Multivariate approach for brain decomposable connectivity networks

This paper deals with the analysis of brain functional network using fMRI data. It recapitulates the concept of decomposable connectivity graph. Graphs are a usual tool to represent complex systems behavior, although edge strength estimation issues have not yet received a universally adopted solution. In the framework of linear Gaussian instantaneous exchanges, the well known partial correlation is usually introduced. However its estimation remains a challenge for highly connected or dense systems. Here, we propose to combine a wavelet decomposition and a graphical Gaussian model approach relying on decomposable graphs. This is shown to improve the estimations of brain function networks in the presence of long range dependence; the results are compared to those obtained with classical partial correlation estimators.