Paper Info
Title
Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes.
Abstract
Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian “scalar-on-image” regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.
Year
DOI
Venue
2013
10.1016/j.neuroimage.2013.06.020
NeuroImage
Keywords
Field
DocType
Multiple sclerosis,Diffusion tensor imaging,Ising prior,Binary Markov random field
Voxel,Developmental psychology,Diffusion MRI,White matter,Regression,Regression analysis,Fractional anisotropy,Psychology,Bayesian probability,Bayes' theorem
Journal
Volume
ISSN
Citations 
83
1053-8119
3
PageRank 
References 
Authors
0.46
17
5
Name
Order
Citations
PageRank
Lei Huang171.22
Jeff Goldsmith2122.35
Philip T. Reiss3221.95
Daniel S. Reich420915.94
Ciprian M Crainiceanu56110.28