Fibre-selective recording from the peripheral nerves of frogs using a multi-electrode cuff

Time series classification of multi-channel nerve cuff recordings using deep learning

Neurostimulation and neural recording are crucial to develop neuroprostheses that can restore function to individuals living with disabilities. While neurostimulation has been successfully translated into clinical use for several applications, it remains challenging to robustly collect and interpret neural recordings, especially for chronic applications. Nerve cuff electrodes offer a viable option for recording nerve signals, with long-term implantation success. However, nerve cuff electrodes' signals have low signal-to-noise ratios, resulting in reduced selectivity between neural pathways. The objective of this study was to determine whether deep learning techniques, specifically networks tailored for time series applications, can increase the recording selectivity achievable using multi-contact nerve cuff electrodes. We compared several neural network architectures, the impact and trade-off of window length on classification performance, and the benefit of data augmentation. Evaluation was carried out using a previously collected dataset of 56-channel nerve cuff recordings from the sciatic nerve of Long-Evans rats, which included afferent signals evoked using three types of mechanical stimuli. Through this study, the best model achieved an accuracy of 0.936 ± 0.084 and an F1-score of 0.917 ± 0.103, using 50 ms windows of data and an augmented training set. These results demonstrate the effectiveness of applying CNNs designed for time-series data to peripheral nerve recordings, and provide insights into the relationship between window duration and classification performance in this application.

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.

An in-vitro system for closed loop neuromodulation of peripheral nerves

Current neuromodulation research relies heavily on in-vivo animal experiments for developing novel devices and paradigms, which can be costly, time-consuming, and ethically contentious. As an alternative to this, in-vitro systems are being developed for examining explanted tissue in a controlled environment. However, these systems are typically tailored for cellular studies. Thus, this paper describes the development of an in-vitro system for electrically recording and stimulating large animal nerves. This is demonstrated experimentally using explanted pig ulnar nerves, which show evoked compound action potentials (eCAPs) when stimulated. These eCAPs were examined both in the time and velocity domain at a baseline temperature of 20° C, and at temperatures increasing up to those seen in-vivo (37°C). The results highlight that as the temperature is increased within the in-vitro system, faster conduction velocities (CVs) similar to those present in-vivo can be observed. To our knowledge, this is the first time an in-vitro peripheral nerve system has been validated against in-vivo data, which is crucial for promoting more widespread adoption of such systems for the optimisation of neural interfaces.

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.

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.

Classification of directionally specific vagus nerve activity using an upper airway obstruction model in anesthetized rodents

Electrical signals from the peripheral nervous system have the potential to provide the necessary motor, sensory or autonomic information for implementing closed-loop control of neuroprosthetic or neuromodulatory systems. However, developing methods to recover information encoded in these signals is a significant challenge. Our goal was to test the feasibility of measuring physiologically generated nerve action potentials that can be classified as sensory or motor signals. A tetrapolar recording nerve cuff electrode was used to measure vagal nerve (VN) activity in a rodent model of upper airway obstruction. The effect of upper airway occlusions on VN activity related to respiration (RnP) was calculated and compared for 4 different cases: (1) intact VN, (2) VN transection only proximal to recording electrode, (3) VN transection only distal to the recording electrode, and (4) transection of VN proximal and distal to electrode. We employed a Support Vector Machine (SVM) model with Gaussian Kernel to learn a model capable of classifying efferent and afferent waveforms obtained from the tetrapolar electrode. In vivo results showed that the RnP values decreased significantly during obstruction by 91.7% ± 3.1%, and 78.2% ± 3.4% for cases of intact VN or proximal transection, respectively. In contrast, there were no significant changes for cases of VN transection at the distal end or both ends of the electrode. The SVM model yielded an 85.8% accuracy in distinguishing motor and sensory signals. The feasibility of measuring low-noise directionally-sensitive neural activity using a tetrapolar nerve cuff electrode along with the use of an SVM classifier was shown. Future experimental work in chronic implant studies is needed to support clinical translatability.

Compliant peripheral nerve interfaces

Peripheral nerve interfaces (PNIs) record and/or modulate neural activity of nerves, which are responsible for conducting sensory-motor information to and from the central nervous system, and for regulating the activity of inner organs. PNIs are used both in neuroscience research and in therapeutical applications such as precise closed-loop control of neuroprosthetic limbs, treatment of neuropathic pain and restoration of vital functions (e.g. breathing and bladder management). Implantable interfaces represent an attractive solution to directly access peripheral nerves and provide enhanced selectivity both in recording and in stimulation, compared to their non-invasive counterparts. Nevertheless, the long-term functionality of implantable PNIs is limited by tissue damage, which occurs at the implant-tissue interface, and is thus highly dependent on material properties, biocompatibility and implant design. Current research focuses on the development of mechanically compliant PNIs, which adapt to the anatomy and dynamic movements of nerves in the body thereby limiting foreign body response. In this paper, we review recent progress in the development of flexible and implantable PNIs, highlighting promising solutions related to materials selection and their associated fabrication methods, and integrated functions. We report on the variety of available interface designs (intraneural, extraneural and regenerative) and different modulation techniques (electrical, optical, chemical) emphasizing the main challenges associated with integrating such systems on compliant substrates.

Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.

Simulating bidirectional peripheral neural interfaces in EIDORS

Bioelectronic neural interfaces that deliver adaptive therapeutic stimulation in an intelligent manner must be able to sense and stimulate activity within the same nerve. Existing minimally-invasive peripheral neural interfaces can provide a read-out of the aggregate level of activity via electrical recordings of nerve activity, but these recordings are limited in terms of their specificity. Computational simulations can provide fine-grained insight into the contributions of different neural populations to the extracellular recording, but integration of the signals from individual nerve fibers requires knowledge of spread of current in the complex (heterogenous, anisotropic) extracellular space. We have developed a model which uses the open-source EIDORS package for extracellular stimulation and recording in the pelvic nerve. The pelvic nerve is the primary source of autonomic innervation to the pelvic organs, and a prime target for electrical stimulation to treat a variety of voiding disorders. We simulated recordings of spontaneous and electrically-evoked activity using biophysical models for myelinated and unmyelinated axons. As expected, stimulus thresholds depended strongly on both fibre type and electrode-fibre distance. In conclusion, EIDORS can be used to accurately simulate extracellular recording in complex, heterogenous neural geometries.

Array processing of neural signals recorded from the peripheral nervous system for the classification of action potentials

BACKGROUND

Recording from the peripheral nervous system is key in the development of implantable neural interfaces. Despite a long history of using implantable electrodes for neuro-stimulation, it is difficult to make recordings from the nerves as signal amplitudes are often too small to be detected. Methods exist that are suitable for recording evoked potentials, but these require artificial stimulation of the nerve and thus have limited use in implanted neural interfaces.

NEW METHOD

In order to address these issues new methods are developed to analyse spontaneously occurring action potentials by extending an approach called velocity selective recording, which uses longitudinally spaced electrodes to record action potentials as they propagate. The new methods using image processing techniques to automatically identify and classify action potentials without any prior knowledge of their morphology.

RESULTS

Simulations are developed to test the methods, and a detailed experimental validation is performed using in-vivo recordings from the L5 dorsal rootlet of rat. Results show that this new approach can discriminate action potentials from both simulated and real recordings and the experimental validation demonstrates an ability to detect dermal stimulation by changes in the firing patterns of different axons.

COMPARISON TO EXISTING METHODS

This framework, unlike existing methods, is intrinsically suitable for recordings of spontaneous neural activity. Further it improves upon both the computational complexity and the overall performance of existing methods.

CONCLUSION

It is possible to perform on-line discrimination and identification of action potentials without any prior knowledge of their morphology using new image processing inspired methods.

The Effects of the Presence of Multiple Conduction Velocities in the Analysis of Electrically-Evoked Compound Action Potentials (eCAPs)

New methods for the analysis of electrically-evoked compound action potentials (eCAPs) are described. Mammalian nerves tend to have broad multi-modal distributions of fibre diameters, which translates into a spread of conduction velocities. The method of velocity selective recording (VSR) is unable to distinguish between this spectral spread and the transfer function of the system. The concept of the velocity impulse function (VIF) is introduced as a tool to differentiate between these signal and system attributes. The new methods enable separate estimates of velocity spectral broadening and signal-to-noise ratio (SNR) to be obtained.

Feasibility of differentially measuring afferent and efferent neural activity with a single nerve cuff electrode

OBJECTIVE

Advances in electrode technology have facilitated the development of neuroprostheses for restoring motor/sensory function in disabled individuals. Information extracted from a whole nerve, recorded using cuffs, can provide signals that control the operation of neuroprostheses. However, the amount of information that can be extracted from a tripolar cuff-which provides the highest signal-to-noise ratio (SNR)-is limited. The physical symmetry of the tripolar cuff results in neural recordings that cannot differentiate afferent versus efferent signals. In this study, we introduced a tetrapolar cuff to achieve low-noise and directionally sensitive recording.

APPROACH

The tetrapolar cuff was initially designed using a computational approach. A finite element model was used to solve the electric potential generated at the electrode contacts by active electrical sources, such as the nodes of Ranvier and an artifact noise source. The resulting single fiber action potentials (SFAPs) and artifact noise signals (ANS) were used to characterize the performance of the tetrapolar configuration of the electrode length (EL) and electrode edge length (EEL) on simulated SFAP and ANS. The feasibility of using a tetrapolar cuff to differentiate afferent/efferent action potentials by applying potassium chloride in anesthetized rats was also investigated.

MAIN RESULTS

Both the computational and experimental results of this study indicated that directional recording can be achieved using a tetrapolar cuff. Testing different design criteria (e.g. EL and EEL) showed that at EL values above 15 mm and EEL  ⩾  2 mm, the tetrapolar cuffs can yield larger SNRs than equally-sized tripolar cuffs.

SIGNIFICANCE

This study indicated that low-noise directionally sensitive measurement of neural activity can be achieved with a tetrapolar cuff. The experimental results confirmed the feasibility of using a tetrapolar cuff to differentiate afferent/efferent signals by applying potassium chloride. Further work is needed to determine whether the tetrapolar cuff can differentiate afferent/efferent physiologically elicited neural activities.

A review for the peripheral nerve interface designer

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

Classification of naturally evoked compound action potentials in peripheral nerve spatiotemporal recordings

Peripheral neural signals have the potential to provide the necessary motor, sensory or autonomic information for robust control in many neuroprosthetic and neuromodulation applications. However, developing methods to recover information encoded in these signals is a significant challenge. We introduce the idea of using spatiotemporal signatures extracted from multi-contact nerve cuff electrode recordings to classify naturally evoked compound action potentials (CAP). 9 Long-Evan rats were implanted with a 56-channel nerve cuff on the sciatic nerve. Afferent activity was selectively evoked in the different fascicles of the sciatic nerve (tibial, peroneal, sural) using mechano-sensory stimuli. Spatiotemporal signatures of recorded CAPs were used to train three different classifiers. Performance was measured based on the classification accuracy, F₁-score, and the ability to reconstruct original firing rates of neural pathways. The mean classification accuracies, for a 3-class problem, for the best performing classifier was 0.686 ± 0.126 and corresponding mean F₁-score was 0.605 ± 0.212. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates found for the best classifier was 0.728 ± 0.276. The proposed method demonstrates the possibility of classifying individual naturally evoked CAPs in peripheral neural signals recorded from extraneural electrodes, allowing for more precise control signals in neuroprosthetic applications.

Neural microphysiological systems for in vitro modeling of peripheral nervous system disorders

PNS disease pathology is diverse and underappreciated. Peripheral neuropathy may result in sensory, motor or autonomic nerve dysfunction and can be induced by metabolic dysfunction, inflammatory dysfunction, cytotoxic pharmaceuticals, rare hereditary disorders or may be idiopathic. Current preclinical PNS disease research relies heavily on the use of rodent models. In vivo methods are effective but too time-consuming and expensive for high-throughput experimentation. Conventional in vitro methods can be performed with high throughput but lack the biological complexity necessary to directly model in vivo nerve structure and function. In this review, we survey in vitro PNS model systems and propose that 3D-bioengineered microphysiological nerve tissue can improve in vitro–in vivo extrapolation and expand the capabilities of in vitro PNS disease modeling.

A microfabricated nerve-on-a-chip platform for rapid assessment of neural conduction in explanted peripheral nerve fibers

Peripheral nerves are anisotropic and heterogeneous neural tissues. Their complex physiology restricts realistic in vitro models, and high resolution and selective probing of axonal activity. Here, we present a nerve-on-a-chip platform that enables rapid extracellular recording and axonal tracking of action potentials collected from tens of myelinated fibers. The platform consists of microfabricated stimulation and recording microchannel electrode arrays. First, we identify conduction velocities of action potentials traveling through the microchannel and propose a robust data-sorting algorithm using velocity selective recording. We optimize channel geometry and electrode spacing to enhance the algorithm reliability. Second, we demonstrate selective heat-induced neuro-inhibition of peripheral nerve activity upon local illumination of a conjugated polymer (P3HT) blended with a fullerene derivative (PCBM) coated on the floor of the microchannel. We demonstrate the nerve-on-a-chip platform is a versatile tool to optimize the design of implantable peripheral nerve interfaces and test selective neuromodulation techniques ex vivo.

Peripheral nerves have a complex physiology and it is therefore difficult to measure axonal activity in vitro. Here the authors make a nerve-on-a-chip platform to align peripheral nerves and permit measurement of conduction amplitude and velocity along several axons in a single experiment.

Velocity Selective Recording: A Demonstration of Effectiveness on the Vagus Nerve in Pig

Neural interfaces that can both stimulate and record from the peripheral nervous system are an important component of future bioelectronic devices. However, despite a long history of neurostimulation, there has been relatively little success in the design of a chronically implantable device for recording from peripheral nerves. This fundamental road block must be overcome if the design of advanced implantable devices is to continue. In this paper, we demonstrate the effectiveness of one method: velocity selective recording, a method that has been proposed as a tool for online neural recording that does not require training. We present results and analysis from invivo recordings made on the right vagus nerve of pig using a multiple-electrode cuff as a chronically implantable recording array.

Identification of cytokine-specific sensory neural signals by decoding murine vagus nerve activity

The nervous system maintains physiological homeostasis through reflex pathways that modulate organ function. This process begins when changes in the internal milieu (e.g., blood pressure, temperature, or pH) activate visceral sensory neurons that transmit action potentials along the vagus nerve to the brainstem. IL-1β and TNF, inflammatory cytokines produced by immune cells during infection and injury, and other inflammatory mediators have been implicated in activating sensory action potentials in the vagus nerve. However, it remains unclear whether neural responses encode cytokine-specific information. Here we develop methods to isolate and decode specific neural signals to discriminate between two different cytokines. Nerve impulses recorded from the vagus nerve of mice exposed to IL-1β and TNF were sorted into groups based on their shape and amplitude, and their respective firing rates were computed. This revealed sensory neural groups responding specifically to TNF and IL-1β in a dose-dependent manner. These cytokine-mediated responses were subsequently decoded using a Naive Bayes algorithm that discriminated between no exposure and exposures to IL-1β and TNF (mean successful identification rate 82.9 ± 17.8%, chance level 33%). Recordings obtained in IL-1 receptor-KO mice were devoid of IL-1β-related signals but retained their responses to TNF. Genetic ablation of TRPV1 neurons attenuated the vagus neural signals mediated by IL-1β, and distal lidocaine nerve block attenuated all vagus neural signals recorded. The results obtained in this study using the methodological framework suggest that cytokine-specific information is present in sensory neural signals within the vagus nerve.

First demonstration of velocity selective recording from the pig vagus using a nerve cuff shows respiration afferents

Neural interfaces have great potential to treat disease and disability by modulating the electrical signals within the nervous system. However, whilst neural stimulation is a well-established technique, current neural interfaces are limited by poor recording ability. Low signal amplitudes necessitate the use of highly invasive techniques that divide or penetrate the nerve, and as such are unsuitable for chronic implantation. In this paper, we present the first application of the velocity selective recording technique to the detection of respiration activity in the vagus nerve, which is involved with treatments for epilepsy, depression, and rheumatoid arthritis. Further, we show this using a chronically implantable interface that does not divide the nerve. We also validate our recording setup using electrical stimulation and we present an analysis of the recorded signal amplitudes. The recording interface was formed from a cuff containing ten electrodes implanted around the intact right vagus nerve of a Danish Landrace pig. Nine differential amplifiers were connected to adjacent electrodes, and the resulting signals were processed to discriminate neural activity based on conduction velocity. Despite the average single channel signal-to-noise ratio of - 5.8 dB, it was possible to observe distinct action potentials travelling in both directions along the nerve. Further, contrary to expectation given the low signal-to-noise ratio, we have shown that it was possible to identify afferent neural activity that encoded respiration. The significance of this is the demonstration of a chronically implantable method for neural recording, a result that will transform the capabilities of future neuroprostheses.

Microneedle cuff electrodes for extrafascicular peripheral nerve interfacing

OBJECTIVE

The work presented here describes a new tool for peripheral nerve interfacing, called the microneedle cuff (μN-cuff) electrode.

APPROACH

μN arrays are designed and integrated into cuff electrodes for penetrating superficial tissues while remaining non-invasive to delicate axonal tracts.

MAIN RESULTS

In acute testing, the presence of 75 μm height μNs decreased the electrode-tissue interface impedance by 0.34 kΩ, resulting in a 0.9 mA reduction in functional stimulation thresholds and increased the signal-to-noise ratio by 9.1 dB compared to standard (needle-less) nerve cuff electrodes. Preliminary acute characterization suggests that μN-cuff electrodes provide the stability and ease of use of standard cuff electrodes while enhancing electrical interfacing characteristics.

SIGNIFICANCE

The ability to stimulate, block, and record peripheral nerve activity with greater specificity, resolution, and fidelity can enable more precise spatiotemporal control and measurement of neural circuits.

In vitro multichannel single-unit recordings of action potentials from mouse sciatic nerve

Electrode arrays interfacing with peripheral nerves are essential for neuromodulation devices targeting peripheral organs to relieve symptoms. To modulate (i.e., single-unit recording and stimulating) individual peripheral nerve axons remains a technical challenge. Here, we report an in vitro setup to allow simultaneous single-unit recordings from multiple mouse sciatic nerve axons. The sciatic nerve (~30 mm) was harvested and transferred to a tissue chamber, the ~5mm distal end pulled into an adjacent recording chamber filled with paraffin oil. A custom-built multi-wire electrode array was used to interface with split fine nerve filaments. Single-unit action potentials were evoked by electrical stimulation and recorded from 186 axons, of which 49.5% were classed A-type with conduction velocities (CV) greater than 1 m/s and 50.5% were C-type (CV < 1 m/s). The single-unit recordings had no apparent bias towards A- or C-type axons, were robust and repeatable for over 60 minutes, and thus an ideal opportunity to assess different neuromodulation strategies targeting peripheral nerves. For instance, ultrasonic modulation of action potential transmission was assessed using the setup, indicating increased nerve conduction velocity following ultrasound stimulus. This setup can also be used to objectively assess the design of next-generation electrode arrays interfacing with peripheral nerves.

Optimizing the design of bipolar nerve cuff electrodes for improved recording of peripheral nerve activity

OBJECTIVE

Differential measurement of efferent and afferent peripheral nerve activity offers a promising means of improving the clinical utility of implantable neuroprostheses. The tripolar nerve cuff electrode has historically served as the gold standard for achieving high signal-to-noise ratios (SNRs) of the recordings. However, the symmetrical geometry of this electrode array (i.e. electrically-shorted side contacts) precludes it from measuring electrical signals that can be used to obtain directional information. In this study, we investigated the feasibility of using a bipolar nerve cuff electrode to achieve high-SNR of peripheral nerve activity.

APPROACH

A finite element model was implemented to investigate the effects of electrode design parameters-electrode length, electrode edge length (EEL), and a conductive shielding layer (CSL)-on simulated single fiber action potentials (SFAP) and also artifact noise signals (ANS).

MAIN RESULTS

Our model revealed that the EEL was particularly effective in increasing the peak-to-peak amplitude of the SFAP (319%) and reducing the common mode ANS (67%) of the bipolar cuff electrode. By adding a CSL to the bipolar cuff electrode, the SNR was found to be 65.2% greater than that of a conventional tripolar cuff electrode. In vivo experiments in anesthetized rats confirmed that a bipolar cuff electrode can achieve a SNR that is 38% greater than that achieved by a conventional tripolar cuff electrode (p  <  0.05).

SIGNIFICANCE

The current study showed that bipolar nerve cuff electrodes can be designed to achieve SNR levels that are comparable to that of tripolar configuration. Further work is needed to confirm that these bipolar design parameters can be used to record bi-directional neural activity in a physiological setting.

An implantable ENG detector with in-system velocity selective recording (VSR) capability

Detection and classification of electroneurogram (ENG) signals in the peripheral nervous system can be achieved by velocity selective recording (VSR) using multi-electrode arrays. This paper describes an implantable VSR-based ENG recording system representing a significant development in the field since it is the first system of its type that can record naturally evoked ENG and be interfaced wirelessly using a low data rate transcutaneous link. The system consists of two CMOS ASICs one of which is placed close to the multi-electrode cuff array (MEC), whilst the other is mounted close to the wireless link. The digital ASIC provides the signal processing required to detect selectively ENG signals based on velocity. The design makes use of an original architecture that is suitable for implantation and reduces the required data rate for transmission to units placed outside the body. Complete measured electrical data from samples of the ASICs are presented that show that the system has the capability to record signals of amplitude as low as 0.5 μV, which is adequate for the recording of naturally evoked ENG. In addition, measurements of electrically evoked ENG from the explanted sciatic nerves of Xenopus Laevis frogs are presented.

A phantom axon setup for validating models of action potential recordings

Electrode designs and strategies for electroneurogram recordings are often tested first by computer simulations and then by animal models, but they are rarely implanted for long-term evaluation in humans. The models show that the amplitude of the potential at the surface of an axon is higher in front of the nodes of Ranvier than at the internodes; however, this has not been investigated through in vivo measurements. An original experimental method is presented to emulate a single fiber action potential in an infinite conductive volume, allowing the potential of an axon to be recorded at both the nodes of Ranvier and the internodes, for a wide range of electrode-to-fiber radial distances. The paper particularly investigates the differences in the action potential amplitude along the longitudinal axis of an axon. At a short radial distance, the action potential amplitude measured in front of a node of Ranvier is two times larger than in the middle of two nodes. Moreover, farther from the phantom axon, the measured action potential amplitude is almost constant along the longitudinal axis. The results of this new method confirm the computer simulations, with a correlation of 97.6 %.

Implantable neurotechnologies: a review of integrated circuit neural amplifiers

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

Implantable neurotechnologies: a review of micro- and nanoelectrodes for neural recording

Electrodes serve as the first critical interface to the biological organ system. In neuroprosthetic applications, for example, electrodes interface to the tissue for either signal recording or tissue stimulation. In this review, we consider electrodes for recording neural activity. Recording electrodes serve as wiretaps into the neural tissues, providing readouts of electrical activity. These signals give us valuable insights into the organization and functioning of the nervous system. The recording interfaces have also shown promise in aiding treatment of motor and sensory disabilities caused by neurological disorders. Recent advances in fabrication technology have generated wide interest in creating tiny, high-density electrode interfaces for neural tissues. An ideal electrode should be small enough and be able to achieve reliable and conformal integration with the structures of the nervous system. As a result, the existing electrode designs are being shrunk and packed to form small form factor interfaces to tissue. Here, an overview of the historic and state-of-the-art electrode technologies for recording neural activity is presented first with a focus on their development road map. The fact that the dimensions of recording electrode sites are being scaled down from micron to submicron scale to enable dense interfaces is appreciated. The current trends in recording electrode technologies are then reviewed. Current and future considerations in electrode design, including the use of inorganic nanostructures and biologically inspired or biocomapatible materials are discussed, along with an overview of the applications of flexible materials and transistor transduction schemes. Finally, we detail the major technical challenges facing chronic use of reliable recording electrode technology.

Chronic multichannel neural recordings from soft regenerative microchannel electrodes during gait

Reliably interfacing a nerve with an electrode array is one of the approaches to restore motor and sensory functions after an injury to the peripheral nerve. Accomplishing this with current technologies is challenging as the electrode-neuron interface often degrades over time, and surrounding myoelectric signals contaminate the neuro-signals in awake, moving animals. The purpose of this study was to evaluate the potential of microchannel electrode implants to monitor over time and in freely moving animals, neural activity from regenerating nerves. We designed and fabricated implants with silicone rubber and elastic thin-film metallization. Each implant carries an eight-by-twelve matrix of parallel microchannels (of 120 × 110 μm² cross-section and 4 mm length) and gold thin-film electrodes embedded in the floor of ten of the microchannels. After sterilization, the soft, multi-lumen electrode implant is sutured between the stumps of the sciatic nerve. Over a period of three months and in four rats, the microchannel electrodes recorded spike activity from the regenerating sciatic nerve. Histology indicates mini-nerves formed of axons and supporting cells regenerate robustly in the implants. Analysis of the recorded spikes and gait kinematics over the ten-week period suggests firing patterns collected with the microchannel electrode implant can be associated with different phases of gait.

Technology modules from micro- and nano-electronics for the life sciences

The capabilities of modern semiconductor manufacturing offer remarkable possibilities to be applied in life science research as well as for its commercialization. In this review, the technology modules available in micro- and nano-electronics are exemplarily presented for the case of 250 and 130 nm technology nodes. Preparation procedures and the different transistor types as available in complementary metal-oxide-silicon devices (CMOS) and BipolarCMOS (BiCMOS) technologies are introduced as key elements of comprehensive chip architectures. Techniques for circuit design and the elements of completely integrated bioelectronics systems are outlined. The possibility for life scientists to make use of these technology modules for their research and development projects via so-called multi-project wafer services is emphasized. Various examples from diverse fields such as (1) immobilization of biomolecules and cells on semiconductor surfaces, (2) biosensors operating by different principles such as affinity viscosimetry, impedance spectroscopy, and dielectrophoresis, (3) complete systems for human body implants and monitors for bioreactors, and (4) the combination of microelectronics with microfluidics either by chip-in-polymer integration as well as Si-based microfluidics are demonstrated from joint developments with partners from biotechnology and medicine. WIREs Nanomed Nanobiotechnol 2016, 8:355-377. doi: 10.1002/wnan.1367 For further resources related to this article, please visit the WIREs website.

Programmable ExG biopotential front-end IC for wearable applications

This paper presents a configurable CMOS integrated circuit front-end for the recording of a wide range of biopotentials (ExG). The system offers a choice between a single-differential or double-differential recording channel topology, wide continuously adjustable gain range (37-66 dB), selectable CMOS or BJT input stages, offset compensation, differential and buffered single-ended voltage output. Measured results from a prototype manufactured in 0.35 μm CMOS technology are presented. Practical recording examples of the electrocardiogram (ECG) and electromyogram (EMG) confirm its operation. The chip consumes between 110 and 324 μW depending on configuration, occupies a core area of 0.16 mm(2), achieves a CMRR > 97 dB , and 21 nV/√Hz input-referred noise. The chip is suited for combination with a microcontroller in long-term wearable physiological sensing applications.

Improved Signal Processing Methods for Velocity Selective Neural Recording Using Multi-Electrode Cuffs

This paper describes an improved system for obtaining velocity spectral information from electroneurogram recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods (`delay-and-add') with and without additive noise. By contrast to earlier linear methods, the present paper adopts a fundamentally non-linear velocity classification approach based on a type of artificial neural network (ANN). The new method provides a unified approach to the solution of the two main problems of the earlier delay-and-add technique, i.e., a damaging decline in both velocity selectivity and velocity resolution at high velocities. The new method can operate in real-time, is shown to be robust in the presence of noise and also to be relatively insensitive to the form of the action potential waveforms being classified.

A device for emulating cuff recordings of action potentials propagating along peripheral nerves

This paper describes a device that emulates propagation of action potentials along a peripheral nerve, suitable for reproducible testing of bio-potential recording systems using nerve cuff electrodes. The system is a microcontroller-based stand-alone instrument which uses established nerve and electrode models to represent neural activity of real nerves recorded with a nerve cuff interface, taking into consideration electrode impedance, voltages picked up by the electrodes, and action potential propagation characteristics. The system emulates different scenarios including compound action potentials with selectable propagation velocities and naturally occurring nerve traffic from different velocity fiber populations. Measured results from a prototype implementation are reported and compared with in vitro recordings from Xenopus Laevis frog sciatic nerve, demonstrating that the electrophysiological setting is represented to a satisfactory degree, useful for the development, optimization and characterization of future recording systems.