The sensitivity of gradient-echo magnetic resonance imaging (MRI) to changes in cerebral blood oxygenation has been introduced for mapping functional brain activation. To benefit from the high spatial and temporal resolution of the respective dynamic MRI data sets, their analysis requires algorithms that are capable of both precisely delineating task-related activation patterns and demonstrating functional connectivity of interacting areas. Here, we present various strategies for data evaluation by means of correlational analyses that surpass the quality of subtraction-based activation maps by improving both sensitivity and robustness. On a pixel-by-pixel basis the approach correlates signal time courses with a reference function, reflecting the temporal sequence of activated and control states. Extended versions employ the calculation of auto- or cross-correlation functions that increase sensitivity, but require periodic stimulations. Following individual correction for non-specific but correlated signal fluctuations, mapping of task-related coherent activation can be improved using neighborhood principles. Such refined strategies are expected to enhance the usefulness of oxygenation-sensitive MRI for studying the functional anatomy of the human brain under both physiological and pathological conditions.