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

View ORCID ProfileManish Saggar, View ORCID ProfileJames M. Shine, Raphaël Liégeois, Nico U. F. Dosenbach, Damien Fair
doi: https://doi.org/10.1101/2021.08.05.455149
Manish Saggar
aDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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  • For correspondence: saggar@stanford.edu
James M. Shine
bBrain and Mind Center, The University of Sydney, Sydney, New South Wales, Australia
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Raphaël Liégeois
cInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland
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Nico U. F. Dosenbach
dDepartments of Neurology, Radiology, Pediatrics and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
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Damien Fair
eDepartment of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
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Abstract

Even in the absence of external stimuli, neural activity is both highly dynamic and organized across multiple spatiotemporal scales. The continuous evolution of brain activity patterns during rest is believed to help maintain a rich repertoire of possible functional configurations that relate to typical and atypical cognitive phenomena. Whether these transitions or “explorations” follow some underlying arrangement or instead lack a predictable ordered plan remains to be determined. Here, using a precision dynamics approach, we aimed at revealing the rules that govern transitions in brain activity at rest at the single participant level. We hypothesized that by revealing and characterizing the overall landscape of whole brain configurations (or states) we could interpret the rules (if any) that govern transitions in brain activity at rest. To generate the landscape of whole-brain configurations we used Topological Data Analysis based Mapper approach. Across all participants, we consistently observed a rich topographic landscape in which the transition of activity from one state to the next involved a central hub-like “transition state.” The hub topography was characterized as a shared attractor-like basin where all canonical resting-state networks were represented equally. The surrounding periphery of the landscape had distinct network configurations. The intermediate transition state and traversal through it via a topographic gradient seemed to provide the underlying structure for the continuous evolution of brain activity patterns at rest. In addition, differences in the landscape architecture were more consistent within than between subjects, providing evidence of idiosyncratic dynamics and potential utility in precision medicine.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Added online methods section to the main manuscript. Also added supplementary information as a separate pdf.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 07, 2021.
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Precision dynamical mapping using topological data analysis reveals a unique hub-like transition state at rest
Manish Saggar, James M. Shine, Raphaël Liégeois, Nico U. F. Dosenbach, Damien Fair
bioRxiv 2021.08.05.455149; doi: https://doi.org/10.1101/2021.08.05.455149
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Precision dynamical mapping using topological data analysis reveals a unique hub-like transition state at rest
Manish Saggar, James M. Shine, Raphaël Liégeois, Nico U. F. Dosenbach, Damien Fair
bioRxiv 2021.08.05.455149; doi: https://doi.org/10.1101/2021.08.05.455149

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