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Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification
Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374334/ https://www.ncbi.nlm.nih.gov/pubmed/34421530 http://dx.doi.org/10.3389/fnins.2021.720909 |
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author | Zhang, Yangyang Jiang, Xiao Qiao, Lishan Liu, Mingxia |
author_facet | Zhang, Yangyang Jiang, Xiao Qiao, Lishan Liu, Mingxia |
author_sort | Zhang, Yangyang |
collection | PubMed |
description | Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the topological structure (such as modularity) of FBNs. To address this issue, we propose a modular-LASSO feature selection (MLFS) framework that can explicitly model the modularity information to identify discriminative and interpretable features from FBNs for automated AD/MCI classification. Specifically, the proposed MLFS method first searches the modular structure of FBNs through a signed spectral clustering algorithm, and then selects discriminative features via a modularity-induced group LASSO method, followed by a support vector machine (SVM) for classification. To evaluate the effectiveness of the proposed method, extensive experiments are performed on 563 resting-state functional MRI scans from the public ADNI database to identify subjects with AD/MCI from normal controls and predict the future progress of MCI subjects. Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD. |
format | Online Article Text |
id | pubmed-8374334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83743342021-08-20 Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification Zhang, Yangyang Jiang, Xiao Qiao, Lishan Liu, Mingxia Front Neurosci Neuroscience Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the topological structure (such as modularity) of FBNs. To address this issue, we propose a modular-LASSO feature selection (MLFS) framework that can explicitly model the modularity information to identify discriminative and interpretable features from FBNs for automated AD/MCI classification. Specifically, the proposed MLFS method first searches the modular structure of FBNs through a signed spectral clustering algorithm, and then selects discriminative features via a modularity-induced group LASSO method, followed by a support vector machine (SVM) for classification. To evaluate the effectiveness of the proposed method, extensive experiments are performed on 563 resting-state functional MRI scans from the public ADNI database to identify subjects with AD/MCI from normal controls and predict the future progress of MCI subjects. Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD. Frontiers Media S.A. 2021-08-05 /pmc/articles/PMC8374334/ /pubmed/34421530 http://dx.doi.org/10.3389/fnins.2021.720909 Text en Copyright © 2021 Zhang, Jiang, Qiao and Liu. 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 Zhang, Yangyang Jiang, Xiao Qiao, Lishan Liu, Mingxia Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification |
title | Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification |
title_full | Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification |
title_fullStr | Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification |
title_full_unstemmed | Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification |
title_short | Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification |
title_sort | modularity-guided functional brain network analysis for early-stage dementia identification |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374334/ https://www.ncbi.nlm.nih.gov/pubmed/34421530 http://dx.doi.org/10.3389/fnins.2021.720909 |
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