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Bayesian spatial transformation models with applications in neuroimaging data.

ABSTRACT: The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder.

SUBMITTER: Miranda MF

PROVIDER: S-EPMC3864982 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Bayesian spatial transformation models with applications in neuroimaging data.

Miranda Michelle F MF Zhu Hongtu H Ibrahim Joseph G JG

Biometrics 20131015 4

The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an ef ...[more]

PMID: 24128143

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Bayesian spatial transformation models with applications in neuroimaging data.

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Bayesian spatial transformation models with applications in neuroimaging data.

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