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Resting brain dynamics at different timescales capture distinct aspects of human behavior

Author

Listed:
  • Raphaël Liégeois

    (National University of Singapore
    Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne
    University of Geneva)

  • Jingwei Li

    (National University of Singapore)

  • Ru Kong

    (National University of Singapore)

  • Csaba Orban

    (National University of Singapore)

  • Dimitri Van De Ville

    (Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne
    University of Geneva)

  • Tian Ge

    (Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital)

  • Mert R. Sabuncu

    (Cornell University)

  • B. T. Thomas Yeo

    (National University of Singapore
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
    Centre for Cognitive Neuroscience, Duke-NUS Medical School
    National University of Singapore)

Abstract

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts of static functional connectivity (FC), at the resolution of several minutes, have been studied but behavioral correlates of dynamic measures of FC at the resolution of a few seconds remain unclear. Here, using resting-state fMRI and 58 phenotypic measures from the Human Connectome Project, we find that dynamic FC captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported measures (e.g., loneliness or life satisfaction) are equally well explained by static and dynamic FC. Furthermore, behaviorally relevant dynamic FC emerges from the interconnections across all resting-state networks, rather than within or between pairs of networks. Our findings shed new light on the timescales of cognitive processes involved in distinct facets of behavior.

Suggested Citation

  • Raphaël Liégeois & Jingwei Li & Ru Kong & Csaba Orban & Dimitri Van De Ville & Tian Ge & Mert R. Sabuncu & B. T. Thomas Yeo, 2019. "Resting brain dynamics at different timescales capture distinct aspects of human behavior," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10317-7
    DOI: 10.1038/s41467-019-10317-7
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    Cited by:

    1. Manish Saggar & James M. Shine & Raphaël Liégeois & Nico U. F. Dosenbach & Damien Fair, 2022. "Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. Xiaolu Kong & Ru Kong & Csaba Orban & Peng Wang & Shaoshi Zhang & Kevin Anderson & Avram Holmes & John D. Murray & Gustavo Deco & Martijn Heuvel & B. T. Thomas Yeo, 2021. "Sensory-motor cortices shape functional connectivity dynamics in the human brain," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    3. Jianzhong Chen & Angela Tam & Valeria Kebets & Csaba Orban & Leon Qi Rong Ooi & Christopher L. Asplund & Scott Marek & Nico U. F. Dosenbach & Simon B. Eickhoff & Danilo Bzdok & Avram J. Holmes & B. T., 2022. "Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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