Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Xie, Qingsong | - |
dc.contributor.author | Zhang, Xiangfei | - |
dc.contributor.author | Rekik, Islem | - |
dc.contributor.author | Chen, Xiaobo | - |
dc.contributor.author | Mao, Ning | - |
dc.contributor.author | Shen, Dinggang | - |
dc.contributor.author | Zhao, Feng | - |
dc.date.accessioned | 2022-02-27T15:40:16Z | - |
dc.date.available | 2022-02-27T15:40:16Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-07-06 | - |
dc.identifier.issn | 2167-8359 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137147 | - |
dc.description.abstract | The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PEERJ INC | - |
dc.subject | CHILDREN | - |
dc.subject | REGRESSION | - |
dc.subject | STRENGTH | - |
dc.subject | CORTEX | - |
dc.title | Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.7717/peerj.11692 | - |
dc.identifier.scopusid | 2-s2.0-85109303016 | - |
dc.identifier.wosid | 000669951600004 | - |
dc.identifier.bibliographicCitation | PEERJ, v.9 | - |
dc.relation.isPartOf | PEERJ | - |
dc.citation.title | PEERJ | - |
dc.citation.volume | 9 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | CHILDREN | - |
dc.subject.keywordPlus | CORTEX | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | STRENGTH | - |
dc.subject.keywordAuthor | Autism spectrum disorder | - |
dc.subject.keywordAuthor | Central moment feature | - |
dc.subject.keywordAuthor | Cross validation | - |
dc.subject.keywordAuthor | Dynamic functional connectivity network | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Feature selection | - |
dc.subject.keywordAuthor | Functional connectivity | - |
dc.subject.keywordAuthor | Functional magnetic resonance imaging | - |
dc.subject.keywordAuthor | High functional connectivity network | - |
dc.subject.keywordAuthor | Low functional connectivity network | - |
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