Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.
Stat Med
; 43(14): 2713-2733, 2024 Jun 30.
Article
em 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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
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Teorema de Bayes
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Emoções
Limite:
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China