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

Ex-vivo systems for neuromodulation: A comparison of ex-vivo and in-vivo large animal nerve electrophysiology

BACKGROUND

Little research exists on extending ex-vivo systems to large animal nerves, and to the best of our knowledge, there has yet to be a study comparing these against in-vivo data. This paper details the first ex-vivo system for large animal peripheral nerves to be compared with in-vivo results.

NEW METHOD

Detailed ex-vivo and in-vivo closed-loop neuromodulation experiments were conducted on pig ulnar nerves. Temperatures from 20 °C to 37 °C were evaluated for the ex-vivo system. The data were analysed in the time and velocity domains, and a regression analysis established how evoked compound action potential amplitude and modal conduction velocity (CV) varied with temperature and time after explantation.

MAIN RESULTS

Pig ulnar nerves were sustained ex-vivo up to 5 h post-explantation. CV distributions of ex-vivo and in-vivo data were compared, showing closer correspondence at 37 °C. Regression analysis results also demonstrated that modal CV and time since explantation were negatively correlated, whereas modal CV and temperature were positively correlated.

COMPARISON WITH EXISTING METHODS

Previous ex-vivo systems were primarily aimed at small animal nerves, and we are not aware of an ex-vivo system to be directly compared with in-vivo data. This new approach provides a route to understand how ex-vivo systems for large animal nerves can be developed and compared with in-vivo data.

CONCLUSION

The proposed ex-vivo system results were compared with those seen in-vivo, providing new insights into large animal nerve activity post-explantation. Such a system is crucial for complementing in-vivo experiments, maximising collected experimental data, and accelerating neural interface development.

Ladder-based resistance training with the progression of training load altered the tibial nerve ultrastructure and muscle fiber area without altering the morphology of the postsynaptic compartment

Scientific evidence regarding the effect of different ladder-based resistance training (LRT) protocols on the morphology of the neuromuscular system is scarce. Therefore, the present study aimed to compare the morphological response induced by different LRT protocols in the ultrastructure of the tibial nerve and morphology of the motor endplate and muscle fibers of the soleus and plantaris muscles of young adult Wistar rats. Rats were divided into groups: sedentary control (control, n = 9), a predetermined number of climbs and progressive submaximal intensity (fixed, n = 9), high-intensity and high-volume pyramidal system with a predetermined number of climbs (Pyramid, n = 9) and lrt with a high-intensity pyramidal system to exhaustion (failure, n = 9). myelinated fibers and myelin sheath thickness were statistically larger in pyramid, fixed, and failure. myelinated axons were statistically larger in pyramid than in control. schwann cell nuclei were statistically larger in pyramid, fixed, and failure. microtubules and neurofilaments were greater in pyramid than in control. morphological analysis of the postsynaptic component of the plantar and soleus muscles did not indicate any significant difference. for plantaris, the type i myofibers were statistically larger in the pyramid and fixed compared to control. the pyramid, fixed, and failure groups for type ii myofibers had larger csa than control. for soleus, the type i myofibers were statistically larger in the pyramid than in control. pyramid and fixed had larger csa for type ii myofibers than control and failure. the pyramid and fixed groups showed greater mass progression delta than the failure. We concluded that the LRT protocols with greater volume and progression of accumulated mass elicit more significant changes in the ultrastructure of the tibial nerve and muscle hypertrophy without endplate changes.

Selective recording of physiologically evoked neural activity in a mixed autonomic nerve using a minimally invasive array

Real-time closed-loop control of neuromodulation devices requires long-term monitoring of neural activity in the peripheral nervous system. Although many signal extraction methods exist, few are both clinically viable and designed for extracting small signals from fragile peripheral visceral nerves. Here, we report that our minimally invasive recording and analysis technology extracts low to negative signal to noise ratio (SNR) neural activity from a visceral nerve with a high degree of specificity for fiber type and class. Complex activity was recorded from the rat pelvic nerve that was physiologically evoked during controlled bladder filling and voiding, in an extensively characterized in vivo model that provided an excellent test bed to validate our technology. Urethane-anesthetized male rats (n = 12) were implanted with a four-electrode planar array and the bladder instrumented for continuous-flow cystometry, which measures urodynamic function by recording bladder pressure changes during constant infusion of saline. We demonstrated that differential bipolar recordings and cross-correlation analyses extracts afferent and efferent activity, and discriminated between subpopulations of fibers based on conduction velocity. Integrated Aδ afferent fiber activity correlated with bladder pressure during voiding (r2: 0.66 ± 0.06) and was not affected by activating nociceptive afferents with intravesical capsaicin (r2: 0.59 ± 0.14, P = 0.54, and n = 3). Collectively, these results demonstrate our minimally invasive recording and analysis technology is selective in extracting mixed neural activity with low/negative SNR. Furthermore, integrated afferent activity reliably correlates with bladder pressure and is a promising first step in developing closed-loop technology for bladder control.

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh-Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.

A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs

Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable classification based on fibre type. The maximum likelihood estimation method also measures velocity and is frequently used in electromyography but has never been applied to electroneurography. Therefore, this study compares the two algorithms using in-vivo recordings of electrically evoked compound action potentials from the ulnar nerve of a pig. The performance of these algorithms was assessed using the velocity quality factor (Q-factor), computational time and the influence of the number of channels. The results show that the performance of both algorithms is significantly influenced by the number of channels in the recording array, with accuracies ranging from 77% with only two channels to 98% for 11 channels. Both algorithms were comparable in accuracy and Q-factor for all channels, with the delay-and-add having a slight advantage in the Q-factor.

Pig Ulnar Nerve Recording with Sinusoidal and Temporal Interference Stimulation

Temporal interference stimulation has been suggested as a method to reach deep targets during transcutaneous electrical stimulation. Despite its growing use in transcutaneous stimulation therapies, the mechanism of its operation is not fully understood. Recent efforts to fill that gap have focused on computational modelling, in vitro and in vivo experiments relying on physical observations - e.g., sensation or movement. This paper expands the current range of experimental methods by demonstrating in vivo extraneural recordings from the ulnar nerve of a pig while applying temporal interference stimulation at a location targeting a distal part of the nerve. The main aim of the experiment was to compare neural activation using sinusoidal stimulation (100 Hz, 2 kHz, 4 kHz) and temporal interference stimulation (2 kHz and 4 kHz). The recordings showed a significant increase in the magnitude of stimulation artefacts at higher frequencies. While those artefacts could be removed and provided an indication of the depth of modulation, they resulted in the saturation of the amplifiers, limiting the stimulation currents and amplifier gains used. The results of the 100 Hz sine wave stimulation showed clear neural activity correlated to the stimulation waveform. However, this was not observed with temporal interference stimulation. The results suggest that, despite its greater penetration, higher currents might be required to observe a neural response with temporal interference stimulation, and more complex artefact rejection techniques may be required to validate the method.

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.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

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.

Ladder-based resistance training elicited similar ultrastructural adjustments in forelimb and hindlimb peripheral nerves of young adult Wistar rats

To analyze the morphological response induced by high-volume, high-intensity ladder-based resistance training (LRT) on the ultrastructure of the radial (forelimb) and sciatic (hindlimb) nerves of adults Wistar rats. Twenty rats were equally distributed into groups: sedentary (SED) and LRT. After the rodents were subjected to the maximum load (ML) carrying test, the LRT group performed 6-8 progressive climbs (2 × 50% ML, 2 × 75% ML, 2 × 100% ML, and 2 × 100% ML + 30 g) three times per week. After 8 weeks, the radial and sciatic nerves were removed and prepared for transmission electron microscopy. In the radial nerve, myelinated axons cross-sectional area (CSA), unmyelinated axons CSA, myelin sheath thickness, and Schwann cells nuclei area were statistically larger in the LRT group than SED (p < 0.05). Also, the number of microtubules and neurofilaments per field were statistically higher in the LRT group than in SED (p < 0.01). For sciatic nerve, myelinated fibers CSA, unmyelinated axons CSA, myelin sheath thickness, Schwann cells nuclei area, and the number of neurofilaments per field were statistically larger in the LRT group compared to the SED group (p < 0.05). LRT with high-volume and high-intensity effectively induce similar changes in adult Wistar rats' radial and sciatic nerves' ultrastructure.

A dataset of action potentials recorded from the L5 dorsal rootlet of rat using a multiple electrode array

This article describes a dataset of action potentials collected from a neural recording experiment conducted on an adult female Sprague Dawley rat. A teased fascicle from the 5^th Lumbar dorsal rootlet (L5) was fitted to a custom-made electrode array (10 wire hooks connected as isolated dipoles, with an effective inter-channel spacing of 1 mm) and neural signals were recorded both with and without manual stimulation of the corresponding dermatome. The dataset contains 20 recordings in total, 10 were made with the animal at rest and 10 were made during stimulation. Each recording contains 5 channels of raw voltage data obtained after amplification and digitisation. In [1], a new method was proposed for analysing such multi-channel data in order to automatically identify and classify the action potentials that correspond to dermal afferents. This dataset is of exceptionally high quality for a neural recording and will be useful in both the development and testing of new signal processing methods.