Imaging Motor Unit Recruitment Patterns

在骨骼肌中，纤维被分成不同的功能单元，即运动单元（mu），其中纤维群由位于脊髓中的单个运动神经元支配。在肌肉内部和肌肉之间，神经支配纤维的大小及其生理特性是不同的。为了完成任何运动，神经系统必须激活适当数量和组合的mu，以产生整个任务所需的不同程度的力量。量化MU的激活模式提供了一种方法：(1)研究MU如何受神经系统控制而产生力；ii)测量由脑瘫或帕金森氏病等疾病引起的功能障碍；Iii)诊断神经退行性疾病，如运动神经元疾病；iv)估计因疾病、损伤或衰老而发生的MU数变化。目前这样的研究是使用肌电图（EMG）完成的，这是一种记录肌肉纤维被激活时发生的电化学变化的方法。然而，现有的分析肌电信号和提供MU招募模式信息的技术限制了可以研究的运动任务的范围。因此，目前还不可能研究在日常生活行为（如伸手、抓握）或更苛刻的任务（如跑步、通过不同的地形或斜坡）中完成的大多数运动中MU的招募模式。MUs的激活导致肌肉组织运动。肌肉中MU纤维的数量和分布将影响：i)组织运动传播的肌肉面积和ii)当MU被激活时运动传播的时间。因此，激活具有不同特性的mu应该会导致不同的“标志性”肌肉组织运动模式发生。MU激活与组织运动模式之间的关系尚未得到充分探讨，但可以通过超声成像揭示。这些类型的图像已经显示出令人惊讶的小收缩（例如单个肌肉纤维的激活），并且可以很容易地在不同的运动中收集，例如运动，伸手和抓握。因此，我们建议开发新的超声图像分析技术，为研究MU的招募模式提供新的途径，并扩展研究MU性质的条件。我们将开发算法来提供：i)高帧率（每秒1000帧）的3D骨骼肌超声成像；ii)一种定义肌肉运动模板的方法，识别与不同mu激活相关的“标志性”肌肉组织运动模式；iii)使用签名运动模式分析图像序列和识别不同微信号的激活模式的方法。因此，这项工作将为研究大范围不同运动任务中的MU招募提供一种新的方法。此外，我们的工作将提供一种手段：i)收集实验证据，以支持更准确和有效的肌肉数学模型的发展；（二）评价和监测由于老化、受伤、康复、疾病和旨在改善身体健康和福祉的不同形式的治疗而发生的MU特性变化；iii)对MU特性进行非侵入性研究，可应用于人类和动物，并提供来自相同或更少数量动物的新数据。

Within skeletal muscles the fibres are grouped into distinct functional units, motor units (MUs), with groups of fibres innervated by a single motoneuron located in the spinal chord. The size of MUs (number of innervated fibres) and their physiological properties varies within and between muscles. To complete any movement the nervous system must activate the appropriate number and combination of MUs to produce the varying levels of force required throughout the task.Quantifying patterns of MU activation provides a means of i) studying how MUs are controlled by the nervous system to produce force; ii) measuring dysfunction caused by diseases such as cerebral palsy or Parkinson's disease; iii) diagnosing neurodegenerative diseases e.g. motor neurone disease; iv) estimating changes in MU number which occur due to disease, injury or ageing. Currently such studies are completed using electromyography (EMG), a method of recording electrochemical changes in the muscle fibre which occur when it is activated. However, the techniques available to analyse EMG signals and provide information on MU recruitment patterns limit the range of movement tasks that can be studied. So it is not currently possible to study patterns of MU recruitment during the majority of movements completed during acts of daily living (e.g. reaching, grasping) or more demanding tasks (e.g. running, negotiating different terrains or inclines).Activation of MUs causes muscle tissue to move. The number and distribution of the fibres of a MU in a muscle will influence: i) the area of the muscle over which tissue movement spreads and ii) the timing of the spread of the movement which occurs when the MU is activated. Activation of MUs with different properties should therefore cause different 'signature' muscle tissue movement patterns to occur. The relationship between MU activation and tissue movement patterns has not yet been fully explored, but could be revealed using ultrasound imaging. These types of images have already been shown to reveal surprisingly small contractions (e.g. activation of single muscle fibres) and can be easily collected during different movements e.g. locomotion, reaching and grasping. We therefore propose developing novel ultrasound image analysis techniques, to provide a new way of studying MU recruitment patterns and extending the conditions under which MU properties can be studied.We will develop algorithms to provide: i) high frame rate (>1000 frames per second) ultrasound imaging of skeletal muscle in 3D; ii) a means of defining muscle movement templates, identifying the 'signature' muscle tissue movement patterns associated with activation of different MUs; iii) a means of using the signature movement patterns to analyse image sequences and identify activation patterns of different MUs.This work will therefore provide a new method of studying MU recruitment during a wide range of different movement tasks. In addition, our work will provide a means of: i) collecting experimental evidence to underpin the development of more accurate and valid mathematical models of muscle; ii) evaluating and monitoring changes in MU properties which occur as a result of ageing, injury, rehabilitation, disease and different forms of treatment aimed at improving physical health and well-being; iii) non-invasive investigation of MU properties which could be applied in humans and animals and provide novel data from the same or smaller numbers of animals.