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Background: Convergent evidence implicates gut microbiota in human health and disease. Hitherto, relatively few studies have evaluated the gut microbiota profile in individuals with bipolar disorder (BD) relative to healthy controls (HC). Methods: Fecal samples were collected from subjects (aged 18–65) meeting DSM-5-defined criteria for BD and age- and sex-matched HC without current or past history of mental or major medical disorders. Samples were sequenced using Illumina sequencing and association of specific taxa and co-occurrence of taxa with sample groups including the effect of diet was assessed using cluster analysis and analysis of communities of microorganisms (ANCOM). Nutritional composition was evaluated using the Dietary Questionnaire for Epidemiological Studies (DQES v2) Food Frequency Questionnaire. Results: Forty-six subjects were enrolled (n=23 BD, n=23 HC). Cluster analyses did not identify any significant differences between BD and HC (p=0.38). Lower microbiota diversity was observed among BD subjects relative to HC (p=0.04). A greater abundance of a Clostridiaceae OTU was observed among BD subjects when compared to HC and of Collinsella among BD-II subjects relative to BD-I. Cluster analysis revealed that neither diagnosis (p=0.38) nor diet (p=0.43) had a significant effect on overall gut microbiota composition. Limitations: This study has a small sample size and insufficient control for some potential moderating factors (e.g. psychotropic medication and smoking). Conclusion: This study suggests that individuals with BD may have a distinct gut microbiota profile compared to healthy controls, with a greater abundance of Clostridiaceae and Collinsella. These findings need to be replicated in future studies with larger sample sizes. [ABSTRACT FROM AUTHOR]

The authors regret an error in one of the extracted data points in the meta-analysis. The classification accuracy for Serretti et al. (2007) was corrected to 64% (Table 3b). The overall results before and after this correction remain directionally consistent and are summarized below (Figures 2 and 3; Table 2; results subsection 3.6). The authors apologise for any inconvenience caused. [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]

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. [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]