Neuromuscular control of joint stability and knee loading during weight bearing

我的研究计划的长期目标是回答以下问题：(1)有没有最佳的肌肉激活策略来优化膝关节的功能稳定性？(2)中枢神经系统(CNS)使用什么作为输入来管理这个策略？(3)膝关节不稳定的人可以学习这个策略吗？第一个问题是我的NSE研究的基础，为了回答它，我发展了三个免费的研究流：在受控的静态和动态任务中对肌电(EMG)、运动学和地面反作用力(GRF)的活体测量；在这些任务的计算机模拟中，提供EMG、运动学、动力学和中枢控制策略之间的关系；体外模拟我们的体内和硅胶实验结果，作为我们的假设和测试策略的验证，我们认为这是膝关节稳定的关键。*在NSERC过去5年的支持下，我们(我和我的受训人员)开发并验证了一种新的体内方案，以测量在力控制的负重任务中的肌肉激活(3篇论文)，一种新的、更强大的级联非负矩阵分解框架，以确定肌肉激活的协同作用(2篇提交的论文)。一个用于识别肌肉骨骼建模框架中的肌肉抑制并提高其肌力输出有效性的工具(提交的硕士论文中的论文)和一个独特的体外膝关节模拟器，该模拟器具有6个自由度的胫骨-股骨运动学，由所应用的基于肌肉骨骼模型的肌肉载荷决定(提交的硕士论文中有2篇论文)。为了使我们的分析尽可能具有生物逼真度，我们目前正在开发一种肌电增强的肌肉骨骼建模框架，以改进我们对肌肉负荷的估计，这一努力得到了斯坦福大学NIH国家康复研究模拟中心的认可，并为我的实习生提供了奖学金)。这项工作也对我的实习生的成功做出了贡献，因为我目前所有的研究生都获得了资助。*本研究计划的目标是(1)探索神经肌肉协同作用和膝关节关节负荷之间的联系；(2)在膝关节定向稳定性和膝关节上个别肌肉的激活之间建立明确的关系；(3)确定中枢神经系统用来优化稳定性的控制策略。除了这一应用，一个长期目标将是利用训练和神经可塑性来评估人类通过优化训练提高稳定性的能力。这些信息将构成以证据为基础的诊断和干预战略的支柱，重点放在表现出共同稳定性缺陷的个人身上。我们的研究将有助于填补有关下肢神经肌肉控制的知识空白，并可能对NSE社区和行业产生直接影响。

The longterm goal of my research program is to answer the following questions: (1) is there an optimal muscle activation strategy to optimise functional knee joint stability?; (2) what does the central nervous system (CNS) use as input to govern this strategy?; (3) can individuals with knee instability learn this strategy? The first question is the basis of my NSE research and to answer it I have developed three complimentary research streams: in vivo measurements of electromyography (EMG), kinematics and ground reaction forces (GRF) during controlled static and dynamic tasks; in silico modelling of these tasks which provides relationships between the EMG, kinematics, kinetics and CNS control strategies; in vitro simulation of our in vivo and in silico results to serve as a validation for our hypotheses and test strategies we identify as key to knee stabilisation.***With NSERC's support over the past 5 years we (myself and my trainees) have developed and validated a new in vivo protocol to measure muscle activations during force-controlled weight bearing tasks (3 publications), a new and more robust concatenated non-negative matrix factorisation framework to identify muscle activation synergies (2 submitted papers), a tool to identify muscle inhibition in a musculoskeletal modeling framework and improve the validity of their muscle force outputs (paper in submitted MSc thesis) and a unique in vitro knee joint simulator with 6 degree of freedom tibio-femoral kinematics dictated by the applied musculoskeletal-model-based muscle loads (2 papers in a submitted MSc thesis). To make our analyses as biofidelic as possible, we are currently developing an EMG-augmented musculoskeletal modelling framework to improve our estimates of muscle loads, an endeavour recognised by the NIH National Center for Simulation in Rehabilitation Research at Stanford University with a scholarship for my trainee). This work has also contributed to my trainee success, as all my current graduate students hold funding awards.***The objectives of this research program are to (1) explore the link between neuromuscular synergies and joint loading at the knee; (2) establish a clear relationship between directional knee joint stability and the activation of individual muscles crossing the knee joint; (3) identify control strategies used by the central nervous system to optimise stability. Beyond this application, a longterm goal will be to take advantage of training and neural plasticity to evaluate the human capacity to improve stability through optimised training. This information will form the backbone for evidence based diagnostic and intervention strategies focused on individuals who display joint stability deficits. Our research will serve to fill a knowledge gap with respect to neuromuscular control of the lower limb and it can lead to a direct impact on the NSE community and industry.********