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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

文献类型：期刊论文

作者Gao, Jingjing3; Qian, Maomin3; Wang, Zhengning3; Li, Yanling4; Luo, Na5; Xie, Sangma6; Shi, Weiyang5,7; Li, Peng8,9,10; Chen, Jun11; Chen, Yunchun2
刊名SCHIZOPHRENIA BULLETIN
出版日期2024-05-16
页码19
关键词schizophrenia identification multimodal MRI deep graph neural networks image marker gene analysis
ISSN号0586-7614
DOI10.1093/schbul/sbae069
通讯作者Jiang, Tianzai(jiangtz@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词BIOMARKERS ; DIAGNOSIS ; ATLAS
资助项目National Natural Science Foundation of China[62276049] ; National Natural Science Foundation of China[61701078] ; National Natural Science Foundation of China[61872068] ; National Natural Science Foundation of China[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] ; Sichuan Province Science and Technology Support Program[2021YFG0126] ; Sichuan Province Science and Technology Support Program[2021YFG0366] ; Sichuan Province Science and Technology Support Program[2022YFS0180] ; Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China[ZYGX2021YGLH014]
WOS研究方向Psychiatry
语种英语
WOS记录号WOS:001224084300001
出版者OXFORD UNIV PRESS
资助机构National Natural Science Foundation of China ; Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project ; National Key R&D Program of China ; Sichuan Province Science and Technology Support Program ; Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China
源URL[http://ir.ia.ac.cn/handle/173211/58336]  
专题自动化研究所_脑网络组研究中心
通讯作者Jiang, Tianzai
作者单位1.Zhumadian Psychiat Hosp, Zhumadian, Peoples R China
2.Fourth Mil Med Univ, Xijing Hosp, Dept Psychiat, Xian, Peoples R China
3.Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu, Peoples R China
4.Xihua Univ, Sch Elect Engn & Elect Informat, Chengdu, Peoples R China
5.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China
6.Hangzhou Dianzi Univ, Inst Biomed Engn & Instrumentat, Sch Automat, Hangzhou, Peoples R China
7.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
8.Peking Univ Sixth Hosp, Inst Mental Hlth, Beijing, Peoples R China
9.Peking Univ Sixth Hosp, Key Lab Mental Hlth, Minist Hlth, Beijing, Peoples R China
10.Peking Univ Sixth Hosp, Natl Clin Res Ctr Mental Disorders, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Jingjing,Qian, Maomin,Wang, Zhengning,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,2024:19.
APA Gao, Jingjing.,Qian, Maomin.,Wang, Zhengning.,Li, Yanling.,Luo, Na.,...&Jiang, Tianzai.(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,19.
MLA Gao, Jingjing,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):19.

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来源：自动化研究所

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