Biology subjects

Farina, D.

Publications and source records attributed to Farina, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Flexible Control of Motor Units: Is the Multidimensionality of Motor Unit Manifolds a Sufficient Condition?

Understanding flexibility in the neural control of movement requires identifying the distribution of common inputs to the motor units. In this study, we identified large samples of motor units from two lower limb muscles: the vastus lateralis (VL; up to 60 motor units/participant) and the gastrocnemius medialis (GM; up to 67 motor units/participant). First, we applied a linear dimensionality reduction method to assess the dimensionality of the manifolds underlying the motor unit activity. We subsequently investigated the flexibility in motor unit control under two conditions: sinusoidal contractions with torque feedback, and online control with visual feedback on motor unit firing rates. Overall, we found that the activity of GM motor units was effectively captured by a single latent factor defining a unidimensional manifold, whereas the VL motor units were better represented by three latent factors defining a multidimensional manifold. Despite this difference in dimensionality, the recruitment of motor units in the two muscles exhibited similarly low levels of flexibility. Using a spiking network model, we tested the hypothesis that dimensionality derived from factorization does not solely represent descending cortical commands but is also influenced by spinal circuitry. We demonstrated that a heterogeneous distribution of inputs to motor units, or specific configurations of recurrent inhibitory circuits, could produce a multidimensional manifold. This study clarifies an important debated issue, demonstrating that while motor unit firings of a non-compartmentalised muscle can lie in a multidimensional manifold, the central nervous system may still have limited capacity for flexible control of these units. Key pointsO_LITo generate movement, the central nervous system distributes both excitatory and inhibitory inputs to the motor units. C_LIO_LIThe level of flexibility in the neural control of these motor units remains a topic of debate with significant implications for identifying the smallest unit of movement control. C_LIO_LIBy combining experimental data and in silico models, we demonstrated that the activity of a large sample of motor units from a single muscle can be represented by a multidimensional linear manifold; however, these units show very limited flexibility in their recruitment. C_LIO_LIThe dimensionality of the linear manifold may not directly reflect the dimensionality of descending inputs but could instead relate to the organisation of local spinal circuits. C_LI

neuroscience↗

bioRxiv2024

NeuroNella: A Robust Unsupervised Algorithm for Identification of Neural Activity from Multielectrode Arrays

We introduce NeuroNella, an automated algorithm developed for the identification of neuronal activity from multichannel electrode arrays. In evaluations conducted on recordings from implanted probes in the nervous system of rodents and primates, the algorithm demonstrated remarkable accuracy, showcasing an error rate of less than 1% compared to ground-truth patch clamp signals. Notably, the proposed algorithm handles large datasets efficiently without the necessity of a GPU system. The results highlighted the algorithms efficacy in detecting sources in a wide amplitude range and its adaptability in accommodating minor probe shifts. Moreover, the high robustness exhibited by the algorithm in decomposing recordings lasting up to 30 minutes underscores its potential for enabling longitudinal studies and prolonged recording sessions, thus opening new avenues for future brain/machine interface applications.

neuroscience↗

bioRxiv2024

Wearable Neural Interfaces: Real-Time Identification of Motor Neuron Discharges in Dynamic Motor Tasks

ObjectiveRobustness to non-stationary conditions is essential to develop stable and accurate wearable neural interfaces. ApproachWe propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Liebler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time ([~]22 ms of computational time per 100-ms batches). Main resultsThe proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80{degrees}). The rate of agreement, sensitivity, and precision were [≥] 90% in [≥] 80% of the cases in both simulated and experimentally recorded data, according to a two-source validation approach. SignificanceThe findings demonstrate the feasibility of accurately decoding MN discharges in real-time during dynamic contractions from wearable systems mounted at the wrist and forearm. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

neuroscience↗

bioRxiv2024

Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

ObjectiveUltrasound (US) images during a muscle contraction can be decoded into individual motor unit (MU) activity, i.e., trains of neural discharges from the spinal cord. However, current decoding algorithms assume a stationary mixing matrix, i.e. equal mechanical twitches at each discharge. This study aimed to investigate the accuracy of these approaches in non-ideal conditions when the mechanical twitches in response to neural discharges vary over time and are partially fused in tetanic contractions. MethodsWe performed an in silico experiment to study the decomposition accuracy for changes in simulation parameters, including the twitch waveforms, spatial territories, and motoneuron-driven activity. Then, we explored the consistency of the in silico findings with an in vivo experiment on the tibialis anterior muscle at varying contraction forces. ResultsA large population of MU spike trains across different excitatory drives, and noise levels could be identified. The identified MUs with varying twitch waveforms resulted in varying amplitudes of the estimated sources correlated with the ground truth twitch amplitudes. The identified spike trains had a wide range of firing rates, and the later recruited MUs with larger twitch amplitudes were easier to identify than those with small amplitudes. Finally, the in silico and in vivo results were consistent, and the method could identify MU spike trains in US images at least up to 40% of the maximal voluntary contraction force. ConclusionThe decoding method was accurate irrespective of the varying twitch-like shapes or the degree of twitch fusion, indicating robustness, important for neural interfacing applications.

bioengineering↗

bioRxiv2024

The volitional control of individual motor units is constrained within low-dimensional manifolds by common inputs

AbstractThe implementation of low-dimensional movement control by the central nervous system has been debated for decades. In this study, we investigated the dimensionality of the control signals received by spinal motor neurons when controlling either the ankle or knee joint torque. We first identified the low-dimensional latent factors underlying motor unit activity during torque- matched isometric contractions. Subsequently, we evaluated the extent to which motor units could be independently controlled. To this aim, we used an online control paradigm in which participants received the corresponding motor unit firing rates as visual feedback. We identified two main latent factors, regardless of the muscle group (vastus lateralis-medialis and gastrocnemius lateralis-medialis). The motor units of the gastrocnemius lateralis could be controlled largely independently from those of the gastrocnemius medialis during ankle plantarflexion. This dissociation of motor unit activity imposed similar behavior to the motor units that were not displayed in the feedback. Conversely, it was not possible to dissociate the activity of the motor units between the vastus lateralis and medialis muscles during the knee extension tasks. These results demonstrate that the number of latent factors estimated from linear dimensionality reduction algorithms does not necessarily reflect the dimensionality of volitional control of motor units. Overall, individual motor units were never controlled independently of all others but rather belonged to synergistic groups. Together, these findings provide evidence for a low-dimensional control of motor units constrained by common inputs, with notable differences between muscle groups. Significance statementIn this study, we initially examined the latent factors underlying motor unit activity in the vastii or gastrocnemii muscles during torque-matched isometric contractions. We then explored the extent to which these motor units could be controlled independently, using an online control paradigm where participants received visual information on motor unit firing rates. Although participants were able to dissociate the activity of a few motor unit pairs (from the gastrocnemius medialis and lateralis muscles), our results provide direct evidence of a low- dimensional control, constrained by common inputs, limiting flexibility in motor unit recruitment. Furthermore, we show that the number of latent factors identified by dimensionality reduction algorithms does not necessarily reflect the dimensionality of volitional control of motor units.

neuroscience↗

bioRxiv2024

NeuroMechanics: Electrophysiological and Computational Methods to Accurately Estimate the Neural Drive to Muscles in Humans In Vivo

The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.

neuroscience↗

bioRxiv2023

The decoding of extensive samples of motor units in human muscles reveals the rate coding of entire motoneuron pools

Movements are performed by motoneurons transforming synaptic inputs into an activation signal that controls muscle force. The control signal emerges from interactions between ionotropic and neuromodulatory inputs to motoneurons. Critically, these interactions vary across motoneuron pools and differ between muscles. To provide the most comprehensive framework to date of motor unit activity during isometric contractions, we identified the firing activity of extensive samples of motor units in the Tibialis Anterior (129{+/-}44 per participant; n=8) and the Vastus Lateralis (130{+/-}63 per participant; n=8) muscles during isometric contractions of up to 80% of maximal force. From this unique dataset, the rate coding of each motor unit was characterised as the relation between its instantaneous firing rate and the applied force, with the assumption that the linear increase in isometric force reflects a proportional increase in the net synaptic excitatory inputs received by the motoneuron. This relation was characterised with a natural logarithm function that comprised two stages. The initial stage was marked by a steep acceleration of firing rate, which was greater for low- than medium- and high-threshold motor units. The second stage comprised a linear increase in firing rate, which was greater for high- than medium- and low-threshold motor units. Changes in firing rate were largely non-linear during the ramp-up and ramp-down phases of the task, but with significant prolonged firing activity only evident for medium-threshold motor units. Contrary to what is usually assumed, our results demonstrate that the firing rate of each motor unit can follow a large variety of trends with force across the pool. From a neural control perspective, these findings indicate how motor unit pools use gain control to transform inputs with limited bandwidths into an intended muscle force.

neuroscience↗

bioRxiv2023

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, are mainly limited to static contractions and cannot fully represent the dynamic modulation of EMG signals during volitional movements. Here, we overcome these limitations by presenting NeuroMotion, an open-source simulator that provides a full-spectrum synthesis of EMG signals during voluntary movements. NeuroMotion is comprised of three modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, such as muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. Here we also provide representative applications of NeuroMotion. We first show how simulated MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regression. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. NeuroMotion is the first full-spectrum EMG generative model to simulate human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this full-spectrum model will complement experimental approaches and facilitate neuromechanical research. Author summaryNeuromechanical studies investigate how the nervous system and musculoskeletal system interact to generate movements. Such studies heavily rely on simulation models, which provide non-measurable variables to complement the experimental analyses. However, the simulation models of surface electromyography (EMG), the core physiological signal widely used in neuromechanical analyses, are limited to static conditions. We bridged this gap by proposing NeuroMotion, the first full-spectrum EMG simulator that can be used to generate EMG signals during voluntary movements. NeuroMotion integrates a musculoskeletal model, a neural network-based EMG generator, and an advanced motoneuron model. With representative applications of this simulator, we show that it can be used to investigate the variabilities of EMG signals during voluntary movement. We also demonstrate that the synthetic signals generated by NeuroMotion can be used to augment experimental data for regressing joint angles. We expect the functionality provided by NeuroMotion, which is provided open-source, will stimulate progress in neuromechanics.

bioengineering↗

bioRxiv2023

Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

ObjectiveNon-invasive identification of motoneuron (MN) activity is commonly done using (EMG). However, surface EMG (sEMG) signals detect only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times. MethodsHere, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across 10 participants using concomitant sEMG decomposition as the ground truth. Results140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 {+/-} 10.3 %. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin. ConclusionThe proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images. SignificanceThe proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.

bioengineering↗

bioRxiv2023

High-frequency cortical neural inputs to muscles during movement cancellation

Cortical beta (13-30 Hz) and gamma (30-60 Hz) oscillations have been investigated during motor processing. Although they are at frequencies greater than the dynamic bandwidth of muscle contraction, these oscillations are partly transmitted from the cortex to motoneurons and muscles. Little is known about when and why this transmission occurs. We developed an experimental approach to examine these high frequency inputs to motoneurons under different motor states while maintaining a stable force, thus constraining behaviour. We acquired brain and muscle activity during a GO/NO-GO task. In this experiment, the effector muscle for the task (tibialis anterior) was kept tonically active during the trials, while participants (N=9) reacted to sequences of auditory stimuli by either keeping the contraction unaltered ( NO-GO trials), or by quickly performing a ballistic contraction ( GO trials). Motor unit (MU) firing activity was extracted from high-density surface and intramuscular electromyographic signals, and the changes in its spectral contents in the NO-GO trials were analysed. We observed an increase in beta and low-gamma (30-45 Hz) activity post NO-GO cue at the brain and muscle levels. There was also an increase in the activity within 8-12 Hz, which was only observed at the muscle level. Overall, our results suggest that the cortical processing of movement cancellation occurs at least in part via increased power of high-frequency oscillations transmitted downstream to the muscles. These changes occur without alterations in behaviour, suggesting that the downstream transmission of these high-frequency oscillations does not have a direct functional impact.

neuroscience↗

bioRxiv2023

Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

ObjectiveClosed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process. ApproachBuilding on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill - as opposed to merely tracking changes in task success across training - facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen able-bodied and one amputee participated in a 3-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined. Main resultsWe found that the participants accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill. SignificanceWe comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

bioengineering↗

bioRxiv2023

Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals

We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through i) the parameters and steps of the decomposition algorithm, and ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during muscle contractions in humans.

neuroscience↗

bioRxiv2023

Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A validation study using intramuscular and surface EMG

The smallest voluntarily controlled structure of the human body is the motor unit (MU), comprised of a motoneuron and its innervated fibres. MUs have been investigated in neurophysiology research and clinical applications, primarily using electromyographic (EMG) techniques. Nonetheless, EMG (both surface and intramuscular) has a limited detection volume. A recent alternative approach to detect MUs is ultrafast ultrasound (UUS) imaging. The possibility of identifying MU activity from UUS has been shown by blind source separation (BSS) of UUS images. However, this approach has yet to be fully validated for a large population of MUs. Here we validate the BSS method on UUS images using a large population of MUs from eleven participants based on concurrent recordings of either surface or intramuscular EMG from forces up to 30% of the maximum voluntary contraction (MVC) force. We assessed the BSS methods ability to identify MU spike trains from direct comparison with the EMG-derived spike trains as well as twitch areas and temporal profiles from comparison with the spike-triggered-averaged UUS images when using the EMG-derived spikes as triggers. We found a moderate rate of correctly identified spikes (53.0 {+/-} 16.0%) with respect to the EMG-identified firings. However, the MU twitch areas and temporal profiles could still be identified accurately, including at 30% MVC force. These results suggest that the current BSS methods for UUS can accurately identify the location and average twitch of a large pool of MUs in UUS images, providing potential avenues for studying neuromechanics from a large cross-section of the muscle. On the other hand, more advanced methods are needed to address the non-linear summation of velocities for recovering the full spike trains.

bioengineering↗

bioRxiv2023

A novel motoneuron-driven computational muscle model with motor unit resolution for subject-specific simulations of human voluntary muscle contraction

The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The control of motoneurons provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decode the subjects intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research. Author SummaryNeuromuscular computational simulations of human muscle contractions are typically obtained with a mathematical model that transforms an electromyographic signal recorded from the muscle into force. This single-input single-output approach, however, limits the comprehensive description of muscle internal dynamics during contraction because of necessary multiscale simplifications. Here, we advance the state-of-the-art in neuromuscular modelling by proposing a novel mathematical model that describes the force-generating dynamics of the individual motor units that constitute the muscle. For the first time, the control to the population of modelled motor units was inferred from decomposed high-density electromyographic signals. The model was experimentally validated, and the sensitivity of its predictions to different experimental neural controls was assessed. The neuromuscular model, coupled with an image-based musculoskeletal model, includes a novel and advanced neuromechanical model of the motor unit excitation-contraction properties, and is suited for subject-specific simulations of human voluntary contraction, with applications in neurorehabilitation and the control of neuroprosthetics.

bioengineering↗

bioRxiv2023

I-Spin live: An open-source software based on blind-source separation for decoding the activity of spinal alpha motor neurons in real-time

Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit firing activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real-time. Here, we introduce an open-source software that performs real-time decoding of motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are optimised for each motor unit from baseline contractions and then re-applied in real-time during test contractions. In this way, the firing activity of multiple motor neurons can be provided through different forms of visual feedback. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We first validated the software with synthetic EMG signals generated during a range of isometric contraction patterns. We then tested the software on data collected using either surface or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source software provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.

neuroscience↗

bioRxiv2023

Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation

Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.

bioengineering↗

bioRxiv2023

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

The spinal motor neurons are the only neural cells whose individual activity can be non-invasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IED). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56{+/-}14; range: 39-79) and the percentage of early recruited motor units within these samples (29{+/-}14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies. Significance statementThe application of source separation methods to multi-channel EMG signals recorded with grids of electrodes enables users to accurately identify the activity of individual motor units. However, the design parameters of these grids have never been discussed. They are usually arbitrarily fixed, often based on commercial availability. Here, we showed that using larger and denser grids of electrodes than conventionally proposed drastically increases the number of identified motor units. The samples of identified units are more balanced between early- and late-recruited motor units. Thus, these grids provide a more representative sampling of the active motor unit population. Gathering large datasets of motor units using large and dense grids will impact the study of motor control, neuromuscular modelling, and human-machine interfacing.

neuroscience↗

bioRxiv2023

A graph-based approach to identify motor neuron synergies

Multiple studies have experimentally observed common fluctuations in the discharge rates of spinal motor neurons, which have been classically interpreted as generated by correlated synaptic inputs. However, so far it has not been possible to identify the number of inputs, nor their relative strength, received by each motor neuron. This information would reveal the distribution of inputs and dimensionality of the neural control of movement at the motor neuron level. Here, we propose a method that generates networks of correlation between motor neuron outputs to estimate the number of common inputs to motor neurons and their relative strengths. The method is based on force-directed graphs, the hierarchical clustering of motor neurons in the graphs, and the estimation of input strengths based on the graph structure. To evaluate the accuracy and robustness of the method, we simulated 100 motor neurons driven by a known number of inputs with fixed weights. The simulation results showed that 99.2 {+/-} 0.6%, 94.3 {+/-} 2.2 %, and 95.1 {+/-} 2.7 % of the motor neurons were accurately assigned to the input source with the highest weight for simulations with 2, 3, and 4 inputs, respectively. Moreover, the normalised weigths (range 0 to 1) with which each input was transmitted to individual motor neurons were estimated with a root-mean-squared error of 0.11, 0.18, and 0.28 for simulations with 2, 3, and 4 inputs, respectively. These results were robust to errors introduced in the discharge times (as they may occur due to errors by decomposition algorithms), with up to 5% of missing spikes or false positives. We finally applied this method on various experimental datasets to demonstrate typical case scenario when studying the neural control of movement. Overall, these results show that the proposed graph-based method accurately describes the distribution of inputs across motor neurons. Authors summaryAn important characteristics for our understanding of the neural control of natural behaviors if the dimensionality in neural control signals to the musculoskeletal system. This dimensionality in turn depends on the number of synaptic inputs transmitted to the elementary units of this control, i.e., the spinal motor neurons, and on their correlation. We propose a graph-based approach applied to the discharge times of motor neurons to estimate the number of inputs and associated strength transmitted to each motor neuron. For this purpose, we first calculated the correlation between motor neuron smoothed discharge rates, assuming that correlated discharge rates result from the reception of a correlated inputs. Then, we derived networks/graphs in which each node represented a motor neuron and where the nodes were positioned close to each or further apart, depending on the level of correlated activities of the corresponding motor neurons. Using simulations of motor neuron behaviour, we showed that the spatial information embedded in the proposed graphs can be used to accurately estimate the number and the relative strengths of the inputs received by each motor neurons. This method allows to reconstruct the distribution of synaptic inputs to motor neurons from the observed motor neuron activity.

physiology↗