An Objective Quantitative Diagnosis of Depression Using a Local-to-global Multimodal Fusion Graph Neural Network

Overview

Journal Patterns (N Y)

Date 2025 Jan 8

PMID 39776853

Authors

Shuyu Liu

Jingjing Zhou

Xuequan Zhu

Ya Zhang

Xinzhu Zhou

Shaoting Zhang

Zhi Yang

Ziji Wang

Ruoxi Wang

Yizhe Yuan

Xin Fang

Xiongying Chen

Yanfeng Wang

Ling Zhang

Gang Wang

Cheng Jin

Affiliations

Soon will be listed here.

Abstract

This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.75%, an area under the receiver operating characteristic curve (AUROC) of 80.64%, and correctly identified MDD subtypes. The system further discovered distinct brain connectivity patterns in MDD, including reduced functional connectivity between the left gyrus rectus and right cerebellar lobule VIIB, and increased connectivity between the left Rolandic operculum and right hippocampus. Anatomically, MDD is associated with thickness changes of the gray and white matter interface, indicating potential neuropathological conditions or brain injuries.

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