Improving the Eligibility of Task-Based fMRI Studies for Meta-Analysis: A Review and Reporting Recommendations

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 22; no. 1; pp. 5 - 22
• Main Authors: Acar, Freya, Maumet, Camille, Heuten, Talia, Vervoort, Maya, Bossier, Han, Seurinck, Ruth, Moerkerke, Beatrijs
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2024; Springer Nature B.V
• Subjects: Bioinformatics; Biomedical and Life Sciences; Biomedicine; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Functional magnetic resonance imaging; Medical imaging; Meta-analysis; Methods; Neuroimaging; Neurology; Neurosciences; Review; Reviews; fMRI; Review; CBMA; Meta-analysis
• ISSN: 1559-0089; 1539-2791; 1559-0089
• DOI: 10.1007/s12021-023-09643-5

Decisions made during the analysis or reporting of an fMRI study influence the eligibility of that study to be entered into a meta-analysis. In a meta-analysis, results of different studies on the same topic are combined. To combine the results, it is necessary that all studies provide equivalent pieces of information. However, in task-based fMRI studies we see a large variety in reporting styles. Several specific meta-analysis methods have been developed to deal with the reporting practices occurring in task-based fMRI studies, therefore each requiring a specific type of input. In this manuscript we provide an overview of the meta-analysis methods and the specific input they require. Subsequently we discuss how decisions made during the study influence the eligibility of a study for a meta-analysis and finally we formulate some recommendations about how to report an fMRI study so that it complies with as many meta-analysis methods as possible.