The paper, led by Joaquim Radua of IDIBAPS in Barcelona together with Lydia Fortea and an international team spanning Spain, Germany, Italy, the United Kingdom, Sweden and South Korea, introduces the Qual-CBMA checklist: fifteen concrete criteria designed to make the methodological rigor of a coordinate-based meta-analysis visible, reviewable and discussable. The checklist grew out of a Delphi-style iterative process in which the developers of the field’s most widely used CBMA methods drafted an initial proposal and then refined it through two structured rounds of feedback among all authors. Its launch matters because the field’s previous benchmark, the influential Ten Simple Rules for neuroimaging meta-analysis published in 2018, has been cited far more often than it has been followed.

The evidence for that claim is sobering. A 2023 citation analysis found that nearly 8 percent of quotations of the Ten Simple Rules contained errors, including misinterpretation, oversimplification or outright unsupported claims. That pattern is not unique to one guideline. A separate analysis showed that a widely cited report promoting uncorrected statistical thresholds in neuroimaging was routinely invoked to justify their use, even though most of the citing studies deviated substantially from the recommended parameters. And in a now-classic irony of evidence synthesis, a commentary that explicitly criticized the Newcastle–Ottawa scale for assessing observational studies was frequently cited as if it endorsed the scale.

The problem is compounded by the fact that general reporting standards fit CBMAs poorly. PRISMA, the default checklist for systematic reviews, requires items such as a per-study effect size estimate that coordinate-based methods typically cannot supply, while ignoring procedures central to CBMAs, such as transforming all data into a common stereotactic space, ensuring consistent brain coverage across datasets and keeping statistical thresholds uniform across brain regions. When the original guidelines did include a checklist, it was optional, several items demanded open-ended answers in supplementary files without corresponding justifications, and the criteria for compliance were often unclear. The result, an exploratory search by the team suggested, was highly heterogeneous reporting of how specific recommendations had been addressed.

Crucially, the authors emphasize that inconsistent reporting does not mean the underlying work was done badly. A first-ever meta-analysis of a rare condition may legitimately include fewer datasets than the guidelines recommend and still be scientifically valuable. Qual-CBMA is therefore designed as an aid to transparent reporting, not a tribunal. Authors mark each of the fifteen criteria as met, not met or not applicable, and must comment on every response; when a criterion is not met, the explanation must also appear in the manuscript. A high-quality CBMA, in this framework, is not one that ticks every box, but one that clearly explains which boxes it missed and why those omissions are acceptable given the study’s objectives. A criterion marked No is not a negative judgment but a prompt for context.

The fifteen criteria operationalize the earlier ten rules by splitting them into sharper, more testable items. The rule on specifying the research question becomes three criteria covering imaging modality and measure, functional paradigm, and condition or disorder. The rule on data collection becomes two criteria on keyword-based searches across multiple databases and explicit inclusion and exclusion criteria. The rules on spatial standardization become three criteria: statistical thresholds independent of brain region, consistent brain or tissue coverage, and conversion of all coordinates into a common reference space such as MNI. Reporting transparency is split into detailed description of datasets, contrasts and samples, and public availability of the data and code needed for replication, with a manuscript stating only that data are available upon reasonable request falling short of the mark.

Several technical criteria deserve special attention. The checklist asks whether the protocol was preregistered in a public database such as PROSPERO or the Open Science Framework, while explicitly acknowledging that CBMAs require iterative refinement and that deviations from a preregistered plan are expected rather than problematic. It requires that each included study applied a uniform statistical threshold across the whole brain, so that peaks obtained with liberal region-of-interest or small-volume corrections are only usable if they also meet the whole-brain threshold. This prevents results biased toward overrepresented regions and preserves the assumption that every voxel has an a priori equal chance of showing an effect. Double extraction of data by at least two independent researchers, with agreement ideally reported, is also assessed, along with adjustment for multiple general linear model contrasts derived from overlapping samples.

Perhaps the most consequential criterion concerns statistical power. Drawing on large-scale simulations of the activation likelihood estimation method, the authors set a floor of 17 independent peak-coordinate datasets, rising to 23 for structural MRI studies. They also offer an optional weighting scheme: each study counts in proportion to its statistical power, so a one-sample study with 20 participants counts as one dataset, a study with only 12 to 13 participants counts as roughly half, and no study counts for more than 1.25 regardless of size. Studies providing full statistical maps count double, a deliberately conservative correction derived from simulations showing that maps carry far more information than peak coordinates alone, roughly fourfold on average though strongly dependent on effect size. These thresholds echo the optimal information size concept from the GRADE framework for intervention meta-analyses.

Notably, the checklist refuses to grade statistical significance. The authors deliberately excluded it to avoid incentivizing p-hacking, the practice of trying multiple meta-analytic methods or parameter settings until significant results emerge. Instead, they argue, the community should learn to accept large-scale CBMAs that yield no findings after correction for multiple comparisons, and they recommend presenting results across a range of thresholds rather than a single cut-off, noting that differences between analytical approaches in the Nature reproducibility study were less pronounced when compared across multiple thresholds. They also concede the apparent unfairness of requiring even a CBMA with 14 highly significant studies to acknowledge having fewer than 20 datasets, countering that small samples allow random imbalances in confounding variables to drive neuroimaging results, whereas large samples wash them out.

The authors are candid about the checklist’s limitations. Binary criteria and fixed thresholds inevitably create artificial distinctions, such as the meaningless boundary between 16 and 17 datasets, and in areas with very large effect sizes even small studies may detect effects consistently, making the thresholds less informative. The tool is also algorithm-agnostic and cannot capture method-specific optimal practices, and future iterations may add graded criteria or algorithm-specific extensions. The payoff, however, could extend well beyond individual papers: clearly defined criteria with explicit comments enable structured extraction of methodological features across hundreds of CBMAs, opening the door to large-scale meta-research and even automated reviews. The team hopes that authors, reviewers and editors will adopt Qual-CBMA as a shared standard, submitted alongside new meta-analyses, so that transparency in neuroimaging evidence synthesis becomes the norm rather than the exception. The checklist is publicly available, with the R code for the power simulations provided in the article’s supplementary material.

Subject of Research: Development of the Qual-CBMA checklist for evaluating the methodological quality of coordinate-based meta-analyses in neuroimaging

Article Title: Evaluating the methodological quality of coordinate-based meta-analyses: the qual-CBMA checklist

Article References: Radua, J., Fortea, L., Albajes-Eizagirre, A., Eickhoff, C., Fullana, M. A., Fusar-Poli, P., Mataix-Cols, D., Shin, J. I., Solanes, A., Tahmasian, M., Vieta, E., Vilajosana, E., Eickhoff, S. B., & Müller, V. I. (2026). Evaluating the methodological quality of coordinate-based meta-analyses: the qual-CBMA checklist. Frontiers in Neuroimaging, 5, Article 1753543. https://doi.org/10.3389/fnimg.2026.1753543

Image Credits: AI Generated

DOI: 10.3389/fnimg.2026.1753543

Keywords: coordinate-based meta-analysis, neuroimaging, checklist, methodological quality, CBMA, MRI, PET, preregistration, reproducibility, statistical power, publication bias, Ten Simple Rules