A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments

TL;DR

This paper introduces a nonparametric Bayesian method using Dirichlet processes to model dynamic, nonstationary effective connectivity in fMRI data, addressing model uncertainty and providing new insights into brain function.

Contribution

It presents a novel nonparametric Bayesian framework for modeling dynamic effective connectivity in fMRI, incorporating model uncertainty with Dirichlet processes.

Findings

Model demonstrates flexibility and improved performance in simulations

Provides new insights into brain mechanisms during a Stroop task

Addresses model uncertainty in dynamic connectivity analysis

Abstract

Effective connectivity analysis provides an understanding of the functional organization of the brain by studying how activated regions influence one other. We propose a nonparametric Bayesian approach to model effective connectivity assuming a dynamic nonstationary neuronal system. Our approach uses the Dirichlet process to specify an appropriate (most plausible according to our prior beliefs) dynamic model as the "expectation" of a set of plausible models upon which we assign a probability distribution. This addresses model uncertainty associated with dynamic effective connectivity. We derive a Gibbs sampling approach to sample from the joint (and marginal) posterior distributions of the unknowns. Results on simulation experiments demonstrate our model to be flexible and a better candidate in many situations. We also used our approach to analyzing functional Magnetic Resonance Imaging (fMRI) data on a Stroop task: our analysis provided new insight into the mechanism by which an individual brain distinguishes and learns about shapes of objects.