Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United States; Department of Psychiatry, New York University at Langone, New York City, NY, United States.
Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, United Kingdom.
Neuroimage. 2021 Aug 15;237:118203. doi: 10.1016/j.neuroimage.2021.118203. Epub 2021 May 25.
Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.
功能定位器非常有价值,因为它们可以帮助定义感兴趣的区域,提供跨研究比较,最重要的是,允许跨研究和实验室对数据进行聚合和荟萃分析。为了在非人类灵长类动物(NHP)成像社区内实现这些目标,迫切需要使用标准化和经过验证的定位器,以便在不同的小组中轻松实施。本文的目的是概述定位器协议对成像研究的价值,我们描述了一些在 NHP 中常用或新颖的定位器,以及在研究中实施它们的关键。正如在原始 PRIME-DE 提交的静息态成像数据聚合中所显示的那样,我们相信该领域已经准备好将相同的倡议应用于 NHP 成像中的基于任务的功能定位器。通过汇集研究小组收集大型数据集,实施相同的功能定位器,并通过 PRIME-DE 共享定位器和数据,现在可以充分测试它们的稳健性、选择性和特异性。为此,我们回顾了一些常见的定位器,并创建了一个经过良好验证的定位器存储库,这些定位器可通过 PRIME-RE 平台轻松访问和实施。