A MS-lesion pattern discrimination plot based on geostatistics

Brain Behav. 2016 Jan 30;6(3):e00430. doi: 10.1002/brb3.430. eCollection 2016 Mar.


Introduction: A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented.

Methods: A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill.

Results: Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot.

Conclusions: The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.

Keywords: Discrimination; Multiple Sclerosis; geostatistics; lesion; pattern.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Cross-Sectional Studies
  • Humans
  • Magnetic Resonance Imaging / methods
  • Magnetic Resonance Imaging / statistics & numerical data*
  • Multiple Sclerosis / diagnostic imaging*
  • Multiple Sclerosis / epidemiology
  • Multiple Sclerosis / physiopathology
  • Pattern Recognition, Automated / methods*
  • Pilot Projects