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Structure-function clustering in weighted brain networks

Crofts, Jonathan J.; Forrester, Michael; Coombes, Stephen; O’Dea, Reuben D.

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Authors

Jonathan J. Crofts

Michael Forrester



Abstract

Functional networks, which typically describe patterns of activity taking place across the cerebral cortex, are widely studied in neuroscience. The dynamical features of these networks, and in particular their deviation from the relatively static structural network, are thought to be key to higher brain function. The interactions between such structural networks and emergent function, and the multimodal neuroimaging approaches and common analysis according to frequency band motivate a multilayer network approach. However, many such investigations rely on arbitrary threshold choices that convert dense, weighted networks to sparse, binary structures. Here, we generalise a measure of multiplex clustering to describe weighted multiplexes with arbitrarily-many layers. Moreover, we extend a recently-developed measure of structure-function clustering (that describes the disparity between anatomical connectivity and functional networks) to the weighted case. To demonstrate its utility we combine human connectome data with simulated neural activity and bifurcation analysis. Our results indicate that this new measure can extract neurologically relevant features not readily apparent in analogous single-layer analyses. In particular, we are able to deduce dynamical regimes under which multistable patterns of neural activity emerge. Importantly, these findings suggest a role for brain operation just beyond criticality to promote cognitive flexibility.

Citation

Crofts, J. J., Forrester, M., Coombes, S., & O’Dea, R. D. (2022). Structure-function clustering in weighted brain networks. Scientific Reports, 12(1), Article 16793. https://doi.org/10.1038/s41598-022-19994-9

Journal Article Type Article
Acceptance Date Sep 7, 2022
Online Publication Date Oct 6, 2022
Publication Date Oct 6, 2022
Deposit Date Nov 18, 2022
Publicly Available Date Nov 18, 2022
Journal Scientific Reports
Electronic ISSN 2045-2322
Peer Reviewed Peer Reviewed
Volume 12
Issue 1
Article Number 16793
DOI https://doi.org/10.1038/s41598-022-19994-9
Keywords Article, /639/766/530/2801, /639/766/530/2795, /631/114/116/2393, /631/114/116/1925, article
Public URL https://nottingham-repository.worktribe.com/output/12317102
Publisher URL https://www.nature.com/articles/s41598-022-19994-9

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