Bayesian structure learning for dynamic brain connectivity

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Conference article in proceedings
Date
2018-01-01
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Language
en
Pages
11
1436-1446
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Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain, Proceedings of Machine Learning Research, Volume 84
Abstract
Human brain activity as measured by fMRI exhibits strong correlations between brain regions which are believed to vary over time. Importantly, dynamic connectivity has been linked to individual differences in physiology, psychology and behavior, and has shown promise as a biomarker for disease. The state of the art in computational neuroimaging is to estimate the brain networks as relatively short sliding window covariance matrices, which leads to high variance estimates, thereby resulting in high overall error. This manuscript proposes a novel Bayesian model for dynamic brain connectivity. Motivated by the underlying neuroscience, the model estimates covariances which vary smoothly over time, with an instantaneous decomposition into a collection of spatially sparse components – resulting in parsimonious and highly interpretable estimates of dynamic brain connectivity. Simulated results are presented to illustrate the performance of the model even when it is mis-specified. For real brain imaging data with unknown ground truth, in addition to qualitative evaluation, we devise a simple classification task which suggests that the estimated brain networks better capture the underlying structure.
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Andersen , M R , Winther , O , Hansen , L K , Poldrack , R & Koyejo , O 2018 , Bayesian structure learning for dynamic brain connectivity . in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain . Proceedings of Machine Learning Research , vol. 84 , JMLR , pp. 1436-1446 , International Conference on Artificial Intelligence and Statistics , Playa Blanca , Spain , 09/04/2018 . < http://proceedings.mlr.press/v84/andersen18a.html >