Characterizing Motor Unit Mechanics and Muscle Contractile Properties In Vivo

表征体内运动单位力学和肌肉收缩特性

基本信息

批准号：
10527926
负责人：
Jongsang Son
金额：
$ 16.41万
依托单位：
NEW JERSEY INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10527926
关键词：
Agreement Algorithms Amyotrophic Lateral Sclerosis Anatomy Animals Architecture Atrophic Biological Markers Biopsy Clinical Coupling Data Diagnosis Diagnostic Procedure Dimensions Disease Disease Progression Early Diagnosis Electric Stimulation Elements Evaluation Fascicle Fiber Frequencies Functional disorder Goals Gold Hereditary Disease Heterogeneity Human Imaging Device Imaging Techniques Impairment In Vitro Inflammatory Intramuscular Isometric Contraction Link Lower Extremity Maps Measurable Measures Mechanics Methods Modeling Motion Motor Motor Neurons Muscle Muscle Contraction Muscle Fibers Muscle Weakness Muscle function Musculoskeletal Diseases Myopathy Nerve Neuromuscular Diseases Neuropathy Onset of illness Optics Outcome Pattern Physiological Procedures Property Relaxation Research Research Personnel Sampling Series Severities Skeletal Muscle Spatial Distribution Speed Spinal Muscular Atrophy Surface System Techniques Testing Time Time Series Analysis Tissues Training Ultrasonography Upper Extremity base density disability early onset image processing in vivo in vivo imaging independent component analysis individual response muscular structure nervous system disorder neuron loss neurophysiology novel personalized medicine reconstruction relating to nervous system response spatiotemporal tool treatment planning ultrasound

项目摘要

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.

表征体内运动单位力学和肌肉收缩特性肌肉收缩力有潜力作为一种有前途的生物标志物，用于早期检测疾病发作和追踪神经肌肉疾病（NMD）的进展。然而，量化肌肉收缩特性目前尚无法达到标准诊断技术，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基于肌肉活检和体外研究，这不仅具有很强的侵入性，而且还不确定是否可以电刺激引起的肌肉收缩特性反映了自然运动单位力学。更多的重要的是，慢肌纤维不像电刺激那样容易接近，但与临床最相关。神经肌肉疾病的观察，强调需要新的体内技术来了解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方法描述运动单位放电特性，但肌电图不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相互关联，而且密切相关功能上相互作用，仅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位力学。因此，这个 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，以精确捕获神经束运动过程中的运动随意肌肉收缩，以便我们可以表征肌肉收缩特性和运动单位机械师。在目标 1 中，我们将使用研究超声波开发超快超声波成像序列系统，捕捉自愿等长收缩期间的动态肌束运动。我们还将开发一个图像处理方法量化组织速度场，进而识别组织的机械响应单独的主动运动单位（即抽搐列车）。抽搐列车使我们能够估计运动单位放电模式和肌肉收缩特性。在目标 2 中，我们将评估拟议的结果技术与先进的表面肌电图分解技术相比。我们将量化相似度根据超快超声记录和独立估计的运动单位放电模式人体骨骼肌在自愿等长收缩期间的分解肌电图记录。一次—— 系列反卷积方法将用于表征肌肉收缩特性。这一目标将证明所提出的技术可以表征运动单位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来帮助研究人员/临床医生研究了解肌肉骨骼或神经系统肌肉无力的起源疾病，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。