structed for each participant a sFC matrix, W , whose elements were equal to W$_{ij}$ = 1 T − 1 ∑ T t =1 z$_{i}$ ( t ) · z$_{j}$ ( t ), where z$_{i}$ = { z$_{i}$ (1) , . . . , z$_{i}$ ( T ) } is the zero-mean, unit-variance fMRI BOLD time series for region i . We also constructed each participant’s dynamic functional connectivity (dFC) matrix, W = { W (1) , . . . , W ( T − L + 1) } , where W ( t ) = [ W$_{ij}$ ( t )] is the esti- mated connectivity matrix for the time interval beginning at t . This entailed first dividing the regional BOLD time series into overlapping windows of approx- imately 100 seconds in length. With a sampling frequency of f = 1 0 . 645 Hz, this translated to a window of L = 156 time points. The decision to select a window of 100 s was made so that the window was long enough to capture a full cycle of the slowest frequency component. As the high-pass cutoff for the BOLD time series was 0.01 Hz, the shortest possible window was 100 s long (Zalesky and Breakspear, 2015; Leonardi and Van De Ville, 2015). For each window, we then calculated the cross-correlation matrix using only the observations within that window. The cross-correlation was calculated after exponentially discounting the fluctuations of more distant time points so that the correlation coefficients weighed recent events more heavily. In slightly more detail, we define a discounting function for each window: where τ = 1 , . . . , L , w$_{0}$ = (1 − e − 1 $^{/θ}$) / (1 − e − $^{L/θ}$), and θ = L/ 3. Based on this weighting, we calculated for each window: 6 ¯ z$_{i}$ ( t ) = L ∑ τ =1 w ( τ ) z$_{i}$ ( t − L + τ ) (2) w ( τ ) = w$_{0}$e ( τ − L ) /θ (1) Dynamic functional connectivity