Individual variability and connectivity dynamics in modular organization of human cortical functional networks

There is growing evidence for modules in structural networks of brain regions. Modular organization is observed in functional networks as well, where connectivity in networks is quantified based on statistical dependencies between regional time series. Studies of such functional networks derived from human fMRI have shown individual variability in modularity. In parallel, recent studies reported that functional networks during resting state vary on a time scale of tens of seconds. However, little is known about fluctuations of modularity in time-varying functional networks and their relation to individual variations in modularity measured over longer time scales. Here, we relate individual variations and dynamic fluctuations in modularity and investigate connectivity patterns during periods of high and low modularity. After confirming that individual differences in modularity persisted across multiple resting-state sessions, we showed that time-resolved functional connectivity displayed highly modular patterns more frequently in subjects exhibiting high long-time-scale modularity. Time-resolved connectivity patterns in high modularity periods exhibited greater similarity to each other, where the patterns were characterized by dissociation of the default mode network from the attention and primary sensory networks. The connectivity patterns averaged within high modularity periods exhibited lower similarity to the patterns in structural networks, indicating that the high modular state was associated with a shift away from the underlying structural connectivity. Altogether, these results suggest that individual variations in long-time-scale modularity can be traced to individual variations in fluctuations of short-time-scale modularity, which can be characterized by the recurrence of increased default mode network segregation and significant divergence from structural connectivity patterns.