Biocomputation of the Links Between Muscle Morphology, Coordination and Injury

描述（由申请人提供）：肌肉拉伤是运动医学诊所最常见的情况之一。然而，治疗方法是可变的，再损伤率往往很高，这在一定程度上反映了对影响损伤风险的因素缺乏基本的了解。流行的理论认为，损伤是由于活动肌纤维过度劳损造成的。本研究的目的是开发新的生物计算工具来预测和分析与损伤相关的运动中骨骼肌内的应变分布。模型预测将与使用最先进的动态磁共振成像实验获得的应变测量结果进行比较。一旦验证，我们将使用生物计算工具来研究跑步过程中形态和协调如何影响腘绳肌损伤风险。以下是具体目标。目的1将使用动态磁共振成像技术来测量单个腘绳肌在拉伸收缩期间的应变分布，这是一种通常与损伤相关的负荷状态。肌肉之间的比较将提供新的见解，倾向于腘绳肌损伤发生在股二头肌长头。目标2将建立一个生物计算框架来预测运动过程中的肌肉应变分布。该框架将结合肌肉组织行为的有限元模拟和全身运动的动态模拟。将通过比较应变预测与Aim 1中动态图像确定的应变预测来验证这些方法。然后，我们将使用这个框架来研究跑步过程中肌肉兴奋、腿筋组织拉伤和骨骼运动之间的关系。目的3将评估计算模型是否能预测再损伤预防策略。我们将建立并验证因先前腿筋损伤而表现出组织结构残余变化的受试者模型。然后，我们将使用软件框架来确定运动协调如何适应损伤引起的形态学变化。本研究将建立一个生物计算框架，揭示肌肉形态、协调和损伤风险之间的复杂关系，从而为确定康复和损伤预防策略提供新的范式。肌肉拉伤是运动医学诊所最常见的情况之一。然而，治疗肌肉损伤的方法是可变的，再损伤率往往很高。本建议结合新的生物计算工具和成像技术，为预防和恢复腘绳肌损伤建立科学基础。

DESCRIPTION (provided by applicant): Muscle strain injuries are one of the most common conditions seen in sports medicine clinics. However, methods of treatment are variable and re-injury rates tend to be high which, in part, reflects a lack of fundamental understanding of the factors that influence injury risk. The prevailing theory is that injury occurs as a result of excessive strain of active muscle fibers. The goal of this study is to develop novel biocomputational tools to predict and analyze the strain distributions within skeletal muscles during movements associated with injury. Model predictions will be compared with strain measures obtained using state-of-the-art dynamic magnetic resonance imaging experiments. Once validated, we will use the biocomputational tools to investigate how morphology and coordination influence hamstring injury risk during running. Following are the specific aims. Aim 1 will use a dynamic magnetic resonance imaging technique to measure the strain distributions within the individual hamstring muscles during lengthening contractions, a loading condition commonly associated with injury. Comparisons between muscles will provide new insights into the propensity for hamstring injury to occur in the biceps femoris long head. Aim 2 will build a biocomputational framework to predict muscle strain distributions during movement. The framework will couple finite-element simulations of muscle tissue behavior with dynamic simulations of whole body movement. The methods will be validated by comparing strain predictions with those determined from the dynamic images in Aim 1. We will then use the framework to investigate the relationship between muscle excitations, hamstring tissue strains and skeletal movement during running. Aim 3 will evaluate whether computational models predict re-injury prevention strategies. We will build and validate models of subjects who exhibit residual changes in tissue structures as a result of a previous hamstring injury. We will then use the software framework to identify how movement coordination can be adapted to accommodate injury-induced changes in morphology. This research will establish a biocomputational framework that reveals the complex relationship between muscle morphology, coordination and injury risk, thus providing a new paradigm for identifying rehabilitation and injury prevention strategies. Muscle strain injuries are one of the most common conditions seen in sports medicine clinics. However, methods of treating muscle injuries are variable and re-injury rates tend to be high. This proposal couples novel biocomputational tools and imaging techniques to establish a scientific basis for preventing and rehabilitating hamstring muscle injuries.