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基于系统多尺度模型研究肌电图和肌音图的空间分辨率。

Investigating the spatial resolution of EMG and MMG based on a systemic multi-scale model.

机构信息

Institute for Modelling and Simulation of Biomechanical Systems, Pfaffenwaldring 5a, 70569, Stuttgart, Germany.

Stuttgart Centre for Simulation Science (SimTech), Pfaffenwaldring 5a, 70569, Stuttgart, Germany.

出版信息

Biomech Model Mechanobiol. 2022 Jun;21(3):983-997. doi: 10.1007/s10237-022-01572-7. Epub 2022 Apr 20.

DOI:10.1007/s10237-022-01572-7
PMID:35441905
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9132853/
Abstract

While electromyography (EMG) and magnetomyography (MMG) are both methods to measure the electrical activity of skeletal muscles, no systematic comparison between both signals exists. Within this work, we propose a novel in silico model for EMG and MMG and test the hypothesis that MMG surpasses EMG in terms of spatial selectivity, i.e. the ability to distinguish spatially shifted sources. The results show that MMG provides a slightly better spatial selectivity than EMG when recorded directly on the muscle surface. However, there is a remarkable difference in spatial selectivity for non-invasive surface measurements. The spatial selectivity of the MMG components aligned with the muscle fibres and normal to the body surface outperforms the spatial selectivity of surface EMG. Particularly, for the MMG's normal-to-the-surface component the influence of subcutaneous fat is minimal. Further, for the first time, we analyse the contribution of different structural components, i.e. muscle fibres from different motor units and the extracellular space, to the measurable biomagnetic field. Notably, the simulations show that for the normal-to-the-surface MMG component, the contribution from volume currents in the extracellular space and in surrounding inactive tissues, is negligible. Further, our model predicts a surprisingly high contribution of the passive muscle fibres to the observable magnetic field.

摘要

虽然肌电图 (EMG) 和磁肌电图 (MMG) 都是测量骨骼肌电活动的方法,但这两种信号之间没有系统的比较。在这项工作中,我们提出了一种用于 EMG 和 MMG 的新的数值模型,并测试了 MMG 在空间选择性方面优于 EMG 的假设,即区分空间移位源的能力。结果表明,当直接记录在肌肉表面时,MMG 比 EMG 提供稍好的空间选择性。然而,对于非侵入性表面测量,空间选择性存在显著差异。与肌肉纤维对齐并垂直于体表的 MMG 分量的空间选择性优于表面 EMG 的空间选择性。特别是对于 MMG 的体表垂直分量,皮下脂肪的影响最小。此外,我们首次分析了不同结构成分(即来自不同运动单位的肌肉纤维和细胞外空间)对可测量生物磁场的贡献。值得注意的是,模拟表明,对于体表垂直 MMG 分量,细胞外空间和周围非活跃组织中的容积电流的贡献可以忽略不计。此外,我们的模型预测,被动肌肉纤维对可观察磁场的贡献高得惊人。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/32f15920823a/10237_2022_1572_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/4c68e2c0c532/10237_2022_1572_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/60598d374c47/10237_2022_1572_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/d11f5e6e5a2b/10237_2022_1572_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/db038227c182/10237_2022_1572_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/12afa83c1d1e/10237_2022_1572_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/eb29712cb72d/10237_2022_1572_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/32f15920823a/10237_2022_1572_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/4c68e2c0c532/10237_2022_1572_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/60598d374c47/10237_2022_1572_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/d11f5e6e5a2b/10237_2022_1572_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/db038227c182/10237_2022_1572_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/12afa83c1d1e/10237_2022_1572_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/eb29712cb72d/10237_2022_1572_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1047/9132853/32f15920823a/10237_2022_1572_Fig7_HTML.jpg

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