TGNet: tensor-based graph convolutional networks for multimodal brain network analysis
Abstract Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this pape...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-024-00409-6 |
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| author | Zhaoming Kong Rong Zhou Xinwei Luo Songlin Zhao Ann B. Ragin Alex D. Leow Lifang He |
| author_facet | Zhaoming Kong Rong Zhou Xinwei Luo Songlin Zhao Ann B. Ragin Alex D. Leow Lifang He |
| author_sort | Zhaoming Kong |
| collection | DOAJ |
| description | Abstract Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. We evaluate TGNet on four datasets—HIV, Bipolar Disorder (BP), and Parkinson’s Disease (PPMI), Alzheimer’s Disease (ADNI)—demonstrating that it significantly outperforms existing methods for disease classification tasks, particularly in scenarios with limited sample sizes. The robustness and effectiveness of TGNet highlight its potential for advancing multimodal brain network analysis. The code is available at https://github.com/rongzhou7/TGNet . |
| format | Article |
| id | doaj-art-0735d99e13194d539931a11c5f1b7150 |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-0735d99e13194d539931a11c5f1b71502024-12-08T12:20:52ZengBMCBioData Mining1756-03812024-12-0117112410.1186/s13040-024-00409-6TGNet: tensor-based graph convolutional networks for multimodal brain network analysisZhaoming Kong0Rong Zhou1Xinwei Luo2Songlin Zhao3Ann B. Ragin4Alex D. Leow5Lifang He6School of Software Engineering, South China University of TechnologyDepartment of Computer Science and Engineering, Lehigh UniversityDepartment of Computer Science and Engineering, Lehigh UniversityDepartment of Computer Science and Engineering, Lehigh UniversityDepartment of Radiology, Northwestern UniversityDepartment of Psychiatry, University of Illinois ChicagoDepartment of Computer Science and Engineering, Lehigh UniversityAbstract Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. We evaluate TGNet on four datasets—HIV, Bipolar Disorder (BP), and Parkinson’s Disease (PPMI), Alzheimer’s Disease (ADNI)—demonstrating that it significantly outperforms existing methods for disease classification tasks, particularly in scenarios with limited sample sizes. The robustness and effectiveness of TGNet highlight its potential for advancing multimodal brain network analysis. The code is available at https://github.com/rongzhou7/TGNet .https://doi.org/10.1186/s13040-024-00409-6Multimodal brain networksTensorGraph convolutional networkDisease classification |
| spellingShingle | Zhaoming Kong Rong Zhou Xinwei Luo Songlin Zhao Ann B. Ragin Alex D. Leow Lifang He TGNet: tensor-based graph convolutional networks for multimodal brain network analysis BioData Mining Multimodal brain networks Tensor Graph convolutional network Disease classification |
| title | TGNet: tensor-based graph convolutional networks for multimodal brain network analysis |
| title_full | TGNet: tensor-based graph convolutional networks for multimodal brain network analysis |
| title_fullStr | TGNet: tensor-based graph convolutional networks for multimodal brain network analysis |
| title_full_unstemmed | TGNet: tensor-based graph convolutional networks for multimodal brain network analysis |
| title_short | TGNet: tensor-based graph convolutional networks for multimodal brain network analysis |
| title_sort | tgnet tensor based graph convolutional networks for multimodal brain network analysis |
| topic | Multimodal brain networks Tensor Graph convolutional network Disease classification |
| url | https://doi.org/10.1186/s13040-024-00409-6 |
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