Preprocessed functional **data** were decomposed into 25 spatially independent components using the Group ICA fMRI Toolbox (GIFT v2.0a) with the Infomax algorithm (http://icatb.source forge.net/gift/gift_startup.php; Calhoun et al., 2001Calhoun et al., , 2004 
 (S4).
 ICA is a **data** driven technique which decomposes fMRI **data** from linear mixed signals into spatially independent components. Components consist of a time course showing the temporal fluctuations of that component and a spatial map that shows the contribution of every voxel. A strength of ICA is that it is able to separate true neural networks and noise artifacts occurring in similar frequency ranges (Calhoun et al., 2001;van de Ven et al., 2004;Beckmann et al., 2005). Further, it has been shown that group ICA is able to characterize individual variation such that might occur across sessions and participants (Calhoun et al., 2001;Allen et al., 2012). The number of components was set to 25, in line with previous reports on low-order ICA for examining robust resting-state networks Zuo et al., 2010). The set of spatial maps from the groupaverage analysis was used to generate subject-specific versions of the spatial maps and associated time-series using spatiotemporal regression (Beckmann et al., 2009;Filippini et al., 2009). The analysis resulted in subject-specific spatial maps of two sessions per subject, and one spatial map per group-level. Components were scaled to percent signal change for comparison between subjects.