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模板流:多尺度、多物种大脑模型的 FAIR 共享。

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models.

机构信息

Department of Psychology, Stanford University, Stanford, CA, USA.

Department of Bioengineering, Stanford University, Stanford, CA, USA.

出版信息

Nat Methods. 2022 Dec;19(12):1568-1571. doi: 10.1038/s41592-022-01681-2. Epub 2022 Dec 1.

Abstract

Reference anatomies of the brain ('templates') and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR-findable, accessible, interoperable, and reusable-principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.

摘要

脑的参考解剖结构(“模板”)和相应的图谱是报告标准化神经影像学结果的基础。目前,没有模板和图谱的注册中心;因此,这些资源的再分配要么捆绑在现有软件中,要么以特定方式进行,例如从机构站点和通用数据存储库下载。我们引入了 TemplateFlow,这是一个用于人类和非人类大脑模型的公开可用框架。该框架将开放数据库与访问、管理和审查软件相结合,允许科学家根据 FAIR(可发现、可访问、可互操作和可重用)原则共享他们的资源。TemplateFlow 使人们能够从多个方面了解跨物种的大脑,并支持多宇宙分析,以测试结果是否在标准参考、规模以及长期内是否在物种间具有通用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/951d/9718663/4a58aa77adad/41592_2022_1681_Fig1_HTML.jpg

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