2010
DOI: 10.1016/j.neuroimage.2009.10.092
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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 nonindependence 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 serv… Show more

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Cited by 241 publications
(190 citation statements)
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“…To examine the time courses in more detail, we next tested the difference between HC and LC at each time point in each cluster. To avoid circularity, we used leave-one-subject-out (LOSO) cross-validation (Esterman et al 2010). To correct for multiple comparisons, we employed a threshold of p < .005, based on the 10 tests in each region (i.e., 0.05/10; a Bonferroni correction across all tests together would almost certainly be overly conservative given the dependency between tests).…”
Section: Fmri Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…To examine the time courses in more detail, we next tested the difference between HC and LC at each time point in each cluster. To avoid circularity, we used leave-one-subject-out (LOSO) cross-validation (Esterman et al 2010). To correct for multiple comparisons, we employed a threshold of p < .005, based on the 10 tests in each region (i.e., 0.05/10; a Bonferroni correction across all tests together would almost certainly be overly conservative given the dependency between tests).…”
Section: Fmri Resultsmentioning
confidence: 99%
“…To analyze activation on a timepoint-by-timepoint basis within clusters identified in the whole-brain ANOVA while avoiding circular voxel selection, we employed the leave-one-subject out (LOSO) method described by Esterman (2010). In this method, the voxels selected for a given ROI in a given subject are determined by the whole-brain suprathreshold voxels identified in a wholebrain analysis carried out with all the other subjects in the study.…”
Section: Glm Analysismentioning
confidence: 99%
“…To determine if amygdala connectivity patterns predicted behavioral performance, multiple linear regression analyses were completed using a leave‐one‐subject‐out (LOSO) approach (Esterman, Tamber‐Rosenau, Chiu, & Yantis, 2010), which maintains independence between the training and testing datasets. Here, separate group‐level functional connectivity difference analyses using the left and right amygdala ROIs as seed regions were rerun 66 times, each time leaving one participant out.…”
Section: Methodsmentioning
confidence: 99%
“…We therefore extracted the parameter estimates at a voxel level threshold of P < 0.001. The fact that the cluster was not significant in each GLM is expected and can be explained by the loss of one degree of freedom due to the left-out subject and thus the slightly reduced statistical power (Esterman et al, 2010). Fig.…”
Section: Fmri Resultsmentioning
confidence: 85%
“…To get an unbiased estimate of the underlying effect sizes for each significant cluster, we used a leave one subject out crossvalidation procedure (Esterman, Tamber-Rosenau, Chiu, & Yantis, 2010). We repeatedly recalculated the second level GLM, iteratively leaving out one of the subjects in each calculation.…”
Section: Fmri Data Analysismentioning
confidence: 99%