Identification of the laterality of motor unit behavior in female patients with parkinson's disease using high-density surface electromyography

Association between physical fitness tests and neuromuscular properties

PURPOSE

While various fitness tests have been developed to assess physical performances, it is unclear how these tests are affected by differences, such as, in morphological and neural factors. This study was aimed to investigate associations between individual differences in physical fitness tests and neuromuscular properties.

METHODS

One hundred and thirty-three young adults participated in various general physical fitness tests and neuromuscular measurements. The appendicular skeletal muscle mass (ASM) was estimated by bioelectrical impedance analysis. Echo intensity (EI) was evaluated from the vastus lateralis. During submaximal knee extension force, high-density surface electromyography of the vastus lateralis was recorded and individual motor unit firings were detected. Y-intercept (i-MU) and slope (s-MU) from the regression line between the recruitment threshold and motor unit firing rate were calculated.

RESULTS

Stepwise multiple regression analyses revealed that knee extension strength could be explained (adjusted R2 = 0.712) by ASM (β = 0.723), i-MU (0.317), EI (- 0.177), and s-MU (0.210). Five-sec stepping could be explained by ASM (adjusted R2 = 0.212). Grip strength, side-stepping, and standing broad jump could be explained by ASM and echo intensity (adjusted R2 = 0.686, 0.354, and 0.627, respectively). Squat jump could be explained by EI (adjusted R2 = 0.640). Counter-movement jump could be explained by EI and s-MU (adjusted R2 = 0.631). On the other hand, i-MU and s-MU could be explained by five-sec stepping and counter-movement jump, respectively, but the coefficients of determination were low (adjusted R2 = 0.100 and 0.045).

CONCLUSION

Generally developed physical fitness tests were mainly explained by morphological factors, but were weakly affected by neural factors involved in performance.

Influence of forward head posture on muscle activation pattern of the trapezius pars descendens muscle in young adults

Forward head posture (FHP) is a serious problem causing head and neck disability, but the characteristics of muscle activity during long-term postural maintenance are unclear. This study aimed to investigate a comparison of electromyography (EMG) activation properties and subjective fatigue between young adults with and without habitual FHP. In this study, we examined the changes in the spatial and temporal distribution patterns of muscle activity using high-density surface EMG (HD-SEMG) in addition to mean frequency, a conventional measure of muscle fatigue. Nineteen male participants were included in the study (FHP group (n = 9; age = 22.3 ± 1.5 years) and normal group (n = 10; age = 22.5 ± 1.4 years)). Participants held three head positions (e.g., forward, backward, and neutral positions) for a total of 30 min each, and the EMG activity of the trapezius pars descendens muscle during posture maintenance was measured by HD-SEMG. The root mean square (RMS), the modified entropy, and the correlation coefficient were calculated. Additionally, the visual analogue scale (VAS) was evaluated to assess subjective fatigue. The RMS, VAS, modified entropy, and correlation coefficients were significantly higher in the FHP group than in the normal group (p < 0.001). With increasing postural maintenance time, the modified entropy and correlation coefficient values significantly decreased, and the mean frequency and VAS values significantly increased (p < 0.001). Furthermore, the forward position had significantly higher RMS, correlation coefficient, modified entropy, and VAS values than in the neutral position (p < 0.001). The HD-SEMG potential distribution patterns in the FHP group showed less heterogeneity and greater muscle activity in the entire muscle and subjective fatigue than those in the normal group. Excess muscle activity even in the neutral/comfortable position in the FHP group could potentially be a mechanism of neuromuscular conditions in this population.

Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling

Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from decomposition of electromyographical (EMG) signals. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of single-compartment leaky fire-and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. A representative application of the method in MN-driven neuromuscular modelling is also presented. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.

Our experimental understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to the observation of small samples of active MNs obtained from EMG decomposition. EMG decomposition therefore provides an important but incomplete description of the role of individual MNs in the firing activity of the complete MN pool, which limits our understanding of the neural strategies of the whole MN pool and of how the firing activity of each MN contributes to the neural drive to muscle. Here, we combine decomposed high-density EMG (HDEMG) data and a physiologically realistic model of MN population to predict the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. In brief, an experimental estimation of the synaptic current is input to a cohort of MN models, which are calibrated using the available decomposed HDEMG data, and predict the MN spike trains fired by the entire MN population. This novel approach is experimentally validated and applied to muscle force prediction from neuromuscular modelling.

Detecting motor unit abnormalities in amyotrophic lateral sclerosis using high-density surface EMG

OBJECTIVE

The purpose of this study was to detect specific motor unit (MU) abnormalities in people with amyotrophic lateral sclerosis (ALS) compared to controls using high-density surface electromyography (HD-SEMG).

METHODS

Sixteen people with ALS and 16 control subjects. The participants performed ramp up and sustained contractions at 30% of their maximal voluntary contraction. HD-SEMG signals were recorded in the vastus lateralis muscle and decomposed into individual MU firing behavior using a convolution blind source separation method.

RESULTS

In total, 339 MUs were detected (people with ALS; n = 93, control subjects; n = 246). People with ALS showed significantly higher mean firing rate, recruitment threshold, coefficient of variation of the MU firing rate, MU firing rate at recruitment, and motoneurons excitability than those of control subjects (p < 0.001). The number of MU, MU firing rate, recruitment threshold, and MU firing rate at recruitment were significantly correlated with disease severity (p < 0.001). Multivariable analysis revealed that an increased MU firing rate at recruitment was independently associated with ALS.

CONCLUSIONS

These results suggest increased excitability at recruitment, which is consistent with neurodegeneration results in a compensatory increase in MU activity.

SIGNIFICANCE

Abnormal MU firing behavior provides an important physiological index for understanding the pathophysiology of ALS.

Association between the Degree of Pre-Synaptic Dopaminergic Pathway Degeneration and Motor Unit Firing Behavior in Parkinson's Disease Patients

The relationship between motor unit (MU) firing behavior and the severity of neurodegeneration in Parkinson’s disease (PD) is not clear. This study aimed to elucidate the association between degeneration with dopaminergic pathways and MU firing behavior in people with PD. Fourteen females with PD (age, 72.6 ± 7.2 years, disease duration, 3.5 ± 2.1 years) were enrolled in this study. All participants performed a submaximal, isometric knee extension ramp-up contraction from 0% to 80% of their maximal voluntary contraction strength. We used high-density surface electromyography with 64 electrodes to record the muscle activity of the vastus lateralis muscle and decomposed the signals with the convolution kernel compensation technique to extract the signals of individual MUs. We calculated the degree of degeneration of the central lesion-specific binding ratio by dopamine transporter single-photon emission computed tomography. The primary, novel results were as follows: (1) moderate-to-strong correlations were observed between the degree of degeneration of the central lesion and MU firing behavior; (2) a moderate correlation was observed between clinical measures of disease severity and MU firing behavior; and (3) the methods of predicting central nervous system degeneration from MU firing behavior abnormalities had a high detection accuracy with an area under the curve >0.83. These findings suggest that abnormalities in MU activity can be used to predict central nervous system degeneration following PD.