超快超声图像的空间分解以识别运动单位活动 - 与肌内和表面 EMG 的比较研究。

Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A comparative study with intramuscular and surface EMG.

机构信息

Department of Biomedical Engineering, Lund University, Lund, Sweden.

Department of Bioengineering, Imperial College London, London, UK.

出版信息

J Electromyogr Kinesiol. 2023 Dec;73:102825. doi: 10.1016/j.jelekin.2023.102825. Epub 2023 Sep 20.

DOI:10.1016/j.jelekin.2023.102825

PMID:37757604

Abstract

The smallest voluntarily controlled structure of the human body is the motor unit (MU), comprised of a motoneuron and its innervated fibres. MUs have been investigated in neurophysiology research and clinical applications, primarily using electromyographic (EMG) techniques. Nonetheless, EMG (both surface and intramuscular) has a limited detection volume. A recent alternative approach to detect MUs is ultrafast ultrasound (UUS) imaging. The possibility of identifying MU activity from UUS has been shown by blind source separation (BSS) of UUS images, using optimal separation spatial filters. However, this approach has yet to be fully compared with EMG techniques for a large population of unique MU spike trains. Here we identify individual MU activity in UUS images using the BSS method for 401 MU spike trains from eleven participants based on concurrent recordings of either surface or intramuscular EMG from forces up to 30% of the maximum voluntary contraction (MVC) force. We assessed the BSS method's ability to identify MU spike trains from direct comparison with the EMG-derived spike trains as well as twitch areas and temporal profiles from comparison with the spike-triggered-averaged UUS images when using the EMG-derived spikes as triggers. We found a moderate rate of correctly identified spikes (53.0 ± 16.0%) with respect to the EMG-identified firings. However, the MU twitch areas and temporal profiles could still be identified accurately, including at 30% MVC force. These results suggest that the current BSS methods for UUS can accurately identify the location and average twitch of a large pool of MUs in UUS images, providing potential avenues for studying neuromechanics from a large cross-section of the muscle. On the other hand, more advanced methods are needed to address the convolutive and partly non-linear summation of velocities for recovering the full spike trains.

摘要

人体最小的自主控制结构是运动单位（MU），由运动神经元及其支配的纤维组成。MU 已经在神经生理学研究和临床应用中进行了研究，主要使用肌电图（EMG）技术。然而，EMG（表面和肌内）的检测体积有限。最近一种替代方法是使用超快速超声（UUS）成像来检测 MU。通过使用最佳分离空间滤波器对 UUS 图像进行盲源分离（BSS），已经证明了从 UUS 中识别 MU 活动的可能性。然而，这种方法尚未与 EMG 技术在大量独特的 MU 尖峰列车中进行全面比较。在这里，我们使用 BSS 方法从 11 名参与者的 401 个 MU 尖峰列车中识别出单个 MU 活动，这些参与者的记录来自表面或肌内 EMG 的尖峰列车，最大用力的 30%（最大随意收缩（MVC）力。我们评估了 BSS 方法从直接比较与 EMG 衍生的尖峰列车以及与尖峰触发平均 UUS 图像比较的触发时的抽搐区和时间分布来识别 MU 尖峰列车的能力，当使用 EMG 衍生的尖峰作为触发时。我们发现，与 EMG 识别的放电相比，正确识别的尖峰的比例适中（53.0±16.0%）。然而，MU 抽搐区和时间分布仍然可以准确识别，包括在 30% MVC 力下。这些结果表明，当前用于 UUS 的 BSS 方法可以准确识别 UUS 图像中大量 MU 的位置和平均抽搐，为从肌肉的大横截面研究神经力学提供了潜在途径。另一方面，需要更先进的方法来解决速度的卷积和部分非线性求和，以恢复完整的尖峰列车。