Leveraging data-science for discovery in chronic TBI

利用数据科学发现慢性 TBI

• 批准号: 10641318
• 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 2028-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10641318
• 关键词: Acceleration; Activities of Daily Living; Animal Model; Award; Big Data; Big Data Methods; Biological; Blast Injuries; Body part; Central Nervous System Diseases; Chronic; Clinical Trials; Closed head injuries; Common Data Element; Complex; Cortical Contusions; Data; Data Commons; Data Discovery; Data Pooling; Data Science; Data Sources; Deceleration; Dimensions; Disease; FAIR principles; Family suidae; Federal Government; Functional disorder; Funding; Future; Generations; Genetic; Goals; Grant; Healthcare; Heterogeneity; High Prevalence; Housing; Human; Impairment; Infrastructure; Ingestion; Injury; Knowledge; Knowledge Discovery; Knowledge Management; Laboratories; Laboratory Research; Lateral; Limb structure; Link; Liquid substance; Literature; Machine Learning; Military Personnel; Modeling; Modernization; Molecular; Monitor; Monkeys; Motor; Multiple Trauma; Mus; Nervous System Trauma; Neurobiology; Neurocognition; Neurocognitive; Neurologic; Neurosciences; Outcome; Pain; Patients; Pattern; Penetration; Percussion; Personality; Persons; Population; Positioning Attribute; Principal Component Analysis; Process; Rattus; Recovery; Recovery of Function; Rehabilitation therapy; Reproducibility; Research; Research Personnel; Research Project Grants; Resources; Sensory; Shapes; Source; Stress; Syndrome; System; Taxonomy; Testing; Therapeutic; Therapeutic Effect; Time; Translating; Translations; Traumatic Brain Injury; Treatment Efficacy; Veterans; Visualization; Visualization software; Well in self; Work; bench-to-bedside translation; body system; computerized data processing; cost; data curation; data dictionary; data exchange; data integration; data resource; data sharing; data standards; data tools; data visualization; disability; heterogenous data; improved; innovation; insight; long term recovery; military veteran; multidimensional data; nervous system disorder; novel; open data; pre-clinical; precision medicine; productivity loss; psychologic; repository; restoration; shared repository; therapeutic development; therapeutic evaluation; tool; translational potential; user-friendly; wound

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, often accompanied by polytrauma to extremities as well as psychological wounds. 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 over 10000 mice, rats, pigs, monkeys. The proposed VA merit award will expand these data with new data-donations collected from several 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, blast injuries as well as polytrauma and stress models. 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，通常伴随 通过多头骨到四肢以及心理伤口。这会导致多面综合征 跨越异质数据源和多个分析量表。这种多尺度异质性 使用传统的分析方法使慢性TBI难以理解 测试治疗效率的终点。单端点反映了一个复杂系统的一小部分 描述慢性TBI的整体综合征的变化。从这个意义上说，复杂的慢性TBI是 从根本上讲，一个“大数据”问题，需要汇总信息和分析来评估可重复性 基本发现和跨物种翻译。拟议的项目将开发切割的新颖应用 边缘多维分析以大规模整合临床前慢性TBI数据。目标的目标 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 迄今为止的神经曲数据数据，外壳详细的多维结果数据超过10,000只小鼠，大鼠，猪， 猴子。拟议的VA优异奖将通过从几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，爆炸 伤害以及多发性和应力模型。该项目将将这些现有数据资源协调到 一个数据库，从而实现了从数据科学到渲染复杂的最新创新的应用 多维终点数据中的鲁棒综合征模式可以可视化和探索 研究人员以用户友好的方式。该项目将加速数据驱动的科学发现 可重复性，假设产生以及最终用于慢性TBI的精确药物。