Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression
Jingjing Gao1 · Heping Tang1 · Zhengning Wang1 · Yanling Li2 · Na Luo3,4 · Ming Song3,4 · Sangma Xie5 · Weiyang Shi3,4 · Hao Yan6 · Lin Lu6,7 · Jun Yan6,7 · Peng Li6,7 · Yuqing Song6,7 · Jun Chen8 · Yunchun Chen9 · Huaning Wang9 · Wenming Liu9 · Zhigang Li10 · Hua Guo10 · Ping Wan10 · Luxian Lv11,12 · Yongfeng Yang11,12 · Huiling Wang13 · Hongxing Zhang11,12,14 · Huawang Wu15 · Yuping Ning15 · Dai Zhang6,7,16 · Tianzi Jiang3,4,171 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2 School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
3 Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4 Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
5 Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
6 Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
7 Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
8 Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
9 Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an 710032, China
10 Zhumadian Psychiatric Hospital, Zhumadian 463000, China
11 Department of Psychiatry, Henan Mental Hospital, The Second Afliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
12 International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, Xinxiang 453003, China
13 Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
14 Department of Psychology, Xinxiang Medical University, Xinxiang 453003, China
15 The Afliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou 510370, China
16 Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
17 Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China
Abstract
Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Keywords
Schizophrenia; Magnetic resonance imaging; Classifcation; Deep learning; Graph neural network
