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

利用临床前创伤性脑损伤的异质性来推动发现和重现性

• 批准号: 10042756
• 负责人: Austin C Chou
• 金额: $ 6.98万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10042756
• 关键词: Address; Affect; Animal Model; Big Data; Biological; Biological Markers; Biomechanics; Brain region; Chronic; Clinic; Clinical; Closed head injuries; Common Data Element; Complex; Data; Data Analyses; Data Collection; Data Commons; Data Element; Data Science; Data Set; Development; Foundations; Goals; Heterogeneity; Incidence; Individual; Inflammation; Informatics; Injury; Institutes; Machine Learning; Measures; Methods; Modeling; Multivariate Analysis; National Institute of Neurological Disorders and Stroke; Outcome; Pathway interactions; Pattern; Pharmacologic Substance; Population; Positioning Attribute; Pre-Clinical Model; Principal Component Analysis; Publishing; Reproducibility; Research; Severities; Standardization; Synaptic plasticity; Therapeutic; TimeLine; Translating; Translational Research; Translations; Traumatic Brain Injury; behavioral outcome; bench to bedside; biomarker discovery; controlled cortical impact; data curation; data framework; data harmonization; data sharing; disability; experimental study; functional outcomes; genetic manipulation; improved; insight; multidimensional data; multiple datasets; nerve injury; nervous system disorder; neuroinflammation; open data; patient subsets; pre-clinical; pre-clinical research; precision medicine; repository; response; response to injury

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.

创伤性脑损伤（TBI）是神经系统疾病的主要原因，每一种损伤影响超过250万人