SGPP: spatial Gaussian predictive process models for neuroimaging data.

TitleSGPP: spatial Gaussian predictive process models for neuroimaging data.
Publication TypeJournal Article
Year of Publication2014
AuthorsHyun, Jung Won, Yimei Li, John H. Gilmore, Zhaohua Lu, Martin Styner, and Hongtu Zhu
JournalNeuroimage
Volume89
Pagination70-80
Date Published2014 Apr 01
ISSN1095-9572
KeywordsComputer Simulation, Female, Humans, Image Processing, Computer-Assisted, Infant, Lateral Ventricles, Male, Neuroimaging, Normal Distribution, Principal Component Analysis, Spatial Analysis
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.

DOI10.1016/j.neuroimage.2013.11.018
Alternate JournalNeuroimage
Original PublicationSGPP: Spatial Gaussian predictive process models for neuroimaging data.
PubMed ID24269800
PubMed Central IDPMC4134945
Grant ListHD053000 / HD / NICHD NIH HHS / United States
P01CA142538-01 / CA / NCI NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
P50 MH064065 / MH / NIMH NIH HHS / United States
RR025747-01 / RR / NCRR NIH HHS / United States
R01 ES017240 / ES / NIEHS NIH HHS / United States
P30 HD003110 / HD / NICHD NIH HHS / United States
R01 MH070890 / MH / NIMH NIH HHS / United States
R01 HD053000 / HD / NICHD NIH HHS / United States
U01 MH070890 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R21 AG033387 / AG / NIA NIH HHS / United States
AG033387 / AG / NIA NIH HHS / United States
R01 MH060352 / MH / NIMH NIH HHS / United States
MH070890 / MH / NIMH NIH HHS / United States
R01ES17240 / ES / NIEHS NIH HHS / United States
AS1499 / / Autism Speaks / United States
U54 EB005149 / EB / NIBIB NIH HHS / United States
P30 HD03110 / HD / NICHD NIH HHS / United States
MH086633 / MH / NIMH NIH HHS / United States
R01 MH091645 / MH / NIMH NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
MH091645 / MH / NIMH NIH HHS / United States
MH064065 / MH / NIMH NIH HHS / United States