BIBLIOS

Tipo
Artigos em Revista

Tipo de Documento
Artigo Completo

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

Participantes na publicação
Olaf Sporns (Author)
Joshua Faskowitz (Author)
Andreia Sofia Teixeira (Author)
Dep. Informática
Sarah A. Cutts (Author)
Richard F. Betzel (Author)

Resumo
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.


Suporte
Network Neuroscience

Identificadores da Publicação
ISSN - 2472-1751

Editora
MIT Press - Journals



Identificadores do Documento
DOI - https://doi.org/10.1162/netn_a_00182
URL - http://dx.doi.org/10.1162/netn_a_00182

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