fMRI preprocessing
REGRESSION, EVENTS, EVENT, SESSION
Results included in this manuscript come from preprocessing performed using FMRIPREPFunctional data was slice time corrected using 3dTshift from AFNI v16.2.07Physiological noise regressors were extracted applying CompCorUsing 3dDeconvolve (AFNI), we used a deconvolution approach based on multiple linear regression to analyze each functional voxel based on happy and angry events without an assumed hemodynamic response. The hemodynamic responses for each event of interest was estimated using 13 time-shifted tent functions, estimating the BOLD activity from 0 to 18 s after trial onset. In addition to these, nuisance regressors derived from For our a priori hypotheses, we conducted an anatomical region of interest (ROI) analysis by averaging the beta coefficients for two ROIs: right and left amygdala (based on FreeSurfer definitions of the amygdala). For our exploratory whole-brain analyses, we blurred the EPI data by 4 mm and used 3dMVM (AFNI) for a group-level ANOVA with Session (Pre vs Active) by Emotion (Happy vs Angry). As our primary interest was a modulation in emotional activity across session, but as our power was low, we chose a two-step approach. First, we identified regions showing a potential difference in activity based on emotion by a liberal threshold (main effect of emotion 