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Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach

INTRODUCTION: Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these me...

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Autores principales: Zhang, Xiaopan, Liu, Junhong, Chen, Yuan, Jin, Yanan, Cheng, Jingliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994705/
https://www.ncbi.nlm.nih.gov/pubmed/33393200
http://dx.doi.org/10.1002/brb3.2027
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author Zhang, Xiaopan
Liu, Junhong
Chen, Yuan
Jin, Yanan
Cheng, Jingliang
author_facet Zhang, Xiaopan
Liu, Junhong
Chen, Yuan
Jin, Yanan
Cheng, Jingliang
author_sort Zhang, Xiaopan
collection PubMed
description INTRODUCTION: Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance. METHODS: We propose a highly‐available nodes approach for constructing brain network of patients with MCI and AD. With resting‐state functional magnetic resonance imaging (rs‐fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier. RESULTS: Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes. CONCLUSIONS: The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs‐fMRI data for construction and topology analysis brain network.
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spelling pubmed-79947052021-03-29 Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach Zhang, Xiaopan Liu, Junhong Chen, Yuan Jin, Yanan Cheng, Jingliang Brain Behav Original Research INTRODUCTION: Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance. METHODS: We propose a highly‐available nodes approach for constructing brain network of patients with MCI and AD. With resting‐state functional magnetic resonance imaging (rs‐fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier. RESULTS: Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes. CONCLUSIONS: The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs‐fMRI data for construction and topology analysis brain network. John Wiley and Sons Inc. 2021-01-03 /pmc/articles/PMC7994705/ /pubmed/33393200 http://dx.doi.org/10.1002/brb3.2027 Text en © 2021 The Authors. Brain and Behavior published by Wiley Periodicals LLC This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Zhang, Xiaopan
Liu, Junhong
Chen, Yuan
Jin, Yanan
Cheng, Jingliang
Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach
title Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach
title_full Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach
title_fullStr Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach
title_full_unstemmed Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach
title_short Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach
title_sort brain network construction and analysis for patients with mild cognitive impairment and alzheimer's disease based on a highly‐available nodes approach
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994705/
https://www.ncbi.nlm.nih.gov/pubmed/33393200
http://dx.doi.org/10.1002/brb3.2027
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