Imperial College London

Dr Alessandro Del Vecchio

Faculty of EngineeringDepartment of Bioengineering

Visiting Researcher
 
 
 
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Contact

 

+44 (0)7516 143 524a.del-vecchio

 
 
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Location

 

B121Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

62 results found

Dideriksen J, Del Vecchio A, 2023, Adaptations in motor unit properties underlying changes in recruitment, rate coding, and maximum force., J Neurophysiol, Vol: 129, Pages: 235-246

Changes in the discharge characteristics of motor units as well as in the maximum force-producing capacity of the muscle are observed following training, aging, and fatiguability. The ability to measure the adaptations in the neuromuscular properties underlying these changes experimentally, however, is limited. In this study we used a computational model to systematically investigate the effects of various neural and muscular adaptations on motor unit recruitment thresholds, average motor unit discharge rates in submaximal contractions, and maximum force. The primary focus was to identify candidate adaptations that can explain experimentally observed changes in motor unit discharge characteristics after 4 wk of strength training (Del Vecchio A, Casolo A, Negro F, Scorcelletti M, Bazzucchi I, Enoka R, Felici F, Farina D. J Physiol 597: 1873-1887, 2019). The simulation results indicated that multiple combinations of adaptations, likely involving an increase in maximum discharge rate across motor units, may occur after such training. On a more general level, we found that the magnitude of the adaptations scales linearly with the change in recruitment thresholds, discharge rates, and maximum force. In addition, the combination of multiple adaptations can be predicted as the linear sum of their individual effects. Together, this implies that the outcomes of the simulations can be generalized to predict the effect of any combination of neural and muscular adaptations. In this way, the study provides a tool for estimating potential underlying adaptations in neural and muscular properties to explain any change in commonly used measures of rate coding, recruitment, and maximum force.NEW & NOTEWORTHY Our ability to measure adaptations in neuromuscular properties in vivo is limited. Using a computational model, we quantify the effect of multiple neuromuscular adaptations on common measures of motor unit recruitment, rate coding, and force-producing capacity. Scaling and co

Journal article

OKeeffe R, Shirazi SY, Del Vecchio A, Ibáñez J, Mrachacz-Kersting N, Bighamian R, Rizzo J, Farina D, Atashzar SFet al., 2022, Low-frequency motor cortex EEG predicts four levels of rate of change of force during ankle dorsiflexion

<jats:title>Abstract</jats:title><jats:p>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)<jats:italic>MRCP morphological characteristics</jats:italic>in the<jats:italic>δ</jats:italic>-band such as amplitude and timing, (ii)<jats:italic>MRCP statistical characteristics</jats:italic>in the<jats:italic>δ</jats:italic>-band such as mean, standard deviation, and kurtosis, and (iii)<jats:italic>wideband time-frequency features</jats:italic>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<jats:italic>min</jats:italic><jats:sub>1</jats:sub>and<jats:italic>min<jats:sub>n</jats:sub></jats:italic>for the morphological sets. This monotonical response of features explains the observed performance of the<jats:italic>δ</jats:italic>-band MRCP and corresponding high discriminative power. Results from temporal analysis

Journal article

Soteropoulos DS, Del Vecchio A, 2022, Accurate measurement of inhibition and excitation in human motor pools during sensory stimulation

<jats:title>Abstract</jats:title><jats:p>Surface electromyography (sEMG) is a pivotal approach in clinical and basic neurophysiology, allowing us to extract the summed activity of motor units in a given muscle. Due to the bipolar nature of the motor unit action potential, the sEMG is a non-linear representation of their underlying motor unit activity and therefore affected by signal cancellation. It is not clear how this cancellation influences evoked responses in sEMG. The aim of our study was to characterise how representative an evoked sEMG response was to the firing behaviour of the underlying motor pool. To do this, we first simulated a population of motor units (and their action potentials) that responded to a stimulus with a change in firing probability. Their activity was summed then rectified to generate a simulated sEMG signal or was rectified and then summed, to generate a sEMG signal with no cancellation. By comparing the two responses to that of the underlying pool we would then compare for discrepancies. We repeated this process but by using the responses of tibialis anterior motor units to weak tibial nerve stimulation. We find that both for the simulated and experimental data the response measured through the sEMG is almost always an underestimate of the evoked response in the underlying motor pool. This is the case for both inhibitory and excitatory evoked responses. The magnitude of the inaccuracy depends on the size of the evoked response, but it cannot be accounted solely by signal cancellation, suggesting other factors may also contribute.</jats:p>

Journal article

Del Vecchio A, Jones RHA, Schofield IS, Kinfe TM, Ibáñez J, Farina D, Baker SNet al., 2022, Interfacing motor units in non-human primates identifies a principal neural component for force control constrained by the size principle, The Journal of Neuroscience, Vol: 42, Pages: 7383-7399, ISSN: 0270-6474

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 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 with a broad range of rate of force development (1.8 - 38.6 N∙m∙s-1). 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 fully predicted by the motor unit recruitment intervals, so that small shifts in recruitment thresholds happened only during contractions at 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 represented a time-shifted version of the first. 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.

Journal article

Alix-Fages C, Jiménez-Martínez P, de Oliveira DS, Möck S, Balsalobre-Fernández C, Del Vecchio Aet al., 2022, Mental fatigue impairs physical performance but not the neural drive to the muscle

<jats:title>ABSTRACT</jats:title><jats:p>Mental fatigue (MF) does not only affect cognitive but also physical performance. This study aimed to explore the effects of MF on muscle endurance, rate of perceived exertion (RPE), and motor units’ activity. Ten healthy males participated in a randomised crossover study. The subjects attended two identical experimental sessions separated by three days with the only difference of a cognitive task (incongruent Stroop task [ST]) and a control condition (watching a documentary). Perceived MF and motivation were measured for each session at baseline and after each cognitive task. Four contractions at 20% of maximal voluntary contraction (MVIC) were performed at baseline, after each cognitive and after muscle endurance task while measuring motor units by high-density surface electromyography. Muscle endurance until failure at 50% of MVIC was measured after each cognitive task and the RPE was measured right after failure. ST significantly increased MF (p = 0.001) reduced the motivation (p = 0.008) for the subsequent physical task and also impaired physical performance (p = 0.044). However, estimates of common synaptic inputs and motor unit discharge rates as well as RPE were not affected by MF (p&gt; 0.11). In conclusion, MF impairs muscle endurance and motivation for the physical task but not the neural drive to the muscle at any frequency bands. Although it is physiologically possible for mentally fatigued subjects to generate an optimal neuromuscular function, the altered perception and motivation seems to limit physical performance. Our results suggest that the corticospinal pathways are not affected by MF.</jats:p>

Journal article

Skarabot J, Folland JP, Holobar A, Baker SN, Del Vecchio Aet al., 2022, Startling stimuli increase maximal motor unit discharge rate and rate of force development in humans, JOURNAL OF NEUROPHYSIOLOGY, Vol: 128, Pages: 455-469, ISSN: 0022-3077

Journal article

Sîmpetru RC, Arkudas A, Braun DI, Osswald M, de Oliveira DS, Eskofier B, Kinfe TM, Del Vecchio Aet al., 2022, Sensing the Full Dynamics of the Human Hand with a Neural Interface and Deep Learning

<jats:title>Abstract</jats:title><jats:p>Theories about the neural control of movement are largely based on movement-sensing devices that capture the dynamics of predefined anatomical landmarks. However, neuromuscular interfaces such as surface electromyography (sEMG) can potentially overcome the limitations of these technologies by directly sensing the motor commands transmitted to the muscles. This allows for the continuous, real-time prediction of kinematics and kinetics without being limited by the biological and physical constraints that affect motion-based technologies. In this work, we present a deep learning method that can decode and map the electrophysiological activity of the forearm muscles into movements of the human hand. We recorded the kinematics and kinetics of the human hand during a wide range of grasping and individual digit movements covering more than 20 degrees of freedom of the hand at slow (0.5 Hz) and fast (1.5 Hz) movement speeds in healthy participants. The input of the model consists of three-hundred EMG sensors placed only on the extrinsic hand muscles. We demonstrate that our neural network can accurately predict the kinematics and contact forces of the hand even during unseen movements and with simulated real-time resolution. By examining the latent space of the network, we find evidence that it has learned the underlying anatomical and neural features of the sEMG that drive all hand motor behaviours.</jats:p>

Journal article

de Oliveira DS, Casolo A, Balshaw TG, Maeo S, Lanza MB, Martin NRW, Maffulli N, Kinfe TM, Eskofier BM, Folland JP, Farina D, Del Vecchio Aet al., 2022, Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units, JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560

Journal article

Ibanez Pereda J, Zicher B, Brown KE, Rocchi L, Casolo A, Del Vecchio A, Spampinato DA, Vollette C-A, Rothwell JC, Baker SN, Farina Det al., 2022, Standard intensities of transcranial alternating current stimulation over the motor cortex do not entrain corticospinal inputs to motor neurons, The Journal of Physiology, ISSN: 0022-3751

Transcranial alternating current stimulation (TACS) is commonly used to synchronise a cortical area and its outputs to the stimulus waveform, but evidence for this based on brain recordings in humans is challenging. The corticospinal tract transmits beta oscillations (~21Hz) from motor cortex to tonically contracted limb muscles linearly. Therefore, muscle activity may be used to measure 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 indirect evidence for TACS-driven neural entrainment. In the first part of this study, we ran simulations of motor neuron (MN) pools receiving inputs from corticospinal neurons with different levels of beta entrainment. Results suggest that MNs are 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 or the tibialis anterior muscle were tonically contracted. Muscle activity was measured using high-density electromyography, which allowed us to decompose the activity of pools of motor units innervating the muscles. By analysing motor unit pool activity, we observed that none of the TACS conditions could consistently alter the spectral contents of the common neural inputs received by the muscles. These results suggest that 1mA-TACS over motor cortex given at beta frequencies does not entrain corticospinal activity.

Journal article

Hug F, Avrillon S, Sarcher A, Del Vecchio A, Farina Det al., 2022, Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task, JOURNAL OF PHYSIOLOGY-LONDON, ISSN: 0022-3751

Journal article

Hug F, Avrillon S, Sarcher A, Del Vecchio A, Farina Det al., 2022, Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task, The Journal of Physiology, ISSN: 0022-3751

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 of these synergies has been the muscle through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study (n = 10), we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity during an isometric multi-joint task. Specifically, high-density surface electromyography recordings from six lower limb muscles were decomposed into motor neurons spiking activity. We analyzed these activities by identifying their common low-frequency components, from which networks of correlated activity to the motor neurons were derived and interpreted as networks of common synaptic inputs. 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 study supports the theory that movements are produced through the control of small numbers of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy. We provide a new neural framework for a deeper understanding of the structure of common inputs to motor neurons.Abstract figure legend Ten participants performed an isometric multi-joint task, which consisted in producing force on an instrumented pedal. Adhesive grids of 64 electrodes were placed over six lower limb muscles (gastrocnemius medialis [GM] and lateralis [GL], vastus lateralis [VL] and medialis [VM], biceps femoris [BF], semit

Journal article

Bracklein M, Barsakcioglu DY, Del Vecchio A, Ibanez J, Farina Det al., 2022, Reading and modulating cortical beta bursts from motor unit spiking activity, JOURNAL OF NEUROSCIENCE, Vol: 42, ISSN: 0270-6474

Journal article

Nuessel M, Zhao Y, Knorr C, Regensburger M, Stadlbauer A, Buchfelder M, del Vecchio A, Kinfe Tet al., 2022, Deep Brain Stimulation, Stereotactic Radiosurgery and High-Intensity Focused Ultrasound Targeting the Limbic Pain Matrix: A Comprehensive Review, PAIN AND THERAPY, Vol: 11, Pages: 459-476, ISSN: 2193-8237

Journal article

Baker AME, Maffitt NJ, Del Vecchio A, McKeating KM, Baker MR, Baker SN, Soteropoulos DSet al., 2022, Neural Dysregulation in Post-COVID Fatigue

<jats:title>Abstract</jats:title><jats:p>Following infection from SARS-CoV-2, a substantial minority of people develop lingering after-effects known as ‘long COVID’. Fatigue is a common complaint with substantial impact on daily life, but the neural mechanisms behind post-COVID fatigue remain unclear.</jats:p><jats:p>We recruited 37 volunteers with self-reported fatigue after a mild COVID infection and carried out a battery of behavioural and neurophysiological tests assessing the central, peripheral and autonomic nervous systems.</jats:p><jats:p>In comparison to age and sex matched volunteers without fatigue (n=52), we show underactivity in specific cortical circuits, dysregulation of autonomic function, and myopathic change in skeletal muscle. Cluster analysis revealed no sub-groupings, suggesting post-COVID fatigue is a single entity with individual variation, rather than a small number of distinct syndromes. Based on our analysis we were also able to exclude dysregulation in sensory feedback circuits and descending neuromodulatory control.</jats:p><jats:p>These abnormalities on objective tests may indicate novel avenues for principled therapeutic intervention, and could act as fast and reliable biomarkers for diagnosing and monitoring the progression of fatigue over time.</jats:p>

Journal article

Alix-Fages C, Del Vecchio A, Baz-Valle E, Santos-Concejero J, Balsalobre-Fernandez Cet al., 2022, The role of the neural stimulus in regulating skeletal muscle hypertrophy, EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY, Vol: 122, Pages: 1111-1128, ISSN: 1439-6319

Journal article

de Oliveira DS, Casolo A, Balshaw TG, Maeo S, Lanza MB, Martin NRW, Maffulli N, Kinfe TM, Eskofier B, Folland JP, Farina D, Del Vecchio Aet al., 2022, Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>High-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.</jats:p></jats:sec><jats:sec><jats:title>Approach</jats:title><jats:p>We 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 (ACSA<jats:sub>max</jats:sub>), 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 (&gt;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.</jats:p></jats:sec><jats:sec><jats:title>Main results</jats:title><jats:p>ST 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 pos

Journal article

Škarabot J, Folland JP, Holobar A, Baker SN, Del Vecchio Aet al., 2022, Reticulospinal drive increases maximal motoneuron output in humans

<jats:title>Abstract</jats:title><jats:p>Maximal rate of force development in adult humans is determined by the maximal motoneuron output, however the origin of the underlying synaptic inputs remains unclear. Here, we tested a hypothesis that the maximal motoneuron output will increase in response to a startling cue, a stimulus that purportedly activates the pontomedullary reticular formation neurons that make mono- and disynaptic connections to motoneurons via fast-conducting axons. Twenty-two men were required to produce isometric knee extensor forces “as fast and as hard” as possible from rest to 75% of maximal voluntary force, in response to visual (VC), visual-auditory (VAC), or visual-startling cue (VSC). Motoneuron activity was estimated via decomposition of high-density surface electromyogram recordings over the vastus lateralis and medialis muscles. Reaction time was significantly shorter in response to VSC compared to VAC and VC (i.e., the StartReact effect). The VSC further elicited faster neuromechanical responses including a greater number of discharges per motor unit per second and greater maximal rate of force development, with no differences between VAC and VC. We provide evidence, for the first time, that the synaptic input to motoneurons increases in response to a startling cue, suggesting a contribution of subcortical pathways to maximal motoneuron output in humans, likely originating from the pontomedullary reticular formation.</jats:p>

Journal article

Del Vecchio A, Germer C, Kinfe TM, Nuccio S, Hug F, Eskofier B, Farina D, Enoka RMet al., 2022, Common synaptic inputs are not distributed homogeneously among the motor neurons that innervate synergistic muscles

<jats:title>Abstract</jats:title><jats:p>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 <jats:italic>neural modules</jats:italic>, 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.<

Journal article

Del Vecchio A, Casolo A, Dideriksen JL, Aagaard P, Felici F, Falla D, Farina Det al., 2022, Lack of increased rate of force development after strength training is explained by specific neural, not muscular, motor unit adaptations, JOURNAL OF APPLIED PHYSIOLOGY, Vol: 132, Pages: 84-94, ISSN: 8750-7587

Journal article

Del Vecchio A, Jones RHA, Schofield IS, Kinfe TM, Ibáñez J, Farina D, Baker SNet al., 2021, Interfacing Spinal Motor Units in Non-Human Primates Identifies a Principal Neural Component for Force Control Constrained by the Size Principle, Publisher: Cold Spring Harbor Laboratory

<jats:title>ABSTRACT</jats:title><jats:p>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·m·s<jats:sup>-1</jats:sup>). 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

Working paper

Ting JE, Del Vecchio A, Sarma D, Verma N, Colachis SC, Annetta N, Collinger JL, Farina D, Weber DJet al., 2021, Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array, JOURNAL OF NEUROPHYSIOLOGY, Vol: 126, Pages: 2104-2118, ISSN: 0022-3077

Journal article

Casolo A, Del Vecchio A, Balshaw TG, Maeo S, Lanza MB, Felici F, Folland JP, Farina Det al., 2021, Behavior of motor units during submaximal isometric contractions in chronically strength-trained individuals, JOURNAL OF APPLIED PHYSIOLOGY, Vol: 131, Pages: 1584-1598, ISSN: 8750-7587

Journal article

Schrader B, Schrader J, Vaske B, Elsaesser A, Haller H, del Vecchio A, Koziolek M, Gehlenborg E, Lueders Set al., 2021, Football beats hypertension: results of the 3F (Fit&Fun with Football) study, JOURNAL OF HYPERTENSION, Vol: 39, Pages: 2290-2296, ISSN: 0263-6352

Journal article

Nuccio S, Del Vecchio A, Casolo A, Labanca L, Rocchi JE, Felici F, Macaluso A, Mariani PP, Falla D, Farina D, Sbriccoli Pet al., 2021, Deficit in knee extension strength following anterior cruciate ligament reconstruction is explained by a reduced neural drive to the vasti muscles, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 599, Pages: 5103-5120, ISSN: 0022-3751

Journal article

Del Vecchio A, Castellini C, Beckerle P, 2021, Peripheral Neuroergonomics - An Elegant Way to Improve Human-Robot Interaction?, FRONTIERS IN NEUROROBOTICS, Vol: 15, ISSN: 1662-5218

Journal article

Germer CM, Farina D, Elias LA, Nuccio S, Hug F, Del Vecchio Aet al., 2021, Surface EMG crosstalk quantified at the motor unit population level for muscles of the hand, thigh, and calf, Journal of Applied Physiology, Vol: 131, Pages: 808-820, ISSN: 1522-1601

Crosstalk is an important source of error in interpreting surface electromyography (EMG) signals. Here, we aimed at characterizing crosstalk for three groups of synergistic muscles by the identification of individual motor unit action potentials. Moreover, we explored whether spatial filtering (single and double differential) of the EMG signals influences the level of crosstalk. Three experiments were conducted. Participants (total twenty-five) performed isometric contractions at 10% of the maximal voluntary contraction (MVC) with digit muscles and knee extensors, and at 30% MVC with plantar flexors. High-density surface EMG signals were recorded and decomposed into motor unit spike trains. For each muscle, we quantified the crosstalk induced to neighboring muscles and the level of contamination by the nearby muscle activity. We also estimated the influence of crosstalk on the EMG power spectrum and intermuscular correlation. Most motor units (80%) generated significant crosstalk signals to neighboring muscle EMG in monopolar recording mode, but this proportion decreased with spatial filtering (50% and 42% for single and double differential, respectively). Crosstalk induced overestimations of intermuscular correlation and has a small effect on the EMG power spectrum, which indicates that crosstalk is not reduced with high-pass temporal filtering. Conversely, spatial filtering diminished the crosstalk magnitude and the overestimations of intermuscular correlation, confirming to be an effective and simple technique to reduce crosstalk. This paper presents a new method for the identification and quantification of crosstalk at the motor unit level and clarifies the influence of crosstalk on EMG interpretation for muscles with different anatomy.

Journal article

Hug F, Avrillon S, Del Vecchio A, Casolo A, Ibanez J, Nuccio S, Rossato J, Holobar A, Farina Det al., 2021, Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators, Journal of Electromyography and Kinesiology, Vol: 58, Pages: 102548-102548, ISSN: 1050-6411

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.

Journal article

Del Vecchio A, Casolo A, Dideriksen J, Aagaard P, Felici F, Falla D, Farina Det al., 2021, Why humans are stronger but not faster after isometric strength training: specific neural, not muscular, motor unit adaptations

<jats:title>Abstract</jats:title><jats:p>While maximal force increases following short-term isometric strength training, the rate of force development (RFD) may remain relatively unaffected. The underlying neural and muscular mechanisms during rapid contractions after strength training are largely unknown. Since strength training increases the neural drive to muscles, it may be hypothesized that there are distinct neural or muscular adaptations determining the change in RFD independently of an increase in maximal force. Therefore, we examined motor unit population data during the rapid generation of force before and after four weeks of strength training. We observed that strength training did not change the RFD because it did not influence the number of motor units recruited per second or their initial discharge rate during rapid contractions. While strength training did not change motoneuron behaviour in the force increase phase of rapid contractions, it increased the discharge rate of motoneurons (by ∼4 spikes/s) when reaching the plateau phase (∼150 ms) of the rapid contractions, determining an increase in maximal force production. Computer simulations with a motor unit model that included neural and muscular properties, closely matched the experimental observations and demonstrated that the lack of change in RFD following training is primarily mediated by an unchanged maximal recruitment speed of motoneurons. These results demonstrate that maximal force and contraction speed are determined by different adaptations in motoneuron behaviour following strength training and indicate that increases in the recruitment speed of motoneurons are required to evoke training-induced increases in RFD.</jats:p>

Journal article

Ting JE, Vecchio AD, Sarma D, Colachis SC, Annetta NV, Collinger JL, Farina D, Weber DJet al., 2021, Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array

<jats:title>Abstract</jats:title><jats:p>Motor neurons in the brain and spinal cord convey information about motor intent that can be extracted and interpreted to control assistive devices, such as computers, wheelchairs, and robotic manipulators. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use. Here, we demonstrate that a wearable sensor array can detect residual motor unit activity in paralyzed muscles after severe cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor neurons below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the EMG power (RMS) or motor unit firing rates with median classification accuracies &gt;75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88 ± 24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor neurons below the level of injury in a person with tetraplegia after motor complete SCI.</jats:p><jats:sec><jats:title>Significance Statement</jats:title><jats:p>A wearable electrode array and machine learning methods were used to record and decode myoelectric signals and motor unit firing in paralyzed muscles of a person with motor complete tetraplegia. Motor unit action p

Journal article

Hug F, Avrillon S, Del Vecchio A, Casolo A, Ibanez J, Nuccio S, Rossato J, Holobar A, Farina Det al., 2021, Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>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 &gt; 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.</jats:p>

Working paper

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