Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024
IntroductionThere are various methods available for conducting meta-analyses of fMRI data, with coordinate-based meta-analysis (CBMA) being a frequently used approach due to the limited availability of effect size and statistical maps. Since the literature has accumulated many fMRI meta-analyses, se...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1580808/full |
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| author | Andy Wai Kan Yeung Natalie Sui Miu Wong |
| author_facet | Andy Wai Kan Yeung Natalie Sui Miu Wong |
| author_sort | Andy Wai Kan Yeung |
| collection | DOAJ |
| description | IntroductionThere are various methods available for conducting meta-analyses of fMRI data, with coordinate-based meta-analysis (CBMA) being a frequently used approach due to the limited availability of effect size and statistical maps. Since the literature has accumulated many fMRI meta-analyses, several reports have been published to reveal the prevalence of numerous meta-analytic software packages without investigating into the recency of their versions used. To address this gap, a literature survey was conducted to identify the software packages and version numbers used for fMRI meta-analyses published between 2019 and 2024.MethodsThe online databases of Web of Science Core Collection (WOSCC) and Scopus were queried to identify relevant papers. After screening, the analysis consisted of data manually extracted from 820 papers.ResultsThe most frequently used software was GingerALE (407 out of 820 papers, 49.6%), followed by SDM-PSI (27.4%) and Neurosynth (11.0%). Overall, 540 papers (65.9%) fully disclosed the names and version numbers of the software packages used in their analyses, whereas 19 papers (2.3%) reported neither the names nor the version numbers. For GingerALE, the most frequently used versions were 2.3.6 and 3.0.2, but it should be noted that versions prior to 2.3.6 have an issue of inflated false positive rates. For SDM-PSI, the most frequently used versions were 5.141, 5.15, 6.21, and 6.22, but the meta-analytic method adopted for version 6 differs from those used in prior versions.DiscussionTo ensure transparency and reproducibility in research, researchers should clearly report the name and version number of software package used. |
| format | Article |
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| institution | Kabale University |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-682a854bb6bd44fcb13b21c26dfffbe02025-08-20T03:28:55ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-07-011910.3389/fnhum.2025.15808081580808Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024Andy Wai Kan Yeung0Natalie Sui Miu Wong1Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, ChinaOral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, ChinaIntroductionThere are various methods available for conducting meta-analyses of fMRI data, with coordinate-based meta-analysis (CBMA) being a frequently used approach due to the limited availability of effect size and statistical maps. Since the literature has accumulated many fMRI meta-analyses, several reports have been published to reveal the prevalence of numerous meta-analytic software packages without investigating into the recency of their versions used. To address this gap, a literature survey was conducted to identify the software packages and version numbers used for fMRI meta-analyses published between 2019 and 2024.MethodsThe online databases of Web of Science Core Collection (WOSCC) and Scopus were queried to identify relevant papers. After screening, the analysis consisted of data manually extracted from 820 papers.ResultsThe most frequently used software was GingerALE (407 out of 820 papers, 49.6%), followed by SDM-PSI (27.4%) and Neurosynth (11.0%). Overall, 540 papers (65.9%) fully disclosed the names and version numbers of the software packages used in their analyses, whereas 19 papers (2.3%) reported neither the names nor the version numbers. For GingerALE, the most frequently used versions were 2.3.6 and 3.0.2, but it should be noted that versions prior to 2.3.6 have an issue of inflated false positive rates. For SDM-PSI, the most frequently used versions were 5.141, 5.15, 6.21, and 6.22, but the meta-analytic method adopted for version 6 differs from those used in prior versions.DiscussionTo ensure transparency and reproducibility in research, researchers should clearly report the name and version number of software package used.https://www.frontiersin.org/articles/10.3389/fnhum.2025.1580808/fullcoordinate based meta-analysisGingerALESDMneurosynthNiMARE |
| spellingShingle | Andy Wai Kan Yeung Natalie Sui Miu Wong Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024 Frontiers in Human Neuroscience coordinate based meta-analysis GingerALE SDM neurosynth NiMARE |
| title | Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024 |
| title_full | Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024 |
| title_fullStr | Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024 |
| title_full_unstemmed | Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024 |
| title_short | Which software packages did researchers use to meta-analyze fMRI data? A literature survey from 2019 to 2024 |
| title_sort | which software packages did researchers use to meta analyze fmri data a literature survey from 2019 to 2024 |
| topic | coordinate based meta-analysis GingerALE SDM neurosynth NiMARE |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1580808/full |
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