Time series analysis of fMRI data: Spatial modelling and Bayesian computation.

ABSTRACT: Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. Spatial models and Bayesian approaches for inference in such models have advantages over more traditional mass univariate approaches; however, a major challenge for such analyses is the required computation. As a result, the neuroimaging community has embraced approximate Bayesian inference based on mean-field variational Bayes (VB) approximations. These approximations are implemented in standard software packages such as the popular statistical parametric mapping software. While computationally efficient, the quality of VB approximations remains unclear even though they are commonly used in the analysis of neuroimaging data. For reliable statistical inference, it is important that these app

SUBMITTER: Teng M 

PROVIDER: S-EPMC6128670 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature