Emergence of metastable dynamics in functional brain organization via spontaneous fMRI signal and whole-brain computational modeling.

Abstract

Little is known about the mechanisms underlying the resting-state brain organization. This study investigated how metastability, defined as the standard deviation of synchrony described by the Kuramoto order parameter, arises from the structural connectome and relates to empirical measures of metastability in resting-state brain networks. We tested whether spontaneous fMRI brain activity in the functional organization of the human brain operates in a metastable state. We compared between empirical metastability defined in four major resting-state brain networks - auditory network, default mode network, left and right executive control networks - and simulated metastability derived from the Kuramoto model constrained by the empirical anatomical connectivity. Our results show that maximal metastability within resting-state brain networks arises from the model with different coupling strengths. Empirical metastability corresponds to a dynamical region where the simulated metastability is maximized. The emergence of metastable dynamics observed in empirical resting-state functional networks around the region of maximal metastability suggests that such a dynamical regime in the brain may drive the resting state of the brain. Our study may provide a mechanistic explanation of the origin of functional organization of the brain, and may help our understanding of the mechanistic causes of disease.