Characterizing the reduction of stimulation artifact noise in a tripolar nerve cuff electrode by application of a conductive shield layer

Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters

BACKGROUND

Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve.

METHODS

We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve.

RESULTS

Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers.

CONCLUSIONS

Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.

A feasibility study on AI-controlled closed-loop electrical stimulation implants

Miniaturized electrical stimulation (ES) implants show great promise in practice, but their real-time control by means of biophysical mechanistic algorithms is not feasible due to computational complexity. Here, we study the feasibility of more computationally efficient machine learning methods to control ES implants. For this, we estimate the normalized twitch force of the stimulated extensor digitorum longus muscle on n = 11 Wistar rats with intra- and cross-subject calibration. After 2000 training stimulations, we reach a mean absolute error of 0.03 in an intra-subject setting and 0.2 in a cross-subject setting with a random forest regressor. To the best of our knowledge, this work is the first experiment showing the feasibility of AI to simulate complex ES mechanistic models. However, the results of cross-subject training motivate more research on error reduction methods for this setting.

A Microclip Peripheral Nerve Interface (μcPNI) for Bioelectronic Interfacing with Small Nerves

Peripheral nerves carry sensory (afferent) and motor (efferent) signals between the central nervous system and other parts of the body. The peripheral nervous system (PNS) is therefore rich in targets for therapeutic neuromodulation, bioelectronic medicine, and neuroprosthetics. Peripheral nerve interfaces (PNIs) generally suffer from a tradeoff between selectivity and invasiveness. This work describes the fabrication, evaluation, and chronic implantation in zebra finches of a novel PNI that breaks this tradeoff by interfacing with small nerves. This PNI integrates a soft, stretchable microelectrode array with a 2-photon 3D printed microclip (μcPNI). The advantages of this μcPNI compared to other designs are: a) increased spatial resolution due to bi-layer wiring of the electrode leads, b) reduced mismatch in biomechanical properties with the nerve, c) reduced disturbance to the host tissue due to the small size, d) elimination of sutures or adhesives, e) high circumferential contact with small nerves, f) functionality under considerable strain, and g) graded neuromodulation in a low-threshold stimulation regime. Results demonstrate that the μcPNIs are electromechanically robust, and are capable of reliably recording and stimulating neural activity in vivo in small nerves. The μcPNI may also inform the development of new optical, thermal, ultrasonic, or chemical PNIs as well.

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.

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying peripheral interfacing technologies. Here, we present a microscale implantable device - the nanoclip - for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high signal-to-noise ratio recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation within the confines of the small device can achieve multi-dimensional control of a small nerve. These results highlight the potential of new microscale design and fabrication techniques for realizing viable devices for long-term peripheral interfacing.

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.

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.