Leveraging data-science for discovery in chronic TBI

慢性创伤性脑损伤是军队和军队中最常见的神经系统疾病之一。 平民人口，影响到美国多达530万人，并造成760亿美元的医疗保健和损失- 工作效率下降。然而，人们对导致慢性脑损伤的确切神经生物学特征知之甚少 功能障碍和残疾。这种知识的缺乏限制了动物治疗发展的可靠性。 模拟和限制跨物种和人类患者的翻译。问题的一部分是慢性脑外伤 本质复杂，涉及到对最复杂的器官系统的异质性损害。这将导致一个 跨不同数据源和多尺度分析的多方面症状。这 多尺度的异质性使得使用传统的分析方法难以理解慢性脑损伤 专注于测试治疗效果的单一终点。单个端点反映一小部分 描述慢性脑外伤整体综合征的复杂变化系统。从这个意义上说，复杂的慢性 从根本上说，TBI是一个大数据问题，需要汇集信息和分析来评估可重复性 在基本发现和跨物种转化方面。拟议的项目将开发新的应用程序 尖端多维分析，大规模整合临床前慢性脑损伤数据。的目标是 拟议的项目是为临床前发现、重复性测试和 在慢性脑损伤类型内和跨慢性脑损伤类型之间的翻译发现。项目团队已经做好了执行的准备 这个项目考虑到在之前的联邦资金的支持下，它建立了最大的多中心、多物种储存库之一 迄今为止的神经创伤数据，包含了近4000只小鼠、大鼠、猪、 还有猴子。拟议的退伍军人管理局优异奖将使用新的数据扩展这些数据-从5个国家收集的捐款 全美的临床前脑损伤研究实验室，包括慢性(&>1个月)脑损伤穿透性模型 损伤、闭合性头部损伤、反复轻微损伤、加速/减速、侧向液体撞击和冲击波 受伤。该项目将把这些现有的数据资源统一到一个单一的数据池中，从而支持应用程序 数据科学的最新创新，将复杂的多维终端数据转化为强大的 可由研究人员以用户友好的方式可视化和探索的症状模式。该项目 将加速数据驱动的发现、科学的重复性、假设的生成以及最终的精确度 治疗慢性脑外伤的药物。

Chronic traumatic brain injury (TBI) is one of the most prevalent neurological disorders in both military and civilian populations, impacting up to 5.3 million people in the US and costing $76 billion in healthcare and loss- of-productivity. Yet relatively little is known about the precise neurobiological features of chronic TBI leading to dysfunction and disability. This lack of knowledge limits the reliability of therapeutic development in animal models and limits translation across species and into human patients. Part of the problem is that chronic TBI is intrinsically complex, involving heterogeneous damage to the most complex organ system. This results in a multifaceted syndrome spanning across heterogeneous data sources and multiple scales of analysis. This multi-scale heterogeneity makes chronic TBI difficult to understand using traditional analytical approaches that focus on a single endpoint for testing therapeutic efficacy. Single endpoints reflect a small portion of a complex system of changes that describe the holistic syndrome of chronic TBI. In this sense, complex chronic TBI is fundamentally a ‘big-data’ problem requiring pooled information and analytics to evaluate reproducibility in basic discovery and cross-species translation. The proposed project will develop novel applications of cutting edge multidimensional analytics to integrate preclinical chronic TBI data on a large scale. The goal of the proposed project is to develop an integrated workflow for preclinical discovery, reproducibility testing, and translational discovery both within and across chronic TBI types. The project team is well-positioned to execute this project given that with prior federal funding it built one of the largest multicenter, multispecies repositories of neurotrauma data to-date, housing detailed multidimensional outcome data on nearly 4000 mice, rats, pigs, and monkeys. The proposed VA merit award will expand these data with new data-donations collected from 5 preclinical TBI research laboratories across the US, including chronic (>1 month) TBI models of penetrating injury, closed head injuries, repeated mild injuries, acceleration/ deceleration, lateral fluid percussion, and blast injuries. The project will harmonize these existing data resources into a single data pool, enabling application of recent innovations from data science to render complex multidimensional endpoint data into robust syndromic patterns that can be visualized and explored by researchers in a user-friendly manner. The project will accelerate data-driven-discovery, scientific reproducibility, hypothesis-generation, and ultimately precision medicine for chronic TBI.