85 ALTERED VISUOMOTOR PROCESSING IN NA 4 Supplementary materials Detailed description of fMRI preprocessing steps First, we converted all functional and structural DICOM files to Niftii files using dcm2nii.250 Non-brain structures such as skull were removed from the structural image using the Brain Extraction Tool (BET2).130 If BET brain extraction did not lead to a satisfactory removal of skull and a subsequent satisfactory registration, excessive skull was removed manually (n = 1). We calculated the framewise displacement for the functional scans with fsl_motion_outliers and excluded participants with a mean framewise displacement > 0.5mm. The first five volumes of the functional data were removed to allow signal equilibration of the scanner. Images were realigned to correct for motion with fMRI Expert Analysis Tool version 6.00 (FEAT).131 FEAT additionally applied smoothing (Full Width at Half Maximum (FWHM) = 3 mm) and grand mean scaling, to normalize the mean of each voxel, and removed non-brain structures from the functional images. Functional images were registered to both the subject’s structural image and to standard MNI152 image space using FMRIB’s Linear Image Registration Tool (FLIRT).134,135 Registration from high resolution structural to standard space was then further refined using nonlinear registration (FNIRT).132, 133 Motion-related noise was removed from our data using ICAAROMA,136 set to generate 100 independent components per participant, which were manually inspected, heeding the spatial pattern, time series and frequency spectrum,137 to ensure correct classification as noise or signal and were reclassified if needed. Nonaggressive denoisingwas then appliedusing ICA-AROMAwith the reclassified components. Next, we performed nuisance regression on the denoised images using fslmaths and fslglm. This nuisance regression included regressors of white matter and cerebrospinal fluid, as well as 24 motion parameters. White matter and cerebrospinal fluid masks were created through tissue-type segmentation with FMRIB’s Automated Segmentation Tool (FAST),251 eroded with fslmaths and registered to functional space using FLIRT. Average time series of these masks were subtracted using fslmeants and added as regressors. The motion regressors consisted of six primary realignment parameters, the history of these realignment parameters, and the squared parameters.138 After nuisance regression, we temporally high pass filtered the data at 0.01 Hz and applied additional smoothing with a 5.2 mm FWHM Gaussian kernel, amounting to a final smoothing of 6mm FWHM.