Understanding Behavioral Variability in Outcome After SCI

了解 SCI 后结果的行为变异

基本信息

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
项目类别：
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

项目摘要

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.

项目概要现在有机会实现健康管理向个性化生理管理的范式转变。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，以及轮椅和家庭内的嵌入式传感器技术。我们的总体目标是解释捕获的相同生物特征变量中收集到的组合变化在良好对照的动物研究中，我们在自然行为小鼠的 SCI 进展过程中进行了非侵入性治疗。提议应用机器学习算法来识别可预测疾病的“数字生物特征” 出现和/或表达，因此可用于基于反馈的缓解。为了实现这一目标，我们有。精心设计的专业仪器仪表鼠标家笼，配有市售传感器，可实现连续长期非侵入性的家庭笼子捕获这些生物特征，以开发此类数字的原型生物特征。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机（自主神经反射异常）因此，基于笼式传感器的捕获包括所有运动事件、呼吸、心率、三态睡眠、皮肤温度热成像和感官偏好测试。我们的总体假设是，在 SCI 进展过程中连续捕获多个变量将识别与紧急功能障碍相关的新颖的“数字生物特征”。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 模型将用于通过时间对应来量化紧急功能障碍的变异性测量的生物特征变化：[1] T9-10 挫伤 SCI 和 [2] T2-3 完全横切。实验系列中，变量将在位于 SCI 或假手术之前和之后 10 周的环境控制室。生物识别技术将根据 SCI 引起的功能障碍的测量进一步分类为机器学习对感觉和自主神经功能障碍进行更常规的测试，以链接无创生物识别数字生物特征 SCI 后生理行为功能障碍的既定措施。如果成功，实时捕获功能障碍的数字生物特征可能会对这是因为获得的生物特征可以用于 SCI 个体的个体化医学应用。从嵌入式/可穿戴传感器获得的类似捕获的生物特征的模板识别功能在临床人群中。