Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task

Sarcher

et al. 2021

Preprint

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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 through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study, we identified the common synaptic inputs received by motor neurons during an isometric multi-joint task. We decoded the spiking activities of dozens of spinal motor neurons innervating six lower limb muscles in 10 participants. Furthermore, we analyzed these activities by identifying 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. Moreover, we showed that common input to motor neurons is an essential feature of the neural control of movement. We conclude that the central nervous system controls flexible groups of motor neurons by distributing common inputs to substantially reduce the dimensionality of movement control.

Section: Methodological Considerationssupporting

confidence: 64%

Networks of common inputs to motor neurons of the lower limb reveal neural synergies that only partly overlap with muscle innervation

Sarcher

et al. 2021

Preprint

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“…Together with the initial automatic decomposition data, a standardized list of instructions was given to each operator. Specifically, they had to: i) Read a tutorial (Del Vecchio et al, 2020) to make sure that they all had the same basic knowledge on HDsEMG decomposition, ii) Inspect and edit the motor unit spike trains over the whole contraction, iii) Remove unreliable motor units that had a pulse-to-noise ratio lower than 30 dB , as classically done in the literature (Avrillon et al, 2021;Laine et al, 2015), iv) Note the time that they took to edit each file.…”

Section: Manual Edition Of the Decomposition Resultsmentioning

confidence: 99%

Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators

Journal of Electromyography and Kinesiology

Vecchio

et al. 2021

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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.

“…Remove unreliable motor units that had a pulse-to-noise ratio lower than 30 dB (Holobar et al, 2014), as classically done in the literature (Avrillon et al, 2021; Laine et al, 2015),…”

Section: Discussionmentioning

confidence: 99%

Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators

Vecchio

et al. 2021

Preprint

Self Cite

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 contraction 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 x 11 contractions/muscles x 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.