Preprocessing and statistical analysis of the image data were done using SPM12 (SPM12/">www.fil.ion.ucl.ac.uk/spm/software/SPM12/ , accessed 27 July 2020) executed in MATLAB version 8.5 (R2015a) (www.mathworks.com , accessed 27 July 2020). To account for movement of the subject, all fMRI image volumes were realigned to the first volume of the corresponding session and subsequently coregistered to the corresponding structural T1 image using rigid-body transformation. A Volterra expansion was performed on the generated six realignment parameters to model residual movement artefacts (Lund et al., 2005 (link)) resulting in 24 movement parameters, which were later entered into the design matrix as regressors of no interest.
For the T1 image, the origin was set to the AC–PC plane and the images were segmented using the SPM12 segmentation procedure. The produced normalization parameters were then applied to all coregistered fMRI image volumes. Successful normalization to standard Montreal Neurological Institute (MNI) coordinate space was checked at random for each subject using ventricles and brain borders as landmarks. Finally, all fMRI image volumes were smoothed with an isotropic 8-mm full width half maximum Gaussian filter.
For the T1 image, the origin was set to the AC–PC plane and the images were segmented using the SPM12 segmentation procedure. The produced normalization parameters were then applied to all coregistered fMRI image volumes. Successful normalization to standard Montreal Neurological Institute (MNI) coordinate space was checked at random for each subject using ventricles and brain borders as landmarks. Finally, all fMRI image volumes were smoothed with an isotropic 8-mm full width half maximum Gaussian filter.