Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要