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用于增强跨物种研究转化影响的网络模型。

Network models to enhance the translational impact of cross-species studies.

机构信息

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.

Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

出版信息

Nat Rev Neurosci. 2023 Sep;24(9):575-588. doi: 10.1038/s41583-023-00720-x. Epub 2023 Jul 31.

Abstract

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

摘要

神经科学研究通常在动物模型中进行,目的是了解人类状况的特定方面。然而,跨物种的发现的转化仍然是一个重大的挑战。网络科学方法可以通过提供一种将在动物模型研究中确定的小规模细胞过程映射到在人类中观察到的更大规模的区域间电路的方法,来增强跨物种研究的转化影响。在这篇综述中,我们强调了网络科学方法对神经科学中跨物种转化研究的发展的贡献。我们通过探索跨物种转化模型的目标来为我们的讨论奠定基础。然后,我们讨论了如何开发新的工具,使我们能够以细胞分辨率在动物模型中获取全脑数据,为理解大规模脑网络的网络科学方法的跨物种应用提供了前所未有的机会。我们描述了这些工具如何支持跨物种和成像方式的发现的转化,并强调了未来的机会。我们的总体目标是说明如何在人类和动物模型研究中应用网络科学工具,可以深入了解通过非侵入性神经影像学方法观察到的现象背后的神经生物学,并同时提高我们跨物种转化发现的能力。

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