dc.contributor.author | Chén, OY | en_US |
dc.contributor.author | Cao, H | en_US |
dc.contributor.author | Reinen, JM | en_US |
dc.contributor.author | Qian, T | en_US |
dc.contributor.author | Gou, J | en_US |
dc.contributor.author | Phan, H | en_US |
dc.contributor.author | De Vos, M | en_US |
dc.contributor.author | Cannon, TD | en_US |
dc.date.accessioned | 2020-06-17T10:30:33Z | |
dc.date.available | 2019-02-07 | en_US |
dc.date.issued | 2019-03-07 | en_US |
dc.identifier.other | 3879 | |
dc.identifier.other | 3879 | |
dc.identifier.other | 3879 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/65050 | |
dc.description.abstract | The 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.extent | 3879 - ? | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Sci Rep | en_US |
dc.rights | Attribution 3.0 United States | |
dc.rights | This 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.uri | http://creativecommons.org/licenses/by/3.0/us/ | |
dc.subject | Adult | en_US |
dc.subject | Brain | en_US |
dc.subject | Cognition | en_US |
dc.subject | Connectome | en_US |
dc.subject | Female | en_US |
dc.subject | Humans | en_US |
dc.subject | Magnetic Resonance Imaging | en_US |
dc.subject | Male | en_US |
dc.subject | Models, Neurological | en_US |
dc.subject | Models, Psychological | en_US |
dc.subject | Rest | en_US |
dc.subject | Signal Processing, Computer-Assisted | en_US |
dc.subject | Young Adult | en_US |
dc.title | Resting-state brain information flow predicts cognitive flexibility in humans. | en_US |
dc.type | Article | |
dc.rights.holder | © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group | |
dc.identifier.doi | 10.1038/s41598-019-40345-8 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/30846746 | en_US |
pubs.issue | 1 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
pubs.volume | 9 | en_US |
dcterms.dateAccepted | 2019-02-07 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |