Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis
Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical sympt...
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| Format: | Article |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2022.886365/full |
| _version_ | 1828499448488001536 |
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| author | Wenchao Li Jiaqi Zhao Chenyu Shen Jingwen Zhang Ji Hu Mang Xiao Jiyong Zhang Minghan Chen |
| author_facet | Wenchao Li Jiaqi Zhao Chenyu Shen Jingwen Zhang Ji Hu Mang Xiao Jiyong Zhang Minghan Chen |
| author_sort | Wenchao Li |
| collection | DOAJ |
| description | Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset. |
| first_indexed | 2024-12-11T13:24:47Z |
| format | Article |
| id | doaj.art-5c763bcf30e0412c84c1c8a8f32689e5 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-11T13:24:47Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-5c763bcf30e0412c84c1c8a8f32689e52022-12-22T01:05:37ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962022-04-011610.3389/fninf.2022.886365886365Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and AnalysisWenchao Li0Jiaqi Zhao1Chenyu Shen2Jingwen Zhang3Ji Hu4Mang Xiao5Jiyong Zhang6Minghan Chen7Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, ChinaResearch Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, ChinaIntelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, ChinaDepartment of Computer Science, Wake Forest University, Winston-Salem, NC, United StatesIntelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, ChinaSir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, ChinaIntelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, ChinaDepartment of Computer Science, Wake Forest University, Winston-Salem, NC, United StatesAlzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.https://www.frontiersin.org/articles/10.3389/fninf.2022.886365/fullbrain networkamyloid-PETregional brain fusion-graph convolutional network (RBF-GCN)adaptive native node attribute (ANNA)Alzheimer's disease |
| spellingShingle | Wenchao Li Jiaqi Zhao Chenyu Shen Jingwen Zhang Ji Hu Mang Xiao Jiyong Zhang Minghan Chen Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis Frontiers in Neuroinformatics brain network amyloid-PET regional brain fusion-graph convolutional network (RBF-GCN) adaptive native node attribute (ANNA) Alzheimer's disease |
| title | Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis |
| title_full | Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis |
| title_fullStr | Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis |
| title_full_unstemmed | Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis |
| title_short | Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis |
| title_sort | regional brain fusion graph convolutional network for alzheimer s disease prediction and analysis |
| topic | brain network amyloid-PET regional brain fusion-graph convolutional network (RBF-GCN) adaptive native node attribute (ANNA) Alzheimer's disease |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2022.886365/full |
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