Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features
机构：[1]School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China[2]School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China[3]Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China[4]Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, China[5]Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China[6]Institute of Mental Health, Peking University Sixth Hospital, Beijing, China[7]Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China[8]Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China[9]Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an, China[10]Zhumadian Psychiatric Hospital, Zhumadian, China[11]Department of Psychiatry, Henan Mental Hospital, The Second Affliated Hospital of Xinxiang Medical University, Xinxiang, China[12]Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China[13]Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China[14]Department of Psychology, Xinxiang Medical University, Xinxiang, China[15]The Affliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China[16]School of Computer and Information Technology, Shanxi University, Taiyuan, China[17]Department of Psychiatry, First Affliated Hospital of Kunming Medical University, Kunming, China[18]Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China[19]Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China[20]Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou, China[21]Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou, China
Background and Hypothesis Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification.Study Design Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions.Study Results Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research.Conclusions Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.
基金：
National Natural Science Foundation of China [62276049, 61701078, 61872068, 62006038]; Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project [2021ZD0200200]; National Key R&D Program of China [2023YFE0118600]; Sichuan Province Science and Technology Support Program [2019YJ0193, 2021YFG0126, 2021YFG0366, 2022YFS0180]; Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China [ZYGX2021YGLH014]
第一作者机构：[1]School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
通讯作者：
通讯机构：[3]Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China[19]Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China[20]Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou, China[21]Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou, China
推荐引用方式(GB/T 7714)：
Jingjing Gao,Maomin Qian,Zhengning Wang,et al.Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features[J].SCHIZOPHRENIA BULLETIN.2024,doi:10.1093/schbul/sbae069.
APA：
Jingjing Gao,Maomin Qian,Zhengning Wang,Yanling Li,Na Luo...&Tianzai Jiang.(2024).Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.SCHIZOPHRENIA BULLETIN,,
MLA：
Jingjing Gao,et al."Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features".SCHIZOPHRENIA BULLETIN .(2024)
医学