 fMRI Image Analysis Imaging data will be analyzed by means of MATLAB and statistical parametric mapping software SPM99. The images will be resliced and corrected for motion by registration to the first functional image acquired for each subject using a 6 parameter transformation Friston et al 1995. Images will be spatially normalized to a Montreal Neurological Institute template. A high-pass filter will be used to remove low frequency noise. Image smoothing using a 6 mm Gaussian kernel will be used to enhance signal-to-noise ratios and accommodate differences in neuroanatomy to facilitate group comparisons. The delayed cerebral blood flow response to activation conditions will be modeled using the standard hemodynamic response function. Statistical analysis will proceed using a random effects model to examine outcomes of the parametric design of the experiment Buchel et al. 1998. We will model both linear and nonlinear hemodynamic responses in regions of interest: Nacc, amygdala, striatum, thalamus, OFC, ACC, and hippocampus. 