Time-limited self-sustaining rhythms and state transitions in brain networks

Resting-state networks usually show time-limited self-sustaining oscillatory patterns (TLSOPs) with the characteristic features of multiscaled rhythms and frequent switching between different rhythms, but underlying mechanisms remain unclear. To reveal we present a simplified reaction-diffusion model activation propagation to reproduce TLSOPs in real brain networks. We find that reproduced do depending on activating threshold initially chosen nodes. understand an approach dominant paths can be separated into individual denoted by core networks, them implemented time-dependent threshold. Further, based microstates TLSOPs, introduce concept return loop study distribution times it satisfies Weibull distribution. Then, check for data, method shifting window transform continuous time series discrete two-state interestingly also exists resting-state EEG fMRI data. Finally, TLSOP lifetime depends exponentially network size explained theory complete graphs.