Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge. Issue 10 (October 2021)

• Record Type: Journal Article
• Authors: Veronese, Mattia; Rizzo, Gaia; Belzunce, Martin; Schubert, Julia; Searle, Graham; Whittington, Alex; Mansur, Ayla; Dunn, Joel; Reader, Andrew; Gunn, Roger N
• Is Part Of: Journal of cerebral blood flow & metabolism. Volume 41:Issue 10(2021)
• Page Start: 2778
• Page End: 2796
• Publication Date: 2021-10
• Subjects: PET -- data analysis -- data sharing -- reproducibility crisis -- "NRM2018 PET Grand Challenge"; Cerebral circulation -- Periodicals; Brain -- Metabolism -- Periodicals; Brain -- Blood-vessels -- Periodicals; Cerebrovascular disease -- Periodicals; 612.824
• DOI: 10.1177/0271678X211015101
• Languages: English
• ISSNs: 0271-678X

Abstract

The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same data are analysed. This problem is well-known in MRI studies, where the indisputable value of the method has been complicated by a number of studies that produce discrepant results. However, any research domain with complex data and flexible analytical procedures can experience a similar lack of reproducibility. In this paper we investigate this issue for brain PET imaging. During the 2018 NeuroReceptor Mapping conference, the brain PET community was challenged with a computational contest involving a simulated neurotransmitter release experiment. Fourteen international teams analysed the same imaging dataset, for which the ground-truth was known. Despite a plurality of methods, the solutions were consistent across participants, although not identical. These results should create awareness that the increased sharing of PET data alone will only be one component of enhancing confidence in neuroimaging results and that it will be important to complement this with full details of the analysis pipelines and procedures that have been used to quantify data.