Neurobiology Research Unit, Rigshospital and University of Copenhagen, Copenhagen, Denmark.
Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
J Cereb Blood Flow Metab. 2020 Aug;40(8):1576-1585. doi: 10.1177/0271678X20905433. Epub 2020 Feb 16.
It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.
越来越多的人担心神经影像学研究的结果往往无法重现。为了应对这一问题,磁共振(MR)神经影像学界基于如何组织和共享 MR 神经影像学数据的共识,提出了采集标准并创建了数据共享平台。在这里,我们采取类似的方法来处理正电子发射断层扫描(PET)数据。为了便于比较研究结果,我们首先为 PET 神经影像学数据推荐了示踪剂特性、图像采集、图像预处理和结果评估的出版标准。本文的共同作者代表了全球 25 个以上的 PET 中心,投票将信息分为强制性、推荐性和选择性。其次,我们描述了一个框架,以促进中心内和中心之间的数据归档和数据共享。由于 PET 神经影像学研究成本高昂,样本量往往较小,并且全球相对较少的站点拥有进行和分析 PET 研究所需的多学科专业知识。数据共享将使来自不同中心的数据集更容易组合,以实现更大的样本量和更强的统计能力来检验假设。通过采用一套通用的最佳数据采集和分析实践,可以增强来自不同中心的数据集的组合。