Potential Reporting Bias in fMRI Studies of the Brain
Open accessAbstract
BACKGROUND: Functional magnetic resonance imaging (fMRI) studies have reported multiple activation foci associated with a variety of conditions, stimuli or tasks. However, most of these studies used fewer than 40 participants. METHODOLOGY: After extracting data (number of subjects, condition studied, number of foci identified and threshold) from 94 brain fMRI meta-analyses (k = 1,788 unique datasets) published through December of 2011, we analyzed the correlation between individual study sample sizes and number of significant foci reported. We also performed an analysis where we evaluated each meta-analysis to test whether there was a correlation between the sample size of the meta-analysis and the number of foci that it had identified. Correlation coefficients were then combined across all meta-analyses to obtain a summary correlation coefficient with a fixed effects model and we combine correlation coefficients, using a Fisher's z transformation. PRINCIPAL FINDINGS: There was no correlation between sample size and the number of foci reported in single studies (r = 0.0050) but there was a strong correlation between sample size and number of foci in meta-analyses (r = 0.62, p<0.001). Only studies with sample sizes <45 identified larger (>40) numbers of foci and claimed as many discovered foci as studies with sample sizes ≥ 45, whereas meta-analyses yielded a limited number of foci relative to the yield that would be anticipated from smaller single studies. CONCLUSIONS: These results are consistent with possible reporting biases affecting small fMRI studies and suggest the need to promote standardized large-scale evidence in this field. It may also be that small studies may be analyzed and reported in ways that may generate a larger number of claimed foci or that small fMRI studies with inconclusive, null, or not very promising results may not be published at all.
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Including original SPMs may avoid reporting biases likely to affect certain brain regions, especially smaller subcortical regions poorly represented in commonly used stereotaxic atlases.
“may avoid reporting biases that are likely to affect certain brain regions, especially smaller subcortical regions that are poorly represented in commonly used stereotaxic atlases of the human brain.15,16”
From Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies (2016) · cites this paper as [15]
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- Authors
- David, S. P., Ware, J. J., Chu, I. M., Loftus, P. D., Fusar-Poli, P., Radua, J., Munafò, M. R., & Ioannidis, J. P. A.
- Affiliations
- SRI International (US)
- University of Bristol (GB)
- Stanford Medicine (US)
- Corresponding author
- David, Sean P.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-07-25
- Volume
- 8
- Issue
- 7
- Pages
- e70104
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3723634
- OpenAlex
- W1975996475
- Cited by
- 130
- References
- 38
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 14 · 2023: 7 · 2022: 6 · 2021: 6 · 2020: 12 · 2019: 9 · 2018: 15 · 2017: 11 · 2016: 15 · 2015: 11 · 2014: 9 · 2013: 1
- FWCI
- 13.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- United Kingdom Clinical Research Collaboration
- Cancer Research UK
- National Institute for Health and Care Research
- British Heart Foundation
- Medical Research Council
- Economic and Social Research Council
- National Institute on Drug Abuse (DA-027331, R21DA027331, DA-017441, R01DA017441)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- reporting bias, functional MRI, sample size, meta-analysis, publication bias, negative results, brain activation, statistical significance thresholds
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Research Design, Statistics as Topic, Databases, Bibliographic, Publication Bias, Sample Size
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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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