Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment

TL;DR

This paper introduces Bayesian causal discovery frameworks leveraging DTI data to improve the accuracy and reliability of brain Effective Connectome estimation from fMRI data, addressing limitations of existing methods.

Contribution

The paper proposes two novel Bayesian causal discovery methods, BGOLEM and BFGES, that incorporate DTI priors to enhance EC detection from fMRI data.

Findings

Bayesian methods outperform traditional causal discovery techniques in accuracy.

Proposed methods yield more reproducible Effective Connectomes.

New metric PFDR effectively assesses causal discovery accuracy.

Abstract

Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome (EC). Although current EC discovery methods have contributed to our understanding of brain organization, their performances are severely constrained by the short sample size and poor temporal resolution of fMRI data, and high dimensionality of the brain connectome. By leveraging the DTI data as prior knowledge, we introduce two Bayesian causal discovery frameworks -- the Bayesian GOLEM (BGOLEM) and Bayesian FGES (BFGES) methods -- that offer significantly more accurate and reliable ECs and address the shortcomings of the existing causal discovery methods in discovering ECs based on only fMRI data. Through a series of simulation studies on synthetic and hybrid (DTI of the Human Connectome Project (HCP) subjects and synthetic fMRI) data, we demonstrate the effectiveness of the proposed methods in discovering EC. To numerically assess the improvement in the accuracy of ECs with our method on empirical data, we first introduce the Pseudo False Discovery Rate (PFDR) as a new computational accuracy metric for causal discovery in the brain. We show that our Bayesian methods achieve higher accuracy than traditional methods on HCP data. Additionally, we measure the reliability of discovered ECs using the Rogers-Tanimoto index for test-retest data and show that our Bayesian methods provide significantly more reproducible ECs than traditional methods. Overall, our study's numerical and graphical results highlight the potential for these frameworks to advance our understanding of brain function and organization significantly.