Journal
NATURE HUMAN BEHAVIOUR
Volume 2, Issue 10, Pages 765-777Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41562-018-0420-6
Keywords
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Funding
- National Institutes of Health (NIH) [NS79698]
- National Science Foundation Graduate Research Fellowship Program [DGE 1106400]
- NIH T32 Ruth L. Kirschstein Institutional National Research Service Award [5T32MH106442-02]
- Singapore MOE Tier 2 [MOE2014-T2-2-016]
- NUS Strategic Research [DPRT/944/09/14]
- NUS SOM Aspiration Fund [R185000271720]
- Singapore NMRC [CBRG/0088/2015]
- NUS YIA
- Singapore National Research Foundation Fellowship
- John D. and Catherine T. MacArthur Foundation
- Alfred P. Sloan Foundation
- Army Research Laboratory
- Army Research Office [W911NF-10-2-0022, W911NF-14-1-0679]
- NIH [2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, R21-M MH-106799]
- Office of Naval Research
- National Science Foundation [BCS-1441502, PHY-1554488, BCS-1631550]
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The human brain network is modular-consisting of communities of tightly interconnected nodes(1). This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities(2,3). A mechanistic understanding of these hubs and how they support cognition has not been demonstrated. Here, we leveraged individual differences in hub connectivity and cognition. We show that a model of hub connectivity accurately predicts the cognitive performance of 476 individuals in 4 distinct tasks. Moreover, there is a general optimal network structure for cognitive performance-individuals with diversely connected hubs and consequent modular brain networks exhibit increased cognitive performance, regardless of the task. Critically, we find evidence consistent with a mechanistic model in which connector hubs tune the connectivity of their neighbours to be more modular while allowing for task appropriate information integration across communities, which increases global modularity and cognitive performance.
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