TY - JOUR
T1 - Smooth scalar-on-image regression via spatial Bayesian variable selection
AU - Goldsmith, Jeff
AU - Huang, Lei
AU - Crainiceanu, Ciprian M.
N1 - Funding Information:
The authors are grateful to Phil Reiss and Lan Huo for providing software and assistance in implementing the FPCR method of Reiss and Ogden (2010). The authors also thank Daniel Reich and Peter Calabresi, who were instrumental in collecting the data for this study. Scans were funded by grants from the National Multiple Sclerosis Society and EMD Serono. We are grateful to Vadim Zippunikov and John Muschelli for sharing their expertise in visualization software.
Funding Information:
The work of Goldsmith and Crainiceanu was supported by award number R01NS060910 from the National Institute of Neurological Disorders and Stroke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke of the National Institutes of Health. Partial support for Goldsmith’s work was provided by training grant 2T32ES012871, from the U.S., NIH, National Institute of Environmental Health Sciences.
PY - 2014
Y1 - 2014
N2 - We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes' inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in the online Appendix (see the "Supplementary Materials" section). We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white-matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
AB - We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes' inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in the online Appendix (see the "Supplementary Materials" section). We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white-matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
KW - Binary Markov random field
KW - Gaussian Markov random field
KW - Markov chain Monte Carlo
UR - http://www.scopus.com/inward/record.url?scp=84901808735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901808735&partnerID=8YFLogxK
U2 - 10.1080/10618600.2012.743437
DO - 10.1080/10618600.2012.743437
M3 - Article
AN - SCOPUS:84901808735
SN - 1061-8600
VL - 23
SP - 46
EP - 64
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
IS - 1
ER -