Enhanced multimodal MRI classification of schizophrenia through cross-attention graph neural networks.
Authors
Affiliations (17)
Affiliations (17)
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China.
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Automation, Chongqing University, Chongqing, 400044, China.
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China; Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China.
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
- Zhumadian Psychiatric Hospital, Zhumadian, China.
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China.
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China; Department of Psychology, Xinxiang Medical University, Xinxiang, China.
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China; Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China; Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou, 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou, 425000, Hunan Province, China. Electronic address: [email protected].
Abstract
We propose TB-GCAN, a tri-branch cross-attention graph neural network for schizophrenia classification using multimodal MRI, including sMRI, fMRI, and DTI. Built on a multi-site dataset of 1191 samples from seven scanning sites, the model exploits atlas-defined one-to-one anatomical correspondence across modalities to enable node-level cross-attention during intermediate representation learning. In 7-site leave-one-site-out evaluation, TB-GCAN achieved 84.63% accuracy and outperformed GAT, GCN, CNN, SVM, and MMGNN in the tri-modal setting. Attention-based region ranking highlighted biologically plausible schizophrenia-related regions, and downstream analyses linked the learned imaging representations to PANSS dimensions and transcriptional programs. Unlike generic multimodal GNNs that learn cross-modal relations from data, TB-GCAN directly leverages atlas-aligned regional correspondence to perform anatomically constrained node-level interaction. These findings indicate that anatomically grounded node-level multimodal fusion can improve classification performance while preserving neurobiological interpretability, thereby providing a principled framework for multimodal schizophrenia classification and biomarker discovery.