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

Calvert, Jonathan S.; Darie, Radu; Parker, Samuel; Shaaya, Elias; Syed, Sohail; McLaughlin, Bryan; Fridley, Jared; Borton, David A.

doi:10.1016/j.neurom.2022.03.007

Cited by 6 publications

(14 citation statements)

References 52 publications

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“…Animals were kept in separate cages in a controlled environment on a 12 h light/dark cycle with ad libitum access to water, and were fed twice daily. Briefly, the sheep underwent laminectomy procedures to surgically implant two 24-contact EES electrode arrays (Micro-Leads Medical, Somerville, MA) placed onto the dorsal aspect of the epidural space of the lumbosacral spinal cord, spanning approximately the L4-L6 vertebral bodies [31]. Additionally, a ground wire and a reference wire were placed into the epidural space and paraspinal musculature, respectively.…”

Section: Study Subjectsmentioning

confidence: 99%

“…The EMG sensors (Trigno Avanti model, Delsys Inc. Natick, MA) were placed longitudinally along the muscle belly following preparation of the attachment site via shaving and cleaning. These muscles were selected based on our previous experience collecting surface EMG in the sheep model [31]. BF is a proximal muscle responsible for hock flexion and hip extension, and GR is a proximal muscle responsible for hip abduction, hock flexion, and hock internal rotation.…”

Section: Data Acquisitionmentioning

confidence: 99%

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

et al. 2022

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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 minutes, capable of producing desired target EMG recruitment during in vivo testing. 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.

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Section: Study Subjectsmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

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

et al. 2022

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“…Real-time analysis of ECAP component and EMG component in ESR during EES may help to optimize EES protocols by enabling real-time adjustment of stimulation contacts and parameters. Moreover, the ECAP component in ESR may provide a clinically viable method to automatically detect lead migration if morphology of the ECAP components changes with migration of the recording or stimulation lead (Calvert et al, 2022). The work by Calvert and colleagues (2022) investigated the migration detection application with paddle leads, which require a laminectomy to place and were designed specifically for the restoration of motor function.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the ECAP component in ESR may provide a clinically viable method to automatically detect lead migration if morphology of the ECAP components changes with migration of the recording or stimulation lead (Calvert et al, 2022). The work by Calvert and colleagues (2022) investigated the migration detection application with paddle leads, which require a laminectomy to place and were designed specifically for the restoration of motor function. Paddle leads are also less common in the treatment of chronic pain than the percutaneously placed cylindrical leads.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Applications of Evoked Responses During Epidural Electrical Stimulation

Verma

Romanauski

Lam

et al. 2023

Preprint

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Epidural electrical stimulation (EES) of the spinal cord has been FDA approved and used therapeutically for decades. However, there is still not a clear understanding of the local neural substrates and consequently the mechanism of action responsible for the therapeutic effects. Epidural spinal recordings (ESR) are collected from the electrodes placed in the epidural space. ESR contains multi-modality signal components such as the evoked neural response (due to tonic or BurstDR waveforms), evoked muscle response, stimulation artifact, and cardiac response. The tonic stimulation evoked compound action potential (ECAP) is one of the components in ESR and has been proposed recently to measure local neural activation during EES. Here, we first review and investigate the referencing strategies, as they apply to ECAP component in ESR in the domestic swine animal model. We then examine how ECAP component can be used to sense lead migration, an adverse outcome following lead placement that can reduce therapeutic efficacy. Lastly, we show and isolate concurrent activation of local back and leg muscles during EES, demonstrating that the ESR obtained from the recording contacts contain both ECAP and EMG components. These findings may further guide the implementation of recording and reference contacts in an implantable EES system and provide preliminary evidence for the utility of ECAP component in ESR to detect lead migration. We expect these results to facilitate future development of EES methodology and implementation of use of different components in ESR to improve EES therapy.

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“…However, there is insufficient understanding of how the evoked responses, such as ECAP signals or the evoked EMGs during spinal cord stimulation, are connected to the therapy outcome. In addition, the stimulation artifact could severely contaminate the evoked responses and it can be easily included in the quantification of the signals (11). As a result, command signals generated by simple quantification of such evoked responses can contain different physiological signals and can be contaminated by stimulation artifact.…”

Section: Introductionmentioning

confidence: 99%

Methods and system for recording human physiological signals from implantable leads during spinal cord stimulation

et al. 2023

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ObjectivesThis article presents a method–including hardware configuration, sampling rate, filtering settings, and other data analysis techniques–to measure evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) in humans with externalized percutaneous electrodes. The goal is to provide a robust and standardized protocol for measuring ECAPs on the non-stimulation contacts and to demonstrate how measured signals depend on hardware and processing decisions.MethodsTwo participants were implanted with percutaneous leads for the treatment of chronic pain with externalized leads during a trial period for stimulation and recording. The leads were connected to a Neuralynx ATLAS system allowing us to simultaneously stimulate and record through selected electrodes. We examined different hardware settings, such as online filters and sampling rate, as well as processing techniques, such as stimulation artifact removal and offline filters, and measured the effects on the ECAPs metrics: the first negative peak (N1) time and peak-valley amplitude.ResultsFor accurate measurements of ECAPs, the hardware sampling rate should be least at 8 kHz and should use a high pass filter with a low cutoff frequency, such as 0.1 Hz, to eliminate baseline drift and saturation (railing). Stimulation artifact removal can use a double exponential or a second-order polynomial. The polynomial fit is 6.4 times faster on average in computation time than the double exponential, while the resulting ECAPs’ N1 time and peak-valley amplitude are similar between the two. If the baseline raw measurement drifts with stimulation, a median filter with a 100-ms window or a high pass filter with an 80-Hz cutoff frequency preserves the ECAPs.ConclusionsThis work is the first comprehensive analysis of hardware and processing variations on the observed ECAPs from SCS leads. It sets recommendations to properly record and process ECAPs from the non-stimulation contacts on the implantable leads.

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

Cited by 6 publications

References 52 publications

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

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

Characterization and Applications of Evoked Responses During Epidural Electrical Stimulation

Methods and system for recording human physiological signals from implantable leads during spinal cord stimulation

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