Time-Varying Dynamic Bayesian Network Learning for an fMRI Study of Emotion Processing
发布时间:2023年11月14日
浏览次数:1459
发布者: Fei Tao
主讲人: Lizhe Sun(BICMR)
活动时间: 从 2023-11-27 15:00 到 16:00
场地: Room 9, Quan Zhai, BICMR
Abstract: This paper presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. Furthermore, the proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by our extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.