Imperial College London
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Dr Andrew Phillips

Faculty of Engineering, Department of Civil and Environmental Engineering

Reader in Structural Biomechanics
 
 
 
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Contact

 

+44 (0)20 7594 6081andrew.phillips Website

 
 
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Assistant

 

Ms Ruth Bello +44 (0)20 7594 6040

 
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Location

 

433Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Caillet:2022:10.1101/2022.02.21.481337,
author = {Caillet, AH and Phillips, ATM and Farina, D and Modenese, L},
doi = {10.1101/2022.02.21.481337},
title = {Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling},
url = {http://dx.doi.org/10.1101/2022.02.21.481337},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>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 decomposition of electromyographical (EMG) signals. 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 single-compartment 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. A representative application of the method in MN-driven neuromuscular modelling is also presented. 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 neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.</jats:p><jats:sec><jats:title>Author Summary</jats:title>&
AU  - Caillet,AH
AU  - Phillips,ATM
AU  - Farina,D
AU  - Modenese,L
DO  - 10.1101/2022.02.21.481337
PY  - 2022///
TI  - Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling
UR  - http://dx.doi.org/10.1101/2022.02.21.481337
ER  -
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