Individual analysis was completed using a general linear model (GLM) in SPM2. Five regressors of interest (anticipation of win $0.2, win $5.0, lose $0.2, lose $5.0 and neutral $0) were convolved with the canonical hemodynamic response function (HRF). Motion parameters were modeled as nuisance regressors to remove residual motion artifacts. Scanner drift and other low frequency noise were removed from the image time series using a 128 second high-pass filter. Two contrasts of interest, anticipation of reward ($0.2 and $5.0 combined) minus neutral incentive and anticipation of loss ($0.2 and $5.0 combined) minus neutral incentive, were calculated for each individual.
