SGPP: spatial Gaussian predictive process models for neuroimaging data
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- 作者：Jung Won Hyun ; Yimei Li ; John H. Gilmore ; Zhaohua Lu ; Martin Styner ; Hongtu Zhu
- 关键词：Cokriging ; Functional principal component analysis ; Missing data ; Prediction ; Simultaneous autoregressive model ; Spatial Gaussian predictive process
- 刊名：NeuroImage
- 出版年：1 April, 2014
- 年：2014
- 卷：89
- 期：Complete
- 页码：70-80
- 全文大小：1839 K
文摘
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.