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To infer interactions from functional magnetic resonance imaging (fMRI) data, structural equation modeling (SEM) as well as dynamic causal modeling (DCM) has been suggested. Directed partial correlation (dPC) is a measure which detects Granger causality in multivariate systems. To demonstrate the strengths as well as the limitations of directed partial correlation we first applied it to simulated data tailored to the problem at hand. Second, after dPC has proven to be useful for fMRI data analysis, we applied it to actual fMRI data.