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dc.contributor.authorZhu, Yifan
dc.contributor.authorLi, Xuesong
dc.contributor.authorSun, Yang
dc.contributor.authorWang, Haixu
dc.contributor.authorGuo, Hua
dc.contributor.authorSui, Jie
dc.date.accessioned2021-08-11T20:30:01Z
dc.date.available2021-08-11T20:30:01Z
dc.date.issued2021-08-02
dc.identifier.citationZhu , Y , Li , X , Sun , Y , Wang , H , Guo , H & Sui , J 2021 , ' Investigating Neural Substrates of Individual Independence and Interdependence Orientations via Efficiency-based Dynamic Functional Connectivity : A Machine Learning Approach ' , IEEE Transactions on Cognitive and Developmental Systems . https://doi.org/10.1109/TCDS.2021.3101643en
dc.identifier.issn2379-8920
dc.identifier.otherPURE: 197972645
dc.identifier.otherPURE UUID: 5c0dc84d-8ed2-4282-a73a-5c0db9147d93
dc.identifier.otherORCID: /0000-0002-4031-4456/work/98628083
dc.identifier.otherScopus: 85112637946
dc.identifier.urihttps://hdl.handle.net/2164/16939
dc.descriptionFundings: Beihang University and Capital Medical University Advanced Innovation Center for Big DataBased Precision Medicine Plan; 10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100000275-Leverhulme Trust;en
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Cognitive and Developmental Systemsen
dc.rights20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Final published version can be found at 10.1109/TCDS.2021.3101643en
dc.subjectself-construalen
dc.subjectresting state functional connectivity (rsFC)en
dc.subjectnodal efficiencyen
dc.subjectdynamic functional connectivityen
dc.subjectBF Psychologyen
dc.subject.lccBFen
dc.titleInvestigating Neural Substrates of Individual Independence and Interdependence Orientations via Efficiency-based Dynamic Functional Connectivity : A Machine Learning Approachen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Psychologyen
dc.description.statusPeer revieweden
dc.description.versionPostprinten
dc.identifier.doihttps://doi.org/10.1109/TCDS.2021.3101643


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