Quantitative motor unit action potential analysis in 2 paraspinal neck muscles in adult Royal Dutch Sport horses

BACKGROUND: Reference values for quantitative electromyography (QEMG) in neck muscles of Royal Dutch Sport horses are lacking. OBJECTIVE: Determine normative data on quantitative motor unit action potential (QMUP) analysis of serratus ventralis cervicis (SV) and brachiocephalicus (BC) muscle. ANIMALS: Seven adult normal horses (mean age 9.5 standard deviation [SD] +/- 2.3 years, mean height 1.64 SD +/- 4.5 cm, and mean rectal temperature 37.6 SD +/- 0.3 degrees C). METHODS: An observational study on QMUP analysis in 6 segments of each muscle was performed with commercial electromyography equipment. Measurements were made according to formerly published methods. Natural logarithm transformed data were tested with ANOVA and posthoc testing according to Bonferroni. RESULTS: Mean duration, amplitude, phases, turns, area, and size index (SI) did not differ significantly among the 6 segments in each muscle. Mean amplitude, number of phases, and SI were significantly (P < .002) higher in SV than BC, 520 versus 448 muV, 3.0 versus 2.8 muV, and 0.48 versus 0.30 muV, respectively. In SV 95% confidence intervals (CI) for amplitude, duration, number of phases, turns, polyphasia area, and SI were 488-551 muV, 4.3-4.6 ms, 2.9-3.0, 2.4-2.6, 7-12%, 382-448, and 0.26-0.70, respectively; in BC this was 412-483 muV, 4.3-4.7 ms, 2.7-2.8, 2.4-2.6, 4-7%, 393-469, and 0.27-0.34, respectively. Maximal voluntary activity expressed by turns/second did not differ significantly between SV and BC with a 95% CI of 132-173 and 137-198, respectively. CONCLUSIONS AND CLINICAL IMPORTANCE: The establishment of normative data makes objective QEMG of paraspinal muscles in horses suspected of cervical neurogenic disorders possible. Differences between muscles should be taken into account.

Muscle fiber and motor unit behavior in the longest human skeletal muscle

The sartorius muscle is the longest muscle in the human body. It is strap-like, up to 600 mm in length, and contains five to seven neurovascular compartments, each with a neuromuscular endplate zone. Some of its fibers terminate intrafascicularly, whereas others may run the full length of the muscle. To assess the location and timing of activation within motor units of this long muscle, we recorded electromyographic potentials from multiple intramuscular electrodes along sartorius muscle during steady voluntary contraction and analyzed their activity with spike-triggered averaging from a needle electrode inserted near the proximal end of the muscle. Approximately 30% of sartorius motor units included muscle fibers that ran the full length of the muscle, conducting action potentials at 3.9 +/- 0.1 m/s. Most motor units were innervated within a single muscle endplate zone that was not necessarily near the midpoint of the fiber. As a consequence, action potentials reached the distal end of a unit as late as 100 ms after initiation at an endplate zone. Thus, contractile activity is not synchronized along the length of single sartorius fibers. We postulate that lateral transmission of force from fiber to endomysium and a wide distribution of motor unit endplates along the muscle are critical for the efficient transmission of force from sarcomere to tendon and for the prevention of muscle injury caused by overextension of inactive regions of muscle fibers.

The stimulus-response curve and motor unit variability in normal subjects and subjects with amyotrophic lateral sclerosis

The behavior and stability of motor units (MUs) in response to electrical stimulation of different intensities can be assessed with the stimulus-response curve, which is a graphical representation of the size of the compound muscle action potential (CMAP) in relation to stimulus intensity. To examine MU characteristics across the whole stimulus range, the variability of CMAP responses to electrical stimulation, and the differences that occur between normal and disease states, the curve was studied in 11 normal subjects and 16 subjects with amyotrophic lateral sclerosis (ALS). In normal subjects, the curve showed a gradual increase in CMAP size with increasing stimulus intensity, although one or two discrete steps were sometimes observed in the upper half of the curve, indicating the activation of large MUs at higher intensities. In ALS subjects, large discrete steps, due to loss of MUs and collateral sprouting, were frequently present. Variability of the CMAP responses was greater than baseline variability, indicating variability of MU responses, and at certain levels this variability was up to 100 mu Vms. The stimulus-response curve shows differences between normal and ALS subjects and provides information on MU activation and variability throughout the curve.

Motor unit number estimation - A Bayesian approach

All muscle contractions are dependent on the functioning of motor units. In diseases such as amyotrophic lateral sclerosis (ALS), progressive loss of motor units leads to gradual paralysis. A major difficulty in the search for a treatment for these diseases has been the lack of a reliable measure of disease progression. One possible measure would be an estimate of the number of surviving motor units. Despite over 30 years of motor unit number estimation (MUNE), all proposed methods have been met with practical and theoretical objections. Our aim is to develop a method of MUNE that overcomes these objections. We record the compound muscle action potential (CMAP) from a selected muscle in response to a graded electrical stimulation applied to the nerve. As the stimulus increases, the threshold of each motor unit is exceeded, and the size of the CMAP increases until a maximum response is obtained. However, the threshold potential required to excite an axon is not a precise value but fluctuates over a small range leading to probabilistic activation of motor units in response to a given stimulus. When the threshold ranges of motor units overlap, there may be alternation where the number of motor units that fire in response to the stimulus is variable. This means that increments in the value of the CMAP correspond to the firing of different combinations of motor units. At a fixed stimulus, variability in the CMAP, measured as variance, can be used to conduct MUNE using the "statistical" or the "Poisson" method. However, this method relies on the assumptions that the numbers of motor units that are firing probabilistically have the Poisson distribution and that all single motor unit action potentials (MUAP) have a fixed and identical size. These assumptions are not necessarily correct. We propose to develop a Bayesian statistical methodology to analyze electrophysiological data to provide an estimate of motor unit numbers. Our method of MUNE incorporates the variability of the threshold, the variability between and within single MUAPs, and baseline variability. Our model not only gives the most probable number of motor units but also provides information about both the population of units and individual units. We use Markov chain Monte Carlo to obtain information about the characteristics of individual motor units and about the population of motor units and the Bayesian information criterion for MUNE. We test our method of MUNE on three subjects. Our method provides a reproducible estimate for a patient with stable but severe ALS. In a serial study, we demonstrate a decline in the number of motor unit numbers with a patient with rapidly advancing disease. Finally, with our last patient, we show that our method has the capacity to estimate a larger number of motor units.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.

Neural adaptations to resistance training - Implications for movement control

It has long been believed that resistance training is accompanied by changes within the nervous system that play an important role in the development of strength. Many elements of the nervous system exhibit the potential for adaptation in response to resistance training, including supraspinal centres, descending neural tracts, spinal circuitry and the motor end plate connections between motoneurons and muscle fibres. Yet the specific sites of adaptation along the neuraxis have seldom been identified experimentally, and much of the evidence for neural adaptations following resistance training remains indirect. As a consequence of this current lack of knowledge, there exists uncertainty regarding the manner in which resistance training impacts upon the control and execution of functional movements. We aim to demonstrate that resistance training is likely to cause adaptations to many neural elements that are involved in the control of movement, and is therefore likely to affect movement execution during a wide range of tasks. We review a small number of experiments that provide evidence that resistance training affects the way in which muscles that have been engaged during training are recruited during related movement tasks. The concepts addressed in this article represent an important new approach to research on the effects of resistance training. They are also of considerable practical importance, since most individuals perform resistance training in the expectation that it will enhance their performance in-related functional tasks.

Age-related differences in rapid muscle activation after rate of force development training of the elbow flexors

In young adults, improvements in the rate of force development as a result of resistance training are accompanied by increases in neural drive in the very initial phase of muscle activation. The purpose of this experiment was to determine if older adults also exhibit similar adaptations in response to rate of force development (RFD) training. Eight young (21-35 years) and eight older (60-79 years) adults were assessed during the production of maximum rapid contractions, before and after four weeks of progressive resistance training for the elbow flexors. Young and older adults exhibited significant increases (P< 0.01) in peak RFD, of 25.6% and 28.6% respectively. For both groups the increase in RFD was accompanied by an increase in the root mean square (RMS) amplitude and in the rate of rise (RER) in the electromyogram (EMG) throughout the initial 100 ms of activation. For older adults, however, this training response was only apparent in the brachialis and brachioradialis muscles. This response was not observed in surface EMG recorded from the biceps brachii muscle during either RFD testing or throughout training, nor was it observed in the pronator teres muscle. The minimal adaptations observed for older adults in the bifunctional muscles biceps brachii and pronator teres are considered to indicate a compromise of the neural adaptations older adults might experience in response to resistance training.

A simulation of the surface EMG for analysis of muscle activity during whole body vibratory stimulation

Many studies have accounted for whole body vibration effects in the fields of exercise physiology, sport and rehabilitation medicine. Generally, surface EMG is utilized to assess muscular activity during the treatment; however, large motion artifacts appear superimposed to the raw signal, making sEMG recording not suitable before any artifact filtering. Sharp notch filters, centered at vibration frequency and at its superior harmonics, have been used in previous studies, to remove the artifacts. [6, 10] However, to get rid of those artifacts some true EMG signal is lost. The purpose of this study was to reproduce the effect of motor-unit synchronization on a simulated surface EMG during vibratory stimulation. In addition, authors mean to evaluate the EMG power percentage in those bands in which are also typically located motion artifact components. Model characteristics were defined to take into account two main aspect: the muscle MUs discharge behavior and the triggering effects that appear during local vibratory stimulation. [7] Inter-pulse-interval, was characterized by a polimodal distribution related to the MU discharge frequency (IPI 55-80ms, σ=12ms) and to the correlation with the vibration period within the range of ±2 ms due to vibration stimulus. [1, 7] The signals were simulated using different stimulation frequencies from 30 to 70 Hz. The percentage of the total simulated EMG power within narrow bands centered at the stimulation frequency and its superior harmonics (± 1 Hz) resulted on average about 8% (± 2.85) of the total EMG power. However, the artifact in those bands may contain more than 40% of the total power of the total signal. [6] Our preliminary results suggest that the analysis of the muscular activity of muscle based on raw sEMG recordings and RMS evaluation, if not processed during vibratory stimulation may lead to a serious overestimation of muscular response.

Simulation of surface EMG for the analysis of muscle activity during whole body vibratory stimulation

This study aims to reproduce the effect of motor-unit synchronization on surface EMG recordings during vibratory stimulation to highlight vibration evoked muscle activity. The authors intended to evaluate, through numerical simulations, the changes in surface EMG spectrum in muscles undergoing whole body vibration stimulation. In some specific bands, in fact, vibration induced motion artifacts are also typically present. In addition, authors meant to compare the simulated EMGs with respect to real recordings in order to discriminate the effect of synchronization of motor units discharges with vibration frequencies from motion artifacts. Computations were performed using a model derived from previous studies and modified to consider the effect of vibratory stimulus, the motor unit synchronization and the endplates-electrodes relative position on the EMG signal. Results revealed that, in particular conditions, synchronization of MUs' discharge generates visible peaks at stimulation frequency and its harmonics. However, only a part of the total power of surface EMGs might be enclosed within artifacts related bands (±1. Hz centered at the stimulation frequency and its superior harmonics) even in case of strong synchronization of motor units discharges with the vibratory stimulus. © 2013 Elsevier Ireland Ltd.

Recruitment of single human low-threshold motor units with increasing loads at different muscle lengths

We investigated the recruitment behaviour of low threshold motor units in flexor digitorum superficialis by altering two biomechanical constraints: the load against which the muscle worked and the initial muscle length. The load was increased using isotonic (low load), loaded dynamic (intermediate load) and isometric (high load) contractions in two studies. The initial muscle position reflected resting muscle length in series A, and a longer length with digit III fully extended in series B. Intramuscular EMG was recorded from 48 single motor units in 10 experiments on five healthy subjects, 21 units in series A and,27 in series B, while subjects performed ramp up, hold and ramp down contractions. Increasing the load on the muscle decreased the force, displacement and firing rate of single motor units at recruitment at shorter muscle lengths (P < 0.001, dependent t-test). At longer muscle lengths this recruitment pattern was observed between loaded dynamic and isotonic contractions, but not between isometric and loaded dynamic contractions. Thus, the recruitment properties of single motor units in human flexor digitorum superficialis are sensitive to changes in both imposed external loads and the initial length of the muscle. (C) 2003 Elsevier Ltd. All rights reserved.

POISSON DISTRIBUTION TO ANALYZE NEAR-THRESHOLD MOTOR EVOKED POTENTIALS

Motor unit action potentials (MUAPs) evoked by repetitive, low-intensity transcranial magnetic stimulation can be modeled as a Poisson process. A mathematical consequence of such a model is that the ratio of the variance to the mean of the amplitudes of motor evoked potentials (MEPs) should provide an estimate of the mean size of the individual MUAPs that summate to generate each MEP. We found that this is, in fact, the case. Our finding thus supports the use of the Poisson distribution to model MEP generation and indicates that this model enables characterization of the motor unit population that contributes to near-threshold MEPs. Muscle Nerve 42: 825-828, 2010