Avoiding non-independence in fMRI data analysis: leave one subject out.
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.
Esterman
Michael
M
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA. esterman@jhu.edu
Tamber-Rosenau
Benjamin J
BJ
Chiu
Yu-Chin
YC
Yantis
Steven
S
D000465:Algorithms; D015982:Bias; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D013223:Statistics as Topic