|Institution:||University of Minnesota|
|Keywords:||Bayesian modeling; fMRI data; Hierarchical priors; Spatiotemporal data; Statistics|
|Full text PDF:||http://hdl.handle.net/11299/170907|
Functional magnetic resonance imaging (fMRI) has recently become a popular tool for studying human brain activity. Despite its widespread use, most existing statistical methods for analyzing fMRI data are problematic. Many methodologies oversimplify the problem for the sake of computational efficiency, often not providing a full statistical model as a result. Other methods are too computationally inefficient to use on large data sets. In this paper, we propose a Bayesian method for analyzing fMRI data that is computationally efficient and provides a full statistical model.