Empirical quantification and computational modeling of spine stability and neuromuscular function during dynamic movements.

动态运动过程中脊柱稳定性和神经肌肉功能的经验量化和计算建模。

• 批准号: RGPIN-2014-05560
• 负责人: Graham, Ryan
• 金额: $ 2.11万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=630171
• 关键词: Empirical; quantification; computational; modeling; spine

The global objective of my research program is to utilize novel techniques to better understand which factors contribute mechanistically to spine injury and impairment. The goal of this grant cycle is to focus specifically on stability, since stability is a fundamental concept that can be used to characterize and evaluate the functioning of a system. A key feature to stability and appropriate neuromuscular function is the ability to effectively respond to internal and external mechanical perturbations, in order to restore an equilibrium posture or movement trajectory during motion. Spine stability is the result of a complex interaction between the osteoligamentous spine, the trunk musculature, and the neural control system; with impairment to any one subsystem, small perturbations can result in the unsuccessful transmission of compressive and shear forces and tissue strain and/or injury. However, despite the knowledge that spine stability is important, its quantification is difficult using presently available imaging, manual testing, and biomechanical modeling techniques. Moreover, to date no empirical method allows measuring stability in static and all the more in dynamic conditions.One promising method assessing spine stability and neuromuscular function during dynamic movements is to calculate local dynamic spine stability from trunk motion data using a nonlinear dynamical systems approach. During repetitive trunk movements it is reasonable to assume that each movement cycle would be similar to every other cycle and the target kinematic trajectory or attractor. Naturally-occurring variance observed in empirical data is thus attributable to mechanical disturbances or control errors that are attenuated in time by the musculoskeletal and nervous systems. Thus, it is logical to calculate stability from the time-dependent growth or attenuation of kinematic variability in state space using the maximum Lyapunov exponent. The proposed research program will build on my previous work and has two overarching objectives: 1) to continue to improve the ability to empirically quantify and model spine stability and neuromuscular function in humans, and 2) to apply these techniques to a variety of movement scenarios to better understand how instability and impaired functioning may act as a biological or biomechanical mechanism of tissue failure and injury. As part of objective 1, we will carry out a series of modeling-based studies that are aimed at: i) further elucidating the relationship between local dynamic spine stability and other stability measures, ii) understanding the relationship between local spine stability and global trunk stability, and iii) beginning the process of modeling the entire Lyapunov spectrum from empirical data. With the knowledge gained, objective 2 will involve applying these advanced stability assessment techniques to various movement tasks to gain a greater understanding of how mechanical loading and tissue altering properties affect (in) stability and neuromuscular function, and vice versa. Lastly, we will assess the relationship between stability and other measures of neuromuscular function (e.g. coordination), in order to fully understand its contributions to healthy movement.As a whole, this combination of novel basic science and computational modeling has the potential to greatly benefit the Canadian natural sciences and engineering fields, as the empirical quantification of spine stability and neuromuscular function during dynamic movement will now be possible; providing us a better biological and mechanical understanding of how many anatomical, physiological, and biomechanical factors contribute mechanistically to tissue strain and/or injury during a variety of movement tasks and conditions.

我的研究计划的总体目标是利用新技术更好地了解哪些因素是导致脊柱损伤和损伤的机械性因素。这一赠款周期的目标是专门关注稳定性，因为稳定性是一个基本概念，可以用来描述和评估一个系统的运作情况。稳定和适当的神经肌肉功能的一个关键特征是能够有效地对内部和外部的机械扰动做出反应，以便在运动过程中恢复平衡的姿势或运动轨迹。脊柱稳定性是骨膜脊柱、躯干肌肉系统和神经控制系统之间复杂相互作用的结果；任何一个子系统的损伤，微小的扰动都可能导致压缩力和剪切力以及组织应变和/或损伤的不成功传递。然而，尽管人们知道脊柱稳定性很重要，但使用目前可用的成像、手动测试和生物力学建模技术很难量化脊柱稳定性。此外，到目前为止，还没有一种经验方法可以测量静态条件下的稳定性，尤其是动态条件下的稳定性。评估动态运动中脊柱稳定性和神经肌肉功能的一个有前途的方法是使用非线性动力学系统方法从躯干运动数据计算局部动态脊柱稳定性。在重复的躯干运动中，可以合理地假设每个运动周期与每隔一个周期和目标运动轨迹或吸引子相似。因此，在经验数据中观察到的自然发生的差异可归因于机械干扰或控制误差，这些误差在时间上被肌肉骨骼和神经系统衰减。因此，使用最大Lyapunov指数根据状态空间中运动学变异性随时间的增长或衰减来计算稳定性是合乎逻辑的。拟议的研究计划将建立在我以前工作的基础上，并有两个主要目标：1)继续提高对人类脊柱稳定性和神经肌肉功能进行实证量化和建模的能力；2)将这些技术应用于各种运动场景，以更好地了解不稳定和功能受损可能如何作为组织衰竭和损伤的生物或生物力学机制。作为目标1的一部分，我们将开展一系列基于模型的研究，旨在：i)进一步阐明局部动态脊柱稳定性与其他稳定性指标之间的关系，ii)了解局部脊柱稳定性与整体躯干稳定性之间的关系，以及iii)开始根据经验数据对整个Lyapunov谱进行建模的过程。有了这些知识，目标2将涉及将这些先进的稳定性评估技术应用于各种运动任务，以更好地了解机械负荷和组织改变特性如何影响稳定性和神经肌肉功能，反之亦然。最后，我们将评估稳定性和其他神经肌肉功能指标(例如协调性)之间的关系，以充分了解它对健康运动的贡献。总的来说，这种新的基础科学和计算建模的结合有可能极大地惠及加拿大的自然科学和工程领域，因为动态运动中脊柱稳定性和神经肌肉功能的经验性量化现在将成为可能；为我们提供了更好的生物和机械理解，使我们更好地了解在各种运动任务和条件下，有多少解剖、生理和生物力学因素对组织疲劳和/或损伤起到机械作用。