Fig. 1.

The procedures for constructing dynamic brain networks and computing dynamic network metrics. (A) The time series for all nodes were divided into a number of continuous time windows. (B) The whole-brain connectivity matrices were calculated within each window to compose a dynamic network, whose temporal variability was then estimated by average dissimilarities between different windows. (C) The dynamic brain networks were further thresholded and binarized with a range of sparsities from 10% to 50%, at which temporal clustering and temporal efficiency were estimated. TR = repetition time.