High-Order Resting-State Functional Connectivity Network for MCI Classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Xiaobo | - |
dc.contributor.author | Zhang, Han | - |
dc.contributor.author | Gao, Yue | - |
dc.contributor.author | Wee, Chong-Yaw | - |
dc.contributor.author | Li, Gang | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-03T20:24:39Z | - |
dc.date.available | 2021-09-03T20:24:39Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-09 | - |
dc.identifier.issn | 1065-9471 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/87625 | - |
dc.description.abstract | Brain functional connectivity ( FC) network, estimated with resting-state functional magnetic resonance imaging ( RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions ( in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These loworder networks ( obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both loworder and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis. (C) 2016 Wiley Periodicals, Inc. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.subject | INVERSE COVARIANCE ESTIMATION | - |
dc.subject | ALZHEIMERS-DISEASE | - |
dc.subject | BRAIN CONNECTIVITY | - |
dc.subject | DIAGNOSIS | - |
dc.subject | REPRESENTATION | - |
dc.subject | DEMENTIA | - |
dc.subject | MODEL | - |
dc.title | High-Order Resting-State Functional Connectivity Network for MCI Classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1002/hbm.23240 | - |
dc.identifier.scopusid | 2-s2.0-84982893690 | - |
dc.identifier.wosid | 000382484600015 | - |
dc.identifier.bibliographicCitation | HUMAN BRAIN MAPPING, v.37, no.9, pp.3282 - 3296 | - |
dc.relation.isPartOf | HUMAN BRAIN MAPPING | - |
dc.citation.title | HUMAN BRAIN MAPPING | - |
dc.citation.volume | 37 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 3282 | - |
dc.citation.endPage | 3296 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.relation.journalWebOfScienceCategory | Neuroimaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | INVERSE COVARIANCE ESTIMATION | - |
dc.subject.keywordPlus | ALZHEIMERS-DISEASE | - |
dc.subject.keywordPlus | BRAIN CONNECTIVITY | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordPlus | DEMENTIA | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | mild cognitive impairment | - |
dc.subject.keywordAuthor | functional connectivity | - |
dc.subject.keywordAuthor | low-order and high-order networks | - |
dc.subject.keywordAuthor | brain disease diagnosis | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.