A mechanistic model of connector hubs, modularity, and cognition

TL;DR

This study presents a mechanistic model linking connector hubs and modular brain networks to enhanced cognitive performance across individuals, emphasizing the importance of diverse hub connectivity for cognition.

Contribution

It introduces a mechanistic model explaining how connector hubs modulate brain modularity to support cognition, validated by individual differences in a large sample.

Findings

Connector hubs predict individual cognitive performance.

Diverse hub connectivity correlates with higher cognition.

Optimal network structure involves increased modularity and diverse connections.

Abstract

The human brain network is modular--comprised of communities of tightly interconnected nodes. This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities. 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 four 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 neighbors to be more modular while allowing for task appropriate information integration across communities, which increases global modularity and cognitive performance.