Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility

• Published in: Neuropsychopharmacology (New York, N.Y.) N.Y.), 2024-11, Vol.50 (1), p.67-84
• Main Authors: Ekhtiari, Hamed, Zare-Bidoky, Mehran, Sangchooli, Arshiya, Valyan, Alireza, Abi-Dargham, Anissa, Cannon, Dara M., Carter, Cameron S., Garavan, Hugh, George, Tony P., Ghobadi-Azbari, Peyman, Juchem, Christoph, Krystal, John H., Nichols, Thomas E., Öngür, Dost, Pernet, Cyril R., Poldrack, Russell A., Thompson, Paul M., Paulus, Martin P.
• Format: Article
• Language: English
• Subjects: 631/378; 631/378/1689; 631/378/2649; 692/308/575; 692/699/476; Artificial intelligence; Behavioral Sciences; Biological Psychology; Check lists; Checklist - methods; Checklist - standards; Functional anatomy; Humans; Medical imaging; Medicine; Medicine & Public Health; Neuroimaging; Neuroimaging - methods; Neuroimaging - standards; Neurosciences; Pharmacotherapy; Psychiatry; Reproducibility; Reproducibility of Results; Research Design - standards; Review Article; Reviews; Structure-function relationships
• ISSN: 0893-133X; 1740-634X; 1740-634X
• DOI: 10.1038/s41386-024-01973-5

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.