resolution=3.12x3.12 mm; slice thickness=4 mm. High resolution anatomical T1 scans were obtained for spatial normalization. Motion was minimized with foam pads and emphasis on importance of keeping still. Data analysis Independent or examined group Demographic, psychometric and task measures. t- χ2-tests differences. Response time (RT) and success rate for each incentive condition were calculated and found normally distributed (KS test, ANOVAs were p’s>.17). Repeated-measures conducted for RT and success rate, separately, assessing performance differences between groups: valence (win/loss) x amount ($0.20/$5). Post-hoc determined the source of any t-tests differences. correlations evaluated relationships between variables. Pearson’s Functional images were reconstructed using an iterative Functional data preprocessing. algorithm (, ). Data were motion corrected using FSL4.0 (Analysis Group, FMRIB, Oxford, UK) (). Runs exceeding 2 mm translation or 2° rotation were excluded. Image processing and statistical analysis used statistical parametric mapping (SPM2, Wellcome Institute of Cognitive Neurology, London, UK). Functional images were spatially normalized to standard stereotactic space as defined by the Montreal Neurological Institute. Images were spatially smoothed with a 6 kernel. Individual analysis a general linear model (GLM) with five Individual subject statistical maps. regressors of interest (for each incentive condition) convolved with the hemodynamic response function (HRF). Motion parameters were modeled as nuisance regressors. Contrasts for anticipation of reward ($0.2 and $5.0 combined) minus neutral and anticipation of loss (combined) minus neutral, were calculated.
resolution=3.12x3.12 mm; slice thickness=4 mm. High resolution anatomical T1 scans were obtained for spatial normalization. Motion was minimized with foam pads and emphasis on importance of keeping still. Data analysis Demographic, psychometric and task measures. Independent t- or χ2-tests examined group differences. Response time (RT) and success rate for each incentive condition were calculated and found normally distributed (KS test, p’s>.17). Repeated-measures ANOVAs were conducted for RT and success rate, separately, assessing performance differences between groups: valence (win/loss) x amount ($0.20/$5). Post-hoc t-tests determined the source of any differences. Pearson’s correlations evaluated relationships between variables. Functional data preprocessing. Functional images were reconstructed using an iterative algorithm (, ). Data were motion corrected using FSL4.0 (Analysis Group, FMRIB, Oxford, UK) (). Runs exceeding 2 mm translation or 2° rotation were excluded. Image processing and statistical analysis used statistical parametric mapping (SPM2, Wellcome Institute of Cognitive Neurology, London, UK). Functional images were spatially normalized to standard stereotactic space as defined by the Montreal Neurological Institute. Images were spatially smoothed with a 6 kernel. Individual subject statistical maps. Individual analysis a general linear model (GLM) with five regressors of interest (for each incentive condition) convolved with the hemodynamic response function (HRF). Motion parameters were modeled as nuisance regressors. Contrasts for anticipation of reward ($0.2 and $5.0 combined) minus neutral and anticipation of loss (combined) minus neutral, were calculated.
PPI_Rewards_RCW.docx