Studying motor end-plate topography by means of scanning-electromyography

An Approach to Maximize Retrograde Transport Based on the Spatial Distribution of Motor Endplates in Mouse Hindlimb Muscles

Knowledge regarding the relationship between muscles and the corresponding motor neurons would allow therapeutic genes to transport into specific spinal cord segments. Retrograde tracing technique by targeting the motor endplate (MEP), a highly specialized structure that offers direct access to the spinal motor neurons, has been used to elucidate the connectivity between skeletal muscles and the innervating motor neuron pools. However, current injection strategies mainly based on blind injection or the local MEP region might lead to an underestimation of the motor neuron number due to the uneven distribution of MEP in skeletal muscles. In this work, we proposed a novel intramuscular injection strategy based on the 3D distribution of the MEPs in skeletal muscles, applied the 3D intramuscular injection to the gastrocnemius and tibialis anterior for retrograde tracing of the corresponding motor neurons, and compared this with the existing injection strategy. The intramuscular diffusion of the tracer demonstrated that 3D injection could maximize the retrograde transport by ensuring a greater uptake of the tracer by the MEP region. In combination with optical clearing and imaging, we performed 3D mapping and quantification of the labeled motor neurons and confirmed that 3D injection could label more motor neurons than the current injection method. It is expected that 3D intramuscular injection strategy will help elucidate the connective relationship between muscles and motor neurons faithfully and becomes a promising tool in the development of gene therapy strategies for motor neuron diseases.

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi-masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. The results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal. Graphical abstract The raw scanning-EMG signal, which can be composed by several discharges of the MU, is processed by the MMLSS algorithm so as to eliminate the artifact interference. Firstly, artifacts are detected for each discharge from the raw signal, obtaining a multi-discharge validity mask that indicates the samples that have been corrupted by artifacts. Secondly, a least-squares smoothing procedure simultaneously operating in the spatial dimension and among the discharges is applied to the raw signal. This second step is performed using only the not contaminated samples according to the validity mask. The resulting MMLSS-processed scanning-EMG signal is clean of artifact interference.

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components.

Graphical Abstract

The effect of recording site on extracted features of motor unit action potential

Motor unit action potential (MUAP), which consists of individual muscle fiber action potentials (MFAPs), represents the electrical activity of the motor unit. The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases. The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units. In order to detect this variation, simulated MUAPs were calculated both at the innervation zone where the MFAPs are more synchronized, and near the tendon, where they show increased temporal dispersion. Reinnervation in neurogenic state increases MUAP amplitude for the recordings at both the innervation zone and near the tendon. However, MUAP duration and the number of peaks significantly increased in a case of myopathy for recordings near the tendon. Furthermore, of the new features, "number of peaks×spike duration" was found as the strongest indicator of MFAP dispersion in myopathy. MUAPs were also recorded from healthy participants in order to investigate the biological counterpart of the simulation data. MUAPs which were recorded near to tendon revealed significantly prolonged duration and decreased amplitude. Although the number of peaks was increased by moving the needle near to tendon, this was not significant.

Innervation zones of fasciculating motor units: observations by a linear electrode array

This study examines the innervation zone (IZ) in the biceps brachii muscle in healthy subjects and those with amyotrophic lateral sclerosis (ALS) using a 20-channel linear electromyogram (EMG) electrode array. Raster plots of individual waveform potentials were studied to estimate the motor unit IZ. While this work mainly focused on fasciculation potentials (FPs), a limited number of motor unit potentials (MUPs) from voluntary activity of 12 healthy and seven ALS subjects were also examined. Abnormal propagation of MUPs and scattered IZs were observed in fasciculating units, compared with voluntarily activated MUPs in healthy and ALS subjects. These findings can be related to muscle fiber reinnervation following motor neuron degeneration in ALS and the different origin sites of FPs compared with voluntary MUPs.

Accuracy of three different techniques for automatically estimating innervation zone location

The purpose of this study was to compare the accuracy of the estimated innervation zone (IZ) locations obtained from cross-correlation, the minimum amplitude, and maximum center frequency criteria. Eight healthy men (mean±SD age=23.0±4.3 yrs) performed isometric muscle actions of the leg extensors, and 15 separate bipolar surface electromyographic (EMG) signals were detected from the vastus lateralis. A custom software program was used to estimate the location of the IZ based on: (1) the EMG channel that demonstrated the lowest amplitude, (2) the EMG channel that showed the highest mean frequency, and (3) the EMG channel that demonstrated the lowest peak cross-correlation between the signals from adjacent channels. The IZ location estimates from the lowest amplitude and highest mean frequency criteria were accurate in only 43.75% and 7.5% of the cases, respectively. The accuracy of the cross-correlation-based method was 90%. The cross-correlation-based method was much more accurate for estimating IZ location than were the lowest amplitude and highest mean frequency criteria. Cross-correlation could potentially be used for estimating the location of the IZ without the need for visual inspection of EMG signals.

A muscle architecture model offering control over motor unit fiber density distributions

The aim of this study was to develop a muscle architecture model able to account for the observed distributions of innervation ratios and fiber densities of different types of motor units in a muscle. A model algorithm is proposed and mathematically analyzed in order to obtain an inverse procedure that allows, by modification of input parameters, control over the output distributions of motor unit fiber densities. The model's performance was tested with independent data from a glycogen depletion study of the medial gastrocnemius of the rat. Results show that the model accurately reproduces the observed physiological distributions of innervation ratios and fiber densities and their relationships. The reliability and accuracy of the new muscle architecture model developed here can provide more accurate models for the simulation of different electromyographic signals.