fMRI data analysis
EVENTS, POSITIVE
A standard pre-processing pipeline using FSL (FMRIB’s Software Library; Version 5.0; Analysis Group, FMRIB, Oxford, United Kingdom) was employed for fMRI pre-processing and analysis. See The time series for outcome valence (i.e., positive feedback, negative feedback) of each condition (i.e., monetary, non-monetary, no feedback) was convolved with a double-gamma canonical hemodynamic response (HRF) function to generate regressors of interest. Corresponding trial onsets for each regressor were concatenated into a single regressor, which was included as a covariate. We modeled trial onsets as a single regressor to minimize collinearity between trial onset and feedback onset events within the model. Additional regressors of no interest included missed trial events, excessive motion outliers, and temporal derivatives of all regressors.Linear contrasts examined differences in activation between positive and negative feedback events, as well as between each of these events and no feedback. To increase statistical power for these contrasts, we created two additional regressors that estimated activity in response to positive feedback (All Positive Feedback) and negative feedback (All Negative Feedback) by collapsing across monetary and non-monetary feedback conditions. Thus, our main contrasts included All Positive Feedback vs. All Negative Feedback, All Positive Feedback vs. No Feedback, and All Negative Feedback vs. No Feedback. We also included contrasts for comparison to baseline activity. Run-level contrasts of parameter estimates were aggregated to generate subject-level parameter estimates for each of our contrasts, which were used for region-of-interest (ROI) analyses, group-level whole-brain analyses, and generalized psychophysiological interaction (gPPI) analyses.