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

Manish Saggar (), James M. Shine, Raphaël Liégeois, Nico U. F. Dosenbach and Damien Fair
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Manish Saggar: Stanford University
James M. Shine: The University of Sydney
Raphaël Liégeois: École Polytechnique Fédérale de Lausanne
Nico U. F. Dosenbach: Washington University School of Medicine
Damien Fair: University of Minnesota Medical School

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-32381-2

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