BIBLIOS

Document type
Journal articles

Document subtype
Full paper

Title
Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series

Participants in the publication
Olaf Sporns (Author)
Joshua Faskowitz (Author)
Andreia Sofia Teixeira (Author)
Dep. Informática
Sarah A. Cutts (Author)
Richard F. Betzel (Author)

Summary
Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter timescales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Unwrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.


Where published
Network Neuroscience

Publication Identifiers
ISSN - 2472-1751

Publisher
MIT Press - Journals



Document Identifiers
DOI - https://doi.org/10.1162/netn_a_00182
URL - http://dx.doi.org/10.1162/netn_a_00182

Rankings
SCOPUS Q1 (2019) - 4.4 - Applied Mathematics
SCIMAGO Q1 (2019) - 1.42 - Computer Science Applications
SCIMAGO Q1 (2019) - 1.42 - Artificial Intelligence