Graphical models for brain connectivity from functional imaging data

This paper proposes a novel approach for analysis of brain connectivity shown in functional MRI (fMRI), using graphical models. Structural equation modeling (SEM) is currently used to model neural systems by using partial covariance values, which is only able to affirm or refute functional connectivity of a previously known anatomical model or select the best fit model from a set of a priori models. Our approach is exploratory in the sense that it does not require a priori model such as an anatomical model. The SEM uses covariances which describe only second order behavior of a network while conditional probabilities used in graphical models, in theory, describe the complete behavior of a network. It renders the interactions among brain regions with conditional densities and allows simulation of disconnectivity of neural systems.