The role of noise modeling in the estimation of resting-state brain effective connectivity

TL;DR

This paper investigates how modeling endogenous fluctuations affects the estimation of effective connectivity in resting-state brain networks using fMRI data, emphasizing the importance of noise modeling assumptions.

Contribution

It introduces a focus on noise modeling in the estimation process of effective connectivity from resting-state fMRI data, highlighting its impact on accuracy.

Findings

Noise modeling significantly influences EC estimation accuracy

Different noise assumptions lead to varying connectivity network structures

Proper noise modeling improves the interpretability of brain connectivity results

Abstract

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.