Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-07-26 DOI:10.1088/1741-2552/ace6fc
Robin Rohlén, Marco Carbonaro, Giacinto L Cerone, Kristen M Meiburger, Alberto Botter, Christer Grönlund
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Abstract

Objective.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.Approach.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.Main results.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.Significance.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.

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超快超声波的空间重复性成分与人体等长收缩时的运动单元活动有关。
目的:超快超声(UUS)成像已被用于检测与单个运动单元(MU)相关的肌肉内机械动力学。从超声波序列中检测单个运动单元需要将速度场分解为多个分量,每个分量由图像和信号组成。这些成分可能与假定的运动单元活动或虚假运动(噪音)有关。通过将信号与针刺肌电图(EMG)获得的 MU 发火进行比较,可以区分假定的 MU 和噪声。在此,我们研究了图像在短暂时间间隔内的可重复性是否可作为区分低力等长收缩中推定 MU 和噪声的标准。方法:UUS 图像和高密度表面肌电图(HDsEMG)同时记录了 5 名健康受试者肱二头肌中 99 个 MU 的活动。通过 HDsEMG 分解确定的 MU 被用作评估超声组件结果的参考。对于每次收缩,来自同一八秒超声波记录的速度序列被分离成连续的两秒时程并进行分解。为了评估组件图像在不同时间段的重复性,我们计算了 Jaccard 相似系数 (JSC)。主要结果:所有与 MU 匹配的成分的 JSC 均大于 0.38,表明它们具有可重复性,并占 HDsEMG 检测到的 MU 的三分之一(1.8 ± 1.6 个匹配,4.9 ± 1.8 个 MU)。可重复成分(JSC > 0.38)占总成分的 14%(6.5 ± 3.3 个成分)。这些发现与我们的假设一致,即序列内重复性可将推定的MU从噪声中区分出来,并可用于减少数据。这项研究为开发独立的方法来识别UUS序列中的MU以及对MU进行实时成像奠定了基础。这些方法与研究肌肉神经力学和设计新型神经接口息息相关。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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