Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites.

ABSTRACT:

Background

Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.

Methods

Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regi

SUBMITTER: Qin K 

PROVIDER: S-EPMC8983334 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature