Multilevel Group Analysis on Bayesian in fMRI Time Series

This paper suggests one method to process fMRI time series based on Bayesian inference for group analysis. The method uses multilevel divided by session, subject and group as pair comparison to reinforce posterior probability in group analysis from single subjects as priors. And also it combines classical statistics, i.e., t-test to obtain voxel activation at subject level as prior for Bayesian inference at group level. It effectively solved computation expensive and complexity. And it shows robust on Bayesian inference for group analysis.