Three-dimensional amplitude characteristics of masseter motor units and representativeness of extracted motor unit samples

Lapatki, Bernd G.; Eiglsperger, Ulrike; Schindler, H. J.; Radeke, Johanna; Holobar, Aleš; Dijk, Hans van

doi:10.1016/j.clinph.2018.12.008

Cited by 8 publications

(9 citation statements)

References 28 publications

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“…Motor unit anatomy refers to the distribution of its fibres across the muscle and the cross-sectional area enclosing these fibres refers to its territory. While muscle anatomy varies greatly both within and between individuals, motor unit territories show common features such as size variation, overlap with other territories, and irregular shape [8][9][10]. Additionally, motor unit types may be restricted to certain parts of the muscle cross-sectional area [11,12].…”

Section: Introductionmentioning

confidence: 99%

Modelling motor units in 3D: Influence on muscle contraction and joint force via a proof of concept simulation

Saini

Klotz

Röhrle

2022

Preprint

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Functional heterogeneity of a skeletal muscle refers to its ability to produce a variety of force vectors through the localised, regional recruitment of motor units. Existing three-dimensional, macroscopic, continuum-mechanical muscle models (3D models) neglect motor unit anatomy and are thus ill-suited to investigate a muscle's functional heterogeneity. Here, we develop a method to generate and enrich 3D models with motor unit anatomy and activity information: First, a virtual innervation process is applied to group idealised computational fibres into motor units. Second, the discrete microstructure is homogenised to form continuous volumetric motor unit distributions. Third, motor unit activity is computed as a function of its firing times and twitch properties. As a proof of concept, we generated multiple masticatory models, each with different (masseter) motor unit distributions. The masticatory models were supplied with identical motor unit activity to simulate a sub-maximal static bite. The virtual innervation method captured physiological hallmark features of motor unit anatomy such as size, shape, position, and overlap. We found that motor unit anatomy significantly influenced bite force direction while having minimal impact on magnitude. Motor unit position played a more significant role than motor unit overlap. These results highlight the importance of modelling motor unit anatomy in understanding movement, particularly for muscles with complex internal architecture. This method provides an environment to investigate the interplay between motor control, skeletal muscle mechanics, and joint kinematics. Such investigations are relevant for the functional heterogeneity of healthy and pathological muscles.

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Section: Introductionmentioning

confidence: 99%

Modelling motor units in 3D: Influence on muscle contraction and joint force via a proof of concept simulation

Saini

Klotz

Röhrle

2022

Preprint

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“…Multi-scanning-EMG is a modified implementation of the scanning-EMG technique that allows to simultaneously record the electrical activity of several MUs in a single scan [136,92,60], i.e., with a single insertion of the scanning electrode. This provides a great advantage over the conventional scanning-EMG technique, in which the recording procedure would have to be repeated as many times as the number of MUs wanted to be investigated, implying one insertion per MU.…”

Section: Multi-scanning-emgmentioning

confidence: 99%

“…Therefore, the trigger electrode must be suitable to record the activity of a wide region of the volume conductor. In [136], two pairs of intramuscular fine-wire electrodes were used as trigger electrode, while in [60], a two-dimensional grid of 256 surface electrodes placed on the skin was used for this purpose. A decomposition algorithm is used to extract the firing pattern of the MUs from the trigger signal.…”

Section: Multi-scanning-emgmentioning

confidence: 99%

“…When multiple MUP discharges per position are recorded, artifacts can be removed by averaging the different discharges existing at each position using mean or median [119]. If this discharge averaging is followed by a spatial median filtering [136,60], a much more effective artifact elimination can be achieved. This is the procedure typically used to eliminate noise in scanning-EMG signals.…”

Section: Spacementioning

confidence: 99%

“…This would not only allow to estimate the parameters of the fibers of several MUs at the same time, but would also give a more detailed image of the muscular section (in an environment close to the needle), allowing to analyze how the fibers of the different MUs are intermingled. Another important advantage of using the multiscanning-EMG technique is that the trigger needle can be replaced by an array of surface electrodes to extract the firing pattern of the MUs [60]. In this way, the recording setup would be simplified, since the same surface electrode array could be used both to extract the MU firing patterns and the rest of physiological information required by the estimation system.…”

Section: Future Linesmentioning

confidence: 99%

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Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

Corera¹,

Rodriguez‐Falces²

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In this thesis, several algorithmic procedures to estimate the MU fiber parameters from the waveform of a scanning-EMG signal (i.e., from the recording of a MUP in multiple positions along a linear corridor) have been developed. The different procedures correspond to different modifications of the scanning-EMG technique, which differ in the number of scanning needles and recording ports used to record the signal. The three proposed variants are: the 1-port recording, based on a single scanning needle with a single recording port placed at one side of the needle (i.e., a single fiber EMG needle); the 2-port recording, based on a single scanning needle, with two recording ports, placed at opposite sides of the needle; and the 4-port recording, based on two scanning needles, each one with two recording ports. The estimation system also uses a linear array of surface-EMG recordings to obtain complementary information about the MU. In this way, the recording setup to achieve the MU parameter estimation consists on a simultaneous surface- and scanning-EMG signal recording of the MUP. The estimation system has been evaluated for the three scanning-EMG recording configurations (1-port, 2-port, and 4-port), and compared to the case in which the estimation is performed from a MUP recorded at a single position. The evaluation has been done in a simulation framework, using state of the art models of the muscle, MUs, recruitment, and needles, and developing specific models for the simultaneous surface- and scanning-EMG recording process. This provides a controlled environment in which the performance of the system can be objectively quantified and evaluated.The results evidence that MU parameters are estimated much more accurately when using the scanning-EMG technique than when using a MUP recorded at a single position, corroborating the hypothesis that the use of signals recorded at multiple positions enhances the parameter estimation. Among the three proposed recording configurations, the poorest estimation results have been obtained for the 1-port configuration which, moreover, is only capable of estimating the MU fibers at one side of the needle. The 2-port configuration gives better results, and allows to estimate the MU fibers at both sides of the needle. The 4-port configuration is the one that provides the best performance, but it has the disadvantage of being the most difficult configuration to be physically implemented. In the view of these results, a deeper evaluation of the 2-port recording configuration is done. This is because it combines a good estimation performance with a relative ease to be physically implemented. An additional effort is done to calculate several global MU parameters, such as the MU fiber density, the average potential propagation velocity of the fibers, and the width of the innervation zone, from the resulting set of estimated fibers. These global MU parameters provide relevant physiological information from a clinical point of view. Hence global parameters connect the estimation system developed in this thesis with a future application in the diagnosis and follow-up of neuromuscular pathologies.

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Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

Corera

Malanda

Rodriguez‐Falces

et al. 2020

Med Biol Eng Comput

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Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked leastsquares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension.We refer to this algorithm as the multi masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. Results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal.

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Three-dimensional amplitude characteristics of masseter motor units and representativeness of extracted motor unit samples

Cited by 8 publications

References 28 publications

Modelling motor units in 3D: Influence on muscle contraction and joint force via a proof of concept simulation

Modelling motor units in 3D: Influence on muscle contraction and joint force via a proof of concept simulation

Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

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