Resting-state functional Connectivity emerges from structurally and dynamically shaped slow linear fluctuations

Deco, Gustavo; Ponce-Alvarez, Adrián; Mantini, Dante; Romani, Gian Luca; Hagmann, Patric; Corbetta, Maurizio

doi:http://dx.doi.org/10.1523/JNEUROSCI.1091-13.2013

Resting-state functional Connectivity emerges from structurally and dynamically shaped slow linear fluctuations

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Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M. Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci. 2013 Jul;33(27):11239-52. DOI 10.1523/JNEUROSCI.1091-13.2013

Abstract

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The/nquestion of how resting-state functional connectivity (FC) emerges from the brain’s anatomical connections has motivated several/nexperimental and computational studies to understand structure–function relationships. However, the mechanistic origin of resting/nstate is obscured by large-scale models’ complexity, and a close structure–function relation is still an open problem. Thus, a realistic but/nsimple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes/nthe realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is/nconstrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose/ntemporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as/nstructured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and/ncrucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure,/nneural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of/nfluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments/npresented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by/nanatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.