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

2018/02/13 by Giulia Prando, Prando, Giulia, Mattia Zorzi +5
Computer Science · Neuroscience · #FOS: Biological sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Neural Networks and Applications #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1802.05533

openalex publication_date 2018/02/13 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28

Abstract

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

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