Publications by authors named "Susan Rotzinger"

46 Publications

White matter microstructure in youth at risk for serious mental illness: A comparative analysis.

Psychiatry Res Neuroimaging 2021 Apr 20;312:111289. Epub 2021 Apr 20.

Department of Radiology, University of Calgary, Calgary, Alberta, Canada; Department of Radiology, Alberta Children's Hospital Research Institute,; Department of Radiology, Child and Adolescent Imaging Research Program, Calgary, Alberta, Canada.

Identifying biomarkers of serious mental illness, such as altered white matter microstructure, can aid in early diagnosis and treatment. White matter microstructure was assessed using constrained spherical deconvolution of diffusion imaging data in a sample of 219 youth (age 12-25 years, 64.84% female) across 8 sites. Participants were classified as healthy controls (HC; n = 47), familial risk for serious mental illness (n = 31), mild-symptoms (n = 37), attenuated syndromes (n = 66), or discrete disorder (n = 38) based on clinical assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values were derived for the whole brain white matter, forceps minor, anterior cingulate, anterior thalamic radiations (ATR), inferior fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF). Linear mixed effects models showed a significant effect of age on MD of the left ATR, left SLF, and left UF, and a significant effect of group on FA for all tracts examined. For most tracts, the discrete disorder group had significantly lower FA than other groups, and the attenuated syndromes group had higher FA compared to HC, with few differences between the remaining groups. White matter differences in MDD are most evident in individuals following illness onset, as few significant differences were observed in the risk phase.
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http://dx.doi.org/10.1016/j.pscychresns.2021.111289DOI Listing
April 2021

Predictors of Quality of Life Improvement with Escitalopram and Adjunctive Aripiprazole in Patients with Major Depressive Disorder: A CAN-BIND Study Report.

CNS Drugs 2021 Apr 16;35(4):439-450. Epub 2021 Apr 16.

Department of Psychiatry, University of British Columbia, 5950 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada.

Background: Non-response to first-line treatment for major depressive disorder (MDD) is common; for such individuals, quality of life (QoL) impairments can be severe. Identifying predictors of QoL changes may support the management of cases with persistent depressive symptoms despite adequate initial pharmacological/psychological treatment.

Objective: The present study aimed to explore predictors of domain-specific QoL improvement following adjunctive aripiprazole treatment for inadequate response to initial antidepressant therapy.

Methods: We evaluated secondary QoL outcomes from a CAN-BIND (Canadian Biomarker Integration Network in Depression) study in patients with MDD who did not respond to an initial 8 weeks of escitalopram and received a further 8 weeks of adjunctive aripiprazole (n = 96). Physical, psychological, social, and environmental QoL domains were assessed using the World Health Organization QoL Scale Brief Version (WHOQOL-BREF). Clinician-rated depressive symptoms were assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS). Functioning was measured with the Sheehan Disability Scale (SDS). Satisfaction with medication was assessed with a single item from the Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF). Exploratory t-tests were used to describe domain score changes. A hierarchical linear regression was used to explore demographic, clinical, and treatment-related predictors of improvement.

Results: Across domains, QoL improved with adjunctive aripiprazole treatment. Satisfaction with medication and MADRS and SDS scores similarly improved. Symptom reduction was a predictor for positive change to physical and psychological QoL; functioning improvements were predictive of increases to all QoL domains. Satisfaction with medication predicted improvements to physical and psychological domains, whereas number of medication trials was a predictor of worsening QoL in the physical domain.

Conclusion: The final model explained the most variance in psychological (68%) and physical (67%) QoL. Less variance was explained for environmental (43%) and social QoL (33%), highlighting a need for further exploration of predictors in these domains. Strategies such as functional remediation may have potential to support QoL for individuals with persistent depressive symptoms.

Clinical Trials Registry: ClinicalTrials.gov identifier: NCT016557.
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http://dx.doi.org/10.1007/s40263-021-00803-2DOI Listing
April 2021

Cognitive Outcomes with Sequential Escitalopram Monotherapy and Adjunctive Aripiprazole Treatment in Major Depressive Disorder: A Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report.

CNS Drugs 2021 Mar 8;35(3):291-304. Epub 2021 Mar 8.

Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada.

Background: Cognitive deficits are detectable in major depressive disorder (MDD). The cognitive impact of antidepressants remains unclear, as does the cognitive effects of aripiprazole in MDD, a commonly used adjunct with putative pro-cognitive properties.

Objectives: In this multi-centre, open-label study, cognitive changes associated with escitalopram monotherapy and adjunctive aripiprazole were examined.

Methods: Acutely depressed participants with MDD (n = 209) received 8 weeks of escitalopram. Non-responders received an additional 8 weeks of adjunctive aripiprazole (ESC-ARI, n = 88), while responders (ESC-CONT, n = 82) continued escitalopram monotherapy (n = 39 lost to attrition). ESC-ARI, ESC-CONT and matched healthy participants (n = 112) completed the Central Nervous System Vital Signs cognitive battery at baseline, 8 and 16 weeks. Linear mixed models compared participants with MDD cognitive trajectories with healthy participants.

Results: Participants with MDD displayed poorer baseline global cognition (assessed via the Neurocognitive Index), composite memory and psychomotor speed vs healthy participants. There were no statistically significant changes in participants with MDD receiving escitalopram monotherapy from baseline to week 8 in the neurocognitive index, reaction time, complex attention, cognitive flexibility, memory or psychomotor speed. Overall symptom severity changes were not associated with cognitive changes. The ESC-CONT group displayed no significant cognitive changes from weeks 8 to 16; reaction time worsened in the ESC-ARI group (p = 0.008) from weeks 8 to 16, independent of symptom change.

Conclusions: Escitalopram monotherapy in acute MDD did not result in significant cognitive improvements. We provide novel evidence that escitalopram continuation in responders does not adversely affect cognition, but adjunctive aripiprazole in escitalopram non-responders worsens reaction time. Treatments targeting cognitive dysfunction are needed in MDD. CLINICALTRIALS.

Gov Identifier: NCT01655706; 2 August, 2012.
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http://dx.doi.org/10.1007/s40263-021-00793-1DOI Listing
March 2021

Impacts on Quality of Life with Escitalopram Monotherapy and Aripiprazole Augmentation in Patients with Major Depressive Disorder: A CAN-BIND Report.

Pharmacopsychiatry 2021 Mar 2. Epub 2021 Mar 2.

Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.

Introduction: Many individuals with major depressive disorder (MDD) do not respond to initial antidepressant monotherapy. Adjunctive aripiprazole is recommended for treatment non-response; however, the impacts on quality of life (QoL) for individuals who receive this second-line treatment strategy have not been described.

Methods: We evaluated secondary QoL outcomes in patients with MDD (n=179). After 8 weeks of escitalopram, non-responders (<50% decrease in clinician-rated depression) were treated with adjunctive aripiprazole for 8 weeks (n=97); responders continued escitalopram (n=82). A repeated-measures ANOVA evaluated change in Quality of Life Enjoyment and Satisfaction Short Form scores. QoL was described relative to normative benchmarks.

Results: Escitalopram responders experienced the most QoL improvements in the first treatment phase. For non-responders, QoL improved with a large effect during adjunctive aripiprazole treatment. At the endpoint, 47% of patients achieving symptomatic remission still had impaired QoL.

Discussion: Individuals who were treated with adjunctive aripiprazole after non-response to escitalopram experienced improved QoL, but a substantial degree of QoL impairment persisted. Since QoL deficits may predict MDD recurrence, attention to ways to support this outcome is required.
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http://dx.doi.org/10.1055/a-1385-0263DOI Listing
March 2021

Genome-wide analysis suggests the importance of vascular processes and neuroinflammation in late-life antidepressant response.

Transl Psychiatry 2021 Feb 15;11(1):127. Epub 2021 Feb 15.

Institute of Medical Science, University of Toronto, Toronto, ON, Canada.

Antidepressant outcomes in older adults with depression is poor, possibly because of comorbidities such as cerebrovascular disease. Therefore, we leveraged multiple genome-wide approaches to understand the genetic architecture of antidepressant response. Our sample included 307 older adults (≥60 years) with current major depression, treated with venlafaxine extended-release for 12 weeks. A standard genome-wide association study (GWAS) was conducted for post-treatment remission status, followed by in silico biological characterization of associated genes, as well as polygenic risk scoring for depression, neurodegenerative and cerebrovascular disease. The top-associated variants for remission status and percentage symptom improvement were PIEZO1 rs12597726 (OR = 0.33 [0.21, 0.51], p = 1.42 × 10) and intergenic rs6916777 (Beta = 14.03 [8.47, 19.59], p = 1.25 × 10), respectively. Pathway analysis revealed significant contributions from genes involved in the ubiquitin-proteasome system, which regulates intracellular protein degradation with has implications for inflammation, as well as atherosclerotic cardiovascular disease (n = 25 of 190 genes, p = 8.03 × 10, FDR-corrected p = 0.01). Given the polygenicity of complex outcomes such as antidepressant response, we also explored 11 polygenic risk scores associated with risk for Alzheimer's disease and stroke. Of the 11 scores, risk for cardioembolic stroke was the second-best predictor of non-remission, after being male (Accuracy = 0.70 [0.59, 0.79], Sensitivity = 0.72, Specificity = 0.67; p = 2.45 × 10). Although our findings did not reach genome-wide significance, they point to previously-implicated mechanisms and provide support for the roles of vascular and inflammatory pathways in LLD. Overall, significant enrichment of genes involved in protein degradation pathways that may be impaired, as well as the predictive capacity of risk for cardioembolic stroke, support a link between late-life depression remission and risk for vascular dysfunction.
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http://dx.doi.org/10.1038/s41398-021-01248-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884410PMC
February 2021

THE DEPRESSION INVENTORY DEVELOPMENT SCALE: Assessment of Psychometric Properties Using Classical and Modern Measurement Theory in a CAN-BIND Trial.

Innov Clin Neurosci 2020 Jul;17(7-9):30-40

Drs. Vaccarino, Evans and Gilbert Evans are with Indoc Research in Toronto, Ontario, Canada.

The goal of the Depression Inventory Development (DID) project is to develop a comprehensive and psychometrically sound rating scale for major depressive disorder (MDD) that reflects current diagnostic criteria and conceptualizations of depression. We report here the evaluation of the current DID item bank using Classical Test Theory (CTT), Item Response Theory (IRT) and Rasch Measurement Theory (RMT). The present study was part of a larger multisite, open-label study conducted by the Canadian Biomarker Integration Network in Depression (ClinicalTrials.gov: NCT01655706). Trained raters administered the 32 DID items at each of two visits (MDD: baseline, n=211 and Week 8, n=177; healthy participants: baseline, n=112 and Week 8, n=104). The DID's "grid" structure operationalizes intensity and frequency of each item, with clear symptom definitions and a structured interview guide, with the current iteration assessing symptoms related to and . Participants were also administered the Montgomery- Åsberg Depression Rating Scale (MADRS) and Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) that allowed DID items to be evaluated against existing "benchmark" items. CTT was used to assess data quality/reliability (i.e., missing data, skewness, scoring frequency, internal consistency), IRT to assess individual item performance by modelling an item's ability to discriminate levels of depressive severity (as assessed by the MADRS), and RMT to assess how the items perform together as a scale to capture a range of depressive severity (item targeting). These analyses together provided empirical evidence to base decisions on which DID items to remove, modify, or advance. Of the 32 DID items evaluated, eight items were identified by CTT as problematic, displaying low variability in the range of responses, floor effects, and/or skewness; and four items were identified by IRT to show poor discriminative properties that would limit their clinical utility. Five additional items were deemed to be redundant. The remaining 15 DID items all fit the Rasch model, with person and item difficulty estimates indicating satisfactory item targeting, with lower precision in participants with mild levels of depression. These 15 DID items also showed good internal consistency (alpha=0.95 and inter-item correlations ranging from r=0.49 to r=0.84) and all items were sensitive to change following antidepressant treatment (baseline vs. Week 8). RMT revealed problematic item targeting for the MADRS and QIDSSR, including an absence of MADRS items targeting participants with mild/moderate depression and an absence of QIDS-SR items targeting participants with mild or severe depression. The present study applied CTT, IRT, and RMT to assess the measurement properties of the DID items and identify those that should be advanced, modified, or removed. Of the 32 items evaluated, 15 items showed good measurement properties. These items (along with previously evaluated items) will provide the basis for validation of a penultimate DID scale assessing and . The strategies adopted by the DID process provide a framework for rating scale development and validation.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839654PMC
July 2020

Cognition and Its Association with Psychosocial and Occupational Functioning during Treatment with Escitalopram in Patients with Major Depressive Disorder: A CAN-BIND-1 Report: La Cognition Et Son Association Avec Le Fonctionnement Psychosocial Et Professionnel Durant Le Traitement Par Escitalopram Chez Des Patients Souffrant De Trouble Dépressif Majeur: Une Étude Can-Bind-1.

Can J Psychiatry 2020 Dec 23:706743720974823. Epub 2020 Dec 23.

Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.

Objectives: Major depressive disorder (MDD) is associated with impairments in both cognition and functioning. However, whether cognitive deficits significantly contribute to impaired psychosocial and occupational functioning, independent of other depressive symptoms, is not well established. We examined the relationship between cognitive performance and functioning in depressed patients before and after antidepressant treatment using secondary data from the first Canadian Biomarker Integration Network in Depression-1 study.

Methods: Cognition was assessed at baseline in unmedicated, depressed participants with MDD ( = 207) using the Central Nervous System Vital Signs computerized battery, psychosocial functioning with the Sheehan Disability Scale (SDS), and occupational functioning with the Lam Employment Absence and Productivity Scale (LEAPS). Cognition ( = 181), SDS ( = 175), and LEAPS ( = 118) were reassessed after participants received 8 weeks of open-label escitalopram monotherapy. A series of linear regressions were conducted to determine (1) whether cognitive functioning was associated with psychosocial and occupational functioning prior to treatment, after adjusting for overall depressive symptom severity and (2) whether changes in cognitive functioning after an 8-week treatment phase were associated with changes in psychosocial and occupational functioning, after adjusting for changes in overall symptom severity.

Results: Baseline global cognitive functioning, after adjusting for depression symptom severity and demographic variables, was associated with the SDS work/study subscale (β = -0.17; = 0.03) and LEAPS productivity subscale (β = -0.17; = 0.05), but not SDS total (β = 0.19; = 0.12) or LEAPS total (β = 0.41; = 0.17) scores. Although LEAPS and SDS scores showed significant improvements after 8 weeks of treatment ( < 0.001), there were no significant associations between changes in cognitive domain scores and functional improvements.

Conclusion: Cognition was associated with occupational functioning at baseline, but changes in cognition were not associated with psychosocial or occupational functional improvements following escitalopram treatment. We recommend the use of more comprehensive functional assessments to determine the impact of cognitive change on functional outcomes in future research.
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http://dx.doi.org/10.1177/0706743720974823DOI Listing
December 2020

Structural covariance pattern abnormalities of insula in major depressive disorder: A CAN-BIND study report.

Prog Neuropsychopharmacol Biol Psychiatry 2020 Dec 6:110194. Epub 2020 Dec 6.

Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada. Electronic address:

Background And Methods: Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs.

Results: The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network.

Conclusions: Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.
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http://dx.doi.org/10.1016/j.pnpbp.2020.110194DOI Listing
December 2020

Symptom Dimension of Interest-Activity Indicates Need for Aripiprazole Augmentation of Escitalopram in Major Depressive Disorder: A CAN-BIND-1 Report.

J Clin Psychiatry 2020 06 16;81(4). Epub 2020 Jun 16.

Members of the CAN-BIND Investigator Team are listed at www.canbind.ca/about-can-bind/our-team/.

Objective: Differential predictors of response to alternative treatment options are needed to improve the outcomes in major depressive disorder. The symptom dimension comprising loss of interest and reduced activity has been reported as a predictor of poor outcome of treatment with antidepressants. We hypothesized that augmentation with partial dopamine agonist aripiprazole will be effective for individuals with pronounced interest-activity symptoms.

Methods: We tested the hypothesis in the 2-phase Canadian Biomarker Integration Network in Depression trial 1 (CAN-BIND-1). All participants had a primary diagnosis of major depressive disorder confirmed with the Mini-International Neuropsychiatric Interview. In phase 1, 188 individuals received escitalopram monotherapy 10-20 mg daily for 8 weeks. In phase 2, nonresponders received augmentation with aripiprazole 2-10 mg daily while responders continued escitalopram monotherapy for another 8 weeks. Outcomes were measured with the Montgomery-Åsberg Depression Rating Scale (MADRS) every 2 weeks. Effects of baseline interest-activity symptoms on outcomes were tested in repeated-measures mixed-effects models.

Results: Higher baseline interest-activity score (indicative of more severe loss of interest and reduction in activity) predicted worse outcome of escitalopram monotherapy in phase 1 (b = 1.75; 95% CI, 0.45 to 3.05; P = .009), but the association disappeared with the augmentation option in phase 2 (b = -0.19; 95% CI, -1.30 to 0.92; P = .739). A significant interaction between the baseline interest-activity score and aripiprazole reflected the opposite direction of the relationship between baseline interest-activity score and degree of improvement with escitalopram monotherapy versus aripiprazole augmentation (b = -1.60; 95% CI, -2.35 to -0.84; P < .001).

Conclusions: Individuals with prominent loss of interest and reduction in activity benefit less from escitalopram monotherapy and more from aripiprazole augmentation. Future trials may test the benefits of early prodopaminergic augmentation guided by interest-activity symptoms.

Trial Registration: ClinicalTrials.gov identifier: NCT01655706.
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http://dx.doi.org/10.4088/JCP.20m13229DOI Listing
June 2020

A randomized, crossover comparison of ketamine and electroconvulsive therapy for treatment of major depressive episodes: a Canadian biomarker integration network in depression (CAN-BIND) study protocol.

BMC Psychiatry 2020 06 2;20(1):268. Epub 2020 Jun 2.

The Royal's Institute of Mental Health Research, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada.

Background: Recent evidence underscores the utility of rapid-acting antidepressant interventions, such as ketamine, in alleviating symptoms of major depressive episodes (MDE). However, to date, there have been limited head-to-head comparisons of intravenous (IV) ketamine infusions with other antidepressant treatment strategies in large randomized trials. This study protocol describes an ongoing multi-centre, prospective, randomized, crossover, non-inferiority trial comparing acute treatment of individuals meeting diagnostic criteria for a major depressive episode (MDE) with ketamine and electroconvulsive therapy (ECT) on efficacy, speed of therapeutic effects, side effects, and health care resource utilization. A secondary aim is to compare a 6-month maintenance strategy for ketamine responders to standard of care ECT maintenance. Finally, through the measurement of clinical, cognitive, neuroimaging, and molecular markers we aim to establish predictors and moderators of treatment response as well as treatment-elicited effects on these outcomes.

Methods: Across four participating Canadian institutions, 240 patients with major depressive disorder or bipolar disorder experiencing a MDE are randomized (1:1) to a course of ECT or racemic IV ketamine (0.5 mg/kg) administered 3 times/week for 3 or 4 weeks. Non-responders (< 50% improvement in Montgomery-Åsberg Depression Rating Scale [MADRS] scores) crossover to receive the alternate treatment. Responders during the randomization or crossover phases then enter the 6-month maintenance phase during which time they receive clinical assessments at identical intervals regardless of treatment arm. ECT maintenance follows standard of care while ketamine maintenance involves: weekly infusions for 1 month, then bi-weekly infusions for 2 months, and finally monthly infusions for 3 months (returning to bi-weekly in case of relapse). The primary outcome measure is change in MADRS scores after randomized treatment as assessed by raters blind to treatment modality.

Discussion: This multi-centre study will help identify molecular, imaging, and clinical characteristics of patients with treatment-resistant and/or severe MDEs who would benefit most from either type of therapeutic strategy. In addition to informing clinical practice and influencing health care delivery, this trial will add to the robust platform and database of CAN-BIND studies for future research and biomarker discovery.

Trial Registration: ClinicalTrials.gov identifier NCT03674671. Registered September 17, 2018.
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http://dx.doi.org/10.1186/s12888-020-02672-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265624PMC
June 2020

Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report.

Neuropsychopharmacology 2020 07;45(8):1390-1397

Institute of Medical Science, University of Toronto, Toronto, ON, Canada.

Anhedonia is thought to reflect deficits in reward processing that are associated with abnormal activity in mesocorticolimbic brain regions. It is expressed clinically as a deficit in the interest or pleasure in daily activities. More severe anhedonia in major depressive disorder (MDD) is a negative predictor of antidepressant response. It is unknown, however, whether the pathophysiology of anhedonia represents a viable avenue for identifying biological markers of antidepressant treatment response. Therefore, this study aimed to examine the relationships between reward processing and response to antidepressant treatment using clinical, behavioral, and functional neuroimaging measures. Eighty-seven participants in the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) protocol received 8 weeks of open-label escitalopram. Clinical correlates of reward processing were assessed at baseline using validated scales to measure anhedonia, and a monetary incentive delay (MID) task during functional neuroimaging was completed at baseline and after 2 weeks of treatment. Response to escitalopram was associated with significantly lower self-reported deficits in reward processing at baseline. Activity during the reward anticipation, but not the reward consumption, phase of the MID task was correlated with clinical response to escitalopram at week 8. Early (baseline to week 2) increases in frontostriatal connectivity during reward anticipation significantly correlated with reduction in depressive symptoms after 8 weeks of treatment. Escitalopram response is associated with clinical and neuroimaging correlates of reward processing. These results represent an important contribution towards identifying and integrating biological, behavioral, and clinical correlates of treatment response. ClinicalTrials.gov: NCT01655706.
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http://dx.doi.org/10.1038/s41386-020-0688-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297974PMC
July 2020

Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report.

Neuropsychopharmacology 2020 07;45(8):1390-1397

Institute of Medical Science, University of Toronto, Toronto, ON, Canada.

Anhedonia is thought to reflect deficits in reward processing that are associated with abnormal activity in mesocorticolimbic brain regions. It is expressed clinically as a deficit in the interest or pleasure in daily activities. More severe anhedonia in major depressive disorder (MDD) is a negative predictor of antidepressant response. It is unknown, however, whether the pathophysiology of anhedonia represents a viable avenue for identifying biological markers of antidepressant treatment response. Therefore, this study aimed to examine the relationships between reward processing and response to antidepressant treatment using clinical, behavioral, and functional neuroimaging measures. Eighty-seven participants in the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) protocol received 8 weeks of open-label escitalopram. Clinical correlates of reward processing were assessed at baseline using validated scales to measure anhedonia, and a monetary incentive delay (MID) task during functional neuroimaging was completed at baseline and after 2 weeks of treatment. Response to escitalopram was associated with significantly lower self-reported deficits in reward processing at baseline. Activity during the reward anticipation, but not the reward consumption, phase of the MID task was correlated with clinical response to escitalopram at week 8. Early (baseline to week 2) increases in frontostriatal connectivity during reward anticipation significantly correlated with reduction in depressive symptoms after 8 weeks of treatment. Escitalopram response is associated with clinical and neuroimaging correlates of reward processing. These results represent an important contribution towards identifying and integrating biological, behavioral, and clinical correlates of treatment response. ClinicalTrials.gov: NCT01655706.
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http://dx.doi.org/10.1038/s41386-020-0688-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297974PMC
July 2020

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

JAMA Netw Open 2020 01 3;3(1):e1918377. Epub 2020 Jan 3.

School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada.

Importance: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression.

Objective: To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression.

Design, Setting, And Participants: This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment.

Interventions: All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment.

Main Outcomes And Measures: The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%.

Results: Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%).

Conclusions And Relevance: These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.
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http://dx.doi.org/10.1001/jamanetworkopen.2019.18377DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991244PMC
January 2020

Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants.

Hum Brain Mapp 2020 04 3;41(6):1400-1415. Epub 2019 Dec 3.

Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

Task-based functional neuroimaging methods are increasingly being used to identify biomarkers of treatment response in psychiatric disorders. To facilitate meaningful interpretation of neural correlates of tasks and their potential changes with treatment over time, understanding the reliability of the blood-oxygen-level dependent (BOLD) signal of such tasks is essential. We assessed test-retest reliability of an emotional conflict task in healthy participants collected as part of the Canadian Biomarker Integration Network in Depression. Data for 36 participants, scanned at three time points (weeks 0, 2, and 8) were analyzed, and intra-class correlation coefficients (ICC) were used to quantify reliability. We observed moderate reliability (median ICC values between 0.5 and 0.6), within occipital, parietal, and temporal regions, specifically for conditions of lower cognitive complexity, that is, face, congruent or incongruent trials. For these conditions, activation was also observed within frontal and sub-cortical regions, however, their reliability was poor (median ICC < 0.2). Clinically relevant prognostic markers based on task-based fMRI require high predictive accuracy at an individual level. For this to be achieved, reliability of BOLD responses needs to be high. We have shown that reliability of the BOLD response to an emotional conflict task in healthy individuals is moderate. Implications of these findings to further inform studies of treatment effects and biomarker discovery are discussed.
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http://dx.doi.org/10.1002/hbm.24883DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267954PMC
April 2020

Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: a CAN-BIND report.

Neuropsychopharmacology 2020 01 14;45(2):283-291. Epub 2019 Oct 14.

Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Finding a clinically useful neuroimaging biomarker that can predict treatment response in patients with major depressive disorder (MDD) is challenging, in part because of poor reproducibility and generalizability of findings across studies. Previous work has suggested that posterior hippocampal volumes in depressed patients may be associated with antidepressant treatment outcomes. The primary purpose of this investigation was to examine further whether posterior hippocampal volumes predict remission following antidepressant treatment. Magnetic resonance imaging (MRI) scans from 196 patients with MDD and 110 healthy participants were obtained as part of the first study in the Canadian Biomarker Integration Network in Depression program (CAN-BIND 1) in which patients were treated for 16 weeks with open-label medication. Hippocampal volumes were measured using both a manual segmentation protocol and FreeSurfer 6.0. Baseline hippocampal tail (Ht) volumes were significantly smaller in patients with depression compared to healthy participants. Larger baseline Ht volumes were positively associated with remission status at weeks 8 and 16. Participants who achieved early sustained remission had significantly greater Ht volumes compared to those who did not achieve remission by week 16. Ht volume is a prognostic biomarker for antidepressant treatment outcomes in patients with MDD.
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http://dx.doi.org/10.1038/s41386-019-0542-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6901577PMC
January 2020

Integrated genome-wide methylation and expression analyses reveal functional predictors of response to antidepressants.

Transl Psychiatry 2019 10 8;9(1):254. Epub 2019 Oct 8.

Department of Psychiatry, McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.

Major depressive disorder (MDD) is primarily treated with antidepressants, yet many patients fail to respond adequately, and identifying antidepressant response biomarkers is thus of clinical significance. Some hypothesis-driven investigations of epigenetic markers for treatment response have been previously made, but genome-wide approaches remain unexplored. Healthy participants (n = 112) and MDD patients (n = 211) between 18-60 years old were recruited for an 8-week trial of escitalopram treatment. Responders and non-responders were identified using differential Montgomery-Åsberg Depression Rating Scale scores before and after treatment. Genome-wide DNA methylation and gene expression analyses were assessed using the Infinium MethylationEPIC Beadchip and HumanHT-12 v4 Expression Beadchip, respectively, on pre-treatment peripheral blood DNA and RNA samples. Differentially methylated positions (DMPs) located in regions of differentially expressed genes between responders (n = 82) and non-responders (n = 95) were identified, and technically validated using a targeted sequencing approach. Three DMPs located in the genes CHN2 (cg23687322, p = 0.00043 and cg06926818, p = 0.0014) and JAK2 (cg08339825, p = 0.00021) were the most significantly associated with mRNA expression changes and subsequently validated. Replication was then conducted with non-responders (n = 76) and responders (n = 71) in an external cohort that underwent a similar antidepressant trial. One CHN2 site (cg06926818; p = 0.03) was successfully replicated. Our findings indicate that differential methylation at CpG sites upstream of the CHN2 and JAK2 TSS regions are possible peripheral predictors of antidepressant treatment response. Future studies can provide further insight on robustness of our candidate biomarkers, and greater characterization of functional components.
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http://dx.doi.org/10.1038/s41398-019-0589-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783543PMC
October 2019

Childhood maltreatment and cognitive functioning in patients with major depressive disorder: a CAN-BIND-1 report.

Psychol Med 2020 11 4;50(15):2536-2547. Epub 2019 Oct 4.

Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.

Background: Patients with major depressive disorder (MDD) display cognitive deficits in acutely depressed and remitted states. Childhood maltreatment is associated with cognitive dysfunction in adults, but its impact on cognition and treatment related cognitive outcomes in adult MDD has received little consideration. We investigate whether, compared to patients without maltreatment and healthy participants, adult MDD patients with childhood maltreatment display greater cognitive deficits in acute depression, lower treatment-associated cognitive improvements, and lower cognitive performance in remission.

Methods: Healthy and acutely depressed MDD participants were enrolled in a multi-center MDD predictive marker discovery trial. MDD participants received 16 weeks of standardized antidepressant treatment. Maltreatment and cognition were assessed with the Childhood Experience of Care and Abuse interview and the CNS Vital Signs battery, respectively. Cognitive scores and change from baseline to week 16 were compared amongst MDD participants with (DM+, n = 93) and without maltreatment (DM-, n = 90), and healthy participants with (HM+, n = 22) and without maltreatment (HM-, n = 80). Separate analyses in MDD participants who remitted were conducted.

Results: DM+ had lower baseline global cognition, processing speed, and memory v. HM-, with no significant baseline differences amongst DM-, HM+, and HM- groups. There were no significant between-group differences in cognitive change over 16 weeks. Post-treatment remitted DM+, but not remitted DM-, scored significantly lower than HM- in working memory and processing speed.

Conclusions: Childhood maltreatment was associated with cognitive deficits in depressed and remitted adults with MDD. Maltreatment may be a risk factor for more severe and persistent cognitive deficits in adult MDD.
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http://dx.doi.org/10.1017/S003329171900268XDOI Listing
November 2020

White Matter Indices of Medication Response in Major Depression: A Diffusion Tensor Imaging Study.

Biol Psychiatry Cogn Neurosci Neuroimaging 2019 10 12;4(10):913-924. Epub 2019 Jun 12.

Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, Ontario, Canada; Imaging Research Center, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada. Electronic address:

Background: While response to antidepressants in major depressive disorder is difficult to predict, characterizing the organization and integrity of white matter in the brain with diffusion tensor imaging (DTI) may provide the means to distinguish between antidepressant responders and nonresponders.

Methods: DTI data were collected at 6 sites (Canadian Biomarker Integration Network in Depression-1 [CAN-BIND-1 study]) from 200 (127 women) depressed and 112 (71 women) healthy participants at 3 time points: at baseline, 2 weeks, and 8 weeks following initiation of selective serotonin reuptake inhibitor treatment. Therapeutic response was established by a 50% reduction of symptoms at 8 weeks. Analysis on responders, nonresponders, and control subjects yielded 4 scalar metrics: fractional anisotropy and mean, axial, and radial diffusivity. Region-of-interest analysis was carried out on 40 white matter regions using a skeletonization approach. Mixed-effects regression was incorporated to test temporal trends.

Results: The data acquired at baseline showed that axial diffusivity in the external capsule, which overlaps the superior longitudinal fasciculus, was significantly associated with medication response. Regression analysis revealed further baseline differences of responders compared with nonresponders in the cingulum regions, sagittal stratum, and corona radiata. Additional group differences relative to control subjects were seen in the internal capsule, posterior thalamic radiation, and uncinate fasciculus. Most effect sizes were moderate (near 0.5), with a maximum of 0.76 in the cingulum-hippocampus region. No temporal changes in DTI metrics were observed over the 8-week study period.

Conclusions: Several DTI measures of altered white matter specifically distinguished medication responders and nonresponders at baseline and show promise for predicting treatment response in depression.
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http://dx.doi.org/10.1016/j.bpsc.2019.05.016DOI Listing
October 2019

A Pilot Study Investigating the Effect of Music-Based Intervention on Depression and Anhedonia.

Front Psychol 2019 8;10:1038. Epub 2019 May 8.

Faculty of Music, University of Toronto, Toronto, ON, Canada.

This study investigated the effect of a music-based intervention on depression and associated symptoms. Twenty individuals formally diagnosed with Major Depressive Disorder and in a current Major Depressive Episode (11 females and 8 males; aged between 26 and 65 years) undertook a 5 weeks intervention consisting of music listening combined with rhythmic sensory stimulation. Participants listened to a set of designed instrumental music tracks embedded with low-frequency sounds (30-70 Hz). The stimuli were delivered for 30 min, 5 times per week, using a portable consumer device with built-in stereo speakers and a low-frequency transducer, which allowed the low-frequency sounds embedded in the music to be experienced as a mild vibrotactile sensation around the lower back. Changes from baseline to post-intervention in measures of depression symptoms, sleep quality, quality of life, anhedonia, and music-reward processing were assessed with clinician-based assessments as well as self-reports and a monetary incentive behavioral task. The study results indicated that there were significant changes from baseline in measures of depression and associated symptoms, including sleep quality, quality of life, and anhedonia. However, individual differences in treatment response need to be considered. These findings corroborate previous evidence that music-based intervention, when added to standard care, is a promising adjunctive treatment for Major Depressive Disorder, and open new avenues to investigate the effect of music-based therapy to ameliorate anhedonia-specific dysfunction in major depressive disorder and other neuropsychiatric disorders.
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http://dx.doi.org/10.3389/fpsyg.2019.01038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517496PMC
May 2019

A Pilot Study Investigating the Effect of Music-Based Intervention on Depression and Anhedonia.

Front Psychol 2019 8;10:1038. Epub 2019 May 8.

Faculty of Music, University of Toronto, Toronto, ON, Canada.

This study investigated the effect of a music-based intervention on depression and associated symptoms. Twenty individuals formally diagnosed with Major Depressive Disorder and in a current Major Depressive Episode (11 females and 8 males; aged between 26 and 65 years) undertook a 5 weeks intervention consisting of music listening combined with rhythmic sensory stimulation. Participants listened to a set of designed instrumental music tracks embedded with low-frequency sounds (30-70 Hz). The stimuli were delivered for 30 min, 5 times per week, using a portable consumer device with built-in stereo speakers and a low-frequency transducer, which allowed the low-frequency sounds embedded in the music to be experienced as a mild vibrotactile sensation around the lower back. Changes from baseline to post-intervention in measures of depression symptoms, sleep quality, quality of life, anhedonia, and music-reward processing were assessed with clinician-based assessments as well as self-reports and a monetary incentive behavioral task. The study results indicated that there were significant changes from baseline in measures of depression and associated symptoms, including sleep quality, quality of life, and anhedonia. However, individual differences in treatment response need to be considered. These findings corroborate previous evidence that music-based intervention, when added to standard care, is a promising adjunctive treatment for Major Depressive Disorder, and open new avenues to investigate the effect of music-based therapy to ameliorate anhedonia-specific dysfunction in major depressive disorder and other neuropsychiatric disorders.
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http://dx.doi.org/10.3389/fpsyg.2019.01038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517496PMC
May 2019

Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.

Neuroimage 2019 08 7;197:589-597. Epub 2019 May 7.

Department of Psychiatry, University of Calgary, Calgary, AB, Canada.

Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets. Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues - Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.
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http://dx.doi.org/10.1016/j.neuroimage.2019.05.017DOI Listing
August 2019

Symptomatic and Functional Outcomes and Early Prediction of Response to Escitalopram Monotherapy and Sequential Adjunctive Aripiprazole Therapy in Patients With Major Depressive Disorder: A CAN-BIND-1 Report.

J Clin Psychiatry 2019 02 5;80(2). Epub 2019 Feb 5.

University of British Columbia and Vancouver Coastal Health Authority, Vancouver, British Columbia, Canada.

Objective: To report the symptomatic and functional outcomes in patients with major depressive disorder (MDD) during a 2-phase treatment trial and to estimate the value of early improvement after 2 weeks in predicting clinical response to escitalopram and subsequently to adjunctive treatment with aripiprazole.

Methods: Participants with MDD (N = 211) identified with the Montgomery-Asberg Depression Rating Scale (MADRS) and confirmed with the Mini-International Neuropsychiatric Interview were recruited from 6 outpatient centers across Canada (August 2013 through December 2016) and treated with open-label escitalopram (10-20 mg) for 8 weeks (Phase 1). Clinical and functional outcomes were evaluated using the MADRS, Quick Inventory of Depressive Symptomatology-Self-Rated (QIDS-SR), Sheehan Disability Scale (SDS), and Lam Employment Absence and Productivity Scale (LEAPS). Participants were evaluated at 8 and 16 weeks for clinical and functional response and remission. Phase 1 responders continued escitalopram while nonresponders received adjunctive aripiprazole (2-10 mg) for a further 8 weeks (Phase 2).

Results: After Phase 1, MADRS response (≥ 50% decrease from baseline) and remission (score ≤ 10) were, respectively, 47% and 31%, and SDS response (score ≤ 12) and remission (score ≤ 6) were, respectively, 53% and 24%. Response to escitalopram was maintained in 91% of participants at week 16, while 61% of the adjunctive aripiprazole group achieved MADRS response during Phase 2. Response and remission rates with the QIDS-SR were lower than with the MADRS. The LEAPS demonstrated significant occupational improvement (P < .05). Early symptomatic improvement predicted outcomes with modest accuracy.

Conclusions: This study demonstrates comparable symptomatic and functional outcomes to those of other large practical-design studies. There was a high response rate with the adjunctive use of aripiprazole in escitalopram nonresponders. Given the limited value of early clinical improvement to predict outcome, integration of clinical and biological markers deserves further exploration.

Trial Registration: ClinicalTrials.gov identifier: NCT01655706.
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http://dx.doi.org/10.4088/JCP.18m12202DOI Listing
February 2019

The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols

J Psychiatry Neurosci 2019 07;44(4):223-236

From the Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alta., Canada (MacQueen, Hassel, Addington, Sharma); the Rotman Research Institute, Baycrest, and Department of Medical Biophysics, University of Toronto, Toronto, Ont., Canada (Arnott, Zamyadi, Strother); the Department of Psychology, Queen’s University, Kingston, Ont., Canada (Bowie, Harkness, Milev); the Department of Radiology, University of Calgary, Calgary, Alta., Canada (Bray, Lebel); the Alberta Children’s Hospital Research Institute, Calgary, Alta., Canada (Bray, Lebel); the Child and Adolescent Imaging Research (CAIR) Program, Calgary, Alta., Canada (Bray, Lebel); the Department of Psychology, Neuroscience and Behaviour, McMaster University, and St. Joseph’s Healthcare Hamilton, Hamilton, Ont., Canada (Hall); the Krembil Research Institute and Centre for Mental Health, University Health Network, Toronto, Ont., Canada (Downar); the Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ont., Canada (Downar); the Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ont., Canada (Downar, Müller, Rizvi, Rotzinger, Kennedy); the Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, Ont., Canada (Foster, Rotzinger, Kennedy); the Department of Psychiatry and Behavioural Neurosciences, McMaster University, and St. Joseph’s Healthcare Hamilton, Hamilton, Ont., Canada (Foster, Frey); the Centre for Youth Bipolar Disorder, Sunnybrook Health Sciences Centre, Toronto, Ont., Canada (Goldstein); the Departments of Psychiatry and Pharmacology, Faculty of Medicine, University of Toronto, Toronto, Ont., Canada (Goldstein); the Department of Computer Science, University of Alberta, Edmonton, Alta., Canada (Harris); the University of British Columbia and Vancouver Coastal Health Authority, Vancouver, B.C., Canada (Lam, Vila-Rodriguez); the Department of Psychiatry, Queen’s University and Providence Care Hospital, Kingston, Ont., Canada (Milev, Soares); the Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont., Canada (Müller); the Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA (Parikh); the Arthur Sommer Rotenberg Suicide and Depression Studies Program, Li Ka Shing Knowledge Institute and St. Michael’s Hospital, Toronto, Ont., Canada (Rizvi); the Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ont., Canada (Rizvi); the Department of Psychiatry, St. Michael’s Hospital, University of Toronto, Toronto, Ont., Canada (Rotzinger, Soares, Yu); McGill University, Montréal, Que., Canada (Turecki); the Douglas Mental Health University Institute, Frank B. Common, Montréal, Que., Canada (Turecki); and the Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Ont., Canada (Kennedy).

Studies of clinical populations that combine MRI data generated at multiple sites are increasingly common. The Canadian Biomarker Integration Network in Depression (CAN-BIND; www.canbind.ca) is a national depression research program that includes multimodal neuroimaging collected at several sites across Canada. The purpose of the current paper is to provide detailed information on the imaging protocols used in a number of CAN-BIND studies. The CAN-BIND program implemented a series of platform-specific MRI protocols, including a suite of prescribed structural and functional MRI sequences supported by real-time monitoring for adherence and quality control. The imaging data are retained in an established informatics and databasing platform. Approximately 1300 participants are being recruited, including almost 1000 with depression. These include participants treated with antidepressant medications, transcranial magnetic stimulation, cognitive behavioural therapy and cognitive remediation therapy. Our ability to analyze the large number of imaging variables available may be limited by the sample size of the substudies. The CAN-BIND program includes a multimodal imaging database supported by extensive clinical, demographic, neuropsychological and biological data from people with major depression. It is a resource for Canadian investigators who are interested in understanding whether aspects of neuroimaging — alone or in combination with other variables — can predict the outcomes of various treatment modalities.
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http://dx.doi.org/10.1503/jpn.180036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6606427PMC
July 2019

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

J Psychiatr Res 2018 04 2;99:62-68. Epub 2018 Feb 2.

Department of Molecular Medicine, Queen's University, Kingston, ON, Canada.

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction.
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http://dx.doi.org/10.1016/j.jpsychires.2017.12.009DOI Listing
April 2018

The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study.

J Affect Disord 2018 02 3;227:542-549. Epub 2017 Nov 3.

Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Psychiatry, University Health Network, Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.

Background: This study aims to compare the effectiveness of EEG frequency band activity including interhemispheric asymmetry and prefrontal theta cordance in predicting response to escitalopram therapy at 8-weeks post-treatment, in a multi-site initiative.

Methods: Resting state 64-channel EEG data were recorded from 44 patients with a diagnosis of major depressive disorder (MDD) as part of a larger, multisite discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). Clinical response was measured at 8-weeks post-treatment as change from baseline Montgomery-Asberg Depression Rating Scale (MADRS) score of 50% or more. EEG measures were analyzed at (1) pre-treatment baseline (2) 2 weeks post-treatment and (3) as an ''early change" variable defined as change in EEG from baseline to 2 weeks post-treatment.

Results: At baseline, treatment responders showed elevated absolute alpha power in the left hemisphere while non-responders showed the opposite. Responders further exhibited a cortical asymmetry in the parietal region. Groups also differed in pre-treatment relative delta power with responders showing greater power in the right hemisphere over the left while non-responders showed the opposite. At 2 weeks post-treatment, responders exhibited greater absolute beta power in the left hemisphere relative to the right and the opposite was noted for non-responders. A reverse pattern was noted for absolute and relative delta power at 2 weeks post-treatment. Responders exhibited early reductions in relative alpha power and early increments in relative theta power. Non-responders showed a significant early increase in prefrontal theta cordance.

Conclusions: Hemispheric asymmetries in the alpha and delta bands at baseline and at 2 weeks post-treatment have moderately strong predictive utility in predicting response to antidepressant treatment.
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http://dx.doi.org/10.1016/j.jad.2017.10.028DOI Listing
February 2018

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression.

Sci Rep 2017 08 7;7(1):7473. Epub 2017 Aug 7.

Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.
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http://dx.doi.org/10.1038/s41598-017-07613-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547036PMC
August 2017

MicroRNAs 146a/b-5 and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes.

Nat Commun 2017 05 22;8:15497. Epub 2017 May 22.

Departments of Psychiatry and Medical Genetics, University of Alberta, Edmonton, Alberta, Canada T6G 2B7.

Antidepressants (ADs) are the most common treatment for major depressive disorder (MDD). However, only ∼30% of patients experience adequate response after a single AD trial, and this variability remains poorly understood. Here, we investigated microRNAs (miRNAs) as biomarkers of AD response using small RNA-sequencing in paired samples from MDD patients enrolled in a large, randomized placebo-controlled trial of duloxetine collected before and 8 weeks after treatment. Our results revealed differential expression of miR-146a-5p, miR-146b-5p, miR-425-3p and miR-24-3p according to treatment response. These results were replicated in two independent clinical trials of MDD, a well-characterized animal model of depression, and post-mortem human brains. Furthermore, using a combination of bioinformatics, mRNA studies and functional in vitro experiments, we showed significant dysregulation of genes involved in MAPK/Wnt signalling pathways. Together, our results indicate that these miRNAs are consistent markers of treatment response and regulators of the MAPK/Wnt systems.
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http://dx.doi.org/10.1038/ncomms15497DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477510PMC
May 2017

Investigation of miR-1202, miR-135a, and miR-16 in Major Depressive Disorder and Antidepressant Response.

Int J Neuropsychopharmacol 2017 08;20(8):619-623

McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada (Drs Fiori, Lopez, Richard-Devantoy, Berlim, Chachamovich, Jollant, and Turecki); Department of Psychiatry, University Health Network, University of Toronto, Toronto, Ontario, Canada (Drs Foster, Rotzinger, and Kennedy).

Background: Major depressive disorder is a debilitating illness, which is most commonly treated with antidepressant drugs. As the majority of patients do not respond on their first trial, there is great interest in identifying biological factors that indicate the most appropriate treatment for each patient. Studies suggest that microRNA represent excellent biomarkers to predict antidepressant response.

Methods: We investigated the expression of miR-1202, miR-135a, and miR-16 in peripheral blood from 2 cohorts of depressed patients who received 8 weeks of antidepressant therapy. Expression was quantified at baseline and after treatment, and its relationship to treatment response and depressive symptoms was assessed.

Results: In both cohorts, responders displayed lower baseline miR-1202 levels compared with nonresponders, which increased following treatment.

Conclusions: Ultimately, our results support the involvement of microRNA in antidepressant response and suggest that quantification of their levels in peripheral samples represents a valid approach to informing treatment decisions.
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http://dx.doi.org/10.1093/ijnp/pyx034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570004PMC
August 2017

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort.

BMC Psychiatry 2016 Apr 16;16:105. Epub 2016 Apr 16.

Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada.

Background: Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD.

Methods: CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response.

Discussion: From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research.

Trial Registration: ClinicalTrials.gov identifier NCT01655706 . Registered July 27, 2012.
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http://dx.doi.org/10.1186/s12888-016-0785-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833905PMC
April 2016

Genetic variation in IL-1β, IL-2, IL-6, TSPO and BDNF and response to duloxetine or placebo treatment in major depressive disorder.

Pharmacogenomics 2015 Nov 10;16(17):1919-29. Epub 2015 Nov 10.

Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction & Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada.

Aim: This study investigated polymorphisms of five inflammatory-related genes for association with duloxetine and placebo response in patients with major depression.

Patients & Methods: Twenty SNPs in IL-1β, IL-2, IL-6, TSPO and BDNF were genotyped in major depressive disorder patients treated with either duloxetine (n = 215) or placebo (n = 235) for up to 8 weeks. Treatment response was measured with the Montgomery-Åsberg Depression Rating Scale.

Results: IL-6 variants rs2066992 and rs10242595 were nominally associated with response to duloxetine (p = 0.047 and p = 0.028, respectively). Notably, the variant rs2066992 was also associated with placebo response (p = 0.026). However, none of our results remained significant after correction for multiple testing.

Conclusion: Our findings tentatively suggest that IL-6 variants play a role in duloxetine and placebo response, which warrants further investigation.
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http://dx.doi.org/10.2217/pgs.15.136DOI Listing
November 2015