The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook
Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging fro...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/13/10/1462 |
| _version_ | 1797574487121592320 |
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| author | Shuoyan Zhang Jiacheng Yang Ying Zhang Jiayi Zhong Wenjing Hu Chenyang Li Jiehui Jiang |
| author_facet | Shuoyan Zhang Jiacheng Yang Ying Zhang Jiayi Zhong Wenjing Hu Chenyang Li Jiehui Jiang |
| author_sort | Shuoyan Zhang |
| collection | DOAJ |
| description | Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging. |
| first_indexed | 2024-03-10T21:23:12Z |
| format | Article |
| id | doaj.art-11fe7517dcc14ab28244768129f04cbe |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T21:23:12Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-11fe7517dcc14ab28244768129f04cbe2023-11-19T15:53:17ZengMDPI AGBrain Sciences2076-34252023-10-011310146210.3390/brainsci13101462The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and OutlookShuoyan Zhang0Jiacheng Yang1Ying Zhang2Jiayi Zhong3Wenjing Hu4Chenyang Li5Jiehui Jiang6School of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaShanghai Institute of Biomedical Engineering, Shanghai University, Shanghai 200444, ChinaNeurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.https://www.mdpi.com/2076-3425/13/10/1462neurological disorderdeep learninggraph neural networkdiagnostic model |
| spellingShingle | Shuoyan Zhang Jiacheng Yang Ying Zhang Jiayi Zhong Wenjing Hu Chenyang Li Jiehui Jiang The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook Brain Sciences neurological disorder deep learning graph neural network diagnostic model |
| title | The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook |
| title_full | The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook |
| title_fullStr | The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook |
| title_full_unstemmed | The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook |
| title_short | The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook |
| title_sort | combination of a graph neural network technique and brain imaging to diagnose neurological disorders a review and outlook |
| topic | neurological disorder deep learning graph neural network diagnostic model |
| url | https://www.mdpi.com/2076-3425/13/10/1462 |
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