Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

Abstract

Objective: The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, i.e., the muscle deformation, has not been widely studied. To address this gap, we analysed the kinematics of muscle units in natural contractions. Approach: We combined high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings, at 1000 frames per second, from the tibialis anterior muscle to measure the motion of the muscular tissue caused by individual MU contractions. The MU discharge times were identified online by decomposition of the HDsEMG and provided as biofeedback to 12 subjects who were instructed to keep the MU active at the minimum discharge rate (9.8 ± 4.7 pulses per second; force less than 10% of the maximum). The series of discharge times were used to identify the velocity maps associated with 51 single muscle unit movements with high spatio-temporal precision, by a novel processing method on the concurrently recorded US images. From the individual MU velocity maps, we estimated the region of movement, the duration of the motion, the contraction time, and the excitation-contraction (E-C) coupling delay. Main results: Individual muscle unit motions could be reliably identified from the velocity maps in 10 out of 12 subjects. The duration of the motion, total contraction time, and E-C coupling were 17.9 ± 5.3 ms, 56.6 ± 8.4 ms, and 3.8 ± 3.0 ms (n = 390 across 10 participants). The experimental measures also provided the first evidence of muscle unit twisting during voluntary contractions and MU territories with distinct split regions. Significance: The proposed method allows for the study of kinematics of individual MU twitches during natural contractions. The described measurements and characterisations open new avenues for the study of neuromechanics in healthy and pathological conditions.

Estimating the neural spike train from an unfused tetanic signal of low threshold motor units using convolutive blind source separation

“…A potential solution may be expanding current sEMG decomposition algorithms 10,11 to include spatial dependence or sparsity in addition to the temporal deconvolution and validating it using an authentic simulation model 28 . Yet, all these approaches need to deal with the motion of non-MU-related structures that hides a large part of the movement caused by a MU in ultrasound images 12 . One potential solution could be to use the spatiotemporal clutter filtering approach to improve the sensitivity to detect microvascular networks or blood flows corrupted by significant tissue or probe motion artefacts 29 .…”

“…Although including the temporal convolutive approach in ultrasound decomposition is promising, Lubel et al (2022) found that the motion of non-MU-related structures hides a large part of the movement caused by a MU in ultrasound images 12 . This finding is in line with the motivation for “over-decomposing” with the ultrasound decomposition method 3 (Fig.…”

Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units

“…Recent innovations have enabled imaging and analysis of MUs using magnetic resonance imaging (MRI) (Birkbeck et al, 2020;Heskamp et al, 2021) and ultrafast ultrasound (UUS) (Deffieux et al, 2008;Grönlund et al, 2013;Lubel et al, 2022). These imaging techniques provide high-resolution spatiotemporal information regarding the kinematics of the muscle fibres that are part of a MU.…”

A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics

“…Ultrasound imaging can detect individual MU activity during voluntary isometric contractions [7,9–16]. The principle of detecting MU activity is based on the depolarization of the MU fibres, resulting in subtle axial displacements.…”

“…Second, displacement velocity images are calculated based on the raw ultrasound (radio frequency) data [18]. Third, the activity of individual MUs is localized in the displacement velocity images through spike-triggered averaging (using neural discharges from EMG concurrently) [16] or using blind source separation (BSS) [7,9–15]. The BSS algorithm extracts spatiotemporal components, where a subset is MUs [14,19].…”