Dynamic functional imaging experiments typically generate large, multivariate data sets that contain considerable spatial and temporal complexity. The goal of this introduction is to present signal-processing techniques that allow the underlying spatiotemporal structure to be readily distilled and that also enable signal versus noise contributions to be separated.