Continuous Sensor-Based Home-Cage Recordings for SCI Research

Biosensor capable technologies have arguably reached a ‘tipping point’ in resolution and affordability. Opportunities now exist to implement a paradigm shift in health management towards individualized physiological monitoring - to predict, to prevent, and to better manage disease. Current strategies focus on wearable technologies and active interactions with personal devices (cellphones/smartwatches). An alternative approach involves embedded monitoring and feedback (via sensing, computing, and actuation) within a home environment or wheelchair. We propose to work in collaboration with Dr. Shawn Hochman at Emory University to leverage affordable miniaturized sensor technologies that report on an individual’s physio-behavioral variables to develop an animal-model prototype – in a home-cage – to test its efficacy in assessing physiologic dysfunction after SCI. Continuous recordings of autonomic and behavioral activity may identify indicators of subsequent disease onset. Indeed, our lab (Rabchevsky et al., 2012) developed a novel algorithm for automated detection of spontaneous autonomic dysreflexia (AD). While pain signaling clearly leads to the expression of AD, other behavioral factors may predict event triggering. Approaches that can detect autonomic imbalance in SCI may predict subsequent disease expression and provide feedback to mitigate expression. Recordable physio-behavioral variables provide important insights into autonomic/behavioral states. The purpose of sensor-based technologies for noncontact non-invasive monitoring and feedback control of animal behavior is to noninvasively record physio-behavioral variables as well as provide real-time sensory feedback to dual-housed rats and mice in their home-cages within the vivarium. Plessey Semiconductors Electric Potential Integrated Circuit (EPIC) sensors [$6] measure changes in the surrounding electric field. When strategically affixed to a home-cage, they easily provide signals corresponding to activity levels and characteristic motor behaviors. These sensors can also reliably detect respiratory rate (RR), HR, and when optimally placed, may detect changes in BP. BP recordings are calculated via measurements of pulse wave velocity from floor embedded sensors. Home-cages will include electric field sensors and inter-individual signal isolation approaches to record autonomic (HR, RR, and BP) and motor activities (general activity level and various motor behaviors). The recorded data is processed by a customized interface in LabVIEW that undergoes Fourier transform-based ongoing detection of RR and user-defined preset RR feedback-based light control. We will evaluate the effects of conditioning on overall autonomic tone, autonomic dysreflexia, as well as the incidence of signature behaviors indicative of spontaneous pain and epochs of anxiety. Accuracy of recorded variables will be validated with currently and widely accepted measurement technologies used in the Rabchevsky lab to evaluate autonomic dysfunction