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 \textbf{(BDyMA)} method to address the challenges in discovering DEC. The presented dynamic causal model enables us to discover bidirected 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 baseline 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.

翻译：理解大脑复杂机制可通过提取动态有效连接组（DEC）得以揭示。近期，基于评分的动态有向无环图（DAG）发现方法在提取因果结构与推断有效连接性方面展现出显著进步。然而，通过这些方法学习DEC仍面临两大主要挑战：一是高维动态DAG发现方法的根本性功能不足，二是fMRI数据质量低下。本文提出结合M-矩阵无环性表征的贝叶斯动态DAG学习（BDyMA）方法，以应对DEC发现中的挑战。所提出的动态因果模型还能发现双向边。BDyMA方法采用无约束框架，在检测高维网络时能获得更精确结果，实现更稀疏的输出，特别适用于DEC提取。此外，BDyMA方法的评分函数允许将先验知识融入动态因果发现过程，进一步提升了结果的准确性。基于合成数据的全面仿真及人类连接组计划（HCP）数据的实验表明，我们的方法可应对上述两大挑战，相比现有最优方法及基线方法能产生更准确可靠的DEC。同时，我们探究了DTI数据作为DEC发现先验知识的可信度，并展示了在流程中融入DTI数据时DEC发现的改进效果。