This talk will discuss the application of Granger causality to fMRI data in the form of Granger causality mapping (GCM), which is used to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the Granger causality concept to define the existence and direction of influence from temporal information in the data. The problems of limited temporal resolution in fMRI, and the hemodynamic source of the signal that makes direct interpretation of fMRI Granger causality as neuronal influence difficult, will be discussed.