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Estimation of the firing behaviour of a complete motoneuron pool by combining EMG signal decomposition and realistic motoneuron modelling

View ORCID ProfileArnault Caillet, View ORCID ProfileAndrew T.M. Phillips, View ORCID ProfileDario Farina, View ORCID ProfileLuca Modenese
doi: https://doi.org/10.1101/2022.02.21.481337
Arnault Caillet
1Department of Civil and Environmental Engineering, Imperial College London, SW7 2AZ, UK
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  • For correspondence: arnault.caillet17@imperial.ac.uk
Andrew T.M. Phillips
1Department of Civil and Environmental Engineering, Imperial College London, SW7 2AZ, UK
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Dario Farina
2Department of Bioengineering, Imperial College London, SW7 2AZ, UK
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Luca Modenese
1Department of Civil and Environmental Engineering, Imperial College London, SW7 2AZ, UK
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Abstract

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 EMG decomposition. 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 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. Representative applications of the method are also presented for the prediction of activity-dependant changes in MN intrinsic properties and in MN-driven neuromuscular modelling. 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 neurophysiology of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.

Author Summary Our 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, and to investigate neurophysiological properties of the human MN population during voluntary contractions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • CONFLICT OF INTEREST STATEMENT: The authors declare no competing financial interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 22, 2022.
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Estimation of the firing behaviour of a complete motoneuron pool by combining EMG signal decomposition and realistic motoneuron modelling
Arnault Caillet, Andrew T.M. Phillips, Dario Farina, Luca Modenese
bioRxiv 2022.02.21.481337; doi: https://doi.org/10.1101/2022.02.21.481337
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Estimation of the firing behaviour of a complete motoneuron pool by combining EMG signal decomposition and realistic motoneuron modelling
Arnault Caillet, Andrew T.M. Phillips, Dario Farina, Luca Modenese
bioRxiv 2022.02.21.481337; doi: https://doi.org/10.1101/2022.02.21.481337

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