Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease
ObjectivesTo propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic character...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1527323/full |
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| author | Ziyi Yuan Zhaodi Huang Chaojun Li Shengrong Li Qingguo Ren Xiaona Xia Qingjun Jiang Daoqiang Zhang Qi Zhu Xiangshui Meng Xiangshui Meng |
| author_facet | Ziyi Yuan Zhaodi Huang Chaojun Li Shengrong Li Qingguo Ren Xiaona Xia Qingjun Jiang Daoqiang Zhang Qi Zhu Xiangshui Meng Xiangshui Meng |
| author_sort | Ziyi Yuan |
| collection | DOAJ |
| description | ObjectivesTo propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.MethodsThe performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity.ResultsBased on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).ConclusionThe multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression. |
| format | Article |
| id | doaj-art-b0d6dc6b07cf4fbca19c5fa69343183d |
| institution | Kabale University |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-b0d6dc6b07cf4fbca19c5fa69343183d2025-02-12T07:26:37ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-02-011710.3389/fnagi.2025.15273231527323Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive diseaseZiyi Yuan0Zhaodi Huang1Chaojun Li2Shengrong Li3Qingguo Ren4Xiaona Xia5Qingjun Jiang6Daoqiang Zhang7Qi Zhu8Xiangshui Meng9Xiangshui Meng10School of Medicine, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Radiology, Meng Chao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, ChinaCollege of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, ChinaCollege of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, ChinaDepartment of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, ChinaDepartment of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, ChinaDepartment of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, ChinaCollege of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, ChinaCollege of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, ChinaDepartment of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, ChinaMedical Imaging and Engineering Intersection Key Laboratory of Qingdao, Qingdao, ChinaObjectivesTo propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.MethodsThe performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity.ResultsBased on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).ConclusionThe multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1527323/fullmiddle cerebral arterystenosismultimodality imagingmild cognitive impairmentMontreal cognitive assessment |
| spellingShingle | Ziyi Yuan Zhaodi Huang Chaojun Li Shengrong Li Qingguo Ren Xiaona Xia Qingjun Jiang Daoqiang Zhang Qi Zhu Xiangshui Meng Xiangshui Meng Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease Frontiers in Aging Neuroscience middle cerebral artery stenosis multimodality imaging mild cognitive impairment Montreal cognitive assessment |
| title | Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease |
| title_full | Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease |
| title_fullStr | Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease |
| title_full_unstemmed | Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease |
| title_short | Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease |
| title_sort | multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno occlusive disease |
| topic | middle cerebral artery stenosis multimodality imaging mild cognitive impairment Montreal cognitive assessment |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1527323/full |
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