  Causal relations among neuronal populations of the brain are studied through
the so-called effective connectivity (EC) network. The latter is estimated from
EEG or fMRI measurements, by inverting a generative model of the corresponding
data. It is clear that the goodness of the estimated network heavily depends on
the underlying modeling assumptions. In this present paper we consider the EC
estimation problem using fMRI data in resting-state condition. Specifically, we
investigate on how to model endogenous fluctuations driving the neuronal
activity.
