Unit Spike Research Articles

A computational approach for generating continuous estimates of motor unit discharge rates and visualizing population discharge characteristics

Objective. Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, (1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and (2) discharge characteristics of large MU populations are often difficult to visualize. Approach. In the present study, we propose support vector regression (SVR) as an improved approach for generating smooth continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions. Main result. Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. Significance. The results and methods outlined here provide an improved method for generating estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.

Online Tracking of the Phase Difference Between Neural Drives to Antagonist Muscle Pairs in Essential Tremor Patients.

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

Changes in synchronization of the motor unit in muscle fatigue condition during the dynamic and isometric contraction in the Biceps Brachii muscle

The fatigue-induced neuromuscular mechanism remains to be fully elucidated. So far, the macroscopic mechanism using global surface electromyogram (sEMG) has been widely investigated. However, the microscopic mechanism using high-level neural information based on motor unit (MU) spike train from the spinal cord lacks attention, especially for the conditions under dynamic contraction task. The synchronization of the MU spike train is generally assumed to be an excellent indicator to represent the activities of spinal nerves. Accordingly, this study employed synchronization of MU spike train decomposed from high-density sEMG (HD-sEMG) to investigate the fatigue condition in muscular contractions within the Biceps Brachii muscle under both isometric and dynamic contraction tasks, giving a complete picture of the microscopic fatigue mechanism. We compared the synchronization of MU in Delta (1–4 Hz), alpha (8–12 Hz), Beta (15–30 Hz), and Gamma (30–60 Hz) frequency bands during the fatigue condition induced by different contractions. Our results showed that MU synchronization increased significantly (p<0.05) in all frequency bands across the two contraction tasks. The results indicate that the microscopic fatigue mechanism of Biceps Brachii muscle does not vary due to different contraction tasks.

Neural control of the healthy pectoralis major from low-to-moderate isometric contractions.

The pectoralis major critically enables arm movement in several directions. However, its neural control remains unknown. High-density electromyography (HD-sEMG) was acquired from the pectoralis major in two sets of experiments in healthy young adults. Participants performed ramp-and-hold isometric contractions in: adduction, internal rotation, flexion, and horizontal adduction at three force levels: 15%, 25%, and 50% scaled to task-specific maximal voluntary force (MVF). HD-sEMG signals were decomposed into motor unit spike trains using a convolutive blind source separation algorithm and matched across force levels using a motor unit matching algorithm. The mean discharge rate and coefficient of variation were quantified across the hold and compared between 15% and 25% MVF across all tasks, whereas comparisons between 25% and 50% MVF were made where available. Mean motor unit discharge rate was not significantly different between 15% and 25% MVF (all P > 0.05) across all tasks or between 25% and 50% MVF in horizontal adduction (P = 0.11), indicating an apparent saturation across force levels and the absence of rate coding. These findings suggest that the pectoralis major likely relies on motor unit recruitment to increase force, providing first-line evidence of motor unit recruitment in this muscle and paving the way for more deliberate investigations of the pectoralis major involvement in shoulder function.NEW & NOTEWORTHY This work is the first to investigate the relative contribution of rate coding and motor unit recruitment in the pectoralis major muscle in several functionally relevant tasks and across varying force levels in healthy adults. Our results demonstrate the absence of motor unit rate coding with an increase in EMG amplitude with increases in force level in all tasks examined, indicating that the pectoralis major relies on motor unit recruitment to increase force.

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time

Objectives. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. Approach. The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: (a) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and (b) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. Main results. The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2–21.4 s epoch−1 vs 6.5–47.8 s epoch−1, respectively) and prediction time (0.04 vs 0.27 s sample−1, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88–0.95). Significance. We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.

Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time

Objective. Surface electromyography (EMG) decomposition techniques can be used to establish human-machine interfacing (HMI), but most investigations are implemented offline due to the computational load of the approach. Here, we generalize the offline decomposition algorithm to identify the motor unit (MU) activities in real time, and we propose a MU-based approach for online simultaneous and proportional control (SPC) of multiple motor tasks. Approach. High-density surface EMG signals recorded from forearm muscles were decomposed into motor unit spike trains (MUSTs) with the proposed decomposition method. The MUSTs were first pooled into clusters in the calibration phase and the cumulative discharges of active MUs in each group were extracted as the control signal for each motor task. Then the subjects were instructed to control a virtual cursor with multiple motor tasks involving grasp and wrist movements. Fifteen able-bodied subjects and two patients with limb deficiency participated in the experiments to validate the proposed control scheme. Main results. On average, over 20 MUSTs were identified in real time with an estimated decomposition accuracy 85%. The cumulative discharge in each pool was highly correlated with the activation of the specific motion (R = 0.93 ± 0.05). Moreover, the proposed MU-based method had superior performance in online tests than conventional myo-control methods based on global EMG features. Significance. These results indicate the feasibility of real-time neural decoding in a non-invasive way. Moreover, the superior performance in online tests proves the potential of the MU-based approach for the SPC, promoting the application of EMG decomposition for HMI systems.

Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119–134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons × 11 contractions/muscles × 4–28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

Determination of wheat spike and spikelet architecture and grain traits using X-ray Computed Tomography imaging

BackgroundWheat spike architecture is a key determinant of multiple grain yield components and detailed examination of spike morphometric traits is beneficial to explain wheat grain yield and the effects of differing agronomy and genetics. However, quantification of spike morphometric traits has been very limited because it relies on time-consuming manual measurements.ResultsIn this study, using X-ray Computed Tomography imaging, we proposed a method to efficiently detect the 3D architecture of wheat spikes and component spikelets by clustering grains based on their Euclidean distance and relative positions. Morphometric characteristics of wheat spikelets and grains, e.g., number, size and spatial distribution along the spike can be determined. Two commercial wheat cultivars, one old, Maris Widgeon, and one modern, Siskin, were studied as examples. The average grain volume of Maris Widgeon and Siskin did not differ, but Siskin had more grains per spike and therefore greater total grain volume per spike. The spike length and spikelet number were not statistically different between the two cultivars. However, Siskin had a higher spikelet density (number of spikelets per unit spike length), with more grains and greater grain volume per spikelet than Maris Widgeon. Spatial distribution analysis revealed the number of grains, the average grain volume and the total grain volume of individual spikelets varied along the spike. Siskin had more grains and greater grain volumes per spikelet from spikelet 6, but not spikelet 1–5, compared with Maris Widgeon. The distribution of average grain volume along the spike was similar for the two wheat cultivars.ConclusionThe proposed method can efficiently extract spike, spikelet and grain morphometric traits of different wheat cultivars, which can contribute to a more detailed understanding of the sink of wheat grain yield.

Neuron‐Gated Silicon Nanowire Field Effect Transistors to Follow Single Spike Propagation within Neuronal Network

Silicon nanowire field effect transistors (SiNW‐FETs) provide a local probe for sensing neuronal activity at the subcellular scale, thanks to their nanometer size and ultrahigh sensitivity. The combination with micropatterning or microfluidic techniques to build model neurons networks above SiNW arrays could allow monitoring of spike propagation and tailor specific stimulations, being useful to investigate network communications at multiple scales, such as plasticity or computing processes. This versatile device could be useful in many research areas, including diagnosis, prosthesis, and health security. Herein, SiNW‐based arrays enable to record electrical signals from matured neurons, such as local field potential and unitary spike within ex vivo preparations and hippocampal neurons grown on chip respectively. Furthermore, the ability to guide neurites above the sensor array during 3 weeks of cultures is demonstrated and the propagation of spikes along cells is followed. The SiNW‐FETs are obtained by a top‐down approach with complementary metal oxide semiconductor (CMOS) technology, showing the possibility to implement them at the manufacturing level. These results confirm further the potentiality of the approach to follow spike propagation over large distances and at precise locations along neuronal cells, by providing a multiscale addressing at the nano and mesoscales.

Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings

Multisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments. Neural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered to extract spiking activity, then the amplitude distribution of samples and quantitative properties of the recorded brain activity (single unit yield, spike amplitude and isolation distance) were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Edge sites outperformed center sites: for all five probe types there was a significant difference in the signal power computed from the amplitude distributions, and edge sites recorded significantly more large amplitude samples both in the positive and negative range. Although the single unit yield was similar between site positions, the difference in spike amplitudes was noticeable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased with decreasing shank width. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1

Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.

Uticaj genotipa i azotne ishrane na varijabilnost prinosa i kvaliteta zrna kod jarog pivskog ječma

Production traits and grain quality of spring malting barley as the major raw material in malt and beer production were evaluated over a period of three years (2012–2014) in the region of Požarevac, Serbia. Four cultivars of two-rowed spring barley released in the Republic of Serbia (‘Novosadski 448’, ‘Novosadski 456’, ‘Dunavac’ and ‘Jadran’) were used. The objective of this research was to assess variability in grain yield and yield components in genetically divergent cultivars of two-rowed spring barley grown at different nitrogen application rates, as dependent on precipitation and air temperature during the growing season. The results showed significant variations in grain yield, yield components and grain protein content across genotypes (G), N application rates (N) and experimental years (E). Significant interdependence was found between grain yield and yield components. Increasing the N application rate up to 135 kg/ha had a positive effect on grain yield, plant height, number of spikes per unit area, spike length and grain protein content. Number of grains per spike and 1,000-kernel weight increased significantly at N application rates up to 105 kg/ha and 75 kg/ha, respectively.

Wrist Torque Estimation via Electromyographic Motor Unit Decomposition and Image Reconstruction.

Neural interface using decomposed motor units (MUs) from surface electromyography (sEMG) has allowed non-invasive access to the neural control signals, and provided a novel approach for intuitive human-machine interaction. However, most of the existing methods based on decomposed MUs merely adopted the discharge rate (DR) as the feature representations, which may lack local information around the discharge instant and ignore the subtle interactions of different MUs. In this study, we proposed an MU-specific image-based scheme for wrist torque estimation. Specifically, the high-density sEMG signals were decoded into motor unit spike trains (MUSTs), and then MU-specific images were reconstructed with MUSTs and corresponding motor unit action potential (MUAP). A convolutional neural network was used to learn representative features from MU-specific images automatically, and further to estimate wrist torques. The results demonstrated that the proposed method outperformed three conventional and a deep-learning regression approaches using DR features, with the estimation accuracy R2 of 0.82 ± 0.09, 0.89 ± 0.06, and nRMSE of 12.6 ± 2.5%, 11.0 ± 3.1% for pronation/supination and flexion/extension, respectively. Further, the analysis of the extracted features from MU-specific images showed a higher correlation than DR for recorded torques, indicating the effectiveness of the proposed method. The outcomes of this study provide a novel and promising perspective for the intuitive control of neural interfacing.

Muscles from the same muscle group do not necessarily share common drive: evidence from the human triceps surae.

It has been proposed that movements are produced through groups of muscles, or motor modules, activated by common neural commands. However, the neural origin of motor modules is still debated. Here, we used complementary approaches to determine: 1) whether three muscles of the same muscle group [soleus, gastrocnemius medialis (GM), and gastrocnemius lateralis (GL)] are activated by a common neural drive, and 2) whether the neural drive to GM and GL could be differentially modified by altering the mechanical requirements of the task. Eighteen human participants performed an isometric standing heel raise and submaximal isometric plantarflexions (10%, 30%, and 50% of maximal effort). High-density surface electromyography recordings were decomposed into motor unit action potentials and coherence analysis was applied on the motor unit spike trains. We identified strong common drive to each muscle but minimal common drive between the muscles. Further, large between-muscle differences were observed during the isometric plantarflexions, such as a delayed recruitment time of GL compared with GM and soleus motor units and opposite time-dependent changes in the estimates of neural drive to muscles during the torque plateau. Finally, the feet position adopted during the heel-raise task (neutral vs. internally rotated) affected only the GL neural drive with no change for GM. These results provide conclusive evidence that not all anatomically defined synergist muscles are controlled by strong common neural drive. Independent drive to some muscles from the same muscle group may allow for more flexible control to comply with secondary goals such as joint stabilization.NEW & NOTEWORTHY In this study, we demonstrated that the three muscles composing the human triceps surae share minimal common drive during isometric contractions. Our results suggest that reducing the number of effectively controlled degrees of freedom may not always be the strategy used by the central nervous system to control movements. Independent control of some, but not all, synergist muscles may allow for more flexible control to comply with secondary goals (e.g., joint stabilization).

Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task.

The interindividual variability in the neural drive sent from the spinal cord to muscles is largely unknown, even during highly constrained motor tasks. Here, we investigated individual differences in the strength of neural drive received by the vastus lateralis (VL) and vastus medialis (VM) during an isometric task. We also assessed the proportion of common neural drive within and between these muscles. Twenty-two participants performed a series of submaximal isometric knee extensions at 25% of their peak torque. High-density surface electromyography recordings were decomposed into motor unit action potentials. Coherence analyses were applied on the motor unit spike trains to assess the degree of neural drive that was shared between motor neurons. Six participants were retested ∼20 mo after the first session. The distribution of the strength of neural drive between VL and VM varied between participants and was correlated with the distribution of normalized interference electromyography (EMG) signals (r > 0.56). The level of within- and between-muscle coherence varied across individuals, with a significant positive correlation between these two outcomes (VL: r = 0.48; VM: r = 0.58). We also observed a large interindividual variability in the proportion of muscle-specific drive, that is, the drive unique to each muscle (VL range: 6%-83%, VM range: 6%-86%). All the outcome measures were robust across sessions, providing evidence that the individual differences did not depend solely on the variability of the measures. Together, these results demonstrate that the neural strategies to control the VL and VM muscles widely vary across individuals, even during a constrained task.NEW & NOTEWORTHY We observed that the distribution of the strength of neural drive between the vastus lateralis and vastus medialis during a single-joint isometric task varied across participants. Also, we observed that the proportion of neural drive that was shared within and between these muscles also varied across participants. These results provide evidence that the neural strategies to control the vastus lateralis and vastus medialis muscles widely vary across individuals, even during a mechanically constrained task.

Analysis of motor unit activities during multiple motor tasks by real-time EMG decomposition: perspective for myoelectric control.

Surface electromyography (EMG) decomposition techniques have been applied for human-machine interfacing by decoding neural information, while most of decomposition approaches work offline. Here, we apply an online decomposition scheme to decode motor unit activities during three motor tasks, and measure the recognition accuracy of motor type and activation level using the decomposition results. High-density surface EMG signal were recorded from forearm muscles of six able-bodied subjects. The EMG signals were decomposed into motor unit spike trains (MUST) with a sliding window of 100 ms. The computation complexity had time consumption < 50 ms in each window. Most identified motor units discharged during only one motor task. On average, over 5 MUSTs were identified for each motion and the recognition accuracy based on motor unit activities was > 99%. The discharge rate of motor units was highly correlated with the activation level of each motion with an average correlation coefficient of 0.94 ± 0.04. These results indicate the feasibility of an online, multi-motion, and proportional control scheme based on neural decoding in a non-invasive way.

Identification and validation of QTL for spike fertile floret and fruiting efficiencies in hexaploid wheat (Triticum aestivum L.).

This study identified and validated two QTL associated with spike fertile floret and fruiting efficiencies. They represent two new loci to use in MAS to improve wheat yield potential. The spike fruiting efficiency (FE-grains per unit spike dry weight at anthesis, GN/SDW) is a promising trait to improve wheat yield potential. It depends on fertile floret efficiency (fertile florets per unit SDW-FFE, FF/SDW) and grain set (grains per fertile floret-GST). Given its difficult measurement, it is often estimated as the grains per unit of nongrain spike dry weight at maturity (FEm). In this study, quantitative trait loci (QTL) were mapped using a double haploid population (Baguette 19/BIOINTA 2002, with high and low FE, respectively) genotyped with the iSelect 90K SNP array and evaluated in five environments. We identified 37 QTL, but two were major with an R2 > 10% and stable for being at least present in three environments: the QFEm.perg-3A (on Chr. 3A, 51.6cM, 685.12Mb) for FEm and the QFFE.perg-5A (on Chr. 5A, 42.1cM, 461.49Mb) for FFE, FE and FEm. Both QTL were validated using two independent F2 populations and KASP markers. For the most promising QTL, QFFE.perg-5A, the presence of the allele for high FFE resulted in + 4% FF, + 9% GN, + 13% GST, + 16% yield gSDW-1 and + 5% yield spike-1. QFEm.perg-3A and QFFE.perg-5A represent two new loci to use in MAS to improve wheat yield potential.

Motor Unit Discharge Variability Is Increased in Mild-To-Moderate Parkinson's Disease.

Individuals with Parkinson's disease (PD) demonstrate deficits in muscle activation such as decreased amplitude and inappropriate bursting. There is evidence that some of these disturbances are more pronounced in extensor vs. flexor muscles. Surface EMG has been used widely to quantify muscle activation deficits in PD, but analysis of discharge of the underlying motor units may provide greater insight and be more sensitive to changes early in the disease. Of the few studies that have examined motor unit discharge in PD, the majority were conducted in the first dorsal interosseous, and no studies have measured motor units from extensor and flexor muscles within the same cohort. The objective of this study was to characterize the firing behavior of single motor units in the elbow flexor and extensor muscles during isometric contractions in people with mild-to-moderate PD. Ten individuals with PD (off-medication) and nine healthy controls were tested. Motor unit spike times were recorded via intramuscular EMG from the biceps and triceps brachii muscles during 30-s isometric contractions at 10% maximum voluntary elbow flexion and elbow extension torque, respectively. We selected variables of mean motor unit discharge rate, discharge variability, and torque variability to evaluate motor abnormalities in the PD group. The effects of group, muscle, and group-by-muscle on each variable were determined using separate linear mixed models. Discharge rate and torque variability were not different between groups, but discharge variability was significantly higher in the PD group for both muscles combined (p < 0.0001). We also evaluated the asymmetry in these motor variables between the triceps and biceps for each individual participant with PD to evaluate whether there was an association with disease severity. The difference in torque variability between elbow flexion and extension was significantly correlated with both the Hoehn and Yahr scale (rho = 0.71) and UPDRS (rho = 0.62). Our findings demonstrate that variability in motor output, rather than decreased discharge rates, may contribute to motor dysfunction in people with mild-to-moderate PD. Our findings provide insight into altered neural control of movement in PD and demonstrate the importance of measuring from multiple muscles within the same cohort.

Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals.

Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was 80% for most conditions. From experimental EMG signals, on average, 12 ± 2 MUSTs were identified from each electrode grid with PNR of 25.0 ± 1.8dB, corresponding to an estimated decomposition accuracy 75%. These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.

Fatigue-related modulation of low-frequency common drive to motor units.

This study investigated fatigue-related modulation of common neural inputs to motor units (MUs) under 5Hz, which determines force precision control. Twenty-seven adults performed a sequence of fatiguing contractions. The participants were assessed with a static isometric index abduction at 20% maximal voluntary contraction in the pre-test and post-test. Discharge characteristics of MUs of the first dorsal interosseous muscle were analyzed with decomposed EMG signals. Along with increases in the mean (58.40 ± 11.76ms → 62.55 ± 10.83ms, P = 0.029) and coefficient of variation (0.204 ± .014 → 0.215 ± 0.017, P = 0.002) in inter-spike intervals, the fatiguing contraction caused reductions in the mean frequency (16.84 ± 3.31Hz → 15.59 ± 3.21Hz, P = 0.027) and spectral dispersions (67.54 ± 4.49 → 62.64 ± 6.76Hz, P = 0.007) of common neural drive, as estimated with smoothed cumulative motor unit spike trains (SCMUSTs). Stabilogram diffusion analysis of SCMUSTs revealed significant fatigue-related reductions in the long-term effective diffusion coefficient (1.91 ± 0.77 Hz2/s → 1.61 ± 0.61 Hz2/s, P = 0.020) and long-term scaling exponent (0.480 ± 0.013 Hz2/s → 0.471 ± 0.017 Hz2/s, P = 0.014). After fatiguing contraction, mutual information of force fluctuations and SCMUSTs was augmented roughly by 12.95% (P = 0.041). Muscular fatigue could compress and shift the low-frequency common drive to MUs toward lower spectral bands, thereby enhancing transmission of twitch forces through the muscle-tendon complex with a low-pass filter property. The fatigue-induced changes involve increased closed-loop control of the common modulation of MU discharge rates.