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Investigating Neural Substrates of Individual Independence and Interdependence Orientations via Efficiency-based Dynamic Functional Connectivity : A Machine Learning Approach

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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.contributor.institutionUniversity of Aberdeen.Psychologyen
dc.date.accessioned2021-08-11T20:30:01Z
dc.date.available2021-08-11T20:30:01Z
dc.date.issued2021-08-02
dc.descriptionFunding: This work was supported in part by the National Natural Science Foundation of China under Grant 62071049 and Grant 61801026; in part by Leverhulme Trust under Grant RPG-2019-010; and in part by the Beihang University and Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan under Grant BHME201907.en
dc.description.statusPeer revieweden
dc.format.extent11
dc.format.extent1033071
dc.identifier197972645
dc.identifier5c0dc84d-8ed2-4282-a73a-5c0db9147d93
dc.identifier85112637946
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, vol. 14, no. 2, pp. 761-771. https://doi.org/10.1109/TCDS.2021.3101643en
dc.identifier.doi10.1109/TCDS.2021.3101643
dc.identifier.iss2en
dc.identifier.issn2379-8920
dc.identifier.otherORCID: /0000-0002-4031-4456/work/98628083
dc.identifier.urihttps://hdl.handle.net/2164/16939
dc.identifier.vol14en
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Cognitive and Developmental Systemsen
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

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