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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Huang L,Goldsmith J,Reiss PT,Reich DS,Crainiceanu CMdoi
10.1016/j.neuroimage.2013.06.020subject
Has Abstractpub_date
2013-12-01 00:00:00pages
210-23eissn
1053-8119issn
1095-9572pii
S1053-8119(13)00657-5journal_volume
83pub_type
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