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A mechanistic model of connector hubs, modularity and cognition

Nature Human Behaviour volume 2, pages 765–777 (2018)Cite this article

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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.

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Fig. 1: Functional connectivity and network science processing workflow.
Fig. 2: Hub diversity and locality, modularity and network connectivity predict cognitive performance.
Fig. 3: Connector hubs and local hubs concurrently facilitate increased modularity and task performance.
Fig. 4: Connectivity between primary sensory, motor, dorsal attention, ventral attention and cingulo-opercular communities mediates the relationship between connector hubs and modularity.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grant numbers NS79698 and the National Science Foundation Graduate Research Fellowship Program under grant number DGE 1106400 to M.A.B. and M.D. M.A.B. also acknowledges NIH T32 Ruth L. Kirschstein Institutional National Research Service Award (5T32MH106442-02). B.T.T.Y. was also supported by 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 and the Singapore National Research Foundation Fellowship (Class of 2017). D.S.B. also acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the NIH (2-R01-DC-009209-11,1R01HD086888-01, R01-MH107235, R01-MH107703 and R21-M MH-106799), the Office of Naval Research and the National Science Foundation (BCS-1441502, PHY-1554488 and BCS-1631550). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Helen Wills Neuroscience Institute and the Department of Psychology, University of California, Berkeley, CA, USA

    Maxwell A. Bertolero & Mark D’Esposito

  2. Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Maxwell A. Bertolero & Danielle S. Bassett

  3. Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Programme, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  4. NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  5. Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  6. Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  7. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

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  1. Maxwell A. Bertolero
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  2. B. T. Thomas Yeo
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  3. Danielle S. Bassett
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Contributions

M.A.B. conceived the analyses. M.A.B., B.T.T.Y., D.S.B. and M.D. collaboratively designed the analyses. M.A.B. executed the analyses. M.A.B., B.T.T.Y., D.S.B. and M.D. collaboratively wrote the paper.

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Correspondence to Maxwell A. Bertolero.

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Bertolero, M.A., Yeo, B.T.T., Bassett, D.S. et al. A mechanistic model of connector hubs, modularity and cognition. Nat Hum Behav 2, 765–777 (2018). https://doi.org/10.1038/s41562-018-0420-6

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