Cited by 1,253Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

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Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations

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Woo, Choong-Wan, Krishnan, Anjali, Wager, Tor D.·2014·NeuroImage

DOI 10.1016/j.neuroimage.2013.12.058PMC PMC4214144

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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.

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“A stepwise thresholding approach was implemented (Woo et al., 2014).”
Method
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Authors: Woo, C.-W., Krishnan, A., & Wager, T. D.
Affiliations: University of Colorado Boulder (US)
Corresponding author: Wager, Tor D.
ORCID: Woo, Choong-Wan
Krishnan, Anjali
Wager, Tor D.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2014-05
Volume: 91
Pages: 412-419
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2013.12.058
PMC: PMC4214144
OpenAlex: W2169463832
Cited by: 1,253
References: 31
Citations by year: 2026: 77 · 2025: 79 · 2024: 81 · 2023: 81 · 2022: 96 · 2021: 110 · 2020: 138 · 2019: 112 · 2018: 158 · 2017: 135 · 2016: 93 · 2015: 77 · 2014: 16
FWCI: 59.70
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Mental Health (R01MH076136)
National Institute on Drug Abuse (R01DA035484)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords: multiple testing correction, spatial specificity, false positives, statistical maps
MeSH terms: Computer Simulation, Data Interpretation, Statistical, False Positive Reactions, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software, Cluster Analysis, Normal Distribution, Signal-To-Noise Ratio, Neuroimaging

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