A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial Network
Purposes For the purpose of predicting the functional brain network and providing reference for studying the evolution patterns of functional brain network, a model of sequential brain function network based on Generative Adversarial Networks has been built. Methods The topological and temporal char...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2023-09-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2114.html |
| _version_ | 1827283457992032256 |
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| author | Zijian WANG Jiayue XUE Pengfei YANG Yiru LI Jie XIANG |
| author_facet | Zijian WANG Jiayue XUE Pengfei YANG Yiru LI Jie XIANG |
| author_sort | Zijian WANG |
| collection | DOAJ |
| description | Purposes For the purpose of predicting the functional brain network and providing reference for studying the evolution patterns of functional brain network, a model of sequential brain function network based on Generative Adversarial Networks has been built. Methods The topological and temporal characteristics of brain function network are captured through Graph Convolutional Network and long-term and short-term memory network separately, and through feature fusion in the whole connection layer to realize the prediction of functional brain network. Findings The accuracy of network prediction with AUC and MAP indicators has been tested. The experimental results show that the AUC and MAP of the proposed method are 0.95 and 0.92, respectively on two different resting state fMRI data. Compared with other link prediction models, this method can achieve better prediction effect on functional brain network. The accurate prediction of brain function network owns a wide application prospect in the field of brain network decoding and brain computer interface. |
| first_indexed | 2024-04-24T09:36:49Z |
| format | Article |
| id | doaj.art-be0dcd00cd5c47b4b6831b5e2022a8a3 |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:36:49Z |
| publishDate | 2023-09-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-be0dcd00cd5c47b4b6831b5e2022a8a32024-04-15T09:17:01ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322023-09-0154583083710.16355/j.tyut.1007-9432.2023.05.0101007-9432(2023)05-0830-08A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial NetworkZijian WANG0Jiayue XUE1Pengfei YANG2Yiru LI3Jie XIANG4College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaPurposes For the purpose of predicting the functional brain network and providing reference for studying the evolution patterns of functional brain network, a model of sequential brain function network based on Generative Adversarial Networks has been built. Methods The topological and temporal characteristics of brain function network are captured through Graph Convolutional Network and long-term and short-term memory network separately, and through feature fusion in the whole connection layer to realize the prediction of functional brain network. Findings The accuracy of network prediction with AUC and MAP indicators has been tested. The experimental results show that the AUC and MAP of the proposed method are 0.95 and 0.92, respectively on two different resting state fMRI data. Compared with other link prediction models, this method can achieve better prediction effect on functional brain network. The accurate prediction of brain function network owns a wide application prospect in the field of brain network decoding and brain computer interface.https://tyutjournal.tyut.edu.cn/englishpaper/show-2114.htmlgenerative adversarial networksequential link predictiongraph convolutional networkfunctional magnetic resonance |
| spellingShingle | Zijian WANG Jiayue XUE Pengfei YANG Yiru LI Jie XIANG A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial Network Taiyuan Ligong Daxue xuebao generative adversarial network sequential link prediction graph convolutional network functional magnetic resonance |
| title | A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial Network |
| title_full | A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial Network |
| title_fullStr | A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial Network |
| title_full_unstemmed | A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial Network |
| title_short | A Method of Link Prediction of Sequential Functional Brain Networks Based on Generative Adversarial Network |
| title_sort | method of link prediction of sequential functional brain networks based on generative adversarial network |
| topic | generative adversarial network sequential link prediction graph convolutional network functional magnetic resonance |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2114.html |
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