The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain

The large-scale functional MRI connectome of the human brain is composed of multiple resting-state networks (RSNs). However, the network dynamics, such as integration and segregation between and within RSNs is largely unknown. To address this question we created high-resolution “frequency graphlets”, connectivity matrices derived across the low-frequency spectrum of the BOLD fMRI resting-state signal (0.01-0.1 Hz) in a cohort of 100 subjects. We then apply and compare graph theoretical measures across the frequency graphlets. Our results show that the within- and between-network connectivity and presence of functional hubs shift as a function of frequency. Furthermore, we show that the small world network property peaks at different frequencies with corresponding spatial connectivity profiles. We conclude that the frequency dependence of the network connectivity and the spatial configuration of functional hubs suggest that the dynamics of large-scale network integration and segregation operate at different time scales.