The role of noise modeling in the estimation of resting-state brain effective connectivity
For researchers using fMRI to infer brain connectivity, this work highlights the importance of noise modeling assumptions, though it is an incremental contribution.
This paper investigates how modeling endogenous fluctuations affects the estimation of effective connectivity from resting-state fMRI data, showing that the choice of noise model significantly impacts network estimation accuracy.
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.