The influence of spatial distribution of motor unit density and level of motor unit recruitment on the estimation of muscle fiber conduction velocity: A simulation study

Background This study aims to investigate how alterations in the spatial distribution of motor unit (MU) density, such as those resulting from neuromuscular disorders, affect the accuracy of muscle fiber conduction velocity (MFCV) estimation, and to examine whether different levels of MU recruitment can mitigate these effects. Methods We used the Fuglevand model to simulate three different MU densities across the muscle: edge-clustered, center-clustered, and a uniform distribution. MFCV was estimated from simulated double differential surface electromyography (sEMG) signals at four MU recruitment levels using a cross-correlation method. Results MU density spatial distribution influenced MFCV estimation accuracy. The highest estimation accuracy was achieved in regions of high MU density and lowest in regions of low MU density. Level of MU recruitment influenced MFCV estimation accuracy, with higher recruitment partially compensating for estimation errors. A lower number of MUs reduced overall estimation accuracy. Conclusions The spatial distribution of MU density is an important factor in MFCV estimation. This study provides quantitative benchmarks for how spatial variations in MU density affect MFCV estimation accuracy. The finding highlights the importance of MU density, recruitment level, and total number of MUs in estimating MFCV, all of which may be altered in patients with neuromuscular disorders.