Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity

Dynamic models of large-scale brain activity have been used for reproducing many empirical findings on human brain functional connectivity. Features that have been shown to be reproducible by comparing modeled to empirical data include functional connectivity measured over several minutes of resting-state functional magnetic resonance imaging, as well as its time-resolved fluctuations on a time scale of tens of seconds. However, comparison of modeled and empirical data has not been conducted yet for fluctuations in global network topology of functional connectivity, such as fluctuations between segregated and integrated topology or between high and low modularity topology. Since these global network-level fluctuations have been shown to be related to human cognition and behavior, there is an emerging need for clarifying their reproducibility with computational models. To address this problem, we directly compared fluctuations in global network topology of functional connectivity between modeled and empirical data, and clarified the degree to which a stationary model of spontaneous brain dynamics can reproduce the empirically observed fluctuations. Modeled fluctuations were simulated using a system of coupled phase oscillators wired according to brain structural connectivity. By performing model parameter search, we found that modeled fluctuations in global metrics quantifying network integration and modularity had more than 80% of magnitudes of those observed in the empirical data. Temporal properties of network states determined based on fluctuations in these metrics were also found to be reproducible, although their spatial patterns in functional connectivity did not perfectly matched. These results suggest that stationary models simulating resting-state activity can reproduce the magnitude of empirical fluctuations in segregation and integration, whereas additional factors, such as active mechanisms controlling non-stationary dynamics and/or greater accuracy of mapping brain structural connectivity, would be necessary for fully reproducing the spatial patterning associated with these fluctuations.