Real-time control of walking using recordings from dorsal root ganglia

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices

BACKGROUND

Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrimination of activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode, achieving state-of-the-art performance but requiring too much data storage and power for a practical implementation on surgically implanted hardware.

OBJECTIVE

To reduce resource utilization for an implantable implementation, with minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact cuff electrode recordings.

METHODS

Neural networks (NNs) were evaluated using rat sciatic nerve recordings previously collected using 56-channel cuff electrodes to capture spatiotemporal neural activity patterns. NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architectures were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture were evaluated based on F1-score, number of weights, and floating-point operations (FLOPs).

RESULTS

NNs were identified that, when compared to ESCAPE-NET, require 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within on-chip memory sizes from published deep learning accelerators fabricated in ASIC technology.

CONCLUSION

Reduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop responsive neural stimulation.

Multiformity of extracellular microelectrode recordings from Aδ neurons in the dorsal root ganglia: a computational modeling study

Microelectrodes serve as a fundamental tool in electrophysiology research throughout the nervous system, providing a means of exploring neural function with a high resolution of neural firing information. We constructed a hybrid computational model using the finite element method and multicompartment cable models to explore factors that contribute to extracellular voltage waveforms that are produced by sensory pseudounipolar neurons, specifically smaller A-type neurons, and that are recorded by microelectrodes in dorsal root ganglia. The finite element method model included a dorsal root ganglion, surrounding tissues, and a planar microelectrode array. We built a multicompartment neuron model with multiple trajectories of the glomerular initial segment found in many A-type sensory neurons. Our model replicated both the somatic intracellular voltage profile of Aδ low-threshold mechanoreceptor neurons and the unique extracellular voltage waveform shapes that are observed in experimental settings. Results from this model indicated that tortuous glomerular initial segment geometries can introduce distinct multiphasic properties into a neuron's recorded waveform. Our model also demonstrated how recording location relative to specific microanatomical components of these neurons, and recording distance from these components, can contribute to additional changes in the multiphasic characteristics and peak-to-peak voltage amplitude of the waveform. This knowledge may provide context for research employing microelectrode recordings of pseudounipolar neurons in sensory ganglia, including functional mapping and closed-loop neuromodulation. Furthermore, our simulations gave insight into the neurophysiology of pseudounipolar neurons by demonstrating how the glomerular initial segment aids in increasing the resistance of the stem axon and mitigating rebounding somatic action potentials.NEW & NOTEWORTHY We built a computational model of sensory neurons in the dorsal root ganglia to investigate factors that influence the extracellular waveforms recorded by microelectrodes. Our model demonstrates how the unique structure of these neurons can lead to diverse and often multiphasic waveform profiles depending on the location of the recording contact relative to microanatomical neural components. Our model also provides insight into the neurophysiological function of axon glomeruli that are often present in these neurons.

Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface

Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( 8×8 ) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was successfully demonstrated in 6 subjects. The number of successful movement sequence trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

Supraspinal control of motoneurons after paralysis enabled by spinal cord stimulation

Spinal cord stimulation (SCS) restores motor control after spinal cord injury (SCI) and stroke. This evidence led to the hypothesis that SCS facilitates residual supraspinal inputs to spinal motoneurons. Instead, here we show that SCS does not facilitate residual supraspinal inputs but directly triggers motoneurons action potentials. However, supraspinal inputs can shape SCS-mediated activity, mimicking volitional control of motoneuron firing. Specifically, by combining simulations, intraspinal electrophysiology in monkeys and single motor unit recordings in humans with motor paralysis, we found that residual supraspinal inputs transform subthreshold SCS-induced excitatory postsynaptic potentials into suprathreshold events. We then demonstrated that only a restricted set of stimulation parameters enables volitional control of motoneuron firing and that lesion severity further restricts the set of effective parameters. Our results explain the facilitation of voluntary motor control during SCS while predicting the limitations of this neurotechnology in cases of severe loss of supraspinal axons.

SUPRASPINAL CONTROL OF MOTONEURONS AFTER PARALYSIS ENABLED BY SPINAL CORD STIMULATION

Spinal cord stimulation (SCS) restores motor control after spinal cord injury (SCI) and stroke. This evidence led to the hypothesis that SCS facilitates residual supraspinal inputs to spinal motoneurons. Instead, here we show that SCS does not facilitate residual supraspinal inputs but directly triggers motoneurons action potentials. However, supraspinal inputs can shape SCS-mediated activity, mimicking volitional control of motoneuron firing. Specifically, by combining simulations, intraspinal electrophysiology in monkeys and single motor unit recordings in humans with motor paralysis, we found that residual supraspinal inputs transform subthreshold SCS-induced excitatory postsynaptic potentials into suprathreshold events. We then demonstrated that only a restricted set of stimulation parameters enables volitional control of motoneuron firing and that lesion severity further restricts the set of effective parameters. Our results explain the facilitation of voluntary motor control during SCS while predicting the limitations of this neurotechnology in cases of severe loss of supraspinal axons.

Miniature battery-free bioelectronics

Miniature wireless bioelectronic implants that can operate for extended periods of time can transform how we treat disorders by acting rapidly on precise nerves and organs in a way that drugs cannot. To reach this goal, materials and methods are needed to wirelessly transfer energy through the body or harvest energy from the body itself. We review some of the capabilities of emerging energy transfer methods to identify the performance envelope for existing technology and discover where opportunities lie to improve how much-and how efficiently-we can deliver energy to the tiny bioelectronic implants that can support emerging medical technologies.

Spinal cord stimulation for spinal cord injury - Where do we stand? A narrative review

Recovery of function following a complete spinal cord injury (SCI) or an incomplete SCI where recovery has plateaued still eludes us despite extensive research. Epidural spinal cord stimulation (SCS) was initially used for managing neuropathic pain. It has subsequently demonstrated improvement in motor function in otherwise non-recovering chronic spinal cord injury in animal and human trials. The mechanisms of how it is precisely effective in doing so will need further research, which would help refine the technology for broader application. Transcutaneous spinal cord stimulation (TSCS) is also emerging as a modality to improve the functional outcome in SCI individuals, especially when coupled with appropriate rehabilitation. Apart from motor recovery, ESCS and TSCS have also shown improvement in autonomic, metabolic, genitourinary, and pulmonary function. Since the literature on this is still in its infancy, with no large-scale randomised trials and different studies using different protocols in a wide range of patients, a review of the present literature is imperative to better understand the latest developments in this field. This article examines the existing literature on the use of SCS for SCI individuals with the purpose of enabling functional recovery. It also examines the voids in the present research, thus providing future directions.

Implant-to-implant wireless networking with metamaterial textiles

Implanted bioelectronic devices can form distributed networks capable of sensing health conditions and delivering therapy throughout the body. Current clinically-used approaches for wireless communication, however, do not support direct networking between implants because of signal losses from absorption and reflection by the body. As a result, existing examples of such networks rely on an external relay device that needs to be periodically recharged and constitutes a single point of failure. Here, we demonstrate direct implant-to-implant wireless networking at the scale of the human body using metamaterial textiles. The textiles facilitate non-radiative propagation of radio-frequency signals along the surface of the body, passively amplifying the received signal strength by more than three orders of magnitude (>30 dB) compared to without the textile. Using a porcine model, we demonstrate closed-loop control of the heart rate by wirelessly networking a loop recorder and a vagus nerve stimulator at more than 40 cm distance. Our work establishes a wireless technology to directly network body-integrated devices for precise and adaptive bioelectronic therapies.

In Memoriam: Professor Richard B. Stein (1940-2020) harnessing insights from the neurophysiology of motor control-from bench to bedside

The role of afferent feedback and central motor drive in muscle activation has a profound impact on our understanding of movement control in health and disease. Dr. Richard B. Stein was a pioneer who made major contributions to the field. In addition to fundamental discoveries using animal models, he translated this to the clinic to benefit patients with spinal cord and other neurological injuries. Along the way, he inspired a generation of scientists around the world.

Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network

To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.

Spinal cord bioelectronic interfaces: opportunities in neural recording and clinical challenges

Bioelectronic stimulation of the spinal cord has demonstrated significant progress in restoration of motor function in spinal cord injury (SCI). The proximal, uninjured spinal cord presents a viable target for the recording and generation of control signals to drive targeted stimulation. Signals have been directly recorded from the spinal cord in behaving animals and correlated with limb kinematics. Advances in flexible materials, electrode impedance and signal analysis will allow SCR to be used in next-generation neuroprosthetics. In this review, we summarize the technological advances enabling progress in SCR and describe systematically the clinical challenges facing spinal cord bioelectronic interfaces and potential solutions, from device manufacture, surgical implantation to chronic effects of foreign body reaction and stress-strain mismatches between electrodes and neural tissue. Finally, we establish our vision of bi-directional closed-loop spinal cord bioelectronic bypass interfaces that enable the communication of disrupted sensory signals and restoration of motor function in SCI.

When Spinal Neuromodulation Meets Sensorimotor Rehabilitation: Lessons Learned From Animal Models to Regain Manual Dexterity After a Spinal Cord Injury

Electrical neuromodulation has strongly hit the foundations of spinal cord injury and repair. Clinical and experimental studies have demonstrated the ability to neuromodulate and engage spinal cord circuits to recover volitional motor functions lost after the injury. Although the science and technology behind electrical neuromodulation has attracted much of the attention, it cannot be obviated that electrical stimulation must be applied concomitantly to sensorimotor rehabilitation, and one would be very difficult to understand without the other, as both need to be finely tuned to efficiently execute movements. The present review explores the difficulties faced by experimental and clinical neuroscientists when attempting to neuromodulate and rehabilitate manual dexterity in spinal cord injured subjects. From a translational point of view, we will describe the major rehabilitation interventions employed in animal research to promote recovery of forelimb motor function. On the other hand, we will outline some of the state-of-the-art findings when applying electrical neuromodulation to the spinal cord in animal models and human patients, highlighting how evidences from lumbar stimulation are paving the path to cervical neuromodulation.

Epidural spinal cord stimulation as an intervention for motor recovery after motor complete spinal cord injury

Spinal cord injury (SCI) commonly results in permanent loss of motor, sensory, and autonomic function. Recent clinical studies have shown that epidural spinal cord stimulation may provide a beneficial adjunct for restoring lower extremity and other neurological functions. Herein, we review the recent clinical advances of lumbosacral epidural stimulation for restoration of sensorimotor function in individuals with motor complete SCI and we discuss the putative neural pathways involved in this promising neurorehabilitative approach. We focus on three main sections: review recent clinical results for locomotor restoration in complete SCI; discuss the contemporary understanding of electrical neuromodulation and signal transduction pathways involved in spinal locomotor networks; and review current challenges of motor system modulation and future directions toward integrative neurorestoration. The current understanding is that initial depolarization occurs at the level of large diameter dorsal root proprioceptive afferents that when integrated with interneuronal and latent residual supraspinal translesional connections can recruit locomotor centers and augment downstream motor units. Spinal epidural stimulation can initiate excitability changes in spinal networks and supraspinal networks. Different stimulation parameters can facilitate standing or stepping, and it may also have potential for augmenting myriad other sensorimotor and autonomic functions. More comprehensive investigation of the mechanisms that mediate the transformation of dysfunctional spinal networks to higher functional states with a greater focus on integrated systems-based control system may reveal the key mechanisms underlying neurological augmentation and motor restoration after severe paralysis.

High-density neural recordings from feline sacral dorsal root ganglia with thin-film array

Objective. Dorsal root ganglia (DRG) are promising sites for recording sensory activity. Current technologies for DRG recording are stiff and typically do not have sufficient site density for high-fidelity neural data techniques.Approach. In acute experiments, we demonstrate single-unit neural recordings in sacral DRG of anesthetized felines using a 4.5µm thick, high-density flexible polyimide microelectrode array with 60 sites and 30-40µm site spacing. We delivered arrays into DRG with ultrananocrystalline diamond shuttles designed for high stiffness affording a smaller footprint. We recorded neural activity during sensory activation, including cutaneous brushing and bladder filling, as well as during electrical stimulation of the pudendal nerve and anal sphincter. We used specialized neural signal analysis software to sort densely packed neural signals.Main results. We successfully delivered arrays in five of six experiments and recorded single-unit sensory activity in four experiments. The median neural signal amplitude was 55μV peak-to-peak and the maximum unique units recorded at one array position was 260, with 157 driven by sensory or electrical stimulation. In one experiment, we used the neural analysis software to track eight sorted single units as the array was retracted ∼500μm.Significance. This study is the first demonstration of ultrathin, flexible, high-density electronics delivered into DRG, with capabilities for recording and tracking sensory information that are a significant improvement over conventional DRG interfaces.

Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion

OBJECTIVE

The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord for decoding the hindlimb kinematics during walking on the treadmill.

APPROACH

Two different experimental paradigms (i.e., active and passive) were used in the current study. During active experiments, five cats were trained to walk bipedally while their hands kept on the front frame of the treadmill for balance or to walk quadrupedally. During passive experiments, the limb was passively moved by the experimenter. Local field potential (LFP) activity was recorded using a microwire array implanted in the dorsal column (DC) and lateral column (LC) of the L3-L4 spinal segments. The amplitude and frequency components of the LFP formed the feature set and the elastic net regularization was used to decode the hindlimb joint angles.

MAIN RESULTS

The results show that there is no significant difference between the information content of the signals recorded from the DC and LC regions during walking on the treadmill, but the information content of the DC is significantly higher than that of the LC during passively applied movement of the hindlimb in the anesthetized cats. Moreover, the decoding performance obtained using the recorded signals from the DC is comparable with that from the LC during locomotion. But, the decoding performance obtained using the recording channels in the DC is significantly better than that obtained using the signals recorded from the LC. The long-term analysis shows that robust decoding performance can be achieved over 2-3 months without a significant decrease in performance.

SIGNIFICANCE

This work presents a promising approach to developing a natural and robust motor neuroprosthesis device using descending neural signals to execute the movement and ascending neural signals as the feedback information for control of the movement.

Pavlovian control of intraspinal microstimulation to produce over-ground walking

OBJECTIVE

Neuromodulation technologies are increasingly used for improving function after neural injury. To achieve a symbiotic relationship between device and user, the device must augment remaining function, and independently adapt to day-to-day changes in function. The goal of this study was to develop predictive control strategies to produce over-ground walking in a model of hemisection spinal cord injury (SCI) using intraspinal microstimulation (ISMS).

APPROACH

Eight cats were anaesthetized and placed in a sling over a walkway. The residual function of a hemisection SCI was mimicked by manually moving one hind-limb through the walking cycle. ISMS targeted motor networks in the lumbosacral enlargement to activate muscles in the other, presumably 'paralyzed' limb, using low levels of current (<130 μA). Four people took turns to move the 'intact' limb, generating four different walking styles. Two control strategies, which used ground reaction force and angular velocity information about the manually moved 'intact' limb to control the timing of the transitions of the 'paralyzed' limb through the step cycle, were compared. The first strategy used thresholds on the raw sensor values to initiate transitions. The second strategy used reinforcement learning and Pavlovian control to learn predictions about the sensor values. Thresholds on the predictions were then used to initiate transitions.

MAIN RESULTS

Both control strategies were able to produce alternating, over-ground walking. Transitions based on raw sensor values required manual tuning of thresholds for each person to produce walking, whereas Pavlovian control did not. Learning occurred quickly during walking: predictions of the sensor signals were learned rapidly, initiating correct transitions after ≤4 steps. Pavlovian control was resilient to different walking styles and different cats, and recovered from induced mistakes during walking.

SIGNIFICANCE

This work demonstrates, for the first time, that Pavlovian control can augment remaining function and facilitate personalized walking with minimal tuning requirements.

Spatial models of cell distribution in human lumbar dorsal root ganglia

Dorsal root ganglia (DRG), which contain the somata of primary sensory neurons, have increasingly been considered as novel targets for clinical neural interfaces, both for neuroprosthetic and pain applications. Effective use of either neural recording or stimulation technologies requires an appropriate spatial position relative to the target neural element, whether axon or cell body. However, the internal three-dimensional spatial organization of human DRG neural fibers and somata has not been quantitatively described. In this study, we analyzed 202 cross-sectional images across the length of 31 human L4 and L5 DRG from 10 donors. We used a custom semi-automated graphical user interface to identify the locations of neural elements in the images and normalize the output to a consistent spatial reference for direct comparison by spinal level. By applying a recursive partitioning algorithm, we found that the highest density of cell bodies at both spinal levels could be found in the inner 85% of DRG length, the outer-most 25-30% radially, and the dorsal-most 69-76%. While axonal density was fairly homogeneous across the DRG length, there was a distinct low density region in the outer 7-11% radially. These findings are consistent with previous qualitative reports of neural distribution in DRG. The quantitative measurements we provide will enable improved targeting of future neural interface technologies and DRG-focused pharmaceutical therapies, and provide a rigorous anatomical description of the bridge between the central and peripheral nervous systems.

Estimation of Bladder Pressure and Volume from the Neural Activity of Lumbosacral Dorsal Horn Using a Long-Short-Term-Memory-based Deep Neural Network

In this paper, we propose a deep recurrent neural network (DRNN) for the estimation of bladder pressure and volume from neural activity recorded directly from spinal cord gray matter neurons. The model was based on the Long Short-Term Memory (LSTM) architecture, which has emerged as a general and effective model for capturing long-term temporal dependencies with good generalization performance. In this way, training the network with the data recorded from one rat could lead to estimating the bladder status of different rats. We combined modeling of spiking and local field potential (LFP) activity into a unified framework to estimate the pressure and volume of the bladder. Moreover, we investigated the effect of two-electrode recording on decoding performance. The results show that the two-electrode recordings significantly improve the decoding performance compared to single-electrode recordings. The proposed framework could estimate bladder pressure and volume with an average normalized root-mean-squared (NRMS) error of 14.9 ± 4.8% and 19.7 ± 4.7% and a correlation coefficient (CC) of 83.2 ± 3.2% and 74.2 ± 6.2%, respectively. This work represents a promising approach to the real-time estimation of bladder pressure/volume in the closed-loop control of bladder function using functional electrical stimulation.

A review for the peripheral nerve interface designer

Informational density and relative accessibility of the peripheral nervous system make it an attractive site for therapeutic intervention. Electrode-based electrophysiological interfaces with peripheral nerves have been under development since the 1960s and, for several applications, have seen widespread clinical implementation. However, many applications require a combination of neural target resolution and stability which has thus far eluded existing peripheral nerve interfaces (PNIs). With the goal of aiding PNI designers in development of devices that meet the demands of next-generation applications, this review seeks to collect and present practical considerations and best practices which emerge from the literature, including both lessons learned during early PNI development and recent ideas. Fundamental and practical principles guiding PNI design are reviewed, followed by an updated and critical account of existing PNI designs and strategies. Finally, a brief survey of in vitro and in vivo PNI characterization methods is presented.

A probabilistic recurrent neural network for decoding hind limb kinematics from multi-segment recordings of the dorsal horn neurons

OBJECTIVE

Providing accurate and robust estimates of limb kinematics from recorded neural activities is prominent in closed-loop control of functional electrical stimulation (FES). A major issue in providing accurate decoding the limb kinematics is the decoding model. The primary goal of this study is to develop a decoding approach to model the dynamic interactions of neural systems for accurate decoding. Another critical issue is to find reliable recording sites. Up to now, neural recordings from spinal neural activities were investigated. In this paper, the neural recordings from different vertebrae in decoding limb kinematics are investigated.

APPROACH

In the current study, a new generative probabilistic model with explicit considering the joint density is developed. Then, an adaptive discriminative learning algorithm is proposed for learning the model. It will be shown that the proposed generative process can be implemented by a recurrent neural network (RNN) with specific structure. We record the neural activities from dorsal horn neurons by using three electrodes placed in the L4, L5, and L6 vertebrae in anesthetized cats.

MAIN RESULTS

Information theoretic analysis on single-joint movement and multi-segment recordings implies the rostrocaudal distribution of kinematic information. It is demonstrated that during hip movement, best decoding performance is achieved by L4 recordings. For knee and ankle movements, best decodings are achieved by L5, and L6 recordings respectively. It is also shown that the decoding accuracy using multi-segment recordings outperforms decoding accuracy obtained by single-segment recording in multi-joint movement. The results also confirm the superiority of the proposed probabilistic recurrent neural network (PRNN) over the conventional recurrent neural network and Kalman filter ([Formula: see text]).

SIGNIFICANCE

Multi-segment recordings from dorsal horn neurons as well as the proposed probabilistic recurrent network model provide a promising approach for robust and accurate decoding limb kinematics.

Recording single- and multi-unit neuronal action potentials from the surface of the dorsal root ganglion

The dorsal root ganglia (DRG) contain cell bodies of primary afferent neurons, which are frequently studied by recording extracellularly with penetrating microelectrodes inserted into the DRG. We aimed to isolate single- and multi-unit activity from primary afferents in the lumbar DRG using non-penetrating electrode arrays and to characterize the relationship of that activity with limb position and movement. The left sixth and seventh lumbar DRG (L6-L7) were instrumented with penetrating and non-penetrating electrode arrays to record neural activity during passive hindlimb movement in 7 anesthetized cats. We found that the non-penetrating arrays could record both multi-unit and well-isolated single-unit activity from the surface of the DRG, although with smaller signal to noise ratios (SNRs) compared to penetrating electrodes. Across all recorded units, the median SNR was 1.1 for non-penetrating electrodes and 1.6 for penetrating electrodes. Although the non-penetrating arrays were not anchored to the DRG or surrounding tissues, the spike amplitudes did not change (<1% change from baseline spike amplitude) when the limb was moved passively over a limited range of motion (~20 degrees at the hip). Units of various sensory fiber types were recorded, with 20% of units identified as primary muscle spindles, 37% as secondary muscle spindles, and 24% as cutaneous afferents. Our study suggests that non-penetrating electrode arrays can record modulated single- and multi-unit neural activity of various sensory fiber types from the DRG surface.

Effect of anesthesia on motor responses evoked by spinal neural prostheses during intraoperative procedures

OBJECTIVE

The overall goal of this study was to investigate the effects of various anesthetic protocols on the intraoperative responses to intraspinal microstimulation (ISMS). ISMS is a neuroprosthetic approach that targets the motor networks in the ventral horns of the spinal cord to restore function after spinal cord injury. In preclinical studies, ISMS in the lumbosacral enlargement produced standing and walking by activating networks controlling the hindlimb muscles. ISMS implants are placed surgically under anesthesia, and refinements in placement are made based on the evoked responses. Anesthesia can have a significant effect on the responses evoked by spinal neuroprostheses; therefore, in preparation for clinical testing of ISMS, we compared the evoked responses under a common clinical neurosurgical anesthetic protocol with those evoked under protocols commonly used in preclinical studies.

APPROACH

Experiments were conducted in seven pigs. An ISMS microelectrode array was implanted in the lumbar enlargement and responses to ISMS were measured under three anesthetic protocols: (1) isoflurane, an agent used pre-clinically and clinically, (2) total intravenous anesthesia (TIVA) with propofol as the main agent commonly used in clinical neurosurgical procedures, (3) TIVA with sodium pentobarbital, an anesthetic agent used mostly preclinically. Responses to ISMS were evaluated based on stimulation thresholds, movement kinematics, and joint torques. Motor evoked potentials (MEP) and plasma concentrations of propofol were also measured.

MAIN RESULTS

ISMS under propofol anesthesia produced large and functional responses that were not statistically different from those produced under pentobarbital anesthesia. Isoflurane, however, significantly suppressed the ISMS-evoked responses.

SIGNIFICANCE

This study demonstrated that the choice of anesthesia is critical for intraoperative assessments of motor responses evoked by spinal neuroprostheses. Propofol and pentobarbital anesthesia did not overly suppress the effects of ISMS; therefore, propofol is expected to be a suitable anesthetic agent for clinical intraoperative testing of an intraspinal neuroprosthetic system.

A speed-adaptive intraspinal microstimulation controller to restore weight-bearing stepping in a spinal cord hemisection model

OBJECTIVE

The goal of this study was to develop control strategies to produce alternating, weight-bearing stepping in a cat model of hemisection spinal cord injury (SCI) using intraspinal microstimulation (ISMS).

APPROACH

Six cats were anesthetized and the functional consequences of a hemisection SCI were simulated by manually moving one hind-limb through the gait cycle over a moving treadmill belt. ISMS activated the muscles in the other leg by stimulating motor networks in the lumbosacral enlargement using low levels of current (<110 µA). The control strategy used signals from ground reaction forces and angular velocity from the manually-moved limb to anticipate states of the gait cycle, and controlled ISMS to move the other hind-limb into the opposite state. Adaptive control strategies were developed to ensure weight-bearing at different stepping speeds. The step period was predicted using generalizations obtained through four supervised machine learning algorithms and used to adapt the control strategy for faster steps.

MAIN RESULTS

At a single speed, 100% of the steps had sufficient weight-bearing; at faster speeds without adaptation, 97.6% of steps were weight-bearing (significantly less than that for single speed; p  =  0.002). By adapting the control strategy for faster steps using the predicted step period, weight-bearing was achieved in more than 99% of the steps in three of four methods (significantly more than without adaptation p  <  0.04). Overall, a multivariate model tree increased the number of weight-bearing steps, restored step symmetry, and maintained alternation at faster stepping speeds.

SIGNIFICANCE

Through the adaptive control strategies guided by supervised machine learning, we were able to restore weight-bearing and maintain alternation and step symmetry at varying stepping speeds.

Therapeutic Stimulation for Restoration of Function After Spinal Cord Injury

Paralysis due to spinal cord injury can severely limit motor function and independence. This review summarizes different approaches to electrical stimulation of the spinal cord designed to restore motor function, with a brief discussion of their origins and the current understanding of their mechanisms of action. Spinal stimulation leads to impressive improvements in motor function along with some benefits to autonomic functions such as bladder control. Nonetheless, the precise mechanisms underlying these improvements and the optimal spinal stimulation approaches for restoration of motor function are largely unknown. Finally, spinal stimulation may augment other therapies that address the molecular and cellular environment of the injured spinal cord. The fact that several stimulation approaches are now leading to substantial and durable improvements in function following spinal cord injury provides a new perspectives on the previously "incurable" condition of paralysis.

Block-based robust control of stepping using intraspinal microstimulation

OBJECTIVE

The problem of motor control using intraspinal microstimulation (ISMS) can be approached at two levels of the motor system: individual muscles (motor pools) and motor primitives. The major challenges of direct ISMS at the level of individual muscle are the number of electrodes that are required to be implanted in order to recruit all muscles involving the motion and muscle selectivity. One solution to cope with these problems is the control of movement generated by appropriate combination of the movement primitives. In this paper, we proposed a robust control framework using primitives for fully automatic block-based control of the motion through ISMS.

APPROACH

The control framework is based on an adaptive fuzzy terminal sliding mode control. The biggest advantage of the controller is the fast convergence compared to the conventional sliding mode control.

MAIN RESULTS

The experiments were conducted on spinally-intact anesthetized cats. Based on electromyography activity of the hindlimbs muscles, different movement blocks were defined. The results of block-based air-stepping control show that the proposed control framework could generate the gait cycle with good tracking performance. The averages of tracking error, over five cats, were 9.3%, 11.2%, and 16.1%, for the ankle, knee, and hip joints, respectively. The results of walking control on the moving treadmill demonstrated that the gait cycle can be generated only with two movement blocks for each leg.

SIGNIFICANCE

The results of the current study demonstrated that the normal gait pattern can be achieved by tracking control of the movement blocks using ISMS, while the controller requires no offline learning phase and no pre-adjustment of the stimulation level. The controller is able to automatically regulate the interactions between movement blocks without any preprogrammed block activities.

Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings

Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems.

Flexible microelectrode array for interfacing with the surface of neural ganglia

OBJECTIVE

The dorsal root ganglia (DRG) are promising nerve structures for sensory neural interfaces because they provide centralized access to primary afferent cell bodies and spinal reflex circuitry. In order to harness this potential, new electrode technologies are needed which take advantage of the unique properties of DRG, specifically the high density of neural cell bodies at the dorsal surface. Here we report initial in vivo results from the development of a flexible non-penetrating polyimide electrode array interfacing with the surface of ganglia.

APPROACH

Multiple layouts of a 64-channel iridium electrode (420 µm²) array were tested, with pitch as small as 25 µm. The buccal ganglia of invertebrate sea slug Aplysia californica were used to develop handling and recording techniques with ganglionic surface electrode arrays (GSEAs). We also demonstrated the GSEA's capability to record single- and multi-unit activity from feline lumbosacral DRG related to a variety of sensory inputs, including cutaneous brushing, joint flexion, and bladder pressure.

MAIN RESULTS

We recorded action potentials from a variety of Aplysia neurons activated by nerve stimulation, and units were observed firing simultaneously on closely spaced electrode sites. We also recorded single- and multi-unit activity associated with sensory inputs from feline DRG. We utilized spatial oversampling of action potentials on closely-spaced electrode sites to estimate the location of neural sources at between 25 µm and 107 µm below the DRG surface. We also used the high spatial sampling to demonstrate a possible spatial sensory map of one feline's DRG. We obtained activation of sensory fibers with low-amplitude stimulation through individual or groups of GSEA electrode sites.

SIGNIFICANCE

Overall, the GSEA has been shown to provide a variety of information types from ganglia neurons and to have significant potential as a tool for neural mapping and interfacing.

Decoding hind limb kinematics from neuronal activity of the dorsal horn neurons using multiple level learning algorithm

Decoding continuous hind limb joint angles from sensory recordings of neural system provides a feedback for closed-loop control of hind limb movement using functional electrical stimulation. So far, many attempts have been done to extract sensory information from dorsal root ganglia and sensory nerves. In this work, we examine decoding joint angles trajectories from the single-electrode extracellular recording of dorsal horn gray matter of the spinal cord during passive limb movement in anesthetized cats. In this study, a processing framework based on ensemble learning approach is propose to combine firing rate (FR) and interspike interval (ISI) information of the neuronal activity. For this purpose, a stacked generalization approach based on recurrent neural network is proposed to enhance decoding accuracy of the movement kinematics. The results show that the high precision neural decoding of limb movement can be achieved even with a single electrode implanted in the spinal cord gray matter.

Peptide modification of polyimide-insulated microwires: Towards improved biocompatibility through reduced glial scarring

The goal of this study is to improve the integration of implanted microdevices with tissue in the central nervous system (CNS). The long-term utility of neuroprosthetic devices implanted in the CNS is affected by the formation of a scar by resident glial cells (astrocytes and microglia), limiting the viability and functional stability of the devices. Reduction in the proliferation of glial cells is expected to enhance the biocompatibility of devices. We demonstrate the modification of polyimide-insulated microelectrodes with a bioactive peptide KHIFSDDSSE. Microelectrode wires were functionalized with (3-aminopropyl) triethoxy silane (APTES); the peptide was then covalently bonded to the APTES. The soluble peptide was tested in 2D mixed cultures of astrocytes and microglia, and reduced the proliferation of both cell types. The interactions of glial cells with the peptide-modified wires was then examined in 3D cell-laden hydrogels by immunofluorescence microscopy. As expected for uncoated wires, the microglia were first attracted to the wire (7days) followed by astrocyte recruitment and hypertrophy (14days). For the peptide-treated wires, astrocytes coated the wires directly (24h), and formed a thin, stable coating without evidence of hypertrophy, and the attraction of microglia to the wire was significantly reduced. The results suggest a mechanism to improve tissue integration by promoting uniform coating of astrocytes on a foreign body while lessening the reactive response of microglia. We conclude that the bioactive peptide KHIFSDDSSE may be effective in improving the biocompatibility of neural interfaces by both reducing acute glial reactivity and generating stable integration with tissue.

STATEMENT OF SIGNIFICANCE

The peptide KHIFSDDSSE has previously been shown in vitro to both reduce the proliferation of astrocytes, and to increase the adhesion of astrocyte to glass substrates. Here, we demonstrate a method to apply uniform coatings of peptides to microwires, which could readily be generalized to other peptides and surfaces. We then show that when peptide-modified wires are inserted into 3D cell-laden hydrogels, the normal cellular reaction (microglial activation followed by astrocyte recruitment and hypertrophy) does not occur, rather astrocytes are attracted directly to the surface of the wire, forming a relatively thin and uniform coating. This suggests a method to improve tissue integration of implanted devices to reduce glial scarring and ultimately reduce failure of neural interfaces.

Neurophysiology and neural engineering: a review

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight.

Dorsal root ganglia neurite outgrowth measured as a function of changes in microelectrode array resistance

Current research in prosthetic device design aims to mimic natural movements using a feedback system that connects to the patient's own nerves to control the device. The first step in using neurons to control motion is to make and maintain contact between neurons and the feedback sensors. Therefore, the goal of this project was to determine if changes in electrode resistance could be detected when a neuron extended a neurite to contact a sensor. Dorsal root ganglia (DRG) were harvested from chick embryos and cultured on a collagen-coated carbon nanotube microelectrode array for two days. The DRG were seeded along one side of the array so the processes extended across the array, contacting about half of the electrodes. Electrode resistance was measured both prior to culture and after the two day culture period. Phase contrast images of the microelectrode array were taken after two days to visually determine which electrodes were in contact with one or more DRG neurite or tissue. Electrodes in contact with DRG neurites had an average change in resistance of 0.15 MΩ compared with the electrodes without DRG neurites. Using this method, we determined that resistance values can be used as a criterion for identifying electrodes in contact with a DRG neurite. These data are the foundation for future development of an autonomous feedback resistance measurement system to continuously monitor DRG neurite outgrowth at specific spatial locations.

Chronic monitoring of lower urinary tract activity via a sacral dorsal root ganglia interface

OBJECTIVE

Our goal is to develop an interface that integrates chronic monitoring of lower urinary tract (LUT) activity with stimulation of peripheral pathways.

APPROACH

Penetrating microelectrodes were implanted in sacral dorsal root ganglia (DRG) of adult male felines. Peripheral electrodes were placed on or in the pudendal nerve, bladder neck and near the external urethral sphincter. Supra-pubic bladder catheters were implanted for saline infusion and pressure monitoring. Electrode and catheter leads were enclosed in an external housing on the back. Neural signals from microelectrodes and bladder pressure of sedated or awake-behaving felines were recorded under various test conditions in weekly sessions. Electrodes were also stimulated to drive activity.

MAIN RESULTS

LUT single- and multi-unit activity was recorded for 4-11 weeks in four felines. As many as 18 unique bladder pressure single-units were identified in each experiment. Some channels consistently recorded bladder afferent activity for up to 41 d, and we tracked individual single-units for up to 23 d continuously. Distension-evoked and stimulation-driven (DRG and pudendal) bladder emptying was observed, during which LUT sensory activity was recorded.

SIGNIFICANCE

This chronic implant animal model allows for behavioral studies of LUT neurophysiology and will allow for continued development of a closed-loop neuroprosthesis for bladder control.

Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems.

Surgical Neurostimulation for Spinal Cord Injury

Traumatic spinal cord injury (SCI) is a devastating neurological condition characterized by a constellation of symptoms including paralysis, paraesthesia, pain, cardiovascular, bladder, bowel and sexual dysfunction. Current treatment for SCI involves acute resuscitation, aggressive rehabilitation and symptomatic treatment for complications. Despite the progress in scientific understanding, regenerative therapies are lacking. In this review, we outline the current state and future potential of invasive and non-invasive neuromodulation strategies including deep brain stimulation (DBS), spinal cord stimulation (SCS), motor cortex stimulation (MCS), transcutaneous direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) in the context of SCI. We consider the ability of these therapies to address pain, sensorimotor symptoms and autonomic dysregulation associated with SCI. In addition to the potential to make important contributions to SCI treatment, neuromodulation has the added ability to contribute to our understanding of spinal cord neurobiology and the pathophysiology of SCI.

A Fuzzy Controller for Movement Stabilization Using Afferent Control: Controller Synthesis and Simulation

Stimulation of spinal sensorimotor circuits can improve motor control in animal models and humans with spinal cord injury (SCI). More recent evidence suggests that the stimulation increases the level of excitability in the spinal circuits, activates central pattern generators, and it is also able to recruit distinctive afferent pathways connected to specific sensorimotor circuits. In addition, the stimulation generates well-defined responses in leg muscles after each pulse. The problem is that in most of the neuromodulation devices, electrical stimulation parameters are regulated manually and stay constant during movement. Such a technique is likely suboptimal to intercede maximum therapeutic effects in patients. Therefore, in this article, a fuzzy controller has been designed to control limb kinematics during locomotion using the afferent control in a neuromechanical model without supraspinal drive simulating post-SCI situation. The proposed controller automatically tunes the weights of group Ia afferent inputs of the spinal cord to reset the phase appropriately during the reaction to an external perturbation. The kinematic motion data and weights of group Ia afferent inputs were the input and output of the controller, respectively. Simulation results showed the acceptable performance of the controller to establish adaptive locomotion against the perturbing forces based on the phase resetting of the walking rhythm.

A brain-spine interface alleviating gait deficits after spinal cord injury in primates

Spinal cord injury disrupts the communication between the brain and the spinal circuits that orchestrate movement. To bypass the lesion, brain–computer interfaces1–3 have directly linked cortical activity to electrical stimulation of muscles, which have restored grasping abilities after hand paralysis1,4. Theoretically, this strategy could also restore control over leg muscle activity for walking5. However, replicating the complex sequence of individual muscle activation patterns underlying natural and adaptive locomotor movements poses formidable conceptual and technological challenges6,7. Recently, we showed in rats that epidural electrical stimulation of the lumbar spinal cord can reproduce the natural activation of synergistic muscle groups producing locomotion8–10. Here, we interfaced leg motor cortex activity with epidural electrical stimulation protocols to establish a brain–spinal interface that alleviated gait deficits after a spinal cord injury in nonhuman primates. Rhesus monkeys were implanted with an intracortical microelectrode array into the leg area of motor cortex; and a spinal cord stimulation system composed of a spatially selective epidural implant and a pulse generator with real-time triggering capabilities. We designed and implemented wireless control systems that linked online neural decoding of extension and flexion motor states with stimulation protocols promoting these movements. These systems allowed the monkeys to behave freely without any restrictions or constraining tethered electronics. After validation of the brain–spinal interface in intact monkeys, we performed a unilateral corticospinal tract lesion at the thoracic level. As early as six days post-injury and without prior training of the monkeys, the brain–spinal interface restored weight-bearing locomotion of the paralyzed leg on a treadmill and overground. The implantable components integrated in the brain–spinal interface have all been approved for investigational applications in similar human research, suggesting a practical translational pathway for proof-of-concept studies in people with spinal cord injury.

A Mixed-Signal VLSI System for Producing Temporally Adapting Intraspinal Microstimulation Patterns for Locomotion

Neural pathways can be artificially activated through the use of electrical stimulation. For individuals with a spinal cord injury, intraspinal microstimulation, using electrical currents on the order of 125 μ A, can produce muscle contractions and joint torques in the lower extremities suitable for restoring walking. The work presented here demonstrates an integrated circuit implementing a state-based control strategy where sensory feedback and intrinsic feed forward control shape the stimulation waveforms produced on-chip. Fabricated in a 0.5 μ m process, the device was successfully used in vivo to produce walking movements in a model of spinal cord injury. This work represents progress towards an implantable solution to be used for restoring walking in individuals with spinal cord injuries.

A modular robust control framework for control of movement elicited by multi-electrode intraspinal microstimulation

OBJECTIVE

An important issue in restoring motor function through intraspinal microstimulation (ISMS) is the motor control. To provide a physiologically plausible motor control using ISMS, it should be able to control the individual motor unit which is the lowest functional unit of motor control. By focal stimulation only a small group of motor neurons (MNs) within a motor pool can be activated. Different groups of MNs within a motor pool can potentially be activated without involving adjacent motor pools by local stimulation of different parts of a motor pool via microelectrode array implanted into a motor pool. However, since the system has multiple inputs with single output during multi-electrode ISMS, it poses a challenge to movement control. In this paper, we proposed a modular robust control strategy for movement control, whereas multi-electrode array is implanted into each motor activation pool of a muscle.

APPROACH

The controller was based on the combination of proportional-integral-derivative and adaptive fuzzy sliding mode control. The global stability of the controller was guaranteed.

MAIN RESULTS

The results of the experiments on rat models showed that the multi-electrode control can provide a more robust control and accurate tracking performance than a single-electrode control. The control output can be pulse amplitude (pulse amplitude modulation, PAM) or pulse width (pulse width modulation, PWM) of the stimulation signal. The results demonstrated that the controller with PAM provided faster convergence rate and better tracking performance than the controller with PWM.

SIGNIFICANCE

This work represents a promising control approach to the restoring motor functions using ISMS. The proposed controller requires no prior knowledge about the dynamics of the system to be controlled and no offline learning phase. The proposed control design is modular in the sense that each motor pool has an independent controller and each controller is able to control ISMS through an array of microelectrodes.

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury

Neuromodulation of spinal sensorimotor circuits improves motor control in animal models and humans with spinal cord injury. With common neuromodulation devices, electrical stimulation parameters are tuned manually and remain constant during movement. We developed a mechanistic framework to optimize neuromodulation in real time to achieve high-fidelity control of leg kinematics during locomotion in rats. We first uncovered relationships between neuromodulation parameters and recruitment of distinct sensorimotor circuits, resulting in predictive adjustments of leg kinematics. Second, we established a technological platform with embedded control policies that integrated robust movement feedback and feed-forward control loops in real time. These developments allowed us to conceive a neuroprosthetic system that controlled a broad range of foot trajectories during continuous locomotion in paralyzed rats. Animals with complete spinal cord injury performed more than 1000 successive steps without failure, and were able to climb staircases of various heights and lengths with precision and fluidity. Beyond therapeutic potential, these findings provide a conceptual and technical framework to personalize neuromodulation treatments for other neurological disorders.

Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury

Electrical neuromodulation of lumbar segments improves motor control after spinal cord injury in animal models and humans. However, the physiological principles underlying the effect of this intervention remain poorly understood, which has limited this therapeutic approach to continuous stimulation applied to restricted spinal cord locations. Here, we developed novel stimulation protocols that reproduce the natural dynamics of motoneuron activation during locomotion. For this, we computed the spatiotemporal activation pattern of muscle synergies during locomotion in healthy rats. Computer simulations identified optimal electrode locations to target each synergy through the recruitment of proprioceptive feedback circuits. This framework steered the design of spatially selective spinal implants and real–time control software that modulate extensor versus flexor synergies with precise temporal resolution. Spatiotemporal neuromodulation therapies improved gait quality, weight–bearing capacities, endurance and skilled locomotion in multiple rodent models of spinal cord injury. These new concepts are directly translatable to strategies to improve motor control in humans.

Spinal primitives and intra-spinal micro-stimulation (ISMS) based prostheses: a neurobiological perspective on the "known unknowns" in ISMS and future prospects

The current literature on Intra-Spinal Micro-Stimulation (ISMS) for motor prostheses is reviewed in light of neurobiological data on spinal organization, and a neurobiological perspective on output motor modularity, ISMS maps, stimulation combination effects, and stability. By comparing published data in these areas, the review identifies several gaps in current knowledge that are crucial to the development of effective intraspinal neuroprostheses. Gaps can be categorized into a lack of systematic and reproducible details of: (a) Topography and threshold for ISMS across the segmental motor system, the topography of autonomic recruitment by ISMS, and the coupling relations between these two types of outputs in practice. (b) Compositional rules for ISMS motor responses tested across the full range of the target spinal topographies. (c) Rules for ISMS effects' dependence on spinal cord state and neural dynamics during naturally elicited or ISMS triggered behaviors. (d) Plasticity of the compositional rules for ISMS motor responses, and understanding plasticity of ISMS topography in different spinal cord lesion states, disease states, and following rehabilitation. All these knowledge gaps to a greater or lesser extent require novel electrode technology in order to allow high density chronic recording and stimulation. The current lack of this technology may explain why these prominent gaps in the ISMS literature currently exist. It is also argued that given the "known unknowns" in the current ISMS literature, it may be prudent to adopt and develop control schemes that can manage the current results with simple superposition and winner-take-all interactions, but can also incorporate the possible plastic and stochastic dynamic interactions that may emerge in fuller analyses over longer terms, and which have already been noted in some simpler model systems.

Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: toward a next generation closed-loop neural prosthesis

Movement is planned and coordinated by the brain and carried out by contracting muscles acting on specific joints. Motor commands initiated in the brain travel through descending pathways in the spinal cord to effector motor neurons before reaching target muscles. Damage to these pathways by spinal cord injury (SCI) can result in paralysis below the injury level. However, the planning and coordination centers of the brain, as well as peripheral nerves and the muscles that they act upon, remain functional. Neuroprosthetic devices can restore motor function following SCI by direct electrical stimulation of the neuromuscular system. Unfortunately, conventional neuroprosthetic techniques are limited by a myriad of factors that include, but are not limited to, a lack of characterization of non-linear input/output system dynamics, mechanical coupling, limited number of degrees of freedom, high power consumption, large device size, and rapid onset of muscle fatigue. Wireless multi-channel closed-loop neuroprostheses that integrate command signals from the brain with sensor-based feedback from the environment and the system's state offer the possibility of increasing device performance, ultimately improving quality of life for people with SCI. In this manuscript, we review neuroprosthetic technology for improving functional restoration following SCI and describe brain-machine interfaces suitable for control of neuroprosthetic systems with multiple degrees of freedom. Additionally, we discuss novel stimulation paradigms that can improve synergy with higher planning centers and improve fatigue-resistant activation of paralyzed muscles. In the near future, integration of these technologies will provide SCI survivors with versatile closed-loop neuroprosthetic systems for restoring function to paralyzed muscles.

Personalized neuroprosthetics

Decades of technological developments have populated the field of neuroprosthetics with myriad replacement strategies, neuromodulation therapies, and rehabilitation procedures to improve the quality of life for individuals with neuromotor disorders. Despite the few but impressive clinical successes, and multiple breakthroughs in animal models, neuroprosthetic technologies remain mainly confined to sophisticated laboratory environments. We summarize the core principles and latest achievements in neuroprosthetics, but also address the challenges that lie along the path toward clinical fruition. We propose a pragmatic framework to personalize neurotechnologies and rehabilitation for patient-specific impairments to achieve the timely dissemination of neuroprosthetic medicine.