Health effects of lesion localization in multiple sclerosis: Spatial registration and confounding adjustment Academic Article Article uri icon


MeSH Major

  • Brain
  • Image Processing, Computer-Assisted
  • Machine Learning
  • Magnetic Resonance Imaging


  • © 2014 Eloyan et al. Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

publication date

  • September 18, 2014



  • Academic Article


Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0107263

PubMed ID

  • 25233361

Additional Document Info


  • 9


  • 9