10.6084/m9.figshare.5306059.v1
Jairo A. Fúquene Patiño
Jairo A. Fúquene
Patiño
Brenda Betancourt
Brenda
Betancourt
João B. M. Pereira
João B. M.
Pereira
A weakly informative prior for Bayesian dynamic model selection with applications in fMRI
Taylor & Francis Group
2017
Dynamic linear models
beta prime prior
sparsity
functional magnetic imaging data
2017-08-12 06:59:20
Journal contribution
https://tandf.figshare.com/articles/journal_contribution/A_weakly_informative_prior_for_Bayesian_dynamic_model_selection_with_applications_in_fMRI/5306059
<p>In recent years, Bayesian statistics methods in neuroscience have been showing important advances. In particular, detection of brain signals for studying the complexity of the brain is an active area of research. Functional magnetic resonance imagining (fMRI) is an important tool to determine which parts of the brain are activated by different types of physical behavior. According to recent results, there is evidence that the values of the connectivity brain signal parameters are close to zero and due to the nature of time series fMRI data with high-frequency behavior, Bayesian dynamic models for identifying sparsity are indeed far-reaching. We propose a multivariate Bayesian dynamic approach for model selection and shrinkage estimation of the connectivity parameters. We describe the coupling or lead-lag between any pair of regions by using mixture priors for the connectivity parameters and propose a new weakly informative default prior for the state variances. This framework produces one-step-ahead proper posterior predictive results and induces shrinkage and robustness suitable for fMRI data in the presence of sparsity. To explore the performance of the proposed methodology, we present simulation studies and an application to functional magnetic resonance imaging data.</p>