Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative mod- els for brain activation patterns. This framework is applied to modeling of neuronal circuits associated with reward. The novelty of our frame- work from a Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity. Such interactivity mod- els which are derived from fMRI data are then validated through a group classification task.

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative models forbrain activation patterns. This framework is applied to modeling of neuronal circuits associated with reward. The novelty of our framework froma Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity. Such interactivity models whichare derived from fMRI data are then validated through a group classification task.