Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders.

Background: There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable.

Objective: The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform.

Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity.

Vector auto-regressive (VAR) models typically form the basis for constructing directed graphical models for investigating connectivity in a brain network with brain regions of interest (ROIs) as nodes. There are limitations in the standard VAR models. The number of parameters in the VAR model increases quadratically with the number of ROIs and linearly with the order of the model and thus due to the large number of parameters, the model could pose serious estimation problems.

Investigating brain connectivity using mixed effects vector autoregressive models.

We propose a mixed-effects vector auto-regressive (ME-VAR) model for studying brain effective connectivity. One common approach to investigating inter-regional associations in brain activity is the multivariate auto-regressive (VAR) model. The standard VAR model unrealistically assumes the connectivity structure to be identical across all participants in a study and therefore, could yield misleading results.

Fronto-temporal spontaneous resting state functional connectivity in pediatric bipolar disorder.

Background: The recent upsurge in interest about pediatric bipolar disorder (BD) has spurred the need for greater understanding of its neurobiology. Structural and functional magnetic resonance imaging studies have implicated fronto-temporal dysfunction in pediatric BD. However, recent data suggest that task-dependent neural changes account for a small fraction of the brain's energy consumption.