Hierarchical Dirichlet Processes for Modeling fMRI Brain Activation Patterns

Seyoung Kim

U. of California (Irvine)


In this talk I will describe the use of Dirichlet process mixtures for modeling of spatial patterns across multiple images. A motivating application is the analysis of functional magnetic resonance imaging (fMRI) data for brain image. I present a mixture-based response-surface technique for extracting and characterizing spatial clusters of image intensity. This approach provides a richer and more powerful representation of the image data compared to more traditional voxel-based hypothesis testing methods. Dirichlet process priors are used to automatically select the number of activation clusters in a single image. This model can be further extended with hierarchical Dirichlet processes to extract common activation clusters from multiple images. In addition, I combine hierarchical Dirichlet processes with random effects model to capture the image-level variabilities in the shape of local clusters.

I describe an MCMC sampling method to simultaneously estimate both the shape parameters and the number of local activations, and demonstrate the application of the algorithm to fMRI brain images.


Seyoung Kim is a Ph.D. candidate in the Department of Computer Science at UC, Irvine. Her research interests include statistical machine learning and Bayesian methods.
Maintained by: Fei Sha