Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing
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Wiley Open Access
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| Formato: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867013601573732352 |
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| author | Lizhe Sun Aiying Zhang Faming Liang |
| author_facet | Lizhe Sun Aiying Zhang Faming Liang Lizhe Sun Aiying Zhang Faming Liang |
| collection | Wiley Open Access |
| contents | Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing Lizhe Sun Aiying Zhang Faming Liang Statistics in Medicine 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. 10.1002/sim.10096 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1002/sim.10096 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10096 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing Lizhe Sun Aiying Zhang Faming Liang Statistics in Medicine Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing Lizhe Sun Aiying Zhang Faming Liang Statistics in Medicine 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. 10.1002/sim.10096 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10096 |