Format

Send to

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

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

Author information

1
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA. esterman@jhu.edu

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.

PMID:
20006712
PMCID:
PMC2823971
DOI:
10.1016/j.neuroimage.2009.10.092
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center