Leveraging data-science for discovery in chronic TBI

利用数据科学发现慢性 TBI

基本信息

批准号：
10269003
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
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10269003
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 Persons 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 translation body system computerized data processing cost data dictionary data exchange data integration data repository data resource data reuse data sharing digital object identifier disability fluid percussion injury heterogenous data improved innovation insight military veteran multidimensional data nervous system disorder novel pre-clinical precision medicine productivity loss repository restoration therapeutic development therapeutic evaluation tool translational potential user-friendly

项目摘要

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）是军事和军事领域最常见的神经系统疾病之一。平民人口，影响了美国多达 530 万人，造成了 760 亿美元的医疗保健和损失—— 然而，对于导致慢性 TBI 的确切神经生物学特征知之甚少。这种知识的缺乏限制了动物治疗开发的可靠性。模型并限制了跨物种和人类患者的转化，部分问题在于慢性 TBI 是一种慢性 TBI。本质上很复杂，涉及对最复杂的器官系统的异质损伤，这导致了。跨越异构数据源和多种分析尺度的多方面综合症。多尺度异质性使得慢性 TBI 难以使用传统的分析方法来理解，专注于测试治疗效果的单一终点单一终点反映了一小部分。复杂的变化系统描述了慢性 TBI 的整体综合征，从这个意义上说，复杂的慢性。 TBI 从根本上来说是一个“大数据”问题，需要汇总信息和分析来评估可重复性拟议的项目将开发基础发现和跨物种翻译的新颖应用。尖端的多维分析，大规模整合临床前慢性 TBI 数据。拟议的项目是开发一个用于临床前发现、再现性测试和项目团队有能力执行慢性 TBI 类型内部和跨类型的转化发现。鉴于该项目在之前的联邦资助下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，包含近 4000 只小鼠、大鼠、猪、拟议的 VA 优异奖将通过从 5 个组织收集的新数据捐赠来扩展这些数据。美国各地的临床前 TBI 研究实验室，包括穿透性慢性（>1 个月）TBI 模型损伤、闭合性头部损伤、重复轻度损伤、加速/减速、侧面液体冲击和爆炸该项目将把这些现有的数据资源整合到一个数据池中，以便于应用。数据科学的最新创新将复杂的多维端点数据呈现为强大的研究人员可以以用户友好的方式可视化和探索综合症模式。将加速数据驱动的发现、科学的可重复性、假设的生成以及最终的精确度治疗慢性 TBI 的药物。