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SGPP: spatial Gaussian predictive process models for neuroimaging 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 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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Hyun JW,Li Y,Gilmore JH,Lu Z,Styner M,Zhu H

doi

10.1016/j.neuroimage.2013.11.018

subject

Has Abstract

pub_date

2014-04-01 00:00:00

pages

70-80

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(13)01110-5

journal_volume

89

pub_type

杂志文章

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