The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network.

ABSTRACT:

Introduction

The early diagnosis of major depressive disorder (MDD) is very important for patients that suffer from severe and irreversible consequences of depression. It has been indicated that functional connectivity (FC) analysis based on functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers for clinical diagnosis. However, previous studies mainly focus on brain disease classification in small sample sizes, which may lead to dramatic divergences in classification accuracy.

Methods

This paper attempts to address this limitation by applying the deep graph convolutional neural network (DGCNN) method on a large multi-site MDD dataset. The resting-state fMRI data are acquired from 830 MDD patients and 771 normal controls (NC) shared by the REST-meta

SUBMITTER: Zhu M 

PROVIDER: S-EPMC9908753 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature