Leveraging Heterogeneity in Preclinical Traumatic Brain Injury to Drive Discovery and Reproducibility

创伤性脑损伤(TBI)是神经系统疾病的主要原因，每个人都有超过250万人受到影响 一年来，没有一种治疗方法成功地从替补席转移到临床。TBI是一个宽泛的术语，包括 一组极其不同的损伤，根据原因、严重程度、生物力学和各种复杂的 共同导致慢性残疾的继发性伤害反应。当前的临床前研究绕过了 依赖于特定的临床前动物模型模拟脑损伤亚群的脑损伤异质性问题 患者和特定的继发性损伤机制，每项研究都侧重于有限的、个别的途径。 相反，该建议旨在通过将TBI视为一个“大数据”问题来解决TBI的异质性，并 将多维数据集合在一起进行分析。数据协调框架和 将开发管理程序，并从使用各种临床前实验室的联盟中获得数据集 TBI模型将被收集并整理成一个开放数据共享空间(ODC-TBI)。利用机器学习和 多维分析，拟议的研究将直接利用合并数据集中的TBI异质性 识别TBI的持久特征，以支持翻译研究。通过创建临床前TBI ODC和 应用机器学习整合临床前脑损伤模型的异质性，该项目将揭示 不同类型损伤的颅脑损伤的多维特征及其继发性损伤的多样性 这些机制相互作用，并最终影响受伤结果。在整个项目时间表中，新数据集将 根据既定的框架，继续统一到ODC-TBI。ODC-TBI将成为 第一个开放的多中心、多模型的临床前脑损伤数据存储库，将使数据科学的应用成为可能 到了TBI领域。此外，ODC-TBI和在整个项目中实施的方法将是公开的 共享以提高TBI研究的重现性。加上多维分析，将提供 通过对TBI异构性的定量和定性理解，该项目旨在最终加速数据- TBI的驱动发现和精准医学。

Traumatic brain injury (TBI) is a leading cause of neurological disorders and affects over 2.5 million people each year, yet no treatment has successfully translated from bench to clinic. TBI is a broad term and encompasses an extremely heterogeneous set of injuries differing by cause, severity, biomechanics, and the varied, complex secondary injury responses that collectively result in chronic disabilities. Current preclinical research circumvents the issue of TBI heterogeneity by relying on specific preclinical animal models that mimic subpopulations of patients and particular secondary injury mechanisms with each study focusing on limited, individual pathways. This proposal instead aims to tackle TBI heterogeneity by approaching TBI as a “big data” problem and aggregating and analyzing the multidimensional data collectively. A framework for data harmonization and curation will be developed, and datasets from a consortium of preclinical labs employing a variety of preclinical TBI models will be collected and curated into an open data commons (ODC-TBI). Utilizing machine learning and multidimensional analytics, the proposed research will directly leverage TBI heterogeneity in the merged dataset to identify persistent features of TBI to empower translational research. By creating a preclinical TBI ODC and applying machine learning to integrate the heterogeneity of preclinical TBI models, the project will reveal multidimensional features of TBI across heterogeneous injuries and characterize how diverse secondary injury mechanisms interact and ultimately affect injury outcome. Throughout the project's timeline, new datasets will continue to be harmonized into the ODC-TBI according to the established framework. The ODC-TBI will be the first open multicenter, multi-model repository of preclinical TBI data and will enable the application of data science to the field of TBI. Furthermore, the ODC-TBI and the methods implemented throughout the project will be openly shared to improve reproducibility of TBI research. Together with the multidimensional analysis that will provide quantitative and qualitative understanding of TBI heterogeneity, the project aims to ultimately accelerate data- driven discovery and precision medicine for TBI.