title: A nonparametric bayesian approach to detecting spatial activation patterns in fMRI data.
abstract: Traditional techniques for statistical fMRI analysis are often based on thresholding of individual voxel values or averaging voxel values over a region of interest. In this paper we present a mixture-based response-surface technique for extracting and characterizing spatial clusters of activation patterns from fMRI data. Each mixture component models a local cluster of activated voxels with a parametric surface function. A novel aspect of our approach is the use of Bayesian nonparametric methods to automatically select the number of activation clusters in an image. We describe an MCMC sampling method to estimate both parameters for shape features and the number of local activations at the same time, and illustrate the application of the algorithm to a number of different fMRI brain images.
authors.affiliation: Bren School of Information and Computer Sciences University of California, Irvine, CA 92697-3425, USA. sykim@ics.uci.edu
authors.forename: Seyoung
authors.initials: S
authors.lastname: Kim
authors.forename: Padhraic
authors.initials: P
authors.lastname: Smyth
authors.forename: Hal
authors.initials: H
authors.lastname: Stern
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity