Hierarchical Dirichlet Processes for Modeling fMRI Brain Activation Patterns
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.
Bio: Seyoung Kim is a Ph.D. candidate in the Department of Computer Science
at UC, Irvine. Her research interests include statistical machine learning and