SGPP: Spatial Gaussian Predictive Process Models for Neuroimaging Data

• Activity Number: 107
• Type: Invited
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Hongtu Zhu*+ and Jung Won Hyun and Yimei Li
• Companies: University of North Carolina at Chapel Hill and St. Jude Children's Research Hospital and St. Jude Children's Research Hospital
• Keywords: Functional principal component analysis ; Simultaneous autoregressive model ; Spatial Gaussian predictive process ; Cokriging ; Prediction ; Missing Data

Abstract

The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also characterize spatial dependence in neuroimaging data. The SGPP model uses a functional principal component model to capture medium-to-long-range (or global) spatial dependence, while SGPP uses a multivariate simultaneous autoregressive model to capture short-range (or local) spatial dependence. We propose a three-stage estimation procedure to simultaneously estimate varying regression coefficients across voxels and the global and local spatial dependence structures. Furthermore, we develop a predictive method by employing a cokriging technique. Simulation studies and real data analysis are used to evaluate the prediction accuracy of SGPP and show that SGPP significantly outperforms several competing methods, such as voxel-wise linear model, in prediction.