  Understanding the complex mechanisms of the brain can be unraveled by
extracting the Dynamic Effective Connectome (DEC). Recently, score-based
Directed Acyclic Graph (DAG) discovery methods have shown significant
improvements in extracting the causal structure and inferring effective
connectivity. However, learning DEC through these methods still faces two main
challenges: one with the fundamental impotence of high-dimensional dynamic DAG
discovery methods and the other with the low quality of fMRI data. In this
paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity
characterization (BDyMA) method to address the challenges in discovering DEC.
The presented dynamic causal model enables us to discover direct feedback loop
edges as well. Leveraging an unconstrained framework in the BDyMA method leads
to more accurate results in detecting high-dimensional networks, achieving
sparser outcomes, making it particularly suitable for extracting DEC.
Additionally, the score function of the BDyMA method allows the incorporation
of prior knowledge into the process of dynamic causal discovery which further
enhances the accuracy of results. Comprehensive simulations on synthetic data
and experiments on Human Connectome Project (HCP) data demonstrate that our
method can handle both of the two main challenges, yielding more accurate and
reliable DEC compared to state-of-the-art and traditional methods.
Additionally, we investigate the trustworthiness of DTI data as prior knowledge
for DEC discovery and show the improvements in DEC discovery when the DTI data
is incorporated into the process.
