ArticleNeuroImage2014
SGPP: spatial Gaussian predictive process models for neuroimaging data.
Article in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
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- The normative modelling framework for traumatic brain injury.Brain : a journal of neurology · 2025Review
- Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.Journal of the American Statistical Association · 2024Article
- Evidence of Neurovascular Water Exchange and Endothelial Vascular Dysfunction in Schizophrenia: An Exploratory Study.Schizophrenia bulletin · 2023Article
- Cortical thickness analysis in temporal lobe epilepsy using fully Bayesian spectral method in magnetic resonance imaging.BMC medical imaging · 2022Article
- The stage-specifically accelerated brain aging in never-treated first-episode patients with depression.Human brain mapping · 2021Article
- Double-wavelet transform for multisubject task-induced functional magnetic resonance imaging data.Biometrics · 2019Article
- Spatially Adaptive Varying Correlation Analysis for Multimodal Neuroimaging Data.IEEE transactions on medical imaging · 2019Article
- Fully Bayesian spectral methods for imaging data.Biometrics · 2018Article
- A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.NeuroImage · 2017Article
- Multivariate semiparametric spatial methods for imaging data.Biostatistics (Oxford, England) · 2017Article
- Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies.Biological psychiatry · 2016Article
- Article
- Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.Journal of the American Statistical Association · 2014Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.