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.

Altered global brain signal in schizophrenia

“…Of note, G and w are effective parameters that describe the net contribution of excitatory and inhibitory coupling at the circuit level (20) (see SI Appendix for details). The pattern of functional connectivity in the model best matches human patterns when the values of w and G set the model in a regime near the edge of instability (19). However, GS and local variance properties derived from the model had not been examined previously, nor related to clinical observations.…”

“…This finding is supported by the attenuation of SCZ effects after GSR. To explore potential neurobiological mechanisms underlying such increases, we used a validated, parsimonious, biophysically based computational model of resting-state fluctuations in multiple parcellated brain regions (19). This model generates simulated BOLD signals for each of its nodes (n = 66) (Fig.…”

“…Finally, we used biologically informed computational modeling (19,20) to explore how alterations in local circuit parameters could impact emergent GS alterations, as observed in SCZ.…”

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

“…Thereafter, a lot of interest has been devoted to deepening the understanding of how anatomical constraints shape functional connectivity (Honey et al 2010;Breakspear et al 2010;Cabral et al 2011;Deco et al 2012), and how this relationship can be affected by different pathologies (de Kwaasteniet et al 2013;van Schouwenburg et al 2013). In most of these studies, either the dynamics of FC are not taken into account, or it is modeled, but the information coming from the data and used to assess models is deduced with a static approach of FC [e.g., (Deco et al 2013b)]. To our knowledge, our paper is the first purely data-driven attempt to study the dynamical relationship between SC and FC.…”

Mouse optical imaging for understanding resting-state functional connectivity in human fMRI

“…In contrast with the traditional analysis of “static” FC using many minutes of scans, the temporal fluctuation of FC across short time windows gives the dynamic aspect of FC that could provide information on the functional organizations of healthy and diseased brains that is inaccessible with static FC [17–19]. The presence of temporal fluctuations in FC has also influenced theoreticians to constrain realistic models of brain networks [20–22]. However, it is unclear whether the fluctuations of FC measured in hemodynamics reflect the dynamics of underlying neural activity.…”