The purpose of our review was to compare the distribution of motor unit properties across human muscles of different sizes and recruitment ranges. Although motor units can be distinguished based on ...several different attributes, we focused on four key parameters that have a significant influence on the force produced by muscle during voluntary contractions: the number of motor units, average innervation number, the distributions of contractile characteristics, and discharge rates within motor unit pools. Despite relatively few publications on this topic, current data indicate that the most influential factor in the distribution of these motor unit properties between muscles is innervation number. Nonetheless, despite a fivefold difference in innervation number between a hand muscle (first dorsal interosseus) and a lower leg muscle (tibialis anterior), the general organization of their motor unit pools, and the range of discharge rates appear to be relatively similar. These observations provide foundational knowledge for studies on the control of movement and the changes that occur with aging and neurological disorders.
Surface electromyographic (EMG) signal amplitude is typically used to compare the neural drive to muscles. We experimentally investigated this association by studying the motor unit (MU) behavior and ...action potentials in the vastus medialis (VM) and vastus lateralis (VL) muscles. Eighteen participants performed isometric knee extensions at four target torques 10, 30, 50, and 70% of the maximum torque (MVC) while high-density EMG signals were recorded from the VM and VL. The absolute EMG amplitude was greater for VM than VL ( P < 0.001), whereas the EMG amplitude normalized with respect to MVC was greater for VL than VM ( P < 0.04). Because differences in EMG amplitude can be due to both differences in the neural drive and in the size of the MU action potentials, we indirectly inferred the neural drives received by the two muscles by estimating the synaptic inputs received by the corresponding motor neuron pools. For this purpose, we analyzed the increase in discharge rate from recruitment to target torque for motor units matched by recruitment threshold in the two muscles. This analysis indicated that the two muscles received similar levels of neural drive. Nonetheless, the size of the MU action potentials was greater for VM than VL ( P < 0.001), and this difference explained most of the differences in EMG amplitude between the two muscles (~63% of explained variance). These results indicate that EMG amplitude, even following normalization, does not reflect the neural drive to synergistic muscles. Moreover, absolute EMG amplitude is mainly explained by the size of MU action potentials. NEW & NOTEWORTHY Electromyographic (EMG) amplitude is widely used to compare indirectly the strength of neural drive received by synergistic muscles. However, there are no studies validating this approach with motor unit data. Here, we compared between-muscles differences in surface EMG amplitude and motor unit behavior. The results clarify the limitations of surface EMG to interpret differences in neural drive between muscles.
Introduction/Aims
The motor unit size index (MUSIX) may provide insight into reinnervation patterns in diseases such as amyotrophic lateral sclerosis (ALS). However, it is not known whether MUSIX ...detects clinically relevant changes in reinnervation, or if all muscles manifest changes in MUSIX in response to reinnervation after motor unit loss.
Methods
Fifty‐seven patients with ALS were assessed at 3‐month intervals for 12 months in four centers. Muscles examined were abductor pollicis brevis, abductor digiti minimi, biceps brachii, and tibialis anterior. Results were split into two groups: muscles with increases in MUSIX and those without increases. Longitudinal changes in MUSIX, motor unit number index (MUNIX), compound muscle action potential amplitude, and Medical Research Council strength score were investigated.
Results
One hundred thirty‐three muscles were examined. Fifty‐nine percent of the muscles exhibited an increase in MUSIX during the study. Muscles with MUSIX increases lost more motor units (58% decline in MUNIX at 12 months, P < .001) than muscles that did not increase MUSIX (34.6% decline in MUNIX at 12 months, P < .001). However, longitudinal changes in muscle strength were similar. When motor unit loss was similar, the absence of a MUSIX increase was associated with a significantly greater loss of muscle strength (P = .002).
Discussion
MUSIX increases are associated with greater motor unit loss but relative preservation of muscle strength. Thus, MUSIX appears to be measuring a clinically relevant response that can provide a quantitative outcome measure of reinnervation in clinical trials. Furthermore, MUSIX suggests that reinnervation may play a major role in determining the progression of weakness.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
We proposed a new method for the identification and quantification of cross talk at the motor unit level. We show that surface EMG cross talk can lead to physiological misinterpretations of EMG ...signals such as overestimations in the muscle activity and intermuscular correlation. Cross talk had little influence on the EMG power spectrum, which indicates that conventional temporal filtering cannot minimize cross talk. Spatial filter (single and double differential) effectively reduces but not abolish cross talk.
Cross talk is an important source of error in interpreting surface electromyography (EMG) signals. Here, we aimed at characterizing cross talk for three groups of synergistic muscles by the identification of individual motor unit action potentials. Moreover, we explored whether spatial filtering (single and double differential) of the EMG signals influences the level of cross talk. Three experiments were conducted. Participants (total 25) performed isometric contractions at 10% of the maximal voluntary contraction (MVC) with digit muscles and knee extensors and at 30% MVC with plantar flexors. High-density surface EMG signals were recorded and decomposed into motor unit spike trains. For each muscle, we quantified the cross talk induced to neighboring muscles and the level of contamination by the nearby muscle activity. We also estimated the influence of cross talk on the EMG power spectrum and intermuscular correlation. Most motor units (80%) generated significant cross-talk signals to neighboring muscle EMG in monopolar recording mode, but this proportion decreased with spatial filtering (50% and 42% for single and double differential, respectively). Cross talk induced overestimations of intermuscular correlation and has a small effect on the EMG power spectrum, which indicates that cross talk is not reduced with high-pass temporal filtering. Conversely, spatial filtering reduced the cross-talk magnitude and the overestimations of intermuscular correlation, confirming to be an effective and simple technique to reduce cross talk. This paper presents a new method for the identification and quantification of cross talk at the motor unit level and clarifies the influence of cross talk on EMG interpretation for muscles with different anatomy.
NEW & NOTEWORTHY We proposed a new method for the identification and quantification of cross talk at the motor unit level. We show that surface EMG cross talk can lead to physiological misinterpretations of EMG signals such as overestimations in the muscle activity and intermuscular correlation. Cross talk had little influence on the EMG power spectrum, which indicates that conventional temporal filtering cannot minimize cross talk. Spatial filter (single and double differential) effectively reduces but not abolish cross talk.
Recent work demonstrated that it is possible to identify motor unit discharge times from high-density surface EMG (HDEMG) decomposition. Since then, the number of studies that use HDEMG decomposition ...for motor unit investigations has increased considerably. Although HDEMG decomposition is a semi-automatic process, the analysis and interpretation of the motor unit pulse trains requires a thorough inspection of the output of the decomposition result. Here, we report guidelines to perform an accurate extraction of motor unit discharge times and interpretation of the signals. This tutorial includes a discussion of the differences between the extraction of global EMG signal features versus the identification of motor unit activity for physiological investigations followed by a comprehensive guide on how to acquire, inspect, and decompose HDEMG signals, and robust extraction of motor unit discharge characteristics.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The relatively large pick-up volume of surface electrodes has for long motivated the concern that muscles other than that of interest may contribute to surface electromyograms (EMGs). Recent findings ...suggest however the pick-up volume of surface electrodes may be smaller than previously appreciated, possibly leading to the detection of surface EMGs insensitive to muscle activity. Here we combined surface and intramuscular recordings to investigate how comparably action potentials from gastrocnemius and soleus are represented in surface EMGs detected with different inter-electrode distances. We computed the firing instants of motor units identified from intramuscular EMGs detected from gastrocnemius and soleus while five participants stood upright. We used these instants to trigger and average surface EMGs detected from multiple skin regions along gastrocnemius. Results from 66 motor units (whereof 31 from gastrocnemius) revealed the surface-recorded amplitude of soleus action potentials was 6% of that of gastrocnemius and did not decrease for inter-electrode distances smaller than 4 cm. Gastrocnemius action potentials were more likely detected for greater inter-electrode distances and their amplitude increased steeply up to 5 cm inter-electrode distance. These results suggest that reducing inter-electrode distance excessively may result in the detection of surface EMGs insensitive to gastrocnemius activity without substantial attenuation of soleus crosstalk.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Pain induces changes in motor performance, motor unit recruitment, and rate coding behavior that varies across different contraction speeds. Here we show that that pain reduces motor unit discharge ...rate and prolongs the neuromechanical delay at slow contraction speeds only. This new evidence suggests that there are differential nociceptive inhibitory effects across the motor unit pool, which allows fast submaximal contractions to be exerted despite the presence of pain.
At high forces, the discharge rates of lower- and higher-threshold motor units (MU) are influenced in a different way by muscle pain. These differential effects may be particularly important for performing contractions at different speeds since the proportion of lower- and higher-threshold MUs recruited varies with contraction velocity. We investigated whether MU discharge and recruitment strategies are differentially affected by pain depending on their recruitment threshold (RT), across a range of contraction speeds. Participants performed ankle dorsiflexion sinusoidal-isometric contractions at two frequencies (0.25 and 1 Hz) and two modulation amplitudes 5% and 10% of the maximum voluntary contraction (MVC) with a mean target torque of 20%MVC. High-density surface electromyography recordings from the tibialis anterior muscle were decomposed and the same MUs were tracked across painful (hypertonic saline injection) and nonpainful conditions. Torque variability, mean discharge rate (MDR), DR variability (DRvar), RT, and the delay between the cumulative spike train and the resultant torque output (neuromechanical delay, NMD) were assessed. The average RT was greater at faster contraction velocities ( P = 0.01) but was not affected by pain. At the fastest contraction speed, torque variability and DRvar were reduced ( P < 0.05) and MDR was maintained. Conversely, MDR decreased and DRvar and NMD increased significantly during pain at slow contraction speeds ( P < 0.05). These results show that reductions in contraction amplitude and increased recruitment of higher-threshold MUs at fast contraction speeds appear to compensate for the inhibitory effect of nociceptive inputs on lower-threshold MUs, allowing the exertion of fast submaximal contractions during pain.
NEW & NOTEWORTHY Pain induces changes in motor performance, motor unit recruitment, and rate coding behavior that varies across different contraction speeds. Here we show that that pain reduces motor unit discharge rate and prolongs the neuromechanical delay at slow contraction speeds only. This new evidence suggests that there are differential nociceptive inhibitory effects across the motor unit pool, which allows fast submaximal contractions to be exerted despite the presence of pain.
Correlation between motor unit discharge times, often referred to as motor unit synchronization, is determined by common synaptic input to motor neurons. Although it has been largely speculated that ...synchronization should influence the rate of force development, the association between the degree of motor unit synchronization and rapid force generation has not been determined. In this study, we examined this association with both simulations and experimental motor unit recordings. The analysis of experimental motor unit discharges from the tibialis anterior muscle of 20 healthy individuals during rapid isometric contractions revealed that the average motor unit discharge rate was associated with the rate of force development. Moreover, the extent of motor unit synchronization was entirely determined by the average motor unit discharge rate (
> 0.7,
< 0.0001). The simulation model demonstrated that the relative proportion of common synaptic input received by motor neurons, which determines motor unit synchronization, does not influence the rate of force development (
= 0.03,
> 0.05). Nonetheless, the estimates of correlation between motor unit spike trains were significantly correlated with the rate of force generation (
> 0.8,
< 0.0001). These results indicate that the average motor unit discharge rate, but not the degree of motor unit synchronization, contributes to most of the variance of human contractile speed among individuals. In addition, estimates of correlation between motor unit discharge times depend strongly on the number of identified motor units and therefore are not indicative of the strength of common input.
It is commonly assumed that motor unit synchronization has an impact on the rate of force development of a muscle. Here we present computer simulations and experimental data of human tibialis anterior motor units during rapid contractions that show that motor unit synchronization is not a determinant of the rate of force production. This conclusion clarifies the neural determinants of rapid force generation.
Approaches for validating motor unit firing times following surface electromyographic (EMG) signal decomposition with the precision decomposition III (PDIII) algorithm have not been agreed upon. Two ...approaches have been common: (1) “reconstruct-and-test” and (2) spike-triggered averaging (STA). We sought to compare motor unit results following the application of these approaches. Surface EMG signals were recorded from the vastus lateralis of 13 young males performing trapezoidal, isometric knee extensions at 50% and 80% of maximum voluntary contraction (MVC) force. The PDIII algorithm was used to quantify motor unit firing rates. Motor units were excluded using eight combinations of the reconstruct-and-test approach with accuracy thresholds of 0, 90, 91, and 92% with and without STA. The mean firing rate versus recruitment threshold relationship was minimally affected by STA. At 80% MVC, slopes acquired at the 0% accuracy threshold were significantly greater (i.e., less negative) than when 91% (
p
= .010) and 92% (
p
= .030) accuracy thresholds were applied. The application of STA has minimal influence on surface EMG signal decomposition results. Stringent reconstruct-and-test accuracy thresholds influence motor unit-derived relationships at high forces, perhaps explained through the increased presence of large motor unit action potentials. Investigators using the PDIII algorithm can expect negligible changes in motor unit-derived linear regression relationships with the application of secondary validation procedures.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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