Department of Biomedical Engineering, Lund University, Lund, Sweden.
Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden.
J Neural Eng. 2023 Jul 26;20(4). doi: 10.1088/1741-2552/ace6fc.
Ultrafast ultrasound (UUS) imaging has been used to detect intramuscular mechanical dynamics associated with single motor units (MUs). Detecting MUs from ultrasound sequences requires decomposing a velocity field into components, each consisting of an image and a signal. These components can be associated with putative MU activity or spurious movements (noise). The differentiation between putative MUs and noise has been accomplished by comparing the signals with MU firings obtained from needle electromyography (EMG). Here, we examined whether the repeatability of the images over brief time intervals can serve as a criterion for distinguishing putative MUs from noise in low-force isometric contractions.UUS images and high-density surface EMG (HDsEMG) were recorded simultaneously from 99 MUs in the biceps brachii of five healthy subjects. The MUs identified through HDsEMG decomposition were used as a reference to assess the outcomes of the ultrasound-based components. For each contraction, velocity sequences from the same eight-second ultrasound recording were separated into consecutive two-second epochs and decomposed. To evaluate the repeatability of components' images across epochs, we calculated the Jaccard similarity coefficient (JSC). JSC compares the similarity between two images providing values between 0 and 1. Finally, the association between the components and the MUs from HDsEMG was assessed.All the MU-matched components had JSC > 0.38, indicating they were repeatable and accounted for about one-third of the HDsEMG-detected MUs (1.8 ± 1.6 matches over 4.9 ± 1.8 MUs). The repeatable components (JSC > 0.38) represented 14% of the total components (6.5 ± 3.3 components). These findings align with our hypothesis that intra-sequence repeatability can differentiate putative MUs from noise and can be used for data reduction.This study provides the foundation for developing stand-alone methods to identify MU in UUS sequences and towards real-time imaging of MUs. These methods are relevant for studying muscle neuromechanics and designing novel neural interfaces.
超快速超声 (UUS) 成像已被用于检测与单个运动单位 (MU) 相关的肌肉内机械动力学。从超声序列中检测 MU 需要将速度场分解为组成部分，每个组成部分都由图像和信号组成。这些组件可以与假定的 MU 活动或虚假运动（噪声）相关联。通过将信号与从针式肌电图 (EMG) 获得的 MU 发射进行比较，可以区分假定的 MU 和噪声。在这里，我们研究了在短暂的时间间隔内，图像的可重复性是否可以作为区分低力等长收缩中假定的 MU 和噪声的标准。UUS 图像和高密度表面肌电图 (HDsEMG) 同时从五名健康受试者的肱二头肌中记录了 99 个 MU。通过 HDsEMG 分解确定的 MU 被用作评估超声基组件结果的参考。对于每个收缩，来自相同 8 秒超声记录的速度序列被分成连续的 2 秒时段并进行分解。为了评估组件图像在各时段之间的可重复性，我们计算了 Jaccard 相似系数 (JSC)。JSC 比较两个图像之间的相似性，提供 0 到 1 之间的值。最后，评估了组件与来自 HDsEMG 的 MU 之间的关联。所有与 MU 匹配的组件的 JSC 均大于 0.38，表明它们是可重复的，占 HDsEMG 检测到的 MU 的三分之一左右（4.9 ± 1.8 MU 中有 1.8 ± 1.6 个匹配）。可重复的组件（JSC > 0.38）占总组件的 14%（6.5 ± 3.3 个组件）。这些发现与我们的假设一致，即序列内的可重复性可以区分假定的 MU 和噪声，并可用于数据减少。这项研究为开发在 UUS 序列中识别 MU 的独立方法以及实现 MU 的实时成像提供了基础。这些方法与研究肌肉神经力学和设计新型神经接口有关。
