Leveraging data-science for discovery in chronic TBI

利用数据科学发现慢性 TBI

• 批准号: 9742296
• 负责人: ADAM R FERGUSON
• 金额: --
• 依托单位: VETERANS AFFAIRS MED CTR SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9742296
• 关键词: Acceleration; Activities of Daily Living; Animal Model; Award; Big Data; Big Data Methods; Biological; Blast Injuries; Categories; Central Nervous System Diseases; Chronic; Clinical; Closed head injuries; Common Data Element; Complex; Cortical Contusions; Data; Data Commons; Data Pooling; Data Provenance; Data Science; Data Sources; Deceleration; Disease; FAIR principles; Family suidae; Federal Government; Functional disorder; Funding; Future; Generations; Genetic; Goals; Grant; Healthcare; Heterogeneity; High Prevalence; Housing; Human; Impairment; Incentives; Infrastructure; Ingestion; Injury; Knowledge; Knowledge Discovery; Laboratory Research; Lateral; Link; Liquid substance; Literature; Machine Learning; Military Personnel; Modeling; Modernization; Molecular; Monkeys; Motor; Mus; National Institute of Neurological Disorders and Stroke; Nervous System Trauma; Neurobiology; Neurocognition; Neurologic; Neurosciences; Outcome; Patients; Pattern; Percussion; Personality; Population; Positioning Attribute; Principal Component Analysis; Process; Rattus; Recovery; Recovery of Function; Reproducibility; Research; Research Personnel; Research Project Grants; Rodent; Shapes; Source; Syndrome; System; Taxonomy; Testing; Therapeutic; Therapeutic Effect; Time; Translating; Translations; Traumatic Brain Injury; Treatment Efficacy; Vertebral column; Veterans; Well in self; analytical tool; bench to bedside; body system; computerized data processing; cost; data dictionary; data exchange; data integration; data resource; data reuse; data sharing; data warehouse; digital object identifier; disability; fluid percussion injury; heterogenous data; improved; innovation; insight; multidimensional data; nervous system disorder; novel; pre-clinical; precision medicine; productivity loss; repository; restoration; therapeutic development; therapeutic evaluation; tool; user-friendly

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.

慢性创伤性脑损伤（TBI）是军事和 平民人口影响美国多达530万人，耗资760亿美元的医疗保健和损失 - 生产力。然而，关于慢性TBI的精确神经生物学特征的了解相对较少 功能障碍和残疾。缺乏知识限制了动物热发展的可靠性 模型和限制跨物种和人类患者的翻译。问题的一部分是慢性TBI是 本质上复杂，涉及对最复杂的器官系统的异质损害。这导致 多方面综合征跨越异质数据源和多个分析量表。这 多尺度异质性使慢性TBI难以使用传统的分析方法来理解 专注于测试治疗效率的单个端点。单端点反映了一小部分 描述慢性TBI的整体综合征的复杂变化系统。从这个意义上说，复杂的慢性 TBI从根本上是一个“大数据”问题，需要汇总信息和分析来评估可重复性 在基本发现和跨物种翻译中。拟议的项目将开发新的应用 尖端多维分析，以大规模整合临床前慢性TBI数据。目标 拟议的项目是为临床前发现，可重复性测试和 慢性TBI类型内部和跨性别的转化发现。项目团队的执行良好 该项目鉴于有了以前的联邦资助，它建立了最大的多中心之一的多人存储库之一 迄今为止的神经曲菌数据，近4000只小鼠，大鼠，猪，，详细的多维结果数据 和猴子。拟议的VA功绩奖将通过从5个收集的新数据介绍来扩展这些数据 全美的临床前TBI研究实验室，包括慢性（> 1个月）TBI穿透模型 受伤，闭合头部受伤，重复轻度损伤，加速/减速，侧液打击乐和爆炸 受伤。该项目将将这些现有数据资源协调到一个数据库中，从而实现应用程序 从数据科学到将复杂的多维终点数据呈现到鲁棒的最新创新 研究人员可以用用户友好的方式对综合征模式进行可视化和探索。项目 将加速数据驱动的发现，科学可重复性，假设生成，并最终精确 慢性TBI的医学。