Functional connectivity modelling in fMRI based on causal networks

Abstract

We apply the principle of causal networks to develop a new tool for connectivity analysis in functional magnetic resonance imaging (fMRI). The connections between active brain regions are modelled as causal relationships in a causal network. The causal networks are based on the notion of d-separation in a graph-theoretic context or, equivalently, on the notion of conditional independence in a statistical context. Since relationships between brain regions are believed to be nonlinear in nature, we express the conditional dependencies between the brain regions' activities in terms of conditional mutual information. The density estimates needed for computing the conditional mutual information are obtained with topographic maps, trained with the kernel-based maximum entropy rule (kMER).