A method for quantitative spatial analysis of immunolabeled fibers at regenerative electrode interfaces.

BACKGROUND: Regenerative electrodes are being explored as robust peripheral nerve interfaces for neuro-prosthetic control and sensory feedback. Current designs differ in electrode number, spatial arrangement, and porosity which impacts the regeneration, activation, and spatial distribution of fibers at the device interface. Knowledge of sensory and motor fiber distributions are important in optimizing selective fiber activation and recording. NEW METHOD: We use confocal microscopy and immunofluorescence methods to conduct spatial analysis of immunolabeled fibers across whole nerve cross sections. RESULTS: This protocol was implemented to characterize motor fiber distribution within 3 macro-sieve electrode regenerated (MSE), 3 silicone-conduit regenerated, and 3 unmanipulated control rodent sciatic nerves. Total motor fiber counts were 1485 [SD: +/- 50.11], 1899 [SD: +/- 359], and 5732 [SD: +/- 1410] for control, MSE, and conduit nerves respectively. MSE motor fiber distributions exhibited evidence of deviation from complete spatial randomness and evidence of dispersion and clustering tendencies at varying scales. Notably, MSE motor fibers exhibited clustering within the central portion of the cross section, whereas conduit regenerated motor fibers exhibited clustering along the periphery. COMPARISON WITH EXISTING METHODS: Prior exploration of fiber distributions at regenerative interfaces was limited to either quadrant-based density analysis of randomly sampled subregions or qualitative description. This method extends existing sample preparation and microscopy techniques to quantitatively assess immunolabeled fiber distributions within whole nerve cross-sections. CONCLUSIONS: This approach is an effective way to examine the spatial organization of fiber subsets at regenerative electrode interfaces, enabling robust assessment of fiber distributions relative to electrode arrangement.

Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.

The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.

Simultaneous modulation of pulse charge and burst period elicits two differentiable referred sensations.

Objective.To investigate the feasibility of delivering multidimensional feedback using a single channel of peripheral nerve stimulation by complementing intensity percepts with flutter frequency percepts controlled by burst period modulation.Approach.Two dimensions of a distally referred sensation were provided simultaneously: intensity was conveyed by the modulation of the pulse charge rate inside short discrete periods of stimulation referred to as bursts and frequency was conveyed by the modulation of the period between bursts. For this approach to be feasible, intensity percepts must be perceived independently of frequency percepts. Two experiments investigated these interactions. A series of two alternative forced choice tasks (2AFC) were used to investigate burst period modulation's role in intensity discernibility. Magnitude estimation tasks were used to determine any interactions in the gradation between the frequency and intensity percepts.Main results.The 2AFC revealed that burst periods can be individually differentiated as a gradable frequency percept in peripheral nerve stimulation. Participants could correctly rate a perceptual scale of intensity and frequency regardless of the value of the second, but the dependence of frequency differentiability on charge rate indicates that frequency was harder to detect with weaker intensity percepts. The same was not observed in intensity differentiability as the length of burst periods did not significantly alter intensity differentiation. These results suggest multidimensional encoding is a promising approach for increasing information throughput in sensory feedback systems if intensity ranges are selected properly.Significance.This study offers valuable insights into haptic feedback through the peripheral nervous system and demonstrates an encoding approach for neural stimulation that may offer enhanced information transfer in virtual reality applications and sensory-enabled prosthetic systems. This multidimensional encoding strategy for sensory feedback may open new avenues for enriched control capabilities.

Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation.

As both the proportion of older people and the length of life increases globally, a rise in age-related degenerative diseases, disability, and prolonged dependency is projected. However, more sophisticated biomedical materials, as well as an improved understanding of human disease, is forecast to revolutionize the diagnosis and treatment of conditions ranging from osteoarthritis to Alzheimer's disease as well as impact disease prevention. Another, albeit quieter, revolution is also taking place within society: human augmentation. In this context, humans seek to improve themselves, metamorphosing through self-discipline or more recently, through use of emerging medical technologies, with the goal of transcending aging and mortality. In this review, and in the pursuit of improved medical care following aging, disease, disability, or injury, we first highlight cutting-edge and emerging materials-based neuroprosthetic technologies designed to restore limb or organ function. We highlight the potential for these technologies to be utilized to augment human performance beyond the range of natural performance. We discuss and explore the growing social movement of human augmentation and the idea that it is possible and desirable to use emerging technologies to push the boundaries of what it means to be a healthy human into the realm of superhuman performance and intelligence. This potential future capability is contrasted with limitations in the right-to-repair legislation, which may create challenges for patients. Now is the time for continued discussion of the ethical strategies for research, implementation, and long-term device sustainability or repair.

Increasing the stability of electrolyte-gated organic synaptic transistors for neuromorphic implants.

Electrolyte-gated organic synaptic transistors (EGOSTs) can have versatile synaptic plasticity in a single device, so they are promising as components of neuromorphic implants that are intended for use in neuroprosthetic electronic nerves that are energy-efficient and have simple system structure. With the advancement in transistor properties of EGOSTs, the commercialization of neuromorphic implants for practical long-term use requires consistent operation, so they must be stable in vivo. This requirement demands strategies that maintain electronic and ionic transport in the devices while implanted in the human body, and that are mechanically, environmentally, and operationally stable. Here, we cover the structure, working mechanisms, and electrical responses of EGOSTs. We then focus on strategies to ensure their stability to maintain these characteristics and prevent adverse effects on biological tissues. We also highlight state-of-the-art neuromorphic implants that incorporate these strategies. We conclude by presenting a perspective on improvements that are needed in EGOSTs to develop practical, neuromorphic implants that are long-term useable.

Organ Neuroprosthetics: Connecting Transplanted and Artificial Organs with the Nervous System.

Implantable neural interfaces with the central and peripheral nervous systems are currently used to restore sensory, motor, and cognitive functions in disabled people with very promising results. They have also been used to modulate autonomic activities to treat diseases such as diabetes or hypertension. Here, this study proposes to extend the use of these technologies to (re-)establish the connection between new (transplanted or artificial) organs and the nervous system in order to increase the long-term efficacy and the effective biointegration of these solutions. In this perspective paper, some clinically relevant applications of this approach are briefly described. Then, the choices that neural engineers must implement about the type, implantation location, and closed-loop control algorithms to successfully realize this approach are highlighted. It is believed that these new "organ neuroprostheses" are going to become more and more valuable and very effective solutions in the years to come.

Gaze-contingent processing improves mobility, scene recognition and visual search in simulated head-steered prosthetic vision.

Objective.The enabling technology of visual prosthetics for the blind is making rapid progress. However, there are still uncertainties regarding the functional outcomes, which can depend on many design choices in the development. In visual prostheses with a head-mounted camera, a particularly challenging question is how to deal with the gaze-locked visual percept associated with spatial updating conflicts in the brain. The current study investigates a recently proposed compensation strategy based on gaze-contingent image processing with eye-tracking. Gaze-contingent processing is expected to reinforce natural-like visual scanning and reestablished spatial updating based on eye movements. The beneficial effects remain to be investigated for daily life activities in complex visual environments.Approach.The current study evaluates the benefits of gaze-contingent processing versus gaze-locked and gaze-ignored simulations in the context of mobility, scene recognition and visual search, using a virtual reality simulated prosthetic vision paradigm with sighted subjects.Main results.Compared to gaze-locked vision, gaze-contingent processing was consistently found to improve the speed in all experimental tasks, as well as the subjective quality of vision. Similar or further improvements were found in a control condition that ignores gaze-dependent effects, a simulation that is unattainable in the clinical reality.Significance.Our results suggest that gaze-locked vision and spatial updating conflicts can be debilitating for complex visually-guided activities of daily living such as mobility and orientation. Therefore, for prospective users of head-steered prostheses with an unimpaired oculomotor system, the inclusion of a compensatory eye-tracking system is strongly endorsed.

Merging Humans and Neuroprosthetics through Regenerative Peripheral Nerve Interfaces.

Limb amputations can be devastating and significantly affect an individual's independence, leading to functional and psychosocial challenges in nearly 2 million people in the United States alone. Over the past decade, robotic devices driven by neural signals such as neuroprostheses have shown great potential to restore the lost function of limbs, allowing amputees to regain movement and sensation. However, current neuroprosthetic interfaces have challenges in both signal quality and long-term stability. To overcome these limitations and work toward creating bionic limbs, the Neuromuscular Laboratory at University of Michigan Plastic Surgery has developed the Regenerative Peripheral Nerve Interface (RPNI). This surgical construct embeds a transected peripheral nerve into a free muscle graft, effectively amplifying small peripheral nerve signals to provide enhanced control signals for a neuroprosthetic limb. Furthermore, the RPNI has the potential to provide sensory feedback to the user and facilitate neuroprosthesis embodiment. This review focuses on the animal studies and clinical trials of the RPNI to recapitulate the promising trajectory toward neurobionics where the boundary between an artificial device and the human body becomes indistinct. This paper also sheds light on the prospects of the improvement and dissemination of the RPNI technology.

Assessing the manufacturable 32-channel cochlear electrode array: evaluation results for clinical trials.

Reliability evaluation results of a manufacturable 32-channel cochlear electrode array are reported in this paper. Applying automated laser micro-machining process and a layer-by-layer silicone deposition scheme, authors developed the manufacturing methods of the electrode array for fine patterning and mass production. The developed electrode array has been verified through the requirements specified by the ISO Standard 14708-7. And the insertion trauma of the electrode array has been evaluated based on human temporal bone studies. According to the specified requirements, the electrode array was assessed through elongation & insulation, flexural, and fatigue tests. In addition, Temporal bone study was performed using eight fresh-frozen cadaver temporal bones with the electrode arrays inserted via the round window. Following soaking in saline condition, the impedances between conducting wires of the electrode array were measured over 100 kΩ (the pass/fail criterion). After each required test, it was shown that the electrode array maintained the electrical continuity and insulation condition. The average insertion angle of the electrode array inside the scala tympani was 399.7°. The human temporal bone studies exhibited atraumatic insertion rate of 60.3% (grade 0 or 1). The reliability of the manufacturable electrode array is successfully verified in mechanical, electrical, and histological aspects. Following the completion of a 32-channel cochlear implant system, the performance and stability of the 32-channel electrode array will be evaluated in clinical trials.

Invariant neural dynamics drive commands to control different movements.

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface (BMI) that transforms rhesus macaques' motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

Uncovering and leveraging the return of voluntary motor programs after paralysis using a bi-cortical neuroprosthesis.

Rehabilitative and neuroprosthetic approaches after spinal cord injury (SCI) aim to reestablish voluntary control of movement. Promoting recovery requires a mechanistic understanding of the return of volition over action, but the relationship between re-emerging cortical commands and the return of locomotion is not well established. We introduced a neuroprosthesis delivering targeted bi-cortical stimulation in a clinically relevant contusive SCI model. In healthy and SCI cats, we controlled hindlimb locomotor output by tuning stimulation timing, duration, amplitude, and site. In intact cats, we unveiled a large repertoire of motor programs. After SCI, the evoked hindlimb lifts were highly stereotyped, yet effective in modulating gait and alleviating bilateral foot drag. Results suggest that the neural substrate underpinning motor recovery had traded-off selectivity for efficacy. Longitudinal tests revealed that the return of locomotion after SCI was correlated with recovery of the descending drive, which advocates for rehabilitation interventions directed at the cortical target.

[New perspectives in orthopedics : Developments through neurotechnology and metaverse]. / Neue Perspektiven in der Orthopädie : Entwicklungen durch Neurotechnologie und Metaversum.

The combination of neurotechnology and metaverse holds high potentials for orthopedics, as it offers a broad spectrum of possibilities to overcome the limits of traditional medical care. The vision of a medical metaverse providing the infrastructure as a link for innovative technologies opens up new opportunities for therapy, medical collaborations and practical, personalized training for aspiring physicians. However, risks and challenges, such as security and privacy, health-related issues, acceptance by patients and doctors, as well as technical hurdles and access to the technologies, remain. Hence, future research and development is paramount. Nonetheless, due to technological progress, the exploration of new research areas, and the improved availability of the technologies paired with cost reduction, the prospects for neurotechnology and metaverse in orthopedics are promising.

Human-machine interface for two-dimensional steering control with the auricular muscles.

Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be found in this direction, there is still room for improvement both from a decoding, hardware, and subject-motor learning perspective. Here we show, in a series of experiments with non-disabled participants, a novel decoding and training paradigm allowing naïve participants to use their auricular muscles (AM) to control two degrees of freedom with a virtual cursor. AMs are particularly interesting because they are vestigial muscles and are often preserved after neurological diseases. Our method relies on the use of surface electromyographic records and the use of contraction levels of both AMs to modulate the velocity and direction of a cursor in a two-dimensional paradigm. We used a locking mechanism to fix the current position of each axis separately to enable the user to stop the cursor at a certain location. A five-session training procedure (20-30 min per session) with a 2D center-out task was performed by five volunteers. All participants increased their success rate (Initial: 52.78 ± 5.56%; Final: 72.22 ± 6.67%; median ± median absolute deviation) and their trajectory performances throughout the training. We implemented a dual task with visual distractors to assess the mental challenge of controlling while executing another task; our results suggest that the participants could perform the task in cognitively demanding conditions (success rate of 66.67 ± 5.56%). Finally, using the Nasa Task Load Index questionnaire, we found that participants reported lower mental demand and effort in the last two sessions. To summarize, all subjects could learn to control the movement of a cursor with two degrees of freedom using their AM, with a low impact on the cognitive load. Our study is a first step in developing AM-based decoders for HMIs for people with motor disabilities, such as spinal cord injury.

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes.

Objective.Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon the implantation procedure and the subject's anatomy. Currently, few alternatives exist for the determination of the target subject structural and functional anatomy, and statistical characterizations from cadaveric samples are limited because of their high cost. We propose an optimization workflow that can guide both the pre-surgical planning and the determination of maximally selective multisite stimulation protocols for implants consisting of several intraneural electrodes, and we characterize its performance in silico. We show that the availability of structural and functional information leads to very high performances and allows taking informed decisions on neuroprosthetic design.Approach.We employ hybrid models (HMs) of neuromodulation in conjunction with a machine learning-based surrogate model to determine fiber activation under electrical stimulation, and two steps of optimization through particle swarm optimization to optimize in silico implant geometry, implantation and stimulation protocols using morphological data from the human median nerve at a reduced computational cost.Main results.Our method allows establishing the optimal geometry of multi-electrode transverse intra-fascicular multichannel electrode implants, the optimal number of electrodes to implant, their optimal insertion, and a set of multipolar stimulation protocols that lead in silico to selective activation of all the muscles innervated by the human median nerve.Significance.We show how to use effectively HMs for optimizing personalized neuroprostheses for motor function restoration. We provide in-silico evidences about the potential of multipolar stimulation to increase greatly selectivity. We also show that the knowledge of structural and functional anatomies of the target subject leads to very high selectivity and motivate the development of methods for theirin vivocharacterization.

An Aqueous Electrolyte Gated Artificial Synapse with Synaptic Plasticity Selectively Mediated by Biomolecules.

The emulation of functions and behaviors of biological synapses using electronic devices has inspired the development of artificial neural networks (ANNs) in biomedical interfaces. Despite the achievements, artificial synapses that can be selectively responsive to non-electroactive biomolecules and directly operate in biological environments are still lacking. Herein, we report an artificial synapse based on organic electrochemical transistors and investigate the selective modulation of its synaptic plasticity by glucose. The enzymatic reaction between glucose and glucose oxidase results in long-term modulation of the channel conductance, mimicking selective binding of biomolecules to their receptors and consequent long-term modulation of the synaptic weight. Moreover, the device shows enhanced synaptic behaviors in the blood serum at a higher glucose concentration, which suggests its potential application in vivo as artificial neurons. This work provides a step towards the fabrication of ANNs with synaptic plasticity selectively mediated by biomolecules for neuro-prosthetics and human-machine interfaces.

Peripheral nerve stimulation enables somatosensory feedback while suppressing phantom limb pain in transradial amputees.

To simultaneously treat phantom limb pain (PLP) and restore somatic sensations using peripheral nerve stimulation (PNS), two bilateral transradial amputees were implanted with stimulating electrodes in the proximity of the medial, ulnar and radial nerves. Application of PNS evoked tactile and proprioceptive sensations in the phantom hand. Both patients learned to determine the shape of invisible objects by scanning a computer tablet with a stylus while receiving feedback based on PNS or transcutaneous electrical nerve stimulation (TENS). Оne patient learned to use PNS as feedback from the prosthetic hand that grasped objects of different sizes. PNS abolished PLP completely in one patient and reduced it by 40-70% in the other. We suggest incorporating PNS and/or TENS in active tasks to reduce PLP and restore sensations in amputees.

Long-term upper-extremity prosthetic control using regenerative peripheral nerve interfaces and implanted EMG electrodes.

Objective.Extracting signals directly from the motor system poses challenges in obtaining both high amplitude and sustainable signals for upper-limb neuroprosthetic control. To translate neural interfaces into the clinical space, these interfaces must provide consistent signals and prosthetic performance.Approach.Previously, we have demonstrated that the Regenerative Peripheral Nerve Interface (RPNI) is a biologically stable, bioamplifier of efferent motor action potentials. Here, we assessed the signal reliability from electrodes surgically implanted in RPNIs and residual innervated muscles in humans for long-term prosthetic control.Main results.RPNI signal quality, measured as signal-to-noise ratio, remained greater than 15 for up to 276 and 1054 d in participant 1 (P1), and participant 2 (P2), respectively. Electromyography from both RPNIs and residual muscles was used to decode finger and grasp movements. Though signal amplitude varied between sessions, P2 maintained real-time prosthetic performance above 94% accuracy for 604 d without recalibration. Additionally, P2 completed a real-world multi-sequence coffee task with 99% accuracy for 611 d without recalibration.Significance.This study demonstrates the potential of RPNIs and implanted EMG electrodes as a long-term interface for enhanced prosthetic control.

A bi-cortical neuroprosthesis to modulate locomotion after incomplete spinal cord injury.

Neuroprosthetic strategies seek to immediately alleviate deficits and reinstate voluntary control of movement. To facilitate recovery, it is crucial to gain a comprehensive understanding of the mechanisms involved in the return of intentional movement. Nevertheless, the precise relationship between the resurgence of cortical commands and the recovery of locomotion remains somewhat elusive. In the study conducted by Duguay, Bonizzato, Delivet-Mongrain, Fortier-Lebel and Martinez, we introduced a neuroprosthesis designed to deliver precise bi-cortical stimulation in a clinically relevant contusive spinal cord injury model. We conducted experiments in both healthy and spinal cord injured cats, where we fine-tuned the timing, duration, amplitude, and site of stimulation to modulate hindlimb locomotor output. In healthy cats, we observed a wide range of motor programs. However, after spinal cord injury, the induced hindlimb movements became highly stereotyped but were effective in modulating gait and reducing bilateral foot dragging. These results suggest that the neural basis for motor recovery traded off selectivity for effectiveness. Through a series of longitudinal assessments, we found that the restoration of locomotion following spinal cord injury was closely linked to the recovery of the descending neural drive. This underscores the importance of directing rehabilitation interventions toward the cortical target. The study results are discussed in terms of their impact and limitations.