Modulations in motor unit discharge are related to changes in fascicle length during isometric contractions

Abstract

Previous studies assessing relationships between muscle mechanics and neural activity have concurrently assessed changes in fascicle length (FL) and neural activation with electromyography (EMG) approaches with low spatial sampling from different muscle regions. This project aimed to assess changes in FL and motor unit (MU) firing, simultaneously, on the same region of interest, with high-density EMG electrodes transparent to ultrasound (HDEMG-US). EMG signals and ultrasound images were recorded simultaneously from the tibialis anterior of 10 participants, using a silicon matrix of 32 electrodes, while performing sustained-isometric ankle-dorsiflexion contractions, at diverse joint positions (0° and 30° plantar flexion) and torques (20% and 40% of maximum). EMG signals were decomposed into individual MUs and changes in FL were assessed with a fascicle-tracking algorithm. MU firing data was converted into a cumulative spike train (CST) that was cross-correlated with dorsiflexion torque (CST-torque) and FL (CST-FL). On average, 7 (3) MUs were identified across contractions. Cross-correlations showed that CST could explain on average 60% (range: 31-85%) and 71% (range: 0.31-0.88) of the variance in FL and torque, respectively. Cross-correlation lags revealed that the delay between CST-FL (~75ms) was smaller than CST-torque (~150ms, p<0.001). Finally, we could observe that both delays increased by ~20ms at 30° of plantar flexion (p<0.05). This study is the first to demonstrate the feasibility of recording single MU activity with HDEMG-US whilst simultaneously evaluating changes in FL. The findings show a close relationship between dorsiflexion torque, FL and CST, also allowing quantification of the delay between each of these signals.

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

“…In Figure 13, the decreasing eND N and eND N r (orange and blue dotted traces) may accurately describe a gradually increasing sharing of the ankle torque between synergistic muscles initially taken by the GM muscle, which the constant validation trace does not capture. To answer such limitations, the predicted eND N should be validated against a direct measure of muscle force, which can be performed as in other recent studies (Dick et al, 2017; Martinez-Valdes et al, 2021; Waasdorp et al, 2021) with ultrasound measurements of the muscle fascicle or of the muscle tendon concurrently obtained with (HD)EMG recordings of the muscle activity.…”

“…In Figure 13, the decreasing 𝑒𝑁𝐷 𝑁 and 𝑒𝑁𝐷 𝑁 𝑟 (orange and blue dotted traces) may accurately describe a gradually increasing sharing of the ankle torque between synergistic muscles initially taken by the GM muscle, which the constant validation trace does not capture. To answer such limitations, the predicted 𝑒𝑁𝐷 𝑁 should be validated against a direct measure of muscle force, which can be performed as in other recent studies [13,14,117] Limitation 2 -Sensitivity of the method to input HDEMG data While the method predicts a list of simulated spike trains 𝑠𝑝 𝑗 𝑠𝑖𝑚 (𝑡) and a 𝑒𝑁𝐷 𝑁 that more accurately describes the MN pool behaviour than the experimental 𝑠𝑝 𝑖 𝑒𝑥𝑝 (𝑡) and 𝑒𝑁𝐷 𝑁 𝑟 , as discussed previously, the accuracy of these predictions (Figure 12 and Figure 13) remains sensitive to the distribution in the entire MN pool of the 𝑁 𝑟 MNs identified experimentally, reported in the third column of Table 2.…”

“…Identifying the recruitment and firing dynamics of MNs is fundamental for understanding the neural strategies controlling human voluntary motion, with applications in sport sciences (Felici & Del Vecchio, 2020; Watanabe, K. et al, 2021; Maeda et al, 2021), and neurological and musculoskeletal rehabilitation (Jordanić et al, 2016; Fang et al, 2020; Pilkar et al, 2020; Kisiel-Sajewicz et al, 2020; Nishikawa et al, 2021). Determining the MN-specific contributions to the MN population activity also allows more realistic control of neuromuscular models (Callahan et al, 2013; Potvin & Fuglevand, 2017; Kim & Kim, 2018; Carriou et al, 2019), investigation of muscle neuromechanics (Waasdorp et al, 2021; Martinez-Valdes et al, 2021), prediction of limb motion from MN-specific behaviour (Chen et al, 2020), or improvement in human-machine interfacing and neuroprosthetics (Farina et al, 2017; Farina et al, 2021).…”

“…Determining the MN-specific contributions to the MN population activity also allows more realistic control of neuromuscular models [9][10][11][12], investigation of muscle neuromechanics [13,14], prediction of limb motion from MN-specific behaviour [15], or improvement in human-machine interfacing and neuroprosthetics [16,17].…”

“…Identifying the recruitment and firing dynamics of MNs is fundamental for understanding the neural strategies controlling human voluntary motion, with applications in sport sciences [1-3], and neurological and musculoskeletal rehabilitation [4-8]. Determining the MN-specific contributions to the MN population activity also allows more realistic control of neuromuscular models [9-12], investigation of muscle neuromechanics [13,14], prediction of limb motion from MN-specific behaviour [15], or improvement in human-machine interfacing and neuroprosthetics [16,17].…”