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Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network
BACKGROUND: Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237212/ https://www.ncbi.nlm.nih.gov/pubmed/35774112 http://dx.doi.org/10.3389/fnagi.2022.866230 |
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author | Zhou, Yu Si, Xiaopeng Chao, Yi-Ping Chen, Yuanyuan Lin, Ching-Po Li, Sicheng Zhang, Xingjian Sun, Yulin Ming, Dong Li, Qiang |
author_facet | Zhou, Yu Si, Xiaopeng Chao, Yi-Ping Chen, Yuanyuan Lin, Ching-Po Li, Sicheng Zhang, Xingjian Sun, Yulin Ming, Dong Li, Qiang |
author_sort | Zhou, Yu |
collection | PubMed |
description | BACKGROUND: Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance. METHODS: Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier. RESULTS: (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI. CONCLUSION: Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD. |
format | Online Article Text |
id | pubmed-9237212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92372122022-06-29 Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network Zhou, Yu Si, Xiaopeng Chao, Yi-Ping Chen, Yuanyuan Lin, Ching-Po Li, Sicheng Zhang, Xingjian Sun, Yulin Ming, Dong Li, Qiang Front Aging Neurosci Neuroscience BACKGROUND: Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance. METHODS: Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier. RESULTS: (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI. CONCLUSION: Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD. Frontiers Media S.A. 2022-06-14 /pmc/articles/PMC9237212/ /pubmed/35774112 http://dx.doi.org/10.3389/fnagi.2022.866230 Text en Copyright © 2022 Zhou, Si, Chao, Chen, Lin, Li, Zhang, Sun, Ming and Li. 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 Zhou, Yu Si, Xiaopeng Chao, Yi-Ping Chen, Yuanyuan Lin, Ching-Po Li, Sicheng Zhang, Xingjian Sun, Yulin Ming, Dong Li, Qiang Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network |
title | Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network |
title_full | Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network |
title_fullStr | Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network |
title_full_unstemmed | Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network |
title_short | Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network |
title_sort | automated classification of mild cognitive impairment by machine learning with hippocampus-related white matter network |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237212/ https://www.ncbi.nlm.nih.gov/pubmed/35774112 http://dx.doi.org/10.3389/fnagi.2022.866230 |
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