Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism.

ABSTRACT:

Background

Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences.

Aims

To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity.

Method

We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups.

Results

stDNN achieved consistently high classification accu

SUBMITTER: Supekar K 

PROVIDER: S-EPMC9376194 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature