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

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.