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

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