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An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease
In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on h...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689953/ https://www.ncbi.nlm.nih.gov/pubmed/36359476 http://dx.doi.org/10.3390/diagnostics12112632 |
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author | Wang, Xinlei Xin, Junchang Wang, Zhongyang Li, Chuangang Wang, Zhiqiong |
author_facet | Wang, Xinlei Xin, Junchang Wang, Zhongyang Li, Chuangang Wang, Zhiqiong |
author_sort | Wang, Xinlei |
collection | PubMed |
description | In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods. |
format | Online Article Text |
id | pubmed-9689953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96899532022-11-25 An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease Wang, Xinlei Xin, Junchang Wang, Zhongyang Li, Chuangang Wang, Zhiqiong Diagnostics (Basel) Article In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods. MDPI 2022-10-30 /pmc/articles/PMC9689953/ /pubmed/36359476 http://dx.doi.org/10.3390/diagnostics12112632 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Xinlei Xin, Junchang Wang, Zhongyang Li, Chuangang Wang, Zhiqiong An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease |
title | An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease |
title_full | An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease |
title_fullStr | An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease |
title_full_unstemmed | An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease |
title_short | An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease |
title_sort | evolving hypergraph convolutional network for the diagnosis of alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689953/ https://www.ncbi.nlm.nih.gov/pubmed/36359476 http://dx.doi.org/10.3390/diagnostics12112632 |
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