Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity

Abstract

Spatial patterns of coherent activity across different brain areas have been identified during/nthe resting-state fluctuations of the brain. However, recent studies indicate that resting-state/nactivity is not stationary, but shows complex temporal dynamics. We were interested in the/nspatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals/nfrom human subjects.We found that the global phase synchrony of the BOLD signals/nevolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations/nreflect the transient formation and dissolution of multiple communities of synchronized brain/nregions. Synchronized communities reoccurred intermittently in time and across scanning/nsessions. We found that the synchronization communities relate to previously defined functional/nnetworks known to be engaged in sensory-motor or cognitive function, called restingstate/nnetworks (RSNs), including the default mode network, the somato-motor network, the/nvisual network, the auditory network, the cognitive control networks, the self-referential network,/nand combinations of these and other RSNs. We studied the mechanism originating/nthe observed spatiotemporal synchronization dynamics by using a network model of phase/noscillators connected through the brain’s anatomical connectivity estimated using diffusion/nimaging human data. The model consistently approximates the temporal and spatial synchronization/npatterns of the empirical data, and reveals that multiple clusters that transiently/nsynchronize and desynchronize emerge from the complex topology of anatomical connections,/nprovided that oscillators are heterogeneous.