Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhou, Yueying | - |
dc.contributor.author | Zhang, Limei | - |
dc.contributor.author | Teng, Shenghua | - |
dc.contributor.author | Qiao, Lishan | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-02T01:39:40Z | - |
dc.date.available | 2021-09-02T01:39:40Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2018-12-18 | - |
dc.identifier.issn | 1662-4548 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/70884 | - |
dc.description.abstract | High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | FRONTIERS MEDIA SA | - |
dc.subject | MILD COGNITIVE IMPAIRMENT | - |
dc.subject | ALZHEIMERS-DISEASE | - |
dc.subject | BRAIN NETWORKS | - |
dc.subject | REPRESENTATION | - |
dc.subject | DIAGNOSIS | - |
dc.subject | BIOMARKER | - |
dc.subject | DISORDER | - |
dc.subject | MOTION | - |
dc.subject | CORTEX | - |
dc.subject | GRAPH | - |
dc.title | Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.3389/fnins.2018.00959 | - |
dc.identifier.scopusid | 2-s2.0-85079125344 | - |
dc.identifier.wosid | 000453692300001 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN NEUROSCIENCE, v.12 | - |
dc.relation.isPartOf | FRONTIERS IN NEUROSCIENCE | - |
dc.citation.title | FRONTIERS IN NEUROSCIENCE | - |
dc.citation.volume | 12 | - |
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.journalWebOfScienceCategory | Neurosciences | - |
dc.subject.keywordPlus | MILD COGNITIVE IMPAIRMENT | - |
dc.subject.keywordPlus | ALZHEIMERS-DISEASE | - |
dc.subject.keywordPlus | BRAIN NETWORKS | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | BIOMARKER | - |
dc.subject.keywordPlus | DISORDER | - |
dc.subject.keywordPlus | MOTION | - |
dc.subject.keywordPlus | CORTEX | - |
dc.subject.keywordPlus | GRAPH | - |
dc.subject.keywordAuthor | high-order correlation | - |
dc.subject.keywordAuthor | functional connectivity network | - |
dc.subject.keywordAuthor | dynamic network | - |
dc.subject.keywordAuthor | modularity | - |
dc.subject.keywordAuthor | mild cognitive impairment | - |
dc.subject.keywordAuthor | autism spectrum disorder | - |
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.