Although functional magnetic resonance imaging (fMRI) methods yield rich temporal and spatial data for even a single subject, universally accepted data analysis techniques have not been developed that use all the potential information from fMRI of the brain. Specifically, temporal correlations and confounds are a problem in assessing change within pixels. Spatial correlations across pixels are a problem in determining regions of activation and in correcting for multiple significance tests. We propose methods that address these issues in the analysis of task-related changes in mean signal intensity for individual subjects. Our approach to temporally based problems within pixels is to employ a model based on autoregressive-moving average (ARMA or ''Box-Jenkins'') time series methods, which we call CARMA (Contrasts and ARMA). To adjust for performing multiple significance tests across pixels, taking into account between-pixel correlations, we propose adjustment of P values with ''resampling methods.'' Our objective is to produce two-or three-dimensional brain maps that provide, at each pixel in the map, an estimated P value with absolute meaning. That is, each P value approximates the probability of having obtained by chance the observed signal effect at that pixel, given that the null hypothesis is true. Simulated and real data examples are provided.