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Farina, D.

Publications and source records attributed to Farina, D..

At least 55 records · Page 3Linked to original sources

Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion

Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross-correlations between smoothed discharge rates of motor neurons. In Part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross-correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In Part 2, sixteen individuals performed plantarflexion at three ankle angles while we recorded electromyographic signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable with no significant differences in the distribution of motor neuron weights across ankle angles (p=0.62). When the cross-correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control. Key points summaryO_LIThe central nervous system may generate movements by activating groups of motor neurons (synergies) with common inputs. C_LIO_LIWe show here that two main sources of common inputs drive the motor neurons innervating the triceps surae muscles during isometric ankle plantarflexions. C_LIO_LIWe report that the distribution of these common inputs is globally invariant despite changing the mechanical constraints of the tasks, i.e., the ankle angle. C_LIO_LIThese results suggest the functional relevance of the modular organization of the central nervous system to control movements. C_LI

neuroscience↗

Low-frequency motor cortex EEG predicts four levels of rate of change of force during ankle dorsiflexion

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal recorded from the motor cortex and its neighboring cortical areas. Since the MRCP encodes motor intention and execution, it may be utilized as an interface between patients and neurorehabilitation technologies. This study investigates the EEG signal recorded from the Cz electrode to discriminate between four levels of rate of force development (RFD) of the tibialis anterior muscle. For classification, three feature sets were evaluated to describe the EEG traces. These were (i) MRCP morphological characteristics in the{delta} -band such as amplitude and timing, (ii) MRCP statistical characteristics in the{delta} -band such as mean, standard deviation, and kurtosis, and (iii) wideband time-frequency features in the 0.5-90 Hz range. Using a support vector machine for classification, the four levels of RFD were classified with a mean (SD) accuracy of 82% (7%) accuracy when using the time-frequency feature space, and with an accuracy of 75% (12%) when using the MRCP statistical characteristics. It was also observed that some of the key features from the statistical and morphological sets responded monotonically to the intensity of the RFD. Examples are slope and standard deviation in the (0, 1)s window for the statistical, and min1 and minn for the morphological sets. This monotonical response of features explains the observed performance of the{delta} -band MRCP and corresponding high discriminative power. Results from temporal analysis considering the pre-movement phase ((-3, 0)s) and three windows of the post-movement phase ((0, 1)s, (1, 2)s, and (2, 3)s)) suggest that the complete MRCP waveform represents high information content regarding the planning, execution, duration, and ending of the isometric dorsiflexion task using the tibialis anterior muscle. Results shed light on the role of{delta} -band in translating to motor command, with potential applications in neural engineering systems.

bioengineering↗

Non-invasive muscle biopsy: estimation of muscle fibre size from a neuromuscular interface

Because of the biophysical relation between muscle fibre diameter and the propagation velocity of action potentials along the muscle fibres, motor unit conduction velocity (MUCV) could be a non-invasive index of muscle fibre size in humans. However, the relation between MUCV and fibre size has been only assessed indirectly in animal models and in human patients with invasive intramuscular EMG recordings, or it has been mathematically derived from computer simulations. By combining advanced non-invasive techniques to record motor unit activity in vivo, i.e., high-density surface EMG, with the gold standard technique for muscle tissue sampling, i.e., muscle biopsy, here we investigated the relation between the conduction velocity of populations of motor units identified from the biceps brachii muscle, and muscle fibre diameter. Moreover, we demonstrate the possibility to predict muscle fibre diameter (R2 = 0.66) and cross-sectional area (R2 = 0.65) from conduction velocity estimates with low systematic bias (~2% and ~4% respectively) and a relatively low margin of individual error (~8% and ~16%, respectively). The proposed neuromuscular interface opens new perspectives in the use of high-density EMG as a non-invasive tool to estimate muscle fibre size without the need of surgical biopsy sampling. The non-invasive nature of high-density surface EMG for the assessment of muscle fibre size may be useful in studies monitoring child development, aging, space and exercise physiology. SIGNIFICANCE STATEMENTOur study explored the relation between the conduction velocity of populations of motor units and muscle fibre size in healthy humans. Our results provide in vivo evidence that a high-density surface EMG-derived physiological parameter, i.e. motor unit conduction velocity, can be adopted to estimate muscle fibre size, without the need of surgical biopsy sampling. Here we propose a neuromuscular interface that opens new perspectives not only in the study of neuromuscular disorders, but also in other fields where the non-invasive and painless determination of muscle fibre and motor unit size becomes a priority, such as in aging, space and exercise physiology.

physiology↗

Hill-type computational models of muscle-tendon actuators: a systematic review

Backed by a century of research and development, Hill-type models of skeletal muscle, often including a muscle-tendon complex and neuromechanical interface, are widely used for countless applications. Lacking recent comprehensive reviews, the field of Hill-type modelling is, however, dense and hard-to-explore, with detrimental consequences on innovation. Here we present the first systematic review of Hill-type muscle modelling. It aims to clarify the literature by detailing its contents and critically discussing the state-of-the-art by identifying the latest advances, current gaps, and potential future directions in Hill-type modelling. For this purpose, fifty-seven criteria-abiding Hill-type models were assessed according to a completeness evaluation, which identified the modelled muscle properties, and a modelling evaluation, which considered the level of validation and reusability of the models, as well as their modelling strategy and calibration. It is concluded that most models (1) do not significantly advance beyond historical gold standards, (2) neglect the importance of parameter identification, (3) lack robust validation, and (4) are not reusable in other studies. Besides providing a convenient tool supported by extensive supplementary material for navigating the literature, the results of this review highlight the need for global recommendations in Hill-type modelling to optimize inter-study consistency, knowledge transfer, and model reusability.

bioinformatics↗

Concurrent assessment of individual motoneuron discharges and muscle unit displacement velocity in humans by high-density EMG and ultrafast ultrasound

ObjectiveThe study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, the muscle deformation, has not been widely studied. An important example of this is excitation-contraction coupling (E-C coupling) - the process by which electrical excitation is converted into contraction in the muscle fibres. Despite this being a clear marker for progression of certain diseases, it cannot be measured in vivo in natural contractions. To address this, we analyse the kinematics of muscle units in natural contractions. ApproachWe combine high density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings of a mildly contracted muscle (tibialis anterior) to measure the deformation of the muscular tissue caused by individual MU twitches (decomposed from the HDsEMG). With a novel analysis on the US images we identified, with high spatio-temporal precision, the velocity maps associated with single muscle unit movements. From the individual MU profiles obtained from the velocity maps the region of movement, the duration of the mechanical twitch, the total and active contraction times, and the activation time (equivalent to E-C coupling) were computed. Main resultsThe E-C coupling was 3.8 {+/-} 3.0 ms (n = 390), providing the first measurement of this value in for single MUs in non-stimulated contractions. Furthermore, the experimental measures provided the first evidence of single muscle unit twisting during voluntary contractions and showed the presence of MUs with territories with multiple distinct split regions across the muscle region. SignificanceWe show that the combined use of HDsEMG and ultrafast US can allow for the study of kinematics of individual MU twitches, including measurement of the excitation-contraction coupling time under natural neural control conditions. These measurements and characterisations open new avenues for study of neuromechanics in healthy and pathological conditions.

bioengineering↗

Estimation of the firing behaviour of a complete motoneuron pool by combining EMG 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. Author SummaryOur 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.

neuroscience↗

Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units

ObjectiveHigh-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability. ApproachWe investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED - subcutaneous fat thickness), maximum anatomical cross-sectional area (ACSAmax), and fiber CSA. For this purpose, we recorded HD-sEMG signals, ultrasound, magnetic resonance imaging, and muscle biopsy of the biceps brachii muscle from two groups of participants - untrained-controls (UT=14) and strength-trained (>3 years of training, ST=16) - while they performed isometric ramp contractions with elbow flexors (at 15, 35, 50 and 70% maximum voluntary torque - MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied. Main resultsST subjects showed lower MED (UT: 4.8 {+/-} 1.4 vs. ST: 3.7 {+/-} 0.8 mm) associated to a greater number of identified motor units (UT: 21.3 {+/-} 10.2 vs. ST: 29.2 {+/-} 11.8 MUs/subject). Both groups showed a negative correlation between MED and the number of identified MUs at low forces (r= -0.6, p=0.002 at 15% MVT). Moreover, the number of identified MUs was positively correlated to the distribution of MU territories (r=0.56, p=0.01) and ACSAmax (r=0.48, p=0.03) at 15% MVT. By accounting for all anatomical parameters, we were able to partly predict the number of decomposed MUs at low but not at high forces. SignificanceOur results confirmed the influence of subcutaneous tissue on the quality of HD-sEMG signals and demonstrated that MU spatial distribution and ACSAmax are also relevant parameters of influence for current decomposition algorithms.

bioengineering↗

ACCURATE DECODING OF THE SPINAL CORD OUTPUT IN HUMANS WITH IMPLANTED HIGH-DENSITY ELECTRODE ARRAYS

Invasive electromyography opened a new window to explore motoneuron behaviour in vivo. However, the technique is limited by the small fraction of active motoneurons that can be concurrently detected, precluding a population analysis in natural tasks. Here, we developed a high-density intramuscular electrode for in vivo human recordings along with a fully automatic methodology that could detect the discharges of action potentials of up to 67 concurrently active motoneurons with 99% accuracy. These data revealed that motoneurons of the same pool receive common synaptic input at frequencies up to 75 Hz and that late recruited motoneurons inhibit the discharges of those recruited earlier. These results constitute an important step in the population coding analysis of the human motor system in vivo.

neuroscience↗

Orphan nuclear receptors Err2 and 3 promote a feature-specific terminal differentiationprogram underlying gamma motor neuron function and proprioceptive movement control

Motor neurons are commonly thought of as mere relays between the central nervous system and the movement apparatus, yet, in mammals about one-third of them function exclusively as regulators of muscle proprioception. How these gamma motor neurons acquire properties to function differently from the muscle force-producing alpha motor neurons remains unclear. Here, we found that upon selective loss of the orphan nuclear receptors Err2 and Err3 (Err2/3) in mice, gamma motor neurons acquire characteristic structural (e.g. synaptic wiring), but not functional (e.g. physiological firing rates) properties necessary for regulating muscle proprioception, thus disrupting gait and precision movements in vivo. Moreover, Err2/3 operate via transcriptional activation of neural activity modulators, one of which (Kcna10) promoted gamma motor neuron functional properties. Our work identifies a long-sought mechanism specifying gamma motor neuron properties necessary for proprioceptive movement control, which implies a feature-specific terminal differentiation program implementing neuron subtype-specific functional but not structural properties. SummaryThe transcription factors Err2 and 3 promote functional properties in a subset of motor neurons necessary for executing precise movements.

neuroscience↗

Common synaptic inputs are not distributed homogeneously among the motor neurons that innervate synergistic muscles

The force generated by the muscles involved in an action is produced by common synaptic inputs received by the engaged motor neurons. The purpose of our study was to identify the low-dimensional latent components, defined hereafter as neural modules, underlying the discharge rates of the motor units from two knee extensors (vastus medialis and lateralis) and two hand muscles (index and thumb muscles) during isometric contractions. The neural modules were extracted by factor analysis from the pooled motor units and no assumptions were made regarding the orthogonality of the modules or the association between the modules and each muscle. Factor analysis identified two independent neural modules that captured most of the covariance in the discharge rates of the motor units in the synergistic muscles. Although the neural modules were strongly correlated with the discharge rates of motor units in each of the synergistic pair of muscles, not all motor units in a muscle were correlated with the neural module for that muscle. The distribution of motor units across the pair of neural modules differed for each muscle: 80% of the motor units in first dorsal interosseous were more strongly correlated with the neural module for that muscle, whereas the proportion was 70%, 60%, and 45% for the thenar, vastus medialis, and vastus lateralis muscles. All other motor units either belonged to both modules or to the module for the other muscle (15% for vastus lateralis). Based on a simulation of 480 integrate-and-fire neurons receiving independent and common inputs, we demonstrate that factor analysis identifies the three neural modules with high levels of accuracy. Our results indicate that the correlated discharge rates of motor units arise from at least two sources of common synaptic input that are not distributed homogeneously among the motor neurons innervating synergistic muscles.

neuroscience↗

Transcranial alternating current stimulation does not modulate corticospinal activity in humans

Transcranial alternating current stimulation (TACS) is commonly used to synchronise the output of a cortical area to other parts of the nervous system, but evidence for this based on brain recordings in humans is challenging. The brain transmits beta oscillations (~21Hz) to tonically contracted limb muscles linearly and through the fastest corticospinal pathways. Therefore, muscle activity may be used as a proxy measure for the level of beta entrainment in the corticospinal tract due to TACS over motor cortex. Here, we assessed if TACS is able to modulate the neural inputs to muscles, which would provide an indirect evidence for TACS-driven neural entrainment. In the first part of this study, we ran a series of simulations of motor neuron (MN) pools receiving inputs from corticospinal neurons with different levels of beta entrainment. Results indicated that MNs should be highly sensitive to changes in corticospinal beta activity. Then, we ran experiments on healthy human subjects (N=10) in which TACS (at 1mA) was delivered over the motor cortex at 21Hz (beta stimulation), or at 7Hz or 40Hz (control conditions) while the abductor digiti minimi (ADM) or the tibialis anterior muscle (TA) were tonically contracted. Muscle activity was measured using high-density electromyography, which allowed us to decompose the spiking activity of pools of motor units innervating the studied muscles. By analysing motor unit pool activity, we observed that none of the tested TACS conditions could consistently alter the spectral characteristics of the common neural inputs received by the muscles. These results suggest that 1mA-TACS over motor cortex given at frequencies in the beta band does not affect corticospinal beta entrainment. Highlights- TACS is commonly used to entrain the communication between brain regions - It is challenging to find direct evidence supporting TACS-driven neural entrainment - Simulations show that motor neurons are sensitive to corticospinal beta entrainment - Motor unit activity from human muscles does not support TACS-driven entrainment

neuroscience↗

Interfacing Spinal Motor Units in Non-Human Primates Identifies a Principal Neural Component for Force Control Constrained by the Size Principle

Motor units convert the last neural code of movement into muscle forces. The classic view of motor unit control is that the central nervous system sends common synaptic inputs to motoneuron pools and that motoneurons respond in an orderly fashion dictated by the size principle. This view however is in contrast with the large number of dimensions observed in motor cortex which may allow individual and flexible control of motor units. Evidence for flexible control of motor units may be obtained by tracking motor units longitudinally during the performance of tasks with some level of behavioural variability. Here we identified and tracked populations of motor units in the brachioradialis muscle of two macaque monkeys during ten sessions spanning over one month during high force isometric contractions with a broad range of rate of force development (1.8 - 38.6 N{middle dot}m{middle dot}s-1). During the same sessions we recorded intramuscular EMG signals from 16 arm muscles of both limbs and elicited the full recruitment through neural stimulation of the median and deep radial nerves. We found a very stable recruitment order and discharge characteristics of the motor units over sessions and contraction trials. The small deviations from orderly recruitment were observed between motor units with close recruitment thresholds, and only during high rate of force development. Moreover, we also found that one component explained more than ~50% of the motor unit discharge rate variance, and that the remaining components could be described as a time-shifted version of the first, as it could be predicted from the interplay between the size principle of recruitment and one common input. In conclusion, our results show that motoneurons recruitment is determined by the interplay of the size principle and common input and that this recruitment scheme is not violated over time nor by the speed of the contractions.

neuroscience↗

Functional connectivity networks of common synaptic inputs to motor neurons reveal neural spinal synergies during a multi-joint task

Movements are reportedly controlled through the combination of synergies that generate specific motor outputs by imposing an activation pattern on a group of muscles. To date, the smallest unit of analysis has been the muscle. In this human study, we decoded the spiking activities of spinal motor neurons innervating six lower limb muscles during an isometric multi-joint task. We identified their common low-frequency components, from which networks of common synaptic inputs to the motor neurons were derived. The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). In addition, groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles - including distant muscles - received common inputs. Our results provide evidence of a synergistic control of a multi-joint motor task at the spinal motor-neuron level. TeaserThe generation of movement involves the activation of many spinal motor neurons from multiple muscles. A central and unresolved question is how these motor neurons are controlled to allow flexibility for adaptation to various mechanical constraints. Since the computational load of controlling each motor neuron independently would be extremely large, the central nervous system presumably adopts dimensionality reduction. We identified networks of functional connectivity between spinal motor neurons based on the common synaptic inputs they receive during a multi-joint task. Our findings revealed functional groupings of motor neurons in a low dimensional space. These groups did not necessarily overlap with the muscle anatomy. We provide a new neural framework for a deeper understanding of movement control in health and disease.

neuroscience↗

Mathematical Relationships between Motoneuron Properties Derived by Empirical Data Analysis: Size Determines All Motoneuron Properties

Our understanding of the behaviour of spinal alpha-motoneurons (MNs) in mammals partly relies on our knowledge of the relationships between MN membrane properties, such as MN size, resistance, rheobase, capacitance, time constant, axonal conduction velocity and afterhyperpolarization period. We reprocessed the data from 40 experimental studies in adult cat, rat and mouse MN preparations, to empirically derive a set of quantitative mathematical relationships between these MN electrophysiological and anatomical properties. This validated mathematical framework, which supports past findings that the MN membrane properties are all related to each other and clarifies the nature of their associations, is besides consistent with the Hennemans size principle and Ralls cable theory. The derived mathematical relationships provide a convenient tool for neuroscientists and experimenters to complete experimental datasets, to explore relationships between pairs of MN properties never concurrently observed in previous experiments, or to investigate inter-mammalian-species variations in MN membrane properties. Using this mathematical framework, modelers can build profiles of inter-consistent MN-specific properties to scale pools of MN models, with consequences on the accuracy and the interpretability of the simulations.

neuroscience↗

The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces provide a promising basis for human motor augmentation by extracting potential high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurones to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy.

neuroscience↗

Double nerve transfer to a single target muscle: experimental model in the upper extremity

Surgical nerve transfers are used to efficiently treat peripheral nerve injuries, neuromas, phantom limb pain or improve bionic prosthetic control. Commonly, one donor nerve is transferred to one target muscle. However, the transfer of multiple nerves onto a single target muscle may increase the number of muscle signals for myoelectric prosthetic control and facilitate the treatment of multiple neuromas. Currently, no experimental models are available for multiple nerve transfers to a common target muscle in the upper extremity. This study describes a novel experimental model to investigate the neurophysiological effects of peripheral double nerve transfers. For this purpose, we developed a forelimb model to enable tension-free transfer of one or two donor nerves in the upper extremity. Anatomic dissections were performed to design the double nerve transfer model (n=8). In 62 male Sprague-Dawley rats the ulnar nerve of the antebrachium alone (n=30) or together with the anterior interosseus nerve (n=32) was transferred to reinnervate the long head of the biceps brachii. Before neurotization, the motor branch to the biceps long head was transected at the motor entry point and resected up to its original branch to prevent auto-reinnervation. In all animals, coaptation of both nerves to the motor entry point could be performed tension-free. Mean duration of the procedure was 49 {+/-} 13 min for the single nerve transfer and 78 {+/-} 20 min for the double nerve transfer. Twelve weeks after surgery, muscle response to neurotomy, behavioral testing, retrograde labeling and structural analyses were performed to assess reinnervation. These analyses indicated that all nerves successfully reinnervated the target muscle. No aberrant reinnervation was observed by the originally innervating nerve. Our observations suggest a minimal burden for the animal with no signs of functional deficit in daily activities or auto-mutilation in both procedures. Furthermore, standard neurophysiological analyses for nerve and muscle regeneration were applicable. This newly developed nerve transfer model allows for the reliable and standardized investigation of neural and functional changes following the transfer of multiple donor nerves to one target muscle.

neuroscience↗

Ultra fast and highly realistic numerical modelling of the surface EMG

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains. It is currently a crucial component of control systems in robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these new applications. Deep learning approaches have shown the highest potential in this regard. To be effective, deep learning requires a large amount of high-quality annotated data for training; the only option today is the use of experimental electromyography data. Yet the acquisition and labelling of training data is time-consuming and expensive. Moreover, the high-quality annotation of this data is often not possible because the ground truth labels are hidden. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient and realistic models. Here, we present a new highly realistic and ultra-fast computational model tailored for the training of deep learning algorithms. For the first time, we are able to simulate arbitrary large datasets of realistic electromyography signals with high internal variability and leverage it to train deep learning algorithms. Because the computational model provides access to all the hidden parameters of the simulation, it also allows us to use some annotation strategies that are impossible with experimental data. We believe that this concept of Myoelectric Digital Twin allows new unprecedented approaches to muscular signals decoding and will accelerate the development of human-machine interfaces.

bioengineering↗

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

Despite the promising features of neural interfaces, their trade-off between information transfer and invasiveness has limited translation and viability outside research settings. Here, we present a non-invasive neural interface that provides access to spinal motoneuron activities from a sensor band at the wrist. The interface decodes electric signals present at the tendon endings of the forearm muscles by using a model of signal generation and deconvolution. First, we evaluated the reliability of the interface to detect motoneuron firings, and thereafter we used the decoded neural activity for the prediction of finger movements in offline and real-time conditions. The results showed that motoneuron activity decoded from the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time control. These findings demonstrate the feasibility of a wearable, non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

neuroscience↗