Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing

• Published in: Statistics in medicine Vol. 43; no. 14; pp. 2713 - 2733
• Main Authors: Sun, Lizhe, Zhang, Aiying, Liang, Faming
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 30.06.2024; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Bayesian analysis; Brain - diagnostic imaging; Brain - physiology; brain connectivity; Computer Simulation; dynamic Bayesian network; Emotions; Emotions - physiology; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Markov Chains; Markov neighborhood regression; Regression analysis; sparse graphical model; variable selection; brain connectivity; Markov neighborhood regression; variable selection; dynamic Bayesian network; sparse graphical model
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.10096

This article 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. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by 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.