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 现在有机会实施健康管理范式转变向个性化的物理学 行为（生物识别）监测 - 预测，预防和更好地管理疾病 技术以及轮椅和家庭内部的嵌入式传感器技术。 我们广泛的目标是解释捕获的相同生物特征变量中收集的组合变化 在自然行为的小鼠中SCI进展过程中无创的。在控制良好的动物研究中，我们 提出应用机器学习算法以识别预测疾病的“数字生物签名”的建议 出现和/或表达，因此用于基于反馈的缓解措施。为了实现这一目标，我们有 带有市售传感器的工程专业仪器式家用式主页蛋白盘可实现连续 长期无创的家庭笼捕获这些生物识别技术以原型开发这种数字 生物签名。 重点是理解睡眠障碍出现，神经性疼痛的暂时相互关系， 温度调节功能障碍，心肺功能障碍和自主神经危机（自主性全自主义疾病）之后 科学。根据所有运动事件，呼吸，心率，三态睡眠， 皮肤温度热学和感觉偏好测试。 我们的总体假设是，在SCI的进展过程中，连续捕获几个变量合并 将确定与新兴功能障碍联系起来的新型“数字生物签名”。长期目标是合并 将数字生物签名捕获为限制疾病表达的基于实时反馈的方法。 两种SCI模型将用于量化与临时通信的紧急功能障碍中的可变性 测得的生物识别技术的改变：[1] T9-10挫伤SCI和[2] T2-3完整翻译。两者 实验系列，变量将在位于专业的仪器笼中连续捕获 SCI或假手术后的前后10周，环境控制的腔室。被捕获 基于科学诱导的功能障碍的测量，生物识别技术将进一步分类为机器学习 更常规的感官和自主功能障碍来链接非侵入性生物识别数字生物签名 SCI后，采用已建立的措施进行了生理行为的功能障碍。 如果成功，实时捕获功能障碍的数字生物签名可​​能会转化 SCI个体中的个性化医学应用。这是因为获得的生物签名可​​能会服务 从嵌入/可穿戴传感器获得的类似捕获的生物识别的模板识别函数 在临床人群中。