Avoiding non-independence in fMRI data analysis: leave one subject out

Neuroimage. 2010 Apr 1;50(2):572-6. doi: 10.1016/j.neuroimage.2009.10.092. Epub 2009 Dec 16.


Concerns regarding certain fMRI data analysis practices have recently evoked lively debate. The principal concern regards the issue of non-independence, in which an initial statistical test is followed by further non-independent statistical tests. In this report, we propose a simple, practical solution to reduce bias in secondary tests due to non-independence using a leave-one-subject-out (LOSO) approach. We provide examples of this method, show how it reduces effect size inflation, and suggest that it can serve as a functional localizer when within-subject methods are impractical.

Publication types

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

MeSH terms

  • Algorithms
  • Bias
  • Brain Mapping / methods*
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging*
  • Statistics as Topic