title: 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 question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.
authors.affiliation: Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Deco
authors.forename: Adrián
authors.initials: A
authors.lastname: Ponce-Alvarez
authors.forename: Dante
authors.initials: D
authors.lastname: Mantini
authors.forename: Gian Luca
authors.initials: GL
authors.lastname: Romani
authors.forename: Patric
authors.initials: P
authors.lastname: Hagmann
authors.forename: Maurizio
authors.initials: M
authors.lastname: Corbetta
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000328:Adult; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D005260:Female; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D012146:Rest / Q000502:physiology*; D055815:Young Adult