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| Seminar Schedule (Seminar Organizer: Prof. Ziv Bar-Joseph) | |
Machine Learning Lunchtime Chats
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Spring 2007 Seminar
Date: February 19, 2007 Time: 1:00 PM - 00:00 AM Location: 1305 Newell-Simon Hall Speaker: Seyoung Kim Graduate Student Title: Hierarchical Dirichlet Processes for Modeling fMRI Brain Activation Patterns Abstract: This talk 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.