Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes

Lei Huang, Jeff Goldsmith, Philip T. Reiss, Daniel S. Reich, Ciprian M. Crainiceanu

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)210-223
Number of pages14
JournalNeuroImage
Volume83
DOIs
StatePublished - Dec 1 2013

Keywords

  • Binary Markov random field
  • Diffusion tensor imaging
  • Ising prior
  • Multiple sclerosis

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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