Harnessing big-data for plasticity and rehabilitation in translational SCI

利用大数据实现转化 SCI 的可塑性和康复

• 批准号: 10311556
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10311556
• 关键词: American; Artificial Intelligence; Autonomic Dysfunction; Award; Behavior; Behavior assessment; Big Data; Big Data to Knowledge; Cellular biology; Cervical; Cervical spinal cord injury; Chronic; Clinical; Collaborations; Complex; Data; Data Analyses; Data Collection; Data Commons; Data Science; Databases; Development; Devices; Disease; Economic Burden; Electronic Health Record; Electrophysiology (science); Ensure; FAIR principles; FDA approved; Forelimb; Funding; Future; Goals; Hand functions; Health; High Prevalence; Histology; Human; Image; Individual; Infrastructure; Ingestion; Injury; Investments; Knowledge Discovery; Laboratories; Learning; Machine Learning; Measures; Medical; Modeling; Modernization; Molecular Biology; Motor; Natural regeneration; Neurologic; Neurostimulation procedures of spinal cord tissue; Pain; Paralysed; Performance; Physiology; Population; Positioning Attribute; Preparation; Primates; Programming Languages; Public Health Informatics; Pythons; Quality of life; Recovery; Regenerative research; Rehabilitation therapy; Research; Resolution; Retrieval; Robotics; Rodent Model; Safety; Sensory; Side; Site; Spinal cord injury; Structure; Syndrome; System; Taxonomy; Technology; Testing; Therapeutic; Time; Training; Translating; Translations; Veterans; Work; brain machine interface; care burden; clinical translation; cloud based; cortex mapping; cost; dashboard; data ingestion; data integration; data reuse; data sharing; data streams; deep neural network; digital; efficacy study; electronic data; federated computing; functional restoration; grasp; health record; improved; informatics tool; innovation; kinematics; machine learning pipeline; multidimensional data; multimodal data; multimodality; neurological rehabilitation; neurophysiology; neuroregulation; nonhuman primate; novel; novel therapeutics; open source; persistent symptom; pre-clinical; precision medicine; productivity loss; regenerative rehabilitation; regenerative therapy; research clinical testing; robot rehabilitation; robotic device; safety study; shared database; spasticity; structured data; success; therapeutic candidate; tool; translation to humans; translational pipeline; translational study; translational therapeutics; usability

Spinal cord injury and disorders (SCI/D) are substantial health concerns impacting veterans at a higher rate than the civilian population. The total economic burden of SCI/D is estimated at $9 billion/year to $400 billion in lifetime medical and loss-of-productivity costs. The most common clinical presentation is high cervical SCI/D which produces a broad spectrum of issues, including loss of hand function, autonomy, sensory changes, spasticity, pain and autonomic dysfunction, profoundly impacting quality of life. Restoring these functions is the goal of regenerative and rehabilitative therapeutic approaches for SCI/D. The VA Gordon Mansfield Spinal Cord Injury Consortium (VA-GMSCIC) is a VA-funded effort to develop late-stage translational therapeutics in a nonhuman primate (NHP) model in preparation for testing emergent therapeutic approaches clinically. Prior and current funding has focused on multifaceted data collection on each subject with 5 different centers collaboratively collecting data, each within their specific domain of expertise (physiology, behavior, histology, neurorehabilitation, and molecular biology). This is an ideal use of the NHP model, as maximal information is collected about the performance of therapeutic approaches in a small number of NHPs. Data from this important model is characterized by the classic ‘3Vs of Big Data’: high volume (large images), high variety (multi-modal data), and high velocity (robotic rehab; physiology; neuromodulation), providing both a challenge and opportunity for novel discoveries. Application of modern data science tools can help deliver on the promise of translational precision medicine for SCI/D. As our prior work demonstrates, effective management of VA NHP big data enables us to effectively harness VA-GMSCIC data to drive new discoveries. However, integrating these NHP big data requires ongoing data-driven integration of robotic rehab, kinematics, histology, and medical information. Extraction of meaningful discoveries requires extensive computational work. The purpose of the proposed renewal is to build on our ongoing success in assembling a data commons for the VA-GMSCIC by integrating new types of high-resolution data in support of safety/efficacy studies of novel therapeutics. Our data science team is well positioned to achieve this goal. Our team has provided analytical support for the VA-GMSCIC, helping to integrate data from UCSD, UCLA, UCI, UC Davis and UCSF for testing SCI rehab and regenerative therapies in NHPs for over 13 years. We have supported development of different injury models, behavioral assessments, electrophysiology, kinematic measures, and therapeutic approaches in 100+ subjects. Under our current merit award (ending Nov 2020), our team built on this historical background to establish a functional primate data commons (PDC-SCI) infrastructure that enables rapid, structured data sharing, data integration, and analytics support across the VA-GMSCIC sites. The project has helped the VA- GMSCIC evolve from focused discovery projects to late-stage translational studies with highly-sophisticated, large-scale “big-data” collection. We aim to expand our knowledge-discovery pipeline for these critical translational SCI/D big data to support planned safety/efficacy studies. Specifically, the renewal will build on our successes and expand the scope of our work by supporting integration of: Aim 1) translational electronic health records (tEHR), Aim 2) advanced robotic rehabilitation data, Aim 3) neuromodulation data from brain- machine interfaces, and Aim 4) advanced machine learning analytical pipelines for rapidly integrating multidimensional data. The goal is to help VA-GMSCIC efficiently test important therapeutic candidates for translation to humans while promoting modern data stewardship adhering to the federally-endorsed FAIR (Findable, Accessible, Interoperable, and Reusable) data sharing principles. This will ensure that the existing VA investment in data collection is leveraged to the maximal extent through digital technologies for enduring knowledge-discovery from this valuable NHP model of SCI/D.

脊髓损伤和疾病（SCI/D）是影响退伍军人的重大健康问题， 比平民人口。SCI/D的总经济负担估计为90亿美元/年至4000亿美元/年。 终身医疗和生产力损失成本。最常见的临床表现是高位颈脊髓损伤/脊髓损伤 这产生了广泛的问题，包括手部功能的丧失，自主性，感觉变化， 痉挛，疼痛和自主神经功能障碍，深刻影响生活质量。恢复这些功能是 SCI/D的再生和康复治疗方法的目标。弗吉尼亚州戈登曼斯菲尔德脊柱 脊髓损伤联盟（VA-GMSCIC）是VA资助的一项努力，旨在开发脊髓损伤的晚期转化疗法。 非人灵长类动物（NHP）模型，为临床测试紧急治疗方法做准备。之前 目前的资金集中在5个不同中心对每个主题进行多方面的数据收集 协作收集数据，每个都在他们的特定专业领域（生理学，行为学，组织学， 神经康复和分子生物学）。这是NHP模型的一个理想用途，因为最大信息是 收集了关于治疗方法在少数NHP中的性能。数据从该 一个重要的模型具有经典的“大数据3V”特征：高容量（大图像）、高多样性 （多模态数据）和高速（机器人康复;生理学;神经调节），这两个方面都是一个挑战。 和新发现的机会。现代数据科学工具的应用可以帮助实现承诺 转化型精准医学的研究正如我们先前的工作所证明的那样，有效的VA管理 NHP大数据使我们能够有效地利用VA-GMSCIC数据来推动新的发现。然而，在这方面， 整合这些NHP大数据需要机器人康复，运动学，组织学， 和医疗信息。提取有意义的发现需要大量的计算工作。的 建议更新的目的是建立在我们不断成功地为 VA-GMSCIC通过整合新型高分辨率数据，支持新型药物的安全性/疗效研究 治疗学我们的数据科学团队有能力实现这一目标。我们的团队提供了分析 支持VA-GMSCIC，帮助整合来自UCSD、UCLA、UCI、UC Davis和UCSF的数据进行测试 SCI康复和再生治疗在NHP超过13年。我们支持发展不同的 损伤模型、行为评估、电生理学、运动学测量和治疗方法， 100多名受试者。在我们目前的优秀奖（截至2020年11月）下，我们的团队在这一历史背景下建立了 建立一个功能性灵长类动物数据共享（PDC-SCI）基础设施， 跨VA-GMSCIC站点的共享、数据集成和分析支持。该项目帮助了VA- GMSCIC从重点发现项目发展到后期转化研究， 大规模的“大数据”收集。我们的目标是扩大我们的知识发现管道，为这些关键 翻译SCI/D大数据，以支持计划的安全性/有效性研究。具体而言，更新将建立在 我们的成功和扩大我们的工作范围，通过支持整合：目标1）翻译电子 健康记录（tEHR），目标2）先进的机器人康复数据，目标3）来自大脑的神经调节数据- 机器接口和Aim 4）先进的机器学习分析管道，用于快速集成 多维数据目标是帮助VA-GMSCIC有效地测试重要的治疗候选药物， 在促进现代数据管理的同时， （可查找、可扩展、可互操作和可重用）数据共享原则。这将确保现有的 VA在数据收集方面的投资通过数字技术得到最大程度的利用， 从SCI/D这一有价值的NHP模型中发现知识。