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Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data
The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer’s disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917197/ https://www.ncbi.nlm.nih.gov/pubmed/35277565 http://dx.doi.org/10.1038/s41598-022-08231-y |
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author | Jitsuishi, Tatsuya Yamaguchi, Atsushi |
author_facet | Jitsuishi, Tatsuya Yamaguchi, Atsushi |
author_sort | Jitsuishi, Tatsuya |
collection | PubMed |
description | The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer’s disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD. |
format | Online Article Text |
id | pubmed-8917197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89171972022-03-16 Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data Jitsuishi, Tatsuya Yamaguchi, Atsushi Sci Rep Article The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer’s disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD. Nature Publishing Group UK 2022-03-11 /pmc/articles/PMC8917197/ /pubmed/35277565 http://dx.doi.org/10.1038/s41598-022-08231-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jitsuishi, Tatsuya Yamaguchi, Atsushi Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data |
title | Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data |
title_full | Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data |
title_fullStr | Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data |
title_full_unstemmed | Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data |
title_short | Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data |
title_sort | searching for optimal machine learning model to classify mild cognitive impairment (mci) subtypes using multimodal mri data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917197/ https://www.ncbi.nlm.nih.gov/pubmed/35277565 http://dx.doi.org/10.1038/s41598-022-08231-y |
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