SGPP: spatial Gaussian predictive process models for neuroimaging data.
SGPP: spatial Gaussian predictive process models for neuroimaging data.
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DOI：
10.1016/j.neuroimage.2013.11.018
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发表时间：
2014-04-01
期刊：
影响因子：
5.7
通讯作者：
Zhu H
中科院分区：
文献类型：
--
作者：
Hyun JW;Li Y;Gilmore JH;Lu Z;Styner M;Zhu H
本文的目的是开发一个空间高斯预测过程(SGPP)框架，通过使用一组感兴趣的协变量，如年龄和诊断状态，以及现有的神经成像数据集，准确地预测神经成像数据。为了实现更好的预测，我们不仅描述了神经成像数据和协变量之间的空间关联，而且还显式地建模了神经成像数据中的空间相关性。SGPP模型使用函数主成分模型来捕捉中长期(或全局)的空间相关性，而SGPP模型使用多变量同时自回归模型来捕捉不同成像模式的短期(或局部)空间相关性以及交叉相关性。我们提出了一种三阶段估计过程来同时估计体素以及全局和局部空间相关性结构中的变化的回归系数。此外，我们发展了一种预测方法，通过使用协同克里格技术来利用空间相关性和交叉相关性，这对于缺失成像数据的填补是有用的。通过仿真研究和实际数据分析，对SGPP的预测精度进行了评估，结果表明，SGPP的预测性能明显优于体素线性模型等几种竞争方法。虽然我们在神经发育的临床研究中主要关注侧脑室表面的形态计量学变化，但预计SGPP也适用于其他成像方式和特征。
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 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.
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影响因子：
5.7
作者：
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通讯作者：
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影响因子：
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通讯作者：
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DOI：
10.1111/j.1467-9868.2008.00663.x
发表时间：
2008-09-01
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
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通讯作者：
Sang H