Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783669810022842368 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7994705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhangxiaopan brainnetworkconstructionandanalysisforpatientswithmildcognitiveimpairmentandalzheimersdiseasebasedonahighlyavailablenodesapproach AT liujunhong brainnetworkconstructionandanalysisforpatientswithmildcognitiveimpairmentandalzheimersdiseasebasedonahighlyavailablenodesapproach AT chenyuan brainnetworkconstructionandanalysisforpatientswithmildcognitiveimpairmentandalzheimersdiseasebasedonahighlyavailablenodesapproach AT jinyanan brainnetworkconstructionandanalysisforpatientswithmildcognitiveimpairmentandalzheimersdiseasebasedonahighlyavailablenodesapproach AT chengjingliang brainnetworkconstructionandanalysisforpatientswithmildcognitiveimpairmentandalzheimersdiseasebasedonahighlyavailablenodesapproach AT brainnetworkconstructionandanalysisforpatientswithmildcognitiveimpairmentandalzheimersdiseasebasedonahighlyavailablenodesapproach |