Characterizing Motor Unit Mechanics and Muscle Contractile Properties In Vivo

在体内表征运动单元力学和肌肉收缩特性 肌肉收缩性有可能作为一个有前途的生物标志物，用于检测疾病发作更早， 追踪神经肌肉疾病（NMD）的进展。然而，量化肌肉收缩特性 目前还没有达到标准的诊断技术，主要是因为缺乏体内技术 可以很容易地应用于真实的临床环境。量化肌肉收缩特性的黄金标准 是基于肌肉活检和体外研究，这不仅是非常侵入性的，而且还不确定是否 由电刺激引起的肌肉收缩特性反映了自然的运动单元力学。更 重要的是，慢收缩纤维，不像电刺激那样容易接近，是与临床最相关的。 观察神经肌肉疾病，强调需要新的体内技术，以了解 在自愿收缩期间的收缩特性。表面或肌内肌电图是一种潜在的替代， 描述了运动单位放电特性，但EMG没有提供有关肌肉收缩的定量数据 特性.由于神经和肌肉机制不仅在解剖学上有联系， 在功能上相互作用，仅仅一部分信息不足以全面了解肌肉 机械功能因此，迫切需要开发新的体内技术来表征 肌肉收缩特性以及运动单元力学。因此，R21项目的主要目标是 开发一种新的基于体内超声成像的框架，以精确捕获 随意肌肉收缩，以便我们可以表征肌肉收缩特性和运动单位 力学在目标1中，我们将开发一种超快超声成像序列， 系统，以捕捉动态束运动过程中自愿等长收缩。我们还将开发一个 图像处理方法来量化组织速度场，进而识别组织的机械响应 单独的主动马达单元（即，twitch trains）。抽搐序列可以让我们估计运动单位放电 模式和肌肉收缩特性。在目标2中，我们将评估拟议的 技术相比，先进的表面肌电分解技术。我们将量化 运动单位放电模式独立估计从超快超声记录和 在随意等长收缩期间来自人类骨骼肌的分解EMG记录。一段时间- 系列反卷积方法将用于表征肌肉收缩特性。这一目标将 证明了所提出的技术可以表征运动单元力学和肌肉的可行性 人体骨骼肌在体内的收缩特性。该项目将提供一个强大的工具，以帮助 研究人员/临床医生研究了解肌肉骨骼或神经系统中肌无力的起源 疾病，诊断遗传性疾病，炎症性疾病的早期肌肉变化，或检测异常 进行性神经系统疾病中的肌肉活动。

Characterizing motor unit mechanics and muscle contractile properties in vivo Muscle contractility has the potential as a promising biomarker for detecting disease onset earlier and tracking the progress of neuromuscular diseases (NMDs). However, quantifying muscle contractile properties is not currently within reach of standard diagnostic techniques, mainly because of a lack of in vivo techniques that can readily be applied in a real clinical setting. The gold standard to quantify muscle contractile properties is based on muscle biopsy and on in vitro studies, which is not only very invasive but also uncertain whether muscle contractile properties induced by electrical stimulation reflect natural motor unit mechanics. More importantly, slow-twitch fibers, not as accessible by electrical stimulation, are the most relevant to clinical observations in neuromuscular diseases, emphasizing the need for new in vivo technique to understand the contractile properties during voluntary contractions. Surface or intramuscular EMG is a potential alternative to describe motor unit discharge properties, but EMG does not provide quantitative data about muscle contractile properties. As both neural and muscular mechanisms are not only linked anatomically but also closely interacted functionally, just one part of the information is not sufficient to comprehensively understand muscle mechanical function. There is therefore a profound need to develop new in vivo techniques to characterize muscle contractile properties as well as motor unit mechanics. Accordingly, the main goal of this R21 project is to develop a new in vivo ultrasound imaging-based framework to precisely capture fascicle motion during voluntary muscle contractions so that we can characterize muscle contractile properties and motor unit mechanics. In Aim 1, we will develop an ultrafast ultrasound imaging sequence, using a research ultrasound system, to capture dynamic fascicle motion during voluntary isometric contractions. We will also develop an image processing method to quantify the tissue velocity field and in turn to identify mechanical responses of individual active motor units (i.e., twitch trains). The twitch trains allow us to estimate motor unit discharge patterns and muscle contractile properties. In Aim 2, we will evaluate the outcomes from the proposed technique compared to the advanced surface EMG decomposition technique. We will quantify the similarity of motor unit discharge patterns independently estimated from both ultrafast ultrasound recordings and decomposition EMG recordings from human skeletal muscles during voluntary isometric contractions. A time- series deconvolution method will be used to characterize muscle contractile properties. This aim will demonstrate the feasibility that the proposed technique can characterize motor unit mechanics and muscle contractile properties of human skeletal muscle in vivo. This project will provide a powerful tool to help researchers/clinicians study understand the origins of muscle weakness in musculoskeletal or neurological disorders, diagnose early muscle changes in inherited diseases, in inflammatory diseases, or detect abnormal muscle activities in progressive nervous system disease.