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Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest

Author

Listed:
  • Manish Saggar

    (Stanford University)

  • James M. Shine

    (The University of Sydney)

  • Raphaël Liégeois

    (École Polytechnique Fédérale de Lausanne
    University of Geneva)

  • Nico U. F. Dosenbach

    (Washington University School of Medicine)

  • Damien Fair

    (University of Minnesota Medical School)

Abstract

In the absence of external stimuli, neural activity continuously evolves from one configuration to another. Whether these transitions or explorations follow some underlying arrangement or lack a predictable ordered plan remains to be determined. Here, using fMRI data from highly sampled individuals (~5 hours of resting-state data per individual), we aimed to reveal the rules that govern transitions in brain activity at rest. Our Topological Data Analysis based Mapper approach characterized a highly visited transition state of the brain that acts as a switch between different neural configurations to organize the spontaneous brain activity. Further, while the transition state was characterized by a uniform representation of canonical resting-state networks (RSNs), the periphery of the landscape was dominated by a subject-specific combination of RSNs. Altogether, we revealed rules or principles that organize spontaneous brain activity using a precision dynamics approach.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32381-2
    DOI: 10.1038/s41467-022-32381-2
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