Physiological robustness of MScanFit to simulated motor unit loss and remodelling
- 1University of Alberta
Abstract
Introduction: MScanFit estimates motor unit number and size from compound muscle action potential (CMAP) scans, but its accuracy may depend on the physiological processes that shape motor unit loss and remodelling. We reproduced the general dynamic remodelling framework used in the original MScanFit simulation and expanded it to test MScanFit robustness across physiologically heterogeneous conditions. Methods: Generative computational models simulated CMAP scans from motor unit pools undergoing progressive loss from 160 to 5 surviving motor units. Twelve remodelling conditions combined denervation pattern (random or selective), reinnervation method (random, distributive, or size-weighted), and neuromuscular resilience (20% or 60%); two additional conditions modelled denervation without collateral reinnervation. MScanFit estimates were compared with the known motor unit numbers and mean motor unit sizes used to generate each scan. Results: MScanFit reproduced the principal behaviour reported in the original simulation and generally tracked progressive motor unit loss across heterogeneous remodelling conditions. However, motor unit number estimation (MUNE) error changed systematically with remodelling physiology. Selective denervation and distributive reinnervation increased error at several intermediate stages of motor unit loss, whereas 60% resilience produced greater error than 20% resilience at every stage after remodelling began. Motor unit size estimates also tracked the underlying increase in mean unit size, but their accuracy was more strongly affected by remodelling, particularly with 60% resilience and advanced motor unit loss. Conclusion: MScanFit motor unit number estimates were broadly robust to substantial motor unit loss and neuromuscular remodelling but were not physiologically invariant. Denervation and collateral reinnervation systematically influenced the magnitude and direction of estimation error, indicating that variation in MScanFit performance can arise from differences in the underlying motor unit population. These findings support MScanFit as a robust measure of motor unit loss across heterogeneous neuromuscular phenotypes and show that some of its estimation variability has a physiological basis.
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BibTeX
@misc{ahmed2026physiological,
title = {{Physiological robustness of MScanFit to simulated motor unit loss and remodelling}},
author = {D. Ahmed and M. Almokdad and K. E. Jones},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.28.747933},
url = {https://www.biorxiv.org/content/10.64898/2026.08.28.747933v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Physiological robustness of MScanFit to simulated motor unit loss and remodelling
AU - D. Ahmed
AU - M. Almokdad
AU - K. E. Jones
PY - 2026
DA - 2026/08/28
PB - bioRxiv
DO - 10.64898/2026.08.28.747933
UR - https://www.biorxiv.org/content/10.64898/2026.08.28.747933v1
AB - Introduction: MScanFit estimates motor unit number and size from compound muscle action potential (CMAP) scans, but its accuracy may depend on the physiological processes that shape motor unit loss and remodelling. We reproduced the general dynamic remodelling framework used in the original MScanFit simulation and expanded it to test MScanFit robustness across physiologically heterogeneous conditions. Methods: Generative computational models simulated CMAP scans from motor unit pools undergoing progressive loss from 160 to 5 surviving motor units. Twelve remodelling conditions combined denervation pattern (random or selective), reinnervation method (random, distributive, or size-weighted), and neuromuscular resilience (20% or 60%); two additional conditions modelled denervation without collateral reinnervation. MScanFit estimates were compared with the known motor unit numbers and mean motor unit sizes used to generate each scan. Results: MScanFit reproduced the principal behaviour reported in the original simulation and generally tracked progressive motor unit loss across heterogeneous remodelling conditions. However, motor unit number estimation (MUNE) error changed systematically with remodelling physiology. Selective denervation and distributive reinnervation increased error at several intermediate stages of motor unit loss, whereas 60% resilience produced greater error than 20% resilience at every stage after remodelling began. Motor unit size estimates also tracked the underlying increase in mean unit size, but their accuracy was more strongly affected by remodelling, particularly with 60% resilience and advanced motor unit loss. Conclusion: MScanFit motor unit number estimates were broadly robust to substantial motor unit loss and neuromuscular remodelling but were not physiologically invariant. Denervation and collateral reinnervation systematically influenced the magnitude and direction of estimation error, indicating that variation in MScanFit performance can arise from differences in the underlying motor unit population. These findings support MScanFit as a robust measure of motor unit loss across heterogeneous neuromuscular phenotypes and show that some of its estimation variability has a physiological basis.
ER -