Resting state networks in empirical and simulated dynamic functional connectivity

Abstract It is well‐established that patterns of functional connectivity (FC) ‐ measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging ‐ are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio‐temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non‐stationary dynamics is an active field of inquiry. In this study, we use a whole‐brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio‐temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (“communities”) that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio‐temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio‐temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model‐based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations. HighlightsTensor decomposition is used to obtain resting state networks from dynamic FC (dFC).The largest 2% of the dFC values are sufficient to recover resting state networks.Mutual information distinguishes surrogate from real data better than correlation.RSNs also emerge from the dFC of data simulated with a dynamic mean field model.Realistic RSNs are obtained by connecting model nodes via effective connectivity.