Bursty and persistent properties of large-scale brain networks revealed with a point-based method for dynamic functional connectivity

In this paper, we present a novel and versatile method to study the dynamics of resting-state fMRI brain connectivity with a high temporal sensitivity. Whereas most existing methods often rely on dividing the time-series into larger segments of data (i.e. so called sliding window techniques), the point-based method (PBM) proposed here provides an estimate of brain connectivity at the level of individual sampled time-points. The achieved increase in temporal sensitivity, together with temporal graph network theory allowed us to study functional integration between, as well as within, resting-state networks. Our results show that functional integrations between two resting-state networks predominately occurs in bursts of activity with intermittent periods of less connectivity, whereas the functional connectivity within resting-state networks is characterized by a tonic/periodic connectivity pattern. Moreover, the point-based approach allowed us to estimate the persistency of brain connectivity, i.e. the duration of the intrinsic trace or memory of resting-state connectivity patterns. The described point-based method of dynamic resting-state functional connectivity allows for a detailed and expanded view on the temporal dynamics of resting-state connectivity that provides novel insights into how neuronal information processing is integrated in the human brain at the level of large-scale networks.