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dc.contributor.authorChén, OYen_US
dc.contributor.authorCao, Hen_US
dc.contributor.authorReinen, JMen_US
dc.contributor.authorQian, Ten_US
dc.contributor.authorGou, Jen_US
dc.contributor.authorPhan, Hen_US
dc.contributor.authorDe Vos, Men_US
dc.contributor.authorCannon, TDen_US
dc.date.accessioned2020-06-17T10:30:33Z
dc.date.available2019-02-07en_US
dc.date.issued2019-03-07en_US
dc.identifier.other3879
dc.identifier.other3879
dc.identifier.other3879en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/65050
dc.description.abstractThe human brain is a dynamic system, where communication between spatially distinct areas facilitates complex cognitive functions and behaviors. How information transfers between brain regions and how it gives rise to human cognition, however, are unclear. In this article, using resting-state functional magnetic resonance imaging (fMRI) data from 783 healthy adults in the Human Connectome Project (HCP) dataset, we map the brain's directed information flow architecture through a Granger-Geweke causality prism. We demonstrate that the information flow profiles in the general population primarily involve local exchanges within specialized functional systems, long-distance exchanges from the dorsal brain to the ventral brain, and top-down exchanges from the higher-order systems to the primary systems. Using an information flow map discovered from 550 subjects, the individual directed information flow profiles can significantly predict cognitive flexibility scores in 233 novel individuals. Our results provide evidence for directed information network architecture in the cerebral cortex, and suggest that features of the information flow configuration during rest underpin cognitive ability in humans.en_US
dc.format.extent3879 - ?en_US
dc.languageengen_US
dc.relation.ispartofSci Repen_US
dc.rightsAttribution 3.0 United States
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectAdulten_US
dc.subjectBrainen_US
dc.subjectCognitionen_US
dc.subjectConnectomeen_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectMagnetic Resonance Imagingen_US
dc.subjectMaleen_US
dc.subjectModels, Neurologicalen_US
dc.subjectModels, Psychologicalen_US
dc.subjectResten_US
dc.subjectSignal Processing, Computer-Assisteden_US
dc.subjectYoung Adulten_US
dc.titleResting-state brain information flow predicts cognitive flexibility in humans.en_US
dc.typeArticle
dc.rights.holder© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
dc.identifier.doi10.1038/s41598-019-40345-8en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/30846746en_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
pubs.volume9en_US
dcterms.dateAccepted2019-02-07en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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Attribution 3.0 United States
Except where otherwise noted, this item's license is described as Attribution 3.0 United States