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Antidepressant pharmacotherapy dominates current treatment in psychiatry, including treatment for major depressive disorder (MDD). However, the current trial-and-error process of medication selection contributes to treatment failure and unnecessarily exposes patients to lengthy and insufficient treatment trials. Notably, improvements in measures of cognition have been demonstrated to occur early during treatment and prior to improvements in clinical state. Cognitions have been categorized based on emotional valence (i.e., cold versus hot cognitions). Cold cognitions describe cognitive operations that are relevant to the processing of non-emotional information. The current analysis investigates whether early changes in cold cognition can predict response after 8 weeks of vortioxetine treatment in adults with MDD. This was secondary analysis of an 8-week, open-label study. Cognition was assessed at week 0 and week 2 to measure early cognitive change. Depressive symptom severity was assessed at week 0 and week 8 to measure treatment response. Eighty-one subjects were analyzed using binomial logistic regression models. Early change in cognition was a non-significant predictor of response (p = 0.845, SE = 0.599, OR = 1.124), which may have resulted from high data variability. The overall predictive accuracy of the model was low (sensitivity = 37.5%, specificity = 89.8%, PPV = 70.6%, NPV = 68.8%). Future studies should include larger samples and stratify patients based on potentially moderating variables, such as baseline cognitive impairment and occupation. Stratification would likely produce more homogenous samples, reducing the amount of variability observed for early cognitive change., Competing Interests: Declaration of Competing Interest Roger S. McIntyre is a consultant to speak on behalf for, and/or has received research support from Lundbeck, Janssen, Shire, Purdue, Pfizer, Otsuka, Allergan, Takeda, Neurocrine, Sunovion, Stanley Medical Research Institute, and CIHR/GACD/Chinese National Natural Research Foundation. All other authors have no conflicts of interest to disclose., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: The aim of this study was to examine whether changes in physical activity predicted reductions in depression during the 8 weeks of antidepressant treatment with vortioxetine., Methods: One hundred individuals were recruited for the (THINC-it ®)-sensitivity to change study. Self-reported moderate-to-vigorous physical activity (MVPA) and depression severity were assessed at baseline, week 4 and week 8. Linear mixed model analyses were performed to examine whether increases in MVPA were associated with reduction in depression severity over the course of treatment and hierarchical logistic regression analyses were performed to assess whether treatment response (responders vs. non-responders) at week 8 was predicted by early change in physical activity (MVPA at week 4), after controlling for individuals' demographics (sex, age, race, education level, BMI) and baseline MVPA and depression severity., Results: After controlling for individuals' demographics, a significant increase in MVPA predicted reduction in depression severity, β = -2.06, 95% CI -3.18, -0.94, p <0.001. Individuals with more physical activity at week 4 relative to baseline had higher odds of treatment response at endpoint, OR 1.97, 95% CI 1.11 - 3.48, p <0.05. Twenty-one percent of total variance of depression severity was explained by change in MVPA., Conclusions: The study suggests that early increases in physical activity may be a behavioral marker of antidepressant treatment response. The inclusion of physical activity measures in future clinical treatment trials of depression is recommended to explore whether changes in physical activity mediate or moderate reductions in depression severity associated with the primary treatment., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Sleep disturbances are frequently reported in patients with major depressive disorder. We aimed to investigate the effects of vortioxetine on sleep quality and association between changes in sleep and treatment response., Methods: This study is a post-hoc analysis of a clinical trial that sought to evaluate the sensitivity to cognitive change of THINC-integrated tool in patients with major depressive disorder. In total, 92 patients (aged 18 to 65) meeting Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for moderate or severe major depressive disorder and 54 healthy controls were included. All patients received open-label vortioxetine (10-20 mg/day, flexibly dosed) for 8 weeks. Herein, the primary outcomes of interest were changes in sleep, as measured by the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and Insomnia Severity Index, between weeks 0, 2, and 8. The association between changes in sleep and depressive symptom severity was secondarily assessed., Results: We observed that sleep, as indicated by scores of Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and Insomnia Severity Index, was significantly poorer in patients with major depressive disorder compared to healthy controls at weeks 0, 2, and 8 ( p < 0.05). Among patients with major depressive disorder, we observed significant improvements on the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and Insomnia Severity Index between weeks 0 and 8 ( p < 0.05). We observed a significant association between improvements on the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and Insomnia Severity Index and improvement of depressive symptoms., Conclusion: Improvement of depressive symptoms in major depressive disorder patients treated with vortioxetine was associated with significant improvements in sleep. Furthermore, improvements in sleep were predictive of antidepressant response and were linearly correlated with improvement in overall depressive symptom severity.

Background: It is well established that deficits in motivation, reward, and cognition are common during and in between syndromal episodes of depression as part of Major Depressive Disorder (MDD). Informed by evidence indicating functional and structural interconnectivity between cognitive and reward brain circuits, we preliminarily evaluate the association between measures of cognitive performance and reward/motivation., Methods: This is a post-hoc analysis of a primary study (i.e. the THINC-it sensitivity to change study). Adults (18-65 years of age) meeting DSM-5 criteria for MDD, single-episode or recurrent confirmed by M.I.N.I. with moderate severity or greater (i.e. Montgomery Asberg Depression Rating Scale ≥20). All eligible subjects received vortioxetine 10-20 mg open-label for 8 weeks. The Effort Expenditure Reward Task (EEfRT) was the principal measure of motivation and reward. We directly compare the effects of cognitive measures and depressive symptoms on effort-based decision-making using the THINC-it composite score and MADRS total score., Results: Twenty-one participants with MDD (Mean age = 38.47, SD = 12.85) and 20 healthy volunteers (Mean age = 41.50, SD = 14.21) completed the optional EEfRT task. Amongst individuals with MDD, performance in processing speed, executive function (i.e. Trails B) and overall composite cognitive score was positively associated with the proportion of hard-task choices in the high reward condition (i.e. greater reward valuation). Across both groups, a greater probability (χ 2  = 1.137) and magnitude of reward (χ 2  = 0.045) was associated with increased effort (i.e. choosing the hard task more frequently). Using fully factored GEE models, we observed a positive association between performance on the Trails test (β = 2.223, SE = 0.928, p = 0.017) as well as the composite score (β = 0.978, SE = 0.0.459, p = 0.033), and greater effort for high rewards. In addition, it was observed that a positive association (i.e. greater effort for reward in higher probability) was observed with depressive symptoms and overall cognitive measures., Conclusion: Herein, we observed that an association exists between overall cognitive function, notably processing speed and executive function and reward function. Specifically, a greater effort for hard task rewards (using the EEfRT task) was manifested in individuals exhibiting higher levels of cognitive performance in a well-characterized sample of MDD treated with Vortioxetine., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Anhedonia is defined as a diminished ability to experience interest or pleasure, and is a critical psychopathological dimension of major depressive disorder (MDD). The purpose of the current systematic review is to evaluate the therapeutic efficacy of pharmacological treatments on measures of anhedonia in adults with MDD. Electronic databases Cochrane Library (CENTRAL), Ovid MEDLINE, PubMed, PsycINFO, and Google Scholar were searched from inception to June 1, 2018 for longitudinal studies utilizing pharmacotherapy for the treatment of anhedonia in patients with MDD. A total of 17 eligible studies were identified (i.e., evaluated the effects of pharmacotherapy on a measure of anhedonia). Among the identified studies, the efficacy of 14 different pharmacotherapies on measures of anhedonia were evaluated, including melatonergic agents (i.e. agomelatine), monoaminergic agents (i.e. moclobemide, clomipramine, bupropion, venlafaxine, fluoxetine, amitifadine and levomilnacipran, escitalopram, and sertraline), glutamatergic agents (i.e., ketamine and riluzole), stimulants (i.e., methylphenidate), and psychedelics (i.e., psilocybin). Based on the available evidence, most antidepressants demonstrated beneficial effects on measures of anhedonia as well as the other depressive symptoms. Only escitalopram/riluzole combination treatment was ineffective in treating symptoms of anhedonia in MDD. Continued research is warranted to further support the efficacy of mechanistically-distinct antidepressants in treating symptoms of anhedonia in MDD. Future research should also aim to parse out the heterogeneous effects of different pharmacotherapies on anhedonic symptoms., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations., Methods: We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted., Results: We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88])., Limitations: Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm., Conclusions: Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders., (Copyright © 2018. Published by Elsevier B.V.)

Background: Despite the widespread use of antidepressants in clinical practice, the current trial-and-error approach to medication selection contributes to treatment failure and underscores the need to identify reliable predictors of antidepressant response. Since changes in measures of cognition have been reported to occur early in treatment and prior to improvements in overall mood symptoms, the present review aims to determine whether early changes in measures of cognition can predict response in individuals with MDD., Methods: A systematic review of studies evaluating early cognitive change as a predictor of later treatment response in MDD was conducted using PubMed/Medline, Embase and PsychINFO., Results: A total of seven articles were identified. The available evidence suggests the early changes in cognition may predict treatment response in individuals with MDD. This was shown across antidepressant classes (i.e., SSRIs, SNRIs, NRIs, melatonergic antidepressants) and forms of therapy (i.e., pharmacotherapy, rTMS). The results depict an emerging trend towards early changes in facial emotion recognition (i.e., a hot cognitive process) as a predictor of treatment outcome., Limitations: Our qualitative analysis reflects a very limited number of studies. Moreover, there was significant heterogeneity in the evaluation of cognition across studies. Future research should aim to parse out this heterogeneity by evaluating the relative predictive value of different measures of cognition., Conclusion: The identification of reliable early treatment predictors of antidepressant response would be clinically significant, enabling clinicians to more accurately evaluate the efficacy of selected treatment avenues., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: Extant evidence indicates that ketamine exerts rapid antidepressant effects in treatment-resistant depressive (TRD) symptoms as a part of major depressive disorder (MDD) and bipolar disorder (BD). The identification of depressed sub-populations that are more likely to benefit from ketamine treatment remains a priority. In keeping with this view, the present narrative review aims to identify the pretreatment predictors of response to ketamine in TRD as part of MDD and BD., Method: Electronic search engines PubMed/MEDLINE, ClinicalTrials.gov, and Scopus were searched for relevant articles from inception to January 2018. The search term ketamine was cross-referenced with the terms depression, major depressive disorder, bipolar disorder, predictors , and response and/or remission ., Results: Multiple baseline pretreatment predictors of response were identified, including clinical (i.e., Body Mass Index (BMI), history of suicide, family history of alcohol use disorder), peripheral biochemistry (i.e., adiponectin levels, vitamin B12 levels), polysomnography (abnormalities in delta sleep ratio), neurochemistry (i.e., glutamine/glutamate ratio), neuroimaging (i.e., anterior cingulate cortex activity), genetic variation (i.e., Val66Met BDNF allele), and cognitive functioning (i.e., processing speed). High BMI and a positive family history of alcohol use disorder were the most replicated predictors., Conclusions: A pheno-biotype of depression more, or less likely, to benefit with ketamine treatment is far from complete. Notwithstanding, metabolic-inflammatory alterations are emerging as possible pretreatment response predictors of depressive symptom improvement, most notably being cognitive impairment. Sophisticated data-driven computational methods that are iterative and agnostic are more likely to provide actionable baseline pretreatment predictive information.