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Effective connectivity analysis provides an understanding of the functionalorganization of the brain by studying how activated regions influence oneother. We propose a nonparametric Bayesian approach to model effectiveconnectivity assuming a dynamic nonstationary neuronal system. Our approachuses the Dirichlet process to specify an appropriate (most plausible accordingto our prior beliefs) dynamic model as the "expectation" of a set of plausiblemodels upon which we assign a probability distribution. This addresses modeluncertainty associated with dynamic effective connectivity. We derive a Gibbssampling approach to sample from the joint (and marginal) posteriordistributions of the unknowns. Results on simulation experiments demonstrateour model to be flexible and a better candidate in many situations. We alsoused our approach to analyzing functional Magnetic Resonance Imaging (fMRI)data on a Stroop task: our analysis provided new insight into the mechanism bywhich an individual brain distinguishes and learns about shapes of objects.

A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments