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Background: Herein, we sought to determine the sensitivity to change in cognitive function, as measured by the THINC-it tool, in a sample of adults with major depressive disorder (MDD) receiving standardized antidepressant therapy. Methods: Adults meeting the DSM-5 criteria for MDD with at least moderate depressive symptom severity [ i.e. , Montgomery Åsberg Depression Rating Scale (MADRS) total score ≥ 20] were treated with open-label vortioxetine (10–20 mg/day, flexibly-dosed) for 8 weeks. The previously validated THINC-it tool was the primary dependent measure. The THINC-it tool was validated against the paper and pencil version of the Digit Symbol Substitution Test (DSST) and the Trails Making Test B (TMTB). Results: After 8 weeks of treatment, adults with MDD exhibited improvement in cognitive function relative to healthy controls (e.g. , processing speed) (p = 0.031). A subdomain measure of working memory (i.e. , symbol check; SC) exhibited significant improvement at Weeks 2 and 8 in latency (p = 0.032), SC accuracy (p = 0.046), and objective z-score (p = 0.001) independent of depressive symptoms. A linear regression analysis determined that the THINC-it tool measures of processing speed, as well as executive function were significantly associated with changes observed on the pencil and paper version the Digit Symbol Substitution Test (DSST) (p = 0.002) and in Trails Making Test B (TMTB) (p = 0.003), respectively. Conclusion: The THINC-it tool demonstrates sensitivity to change in adults with MDD and is highly correlated with improvements on pencil and paper versions of DSST and TMTB. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT03053362. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights • Numerous studies indicate that probiotics have anti-inflammatory effects. • Immune-inflammatory modulation may mediate the antidepressant effects of probiotics. • Probiotics may be more effective in depressed subgroups with elevated inflammation. • Future studies should parse out the heterogeneous effects of different probiotics. Abstract During the past decade, there has been renewed interest in the relationship between brain-based disorders, the gut microbiota, and the possible beneficial effects of probiotics. Emerging evidence suggests that modifying the composition of the gut microbiota via probiotic supplementation may be a viable adjuvant treatment option for individuals with major depressive disorder (MDD). Convergent evidence indicates that persistent low-grade inflammatory activation is associated with the diagnosis of MDD as well as the severity of depressive symptoms and probability of treatment response. The objectives of this review are to (1) evaluate the evidence supporting an anti-inflammatory effect of probiotics and (2) describe immune system modulation as a potential mechanism for the therapeutic effects of probiotics in populations with MDD. A narrative review of studies investigating the effects of probiotics on systemic inflammation was conducted. Studies were identified using PubMed/Medline, Google Scholar, and clinicaltrials.gov (from inception to November 2017) using the following search terms (and/or variants): probiotic , inflammation , gut microbiota , and depression. The available evidence suggests that probiotics should be considered a promising adjuvant treatment to reduce the inflammatory activation commonly found in MDD. Several controversial points remain to be addressed including the role of leaky gut, the role of stress exposure, and the role of blood-brain-barrier permeability. Taken together, the results of this review suggest that probiotics may be a potentially beneficial, but insufficiently studied, antidepressant treatment intervention. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate sex differences in discrete domains of psychopathology as mediators of functional disability among individuals with Major Depressive Disorder (MDD). Adults (ages 18–65) with moderate-to-severe MDD ( n = 100) and age-, sex-, and education-matched healthy controls (HC; n = 100) participated in a clinical trial validating the THINC-integrated tool, a newly developed cognitive assessment tool for patients with MDD. Variables assessed as possible mediators included depression symptom severity, anxiety symptoms, sleep disturbance, perceived cognitive deficits, and objective cognitive performance. Functional disability was assessed using the total score on the Sheehan Disability Scale. Separate mediation analyses were conducted for men and women. No significant differences were detected between men and women on the assessed domains of psychopathology or functional disability ( p s > 0.05). However, the mediation analyses demonstrated different patterns with respect to determinants of functional disability in MDD between men and women. Functional disability was mediated by anxiety (95% CI: −3.17, −0.28) and sleep disturbance (95% CI: −0.69, −0.05) among men and by depressive symptom severity (95% CI: −7.82, −0.32) among women. These preliminary results instantiate the need to dimensionalize psychopathology in MDD. Our results at least in part support the hypothesis that, consistent with the sex differences in the prevalence and illness presentation of MDD, determinants of functional outcomes also differ between men and women, underscoring the need to consider sex differences in order to improve functional outcomes in the treatment of MDD. [ABSTRACT FROM AUTHOR]