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A mechanistic model of connector hubs, modularity and cognition.

ABSTRACT: The human brain network is modular-comprised 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 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.

SUBMITTER: Bertolero MA

PROVIDER: S-EPMC6322416 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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A mechanistic model of connector hubs, modularity and cognition.

Bertolero Maxwell A MA Yeo B T Thomas BTT Bassett Danielle S DS D'Esposito Mark M

Nature human behaviour 20180903 10

The human brain network is modular-comprised of communities of tightly interconnected nodes<sup>1</sup>. This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities<sup>2,3</sup>. 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 ...[more]

PMID: 30631825

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Project description:Cognitive and movement processes involved integration of several large-scale brain networks. Central to these integrative processes are connector hubs, brain regions characterized by strong connections with multiple networks. Growing evidence suggests that many neurodegenerative and psychiatric disorders are associated with connector hub dysfunctions. Using a network metric called functional connectivity overlap ratio, we investigated connector hub alterations in Parkinson's disease. Resting-state functional MRI data from 99 patients (male/female = 44/55) and 99 age- and sex-matched healthy controls (male/female = 39/60) participating in our cross-sectional study were used in the analysis. We have identified two sets of connector hubs, mainly located in the sensorimotor cortex and cerebellum, with significant connectivity alterations with multiple resting-state networks. Sensorimotor connector hubs have impaired connections primarily with primary processing (sensorimotor, visual), visuospatial, and basal ganglia networks, whereas cerebellar connector hubs have impaired connections with basal ganglia and executive control networks. These connectivity alterations correlated with patients' motor symptoms. Specifically, values of the functional connectivity overlap ratio of the cerebellar connector hubs were associated with tremor score, whereas that of the sensorimotor connector hubs with postural instability and gait disturbance score, suggesting potential association of each set of connector hubs with the disorder's two predominant forms, the akinesia/rigidity and resting tremor subtypes. In addition, values of the functional connectivity overlap ratio of the sensorimotor connector hubs were highly predictive in classifying patients from controls with an accuracy of 75.76%. These findings suggest that, together with the basal ganglia, cerebellar and sensorimotor connector hubs are significantly involved in Parkinson's disease with their connectivity dysfunction potentially driving the clinical manifestations typically observed in this disorder.

Dataset Information

A mechanistic model of connector hubs, modularity and cognition.

Publications

A mechanistic model of connector hubs, modularity and cognition.

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