title: 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.
authors.affiliation: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.
authors.forename: Michelle F
authors.initials: MF
authors.lastname: Miranda
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D001289:Attention Deficit Disorder with Hyperactivity / Q000473:pathology*; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D002648:Child; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D009415:Nerve Net / Q000473:pathology; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D062211:Spatio-Temporal Analysis
keywords: Bayesian analysis; Big data; Box-Cox transformation; Gaussian Markov random field; MCMC; Neuroimaging data