Spatiotemporal Distribution of Electrically Evoked Spinal Compound Action Potentials During Spinal Cord Stimulation.

OBJECTIVES: Recent studies using epidural spinal cord stimulation (SCS) have demonstrated restoration of motor function in individuals previously diagnosed with chronic spinal cord injury (SCI). In parallel, the spinal evoked compound action potentials (ECAPs) induced by SCS have been used to gain insight into the mechanisms of SCS-based chronic pain therapy and to titrate closed-loop delivery of stimulation. However, the previous characterization of ECAPs recorded during SCS was performed with one-dimensional, cylindrical electrode leads. Herein, we describe the unique spatiotemporal distribution of ECAPs induced by SCS across the medial-lateral and rostral-caudal axes of the spinal cord, and their relationship to polysynaptic lower-extremity motor activation. MATERIALS AND METHODS: In each of four sheep, two 24-contact epidural SCS arrays were placed on the lumbosacral spinal cord, spanning the L3 to L6 vertebrae. Spinal ECAPs were recorded during SCS from nonstimulating contacts of the epidural arrays, which were synchronized to bilateral electromyography (EMG) recordings from six back and lower-extremity muscles. RESULTS: We observed a triphasic P1, N1, P2 peak morphology and propagation in the ECAPs during midline and lateral stimulation. Distinct regions of lateral stimulation resulted in simultaneously increased ECAP and EMG responses compared with stimulation at adjacent lateral contacts. Although EMG responses decreased during repetitive stimulation bursts, spinal ECAP amplitude did not significantly change. Both spinal ECAP responses and EMG responses demonstrated preferential ipsilateral recruitment during lateral stimulation compared with midline stimulation. Furthermore, EMG responses were correlated with stimulation that resulted in increased ECAP amplitude on the ipsilateral side of the electrode array. CONCLUSIONS: These results suggest that ECAPs can be used to investigate the effects of SCS on spinal sensorimotor networks and to inform stimulation strategies that optimize the clinical benefit of SCS in the context of managing chronic pain and the restoration of sensorimotor function after SCI.

An active electronic bidirectional interface for high resolution interrogation of the spinal cord.

Epidural electrical stimulation (EES) has shown promise as both a clinical therapeutic tool and research aid in the study of nervous system function. However, available clinical paddles are limited to using a small number of contacts due to the burden of wires necessary to connect each contact to the therapeutic device. Here, we introduce for the first time the integration of a hermetic active electronic multiplexer onto the electrode paddle array itself, removing this interconnect limitation. We evaluated the chronic implantation of an active electronic 60-contact paddle (the HD64) on the lumbosacral spinal cord of two sheep. The HD64 was implanted for 13 months and 15 months, with no device-related malfunctions or adverse events. We identified increased selectivity in EES-evoked motor responses using dense stimulating bipoles. Further, we found that dense recording bipoles decreased the spatial correlation between channels during recordings. Finally, spatial electrode encoding enabled a neural network to accurately perform EES parameter inference for unseen stimulation electrodes, reducing training data requirements. A high-density EES paddle, containing active electronics safely integrated into neural interfaces, opens new avenues for the study of nervous system function and new therapies to treat neural injury and dysfunction.

A Review of Functional Restoration From Spinal Cord Stimulation in Patients With Spinal Cord Injury.

Traumatic spinal cord injury often leads to loss of sensory, motor, and autonomic function below the level of injury. Recent advancements in spinal cord electrical stimulation (SCS) for spinal cord injury have provided potential avenues for restoration of neurologic function in affected patients. This review aims to assess the efficacy of spinal cord stimulation, both epidural (eSCS) and transcutaneous (tSCS), on the return of function in individuals with chronic spinal cord injury. The current literature on human clinical eSCS and tSCS for spinal cord injury was reviewed. Seventy-one relevant studies were included for review, specifically examining changes in volitional movement, changes in muscle activity or spasticity, or return of cardiovascular pulmonary, or genitourinary autonomic function. The total participant sample comprised of 327 patients with spinal cord injury, each evaluated using different stimulation protocols, some for sensorimotor function and others for various autonomic functions. One hundred eight of 127 patients saw improvement in sensorimotor function, 51 of 70 patients saw improvement in autonomic genitourinary function, 32 of 32 patients saw improvement in autonomic pulmonary function, and 32 of 36 patients saw improvement in autonomic cardiovascular function. Although this review highlights SCS as a promising therapeutic neuromodulatory technique to improve rehabilitation in patients with SCI, further mechanistic studies and stimulus parameter optimization are necessary before clinical translation.

Fast inference of spinal neuromodulation for motor control using amortized neural networks.

Objective.Epidural electrical stimulation (EES) has emerged as an approach to restore motor function following spinal cord injury (SCI). However, identifying optimal EES parameters presents a significant challenge due to the complex and stochastic nature of muscle control and the combinatorial explosion of possible parameter configurations. Here, we describe a machine-learning approach that leverages modern deep neural networks to learn bidirectional mappings between the space of permissible EES parameters and target motor outputs.Approach.We collected data from four sheep implanted with two 24-contact EES electrode arrays on the lumbosacral spinal cord. Muscle activity was recorded from four bilateral hindlimb electromyography (EMG) sensors. We introduce a general learning framework to identify EES parameters capable of generating desired patterns of EMG activity. Specifically, we first amortize spinal sensorimotor computations in a forward neural network model that learns to predict motor outputs based on EES parameters. Then, we employ a second neural network as an inverse model, which reuses the amortized knowledge learned by the forward model to guide the selection of EES parameters.Main results.We found that neural networks can functionally approximate spinal sensorimotor computations by accurately predicting EMG outputs based on EES parameters. The generalization capability of the forward model critically benefited our inverse model. We successfully identified novel EES parameters, in under 20 min, capable of producing desired target EMG recruitment duringin vivotesting. Furthermore, we discovered potential functional redundancies within the spinal sensorimotor networks by identifying unique EES parameters that result in similar motor outcomes. Together, these results suggest that our framework is well-suited to probe spinal circuitry and control muscle recruitment in a completely data-driven manner.Significance.We successfully identify novel EES parameters within minutes, capable of producing desired EMG recruitment. Our approach is data-driven, subject-agnostic, automated, and orders of magnitude faster than manual approaches.