Functional connectivity modelling in fMRI based on causal networks

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).