Conditional independence graphs for functional connectivity in functional MRI

A recent concern in BOLD fMRI data analysis is extraction of connectivity information between regions. Functional connectivity and effective connectivity are the two notions defined so far, but the former lacks the ability to reveal direct interactions, while use of the latter requires the connectivity model to be set a priori. We propose that conditional independence graphs be considered as a proper way to extract and represent direct interactions between regions. We show how robust and fast statistical inference can be conducted on real data without need of any prior model.