Exploring functional brain dynamics via a Bayesian connectivity change point model

Zhichao Lian, Xiang Li, Jianchuan Xing, Jinglei Lv, Xi Jiang, Dajiang Zhu, Shu Zhang, Jiansong Xu, Marc N. Potenza, Tianming Liu, Jing Zhang

Abstract: Multiple recent neuroimaging studies have demonstrated that the human brain's function undergoes remarkable temporal dynamics. However, quantitative characterization and modeling of such functional dynamics have been rarely explored. To fill this gap, we presents a novel Bayesian connectivity change point model (BCCPM), to analyze the joint probabilities among the nodes of brain networks between different time periods and statistically determine the boundaries of temporal blocks to estimate the change points. Intuitively, the determined change points represent the transitions of functional interaction patterns within the brain networks and can be used to investigate temporal functional brain dynamics. The BCCPM has been evaluated and validated by synthesized data. Also, the BCCPM has been applied to a real block-design task-based fMRI dataset and interesting results were obtained.