Brain imaging by PET, MR, EEG, and MEG has become a cornerstone in systems level neuroscience. Statistical analyses of neuroimage datasets face many interesting challenges including non-linearity and multi-scale spatial and temporal dynamics. The objectives of neuroimaging are dual, we are interested in the most accurate, i.e., predictive, statistical model, but equally important is model interpretation and visualization which often takes the form of “brain mapping”. I will introduce some current machine learning strategies invoked for explorative and hypothesis driven neuroimage modeling, and present a general framework for model evaluation, interpretation, and visualization based on computer intensive data re-sampling schemes. Within the framework we obtain both an unbiased estimate of the predictive performance and of the reliability of the brain map visualization.