Prior to all analysis, the first three volumes were discarded to remove artifacts associated with scanner disequilibrium. Afterward, all preprocessing steps were run with fMRIPrep version 1.4.1 (Esteban et al., 2019) and involved the following: 1) motion correction using MCFLIRT (Jenkinson et al., 2002); 2) slice time correction using 3dTshift from AFNI 20160207 (Cox and Hyde, 1997); 3) spatial smoothing with a Gaussian kernel of 6mm full-width half-maximum, co-registering to MNI; and 4) non-aggressive denoising with ICA-AROMA (Pruim et al., 2015). After preprocessing with fMRIPrep, nuisance regression and bandpass filtering (0.009–0.08 Hz) were done with AFNI 3dTproject. Nuisance regressors included mean signal from the white matter and cerebrospinal fluid masks (95% probability masks) extracted from the non-aggressively denoised, unsmoothed BOLD. Lastly, the data were normalized and scaled.
