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Objectives: Immigrant women living in Canada present with higher rates of prenatal depressive symptomatology than Canadian-born women; however, the associated psychosocial correlates remain understudied. Antenatal depression and stress negatively affect maternal health and infant development, in part through changes in maternal hypothalamic-pituitary-adrenal (HPA) axis activity. We aimed to examine the factors associated with prenatal depressive symptoms, including altered HPA axis function, in an ethnically diverse community sample of Canadian immigrant women. Methods: Seventy-eight pregnant immigrant women were recruited from the community at 19 weeks' gestation and provided information on health, mood, stressful life events (SLEs), and social support. Fifty-seven of these women also provided saliva samples for measurement of the cortisol awakening response and nighttime cortisol levels. Results: Seventeen per cent of the sample had a high level of prenatal depressive symptoms, and these women reported more perceived stress, more somatic symptoms, lower social support, and were less often working or attending school during pregnancy. Controlling for wake time, parity, and region of origin, high levels of prenatal depressive symptoms were associated with elevated nighttime cortisol levels, whereas SLEs were not associated with any measures of HPA axis activity. Conclusions: High levels of prenatal depressive symptoms are common in immigrant women living in Canada, and are associated with identifiable factors. Preliminary evidence suggests a similar pattern of HPA axis activity characterizing depressive symptomatology in this subpopulation as previously seen in clinically depressed patients. [ABSTRACT FROM AUTHOR]

The Premenstrual Symptoms Screening Tool was modified for use in adolescents and piloted in 578 girls at three international sites. Nearly one third (29.6%) reported experiencing severe PMS or PMDD, with irritability being the most commonly reported symptom. Rates of menstrual-related pain were high, particularly in those with severe PMS or PMDD. Severe PMS and PMDD present with similar rates and symptoms in adolescents as in adults, and the Premenstrual Symptoms Screening Tool modified for adolescents is a fast, reliable tool to screen for these syndromes in adolescents. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Current pharmacological agents for depression have limited efficacy in achieving remission. Developing and validating new medications is challenging due to limited biological targets. This study aimed to link electrophysiological data and symptom improvement to better understand mechanisms underlying treatment response. Longitudinal changes in neural oscillations were assessed using resting-state electroencephalography (EEG) data from two Canadian Biomarker Integration Network in Depression studies, involving pharmacological and cognitive behavioral therapy (CBT) trials. Patients in the pharmacological trial received eight weeks of escitalopram, with treatment response defined as ≥ 50% decrease in Montgomery-Åsberg Depression Rating Scale (MADRS). Early (baseline to week 2) and late (baseline to week 8) changes in neural oscillation were investigated using relative power spectral measures. An association was found between an initial increase in theta and symptom improvement after 2 weeks. Additionally, late increases in delta and theta, along with a decrease in alpha, were linked to a reduction in MADRS after 8 weeks. These late changes were specifically observed in responders. To assess specificity, we extended our analysis to the independent CBT cohort. Responders exhibited an increase in delta and a decrease in alpha after 2 weeks. Furthermore, a late (baseline to week 16) decrease in alpha was associated with symptom improvement following CBT. Results suggest a common late decrease in alpha across both treatments, while modulatory effects in theta may be specific to escitalopram treatment. This study offers insights into electrophysiological markers indicating a favorable response to antidepressants, enhancing our comprehension of treatment response mechanisms in depression., (© 2024. The Author(s).)

Background: The Canadian Network for Mood and Anxiety Treatments (CANMAT) last published clinical guidelines for the management of major depressive disorder (MDD) in 2016. Owing to advances in the field, an update was needed to incorporate new evidence and provide new and revised recommendations for the assessment and management of MDD in adults., Methods: CANMAT convened a guidelines editorial group comprised of academic clinicians and patient partners. A systematic literature review was conducted, focusing on systematic reviews and meta-analyses published since the 2016 guidelines. Recommendations were organized by lines of treatment, which were informed by CANMAT-defined levels of evidence and supplemented by clinical support (consisting of expert consensus on safety, tolerability, and feasibility). Drafts were revised based on review by patient partners, expert peer review, and a defined expert consensus process., Results: The updated guidelines comprise eight primary topics, in a question-and-answer format, that map a patient care journey from assessment to selection of evidence-based treatments, prevention of recurrence, and strategies for inadequate response. The guidelines adopt a personalized care approach that emphasizes shared decision-making that reflects the values, preferences, and treatment history of the patient with MDD. Tables provide new and updated recommendations for psychological, pharmacological, lifestyle, complementary and alternative medicine, digital health, and neuromodulation treatments. Caveats and limitations of the evidence are highlighted., Conclusions: The CANMAT 2023 updated guidelines provide evidence-informed recommendations for the management of MDD, in a clinician-friendly format. These updated guidelines emphasize a collaborative, personalized, and systematic management approach that will help optimize outcomes for adults with MDD., Competing Interests: Declaration of Conflicting InterestsThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Camelia Adams has nothing to disclose. Anees Bahji has nothing to disclose. Serge Beaulieu reports research grants, personal fees, and/or nonfinancial support from Abbvie, Boehringer-Ingelheim, Canadian Institutes for Health Research (CIHR), DiaMentis, Idorsia, Eisai, Janssen, and Otsuka-Lundbeck. Venkat Bhat reports research grants from the American Foundation for Suicide Prevention, AMS, Brain and Behaviour Research Foundation, CIHR, Department of Defense, Eisai, New Frontiers in Research Fund, Novartis, Ontario Ministry of Health, Roche, Royal College of Physicians and Surgeons of Canada, and the University of Toronto. Pierre Blier received research grants and/or personal fees from Abbvie, Allergan, CIHR, Janssen, Lundbeck, Merck, Ontario Brain Institute (OBI), Otsuka, Pfizer, University of Ottawa Medical Research Fund, and VilaNova. Daniel M. Blumberger reports research grants, nonfinancial support, and/or other support from Brain Canada, CIHR, Magventure, National Institutes of Health (NIH), and Welcony Inc. Elisa Brietzke reports research grants from Queen’s University Centre for Neuroscience and the PSI Foundation. Trisha Chakrabarty has nothing to disclose. Dr. André Do reports research grants and/or personal fees from AbbVie, CIHR, Janssen, and Otsuka. Benicio N. Frey has nothing to disclose. Dr. Peter Giacobbe reports research grants from CIHR and salary support from the Academic Scholars Fund, Department of Psychiatry, University of Toronto. David David Gratzer has nothing to disclose. Dr. Sophie Grigoriadis reports personal fees from the Canadian Pharmacists Association, Norton, and UptoDate. Jeffrey Habert reports research grants, personal fees and/or other support from Abbvie, Amgen, Astra-Zeneca, Bausch, Bayer, BMS, Boehringer-Ingelheim, Eisai, Eli-Lilly, Elvium, GSK, HLS, Idorsia, Janssen, Lundbeck, Novartis, Novo-Nordisk, Otsuka, Pfizer, and Valeo. M. Ishrat Husain reports research grants and/or other support from the Centre for Addiction and Mental Health (CAMH) Foundation, CIHR, COMPASS Pathfinder, MindSet Pharma, Psyched Therapeutics, University of Toronto, and Wake Network. Zahinoor Ismail reports personal fees from Lundbeck and Otsuka. Sidney H. Kennedy reports research grants, personal fees and/or other support from Abbott, Abbvie, Boehringer-Ingelheim, Brain Canada, Canada’s Strategy for Patient-Oriented Research (SPOR), CIHR, -, Janssen, Lundbeck, Merck, -, OBI, Otsuka, - Servier, and Sunovion. Raymond W. Lam reports research grants, personal fees and/or other support from AbbVie, Asia-Pacific Economic Cooperation (APEC), Bausch, BC Leading Edge Endowment Fund, Brain Canada, CANMAT, Carnot, CIHR, Grand Challenges Canada, Healthy Minds Canada, Janssen, Lundbeck, Michael Smith Health Research BC, MITACS, Neurotorium, OBI, Otsuka, Pfizer/Viatris, Unity Health, and VGH-UBCH Foundation. Alexander McGirr reports other support from Allergan Canada; he holds a patent 63/193,643 pending. Roger S. McIntyre reports research grants and/or personal fees from Abbvie, Alkermes, Atai Life Sciences, Axsome, Bausch, Biogen, Boehringer Ingelheim, Braxia Scientific Corp., CIHR, Eisai, Global Alliance for Chronic Diseases, Intra-Cellular, Janssen, Kris, Lundbeck, Milken Institute, Mitsubishi Tanabe, National Natural Science Foundation of China, Neumora Therapeutics, Neurocrine, NewBridge Pharmaceuticals, Novo Nordisk, Otsuka, Pfizer, Purdue, Sage, Sanofi, Sunovion, Takeda, and Viatris. Erin E. Michalak reports research grants from the Otsuka-Lundbeck Alliance. Roumen V. Milev reports research grants and/or personal fees from AbbVie, Allergan, Canadian Biomarker Integration Network in Depression (CAN-BIND), CIHR, Eisai, Janssen, KYE, Lallemand, Lundbeck, Neonmind, Nubiyota, OBI, Ontario Mental Health Foundation, Otsuka, and Sunovion. Daniel J. Müller reports research grants or personal fees from CAMH AFP Innovation Fund, CAMH Foundation, CIHR, Novagenic, Nubiyota, and OBI. Sagar V. Parikh is the Medical Director for the National Network of Depression Centers and reports research grants, honoraria and/or personal fees from Aifred, Boehringer Ingelheim, Janssen, Medscape, Mensante, Merck, Myriad/Assurex, Otsuka, and Sage; and private shares in Mensante. Lena S. Quilty has nothing to disclose. Arun V. Ravindran has nothing to disclose. Nisha Ravindran has nothing to disclose. Johanne Renaud has nothing to disclose. Joshua D. Rosenblat reports research grants and/or personal fees from Academic Scholars Award, Allergan, American Psychiatric Association, American Society of Psychopharmacology, Boehringer Ingelheim, Braxia Health (Chief Medical and Scientific Officer of Braxia Scientific; Medical Director of the Canadian Rapid Treatment Centre of Excellence [Braxia Health]), Brain and Cognition Discovery Foundation, Canadian Cancer Society, Canadian Psychiatric Association, CIHR, COMPASS, iGan, Janssen, Joseph M. West Family Memorial Fund, Labatt Brain Health Network, Lundbeck, Physician Services Inc. (PSI) Foundation, Sunovion, Timeposters Fellowship, University Health Network Centre for Mental Health, and University of Toronto. Zainab Samaan has nothing to disclose. Gayatri Saraf has nothing to disclose. Kathryn Schade has nothing to disclose. Ayal Schaffer reports personal fees from AbbVie, Lundbeck, and Otsuka. Mark Sinyor has nothing to disclose. Claudio N. Soares reports research grants and/or personal fees from Bayer, Boehinger-Ingelheim, Clairvoyant Therapeutics, Diamond Therapeutics, Eisai, OBI, and Otsuka, Jennifer Swainson reports personal fees, nonfinancial support, and/or other support from AbbVie, Bausch, Eisai, Idorsia, Janssen, Lundbeck, Newly Institute, and Otsuka. Valerie H. Taylor has nothing to disclose. Smadar V. Tourjman reports personal fees from AbbVie, Elvium, Esai, Idorsia, Janssen, Lundbeck, Otsuka, Pfizer, Sunovion, and Takeda. Rudolf Uher has nothing to disclose. Michael van Ameringen reports research grants, personal fees, and/or other support from Abbvie, Bausch Health, Biohaven, Boehringer Ingelheim, CIHR, Elvium (Purdue), Jazz, Lundbeck, Michael G. DeGroote Centre for Medicinal Cannabis Research, Otsuka, Sunovion, Takeda, UptoDate, and Vistagen. Gustavo Vazquez reports personal fees for Abbvie, Allergan, Asofarma, Elea/Phoenix, Eurofarma, Gador, Janssen, Lundbeck/Otsuka, NeonMind Biosciences, Psicofarma, Raffo, Sunovion, and Tecnofarma. Simone Vigod reports royalties from UpToDate. Daphne Voineskos holds the Labatt Family Professorship in Depression Biology, a University Named Professorship at the University of Toronto; she reports research grants from CIHR, CAMH, Centre for Mental Health at University Health Network, Department of Psychiatry at the University of Toronto, and the National Institute of Mental Health. Lakshmi N. Yatham reports research grants and/or personal fees from Abbvie, Alkermes, Allergan, Dainippon Sumitomo, Gedeon Richter, GSK, Intracellular Therapies, Merck, Sanofi, and Sunovion.

Objectives: Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6 , and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1 ), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample., Methods: A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n  = 91), while responders continued ESC (i.e., ESC-Only, n  = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models., Results: In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F (2,54) = 8.00, p  < 0.001, q  = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF ( r  = -0.42, p  = 0.004, q  = 0.034) and SS ( r  = -0.43, p  = 0.003, q  = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction ( r  = -0.39, p  = 0.009, q  = 0.052)., Conclusions: CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.

Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g., sad, miserable) and oneself, often persist following depressive episodes and may represent a cognitive biomarker for future recurrences. Thus, we examined whether SDAs, and changes in SDAs over time, prospectively predict depressive recurrence among treatment responders in the CAN-BIND Wellness Monitoring for MDD Study, a prospective cohort study conducted across 5 clinical centres. A total of 96 patients with MDD responding to various treatments were followed an average of 1.01 years. Participants completed the Depression Implicit Association Test (DIAT) - a computer-based measure of SDAs - every 8 weeks on a tablet device. Survival analyses indicated that greater SDAs at baseline and increases in SDAs over time predicted shorter time to MDD recurrence, even after accounting for depressive symptom severity. The findings show that SDAs are a robust prognostic indicator of risk for MDD recurrence, and that the DIAT may be a feasible and low-cost clinical screening tool. SDAs also represent a potential mechanism underlying the course of recurrent depression and are a promising target for relapse prevention interventions., Competing Interests: Declaration of Competing Interest KR, JM, IJT, TC, JF, BNF, KLH, KH, LCQ, and RU disclose no relevant conflict of interests. QSL is an employee of Janssen Research and Development, LLC. RM has received consulting and speaking honoraria from AbbVie, Eisai, Janssen, Lallemand, Lundbeck, Neonmind, and Otsuka, and research grants from CAN-BIND, CIHR, Janssen, Nubiyota, OBI and OMHF. SR has grant funding from Ontario Brain Institute and holds a patent “Teneurin C-Terminal Associated Peptides (TCAP) and methods and uses thereof.” CNS has received consulting honoraria from Eisai, Lundbeck, Otsuka, Diamond Therapeutics and Bayer and research grants from Ontario Brain Institute, SEAMO Innovation Funds and Clairvoyant. SHK has received research funding or honoraria from Abbvie, Abbott, Brain Canada, Boehringer-Ingelheim, Canadian Institutes of Health Research, Janssen, Lundbeck, Lundbeck Institute, Merck, Neurocrine, Ontario Brain Institute, Otsuka, Pfizer, SPOR (Canada's Strategy for Patient-Oriented Research), Sunovion and Servier; and holds stock/stock options in Field Trip Health. RWL has received honoraria for ad hoc speaking or advising/consulting, or received research funds, from Abbvie, Asia-Pacific Economic Cooperation, Bausch, BC Leading Edge Foundation, Brain Canada, Canadian Institutes of Health Research, Canadian Network for Mood and Anxiety Treatments, Healthy Minds Canada, CAN-BIND Solutions, Janssen, Lundbeck, Medscape, Michael Smith Foundation for Health Research, MITACS, Neurotorium, Ontario Brain Institute, Otsuka, Pfizer/Viatris, Unity Health, and VGH-UBCH Foundation., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment., Methods: Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n = 175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP)., Results: Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology-Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains., Conclusions: Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility., Trial Registration: ClinicalTrials.gov identifiers NCT00021528 and NCT01655706., (© Copyright 2023 Physicians Postgraduate Press, Inc.)

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition., Competing Interests: Declaration of Competing Interest RVM has received consulting and speaking honoraria from AbbVie, Allergan, Eisai, Janssen, KYE, Lallemand, Lundbeck, Otsuka, and Sunovion, and research grants from CAN-BIND, CIHR, Janssen, Lallemand, Lundbeck, Nubiyota, OBI and OMHF. SR holds a patent “Teneurin C-Terminal Associated Peptides (TCAP) and methods and uses thereof. SHK has received research funding or honoraria from the following sources: Abbott, Alkermes, Allergan Abbvie, Brain Canada, Canadian Institutes for Health Research, Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund, Otsuka, Pfizer, Servier, Sunovion and Xian-Janssen and holds stock in Field Trip Health. RWL has received honoraria for ad hoc speaking or advising/consulting, or received research funds, from: Abbvie, Asia-Pacific Economic Cooperation, Bausch, BC Leading Edge Foundation, Brain Canada, Canadian Institutes of Health Research, Canadian Network for Mood and Anxiety Treatments, CAN-BIND Solutions, Carnot, Grand Challenges Canada, Healthy Minds Canada, Janssen, Lundbeck, Medscape, Michael Smith Foundation for Health Research, MITACS, Neurotorium, Ontario Brain Institute, Otsuka, Pfizer/Viatris, Shanghai Mental Health Center, Sunnybrook Health Sciences centre, Unity Health, Vancouver Coastal Health Research Institute, and VGH-UBCH Foundation. JJN received unrestricted research funding from the BC Cancer Foundation, with funds originating from Pfizer. CNS has received consulting honoraria from Eisai, Lundbeck, Otsuka, Diamond Therapeutics and Bayer and research grants from Ontario Brain Institute, SEAMO Innovation Funds and Clairvoyant; VHT has received consulting honoraria from Abbvie and research grants from Ontario Brain Institute, MITACS, Brain Canada, Canadian Institute of Health Research. YSL, BC, BNF, DJM, and RU have no disclosures., (Copyright © 2023. Published by Elsevier B.V.)

Background: Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers., Methods: In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively., Results: A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction., Conclusions: A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

The P-glycoprotein efflux pump, encoded by the ABCB1 gene, has been shown to alter concentrations of various antidepressants in the brain. In this study, we conducted a systematic review and meta-analysis to investigate the association between six ABCB1 single-nucleotide polymorphisms (SNPs; rs1045642, rs2032582, rs1128503, rs2032583, rs2235015, and rs2235040) and antidepressant treatment outcomes in individuals with major depressive disorder (MDD), including new data from the Canadian Biomarker and Integration Network for Depression (CAN-BIND-1) cohort. For the CAN-BIND-1 sample, we applied regression models to investigate the association between ABCB1 SNPs and antidepressant treatment response, remission, tolerability, and antidepressant serum levels. For the meta-analysis, we systematically summarized pharmacogenetic evidence of the association between ABCB1 SNPs and antidepressant treatment outcomes. Studies were included in the meta-analysis if they investigated at least one ABCB1 SNP in individuals with MDD treated with at least one antidepressant. We did not find a significant association between ABCB1 SNPs and antidepressant treatment outcomes in the CAN-BIND-1 sample. A total of 39 studies were included in the systematic review. In the meta-analysis, we observed a significant association between rs1128503 and treatment response (T vs. C-allele, odds ratio = 1.30, 95% confidence interval = 1.15-1.48, P value (adjusted) = 0.024, n = 2,526). We did not find associations among the six SNPs and treatment remission nor tolerability. Our findings provide limited evidence for an association between common ABCB1 SNPs and antidepressant outcomes, which do not support the implementation of ABCB1 genotyping to inform antidepressant treatment at this time. Future research, especially on rs1128503, is recommended., (© 2023 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient., (© 2023. The Author(s).)

Background: Major depressive disorder (MDD) is associated with various cognitive impairments, including response inhibition. Deficits in response inhibition may also underlie poor antidepressant treatment response. Recent studies revealed that the neurobiological correlates of response inhibition can predict response to pharmacological treatments. However, the generalizability of this finding to first-line nonpharmacological treatments, particularly cognitive behavioral therapy, remains to be investigated., Methods: Data from two independent treatment protocols were combined, one in which 65 patients with MDD underwent treatment with escitalopram, and the other in which 41 patients with MDD underwent a course of cognitive behavioral therapy. A total of 25 healthy control subjects were also recruited. Neural correlates of response inhibition were captured by participants completing a Go/NoGo task during electroencephalography recording. Response inhibition-related measures of interest included the amplitudes of the N2 and P3 event-related potentials., Results: Pretreatment P3 amplitude, which has been linked to both the motor and cognitive aspects of response inhibition, was a significant predictor of change in depressive symptoms following escitalopram and cognitive behavioral therapy treatment. A greater pretreatment P3 amplitude was associated with a greater reduction in depressive severity. In addition, the pretreatment P3 amplitude was found to be significantly greater at baseline in remitters than in nonremitters and healthy control subjects., Conclusions: The integrity of response inhibition may be critical for a successful course of pharmacological or psychological treatment for MDD. Electrophysiological correlates of response inhibition may have utility as a general prognostic marker of treatment response in MDD. Future studies may investigate the benefit of preceding first-line treatments with interventions that improve response inhibition in MDD., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Introduction: An increasing number of studies are examining the link between the endocannabinoidome and major depressive disorder (MDD). We conducted an exploratory analysis of this system to identify potential markers of treatment outcomes., Methods: The dataset of the Canadian Biomarker Integration Network in Depression-1 study, consisting of 180 patients with MDD treated for eight weeks with escitalopram followed by eight weeks with escitalopram alone or augmented with aripiprazole was analyzed. Association between response Montgomery-Asberg Depression Rating Scale (MADRS; score reduction≥50%) or remission (MADRS score≤10) at weeks 8 and 16 and single nucleotide polymorphisms (SNPs), methylation, and mRNA levels of 33 endocannabinoid markers were examined. A standard genome-wide association studies protocol was used for identifying SNPs, and logistic regression was used to assess methylation and mRNA levels., Results: Lower methylation of CpG islands of the diacylglycerol lipase alpha gene ( DAGLA ) was associated with non-remission at week 16 ( DAGLA ; OR=0.337, p<0.003, q=0.050). Methylation of DAGLA was correlated with improvement in Clinical Global Impression (p=0.026), Quick Inventory of Depressive Symptomatology (p=0.010), and Snaith-Hamilton Pleasure scales (p=0.028). We did not find any association between SNPs or mRNA levels and treatment outcomes., Discussion: Methylation of DAGLA is a promising candidate as a marker of treatment outcomes for MDD and needs to be explored further., Competing Interests: There was no role of the following granting agencies/sponsors in the design, collection of data, analysis, interpretation of data, writing of this paper, and decision to submit the paper for publication. Muhammad I. Husain receives research support from the Brain and Behavior Research Foundation, Canadian Institutes of Health Research, CAMH Foundation, the PSI Foundation, and the University of Toronto. He has provided consultancy to Mindset Pharma, PsychEd Therapeutics, and Wake Network. He holds stock/stock options in Mindset Pharma. Raymond W. Lam has received honoraria for ad hoc speaking or advising/consulting or received research funds from: Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, Canadian Institutes of Health Research, Canadian Network for Mood and Anxiety Treatments, Grand Challenges Canada, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, Medscape, Michael Smith Foundation for Health Research, MITACS, Myriad Neuroscience, Ontario Brain Institute, Otsuka, Pfizer, Sanofi, Unity Health, Vancouver Coastal Health Research Institute, and VGH-UBCH Foundation. Claudio N. Soares received/has pending grants that include Canadian Institutes for Health Research, Ontario Brain Institute, Ontario Research Fund, and Eisai. He also receives consulting feed or honorarium from Lundbeck, Otsuka, Pfizer, Bayer, and Eisai. Sagar V. Parikh reports research support in the past three years from Takeda, Sage, Janssen, Merck, the Ontario Brain Institute, and the Canadian Institutes for Health Research; honoraria from Aifred, Assurex, Janssen, Lundbeck, Mensante, Neonmind, Otsuka, and Sage; and shares in Neonmind and Mensante. Roumen Milev has received consulting and speaking honoraria from AbbVie, Allergan, Eisai, Janssen, KYE, Lallemand, Lundbeck, Neonmind, Otsuka, and Sunovion, and research grants from CAN-BIND, CIHR, Janssen, Lallemand, Lundbeck, Nubiyota, OBI and OMHF. Jane A. Foster receives consulting and speaking fees from Takeda Canada and RBH. Faranak Farzan received funding from CIHR, NSER, and Michael Smith Foundation for Health Research. Benoit H. Mulsant holds and receives support from the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto. He currently receives research support from Brain Canada, the Canadian Institutes of Health Research, the CAMH Foundation, the Patient-Centered Outcomes Research Institute, the US National Institute of Health, Capital Solution Design LLC (software used in a study funded by CAMH Foundation), and HAPPYneuron (software used in a study funded by Brain Canada). He has been an unpaid consultant to Myriad Neuroscience. Sidney Kennedy received grants/has pending grants, including Abbott, Allergan, Brain Canada, Canadian Institutes for Health Research, Janssen, Lundbeck, Ontario Brain Institute, Ontario Research Fund, Otsuka, Pfizer, Servier, Sunovion, and Xian-Janssen. He also receives consulting fees or honorarium from Abbott, Alkermes, Allergan, Boehringer Ingelheim, Brain Canada, Canadian Institutes for Health Research, Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund, Otsuka, Pfizer, Servier, Sunovion and Xian-Janssen. He has stock/stock options from Field Trip Health. Stefan Kloiber’s work has been supported by the Academic Scholar Award and the Labatt Family Innovation Fund in Brain Health, Department of Psychiatry, University of Toronto. DJM’s research work is supported by the Canadian Institutes of Health Research, the Ontario Brain Foundation, the Alternate Funding Plan of Ontario, and the Centre for Addiction and Mental Health Foundation. GZ’s work has been supported by the Academic Scholar Award and the Labatt Family Innovation Fund in Brain Health, Department of Psychiatry, University of Toronto, in addition to the Brain & Behavior Research Foundation, International Obsessive-Compulsive Disorder Foundation, and the Physicians’ Services Incorporated Foundation in Ontario, Canada. SK has received an honorarium for past consultation from EmpowerPharm. All other authors declare no conflict of interest. Helena K. Kim, Gwyneth Zai, Daniel J. Müller, Benicio N. Frey, Gustavo Turecki, and Shreejoy J. Tripathy have no conflicts to disclose., (Thieme. All rights reserved.)

Objectives: The COVID-19 pandemic has contributed to a shift from in-person to remote mental health care. While remote care methods have long existed, their widespread use is unprecedented. There is little research about mental health care user and provider experiences with this transition, and no published studies to date have compared satisfaction between these groups., Methods: Canadian mental health care users ( n  = 332) and providers ( n  = 107) completed an online self-report survey from October 2020 to February 2021 hosted by the Canadian Biomarker Integration Network in Depression. Using a mixed-methods approach, participants were asked about their use of remote care, including satisfaction, barriers to use, helpful and unhelpful factors, and suggestions for improvement., Results: Overall, 59% to 63% of health care users and 59% of health care providers were satisfied with remote care. Users reported the greatest satisfaction with the convenience of remote care, while providers were most satisfied with the speed of provision of care; all groups were least satisfied with therapeutic rapport. Health care providers were less satisfied with the user-friendliness of remote care ( P  < 0.001) than users, while health care users were less satisfied than providers with continuity of care ( P  < 0.001). The use of a video-based platform was associated with remote care satisfaction among health care users ( P  < 0.02), and qualitative responses support the importance of visual cues in maintaining therapeutic rapport remotely. The majority of users (55%) and providers (87%) reported a likelihood of using remote care after the pandemic., Conclusions: Remote mental health care is generally accepted by both users and providers, and the majority would consider using remote care following the pandemic. Suggestions for improvement include greater use of video, increased attention to body language and eye contact, consistency with in-person care, as well as increased provider training and administrative support.

Dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis is considered one of the mechanisms underlying the development of major depressive disorder (MDD), but the exact nature of this dysfunction is unknown. We investigated the relationship between hypothalamus volume (HV) and blood-derived DNA methylation in MDD. We obtained brain MRI, clinical and molecular data from 181 unmedicated MDD and 90 healthy control (HC) participants. MDD participants received a 16-week standardized antidepressant treatment protocol, as part of the first Canadian Biomarker Integration Network in Depression (CAN-BIND) study. We collected bilateral HV measures via manual segmentation by two independent raters. DNA methylation and RNA sequencing were performed for three key HPA axis-regulating genes coding for the corticotropin-binding protein (CRHBP), glucocorticoid receptor (NR3C1) and FK506 binding protein 5 (FKBP5). We used elastic net regression to perform variable selection and assess predictive ability of methylation variables on HV. Left HV was negatively associated with duration of current episode (ρ = -0.17, p = 0.035). We did not observe significant differences in HV between MDD and HC or any associations between HV and treatment response at weeks 8 or 16, overall depression severity, illness duration or childhood maltreatment. We also did not observe any differentially methylated CpG sites between MDD and HC groups. After assessing functionality by correlating methylation levels with RNA expression of the respective genes, we observed that the number of functionally relevant CpG sites differed between MDD and HC groups in FKBP5 (χ 2 = 77.25, p < 0.0001) and NR3C1 (χ 2 = 7.29, p = 0.007). Cross-referencing functionally relevant CpG sites to those that were highly ranked in predicting HV in elastic net modeling identified one site from FKBP5 (cg03591753) and one from NR3C1 (cg20728768) within the MDD group. Stronger associations between DNA methylation, gene expression and HV in MDD suggest a novel putative molecular pathway of stress-related sensitivity in depression. Future studies should consider utilizing the epigenome and ultra-high field MR data which would allow the investigation of HV sub-fields., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict treatment outcomes based on clinical symptoms and episode features. We sought to independently replicate recent machine learning methodology predicting antidepressant outcomes using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, and then externally validate these methods to train models using data from the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) dataset., Methods: We replicated methodology from Nie et al (2018) using common algorithms based on linear regressions and decision trees to predict treatment-resistant depression (TRD, defined as failing to respond to 2 or more antidepressants) in the STAR*D dataset. We then trained and externally validated models using the clinical features found in both datasets to predict response (≥50% reduction on the Quick Inventory for Depressive Symptomatology, Self-Rated [QIDS-SR]) and remission (endpoint QIDS-SR score ≤5) in the CAN-BIND-1 dataset. We evaluated additional models to investigate how different outcomes and features may affect prediction performance., Results: Our replicated models predicted TRD in the STAR*D dataset with slightly better balanced accuracy than Nie et al (70%-73% versus 64%-71%, respectively). Prediction performance on our external methodology validation on the CAN-BIND-1 dataset varied depending on outcome; performance was worse for response (best balanced accuracy 65%) compared to remission (77%). Using the smaller set of features found in both datasets generally improved prediction performance when evaluated on the STAR*D dataset., Conclusion: We successfully replicated prior work predicting antidepressant treatment outcomes using machine learning methods and clinical data. We found similar prediction performance using these methods on an external database, although prediction of remission was better than prediction of response. Future work is needed to improve prediction performance to be clinically useful., Competing Interests: JJN, TN, YZ, RTN, JC, and RU have no disclosures. BNF has a research grant from Pfizer. RM has received consulting and speaking honoraria from AbbVie, Allergan, Janssen, KYE, Lundbeck, Otsuka, and Sunovion, and research grants from CAN-BIND, CIHR, Janssen, Lallemand, Lundbeck, Nubiyota, OBI and OMHF. SHK has received honoraria or research funds from Abbott, Alkermes, Allergan, BMS, Brain Canada, CIHR, Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund, Otsuka, Pfizer, Servier, Sunovion, Xian-Janssen, and holds stock in Field Trip Health. RWL has received ad hoc speaking/consulting fees or research grants from: Allergan, Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, Canadian Institutes of Health Research (CIHR), Canadian Network for Mood and Anxiety Treatments (CANMAT), Canadian Psychiatric Association, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, MITACS, Myriad Neuroscience, Ontario Brain Institute, Otsuka, Pfizer, University Health Network Foundation, and VGH-UBCH Foundation. CAN-BIND is sponsored by non-for-profit and government funding agencies and by commercial sponsors including Lundbeck, Bristol-Myers Squibb, Pfizer, and Servier. None of the sponsors were involved in study design, conduct of the study, data analysis, data sharing (except for the government funded Ontario Brain Institute, as stated in the Data Availability Statement), interpretation of results, drafting of the manuscript, or final approval and publication of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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.

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.

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.

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.

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., Competing Interests: G. MacQueen reports consultancy/speaker fees from Lundbeck, Pfizer, Johnson & Johnson and Janssen, outside the submitted work. B. Frey reports grants and personal fees from Pfizer and personal fees from Sunovion, outside the submitted work. R. Milev reports grants, nonfinancial support and honoraria from Lundbeck, Janssen and Pfizer; personal fees and honoraria from Sunovion, Shire, Allergan and Otsuka; grants from Boehringer Ingelheim; and grants from the Ontario Brain Institute, the Canadian Institutes for Health Research and CAN-BIND, outside the submitted work. F. Vila-Rodriguez reports nonfinancial support from Magventure during the conduct of the study; grants from the Canadian Institutes for Health Research, Brain Canada, the Michael Smith Foundation for Health Research, and the Vancouver Coastal Health Research Institute; and personal fees from Janssen, outside the submitted work. S. Rizvi reports grants from Pfizer Canada, outside the submitted work. S. Strother reports grants from Canadian Biomarker Integration Network in Depression during the conduct of the study and grants from Ontario Brain Institute, outside the submitted work. He is also the chief scientific officer of the neuroimaging data analysis company ADMdx, Inc (www. admdx.com), which specializes in brain image analysis to enable diagnosis, prognosis and drug effect detection for Alzheimer disease and various other forms of dementia. R. Lam reports grants from Canadian Institutes of Health Research during the conduct of the study; grants from Asia-Pacific Economic Cooperation, VGH-UBCH Foundation, BC LEading Edge Endowment Fund, Janssen, Lundbeck, Pfizer and St. Jude Medical, outside the submitted work; personal fees from Allergan, Akili, CME Institute, Canadian Network for Mood and Anxiety Treatments, Janssen, Lundbeck, Lundbeck Institute, Pfizer, Otsuka, Medscape and Hansoh, outside the submitted work; travel expenses from Asia-Pacific Economic Cooperation outside the submitted work; and stock options from Mind Mental Health Technologies., (© 2019 Joule Inc. or its licensors)

Depression during midlife years may lead to significant sleep disturbances and adversely impact quality of life (QOL). In this report, we examined the effects of treatment with quetiapine extended release (XR), 150-300 mg/day, on sleep and QOL in 23 midlife women with major depressive disorder (MDD). Quetiapine XR improved subjective sleep distress, overall sleep parameters, and sleep-related QOL, ultimately leading to significant improvement in menopause-related QOL. While larger, controlled trials are still awaited, these preliminary results are encouraging and suggest that quetiapine XR may be a useful tool in treating symptomatic midlife women with MDD.