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.
Publication Title
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Recommended Citation
Ray, M., Kang, J., & Zhang, H. (2016). Identifying Activation Centers with Spatial Cox Point Processes Using fMRI Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13 (6), 1130-1141. https://doi.org/10.1109/TCBB.2015.2510007
