title: Identifying Activation Centers with Spatial Cox Point Processes Using fMRI Data.
abstract: We developed a Bayesian clustering method to identify significant regions of brain activation. Coordinate-based meta data originating from functional magnetic resonance imaging (fMRI) were of primary interest. Individual fMRI has the ability to measure the intensity of blood flow and oxygen to a location within the brain that was activated by a given thought or emotion. The proposed method performed clustering on two levels, latent foci center and study activation center, with a spatial Cox point process utilizing the Dirichlet process to describe the distribution of foci. Intensity was modeled as a function of distance between the focus and the center of the cluster of foci using a Gaussian kernel. Simulation studies were conducted to evaluate the sensitivity and robustness of the method with respect to cluster identification and underlying data distributions. We applied the method to a meta data set to identify emotion foci centers.
authors.forename: Meredith
authors.initials: M
authors.lastname: Ray
authors.forename: Jian
authors.initials: J
authors.lastname: Kang
authors.forename: Hongmei
authors.initials: H
authors.lastname: Zhang
mesh_terms: D001921:Brain / Q000502:physiology*; D003071:Cognition; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004644:Emotions / Q000502:physiology*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity