Understanding Behavioral Variability in Outcome After SCI

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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.