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Health Effects of Lesion Localization in Multiple Sclerosis: Spatial Registration and Confounding Adjustment

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Listed:
  • Ani Eloyan
  • Haochang Shou
  • Russell T Shinohara
  • Elizabeth M Sweeney
  • Mary Beth Nebel
  • Jennifer L Cuzzocreo
  • Peter A Calabresi
  • Daniel S Reich
  • Martin A Lindquist
  • Ciprian M Crainiceanu

Abstract

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.

Suggested Citation

  • Ani Eloyan & Haochang Shou & Russell T Shinohara & Elizabeth M Sweeney & Mary Beth Nebel & Jennifer L Cuzzocreo & Peter A Calabresi & Daniel S Reich & Martin A Lindquist & Ciprian M Crainiceanu, 2014. "Health Effects of Lesion Localization in Multiple Sclerosis: Spatial Registration and Confounding Adjustment," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-15, September.
    • Handle: RePEc:plo:pone00:0107263
    DOI: 10.1371/journal.pone.0107263
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    Cited by:

    1. Arnab Hazra & Brian J. Reich & Daniel S. Reich & Russell T. Shinohara & Ana-Maria Staicu, 2019. "A Spatio-Temporal Model for Longitudinal Image-on-Image Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 22-46, April.

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