ETRI Knowledge Sharing Platform : Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Titles

논문 검색
Type		SCI
Year	~	Keyword

List

Journal Article Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Cited 16 time in scopus

Download 131 time Share share

Authors: Sujitha Venkatapathy, Mikhail Votinov, Lisa Wagels, Sangyun Kim, Munseob Lee, Ute Habel, In-Ho Ra, Han-Gue Jo

Abstract: Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

KSP Keywords: Brain functional connectivity, Brain functional networks, Convolutional networks, Cross validation(CV), Ensemble models, Healthcare professionals, Healthy controls, Learning approach, Level of accuracy, Magnetic Resonance imaging(MRI), Magnetic resonance(MR)

This work is distributed under the term of Creative Commons License (CCL)
(CC BY)