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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.

项目总结