Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Xinlei, Xin, Junchang, Wang, Zhongyang, Li, Chuangang, Wang, Zhiqiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784836664893374464
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
work_keys_str_mv AT wangxinlei anevolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT xinjunchang anevolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT wangzhongyang anevolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT lichuangang anevolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT wangzhiqiong anevolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT wangxinlei evolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT xinjunchang evolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT wangzhongyang evolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT lichuangang evolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease
AT wangzhiqiong evolvinghypergraphconvolutionalnetworkforthediagnosisofalzheimersdisease