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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100702/ https://www.ncbi.nlm.nih.gov/pubmed/35571869 http://dx.doi.org/10.3389/fninf.2022.886365 |
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author | Li, Wenchao Zhao, Jiaqi Shen, Chenyu Zhang, Jingwen Hu, Ji Xiao, Mang Zhang, Jiyong Chen, Minghan |
author_facet | Li, Wenchao Zhao, Jiaqi Shen, Chenyu Zhang, Jingwen Hu, Ji Xiao, Mang Zhang, Jiyong Chen, Minghan |
author_sort | Li, Wenchao |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9100702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91007022022-05-14 Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis Li, Wenchao Zhao, Jiaqi Shen, Chenyu Zhang, Jingwen Hu, Ji Xiao, Mang Zhang, Jiyong Chen, Minghan Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2022-04-29 /pmc/articles/PMC9100702/ /pubmed/35571869 http://dx.doi.org/10.3389/fninf.2022.886365 Text en Copyright © 2022 Li, Zhao, Shen, Zhang, Hu, Xiao, Zhang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Li, Wenchao Zhao, Jiaqi Shen, Chenyu Zhang, Jingwen Hu, Ji Xiao, Mang Zhang, Jiyong Chen, Minghan Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis |
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 | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100702/ https://www.ncbi.nlm.nih.gov/pubmed/35571869 http://dx.doi.org/10.3389/fninf.2022.886365 |
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