Resting-state brain information flow predicts cognitive flexibility in humans

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作者
Oliver Y. Chén
Hengyi Cao
Jenna M. Reinen
Tianchen Qian
Jiangtao Gou
Huy Phan
Maarten De Vos
Tyrone D. Cannon
机构
[1] Yale University,Department of Psychology
[2] University of Oxford,Department of Engineering Science
[3] IBM Watson Research,Department of Statistics
[4] Harvard University,Department of Mathematics and Statistics
[5] The City University of New York,Department of Psychiatry
[6] Yale University,School of Computing
[7] Fox Chase Cancer Center,undefined
[8] University of Kent,undefined
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Scientific Reports | / 9卷
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摘要
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.
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