We first demonstrate
less variability of global Pearson
correlations with respect to the two
chosen networks using a sliding - window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in
correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs.