Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.

ABSTRACT:

Background

Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD.

Methods

We studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial

SUBMITTER: Yang J 

PROVIDER: S-EPMC8555083 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature