A causal brain network estimation method leveraging Bayesian analysis and the PC algorithm

Estimating causal brain networks from fMRI data is important in understanding functional human brain connectivity, and current causality estimation methods face various challenges such as high dimensionality and expensive computation. The joint estimation of causal networks between groups shows promising potential to investigate group-related brain connectivity variations. In this paper, we proposed a joint causal brain network estimation method by adding a prior to the popular PC algorithm(1) (by Peter Spirtes and Clark Glymour). The prior is obtained through a fast joint Bayesian analysis (FIBA) and plays a role as a screening step, significantly reducing computational burden of PC algorithm. Moreover, the FIBA also enables us to efficiently address the high dimensionality problem of fMRI data. The experimental results from both simulation data sets and real fMRI data demonstrate the accuracy and efficiency of the proposed method. The specific brain connections identified in schizophrenia patients extend previous research and shed light on other studies of mental disorders.