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Activity Number: 338
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313743
Title: Clustering fMRI Metadata to Identify Significant Regions of Brain Activation
Author(s): Meredith Ray*+ and Hongmei Zhang and Jian Kang
Companies: University of South Carolina and University of Memphis and Emory University
Keywords: fMRI ; Bayesian ; spatial anlaysis ; clustering
Abstract:

We developed a Bayesian clustering method for identifying significant regions of brain activation (foci). Coordinate-based meta data originating from functional magnetic resonance imaging (fMRI), which 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, was of primary interest. FMRI studies have small samples sizes and therefore low testing power and reproducibility, so meta-analyses are implemented to increase the latter. We clustered on two levels, latent foci center and study activation center, with a spatial Poisson 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 Gaussian kernels. Simulation studies were conducted to evaluate the sensitivity and robustness with respect to cluster identification and underlying data distributions. An additional application of the proposed method modeled meta data of emotion foci. Both simulations and real data application produced promising results that highlighted the ability to correctly cluster.


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