deviations. Time courses, with a size of 145 time steps, were orthogonalized with respect to i) linear, quadratic and cubic trends; ii) the 6 realignment parameters and iii) realignment parameters derivatives. The decision to pre-process these nuisances at this point is based on recent recommendations in the field (Vergara et al., 2016). The fMRI data were smoothed using a FWHM Gaussian kernel of size 6 mm. The data were then analyzed with Infomax based gICA (Calhoun et al., 2001; Calhoun and Adali, 2012) with 120 and 100 components for the first and second decomposition levels respectively (Erhardt et al., 2011b). The number of components was determined using ICASSO (Himberg et al., 2004) such that its R-index is close to the minimum and the quality index for any given component is above 0.7. A total of 39 components out of the 100 estimated components were selected based on frequency content and visual inspection in order to include components that were low noise and free of major artifacts (Allen et al., 2011). These 39 components were considered the RSNs of interest. Spatial maps of the 39 components were z-transformed in order to identify the main brain areas included in each RSN. Time
