Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors.

ABSTRACT: Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding

SUBMITTER: Sheynin S 

PROVIDER: S-EPMC8350148 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature