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Background: Bright light therapy has been shown to improve depressive symptoms in patients with nonseasonal major depressive disorder (MDD) but there are few studies examining functional outcomes., Methods: We examined secondary functional outcomes in the 8-week randomized, placebo-sham-controlled LIFE-D trial comparing light therapy, fluoxetine, and the combination in patients with nonseasonal MDD. Functional assessments included the Sheehan Disability Scale (SDS) and, for employed participants, the Lam Employment Absence and Productivity Scale (LEAPS). Analysis of covariance (ANCOVA) was conducted with SDS and LEAPS change scores from baseline to week 8 as dependent variables, treatment modality (light, fluoxetine) as an independent variable, and baseline SDS and LEAPS scores as covariates., Results: Of 122 randomized participants, SDS data were available for 105 and LEAPS data for 70. For the SDS, there were no interaction effects, but there was a significant small- to medium-sized main effect of light treatment on total SDS scores with corresponding significant effects in the Social Life and Family Life domains, but not in the Work/Study domain. There were no significant interaction or main effects with LEAPS scores., Conclusion: Light therapy significantly improved social and family life functioning in patients with MDD. However, work functioning was not significantly improved despite large effect sizes; these results were limited by low statistical power because of small sample sizes. Future studies should use longer treatment durations and be powered to detect clinically relevant differences in functional outcomes., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objective: To determine the objective neurocognitive effects of (1) single-dose ketamine, (2) repeated-dose ketamine, (3) ketamine adjunct to electroconvulsive therapy (ECT), and (4) ketamine as the anesthetic for ECT in major depressive disorder (MDD) and depression in bipolar disorder (BD)., Data Sources: Cochrane, MEDLINE, Embase, and PsycINFO databases were searched on March 19, 2020 (updated July 2, 2020), using the terms terms major depressive disorder bipolar disorder and ketamine and their synonyms. Clinical trial registries (search date May 4, 2020) and reference sections of included articles were also searched. There was no restriction on language or year of publication., Study Selection: Of 4,035 identified articles, 17 met inclusion criteria. Controlled and open-label studies of adults who received at least 1 ketamine treatment for a current major depressive episode, as part of MDD or BD, were included. Only studies measuring cognition using at least 1 validated, objective neurocognitive assessment were eligible., Data Extraction: Results are presented using a narrative review format. Data regarding change in cognitive performance from baseline to end-of-treatment and/or differences in cognition between ketamine and control groups were extracted., Results: There were no negative effects of single- or repeated-dose intravenous ketamine up to 2 weeks post-treatment in MDD. Limited data were available for BD populations, as well as on other routes of ketamine administration., Conclusions: Data to definitively answer the question of whether ketamine has substantive or persistent cognitive effects are insufficient; thus, larger controlled trials measuring cognition as the primary outcome are needed. Future research should focus on different routes of ketamine administration, ketamine enantiomers, and BD populations., (© Copyright 2022 Physicians Postgraduate Press, Inc.)

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment., Methods: We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data ( n  = 377)., Results: Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%., Conclusions: Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.

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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Little is known on the persistence of asthma biologic use in clinical practice., Objective: To evaluate the persistence of asthma biologic use and time to clinical response in clinical practice., Methods: A cohort of people with asthma who used at least 1 asthma biologic was constructed using data from 2003 to 2019 in the OptumLabs Data Warehouse. Treatment persistence was defined by the length of time that a person continuously used an asthma biologic, allowing for a lapse in use up to 4 months before confirming that a person stopped. Clinical response to treatment (defined as a decline in asthma exacerbations of at least 50% compared with the 6 months before starting an asthma biologic) was described over time and in relation to biologic persistence., Results: There were 9575 people who had at least 1 episode of asthma biologic use. There were 5319 people (64%, 95% confidence interval, 63%-65%) who completed 6 months or more on an asthma biologic and 3284 (45%, 95% confidence interval, 44%-46%) who completed 12 months or more. Of people with 1 or more asthma exacerbation 6 months before index biologic use, 63%, 76%, 80%, and 81% realized a 50% or more reduction in postindex asthma exacerbations in the first 6 months, 6 to 12 months, 12 to 18 months, and 18 to 24 months, respectively., Conclusion: Between 48% and 64% of people remained on an asthma biologic for 6 months or more after first use. Most people who achieved a reduction in asthma exacerbations did so in the first 6 months of treatment., (Copyright © 2021 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Objectives: The 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) guidelines provided clinicians with pragmatic treatment recommendations for bipolar disorder (BD). While these guidelines included commentary on how mixed features may direct treatment selection, specific recommendations were not provided-a critical gap which the current update aims to address., Method: Overview of research regarding mixed presentations in BD, with treatment recommendations developed using a modified CANMAT/ISBD rating methodology. Limitations are discussed, including the dearth of high-quality data and reliance on expert opinion., Results: No agents met threshold for first-line treatment of DSM-5 manic or depressive episodes with mixed features. For mania + mixed features second-line treatment options include asenapine, cariprazine, divalproex, and aripiprazole. In depression + mixed features, cariprazine and lurasidone are recommended as second-line options. For DSM-IV defined mixed episodes, with a longer history of research, asenapine and aripiprazole are first-line, and olanzapine (monotherapy or combination), carbamazepine, and divalproex are second-line. Research on maintenance treatments following a DSM-5 mixed presentation is extremely limited, with third-line recommendations based on expert opinion. For maintenance treatment following a DSM-IV mixed episode, quetiapine (monotherapy or combination) is first-line, and lithium and olanzapine identified as second-line options., Conclusion: The CANMAT and ISBD groups hope these guidelines provide valuable support for clinicians providing care to patients experiencing mixed presentations, as well as further influence investment in research to improve diagnosis and treatment of this common and complex clinical state., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Major Depressive Disorder (MDD) is characterized by objective and subjective cognitive deficits. Discrepancies between objective and subjective cognitive performance can reflect under- to over-estimations of cognitive abilities, and these discrepancies are referred to as cognitive self-appraisals. Despite evidence that low self-appraisals are associated with depression, the modifiability of self-appraisals and their association with treatment outcome remains unclear. The current study examined whether self-appraisals change following antidepressant treatment. Furthermore, we investigated the association of self-appraisals with treatment outcome., Methods: As part of the CAN-BIND-1 clinical trial, 154 patients with MDD completed measures of objective and subjective cognitive abilities, depressive symptoms, and functional outcomes (work productivity, psychosocial functioning, and quality of life) at baseline and post-escitalopram treatment. Self-appraisals were calculated based on discrepancies between objective and subjective cognitive abilities, with higher scores indicating overestimation of cognitive abilities., Results: Baseline self-appraisals were not predictive of treatment outcomes. However, self-appraisals increased from pre- to post-treatment. Moreover, pre-post treatment increases in self-appraisals were associated with positive treatment response and remission, decreases in depressive symptoms, and improvements in work productivity, psychosocial functioning, and quality of life., Limitations: The pre-post intervention design precluded examining the temporal precedence of change in self-appraisals versus depressive symptoms and functional outcomes., Conclusions: Findings are the first to demonstrate that self-appraisals are treatment-sensitive and are associated with treatment outcomes and recovery from MDD. Cognitive self-appraisals may represent a key marker of treatment response and a valuable target for assessment and intervention, as well as a potential mechanism underlying risk and recovery., (Copyright © 2021. Published by Elsevier B.V.)

Background: Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes., Methods: Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment., Results: Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy., Conclusions: The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration (Brain-CODE) data platform, each involving MRI data from multiple research institutes, are used to build and test our model. The preliminary results show that a random forest model can be trained to accurately identify MRI sequence types, and to recognize MRI scans that do not belong to any of the known sequence types. Therefore the proposed approach can be used to automate processing of MRI data that involves a large number of variations in sequence names, and to help standardize sequence naming in ongoing data collections. This study highlights the potential of the machine learning approaches in helping manage health data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Liang, Beaton, Arnott, Gee, Zamyadi, Bartha, Symons, MacQueen, Hassel, Lerch, Anagnostou, Lam, Frey, Milev, Müller, Kennedy, Scott, The ONDRI Investigators and Strother.)

Background: Intermittent theta burst stimulation (iTBS) is a newer form of repetitive transcranial magnetic stimulation (rTMS) for patients with treatment resistant depression (TRD). Applying multiple daily iTBS sessions may enable patients to achieve remission more rapidly., Objective: We compared the efficacy and tolerability of a twice-daily versus once-daily iTBS protocol in patients with TRD. We hypothesized that twice-daily iTBS would result in a greater improvement in depression scores compared to once-daily iTBS., Methods: 208 participants (131 females) with TRD were randomized to receive either iTBS (600 pulses) delivered twice-daily with a 54-min interval between treatments or once-daily (1200 pulses) with 1 sham treatment with the same interval between treatments, to ensure equal levels of daily therapeutic contact and blinding of patients and raters. The primary outcome measure was change in depression scores on the Hamilton Rating Scale for Depression (HRSD-17) after 10 days of treatment and 30 days of treatments., Results: HRSD-17 scores improved in both the twice-daily and once-daily iTBS groups; however, these improvements did not significantly differ between the two groups at either the 10-day or 30-day timepoints. Response and remission rates were low (<10%) in both groups after 10 days and consistent with prior reports at 30 days; these rates did not differ between the treatment groups., Conclusions: These results suggest that twice-daily iTBS does not accelerate response to iTBS and is not different from once-daily treatment in terms of improving depressive symptoms in patients with TRD. Clinicaltrials.gov ID: NCT02729792 (https://clinicaltrials.gov/ct2/show/NCT02729792)., Competing Interests: Declaration of competing interest DMB receives research support from CIHR, NIH, Brain Canada and the Temerty Family through the CAMH Foundation and the Campbell Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd. and he has been the site principal investigator of four sponsor-initiated studies for Brainsway Ltd. He also receives in-kind equipment support from Magventure for investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. FVR receives research support from CIHR, Brain Canada, Michael Smith Foundation for Health Research, Vancouver Coastal Health Research Institute, and Weston Brain Institute for investigator-initiated research. Philanthropic support from Seedlings Foundation. In-kind equipment support for this investigator-initiated trial from MagVenture. He has received honoraria for participation in advisory board for Janssen. WW declares no biomedical interests or conflicts. YK declares no biomedical interests or conflicts. MB declares no biomedical interests or conflicts. YN has received a Grant-in-Aid for Young Scientists and Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science (JSPS), research grants from Japan Agency for Medical Research and Development (AMED), investigator-initiated clinical study grants from TEIJIN PHARMA LIMITED and Inter Reha Co., Ltd. YN also receives research grants from Mochida Memorial Foundation for Medical and Pharmaceutical Research and Daiichi Sankyo Scholarship Donation Program. He has received speaker's honoraria from Dainippon Sumitomo Pharma within the past three years. He also receives equipment-in-kind support for an investigator-initiated study from Magventure Inc. And Miyuki Giken Co., Ltd. SY receives research support from ISF (Israel Science Foundation), Minerva Stifung and BrainMarc LTD, Israel. He also received honoraria from Lundbeck, Unipharm, Medison Pharma, and serves at the advisory board of Neurolief LTD, Israel. MI has received consulting fees from Janssen Pharmaceuticals. DV receives support from CAMH and the Department of Psychiatry at the University of Toronto, as well as the Innovation Fund of the Alternative Funding Plan for the Academic Health Sciences Centers of Ontario. DV declares no biomedical interests or conflicts. NJA declares no biomedical interests or conflicts. SHK has research support from Abbott, Allergan, Brain Canada, Canadian Institutes for Health Research (CIHR), Janssen, Lundbeck, Ontario Brain Institute, Ontario Research Fund(ORF), Otsuka, Pfizer and Servier. He has also received consulting fees/honoraria from Abbott, Alkermes, Allergan, Boehringer Ingelheim, Brain Canada, Canadian Institutes for Health Research (CIHR), Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund(ORF), Otsuka, Pfizer, Servier and Sunovion, 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. ZJD has received research and equipment in-kind support for an investigator-initiated study through Brainsway Inc and Magventure Inc. His work is supported by the Canadian Institutes of Health Research (CIHR), the National Institutes of Mental Health (NIMH), Brain Canada and the Temerty Family and Grant Family and through the Centre for Addiction and Mental Health (CAMH) Foundation and the Campbell Institute. JD has received research support from the Arrell Family Foundation, the Buchan Family Foundation, Brain Canada, the Canadian Biomarker Integration Network in Depression, the Canadian Institutes of Health Research (CIHR), the Klarman Family Foundation, NIH, the Ontario Brain Institute, the Toronto General and Western Hospital Foundation, and the Weston Family Foundation; he has received travel stipends from Lundbeck and ANT Neuro; he has served as an advisor for BrainCheck, Restorative Brain Clinics, and TMS Neuro Solutions., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Exposure to stressful life events and individual differences in the personality trait neuroticism are important risk factors that interact to predict major depressive disorder (MDD). Less is known about their effect on treatment response in depression. Here, we examine whether stressful life events experienced prior to and during treatment interact with neuroticism to predict response to 16-week pharmacotherapy for MDD. Participants included 159 outpatients with MDD who were initially treated with 8 weeks of escitalopram. Those who responded to the initial treatment continued on escitalopram monotherapy, whereas non-responders received 8 weeks of adjunctive aripiprazole. Personality was assessed using the NEO-Five Factor Inventory, and stressful life events were assessed using the Life Events and Difficulties Schedule, a rigorous contextual interview that includes independent ratings of threatening life events. High baseline neuroticism was associated with a lower likelihood of response when patients experienced one or more negative life events before treatment. Secondary analyses indicated that this effect was specific to neuroticism, and not better accounted for by its self-criticism or negative affect facets. Our results suggest that assessing personality and stressful life events at baseline can help clinicians assess which patients will respond to antidepressant therapy and which may need treatment augmentation., (© 2021 John Wiley & Sons, Ltd.)

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT-awFC). The novel FATCAT-awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN-BIND-1) study. Large-scale resting-state networks were assessed. We found statistically significant anatomically-weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region-pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

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.)

Objective: To determine the efficacy of measurement-based care (MBC), defined as routinely administered outcome measures with practitioner and patient review to inform clinical decision-making, for adults with depressive disorders., Data Sources: Embase, MEDLINE, PsycINFO, ClinicalTrials.gov, CNKI, and Wanfang Data were searched through July 1, 2020, using search terms for measurement-based care, depression, antidepressant or pharmacotherapy, and randomized controlled trials (RCTs), without language restriction., Study Selection: Of 8,879 articles retrieved, 7 RCTs (2,019 participants) evaluating MBC for depressive disorders, all involving pharmacotherapy, were included., Data Extraction: Two independent reviewers extracted data. The primary outcome was response rate (≥ 50% improvement from baseline to endpoint on a depression scale). Secondary clinical outcomes were remission rate (endpoint score in remission range), difference in endpoint severity, and medication adherence., Results: Meta-analysis with random-effects models found no significant difference between MBC and comparison groups in response rates (3 studies; odds ratio [OR] = 1.66; 95% CI, 0.66-4.17; P  = .279). MBC was associated with significantly greater remission rates (5 studies; OR = 1.83; 95% CI, 1.12-2.97; P  = .015), lower endpoint severity (5 studies; standardized mean difference = 0.53; CI 0.06-0.99; P  = .026), and greater medication adherence (3 studies; OR = 1.68; 95% CI, 1.22-2.30; P  = .001)., Conclusions: Although benefits for clinical response are unclear, MBC is effective in decreasing depression severity, promoting remission, and improving medication adherence in patients with depressive disorders treated with pharmacotherapy. The results are limited by the small number of included trials, high risk of bias, and significant study heterogeneity., (© Copyright 2021 Physicians Postgraduate Press, Inc.)

The pathophysiology of major depressive disorder (MDD) encompasses an array of changes at molecular and neurobiological levels. As chronic stress promotes neurotoxicity there are alterations in the expression of genes and gene-regulatory molecules. The hippocampus is particularly sensitive to the effects of stress and its posterior volumes can deliver clinically valuable information about the outcomes of antidepressant treatment. In the present work, we analyzed individuals with MDD (N = 201) and healthy controls (HC = 104), as part of the CAN-BIND-1 study. We used magnetic resonance imaging (MRI) to measure hippocampal volumes, evaluated gene expression with RNA sequencing, and assessed DNA methylation with the (Infinium MethylationEpic Beadchip), in order to investigate the association between hippocampal volume and both RNA expression and DNA methylation. We identified 60 RNAs which were differentially expressed between groups. Of these, 21 displayed differential methylation, and seven displayed a correlation between methylation and expression. We found a negative association between expression of Brain Abundant Membrane Attached Signal Protein 1 antisense 1 RNA (BASP1-AS1) and right hippocampal tail volume in the MDD group (β = -0.218, p = 0.021). There was a moderating effect of the duration of the current episode on the association between the expression of BASP1-AS1 and right hippocampal tail volume in the MDD group (β = -0.48, 95% C.I. [-0.80, -0.16]. t = -2.95 p = 0.004). In conclusion, we found that overexpression of BASP1-AS1 was correlated with DNA methylation, and was negatively associated with right tail hippocampal volume in MDD., (© 2021. The Author(s).)

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 ( n = 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 ( n = 181), SDS ( n = 175), and LEAPS ( n = 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; P = 0.03) and LEAPS productivity subscale (β = -0.17; P = 0.05), but not SDS total (β = 0.19; P = 0.12) or LEAPS total (β = 0.41; P = 0.17) scores. Although LEAPS and SDS scores showed significant improvements after 8 weeks of treatment ( P < 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.

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., Competing Interests: EM declares no potential conflicts of interest concerning the research, authorship, and/or publication of this article. VB declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. BNF has received research funds from Pfizer. PG has received research support from the Academic Scholar’s Award, University of Toronto Department of Psychiatry, CIHR, PSI, and Veterans Affairs Canada; served on an advisory board for Janssen; and has been an unpaid consultant for St. Jude Medical. WL declared no potential conflicts of interest for the research, authorship, and/or publication of this article. EEM has received funding to support patient education initiatives from Otsuka. TC declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article. SHK has received honoraria or research funds from Abbott, Alkermes, Allergan, Boehringer Ingelheim, Brain Canada, CIHR, Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund, Otsuka, Pfizer, Servier, Sunovion, and Sun Pharmaceuticals, and holds stock in Field Trip Health. RWL has received honoraria or research funds from Allergan, Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, CIHR, CANMAT, Canadian Psychiatric Association, Hansoh, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, MITACS, Myriad Neuroscience, Ontario Brain Institute, Otsuka, Pfizer, St. Jude Medical, University Health Network Foundation, and VGH-UBCH Foundation. RVM 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. DJM declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. SVP has received honoraria or research funds from Assurex, Takeda, Janssen, Mensante, Aifred, Sage. SR holds a patent “Teneurin C-Terminal Associated Peptides (TCAP) and methods and uses thereof.” Inventors: David Lovejoy, RB Chewpoy, Dalia Barsyte, Susan Rotzinger., (Thieme. All rights reserved.)

Background: Measurement-based care (MBC) is an evidence-based practice for depression, but its use by clinicians remains low. Enhanced MBC (eMBC), which uses digital technologies, can help to facilitate the use of MBC by clinicians and patients. Understanding factors that act as barriers and drivers to the implementation of MBC and eMBC is important to support the design of implementation strategies, promoting uptake by clinicians and patients., Objective: This situational analysis identifies barriers and facilitators to the implementation of standard and eMBC at mental health centers in Shanghai, China., Methods: We used mixed methods to develop a comprehensive understanding of the factors influencing MBC and eMBC implementation in Shanghai. This study took place across three mental health centers in Shanghai. We used situational analysis tools to collect contextual information about the three centers, conducted surveys with n = 116 clinicians and n = 301 patients, conducted semi-structured interviews with n = 30 clinicians and six focus groups with a total of n = 19 patients. Surveys were analysed using descriptive statistics, and semi-structured interviews and focus groups were analysed using framework analysis., Results: Several potential barriers and facilitators to MBC and eMBC implementation were identified. Infrastructure, cost, attitudes and beliefs, and perceptions about feasibility and efficacy emerged as both challenges and drivers to MBC and eMBC implementation in Shanghai., Conclusions: The results of this study will directly inform the design of an implementation strategy for MBC and eMBC in Shanghai, that will be tested via a randomized controlled trial. This study contributes to the emerging body of literature on MBC implementation and, to the best of our knowledge, is the first such study to take place in Asia. This study identifies several factors that are relevant to the equitable delivery of MBC, recognizing the need to explicitly address equity concerns in global mental health implementation research., (© 2021. The Author(s).)

Identifying biomarkers of antidepressant response may advance personalized treatment of major depressive disorder (MDD). We aimed to identify longitudinal changes in gene expression associated with response to antidepressants in a sample of MDD patients treated with escitalopram. Patients (N = 153) from the CAN-BIND-1 cohort were treated for 8 weeks, and depressive symptoms were assessed using the Montgomery-Åsberg Depression Rating Scale at 0, 2, 4, 6, and 8 weeks. We identified three groups of patients according to response status: early responders (22.9%), later responders (32.0%), and nonresponders (45.1%). RNA sequencing was performed in blood obtained at weeks 0, 2, and 8. RNA expression was modeled using growth models, and differences in the longitudinal changes in expression according to response were investigated using multiple regression models. The expression of RNAs related to response was investigated in the brains of depressed individuals, as well as in neuronal cells in vitro. We identified four RNAs (CERCAM, DARS-AS1, FAM228B, HBEGF) whose change over time was independently associated with a response status. For all except HBEGF, responders showed higher expression over time, compared to nonresponders. While the change in all RNAs differentiated early responders from nonresponders, changes in DARS-AS1 and HBEGF also differentiated later responders from nonresponders. Additionally, HBEGF was downregulated in the brains of depressed individuals, and increased in response to escitalopram treatment in vitro. In conclusion, using longitudinal assessments of gene expression, we provide insights into biological processes involved in the intermediate stages of escitalopram response, highlighting several genes with potential utility as biomarkers of antidepressant response., (© 2021. The Author(s).)

The utility of cannabinoids and cannabinoid-based products (CBPs) as a pharmacological aid to treat psychiatric disorders in adulthood is still poorly understood despite a number of comprehensive general reviews discussing the topic. With a focus on randomized controlled trial (RCT) data, this review and meta-analysis aimed to aggregate and evaluate all current high-quality (Level-1) research that specifically assessed the effectiveness of a CBP on a diagnosed adult psychiatric disorder. The following databases, from their inception to September 2020, were included in the search: Academic Search Premier, PubMed, Ovid MEDLINE®, Web of Science™, PsycARTICLES, PsycINFO, CINAHL (Nursing and Allied Health), and Scopus. Risk of bias for each study was individually assessed using the revised Cochrane tool. Of the 2397 papers identified, thirty-one RCTs met criteria for inclusion: ten trials focused on treating cannabis use disorder, six on schizophrenia, five on opioid/tobacco use disorder, three on anxiety disorders, two on Tourette's disorder, two on anorexia nervosa, and one trial each for attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and obsessive compulsive disorder. This review finds limited evidence for the effectiveness of CBPs to acutely treat a narrow range of psychiatric symptoms. We report no evidence supporting the mid- to long-range effectiveness of any currently available CBP. In general, quality of the evidence was assessed as low- to moderate. Importantly, none of the studies discussed in this review presently endorse the use of cannabis flower as a method of treatment for any recognized psychiatric disorder. Larger, hypothesis driven RCTs are required prior to making further therapeutic recommendations., (Crown Copyright © 2021. Published by Elsevier Ltd. All rights reserved.)

One of the biggest challenges in treating depression is the heterogeneous and qualitative nature of its clinical presentations. This highlights the need to find quantitative molecular markers to tailor existing treatment strategies to the individual's biological system. In this study, high-resolution metabolic phenotyping of urine and plasma samples from the CAN-BIND study collected before treatment with two common pharmacological strategies, escitalopram and aripiprazole, was performed. Here we show that a panel of LDL and HDL subfractions were negatively correlated with depression in males. For treatment response, lower baseline concentrations of apolipoprotein A1 and HDL were predictive of escitalopram response in males, while higher baseline concentrations of apolipoprotein A2, HDL and VLDL subfractions were predictive of aripiprazole response in females. These findings support the potential of metabolomics in precision medicine and the possibility of identifying personalized interventions for depression., (© 2021. The Author(s).)

Background: The COVID-19 pandemic is expected to have profound mental health impact, including in the Asia Pacific Economic Cooperation (APEC) region. Some populations might be at higher risk of experiencing negative mental health impacts and may encounter increased barriers to accessing mental health care. The pandemic and related restrictions have led to changes in care delivery, including a rapid shift to the use of e-mental health and digital technologies. It is therefore essential to consider needs and opportunities for equitable mental health care delivery to the most at-risk populations. This rapid scoping review: 1) identifies populations in the APEC region that are at higher risk of the negative mental health impacts of COVID-19, 2) identifies needs and gaps in access to standard and e-mental health care among these populations, and 3) explores the potential of e-mental health to address these needs., Methods: We conducted a rapid scoping review following the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We searched Medline, Embase and PsychInfo databases and Google Scholar using a search strategy developed in consultation with a biomedical librarian. We included records related to mental health or psychosocial risk factors and COVID-19 among at-risk groups; that referred to one or more APEC member economies or had a global, thus generalizable, scope; English language papers, and papers with full text available., Results: A total of 132 records published between December 2019 and August 2020 were included in the final analysis. Several priority at-risk populations, risk factors, challenges and recommendations for standard and e-mental health care were identified. Results demonstrate that e-mental health care can be a viable option for care delivery but that specific accessibility and acceptability considerations must be considered. Options for in-person, hybrid or "low-tech" care must also remain available., Conclusions: The COVID-19 pandemic has highlighted the urgent need for equitable standard and e-mental health care. It has also highlighted the persistent social and structural inequities that contribute to poor mental health. The APEC region is vast and diverse; findings from the region can guide policy and practice in the delivery of equitable mental health care in the region and beyond., (© 2021. The Author(s).)

Background: There is limited information about outcomes associated with stopping asthma biologics., Objective: To compare outcomes in people who stopped or continued asthma biologics., Methods: We identified a cohort of people with asthma who stopped or continued asthma biologics in the Optum Labs Database Warehouse, using a propensity matching method for case and control groups with the variables of age, sex, race, region, insurance, income, specialist access, Charlson comorbidity, specific medical conditions, pre-index exacerbation count, pre-index rescue inhaler pharmacy fills, and pre-index inhaled corticosteroid with or without long-acting β-agonist pharmacy fills. Primary outcome used to assess failure of stopping was an increase of 50% or more in the asthma exacerbation rate in the 6 months after discontinuing the biologic compared with the 6-month period before biologic initiation., Results: Among a cohort of 4960 asthma biologic users, 1249 were observed to stop use after 6 to 12 months of use. We identified a matched cohort of 1247 stoppers and 1247 people who continued biologic use for at least 18 months. In the first 6 months after stopping or sham stopping, 10.2% of stoppers and 9.5% of continuers had an increase of 50% or more in asthma exacerbations. We found a similar adjusted odds of failing among stoppers and continuers (odds ratio = 1.085; 95% confidence interval, 0.833-1.413)., Conclusions: An increase in asthma exacerbations is infrequently observed in people who stopped asthma biologics and was observed at similar rates as in matched controls who continued asthma biologics., (Copyright © 2021 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2021. Published by Elsevier B.V.)

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.

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.

Background: We previously reported that bipolar disorder (BD) patients with clinically significant weight gain (CSWG; ⩾7% of baseline weight) in the 12 months after their first manic episode experienced greater limbic brain volume loss than patients without CSWG. It is unknown whether CSWG is also a risk factor for progressive neurochemical abnormalities., Methods: We investigated whether 12-month CSWG predicted greater 12-month decreases in hippocampal N-acetylaspartate (NAA) and greater increases in glutamate + glutamine (Glx) following a first manic episode. In BD patients (n = 58) and healthy comparator subjects (HS; n = 34), we measured baseline and 12-month hippocampal NAA and Glx using bilateral 3-Tesla single-voxel proton magnetic resonance spectroscopy. We used general linear models for repeated measures to investigate whether CSWG predicted neurochemical changes., Results: Thirty-three percent of patients and 18% of HS experienced CSWG. After correcting for multiple comparisons, CSWG in patients predicted a greater decrease in left hippocampal NAA (effect size = -0.52, p = 0.005). CSWG also predicted a greater decrease in left hippocampal NAA in HS with a similar effect size (-0.53). A model including patients and HS found an effect of CSWG on Δleft NAA (p = 0.007), but no diagnosis effect and no diagnosis × CSWG interaction, confirming that CSWG had similar effects in patients and HS., Conclusion: CSWG is a risk factor for decreasing hippocampal NAA in BD patients and HS. These results suggest that the well-known finding of reduced NAA in BD may result from higher body mass index in patients rather than BD diagnosis.

Objective: Bright light therapy is increasingly recommended (alone or in combination with antidepressant medication) to treat symptoms of nonseasonal major depressive disorder (MDD). However, little is known about its impacts on quality of life (QoL), a holistic, patient-valued outcome., Methods: This study utilizes secondary outcome data from an 8-week randomized, controlled, double blind trial comparing light monotherapy ( n = 32), fluoxetine monotherapy ( n = 30), and the combination of these ( n = 27) to placebo ( n = 30). QoL was assessed using the Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF). Treatment-related differences in QoL improvements were assessed using a repeated measures analysis of variance. The influence of potential predictors of QoL (demographic variables and change in depressive symptoms) were investigated via hierarchical linear regression., Results: Q-LES-Q-SF scores significantly improved across all treatment conditions; however, no significant differences were observed between treatment arms. QoL remained poor relative to community norms by the end of the trial period: Across conditions, 70.6% of participants had significantly impaired QoL at the 8-week assessment. Reduction in depressive scores was a significant predictor of improved QoL, with the final model accounting for 54% of variance in QoL change scores., Conclusion: The findings of this study emphasize that improvement in QoL and reduction in depressive symptoms in MDD, while related, cannot be taken to be synonymous. Adjunctive therapies may be required to address unmet QoL needs in patients with MDD receiving antidepressant or light therapies. Further research is required to explore additional predictors of QoL in order to better refine treatments for MDD.

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.

Background: Little is known about adherence to asthma biologics., Research Question: Is adherence to inhaled corticosteroid (ICS) associated with subsequent asthma biologic adherence?, Study Design and Methods: We analyzed individuals with asthma who started asthma biologics in the OptumLab Data Warehouse and used that data until October 2019. We calculated proportion days covered (PDC) for ICS ± long-acting β-agonists in the 6 months before and after asthma biologics were started and asthma biologic PDC for the first 6 months of use. We performed a multivariable analysis to identify factors associated with asthma biologic PDC ≥0.75, ICS PDC ≥0.75 during the 6-month period after asthma biologic were started, and achievement of a ≥50% reduction in asthma exacerbations during the first 6 months of asthma biologic use., Results: We identified 5,319 people who started asthma biologics. The mean PDC for asthma biologics was 0.76 (95% CI, 0.75-0.77) in the first 6 months after starting, higher than the mean PDCs for ICS in the 6 months before (0.44 [95% CI, 0.43-0.45]) and after (0.40 [95% CI, 0.39-0.40]) starting the asthma biologic. PDC ≥0.75 for ICS 6 months before index biologic use is associated with PDC for asthma biologics ≥0.75 (OR, 1.25; 95% CI, 1.10-1.43) and for ICS during the first 6 months of biologic use (OR, 9.93; 95% CI, 8.55-11.53). Neither ICS PDC ≥0.75 (OR, 0.92; 95% CI, 0.74-1.14) nor asthma biologic PDC ≥0.75 (OR, 1.15; 95% CI, 0.97-1.36) is associated with a statistically significant reduction in asthma exacerbations during the first 6 months of asthma biologic use among people with any exacerbation in the 6 months before first use., Interpretation: Adherence to asthma biologic is higher than to ICS and is associated with different factors., (Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Importance: Major depressive episodes in bipolar disorder are common and debilitating. Repetitive transcranial magnetic stimulation is well established in the treatment of major depressive disorder, and the intermittent theta burst stimulation (iTBS) protocol is replacing conventional protocols because of noninferiority and reduced delivery time. However, iTBS has not been adequately studied in bipolar disorder and, therefore, its efficacy is uncertain., Objective: To determine whether iTBS to the left dorsolateral prefrontal cortex (LDLPFC) is safe and efficacious in the treatment of acute bipolar depression., Design, Setting, and Participants: This study was a double-blind, 4-week, randomized clinical trial of iTBS targeting the LDLPFC. Two Canadian academic centers recruited patients between 2016 and 2020. Adults with bipolar disorder type I or type II experiencing an acute major depressive episode were eligible if they had not benefited from a first-line treatment for acute bipolar depression recommended by the Canadian Network for Mood and Anxiety Treatments and were currently treated with a mood stabilizer, an atypical antipsychotic, or their combination. Seventy-one participants were assessed for eligibility, and 37 were randomized to daily sham iTBS or active iTBS using a random number sequence, stratified according to current pharmacotherapy. Data analysis was performed from April to September 2020., Interventions: Four weeks of daily active iTBS (120% resting motor threshold) or sham iTBS to the LDLPFC. Nonresponders were eligible for 4 weeks of open-label iTBS., Main Outcomes and Measures: The primary outcome was the change in score on the Montgomery-Asberg Depression Rating Scale from baseline to study end. Secondary outcomes included clinical response, remission, and treatment-emergent mania or hypomania., Results: The trial was terminated for futility after 37 participants (23 women [62%]; mean [SD] age, 43.86 [13.87] years; age range, 20-68 years) were randomized, 19 to sham iTBS and 18 to active iTBS. There were no significant differences in Montgomery-Asberg Depression Rating Scale score changes (least squares mean difference between groups, -1.36 [95% CI, -8.92 to 6.19; P = .91] in favor of sham iTBS), and rates of clinical response were low in both the double-blind phase (3 of 19 participants [15.8%] in the sham iTBS group and 3 of 18 participants [16.7%] in the active iTBS group) and open-label phase (5 of 21 participants [23.8%]). One active iTBS participant had a treatment emergent hypomania, and a second episode occurred during open-label treatment., Conclusions and Relevance: iTBS targeting the LDLPFC is not efficacious in the treatment of acute bipolar depression in patients receiving antimanic or mood stabilizing agents. Additional research is required to understand how transcranial magnetic stimulation treatment protocols differ in efficacy between unipolar and bipolar depression., Trial Registration: ClinicalTrials.gov Identifier: NCT02749006.

With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3-85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni&#95;refacer, deepdefacer, mri&#95;deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni&#95;refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3-20 years) for afni&#95;refacer and the oldest (44-85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files., Competing Interests: The authors declare that this study received funding from Lundbeck, Bristol-Myers Squibb, Pfizer, and Servier. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. 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. RL has received honoraria or research funds from Allergan, Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, CIHR, CANMAT, Canadian Psychiatric Association, Hansoh, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, MITACS, Myriad Neuroscience, Ontario Brain Institute, Otsuka, Pfizer, St. Jude Medical, University Health Network Foundation, and VGH-UBCH Foundation. SCS is the Chief Scientific Officer of ADMdx, Inc., which receives NIH funding, and he currently has research grants from Brain Canada, Canada Foundation for Innovation (CFI), Canadian Institutes of Health Research (CIHR), and the Ontario Brain Institute in Canada. BF has received a research grant from Pfizer. SK has received research funding or honoraria from Abbott, Alkermes, Allergan, Bristol-Myers Squibb, Brain Canada, Canadian Institutes for Health Research (CIHR), Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute (OBI), Ontario Research Fund (ORF), Otsuka, Pfizer, Servier, Sunovion, and Xian-Janssen. EA has served as a consultant to Roche, has received grant funding from Sanofi Canada and SynapDx, has received royalties from APPI and Springer, and received kind support from AMO Pharmaceuticals, honoraria from Wiley, and honorarium from Simons Foundations. GM has received consultancy/speaker fees from Lundbeck, Pfizer, Johnson & Johnson and Janssen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Theyers, Zamyadi, O'Reilly, Bartha, Symons, MacQueen, Hassel, Lerch, Anagnostou, Lam, Frey, Milev, Müller, Kennedy, Scott, Strother and Arnott.)

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 -6 ) and intergenic rs6916777 (Beta = 14.03 [8.47, 19.59], p = 1.25 × 10 -6 ), 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 -6 , 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 -4 ). 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.

Objective: Patients with major depressive disorder often have limited response to first-line and second-line medications; hence, novel pharmacological treatments are needed for treatment-resistant depression (TRD). Ketamine, an N -methyl-d-aspartate (NMDA) receptor antagonist, has demonstrated rapid antidepressant effects in patients with TRD. The Canadian Network for Mood and Anxiety Treatments (CANMAT) convened a task force to review the evidence for efficacy and safety of racemic ketamine and to provide recommendations for its use in clinical practice., Methods: A systematic review was conducted with computerized search of electronic databases up to January 31, 2020 using combinations of search terms, inspection of bibliographies, and review of other ketamine guidelines and consensus statements. The level of evidence and lines of treatment were assigned according to CANMAT criteria. Recommendations were given in question-answer format., Results: Intravenous (IV) racemic ketamine given as a single infusion has Level 1 evidence for efficacy in adults with TRD. The evidence for multiple infusions, given as an acute series or as ongoing maintenance treatment, is limited to Level 3. Adverse events associated with ketamine infusions include behavioral (e.g., dissociative symptoms) and physiological (e.g., hypertension) events. There is only Level 3 or 4 evidence for non-IV formulations of racemic ketamine. Consensus recommendations are given for clinical administration of IV ketamine including patient selection, facility and personnel issues, monitoring, and maintaining response., Conclusions: Single-dose IV racemic ketamine is a third-line recommendation for adults with TRD. The need for repeated and maintenance ketamine infusions should be carefully assessed on a case-by-case basis with consideration of potential risks and benefits. Because of limited evidence for efficacy and risk for misuse and diversion, the use of oral and other formulations of racemic ketamine should be limited to specialists with ketamine-prescribing expertise and affiliations with tertiary or specialized centers.

A position statement developed by the Canadian Psychiatric Association's (CPA) Research Committee and approved by the CPA's Board of Directors on May 13, 2020.

Bipolar disorder (BD) and major depressive disorder (MDD) have both common and distinct clinical features, that pose both conceptual challenges in terms of their diagnostic boundaries and practical difficulties in optimizing treatment. Multivariate machine learning techniques offer new avenues for exploring these boundaries based on clinical neuroanatomical features. Brain structural data were obtained at 3 T from a sample of 90 patients with BD, 189 patients with MDD, and 162 healthy individuals. We applied sparse partial least squares discriminant analysis (s-PLS-DA) to identify clinical and brain structural features that may discriminate between the two clinical groups, and heterogeneity through discriminative analysis (HYDRA) to detect patient subgroups with reference to healthy individuals. Two clinical dimensions differentiated BD from MDD (area under the curve: 0.76, P < 0.001); one dimension emphasized disease severity as well as irritability, agitation, anxiety and flight of ideas and the other emphasized mostly elevated mood. Brain structural features could not distinguish between the two disorders. HYDRA classified patients in two clusters that differed in global and regional cortical thickness, the distribution proportion of BD and MDD and positive family history of psychiatric disorders. Clinical features remain the most reliable discriminant attributed of BD and MDD depression. The brain structural findings suggests that biological partitions of patients with mood disorders are likely to lead to the identification of subgroups, that transcend current diagnostic divisions into BD and MDD and are more likely to be aligned with underlying genetic variation. These results set the foundation for future studies to enhance our understanding of brain-behavior relationships in mood disorders.

(Reprinted with permission from Lancet Psychiatry 2016; 3: 1138-46)., (Copyright © 2021 by the American Psychiatric Association.)

Background: Condition-specific quality of life (QoL) instruments are more representative of the priorities of people with lived experience. As such, the development of the first and only bipolar disorder (BD) specific measurement instrument, the Quality of Life in Bipolar Disorder (QoL.BD) questionnaire, marked an important step forward for the literature. The present systematic review aims to characterise applications of the QoL.BD in the BD literature and review empirical findings obtained from studies using this measure., Methods: A systematic search identified 37 peer-reviewed publications which reported original empirical data using the QoL.BD in a BD population. No restrictions were placed on language/study type., Results: Adaptations to the QoL.BD displayed appropriate psychometric properties. Although clinical trials were typically underpowered, promising effect sizes for a number of treatment modalities were reported. QoL.BD scores were moderately correlated with depressive symptoms; a number of candidate predictors were identified., Limitations: Due to resource limitations, the present review used one database (Google Scholar), and a single author reviewed articles for eligibility. On balance the risks of missing relevant studies were deemed minimal., Conclusion: A sizeable, international body of evidence now exists regarding the measurement, presentation, and treatment of condition-specific aspects of QoL in BD. Key avenues for future research include large scale, randomized control clinical trials using the QoL.BD as a primary outcome, and granular exploration of potential correlates of QoL.BD domain scores. Finally, longer follow-up periods are required to inform understanding of the dynamic relationship between clinical variables and condition-specific aspects of QoL in BD., (Copyright © 2020. Published by Elsevier B.V.)

Objectives: Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response., Methods: Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data., Results: Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD., Conclusion: We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Despite effective psychological and pharmacological treatments, there is a large unmet burden of illness in post-traumatic stress disorder (PTSD). Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive intervention and a putative treatment strategy for PTSD. The evidence base to date suggests that rTMS targeting the dorsolateral prefrontal cortex (DLPFC), in particular the right DLPFC, leads to improvements in PTSD symptoms. However, optimal stimulation parameters have yet to be determined. In this study, we examine the efficacy of high- and low-frequency rTMS of the right DLPFC using a randomized, double-blind, sham-controlled design in civilian PTSD., Methods: We conducted a 2-week single-site randomized sham-controlled trial of rTMS targeting the right DLPFC. We recruited civilians aged 19 to 70 with PTSD and randomized subjects with allocation concealment to daily 1-Hz rTMS, 10-Hz rTMS, or sham rTMS. The primary outcome was improvement in Clinician Administered PTSD Scale-IV (CAPS-IV). Secondary outcomes included change in depressive and anxiety symptoms., Results: We recruited 31 civilians with PTSD. One 1-Hz-treated patient developed transient suicidal ideation. Analyses revealed significant improvement in CAPS-IV symptoms in the 1-Hz group relative to sham (Hedges' g = -1.07) but not in the 10-Hz group. This was not attributable to changes in anxious or depressive symptomatology. Ten-Hz stimulation appeared to improve depressive symptoms compared to sham., Conclusion: Low-frequency rTMS is efficacious in the treatment of civilian PTSD. Our data suggest that high-frequency rTMS of the right DLPFC is worthy of additional investigation for the treatment of depressive symptoms comorbid with PTSD.

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.

Background: Ketamine is established as a rapid and effective treatment in adults with treatment-resistant depression (TRD). The availability of different formulations and routes of delivery invites the need for evaluating relative effect sizes., Methods: Effect size with respect to depression symptom reduction for each formulation and route of delivery was compared at discrete time-points (i.e., 24 h, 2-6 days, 7-20 days, 21-28 days) in adults with TRD. A random-effects meta-analysis was conducted to evaluate the effect size across intravenous, intranasal and oral routes of administration. Analysis was also conducted evaluating the effect size of racemic ketamine to esketamine., Results: The pooled effect size for intranasal ketamine/esketamine at 24 h was g = 1.247 (n = 5, 95% CI: 0.591-1.903, p < 0.01). At 2-6 days, the pooled effect size for intravenous ketamine/esketamine was g = 0.949 (n = 14, 95% CI: -0.308-2.206, p = 0.139). At 7-20 days, intranasal ketamine had a pooled effect size of g = 1.018 (n = 4, 95% CI: 0.499-1.538, p < 0.01). At 21-28 days, oral ketamine had a pooled effect size of g = 0.633 (n = 2, 95% CI: 0.368-0.898, p < 0.01)., Limitations: Additional comparative studies are needed with regards to the efficacy of different formulations and routes of delivery., Conclusions: The short-term efficacy of intravenous and intranasal ketamine/esketamine for adults with TRD was established. Interpreting the efficacy of oral ketamine was limited by the need for studies with larger samples across independent sites. No conclusions regarding comparative efficacy of the disparate formulations and routes of delivery can be derived from this analysis. Direct comparative studies are needed to further inform treatment options for TRD., (Copyright © 2020. Published by Elsevier B.V.)

Objective: To evaluate the development and implementation of clinical practice guidelines for the management of depression globally., Methods: We conducted a systematic review of existing guidelines for the management of depression in adults with major depressive or bipolar disorder. For each identified guideline, we assessed compliance with measures of guideline development quality (such as transparency in guideline development processes and funding, multidisciplinary author group composition, systematic review of comparative efficacy research) and implementation (such as quality indicators). We compared guidelines from low- and middle-income countries with those from high-income countries., Findings: We identified 82 national and 13 international clinical practice guidelines from 83 countries in 27 languages. Guideline development processes and funding sources were explicitly specified in a smaller proportion of guidelines from low- and middle-income countries (8/29; 28%) relative to high-income countries (35/58; 60%). Fewer guidelines (2/29; 7%) from low- and middle-income countries, relative to high-income countries (22/58; 38%), were authored by a multidisciplinary development group. A systematic review of comparative effectiveness was conducted in 31% (9/29) of low- and middle-income country guidelines versus 71% (41/58) of high-income country guidelines. Only 10% (3/29) of low- and middle-income country and 19% (11/58) of high-income country guidelines described plans to assess quality indicators or recommendation adherence., Conclusion: Globally, guideline implementation is inadequately planned, reported and measured. Narrowing disparities in the development and implementation of guidelines in low- and middle-income countries is a priority. Future guidelines should present strategies to implement recommendations and measure feasibility, cost-effectiveness and impact on health outcomes., ((c) 2020 The authors; licensee World Health Organization.)

Background: Cognitive impairment is a well-recognized symptom of major depressive disorder; however, contributing factors are not fully characterized. The present study examined the neurocognitive profiles and predictors of cognitive impairment in patients with treatment-resistant depression (TRD)., Methods: Moderate to severely depressed TRD patients were compared to matched healthy volunteers (HV) in verbal learning and recall and executive functions. Based on cognitive scores, cluster analysis was performed to identify subsets within the TRD sample. Predictors of cognitive impairment were also investigated., Results: TRD patients showed worse performance in tests assessing verbal memory, executive attentional shifting, and inhibitory control. The cluster analysis revealed two groups: a cognitively impaired (CI) group that showed a generalized deficit across cognitive domains, and a relatively cognitively intact group that performed better than CI in all domains except attentional shifting. A logistic binomial regression of the two groups revealed three significant contributing risk factors for CI: 1) older age, 2) lower premorbid IQ, and 3) benzodiazepine use. Cognitive impairment and benzodiazepine use were associated with worse functioning., Conclusions: Significant cognitive impairment is present in TRD and is associated with worse functioning. Age, lower premorbid IQ, and benzodiazepine use increased the likelihood of generalized cognitive impairment in TRD patients. The detrimental effect of benzodiazepine on cognitive impairment is independent of anxiety symptoms. Further research is needed to characterize the timeline of cognitive impairment in depression., Competing Interests: Declaration Of Competing Interest IJT has served as consultant for Lundbeck Canada, Sumitomo Dainippon, and Community Living British Columbia. DMB has received research support from CIHR, NIH, Brain Canada and the Temerty Family through the CAMH Foundation and the CampbellFamily Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd.  He is the site principal investigator for three sponsor-initiated studies for Brainsway Ltd. He also receives in-kind equipment support from Magventure for two investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. Dr. Downar has received research support from CIHR, NIMH, Brain Canada, the Canadian Biomarker Integration Network in Depression, the Ontario Brain Institute, the Klarman Family Foundation, the Arrell Family Foundation, the Edgestone Foundation, a travel stipend from Lundbeck and from ANT Neuro, an advisor to BrainCheck and in-kind equipment support for this investigator-initiated trial from MagVenture. RWL has received honoraria for ad hoc speaking or advising/consulting, or received research funds, from: Akili, Allergan, Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, Canadian Institutes of Health Research, Canadian Network for Mood and Anxiety Treatments, Canadian Psychiatric Association, CME Institute, Hansoh, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, Medscape, MITACS, Movember Foundation, Ontario Brain Institute, Otsuka, Pfizer, St. Jude Medical, University Health Network Foundation, and VGH-UBCH Foundation. Dr. Vila-Rodriguez receives research support from CIHR, Brain Canada, Michael Smith Foundation for Health Research, Vancouver Coastal Health Research Institute, and in-kind equipment support for this investigator-initiated trial from MagVenture. He has participated in an advisory board for Janssen. EG and RG have no conflicts of interest to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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.