The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI.

ABSTRACT:

Purpose

Multiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI.

Materials and methods

In this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies.

Results

First, we perform a Linear Discriminant Analysis stud

SUBMITTER: Bielczyk NZ 

PROVIDER: S-EPMC5561328 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature