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

Frontiers in Psychiatry

Psychiatry

Sangyun Kim¹  Munseob Lee¹  Lisa Wagels²  Mikhail Votinov²  Ute Habel²  Sujitha Venkatapathy³  Han-Gue Jo³  In-Ho Ra³ 

[1] AI Convergence Research Section, Electronics and Telecommunications Research Institute, Gwangju, Republic of Korea;Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany;Research Center Juelich, Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Juelich, Republic of Korea;School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea;

关键词: major depressive disorder;    deep learning;    graph neural network;    ensemble model;    functional connectivity;   

DOI  :  10.3389/fpsyt.2023.1125339

 received in 2022-12-16, accepted in 2023-03-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

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Copyright © 2023 Venkatapathy, Votinov, Wagels, Kim, Lee, Habel, Ra and Jo.