The general linear model of Statistical Parametric Mapping 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm) was used for whole-brain image analysis. Individual subject data were first realigned to the first volume in the time series to correct for head motion before being spatially normalized into the standard stereotactic space of the Montreal Neurological Institute (MNI) template using a 12-parameter affine model. Next, data were smoothed to minimize noise and residual differences in individual anatomy with a 6 mm FWHM Gaussian filter. Voxel-wise signal intensities were ratio normalized to the whole-brain global mean. Then the ARTifact Detection Tool (ART) was used to generate regressors accounting for images due to large motion (i.e. >0.6 mm relative to the previous time frame) or spikes (i.e. global mean intensity 2.5 standard deviations (S.D.) from the entire time series). Participants for whom more than 5% of acquisition volumes were flagged by ART (VS: n = 38) were removed from analyses. An ROI mask (i.e. 5 mm spheres centered around MNI coordinates, left: x = −12, y = 8, z = −10; right: x = 12, y = 10, z
