Image processing
pain
Preprocessing and first-level analysis of the fMRI data was carried out with the use of SPM12 (SPM; WellcomeTrust Centre for Neuroimaging, London, UK). The functional data were slice-time corrected, motion corrected, realigned to the first image from each run and co-registered to the individual structural images. High-resolution structural images were segmented and normalized to the common MNI space with resampling to 1 mm isometric voxels. The obtained transformation parameters were applied to the functional volumes with resampling to 2 mm isometric voxels. Spatial smoothing with a Gaussian kernel of full-width half-maximum (FWHM) of 6 mm was performed on the normalized functional images. Additional high-pass filters of 256 s were used for the functional data.For the first-level statistical analysis, a general linear model was created including a regressor for each condition (pain Polish, pain Arab, no pain Polish, no pain Arab, fixation cross). This resulted in 5 regressors in total. Additionally, six movement parameters (3 translations, 3 rotations) were included as nuisance regressors. The conditions were modeled using a standard boxcar model. For the second-level analysis, we used the Multivariate and Repeated Measures (MRM) toolboxTo test for a simple effect of pain, at the second level, a two-sample All reported contrasts were thresholded voxel-wise at Subsequently, we conducted Regions of Interest (ROI) analysis based on regions postulated to be involved in empathy, mentalizing, and moral sensitivity in relation to outgroup biasRegions of Interest selected based on Neurosynth survey (only ROIs in the right hemisphere are visualized, ROIs in the left hemisphere were roughly symmetrical, see “The averaged beta estimates for each condition of each task from the ROIs were extracted using in-house Matlab scripts.First, we conducted paired t-tests analysis for pain and no pain conditions for the right and left AI and dACC for each group separately to ensure that painful stories elicited greater BOLD response in the key regions of empathy for physical pain irrespectively of experimental manipulation (neutral vs. hateful).Finally, a series of ANOVAs was conducted on the following ROIs (selected a priori, see Methods): left and right TPJ, mPFC, left and right insula, and left and right dACC. Each ANOVA followed the same full factorial mixed design, with Type of Comments (neutral vs. hateful, between-subject), the Story Type (no pain vs. pain, within-subject), and Protagonist’s Ethnicity (Arab vs. Polish).The analyses were carried out using R software(