Stress Markers for Mental States and Biotypes of Depression and Anxiety: A Scoping Review and Preliminary Illustrative Analysis.

Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety.

Classifying Major Depressive Disorder and Response to Deep Brain Stimulation Over Time by Analyzing Facial Expressions.

Objective: Major depressive disorder (MDD) is a common psychiatric disorder that leads to persistent changes in mood and interest among other signs and symptoms. We hypothesized that convolutional neural network (CNN) based automated facial expression recognition, pre-trained on an enormous auxiliary public dataset, could provide improve generalizable approach to MDD automatic assessment from videos, and classify remission or response to treatment.

Methods: We evaluated a novel deep neural network framework on 365 video interviews (88 hours) from a cohort of 12 depressed patients before and after deep brain stimulation (DBS) treatment.

Addressing the Credit Assignment Problem in Treatment Outcome Prediction using Temporal Difference Learning.

Mental health patients often undergo a variety of treatments before finding an effective one. Improved prediction of treatment response can shorten the duration of trials. A key challenge of applying predictive modeling to this problem is that often the effectiveness of a treatment regimen remains unknown for several weeks, and therefore immediate feedback signals may not be available for supervised learning.

Classifying Depression Severity in Recovery From Major Depressive Disorder via Dynamic Facial Features.

Major depressive disorder is a common psychiatric illness. At present, there are no objective, non-verbal, automated markers that can reliably track treatment response. Here, we explore the use of video analysis of facial expressivity in a cohort of severely depressed patients before and after deep brain stimulation (DBS), an experimental treatment for depression.

Cingulum stimulation enhances positive affect and anxiolysis to facilitate awake craniotomy.

Background: Awake neurosurgery requires patients to converse and respond to visual or verbal prompts to identify and protect brain tissue supporting essential functions such as language, primary sensory modalities, and motor function. These procedures can be poorly tolerated because of patient anxiety, yet acute anxiolytic medications typically cause sedation and impair cortical function.

Methods: In this study, direct electrical stimulation of the left dorsal anterior cingulum bundle was discovered to reliably evoke positive affect and anxiolysis without sedation in a patient with epilepsy undergoing research testing during standard inpatient intracranial electrode monitoring.

Depression Severity Classification from Speech Emotion.

Major Depressive Disorder (MDD) is a common psychiatric illness. Automatically classifying depression severity using audio analysis can help clinical management decisions during Deep Brain Stimulation (DBS) treatment of MDD patients. Leveraging the link between short-term emotions and long-term depressed mood states, we build our predictive model on the top of emotion-based features.

Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression.

We used several metrics of variability to extract unsupervised features from video recordings of patients before and after deep brain stimulation (DBS) treatment for major depressive disorder (MDD). Our goal was to quantify the treatment effects on facial expressivity. Multiscale entropy (MSE) was used to capture the temporal variability in pixel intensity level at multiple time-scales.

The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies.

As genomics advances reveal the cancer gene landscape, a daunting task is to understand how these genes contribute to dysregulated oncogenic pathways. Integration of cancer genes into networks offers opportunities to reveal protein-protein interactions (PPIs) with functional and therapeutic significance. Here, we report the generation of a cancer-focused PPI network, termed OncoPPi, and identification of >260 cancer-associated PPIs not in other large-scale interactomes.

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

Investigation of factors affecting RNA-seq gene expression calls.

RNA-seq enables quantification of the human transcriptome. Estimation of gene expression is a fundamental issue in the analysis of RNA-seq data. However, there is an inherent ambiguity in distinguishing between genes with very low expression and experimental or transcriptional noise.