A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data
We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated observations on an individual or images from different individuals in a clinical study. Instead of taking the traditional approach of voxel-by-voxel analysis, we directly model the shape of activation patterns by representing each activation cluster in an image as a Gaussian-shaped surface. We assume that there is an unknown true template pattern and that each observed image is a noisy realization of this template. We model an individual image using a mixture of experts model with each component representing a spatial activation cluster. Taking a nonparametric Bayesian approach, we use a hierarchical Dirichlet process to extract common activation clusters from multiple images and estimate the number of such clusters automatically. We further extend the model by adding random effects to the shape parameters to allow for image-specific variation in the activation patterns. Using a Bayesian framework, we learn the shape parameters for both image-level activation patterns and the template for the set of images by sampling from the posterior distribution of the parameters. We demonstrate our model on a dataset collected in a large multisite fMRI study.
functional magnetic resonance imaging (fmri) brain activation hierarchical model