The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

Examining hope as a transdiagnostic mechanism of change across anxiety disorders and CBT treatment protocols.

Hope is a trait that represents the capacity to identify strategies or pathways to achieve goals and the motivation or agency to effectively pursue those pathways. Hope has been demonstrated to be a robust source of resilience to anxiety and stress and there is limited evidence that, as has been suggested for decades, hope may function as a core process or transdiagnostic mechanism of change in psychotherapy. The current study examined the role of hope in predicting recovery in a clinical trial in which 223 individuals with 1 of 4 anxiety disorders were randomized to transdiagnostic cognitive behavior therapy (CBT), disorder-specific CBT, or a waitlist controlled condition. Effect size results indicated moderate to large intraindividual increases in hope, that changes in hope were consistent across the five CBT treatment protocols, that changes in hope were significantly greater in CBT relative to waitlist, and that changes in hope began early in treatment. Results of growth curve analyses indicated that CBT was a robust predictor of trajectories of change in hope compared to waitlist, and that changes in hope predicted changes in both self-reported and clinician-rated anxiety. Finally, a statistically significant indirect effect was found indicating that the effects of treatment on changes in anxiety were mediated by treatment effects on hope. Together, these results suggest that hope may be a promising transdiagnostic mechanism of change that is relevant across anxiety disorders and treatment protocols.R01 MH090053 - NIMH NIH HHSAccepted manuscrip

Polygenic risk associated with post-traumatic stress disorder onset and severity.

Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R2 = 4.68%, P = 0.003) and severity (R2 = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R2 = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder

Transcranial direct current stimulation (tDCS) in the treatment of depression: Systematic review and meta-analysis of efficacy and tolerability

BACKGROUND Transcranial direct current stimulation (tDCS) is a potential alternative treatment option for major depressive episodes (MDE). OBJECTIVES We address the efficacy and safety of tDCS in MDE. METHODS The outcome measures were Hedges' g for continuous depression ratings, and categorical response and remission rates. RESULTS A random effects model indicated that tDCS was superior to sham tDCS (k=11, N=393, g=0.30, 95% CI=[0.04, 0.57], p=0.027). Adjunctive antidepressant medication and cognitive control training negatively impacted on the treatment effect. The pooled log odds ratios (LOR) for response and remission were positive, but statistically non-significant (response: k=9, LOR=0.36, 95% CI[-0.16, 0.88], p=0.176, remission: k=9, LOR=0.25, 95% CI [-0.42, 0.91], p=0.468). We estimated that for a study to detect the pooled continuous effect (g=0.30) at 80% power (alpha=0.05), a total N of at least 346 would be required (with the total N required to detect the upper and lower bound being 49 and 12,693, respectively). CONCLUSIONS tDCS may be efficacious for treatment of MDE. The data do not support the use of tDCS in treatment-resistant depression, or as an add-on augmentation treatment. Larger studies over longer treatment periods are needed

Attention, interpretation, and memory biases in subclinical depression: a path analysis approach to test the combined cognitive bias hypothesis

Emotional biases in attention, interpretation, and memory are viewed as important cognitive processes underlying symptoms of depression. To date, there is a limited understanding of the interplay among these processing biases. This study tested the dependence of memory on depression-related biases in attention and interpretation. Subclinically depressed and nondepressed participants completed a computerized version of the scrambled sentences test (measuring interpretation bias) while their eye movements were recorded (measuring attention bias). This task was followed by an incidental free recall test of previously constructed interpretations (measuring memory bias). Path analysis revealed a good fit for the model in which selective orienting of attention was associated with interpretation bias, which in turn was associated with a congruent bias in memory. Also, a good fit was observed for a path model in which biases in the maintenance of attention and interpretation were associated with memory bias. Both path models attained a superior fit compared with path models without the theorized functional relations among processing biases. These findings enhance understanding of how mechanisms of attention and interpretation regulate what is remembered. As such, they offer support for the combined cognitive biases hypothesis or the notion that emotionally biased cognitive processes are not isolated mechanisms but instead influence each other. Implications for theoretical models and emotion regulation across the spectrum of depressive symptoms are discussed

Exploring the Role of Emotion Regulation Difficulties in the Assessment of Mental Disorders

Several studies have been reported in the literature for the automatic detection of mental disorders. It is reported that mental disorders are highly correlated. The exploration of this fact for the automatic detection of mental disorders is yet to explore. Emotion regulation difficulties (ERD) characterize several mental disorders. Motivated by that, we investigated the use of ERD for the detection of two opted mental disorders in this study. For this, we have collected audio-video data of human subjects while conversing with a computer agent based on a specific questionnaire. Subsequently, a subject's responses are collected to obtain the ground truths of the audio-video data of that subject. The results indicate that the ERD can be used as an intermediate representation of audio-video data for detecting mental disorders