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A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps
Open accessAbstract
Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM meta-analysis of studies on the brain response to fearful faces with the results of a pooled analysis of the original individual data. The results showed that ES-SDM is a valid and reliable coordinate-based method, whose performance might be additionally increased by including statistical parametric maps. We anticipate that ES-SDM will be a helpful tool for researchers in the fields of psychiatry, neurology and allied disciplines.
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In the citing paper
“This method, which has been validated and used in several structural and functional fMRI studies creates a brain map of the effect size of the difference between the two conditions (CS+ and CS -)for each study (either from SPMs or from peak information) and afterwards conducts a voxel-wise random-effects meta-analysis (weighting the studies for sample size and variance) 13,[20][21][22]”
From Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies (2016) · cites this paper as [13]
In the citing paper
“Including original SPMs, as opposed to including only peak regional effects (typically reported in tables of statistics), substantially increases the analyses statistical power 13”
From Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies (2016) · cites this paper as [13]
In the citing paper
“Despite providing an optimal balance between sensitivity and false positive rate, 13 the default AES-SDM statistical thresholds were based on uncorrected P-values, which may be seen as a limitation.”
From Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies (2016) · cites this paper as [13]
In the citing paper
“Our study nevertheless has some limitations inherent to most meta-analyses; the different studies included employed various statistical thresholds, and although our methods provide excellent control for false positives it is more difficult to avoid false negatives.13”
From Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies (2016) · cites this paper as [13]
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Metadata
- Authors
- Radua, J., Mataix-Cols, D., Phillips, M. L., El-Hage, W., Kronhaus, D. M., Cardoner, N., & Surguladze, S.
- Affiliations
- University of Pittsburgh (US)
- Cygnet Healthcare Ltd (GB)
- Bellvitge University Hospital (ES)
- King's College London (GB)
- Venue
- European Psychiatry
- Publisher
- Cambridge University Press (CUP)
- Published
- 2011-06-11
- Volume
- 27
- Issue
- 8
- Pages
- 605-611
- ISSN
- 0924-9338 (ISSN-L)
- 1778-3585
- OpenAlex
- W2064405215
- Cited by
- 765
- References
- 32
- Citations by year
- 2026: 63 · 2025: 81 · 2024: 60 · 2023: 73 · 2022: 75 · 2021: 76 · 2020: 57 · 2019: 45 · 2018: 58 · 2017: 45 · 2016: 40 · 2015: 23 · 2014: 26 · 2013: 25 · 2012: 17
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- www.cambridge.org/core/terms (unspecified)
- Locations
- European Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by-nc-nd)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- John D. and Catherine T. MacArthur Foundation
- National Institute for Materials Science
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- activation likelihood estimation, method validation
- MeSH terms
- Adult, Brain, Brain Mapping, Facial Expression, Humans, Magnetic Resonance Imaging, Neuropsychological Tests, Reproducibility of Results, Neuroimaging
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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