Does strict validation criteria for individual motor units alter population-based regression models of the motor unit pool?

• Published in: Experimental brain research 2020-11, Vol.238 (11), p.2475-2485
• Main Authors: Hernandez-Sarabia, Jesus A., Luera, Micheal J., Barrera-Curiel, Alejandra, Estrada, Carlos A., DeFreitas, Jason M.
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Biomedicine; Neurology; Neurosciences; Research Article
• ISSN: 0014-4819; 1432-1106
• DOI: 10.1007/s00221-020-05906-8

The purpose of this study was to determine if the implementation of a strict validation procedure, designed to limit the inclusion of inaccuracies from the decomposition of surface electromyographic (sEMG) signals, affects population-based motor unit (MU) analyses. Four sEMG signals were obtained from the vastus lateralis of 59 participants during isometric contractions at different relative intensities [30%, 70%, and 100% of maximal voluntary contraction (MVC)], and its individual motor unit potential trains (MUPTs) were extracted. The MUPTs were then excluded (ISIval) based on the coefficient of variation and histogram of the interspike intervals (ISI), the absence of additional clusters that reveals missed or additional firings, and more. MU population-based regression models (i.e., modeling the entire motor unit pool) were performed between motor unit potential size (MUP SIZE ), mean firing rate (MFR), and recruitment threshold (RT%) separately for DSDC Only (includes all MUPTs without the additional validation performed) and ISIval data at each contraction intensity. The only significant difference in regression coefficients between DSDC Only and ISIval was for the intercepts of the MUP SIZE /MFR at 100% MVC. The validation had no other significant effect on any of the other regression coefficients for each of the contraction intensities. Our findings suggest that even though the decomposition of surface signals leads to some inaccuracies, these errors have limited effects on the regression models used to estimate the behavior of the whole pool. Therefore, we propose that motor unit population-based regression models may be robust enough to overcome decomposition-induced errors at the individual MU level.