Data preprocessing was accomplished using a combination of tools from AFNI (39) and FSL (40) packages. The structural image was skull stripped, segmented, and then registered to standard Montreal Neurological Institute (MNI) space using a nonlinear registration tool (AFNI’s 3dQwarp). Functional image preprocessing began with de-spiking, slice timing correction, motion correction, spatial smoothing (full width at half maximum = 6 mm). The time series were additionally processed to minimize artifacts from head motion, respiration, cardiac pulsation, and hardware using ANATICOR method (41, 42) by performing motion censoring, nuisance regression (motion parameters and averaged signal from eroded local white matter), and band-pass filtering (0.01 – 0.1 Hz) simultaneously in one regression model. Finally, functional images were registered to MNI space via co-registration to the structural image using boundary-based registration (43) within FSL package.
