Title | Bayesian spatial transformation models with applications in neuroimaging data. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Miranda, Michelle F., Hongtu Zhu, and Joseph G. Ibrahim |
Journal | Biometrics |
Volume | 69 |
Issue | 4 |
Pagination | 1074-83 |
Date Published | 2013 Dec |
ISSN | 1541-0420 |
Keywords | Attention Deficit Disorder with Hyperactivity, Bayes Theorem, Brain, Child, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Models, Statistical, Nerve Net, Neuroimaging, Pattern Recognition, Automated, Spatio-Temporal Analysis |
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. |
DOI | 10.1111/biom.12085 |
Alternate Journal | Biometrics |
Original Publication | Bayesian spatial transformation models with applications in neuroimaging data. |
PubMed ID | 24128143 |
PubMed Central ID | PMC3864982 |
Grant List | AG033387 / AG / NIA NIH HHS / United States R01GM070335 / GM / NIGMS NIH HHS / United States TL1 RR025745 / RR / NCRR NIH HHS / United States P01CA142538-01 / CA / NCI NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States R01 GM070335 / GM / NIGMS NIH HHS / United States RR025747-01 / RR / NCRR NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States P50 CA106991 / CA / NCI NIH HHS / United States R01 CA074015 / CA / NCI NIH HHS / United States MH086633 / MH / NIMH NIH HHS / United States UL1 RR025747 / RR / NCRR NIH HHS / United States T32 CA106209 / CA / NCI NIH HHS / United States P01CA142538 / CA / NCI NIH HHS / United States |
Bayesian spatial transformation models with applications in neuroimaging data.
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