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Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification
Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer’s disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8645991/ https://www.ncbi.nlm.nih.gov/pubmed/34881247 http://dx.doi.org/10.3389/fcell.2021.782727 |
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author | Li, Weikai Xu, Xiaowen Wang, Zhengxia Peng, Liling Wang, Peijun Gao, Xin |
author_facet | Li, Weikai Xu, Xiaowen Wang, Zhengxia Peng, Liling Wang, Peijun Gao, Xin |
author_sort | Li, Weikai |
collection | PubMed |
description | Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer’s disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focused on the single connection pattern for the neuro-disease diagnosis. Thus, such approaches are commonly insufficient to reveal the underlying changes between groups of MCI patients and normal controls (NCs), thereby limiting their performance. In this context, the information associated with multiple patterns (e.g., functional connectivity or effective connectivity) from single-mode data are considered for the MCI diagnosis. In this paper, we provide a novel multiple connection pattern combination (MCPC) approach to combine different patterns based on the kernel combination trick to identify MCI from NCs. In particular, sixty-three MCI cases and sixty-four NC cases from the ADNI dataset are conducted for the validation of the proposed MCPC method. The proposed method achieves 87.40% classification accuracy and significantly outperforms methods that use a single pattern. |
format | Online Article Text |
id | pubmed-8645991 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86459912021-12-07 Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification Li, Weikai Xu, Xiaowen Wang, Zhengxia Peng, Liling Wang, Peijun Gao, Xin Front Cell Dev Biol Cell and Developmental Biology Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer’s disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focused on the single connection pattern for the neuro-disease diagnosis. Thus, such approaches are commonly insufficient to reveal the underlying changes between groups of MCI patients and normal controls (NCs), thereby limiting their performance. In this context, the information associated with multiple patterns (e.g., functional connectivity or effective connectivity) from single-mode data are considered for the MCI diagnosis. In this paper, we provide a novel multiple connection pattern combination (MCPC) approach to combine different patterns based on the kernel combination trick to identify MCI from NCs. In particular, sixty-three MCI cases and sixty-four NC cases from the ADNI dataset are conducted for the validation of the proposed MCPC method. The proposed method achieves 87.40% classification accuracy and significantly outperforms methods that use a single pattern. Frontiers Media S.A. 2021-11-22 /pmc/articles/PMC8645991/ /pubmed/34881247 http://dx.doi.org/10.3389/fcell.2021.782727 Text en Copyright © 2021 Li, Xu, Wang, Peng, Wang and Gao. 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 | Cell and Developmental Biology Li, Weikai Xu, Xiaowen Wang, Zhengxia Peng, Liling Wang, Peijun Gao, Xin Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification |
title | Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification |
title_full | Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification |
title_fullStr | Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification |
title_full_unstemmed | Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification |
title_short | Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification |
title_sort | multiple connection pattern combination from single-mode data for mild cognitive impairment identification |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8645991/ https://www.ncbi.nlm.nih.gov/pubmed/34881247 http://dx.doi.org/10.3389/fcell.2021.782727 |
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