2.5.3. fMRI pre-processing

Standard preprocessing steps were implemented with afni_proc.py; these steps included slice timing, coregistration, smoothing to 6-mm full-width half maximum (FWHM), spatial normalizing to standard Talairach space, and resampling, which resulted in 2.5 mm³ voxels AFNI software (Cox, 1996). Seven regressors of interest were modeled: three for anticipation of social evaluation (nice, mean, and unpredictable), and four for receipt of evaluation (positive evaluation from nice peers, positive evaluation from unpredictable peers, negative evaluation from mean peers, negative evaluation from unpredictable peers). An additional nine regressors of no-interest were also modeled: participants response and classroom selection events, six motion parameters, and linear drift. .

Individual-level regression analyses were carried out with AFNI’s 3dDeconvolve function, in which regressors were time-locked to the onset of each event and convolved with a duration-modulated boxcar regressor. Temporally adjacent TRs with a euclidean-norm motion derivative > 1.0 mm were omitted from the model via censoring. This resulted in a β coefficient and t statistic for each voxel and regressor. Whole-brain percent signal-change maps were generated by dividing signal intensity at each voxel by the mean voxel intensity, and multiplying by 100.