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: 10599836
• 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/10599836
• 关键词: Acceleration; American; Artificial Intelligence; Autonomic Dysfunction; Award; Behavior; Behavior assessment; Big Data; Big Data Methods; 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; Dedications; 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; digital technology; efficacy study; electronic data; federated data; 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; 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）是影响退伍军人的主要健康问题