamine. Arch Gen Psychiatry. 2002;59: Tremblay LK, Naranjo CA, Graham SJ, Herrmann N, Mayberg HS, Hevenor S, Busto UE. Functional neuroanatomical substrates of altered reward processing in major depressive disorder revealed by a dopaminergic probe. Arch Gen Psychiatry. 2005;62: Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, Norquist G, Howland RH, Lebowitz B, McGrath PJ, Shores-Wilson K, Biggs MM, Balasubramani GK, Fava M; STAR*D Study Team. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163: Uher R, Mors O, Rietschel M, Rajewska-Rager A, Petrovic A, Zobel A, Henigsberg N, Mendlewicz J, Aitchison KJ, Farmer A, McGuffin P. Early and delayed onset of response to antidepressants in individual trajectories of change during treatment of major depression: a secondary analysis of data from the Genome-Based Therapeutic Drugs for Depression (GENDEP) study. J Clin Psychiatry. 2011;72: Uher R, Perlis RH, Henigsberg N, Zobel A, Rietschel M, Mors O, Hauser J, Dernovsek MZ, Souery D, Bajs M, Maier W, Aitchison KJ, Farmer A, McGuffin P. Depression symptom dimensions as predictors of antidepressant treatment outcome: replicable evidence for interest-activity symptoms. Psychol Med. 2012;42: Uher R, Farmer A, Maier W, Rietschel M, Hauser J, Marusic A, Mors O, Elkin A, Williamson RJ, Schmael C, Henigsberg N, Perez J, Mendlewicz J, Janzing JG, Zobel A, Skibinska M, Kozel D, Stamp AS, Bajs M, Placentino A, Barreto M, McGuffin P, Aitchison KJ. Measuring depression: comparison and integration of three scales in the GENDEP study. Psychol Med. 2008;38: Valenstein M, Taylor KK, Austin K, Kales HC, McCarthy JF, Blow FC. Benzodiazepine use among depressed patients treated in mental health settings. Am J Psychiatry 2004;161: Vallone D, Picetti R, Borrelli E. Structure and function of dopamine receptors. Neurosci Biobehav Rev. 2000;24: van Zessen R, Phillips JL, Budygin EA, Stuber GD. Activation of VTA GABA neurons disrupts reward consumption. Neuron. 2012;73: Vernaleken I, Fellows C, Janouschek H, Bröcheler A, Veselinovic T, Landvogt C, Boy C, Buchholz HG, Spreckelmeyer K, Bartenstein P,Cumming P, Hiemke C, Rösch F, Schäfer W, Wong DF, Gründer G. Striatal and extrastriatal D2/D3-receptor-binding properties of ziprasidone: a positron emission tomography study with [18F]Fallypride and [11C]raclopride (D2/D3-receptor occupancy of ziprasidone). J Clin Psychopharmacol. 2008;28: Volkow ND, Wang GJ, Fowler JS, Telang F. Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Philos Trans R Soc Lond B Biol Sci. 2008;363: Vrieze E, Ceccarini J, Pizzagalli DA, Bormans G, Vandenbulcke M, Demyttenaere K, Van Laere K, Claes S. Measuring extrastriatal dopamine release during a reward learning task. Hum Brain Mapp. 2013;34:

132 Wacker J, Dillon DG, Pizzagalli DA. The role of the nucleus accumbens and rostral anterior cingulate cortex in anhedonia: integration of resting EEG, fmri, and volumetric techniques. Neuroimage. 2009;46: Wagner HN, Jr., Burns HD, Dannals RF, Wong DF, Langstrom B, Duelfer T, Frost JJ, Ravert HT, Links JM, Rosenbloom SB, Lukas SE, Kramer AV, Kuhar MJ. Imaging dopamine receptors in the human brain by positron tomography. Science. 1983;221: Wallston KA, Wallston BS, DeVellis R. Development of the Multidimensional Health Locus of Control (MHLC) Scales. Health Educ Monogr. 1978;6: Walter M, Henning A, Grimm S, Schulte RF, Beck J, Dydak U, Schnepf B, Boeker H, Boesiger P, Northoff G. The relationship between aberrant neuronal activation in the pregenual anterior cingulate, altered glutamatergic metabolism, and anhedonia in major depression. Arch Gen Psychiatry. 2009;66: Wang L, Leonards CO, Sterzer P, Ebinger M. White matter lesions and depression: a systematic review and meta-analysis. J Psychiatr Res. 2014;56: Wang Y, Xu C, Cao X, Gao Q, Li J, Liu Z, Sun N, Ren Y, Zhang K. Effects of an antidepressant on neural correlates of emotional processing in patients with major depression. Neurosci Lett. 2012;527:55-9. Warden D, Rush AJ, Trivedi MH, Fava M, Wisniewski SR. The STAR*D Project results: a comprehensive review of findings. Curr Psychiatry Rep. 2007;9: Wassum KM, Ostlund SB, Maidment NT, Balleine BW. Distinct opioid circuits determine the palatability and the desirability of rewarding events. Proc Natl Acad Sci U S A. 2009;106: Watson D, Weber K, Assenheimer JS, Clark LA, Strauss ME, McCormick RA. Testing a tripartite model: I. Evaluating the convergent and discriminant validity of anxiety and depression symptom scales. J Abnorm Psychol. 1995;104:3-14. Weissman MM, Sholomskas D, Pottenger M, Prusoff BA, Locke BZ. Assessing depressive symptoms in five psychiatric populations: a validation study. Am J Epidemiol. 1977; Wier LT, Ayers GW, Jackson AS, Rossum AC, Poston WS, Foreyt JP. Determining the amount of physical activity needed for long-term weight control. Int J Obes Relat Metab Disord. 2001;25: Willner P, Hale AS, Argyropoulos S. Dopaminergic mechanism of antidepressant action in depressed patients. J Affect Disord. 2005;86: Winer ES, Veilleux JC, Ginger EJ. Development and validation of the Specific Loss of Interest and Pleasure Scale (SLIPS). J Affect Disord. 2014; : Witkin JM, Marek GJ, Johnson BG, Schoepp DD. Metabotropic glutamate receptors in the control of mood disorders. CNS Neurol Disord Drug Targets 2007;6: Wong DF, Wagner HN, Jr., Dannals RF, Links JM, Frost JJ, Ravert HT, Wilson AA, Rosenbaum AE, Gjedde A, Douglass KH, Effects of age on dopamine and serotonin receptors measured by positron tomography in the living human brain. Science. 1984;226: Wong LS, Eshel G, Dreher J, Ong J, Jackson DM. Role of dopamine and GABA in the control of motor activity elicited from the rat nucleus accumbens. Pharmacol Biochem Behav. 1991;38: World Health Organization. The ICD-10 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. Geneva: World Health Organization,

133 Wu M, Andreescu C, Butters MA, Tamburo R, Reynolds CF 3rd, Aizenstein H. Default-mode network connectivity and white matter burden in late-life depression. Psychiatry Res. 2011;194: Xing G, Ku HC, Xing C. A comparison of the likelihood ratio test and the variance-stabilising transformation-based tests for detecting association of rare variants. Ann Hum Genet. 2013;77: Xu K, Jiang W, Ren L, Ouyang X, Jiang Y, Wu F, Kong L, Womer F, Liu Z, Blumberg HP, Tang Y, Wang F. Impaired interhemispheric connectivity in medication-naive patients with major depressive disorder. J Psychiatry Neurosci. 2013;38:43-8. Yan QS. Activation of 5-HT2A/2C receptors within the nucleus accumbens increases local dopaminergic transmission. Brain Res Bull. 2000;51: Yoshida K, Higuchi H, Kamata M, Yoshimoto M, Shimizu T, Hishikawa Y. Dopamine releasing response in rat striatum to single and repeated electroconvulsive shock treatment. Prog Neuropsychopharmacol Biol Psychiatry. 1997;21: Yucel K, McKinnon MC, Chahal R, Taylor VH, Macdonald K, Joffe R, MacQueen GM. Anterior cingulate volumes in never-treated patients with major depressive disorder. Neuropsychopharmacol. 2008;33: Zarate CA Jr, Du J, Quiroz J, Gray NA, Denicoff KD, Singh J, Charney DS, Manji HK. Regulation of cellular plasticity cascades in the pathophysiology and treatment of mood disorders: role of the glutamatergic system. Ann NY Acad Sci. 2004; 1003: Zarate CA Jr, Singh JB, Carlson PJ, Brutsche NE, Ameli R, Luckenbaugh DA, Charney DS, Manji HK. A randomized trial of an N-methyl-D-aspartate antagonist in treatment-resistant major depression. Arch Gen Psychiatry. 2006;63: Zaslavsky A, Shaul J, Zaborski L, Cioffi M, Cleary P. Combining health plan performance indicators into simpler composite measures. Health Care Financ Rev. 2002;23: Zhou Y, Qin LD, Chen J, Qian LJ, Tao J, Fang YR, Xu JR. Brain microstructural abnormalities revealed by diffusion tensor images in patients with treatment-resistant depression compared with major depressive disorder before treatment. Eur J Radiol. 2011;80: Zubieta JK, Ketter TA, Bueller JA, Xu Y, Kilbourn MR, Young EA, Koeppe RA. Regulation of human affective responses by anterior cingulate and limbic mu-opioid neurotransmission. Arch Gen Psychiatry. 2003;60:

134 122 Appendix 1 Initial version of the Dimensional Anhedonia Rating Scale 34-item Instructions: Please think carefully and provide at least 2 examples of pleasurable activities/experiences for each category. Even if you have not had pleasure from activities/experiences lately, please use the activities/experiences you remember enjoying the most, and answer the questions by how much they apply to you right now. Check the box that best describes how you feel. A. Please list at least 2 of your favourite pastimes/hobbies that are NOT primarily social (Examples: gardening, reading, movies, sports, art, cooking, shopping, driving) Thinking about these activities right now: Not at all Slightly Moderately Mostly Very Much 1. I would enjoy these activities O O O O O 2. I would have to push myself to start these activities O O O O O 3. I would have a desire to participate in these activities O O O O O 4. Once I started these activities it would be easy to continue doing them until it was time to stop O O O O O 5. I would have trouble starting these activities O O O O O 6. I would only enjoy the activities that do not require a lot of effort O O O O O 7. I would need help to do these activities O O O O O 8. I would spend time doing these activities O O O O O 9. I want to do these activities O O O O O 10. These activities would interest me O O O O O 11. These activities would give me pleasure O O O O O

135 123 B. Please list at least 2 of your favourite foods/drinks (Examples: pizza, coffee, wine) Thinking about these foods/drinks right now: Not at all Slightly Moderately Mostly Very Much 12. Having these foods/drinks would satisfy me O O O O O 13. The thought of having my favourite food/drink at the end of the day pleases me O O O O O 14. I would make an effort to get/make these foods/drinks O O O O O 15. I would enjoy these foods/drinks O O O O O 16. I want to have these foods/drinks O O O O O 17. I would eat as much of these foods as I could O O O O O 18. I would only have these foods/drinks if someone encouraged me or provided them to me O O O O O C. Please list at least 2 of your favourite social activities (Examples: making dinner with partner, meeting friends for coffee, spending time with family, volunteering) Thinking about these social activities right now: Not at all Slightly Moderately Mostly Very Much 19. Spending time doing these things would make me happy O O O O O 20. I would be interested in doing things that involve other people O O O O O 21. If I were to participate in things with other people I would do my best to be involved O O O O O 22. It would be difficult to pump myself up to do these activities O O O O O

136 I enjoy the idea of doing these activities O O O O O 24. I would want to participate in activities involving other people O O O O O 25. I would be the one to plan these activities O O O O O 26. I would feel cheerful from participating in these social activities O O O O O 27. I would actively participate in these social activities O O O O O 28. I would have to force myself to get involved in these activities O O O O O D. Please list at least 2 of your favourite sensory experiences (Examples: listening to music, watching a sunset, smell of favourite foods, sensitivity to touch, sex) Thinking about these experiences right now: Not at all Slightly Moderately Mostly Very Much 29. I would actively seek out these experiences O O O O O 30. I get excited thinking about these experiences O O O O O 31. If I were to have these experiences I would savor every moment O O O O O 32. It would be difficult to enjoy these experiences O O O O O 33. I want to have these experiences O O O O O 34. I would make an effort to spend time having these experiences O O O O O

137 125 Appendix 2 Phase 2 Factor Analysis Data Communalities of 34-item scale* Scale Items Extraction Q1-I would enjoy these activities.719 Q2 r -I would have to push myself to start these activities.538 Q3-I would have a desire to participate in these activities.453 Q4-Once I started...easy to continue till it was time to stop.423 Q5 r -I would have trouble starting these activities.434 Q6 r -I would only enjoy activities that do not require a lot of effort.249 Q7 r -I would need help to do these activities.449 Q8-I would spend time doing these activities.691 Q9-I want to do these activities.808 Q10-These activities would interest me.821 Q11-These activities would give me pleasure.599 Q12-Having these foods/drinks would satisfy me.719 Q13-The thought of having my favourite food/drink...pleases me.696 Q14-I would make an effort to get/make these foods/drinks.635 Q15-I would enjoy these foods/drinks.788 Q16-I want to have these foods/drinks.810 Q17-I would eat as much of these foods as I could.571 Q18 r -I would only have these foods/drinks if...encouraged Q19-Spending time doing these activities would make me happy.713 Q20-I would be interested in doing things that involve other people.737 Q21-If I were to participate...i would do my best to be involved.726 Q22 r -It would be difficult to pump myself up to do these activities.619 Q23-I enjoy the idea of doing these activities.661 Q24-I would want to participate in activities involving other people.716 Q25-I would be the one to plan these activities.515 Q26-I would feel cheerful from participating in these social activities.708 Q27-I would actively participate in these social activities.746 Q28 r -I would have to force myself to get involved in these activities.716 Q29-I would actively seek out these experiences.732 Q30-I get excited thinking about these experiences.789 Q31-If I were to have these experiences, I would savour every moment.670 Q32 r -It would be difficult to enjoy these experiences.446 Q33-I want to have these experiences.734 Q34-I would make an effort to spend time having these experiences.612 *All items less than 0.5 were removed. Greyed out items were retained. r Reverse scored items

138 126 Pattern matrix of 34-item DARS scale representing factor loadings of remaining 27 items after removal of communalities<0.5 Scale Items Component Q1-I would enjoy these activities.724 Q2 r -I would have to push myself to start these activities.670 Q8-I would spend time doing these activities.912 Q9-I want to do these activities.952 Q10-These activities would interest me.867 Q11-These activities would give me pleasure.699 Q12-Having these foods/drinks would satisfy me.790 Q13-The thought of having my fav food/drink...pleases me.782 Q14-I would make an effort to get/make these foods/drinks.763 Q15-I would enjoy these foods/drinks.851 Q16-I want to have these foods/drinks.960 Q17-I would eat as much of these foods as I could.729 Q19-Spending time doing these activities would make me happy.643 Q20-I would be interested in doing things that involve other people.827 Q21-If I were to participate...i would do my best to be involved.856 Q22 r -It would be difficult to pump myself up to do these activities.711 Q23-I enjoy the idea of doing these activities.744 Q24-I would want to participate in activities involving other people.872 Q25-I would be the one to plan these activities.801 Q26-I would feel cheerful from participating in these social activities.778 Q27-I would actively participate in these social activities.789 Q28 r -I would have to force myself to get involved in these activities Q29-I would actively seek out these experiences.758 Q30-I get excited thinking about these experiences.871 Q31-If I were to have these experiences, I would savour every.720 moment Q33-I want to have these experiences.823 Q34-I would make an effort to spend time having these experiences.835 * Factor loadings >0.3 have been suppressed.

139 127 Structure matrix of 34-item DARS scale representing correlations of remaining 24 items after removal of reverse-keyed items Scale Items Component Q1-I would enjoy these activities Q8-I would spend time doing these activities Q9-I want to do these activities Q10-These activities would interest me Q11-These activities would give me pleasure Q12-Having these foods/drinks would satisfy me Q13-The thought of having my fav food/drink...pleases me Q14-I would make an effort to get/make these foods/drinks Q15-I would enjoy these foods/drinks Q16-I want to have these foods/drinks Q17-I would eat as much of these foods as I could Q19-Spending time doing these activities would make me happy Q20-I would be interested in doing things that involve other people Q21-If I were to participate...i would do my best to be involved Q23-I enjoy the idea of doing these activities Q24-I would want to participate in activities involving other people Q25-I would be the one to plan theses activities.678 Q26-I would feel cheerful from participating in these social activities Q27-I would actively participate in these social activities Q29-I would actively seek out these experiences Q30-I get excited thinking about these experiences Q31-If I were to have these experiences, I would savour every moment Q33-I want to have these experiences Q34-I would make an effort to spend time having these experiences * Correlations<0.3 have been suppressed.

140 128 Scree Plot of DARS-17 item factor solution Blue like represents factors in the data versus the eigenvalue of the factor.