Format

Send to

Choose Destination
Neuroimage. 2014 May 1;91:412-9. doi: 10.1016/j.neuroimage.2013.12.058. Epub 2014 Jan 8.

Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations.

Author information

1
Department of Psychology and Neuroscience, University of Colorado Boulder, USA; Institute of Cognitive Science, University of Colorado Boulder, USA.
2
Department of Psychology and Neuroscience, University of Colorado Boulder, USA; Institute of Cognitive Science, University of Colorado Boulder, USA. Electronic address: Tor.Wager@Colorado.Edu.

Abstract

Cluster-extent based thresholding is currently the most popular method for multiple comparisons correction of statistical maps in neuroimaging studies, due to its high sensitivity to weak and diffuse signals. However, cluster-extent based thresholding provides low spatial specificity; researchers can only infer that there is signal somewhere within a significant cluster and cannot make inferences about the statistical significance of specific locations within the cluster. This poses a particular problem when one uses a liberal cluster-defining primary threshold (i.e., higher p-values), which often produces large clusters spanning multiple anatomical regions. In such cases, it is impossible to reliably infer which anatomical regions show true effects. From a survey of 814 functional magnetic resonance imaging (fMRI) studies published in 2010 and 2011, we show that the use of liberal primary thresholds (e.g., p<.01) is endemic, and that the largest determinant of the primary threshold level is the default option in the software used. We illustrate the problems with liberal primary thresholds using an fMRI dataset from our laboratory (N=33), and present simulations demonstrating the detrimental effects of liberal primary thresholds on false positives, localization, and interpretation of fMRI findings. To avoid these pitfalls, we recommend several analysis and reporting procedures, including 1) setting primary p<.001 as a default lower limit; 2) using more stringent primary thresholds or voxel-wise correction methods for highly powered studies; and 3) adopting reporting practices that make the level of spatial precision transparent to readers. We also suggest alternative and supplementary analysis methods.

KEYWORDS:

Cluster-extent thresholding; FSL; False discovery rate; Family-wise error rate; Gaussian random fields; Multiple comparisons; Primary threshold; SPM; fMRI

PMID:
24412399
PMCID:
PMC4214144
DOI:
10.1016/j.neuroimage.2013.12.058
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

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