Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.
Stat Med
; 43(14): 2713-2733, 2024 Jun 30.
Article
in En
| MEDLINE
| ID: mdl-38690642
ABSTRACT
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Magnetic Resonance Imaging
/
Bayes Theorem
/
Emotions
Limits:
Humans
Language:
En
Journal:
Stat Med
Year:
2024
Document type:
Article
Affiliation country: