Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates
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Abstract
Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event‐related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one‐sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two‐sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method ( p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.
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The method of correction for multiple comparisons used in the exploratory whole-brain analyses is probably biased toward false positives.
“We note that this method of correction for multiple comparisons is probably biased toward false positives97,98.”
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Functional Magnetic Resonance Imaging
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- Authors
- Eklund, A., Knutsson, H., & Nichols, T. E.
- Affiliations
- Linköping University (SE)
- University of Warwick (GB)
- Corresponding author
- Eklund, Anders
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2018-10-15
- Volume
- 40
- Issue
- 7
- Pages
- 2017-2032
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6445744
- OpenAlex
- W2796214606
- Cited by
- 82
- References
- 69
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 5 · 2023: 8 · 2022: 11 · 2021: 7 · 2020: 14 · 2019: 17 · 2018: 5 · 2017: 1
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Brain Mapping (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Wellcome Trust (100309/Z/12/Z)
- National Institutes of Health (R01 EB015611)
- Knut och Alice Wallenbergs Stiftelse
- Linköpings Universitet
- Vetenskapsrådet (2013‐5229 and 2017‐04889)
- 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
- false positive rate, event-related design, two-sided tests, data cleaning, physiological noise, task-based fMRI, resting-state fMRI, null hypothesis testing, data sharing
- MeSH terms
- Brain, Brain Mapping, Electricity, Humans, Magnetic Resonance Imaging, Cluster Analysis, Artifacts
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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