Ten simple rules for neuroimaging meta-analysis
Open accessAbstract
Neuroimaging has evolved into a widely used method to investigate the functional neuroanatomy, brain-behaviour relationships, and pathophysiology of brain disorders, yielding a literature of more than 30,000 papers. With such an explosion of data, it is increasingly difficult to sift through the literature and distinguish spurious from replicable findings. Furthermore, due to the large number of studies, it is challenging to keep track of the wealth of findings. A variety of meta-analytical methods (coordinate-based and image-based) have been developed to help summarise and integrate the vast amount of data arising from neuroimaging studies. However, the field lacks specific guidelines for the conduct of such meta-analyses. Based on our combined experience, we propose best-practice recommendations that researchers from multiple disciplines may find helpful. In addition, we provide specific guidelines and a checklist that will hopefully improve the transparency, traceability, replicability and reporting of meta-analytical results of neuroimaging data.
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ALE analysis of the unbalanced and balanced datasets used a cluster-forming threshold of p < 0.001 (uncorrected, 1000 permutations) and a cluster-level family-wise error corrected threshold of p < 0.05.
“ALE analysis of the unbalanced and balanced datasets used a cluster-forming threshold of p < 0.001 (uncorrected, 1000 permutations), and a cluster-level family-wise error (FWE) corrected threshold of p < 0.05, as recommended by Müller et al. (2018).”
From Bridging phenomenology and neural mechanisms of inner speech: ALE meta-analysis on egocentricity and spontaneity in a dual-mechanistic framework (2023) · cites this paper as Müller et al. (2018)
Section 8, “Find a balance between sensitivity and susceptibility to false positives” (page unavailable), recommends a voxel-level cluster-forming threshold of p<0.001 and a cluster-level FWE threshold of p<0.05 for ALE meta-analyses. It does not recommend the specified 1,000 permutations; that part of the method is not supported by this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe study systematically synthesised evidence on task-related activation changes following CWMT in line with established methodological standards for neuroimaging meta-analyses.
“In line with established methodological standards for neuroimaging meta-analyses85, we systematically synthesised evidence on task-related activation changes following CWMT.”
From Meta-analysis of computerised working memory training: behavioural gains, training parameters, transfer mechanisms, and neural correlates (2026) · cites this paper as [85]
The meta-analysis followed recent guidelines for meta-analysing neuroimaging studies.
“In the current paper, we follow recent guidelines put forward for metaanalysing neuroimaging studies (Muller et al., 2018).”
From The inhibition of automatic imitation: a meta-analysis and synthesis of fMRI studies (2018) · cites this paper as Muller et al., 2018
A Jackknife analysis was performed to assess the robustness of the main findings.
“To assess the robustness of our main findings, we performed a Jackknife analysis, which consists of repeating the statistical analyses by discarding one study each time, thus demonstrating the stability of the results (Müller et al., 2018).”
From Overlapping and differential neuropharmacological mechanisms of stimulants and nonstimulants for attention-deficit/hyperactivity disorder: A comparative neuroimaging analysis (2024) · cites this paper as Müller et al., 2018
This review adheres to PRISMA guidelines and follows best-practice recommendations for coordinate-based meta-analyses.
“This review adheres to the Preferred Reporting Items for Systematic Reviews and meta-Analysis (PRISMA) guidelines (Page et al., 2021) and follows the best-practice recommendations for conducting coordinate-based meta-analyses (Müller et al., 2018, Tahmasian et al., 2019).”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Müller et al., 2018
The sample of 35 experiments is considered adequate by present guidelines for neuroimaging meta-analyses.
“Notably, this null-finding occurred in a well-powered meta-analysis: the sample of 35 experiments is considered adequate by present guidelines for neuroimaging meta-analyses (Müller et al., 2018, Tahmasian et al., 2019) and gave us more than 80 % power to detect a convergence of activation differences in areas that were identified in just 7 (20 %) of the experiments (Eickhoff et al., 2016)”
From Inhibitory control in obsessive compulsive disorder: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies (2022) · cites this paper as Müller et al., 2018
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- Authors
- Müller, V. I., Cieslik, E. C., Laird, A. R., Fox, P. T., Radua, J., Mataix-Cols, D., Tench, C. R., Yarkoni, T., Nichols, T. E., Turkeltaub, P. E., Wager, T. D., & Eickhoff, S. B.
- Affiliations
- Forschungszentrum Jülich (DE)
- Florida International University (US)
- The University of Texas at San Antonio Health Science Center (US)
- University of Nottingham (GB)
- The University of Texas at Austin (US)
- University of Warwick (GB)
- MedStar National Rehabilitation Hospital (US)
- University of Colorado Boulder (US)
- Corresponding author
- Müller, Veronika I.
- Cieslik, Edna C.
- ORCID
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 84
- Pages
- 151-161
- ISSN
- 0149-7634 (ISSN-L)
- PMC
- PMC5918306
- OpenAlex
- W2768973744
- Cited by
- 805
- References
- 72
- Citations by year
- 2026: 98 · 2025: 113 · 2024: 95 · 2023: 103 · 2022: 127 · 2021: 113 · 2020: 82 · 2019: 53 · 2018: 19 · 2017: 1 · 2016: 1
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- JuSER (Forschungszentrum Jülich) (submittedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, cc-by-nc-nd)
- JuSER (Forschungszentrum Jülich) (submittedVersion, cc-by-nc-nd)
- Funders
- Wellcome Trust (100309/Z/12/Z)
- Horizon 2020 Framework Programme (7202070)
- Deutsche Forschungsgemeinschaft (EI 816/4-1, LA 3071/3-1)
- National Institutes of Health (R01 NS075066-01A1, R01DA035484, 2R01MH076136)
- National Institute of Mental Health (R01-MH074457)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- neuroimaging meta-analysis, best practice guidelines, coordinate-based meta-analysis, replicability, reporting transparency, traceability, spurious findings, functional neuroanatomy, brain-behavior relationship, brain disorders
- MeSH terms
- Humans, Meta-Analysis as Topic, Guidelines as Topic, 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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