Augmenting Quadriplegic Hand Function Using a Sensorimotor Demultiplexing Neural Interface

AbstractBackgroundThe sense of touch is a key component of motor function. Severe spinal cord injury (SCI) should essentially eliminate sensory information transmission to the brain, that originates from skin innervated from below the lesion. We assessed the hypothesis that, following SCI, residual hand sensory information is transmitted to the brain, can be decoded amongst competing sensorimotor signals, and used to enhance the sense of touch via an intracortically controlled closed-loop brain-computer interface (BCI) system.MethodsExperiments were performed with a participant who has an AIS-A C5 SCI and an intracortical recording array implanted in left primary motor cortex (M1). Sensory stimulation and standard clinical sensorimotor functional assessments were used throughout a series of several mechanistic experiments.FindingsOur results demonstrate that residual afferent hand sensory signals surprisingly reach human primary motor cortex and can be simultaneously demultiplexed from ongoing efferent motor intention, enabling closed-loop sensory feedback during brain-computer interface (BCI) operation. The closed-loop sensory feedback system was able to detect residual sensory signals from up to the C8 spinal level. Using the closed-loop sensory feedback system enabled significantly enhanced object touch detection, sense of agency, movement speed, and other sensorimotor functions.InterpretationTo our knowledge, this is the first demonstration of simultaneously decoding multiplexed afferent and efferent activity from human cortex to control multiple assistive devices, constituting a ‘sensorimotor demultiplexing’ BCI. Overall, our results support the hypothesis that sub-perceptual neural signals can be decoded reliably and transformed to conscious perception, significantly augmenting function.FundingInternal funding was provided for this study from Battelle Memorial Institute and The Ohio State University Center for Neuromodulation.</jats:sec

Detecting and Reversing Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

SummaryMyocardial ischemia is spontaneous, usually asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, biological neural networks cannot reliably detect and correct myocardial ischemia on their own. In this study, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements biological neural networks enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ∼92%. ANN-controlled vagus nerve stimulation (VNS) reversed the major biomarkers of myocardial ischemia with no side effects. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse correlates of myocardial ischemia. Lastly, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress states. Overall, these results demonstrate that ANNs can supplement deficient biological neural networks via an artificially intelligent bioelectronic medicine system.</jats:sec