A mechanistic model of connector hubs, modularity and cognition

Maxwell A. Bertolero (), B. T. Thomas Yeo, Danielle S. Bassett and Mark D’Esposito
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Maxwell A. Bertolero: University of California
B. T. Thomas Yeo: National University of Singapore
Danielle S. Bassett: University of Pennsylvania
Mark D’Esposito: University of California

Nature Human Behaviour, 2018, vol. 2, issue 10, 765-777

Abstract: Abstract The human brain network is modular—consisting of communities of tightly interconnected nodes1. This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities2,3. 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.

Date: 2018
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DOI: 10.1038/s41562-018-0420-6

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