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人群神经科学:在保护隐私的同时促进数据共享的策略。

Population Neuroscience: Strategies to Promote Data Sharing While Protecting Privacy.

机构信息

The Terry Fox Research Institute, Vancouver, BC, Canada.

出版信息

Curr Top Behav Neurosci. 2024;68:53-66. doi: 10.1007/7854_2024_467.

Abstract

Population neuroscience aims to advance our understanding of how genetic and environmental factors influence brain development and brain health over the life span, by integrating genomics, epidemiology, and neuroscience at population scale. This big data approach depends on data sharing strategies at both the micro- and macro-level, as well as attention to effective data management and protection of participant privacy. At the micro-level, researchers participate in international consortia that support collaboration, standards, and data sharing. They also seek to link together cohort studies, administrative health databases, and measures of the physical, built, and social environment in creative ways. Large-scale, longitudinal, and multi-modal cohorts are being designed to support explorations of genetic and environmental impacts on the brain. At a macro-level, funding agency policies now require data across health research domains to be managed according to the FAIR (findable, accessible, interoperable, and re-useable) Data principles and made available to the research community in a timely manner to support reproducibility and re-use. Data repositories provide technical infrastructure for storing, accessing, and increasingly also analyzing rich population-level data. Federated and cloud-based approaches are being leveraged to improve the security, remote accessibility, and performance of repositories. Finally, legal frameworks are being developed to facilitate secure health data access, integration, and analysis, providing new opportunities for the field.

摘要

人口神经科学旨在通过整合基因组学、流行病学和神经科学在人口规模上的研究,来深入了解遗传和环境因素如何影响大脑的发育和健康。这种大数据方法依赖于微观和宏观层面的数据共享策略,以及对有效数据管理和参与者隐私保护的关注。在微观层面上,研究人员参与支持合作、标准和数据共享的国际联盟。他们还试图以创造性的方式将队列研究、行政健康数据库以及身体、建筑和社会环境的测量结果联系起来。正在设计大规模、纵向和多模态队列,以支持对大脑的遗传和环境影响的探索。在宏观层面上,资助机构的政策现在要求根据 FAIR(可发现、可访问、可互操作和可重用)数据原则来管理健康研究领域的所有数据,并及时向研究界提供数据,以支持可重复性和再利用。数据存储库为存储、访问,以及越来越多地分析丰富的人群数据提供了技术基础。正在利用联合和基于云的方法来提高存储库的安全性、远程可访问性和性能。最后,正在制定法律框架,以促进安全获取、集成和分析健康数据,为该领域提供新的机会。

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