title: Avoiding non-independence in fMRI data analysis: leave one subject out.
abstract: 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.
authors.affiliation: Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA. esterman@jhu.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Esterman
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Tamber-Rosenau
authors.forename: Yu-Chin
authors.initials: YC
authors.lastname: Chiu
authors.forename: Steven
authors.initials: S
authors.lastname: Yantis
mesh_terms: 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