SGPP: spatial Gaussian predictive process models for neuroimaging data

Authors

Jung Won Hyun, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
Yimei Li, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
John H. Gilmore, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Zhaohua Lu, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Martin Styner, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Hongtu Zhu, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. Electronic address: hzhu@bios.unc.edu.

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 a better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also explicitly model 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 as well as cross-correlations of different imaging modalities. 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 to use the spatial correlations as well as the cross-correlations by employing a cokriging technique, which can be useful for the imputation of missing imaging data. 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. Although we focus on the morphometric variation of lateral ventricle surfaces in a clinical study of neurodevelopment, it is expected that SGPP is applicable to other imaging modalities and features.

Publication Title

NeuroImage

Recommended Citation

Hyun, J. W., Li, Y., Gilmore, J. H., Lu, Z., Styner, M., & Zhu, H. (2014). SGPP: spatial Gaussian predictive process models for neuroimaging data. NeuroImage, 89, 70-80. https://doi.org/10.1016/j.neuroimage.2013.11.018