1,721,596 research outputs found
Gomes et al. 2022. ANGPTL4 is a potential driver of HCV-induced peripheral insulin resistance
No full textDeposit includes raw data, intermediate and final analysis files used to build up figures and tables of the paper "ANGPTL4 is a potential driver of HCV-induced peripheral insulin resistance" by Gomes et al. 2022
Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol
No full textResearch on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods similar to 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift (+/- 1 cm) with respect to the classic features (p<0.05). When even just one channel was noisy, the classification accuracy dropped by similar to 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches.ERC AdvancedGrant DE-MOVE [267888
Power spectrum of the rectified EMG: when and why is rectification beneficial for identifying neural connectivity?
No full textObjective. The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons. Approach. We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals. Main results. Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs. Significance. This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.European Research Council Advanced [267888
Neural correlates of task-related changes in physiological tremor
No full textAppropriate control of muscle contraction requires integration of command signals with sensory feedback. Sensorimotor integration is often studied under conditions in which muscle force is controlled with visual feedback. While it is known that alteration of visual feedback can influence task performance, the underlying changes in neural drive to the muscles are not well understood. In this study, we characterize the frequency content of force fluctuations and neural drive when production of muscle force is target guided versus self guided. In the self-guided condition, subjects performed isometric contractions of the first dorsal interosseous (FDI) muscle while slowly and randomly varying their force level. Subjects received visual feedback of their own force in order to keep contractions between 6% and 10% of maximum voluntary contraction (MVC). In the target-guided condition, subjects used a display of their previously generated force as a target to track over time. During target tracking, force tremor increased significantly in the 3-5 and 7-9 Hz ranges, compared with self-guided contractions. The underlying changes in neural drive were assessed by coherence analysis of FDI motor unit activity. During target-guided force production, pairs of simultaneously recorded motor units showed less coherent activity in the 3-5 Hz frequency range but greater coherence in the 7-9 Hz range than in the self-guided contractions. These results show that the frequency content of common synaptic input to motoneurons is altered when force production is visually guided. We propose that a change in stretch-reflex gain could provide a potential mechanism for the observed changes in force tremor and motor unit coherence
The effective neural drive to muscles is the common synaptic input to motor neurons
No full textWe analysed the transformation of synaptic input to the pool of motor neurons into the neural drive to the muscle. The aim was to explain the relations between common oscillatory signals sent to motor neurons and the effective component of the neural signal sent to muscles as output of the spinal cord circuitries. The approach is based on theoretical derivations, computer simulations, and experiments. It is shown theoretically that for frequencies smaller than the average discharge rates of the motor neurons, the pool of motor neurons determines a pure amplification of the frequency components common to all motor neurons, so that the common input is transmitted almost undistorted and the non-common components are strongly attenuated. The effective neural drive to the muscle thus mirrors the common synaptic input to motor neurons. The simulations with three models of motor neuron confirmed the theoretical results by showing that the coherence function between common input components and the neural drive to the muscle tends to 1 when increasing the number of active motor neurons. This result, which was relatively insensitive to the type of model used, was also supported experimentally by observing that, in the low-pass signal bandwidth, the peak in coherence between groups of motor units of the abductor digiti minimi muscle of five healthy subjects tended to 1 when increasing the number of motor units. These results have implications for our understanding of the neural control of muscles as well as for methods used for estimating the strength of common input to populations of motor neurons.European Research Council Advanced Grant DEMOVE [267888
Robust estimation of average twitch contraction forces of populations of motor units in humans
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Factors influencing the estimates of correlation between motor unit activities in humans
No full textBACKGROUND: Alpha motoneurons receive common synaptic inputs from spinal and supraspinal pathways. As a result, a certain degree of correlation can be observed between motoneuron spike trains during voluntary contractions. This has been studied by using correlation measures in the time and frequency domains. These measures are interpreted as reflecting different types of connectivity in the spinal networks, although the relation between the degree of correlation of the output motoneuron spike trains and of their synaptic inputs is unclear. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we analyze theoretically this relation and we complete this analysis by simulations and experimental data on the abductor digiti minimi muscle. The results demonstrate that correlation measures between motoneuron output spike trains are inherently influenced by the discharge rate and that this influence cannot be compensated by normalization. Because of the influence of discharge rate, frequency domain measures of correlation (coherence) do not identify the full frequency content of the common input signal when computed from pairs of motoneurons. Rather, an increase in sampling rate is needed by using cumulative spike trains of several motoneurons. Moreover, the application of averaging filters to the spike trains influences the magnitude of the estimated correlation levels calculated in the time, but not in the frequency domain (coherence). CONCLUSIONS: It is concluded that the analysis of coherence in different frequency bands between cumulative spike trains of a sufficient number of motoneurons provides information on the spectrum of the common synaptic input. Nonetheless, the absolute values of coherent peaks cannot be compared across conditions with different cumulative discharge rates
Spike-triggered averaging provides inaccurate estimates of motor unit twitch properties under optimal conditions
Full text linkSpike-triggered averaging is a commonly used technique for the estimation of motor unit twitches during voluntary contractions, although the obtained twitch estimates are known to be inaccurate in several conditions. Nevertheless, it is commonly assumed that a careful selection of the triggers may reduce the inaccuracy. This study aimed to analyze the impact of trigger selection criteria and thereby to identify the minimum estimation errors using a computational neuromuscular model. Force signals of five-minute duration were simulated at 10 contraction levels between 1 and 30% of the maximal voluntary contraction level (MVC) for motor unit pools of varying size (100, 300, and 800 motor units). Triggers were selected based on the inter-spike intervals (minimal value: 90–175 ms) and the number of triggers (minimal value: 100–800). The simulation results indicated that a minimum of 400 triggers with inter-spike intervals >125 ms are needed to achieve the most accurate twitch estimates. Even under these conditions, however, a substantial estimation error remained (11.8–31.2% for different twitch parameters for simulations with 100 motor units). The error increased with the innervation number. The study demonstrates the fundamental inaccuracy of twitch estimates from spike-triggered averaging, which has important implications for our understanding of muscular adaptations.</p
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