Understanding Behavioral Variability in Outcome After SCI

了解 SCI 后结果的行为变异

• 批准号: 10528065
• 负责人: SHAWN HOCHMAN
• 金额: $ 42.62万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10528065
• 关键词: Affective; Animal Experimentation; Animal Model; Animals; Autonomic Dysfunction; Autonomic Dysreflexia; Behavior; Behavioral; Biological Markers; Biometry; Blood Pressure; Cardiovascular system; Clinical; Controlled Study; Contusions; Data; Databases; Detection; Development; Disease; Disease Management; Engineering; Event; Feedback; Functional disorder; Goals; Health; Health Technology; Heart Rate; Home; Individual; Lead; Link; Machine Learning; Measures; Mechanics; Modeling; Monitor; Motivation; Motor; Mus; Outcome; Population; Publications; Respiration; Sensory; Series; Signal Transduction; Skin Temperature; Sleep; Sleep Architecture; Sleep disturbances; Stress; Supervision; Technology; Telemetry; Testing; Thermography; Time; Training; Wheelchairs; base; biosignature; clinical application; clinical development; clinically relevant; combinatorial; data mining; digital; digital health; health management; indexing; individualized medicine; insight; instrument; machine learning algorithm; medical specialties; mouse model; novel; novel strategies; painful neuropathy; pre-clinical; predictive marker; preference; prevent; prototype; respiratory; sensor; sensor technology; sham surgery; spatiotemporal; supervised learning; translational impact; unsupervised learning; wearable device; wearable sensor technology

PROJECT SUMMARY Opportunities now exist to implement a paradigm shift in health management towards individualized physio- behavioral (biometric) monitoring - to predict, to prevent, and to better manage disease using wearable technologies, as well as embedded sensor technologies within wheelchairs as well as within the home. Our broad objective is to interpret collected combinatorial changes in the same biometric variables captured noninvasively during the progression of SCI in naturally behaving mice. In well-controlled animal studies, we propose to apply machine learning algorithms to identify ‘digital biosignatures’ that are predictive to disease emergence and/or expression, and therefore of use in feedback-based mitigation. To achieve this, we have engineered specialty instrumented mouse home-cages with commercially available sensors that enable continuous long-term noninvasive home cage capture of these biometrics to prototype development of such digital biosignatures. Emphasis is on understanding temporal interrelations in the emergence of sleep disturbances, neuropathic pain, thermoregulatory dysfunction, cardiorespiratory dysfunction and autonomic crises (autonomic dysreflexia) after SCI. Accordingly, home cage sensor-based capture includes all motor events, respiration, heart rate, 3-state sleep, skin temperature thermography and sensory preference testing. Our overarching hypothesis is that combined continuous capture several variables during the progression of SCI will identify novel ‘digital biosignatures’ that link to emergent dysfunction. The longer-term goal is to incorporate capture of digital biosignatures into real-time feedback-based approaches that limit disease expression. Two SCI models will be used to quantify variability in emergent dysfunction with the temporal correspondence of alterations in measured biometrics: [1] T9-10 contusion SCI and [2] T2-3 complete transection. For both experimental series, variables will be continuously captured in specialty instrumented home cages located in environmentally controlled chambers both before and for 10 weeks after SCI or sham surgery. Captured biometrics will be further categorized for machine learning based on measures of SCI -induced dysfunction from more conventional tests of sensory and autonomic dysfunction to link noninvasive biometric digital biosignatures with established measures physio-behavioral dysfunction after SCI. If successful, capturing digital biosignatures of dysfunction in real time may have translational impact on individualized medicine applications in SCI individuals. This is because acquired biosignatures may then serve a template recognition function from analogously captured biometrics obtained from embedded/wearable sensors in clinical populations.

项目摘要 现在有机会在健康管理方面实现向个性化生理治疗的范式转变， 行为（生物识别）监测-使用可穿戴设备预测、预防和更好地管理疾病 技术，以及嵌入式传感器技术在轮椅以及在家里。 我们的广泛目标是解释收集的组合变化，在相同的生物特征变量捕获 在自然行为小鼠的SCI进展过程中无创地进行。在严格控制的动物研究中，我们 我建议应用机器学习算法来识别预测疾病的“数字生物特征” 出现和/或表达，并因此用于基于反馈的缓解。为了实现这一目标，我们必须 设计的专用仪器化小鼠笼，其具有市售传感器， 长期非侵入性的家庭笼捕获这些生物特征，以原型开发这种数字 生物特征 重点是了解睡眠障碍、神经性疼痛， 体温调节功能障碍、心肺功能障碍和自主神经危象（自主神经反射障碍） SCI.因此，基于家庭笼传感器的捕获包括所有运动事件、呼吸、心率、3状态睡眠， 皮肤温度热成像和感觉偏好测试。 我们的总体假设是，在SCI的进展过程中， 将识别与紧急功能障碍有关的新的“数字生物特征”。长期目标是将 将数字生物特征捕获到基于实时反馈的方法中，以限制疾病的表达。 两个SCI模型将用于量化与时间对应的紧急功能障碍的变化 测量的生物统计学变化：[1] T9-10挫伤SCI和[2] T2-3完全横切。为 实验系列中，变量将在位于 在SCI或假手术之前和之后10周，在环境控制的腔室中进行。捕获 基于SCI引起的功能障碍的测量，生物特征将被进一步分类用于机器学习， 更传统的感觉和自主神经功能障碍测试，以连接非侵入性生物计量数字生物签名 与SCI后的生理行为功能障碍的既定措施。 如果成功的话，在真实的时间内捕获功能障碍的数字生物特征可能会对 在SCI患者中的个体化医疗应用。这是因为获得的生物签名然后可以服务于 从嵌入式/可穿戴传感器获得的模拟捕获生物特征的模板识别功能 在临床人群中。