A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems

A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces

Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity through stimulation or monitor conditions by recording from the peripheral nerves. The recent growth of Machine Learning (ML) has led to the application of a wide variety of ML techniques to PNIs, especially in circumstances where the goal is classification or regression. However, the extent to which ML has been applied to PNIs or the range of suitable ML techniques has not been documented. Therefore, a scoping review was conducted to determine and understand the state of ML in the PNI field. The review searched five databases and included 63 studies after full-text review. Most studies incorporated a supervised learning approach to classify activity, with the most common algorithms being some form of neural network (artificial neural network, convolutional neural network or recurrent neural network). Unsupervised, semi-supervised and reinforcement learning (RL) approaches are currently underutilized and could be better leveraged to improve performance in this domain.

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.

Ultraconformable cuff implants for long-term bidirectional interfacing of peripheral nerves at sub-nerve resolutions

Implantable devices interfacing with peripheral nerves exhibit limited longevity and resolution. Poor nerve-electrode interface quality, invasive surgical placement and development of foreign body reaction combine to limit research and clinical application of these devices. Here, we develop cuff implants with a conformable design that achieve high-quality and stable interfacing with nerves in chronic implantation scenarios. When implanted in sensorimotor nerves of the arm in awake rats for 21 days, the devices record nerve action potentials with fascicle-specific resolution and extract from these the conduction velocity and direction of propagation. The cuffs exhibit high biocompatibility, producing lower levels of fibrotic scarring than clinically equivalent PDMS silicone cuffs. In addition to recording nerve activity, the devices are able to modulate nerve activity at sub-nerve resolution to produce a wide range of paw movements. When used in a partial nerve ligation rodent model, the cuffs identify and characterise changes in nerve C fibre activity associated with the development of neuropathic pain in freely-moving animals. The developed implantable devices represent a platform enabling new forms of fine nerve signal sensing and modulation, with applications in physiology research and closed-loop therapeutics.

Engineered Graphene Material Improves the Performance of Intraneural Peripheral Nerve Electrodes

Limb neuroprostheses aim to restore motor and sensory functions in amputated or severely nerve-injured patients. These devices use neural interfaces to record and stimulate nerve action potentials, creating a bidirectional connection with the nervous system. Most neural interfaces are based on standard metal microelectrodes. In this work, a new generation of neural interfaces which replaces metals with engineered graphene, called EGNITE, is tested. In vitro and in vivo experiments are conducted to assess EGNITE biocompatibility. In vitro tests show that EGNITE does not impact cell viability. In vivo, no significant functional decrease or harmful effects are observed. Furthermore, the foreign body reaction to the intraneural implant is similar compared to other materials previously used in neural interfaces. Regarding functionality, EGNITE devices are able to stimulate nerve fascicles, during two months of implant, producing selective muscle activation with about three times less current compared to larger microelectrodes of standard materials. CNAP elicited by electrical stimuli and ENG evoked by mechanical stimuli are recorded with high resolution but are more affected by decreased functionality over time. This work constitutes further proof that graphene-derived materials, and specifically EGNITE, is a promising conductive material of neural electrodes for advanced neuroprostheses.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

A World Update of Progress in Lower Extremity Transplantation: What's Hot and What's Not

ABSTRACT

The field of vascularized composite allotransplantation (VCA) is the new frontier of solid organ transplantation (SOT). VCA spans life-enhancing/life-changing procedures such as upper extremity, craniofacial (including eye), laryngeal, tracheal, abdominal wall, penis, and lower extremity transplants. VCAs such as uterus transplants are life giving unlike any other SOT. Of all VCAs that have shown successful intermediate- to long-term graft survival with functional and immunologic outcomes, lower extremity VCAs have remained largely underexplored. Lower extremity transplantation (LET) can offer patients with improved function compared to the use of conventional prostheses, reducing concerns of phantom limb pain and stump complications, and offer an option for eligible amputees that either fail prosthetic rehabilitation or do not adapt to prosthetics. Nevertheless, these benefits must be carefully weighed against the risks of VCA, which are not trivial, including the adverse effects of lifelong immunosuppression, extremely challenging perioperative care, and delayed nerve regeneration. There have been 5 lower extremity transplants to date, ranging from unilateral or bilateral to quadrimembral, progressively increasing in risk that resulted in fatalities in 3 of the 5 cases, emphasizing the inherent risks. The advantages of LET over prosthetics must be carefully weighed, demanding rigorous candidate selection for optimal outcomes.

An optoelectronic implantable neurostimulation platform allowing full MRI safety and optical sensing and communication

A novel programmable implantable neurostimulation platform based on photonic power transfer has been developed for various clinical applications with the main focus of being safe to use with MRI scanners. The wires usually conveying electrical current from the neurostimulator to the electrodes are replaced by optical fibers. Photovoltaic cells at the tip of the fibers convert laser light to biphasic electrical impulses together with feedback signals with 54% efficiency. Furthermore, a biocompatible, implantable and ultra-flexible optical lead was developed including custom optical fibers. The neurostimulator platform incorporates advanced signal processing and optical physiological sensing capabilities thanks to a hermetically sealed transparent nonmetallic casing. Skin transparency also allowed the development of a high-speed optical transcutaneous communication channel. This implantable neurostimulation platform was first adapted to a vagus nerve stimulator for the treatment of epilepsy. This neurostimulator has been designed to fulfill the requirements of a class III long-term implantable medical device. It has been proven compliant with standard ISO/TS10974 for 1.5 T and 3 T MRI scanners. The device poses no related threat and patients can safely undergo MRI without specific or additional precautions. Especially, the RF induced heating near the implant remains below 2 °C whatever the MRI settings used. The main features of this unique advanced neurostimulator and its architecture are presented. Its functional performance is evaluated, and results are described with a focus on optoelectronics aspects and MRI safety.

Simple dynamic cell culture system reduces recording noise in microelectrode array recordings

Microelectrode arrays (MEAs) have applications in drug discovery, toxicology, and basic research. They measure the electrophysiological response of tissue cultures to quantify changes upon exposure to biochemical stimuli. Unfortunately, manual addition of chemicals introduces significant noise in the recordings. Here, we report a simple-to-fabricate fluidic system that addresses this issue. We show that cell cultures can be successfully established in the fluidic compartment under continuous flow conditions and that the addition of chemicals introduces minimal noise in the recordings. This dynamic cell culture system represents an improvement over traditional tissue culture wells used in MEAs, facilitating electrophysiology measurements.

Graphical abstract

Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation

One of the critical factors determining the performance of neural interfaces is the electrode material used to establish electrical communication with the neural tissue, which needs to meet strict electrical, electrochemical, mechanical, biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin-film technology and its engineering to form flexible neural interfaces. The developed technology allows the fabrication of small microelectrodes (25 µm diameter) while achieving low impedance (∼25 kΩ) and high charge injection (3-5 mC cm-2). In vivo brain recording performance assessed in rodents reveals high-fidelity recordings (signal-to-noise ratio >10 dB for local field potentials), while stimulation performance assessed with an intrafascicular implant demonstrates low current thresholds (<100 µA) and high selectivity (>0.8) for activating subsets of axons within the rat sciatic nerve innervating tibialis anterior and plantar interosseous muscles. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical (12 week) and intraneural (8 week) implantation. This work describes a graphene-based thin-film microelectrode technology and demonstrates its potential for high-precision and high-resolution neural interfacing.

Fork-shaped neural interface with multichannel high spatial selectivity in the peripheral nerve of a rat

Objective.This study aims to develop and validate a sophisticated fork-shaped neural interface (FNI) designed for peripheral nerves, focusing on achieving high spatial resolution, functional selectivity, and improved charge storage capacities. The objective is to create a neurointerface capable of precise neuroanatomical analysis, neural signal recording, and stimulation.Approach.Our approach involves the design and implementation of the FNI, which integrates 32 multichannel working electrodes featuring enhanced charge storage capacities and low impedance. An insertion guide holder is incorporated to refine neuronal selectivity. The study employs meticulous electrode placement, bipolar electrical stimulation, and comprehensive analysis of induced neural responses to verify the FNI's capabilities. Stability over an eight-week period is a crucial aspect, ensuring the reliability and durability of the neural interface.Main results.The FNI demonstrated remarkable efficacy in neuroanatomical analysis, exhibiting accurate positioning of motor nerves and successfully inducing various movements. Stable impedance values were maintained over the eight-week period, affirming the durability of the FNI. Additionally, the neural interface proved effective in recording sensory signals from different hind limb areas. The advanced charge storage capacities and low impedance contribute to the FNI's robust performance, establishing its potential for prolonged use.Significance.This research represents a significant advancement in neural interface technology, offering a versatile tool with broad applications in neuroscience and neuroengineering. The FNI's ability to capture both motor and sensory neural activity positions it as a comprehensive solution for neuroanatomical studies. Moreover, the precise neuromodulation potential of the FNI holds promise for applications in advanced bionic prosthetic control and therapeutic interventions. The study's findings contribute to the evolving field of neuroengineering, paving the way for enhanced understanding and manipulation of peripheral neural functions.

Using Compound Neural Action Potentials for Functional Validation of a High-Density Intraneural Interface: A Preliminary Study

Compound nerve action potentials (CNAPs) were used as a metric to assess the stimulation performance of a novel high-density, transverse, intrafascicular electrode in rat models. We show characteristic CNAPs recorded from distally implanted cuff electrodes. Evaluation of the CNAPs as a function of stimulus current and calculation of recruitment plots were used to obtain a qualitative approximation of the neural interface's placement and orientation inside the nerve. This method avoids elaborate surgeries required for the implantation of EMG electrodes and thus minimizes surgical complications and may accelerate the healing process of the implanted subject.

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.

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.

Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review

Peripheral nerve interfaces (PNIs) are electrical systems designed to integrate with peripheral nerves in patients, such as following central nervous system (CNS) injuries to augment or replace CNS control and restore function. We review the literature for clinical trials and studies containing clinical outcome measures to explore the utility of human applications of PNIs. We discuss the various types of electrodes currently used for PNI systems and their functionalities and limitations. We discuss important design characteristics of PNI systems, including biocompatibility, resolution and specificity, efficacy, and longevity, to highlight their importance in the current and future development of PNIs. The clinical outcomes of PNI systems are also discussed. Finally, we review relevant PNI clinical trials that were conducted, up to the present date, to restore the sensory and motor function of upper or lower limbs in amputees, spinal cord injury patients, or intact individuals and describe their significant findings. This review highlights the current progress in the field of PNIs and serves as a foundation for future development and application of PNI systems.

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.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.

Toward the Development of User-Centered Neurointegrated Lower Limb Prostheses

The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users' needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.

Recent Advancements in Graphene-Based Implantable Electrodes for Neural Recording/Stimulation

Implantable electrodes represent a groundbreaking advancement in nervous system research, providing a pivotal tool for recording and stimulating human neural activity. This capability is integral for unraveling the intricacies of the nervous system's functionality and for devising innovative treatments for various neurological disorders. Implantable electrodes offer distinct advantages compared to conventional recording and stimulating neural activity methods. They deliver heightened precision, fewer associated side effects, and the ability to gather data from diverse neural sources. Crucially, the development of implantable electrodes necessitates key attributes: flexibility, stability, and high resolution. Graphene emerges as a highly promising material for fabricating such electrodes due to its exceptional properties. It boasts remarkable flexibility, ensuring seamless integration with the complex and contoured surfaces of neural tissues. Additionally, graphene exhibits low electrical resistance, enabling efficient transmission of neural signals. Its transparency further extends its utility, facilitating compatibility with various imaging techniques and optogenetics. This paper showcases noteworthy endeavors in utilizing graphene in its pure form and as composites to create and deploy implantable devices tailored for neural recordings and stimulations. It underscores the potential for significant advancements in this field. Furthermore, this paper delves into prospective avenues for refining existing graphene-based electrodes, enhancing their suitability for neural recording applications in in vitro and in vivo settings. These future steps promise to revolutionize further our capacity to understand and interact with the neural research landscape.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Polyimide biocomposites coated with tantalum pentoxide for stimulation of cell compatibility and enhancement of biointegration for orthopedic implant

Orthopedic implants are an important tool in the treatment of musculoskeletal conditions and helped many patients to improve their quality of life. Various inorganic-organic biocomposites have been broadly investigated particularly in the area of load-bearing orthopedic/dental applications. Polyimide (PI) is a promising organic material and shows excellent mechanical properties, biocompatibility, bio-stability, and its elastic modulus is similar to human bone but it lacks bioactivity, which is very important for cell adhesion and ultimately for bone regeneration. In this research, tantalum pentoxide (Ta2O5) coating was prepared on the surface of PI by polydopamine (PDA) bonding. The results showed that Ta2O5 was evenly coated on the surface of PI, and with the concentration of Ta2O5 in the PDA suspension increased, the content of Ta2O5 particles on the surface of PI increased significantly. In addition, the Ta2O5 coating significantly increased the roughness and hydrophilicity of the PI matrix. Cell experiments showed that PI surface coating Ta2O5 could promote the proliferation, adhesion, and osteogenic differentiation of bone marrow-derived stromal cells (BMSCs). The results demonstrated that fabricating Ta2O5 coating on the surface of PI through PDA bonding could improve the biocompatibility as well as bioactivity of PI, and increase the application potential of PI in the field of bone repair materials.

Water-responsive supercontractile polymer films for bioelectronic interfaces

Connecting different electronic devices is usually straightforward because they have paired, standardized interfaces, in which the shapes and sizes match each other perfectly. Tissue-electronics interfaces, however, cannot be standardized, because tissues are soft1-3 and have arbitrary shapes and sizes4-6. Shape-adaptive wrapping and covering around irregularly sized and shaped objects have been achieved using heat-shrink films because they can contract largely and rapidly when heated7. However, these materials are unsuitable for biological applications because they are usually much harder than tissues and contract at temperatures higher than 90 °C (refs. 8,9). Therefore, it is challenging to prepare stimuli-responsive films with large and rapid contractions for which the stimuli and mechanical properties are compatible with vulnerable tissues and electronic integration processes. Here, inspired by spider silk10-12, we designed water-responsive supercontractile polymer films composed of poly(ethylene oxide) and poly(ethylene glycol)-α-cyclodextrin inclusion complex, which are initially dry, flexible and stable under ambient conditions, contract by more than 50% of their original length within seconds (about 30% per second) after wetting and become soft (about 100 kPa) and stretchable (around 600%) hydrogel thin films thereafter. This supercontraction is attributed to the aligned microporous hierarchical structures of the films, which also facilitate electronic integration. We used this film to fabricate shape-adaptive electrode arrays that simplify the implantation procedure through supercontraction and conformally wrap around nerves, muscles and hearts of different sizes when wetted for in vivo nerve stimulation and electrophysiological signal recording. This study demonstrates that this water-responsive material can play an important part in shaping the next-generation tissue-electronics interfaces as well as broadening the biomedical application of shape-adaptive materials.

Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene Biointerfaces

Severe injuries to the peripheral nervous system (PNS) require Schwann cells to aid in neuronal regeneration. Low-frequency electrical stimulation is known to induce the cogrowth of neurons and Schwann cells in an injured PNS. However, the correlations between electrical stimulation and Schwann cell viability are complex and not well understood. In this work, we develop a machine learning (ML)-integrated workflow that uses conductive hydrogel biointerfaces to evaluate the impacts of fabrication parameters and electrical stimulation on the Schwann cell viability. First, a hydrogel array with varying MXene and peptide loadings is fabricated, which serves as conductive biointerfaces to incubate Schwann cells and introduce various electrical stimulation (at different voltages and frequencies). Upon specific fabrication parameters and stimulation, the cell viability is evaluated and input into an artificial neural network model to train the model. Additionally, a data augmentation method is applied to synthesize 1000-fold virtual data points, enabling the construction of a high-accuracy prediction model (with a testing mean absolute error ≤11%). By harnessing the model's predictive power, we can accurately predict Schwann cell viability based on a given set of fabrication/stimulation parameters. Finally, the SHapley Additive exPlanations model interpretation provides several data-scientific insights that are validated by microscopic cellular observations. Our hybrid approach, involving conductive biointerface fabrication, ML algorithms, and data analysis, offers an unconventional platform to construct a preclinical prediction model at the cellular level.

A review about the role of additives in nerve tissue engineering: growth factors, vitamins, and drugs

Notably the integration of additives such as growth factors, vitamins, and drugs with scaffolds promoted nerve tissue engineering. This study tried to provide a concise review of all these additives that facilitates nerve regeneration. An attempt was first made to provide information on the main principle of nerve tissue engineering, and then to shed light on the effectiveness of these additives on nerve tissue engineering. Our research has shown that growth factors accelerate cell proliferation and survival, while vitamins play an effective role in cell signalling, differentiation, and tissue growth. They can also act as hormones, antioxidants, and mediators. Drugs also have an excellent and necessary effect on this process by reducing inflammation and immune responses. This review shows that growth factors were more effective than vitamins and drugs in nerve tissue engineering. Nevertheless, vitamins were the most commonly used additive in the production of nerve tissue.

Functional assessment of current upper limb prostheses: An integrated clinical and technological perspective

Although recent technological developments in the field of bionic upper limb prostheses, their rejection rate remains excessively high. The reasons are diverse (e.g. lack of functionality, control complexity, and comfortability) and most of these are reported only through self-rated questionnaires. Indeed, there is no quantitative evaluation of the extent to which a novel prosthetic solution can effectively address users' needs compared to other technologies. This manuscript discusses the challenges and limitations of current upper limb prosthetic devices and evaluates their functionality through a standard functional assessment, the Assessment of Capacity for Myoelectric Control (ACMC). To include a good representation of technologies, the authors collect information from participants in the Cybathlon Powered Arm Prostheses Race 2016 and 2020. The article analyzes 7 hour and 41 min of video footage to evaluate the performance of different prosthetic devices in various tasks inspired by activities of daily living (ADL). The results show that commercially-available rigid hands perform well in dexterous grasping, while body-powered solutions are more reliable and convenient for competitive environments. The article also highlights the importance of wrist design and control modality for successful execution of ADL. Moreover, we discuss the limitations of the evaluation methodology and suggest improvements for future assessments. With regard to future development, this work highlights the need for research in intuitive control of multiple degrees of freedom, adaptive solutions, and the integration of sensory feedback.

Magnetic stimulation of the sciatic nerve using an implantable high-inductance coil with low-intensity current

Objective.Magnetic stimulation using implantable devices may offer a promising alternative to other stimulation methods such as transcranial magnetic stimulation (TMS) or electric stimulation using implantable devices. This alternative may increase the selectivity of stimulation compared to TMS, and eliminate the need to expose tissue to metals in the body, as is required in electric stimulation using implantable devices. However, previous studies of magnetic stimulation of the sciatic nerve used large coils, with a diameter of several tens of mm, and a current intensity in the order of kA.Approach.Since such large coils and high current intensity are not suitable for implantable devices, we investigated the feasibility of using a smaller implantable coil and lower current to elicit neuronal responses. A coil with a diameter of 3 mm and an inductance of 1 mH was used as the implantable stimulator.Main results.Beforein vivoexperiments, we used 3D computational models to estimate the minimum stimulus intensity required to elicit neuronal responses, resulting in a threshold current above 3.5 A. Inin vivoexperiments, we observed successful nerve stimulation via compound muscle action potentials elicited in hind-limb muscles when the applied current was above 3.8 A, a significantly reduced current than that used in conventional magnetic stimulation.Significance.We report the feasibility of magnetic stimulation using an implantable millimeter-sized coil and low current of a few amperes to elicit neural responses in peripheral nerves. The proposed method is expected to be an alternative to TMS, with the merit of improved selectivity in stimulation, and to electrical stimulation based on implantable devices, with the merit of avoiding the exposure of conducting metals to neural tissues.

Highly conformable chip-in-foil implants for neural applications

Demands for neural interfaces around functionality, high spatial resolution, and longevity have recently increased. These requirements can be met with sophisticated silicon-based integrated circuits. Embedding miniaturized dice in flexible polymer substrates significantly improves their adaptation to the mechanical environment in the body, thus improving the systems' structural biocompatibility and ability to cover larger areas of the brain. This work addresses the main challenges in developing a hybrid chip-in-foil neural implant. Assessments considered (1) the mechanical compliance to the recipient tissue that allows a long-term application and (2) the suitable design that allows the implant's scaling and modular adaptation of chip arrangement. Finite element model studies were performed to identify design rules regarding die geometry, interconnect routing, and positions for contact pads on dice. Providing edge fillets in the die base shape proved an effective measure to improve die-substrate integrity and increase the area available for contact pads. Furthermore, routing of interconnects in the immediate vicinity of die corners should be avoided, as the substrate in these areas is prone to mechanical stress concentration. Contact pads on dice should be placed with a clearance from the die rim to avoid delamination when the implant conforms to a curvilinear body. A microfabrication process was developed to transfer, align, and electrically interconnect multiple dice into conformable polyimide-based substrates. The process enabled arbitrary die shape and size over independent target positions on the conformable substrate based on the die position on the fabrication wafer.

Evaluating the effect of non-invasive force feedback on prosthetic grasp force modulation in participants with and without limb loss

Grasping an object is one of the most common and complex actions performed by humans. The human brain can adapt and update the grasp dynamics through information received from sensory feedback. Prosthetic hands can assist with the mechanical performance of grasping, however currently commercially available prostheses do not address the disruption of the sensory feedback loop. Providing feedback about a prosthetic hand's grasp force magnitude is a top priority for those with limb loss. This study tested a wearable haptic system, i.e., the Clenching Upper-Limb Force Feedback device (CUFF), which was integrated with a novel robotic hand (The SoftHand Pro). The SoftHand Pro was controlled with myoelectrics of the forearm muscles. Five participants with limb loss and nineteen able-bodied participants completed a constrained grasping task (with and without feedback) which required modulation of the grasp to reach a target force. This task was performed while depriving participants of incidental sensory sources (vision and hearing were significantly limited with glasses and headphones). The data were analyzed with Functional Principal Component Analysis (fPCA). CUFF feedback improved grasp precision for participants with limb loss who typically use body-powered prostheses as well as a sub-set of able-bodied participants. Further testing, that is more functional and allows participants to use all sensory sources, is needed to determine if CUFF feedback can accelerate mastery of myoelectric control or would benefit specific patient sub-groups.

Restoration of sensory information via bionic hands

Individuals who have lost the use of their hands because of amputation or spinal cord injury can use prosthetic hands to restore their independence. A dexterous prosthesis requires the acquisition of control signals that drive the movements of the robotic hand, and the transmission of sensory signals to convey information to the user about the consequences of these movements. In this Review, we describe non-invasive and invasive technologies for conveying artificial sensory feedback through bionic hands, and evaluate the technologies' long-term prospects.

Toward higher-performance bionic limbs for wider clinical use

Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs.

Wearable High Voltage Compliant Current Stimulator for Restoring Sensory Feedback

Transcutaneous Electrical Nerve Stimulation (TENS) is a promising technique for eliciting referred tactile sensations in patients with limb amputation. Although several studies show the validity of this technique, its application in daily life and away from laboratories is limited by the need for more portable instrumentation that guarantees the necessary voltage and current requirements for proper sensory stimulation. This study proposes a low-cost, wearable high-voltage compliant current stimulator with four independent channels based on Components-Off-The-Shelf (COTS). This microcontroller-based system implements a voltage-current converter controllable through a digital-to-analog converter that delivers up to 25 mA to load up to 3.6 kΩ. The high-voltage compliance enables the system to adapt to variations in electrode-skin impedance, allowing it to stimulate loads over 10 kΩ with currents of 5 mA. The system was realized on a four-layer PCB (115.9 mm × 61 mm, 52 g). The functionality of the device was tested on resistive loads and on an equivalent skin-like RC circuit. Moreover, the possibility of implementing an amplitude modulation was demonstrated.

A flexible protruding microelectrode array for neural interfacing in bioelectronic medicine

Recording neural signals from delicate autonomic nerves is a challenging task that requires the development of a low-invasive neural interface with highly selective, micrometer-sized electrodes. This paper reports on the development of a three-dimensional (3D) protruding thin-film microelectrode array (MEA), which is intended to be used for recording low-amplitude neural signals from pelvic nervous structures by penetrating the nerves transversely to reduce the distance to the axons. Cylindrical gold pillars (Ø 20 or 50 µm, ~60 µm height) were fabricated on a micromachined polyimide substrate in an electroplating process. Their sidewalls were insulated with parylene C, and their tips were optionally modified by wet etching and/or the application of a titanium nitride (TiN) coating. The microelectrodes modified by these combined techniques exhibited low impedances (~7 kΩ at 1 kHz for Ø 50 µm microelectrode with the exposed surface area of ~5000 µm²) and low intrinsic noise levels. Their functionalities were evaluated in an ex vivo pilot study with mouse retinae, in which spontaneous neuronal spikes were recorded with amplitudes of up to 66 µV. This novel process strategy for fabricating flexible, 3D neural interfaces with low-impedance microelectrodes has the potential to selectively record neural signals from not only delicate structures such as retinal cells but also autonomic nerves with improved signal quality to study neural circuits and develop stimulation strategies in bioelectronic medicine, e.g., for the control of vital digestive functions.

Peripheral neurostimulation for encoding artificial somatosensations

The direct neural stimulation of peripheral or central nervous systems has been shown as an effective tool to treat neurological conditions. The electrical activation of the nervous sensory pathway can be adopted to restore the artificial sense of touch and proprioception in people suffering from sensory-motor disorders. The modulation of the neural stimulation parameters has a direct effect on the electrically induced sensations, both when targeting the somatosensory cortex and the peripheral somatic nerves. The properties of the artificial sensations perceived, as their location, quality and intensity are strongly dependent on the direct modulation of pulse width, amplitude and frequency of the neural stimulation. Different sensory encoding schemes have been tested in patients showing distinct effects and outcomes according to their impact on the neural activation. Here, I reported the most adopted neural stimulation strategies to artificially encode somatosensation into the peripheral nervous system. The real-time implementation of these strategies in bionic devices is crucial to exploit the artificial sensory feedback in prosthetics. Thus, neural stimulation becomes a tool to directly communicate with the human nervous system. Given the importance of adding artificial sensory information to neuroprosthetic devices to improve their control and functionality, the choice of an optimal neural stimulation paradigm could increase the impact of prosthetic devices on the quality of life of people with sensorimotor disabilities.

Examining the in vivo functionality of the Magnetically Aligned Regenerative Tissue-Engineered Electronic Nerve Interface (MARTEENI)

OBJECTIVE

Although neural-enabled prostheses have been used to restore some lost functionality in clinical trials, they have faced difficulty in achieving high degree of freedom, natural use compared to healthy limbs. This study investigated the in vivo functionality of a flexible and scalable regenerative peripheral-nerve interface suspended within a microchannel-embedded, tissue-engineered hydrogel (the Magnetically Aligned Regenerative Tissue-Engineered Electronic Nerve Interface, MARTEENI) as a potential approach to improving current issues in peripheral nerve interfaces.

APPROACH

Assembled MARTEENI devices were implanted in the gaps of severed sciatic nerves in Lewis rats. Both acute and chronic electrophysiology were recorded, and channel-isolated activity was examined. In terminal experiments, evoked activity during paw compression and stimulus response curves generated from proximal nerve stimulation were examined. Electrochemical impedance spectroscopy was performed to assess the complex impedance of recording sites during chronic data collection. Features of the foreign-body response in non-functional implants were examined using immunohistological methods.

MAIN RESULTS

Channel-isolated activity was observed in acute, chronic, and terminal experiments and showed a typically biphasic morphology with peak-to-peak amplitudes varying between 50 to 500 µV. For chronic experiments, electrophysiology was observed for 77 days post-implant. Within the templated hydrogel, regenerating axons formed minifascicles that varied in both size and axon count and were also found to surround device threads. No axons were found to penetrate the foreign-body response. Together these results suggest the MARTEENI is a promising approach for interfacing with peripheral nerves.

SIGNIFICANCE

Findings demonstrate a high likelihood that observed electrophysiological activity recorded from implanted MARTEENIs originated from neural tissue. The variation in minifascicle size seen histologically suggests that amplitude distributions observed in functional MARTEENIs may be due to a combination of individual axon and mini-compound action potentials. This study provided an assessment of a functional MARTEENI in an in vivo animal model for the first time.

Femtosecond laser hierarchical surface restructuring for next generation neural interfacing electrodes and microelectrode arrays

Long-term implantable neural interfacing devices are able to diagnose, monitor, and treat many cardiac, neurological, retinal and hearing disorders through nerve stimulation, as well as sensing and recording electrical signals to and from neural tissue. To improve specificity, functionality, and performance of these devices, the electrodes and microelectrode arrays-that are the basis of most emerging devices-must be further miniaturized and must possess exceptional electrochemical performance and charge exchange characteristics with neural tissue. In this report, we show for the first time that the electrochemical performance of femtosecond-laser hierarchically-restructured electrodes can be tuned to yield unprecedented performance values that significantly exceed those reported in the literature, e.g. charge storage capacity and specific capacitance were shown to have improved by two orders of magnitude and over 700-fold, respectively, compared to un-restructured electrodes. Additionally, correlation amongst laser parameters, electrochemical performance and surface parameters of the electrodes was established, and while performance metrics exhibit a relatively consistent increasing behavior with laser parameters, surface parameters tend to follow a less predictable trend negating a direct relationship between these surface parameters and performance. To answer the question of what drives such performance and tunability, and whether the widely adopted reasoning of increased surface area and roughening of the electrodes are the key contributors to the observed increase in performance, cross-sectional analysis of the electrodes using focused ion beam shows, for the first time, the existence of subsurface features that may have contributed to the observed electrochemical performance enhancements. This report is the first time that such performance enhancement and tunability are reported for femtosecond-laser hierarchically-restructured electrodes for neural interfacing applications.

A lightweight learning-based decoding algorithm for intraneural vagus nerve activity classification in pigs

OBJECTIVE

Bioelectronic medicine is an emerging field that aims at developing closed-loop neuromodulation protocols for the autonomic nervous system (ANS) to treat a wide range of disorders. When designing a closed-loop protocol for real time modulation of the ANS, the computational execution time and the memory and power demands of the decoding step are important factors to consider. In the context of cardiovascular and respiratory diseases, these requirements may partially explain why closed-loop clinical neuromodulation protocols that adapt stimulation parameters on patient's clinical characteristics are currently missing.

APPROACH

Here, we developed a lightweight learning-based decoder for the classification of cardiovascular and respiratory functional challenges from neural signals acquired through intraneural electrodes implanted in the cervical vagus nerve (VN) of 5 anaesthetized pigs. Our algorithm is based on signal temporal windowing, 9 handcrafted features, and Random Forest (RF) model for classification. Temporal windowing ranging from 50 ms to 1 sec, compatible in duration with cardio-respiratory dynamics, was applied to the data in order to mimic a pseudo real-time scenario.

MAIN RESULTS

We were able to achieve high balanced accuracy (BA) values over the whole range of temporal windowing duration. We identified 500 ms as the optimal temporal windowing duration for both BA values and computational execution time processing, achieving more than 86% for BA and a computational execution time of only ∼6.8 ms. Our algorithm outperformed in terms of balanced accuracy and computational execution time a state of the art decoding algorithm tested on the same dataset [1]. We found that RF outperformed other machine learning models such as Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptrons.

SIGNIFICANCE

Our approach could represent an important step towards the implementation of a closed-loop neuromodulation protocol relying on a single intraneural interface able to perform real-time decoding tasks and selective modulation of the VN.

Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of peripheral nerve interfaces, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

EMG-driven control in lower limb prostheses: a topic-based systematic review

BACKGROUND

The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts.

OBJECTIVE AND METHODOLOGIES

This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers.

RESULTS AND CONCLUSIONS

The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees.

Optimization of immune receptor-related hypersensitive cell death response assay using agrobacterium-mediated transient expression in tobacco plants

BACKGROUND

The study of the regulatory mechanisms of evolutionarily conserved Nucleotide-binding leucine-rich repeat (NLR) resistance (R) proteins in animals and plants is of increasing importance due to understanding basic immunity and the value of various crop engineering applications of NLR immune receptors. The importance of temperature is also emerging when applying NLR to crops responding to global climate change. In particular, studies of pathogen effector recognition and autoimmune activity of NLRs in plants can quickly and easily determine their function in tobacco using agro-mediated transient assay. However, there are conditions that should not be overlooked in these cell death-related assays in tobacco.

RESULTS

Environmental conditions play an important role in the immune response of plants. The system used in this study was to establish conditions for optimal hypertensive response (HR) cell death analysis by using the paired NLR RPS4/RRS1 autoimmune and AvrRps4 effector recognition system. The most suitable greenhouse temperature for growing plants was fixed at 22 °C. In this study, RPS4/RRS1-mediated autoimmune activity, RPS4 TIR domain-dependent cell death, and RPS4/RRS1-mediated HR cell death upon AvrRps4 perception significantly inhibited under conditions of 65% humidity. The HR is strongly activated when the humidity is below 10%. Besides, the leaf position of tobacco is important for HR cell death. Position #4 of the leaf from the top in 4-5 weeks old tobacco plants showed the most effective HR cell death.

CONCLUSIONS

As whole genome sequencing (WGS) or resistance gene enrichment sequencing (RenSeq) of various crops continues, different types of NLRs and their functions will be studied. At this time, if we optimize the conditions for evaluating NLR-mediated HR cell death, it will help to more accurately identify the function of NLRs. In addition, it will be possible to contribute to crop development in response to global climate change through NLR engineering.

On-chip testing of a carbon-based platform for electro-adsorption of glutamate

It is known that excessive concentrations of glutamate in the brain can cause neurotoxicity. A common approach to neutralizing this phenomenon is the use of suppressant drugs. However, excessive dependence on suppressant drugs could potentially lead to adversarial side effects, such as drug addiction. Here, we propose an alternative approach to this problem by controlling excessive amounts of glutamate ions through carbon-based, neural implant–mediated uptake. In this study, we introduce a microfluidic system that enables us to emulate the uptake of glutamate into the carbon matrix. The uptake is controlled using electrical pulses to incorporate glutamate ions into the carbon matrix through electro-adsorption. The effect of electric potential on glutamate ion uptake to control the amount of glutamate released into the microfluidic system was observed. The glutamate concentration was measured using a Ultra Violet-Visible spectrophotometer. The current setup demonstrated that a low pulsatile electric potential (0.5–1.5 V) was able to effectively govern the uptake of glutamate ions. The stimulated carbon matrix was able to decrease glutamate concentration by up to 40%. Furthermore, our study shows that these “entrapped” glutamate molecules can be effectively released upon electrical stimulation, thereby reversing the carbon electrical charge through a process called reverse uptake. A release model was used to study the profile of glutamate release from the carbon matrix at a potential of 0–1.5 V. This study showed that a burst release of glutamate was evident at an applied voltage higher than 0.5 V. Ultimately, the MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) test for cytotoxicity indicated a cell viability of more than 80% for the carbon matrix. This test demonstrates that the carbon matrix can support the proliferation of cells and has a nontoxic composition; thus, it could be accepted as a candidate material for use as neural implants.

Polysaccharide Layer-by-Layer Coating for Polyimide-Based Neural Interfaces

Implantable flexible neural interfaces (IfNIs) are capable of directly modulating signals of the central and peripheral nervous system by stimulating or recording the action potential. Despite outstanding results in acute experiments on animals and humans, their long-term biocompatibility is hampered by the effects of foreign body reactions that worsen electrical performance and cause tissue damage. We report on the fabrication of a polysaccharide nanostructured thin film as a coating of polyimide (PI)-based IfNIs. The layer-by-layer technique was used to coat the PI surface due to its versatility and ease of manufacturing. Two different LbL deposition techniques were tested and compared: dip coating and spin coating. Morphological and physiochemical characterization showed the presence of a very smooth and nanostructured thin film coating on the PI surface that remarkably enhanced surface hydrophilicity with respect to the bare PI surface for both the deposition techniques. However, spin coating offered more control over the fabrication properties, with the possibility to tune the coating's physiochemical and morphological properties. Overall, the proposed coating strategies allowed the deposition of a biocompatible nanostructured film onto the PI surface and could represent a valid tool to enhance long-term IfNI biocompatibility by improving tissue/electrode integration.

Closed-Loop Vagus Nerve Stimulation for the Treatment of Cardiovascular Diseases: State of the Art and Future Directions

The autonomic nervous system exerts a fine beat-to-beat regulation of cardiovascular functions and is consequently involved in the onset and progression of many cardiovascular diseases (CVDs). Selective neuromodulation of the brain-heart axis with advanced neurotechnologies is an emerging approach to corroborate CVDs treatment when classical pharmacological agents show limited effectiveness. The vagus nerve is a major component of the cardiac neuroaxis, and vagus nerve stimulation (VNS) is a promising application to restore autonomic function under various pathological conditions. VNS has led to encouraging results in animal models of CVDs, but its translation to clinical practice has not been equally successful, calling for more investigation to optimize this technique. Herein we reviewed the state of the art of VNS for CVDs and discuss avenues for therapeutic optimization. Firstly, we provided a succinct description of cardiac vagal innervation anatomy and physiology and principles of VNS. Then, we examined the main clinical applications of VNS in CVDs and the related open challenges. Finally, we presented preclinical studies that aim at overcoming VNS limitations through optimization of anatomical targets, development of novel neural interface technologies, and design of efficient VNS closed-loop protocols.

Bidirectional bionic limbs: a perspective bridging technology and physiology

Precise control of bionic limbs relies on robust decoding of motor commands from nerves or muscles signals and sensory feedback from artificial limbs to the nervous system by interfacing the afferent nerve pathways. Implantable devices for bidirectional communication with bionic limbs have been developed in parallel with research on physiological alterations caused by an amputation. In this perspective article, we question whether increasing our effort on bridging these technologies with a deeper understanding of amputation pathophysiology and human motor control may help to overcome pressing stalls in the next generation of bionic limbs.

Semiconducting Polymers for Neural Applications

Electronically interfacing with the nervous system for the purposes of health diagnostics and therapy, sports performance monitoring, or device control has been a subject of intense academic and industrial research for decades. This trend has only increased in recent years, with numerous high-profile research initiatives and commercial endeavors. An important research theme has emerged as a result, which is the incorporation of semiconducting polymers in various devices that communicate with the nervous system—from wearable brain-monitoring caps to penetrating implantable microelectrodes. This has been driven by the potential of this broad class of materials to improve the electrical and mechanical properties of the tissue–device interface, along with possibilities for increased biocompatibility. In this review we first begin with a tutorial on neural interfacing, by reviewing the basics of nervous system function, device physics, and neuroelectrophysiological techniques and their demands, and finally we give a brief perspective on how material improvements can address current deficiencies in this system. The second part is a detailed review of past work on semiconducting polymers, covering electrical properties, structure, synthesis, and processing.

Amputee, clinician, and regulator perspectives on current and prospective upper extremity prosthetic technologies

Existing prosthetic technologies for people with upper limb amputation are being adopted at moderate rates. Once fitted for these devices, many upper limb amputees report not using them regularly or at all. The primary aim of this study was to solicit feedback about prosthetic technology and important device design criteria from amputees, clinicians, and device regulators. We compare these perspectives to identify common or divergent priorities. Twenty-one adults with upper limb loss, 35 clinicians, and 3 regulators completed a survey on existing prosthetic technologies and a conceptual sensorimotor prosthesis driven by implanted myoelectric electrodes with sensory feedback via spinal root stimulation. The survey included questions from the Trinity Amputation and Prosthesis Experience Scale, the Disabilities of the Arm, Shoulder, and Hand, and novel questions about technology acceptance and neuroprosthetic design. User and clinician ratings of satisfaction with existing devices were similar. Amputees were most accepting of the proposed sensorimotor prosthesis (75.5% vs clinicians(68.8%), regulators(67.8%)). Stakeholders valued user-centered outcomes like individualized task goals, improved quality of life, device reliability, and user safety; regulators emphasized these last two. The results of this study provide insight into amputee, clinician, and regulator priorities to inform future upper-limb prosthetic design and clinical trial protocol development.

The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

Decoding information from the peripheral nervous system via implantable neural interfaces remains a significant challenge, considerably limiting the advancement of neuromodulation and neuroprosthetic devices. The velocity selective recording (VSR) technique has been proposed to improve the classification of neural traffic by combining temporal and spatial information through a multi-electrode cuff (MEC). Therefore, this study investigates the feasibility of using the VSR technique to characterise fibre type based on the electrically evoked compound action potentials (eCAP) propagating along the ulnar nerve of pigs in vivo. A range of electrical stimulation parameters (amplitudes of 50 μA-10 mA and pulse durations of 100 μs, 500 μs, 1000 μs, and 5000 μs) was applied on a cutaneous and a motor branch of the ulnar nerve in nine Danish landrace pigs. Recordings were made with a 14 ring MEC and a delay-and-add algorithm was used to convert the eCAPs into the velocity domain. The results revealed two fibre populations propagating along the cutaneous branch of the ulnar nerve, with mean velocities of 55 m/s and 21 m/s, while only one dominant fibre population was found for the motor branch, with a mean velocity of 63 m/s. Because of its simplicity to provide information on the fibre selectivity and direction of propagation of nerve fibres, VSR can be implemented to advance the performance of the bidirectional control of neural prostheses and bioelectronic medicine applications.

Sensorimotor integration within the primary motor cortex by selective nerve fascicle stimulation

KEY POINTS

Cortical integration of sensory inputs is crucial for dexterous movement. Short-latency somatosensory afferent inhibition of motor cortical output is typically produced by peripheral whole-nerve stimulation. We exploited intraneural multichannel electrodes used to provide sensory feedback for prosthesis control to assess whether and how selective intraneural sensory stimulation affects sensorimotor cortical circuits in humans. The activation of the primary somatosensory cortex (S1) was explored by recording scalp somatosensory evoked potentials. Sensorimotor integration was tested by measuring the inhibitory effect of the afferent stimulation on the output of the primary motor cortex (M1) generated by transcranial magnetic stimulation. We demonstrate in humans that selective intraneural sensory stimulation elicits a measurable activation of S1 and that it inhibits the output of M1 at the same time range of whole-nerve superficial stimulation.

ABSTRACT

The integration of sensory inputs in the motor cortex is crucial for dexterous movement. We recently demonstrated that a closed-loop control based on the feedback provided through intraneural multi-channel electrodes implanted in the median and ulnar nerves of a participant with upper limb amputation improved manipulation skills and increased prosthesis embodiment. Here we assessed, in the same participant, whether and how selective intraneural sensory stimulation also elicits a measurable cortical activation and affects sensorimotor cortical circuits. After estimating the activation of the primary somatosensory cortex evoked by intraneural stimulation, sensorimotor integration was investigated by testing the inhibition of primary motor cortex (M1) output to transcranial magnetic stimulation, after both intraneural and perineural stimulation. Selective sensory intraneural stimulation evoked a low-amplitude, 16 ms-latency, parietal response in the same area of the earliest component evoked by whole-nerve stimulation, compatible with fast-conducting afferent fiber activation. For the first time, we show that the same intraneural stimulation was also capable of decreasing M1 output, at the same time range of the short-latency afferent inhibition effect of whole-nerve superficial stimulation. The inhibition generated by the stimulation of channels activating only sensory fibers was stronger than the one due to intraneural or perineural stimulation of channels activating mixed fibers. We demonstrate in a human subject that the cortical sensorimotor integration inhibiting M1 output previously described after the experimental whole-nerve stimulation is present also with a more ecological selective sensory fiber stimulation. Abstract Figure: Double-sided filament electrodes (ds-FILE), bearing 16 active sites, and perineural Cuff electrodes were implanted in the median and ulnar nerve of the arm in a hand amputee (upper left panel, single nerve represented). Selectivity of stimulation (1), evoked activity in the somatosensory cortex (2), and sensorimotor integration (3) were investigated. TMS: transcranial magnetic stimulation. This article is protected by copyright. All rights reserved.

Mangled extremity- Modern concepts in treatment

A mangled extremity is the most devastating limb injury and presents a challenge for the orthopedic surgeon. There are two main treatment options, reconstruction or amputation, but sometimes indications for either are not clear. There are many pro and contra arguments for both options. To make the decision easier numerous score systems have been introduced, but the final decision is based on the judgment and experience of the treating surgeon. Early extremity reconstruction appears to give better results than delayed or late reconstruction and should be the treatment of choice where possible. The goal in reconstruction of a lower extremity is to restore and maintain balance and ambulation, while restoration of an upper extremity's numerous functions is more demanding. In this paper the authors describe and suggest treatment approaches in patients with a severely mangled extremity, including assessment and treatment of all injured tissues, using defined protocols, with special attention to bone stabilization, revascularization, soft-tissue coverage and nerve reconstruction. These have a great impact on the outcome and function of the injured extremity. Rehabilitation and return to the preinjury level is slow and sometimes uncertain.