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...

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Autores principales:	Weikai Li, Xiaowen Xu, Zhengxia Wang, Liling Peng, Peijun Wang, Xin Gao
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	functional connectivity effective connectivity multiview multimodal mild cognitive impairment Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d867a3b99630430599a31d21b43827bd
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spelling	oai:doaj.org-article:d867a3b99630430599a31d21b43827bd2021-11-22T06:48:34ZMultiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification2296-634X10.3389/fcell.2021.782727https://doaj.org/article/d867a3b99630430599a31d21b43827bd2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcell.2021.782727/fullhttps://doaj.org/toc/2296-634XMild 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.Weikai LiWeikai LiXiaowen XuXiaowen XuZhengxia WangLiling PengPeijun WangPeijun WangXin GaoFrontiers Media S.A.articlefunctional connectivityeffective connectivitymultiviewmultimodalmild cognitive impairmentBiology (General)QH301-705.5ENFrontiers in Cell and Developmental Biology, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	functional connectivity effective connectivity multiview multimodal mild cognitive impairment Biology (General) QH301-705.5
spellingShingle	functional connectivity effective connectivity multiview multimodal mild cognitive impairment Biology (General) QH301-705.5 Weikai Li Weikai Li Xiaowen Xu Xiaowen Xu Zhengxia Wang Liling Peng Peijun Wang Peijun Wang Xin Gao Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification
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	article
author	Weikai Li Weikai Li Xiaowen Xu Xiaowen Xu Zhengxia Wang Liling Peng Peijun Wang Peijun Wang Xin Gao
author_facet	Weikai Li Weikai Li Xiaowen Xu Xiaowen Xu Zhengxia Wang Liling Peng Peijun Wang Peijun Wang Xin Gao
author_sort	Weikai Li
title	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_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_sort	multiple connection pattern combination from single-mode data for mild cognitive impairment identification
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/d867a3b99630430599a31d21b43827bd
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Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification

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