Rank-order versus mean based statistics for neuroimaging

Neuroimage. 2007 May 1;35(4):1531-7. doi: 10.1016/j.neuroimage.2006.12.043. Epub 2007 Jan 25.


Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-Parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency--for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Brain / pathology
  • Computer Simulation
  • Data Interpretation, Statistical
  • Epilepsy, Temporal Lobe / pathology
  • False Positive Reactions
  • Hippocampus / pathology
  • Humans
  • Image Processing, Computer-Assisted / statistics & numerical data*
  • Magnetic Resonance Imaging / statistics & numerical data
  • Sample Size
  • Statistics, Nonparametric