Computational Neuromechanics for Stroke Rehabilitation

FreglyStroke是全球成年人长期严重残疾的主要原因。美国每年发生的中风事件超过79.5万例。行走功能障碍是与中风相关的最大身体限制之一。虽然大约三分之二的中风患者恢复了步行功能，但他们的步态缓慢、不对称，代谢效率低下。尽管认识到了这个问题，但有限的证据表明，康复能对行走功能产生有意义的改变。这些发现强调了人们对运动恢复能力的认识上的巨大差距，这是一种迫切的未得到满足的需求，阻碍了促进中风后患者运动功能恢复和恢复的干预措施的发展。长期目标是改善中风后患者的步行功能。这项建议的目标是开发计算机模拟技术，可以预测中风患者的最佳步态模式。这项技术将考虑到中风造成的特定于受试者的神经控制限制和剩余的神经控制能力，以及特定于受试者的肌肉骨骼解剖。当试图预测偏瘫患者理论上能够达到的步态模式时，模拟将针对正常行走功能的两个关键方面--速度和两侧对称性。在未来的项目中，当前和预测的肌肉兴奋模式、关节运动学和关节动力学之间的差异将被用作根据个体受试者选择最有可能恢复正常步态速度和对称性的神经康复治疗方案的基础。智力价值：拟议项目的智力价值是发展了新的神经机械建模方法，使中风患者能够预测最佳可达到的步态模式。新奇的技术方面有三个方面。首先，将开发一种名为统计矩估计的新技术，允许将测量的肌肉电活动信号直接转换为关节力矩。该方法使用统计超定方程组而不是几何欠定方程组来校准必要的模型参数值。第二，现有的肌肉协同分析技术将被扩展到量化特定受试者的神经控制限制和约束步态运动预测。第三，统计矩估计和肌肉协同分析将被纳入现有的预测步态优化的计算框架中。虽然现有的神经力学建模方法只描述了存在实验数据的情况，但我们增强的计算框架将预测尚不存在实验数据的情况。广泛影响：拟议项目的更广泛影响是计算模拟技术的发展，可以为中风神经康复治疗的设计和选择增加客观性。目前的治疗设计范例是高度主观的，主要基于临床医生的经验。因此，通常没有明确的理由来选择一种治疗方法而不是另一种治疗方法，或者选择在所选治疗方法中作为靶点的具体数量。目的预测患者理论上能够达到的步态模式，可以为临床医生提供有价值的新信息，以提高治疗设计过程的有效性。变革性：拟议的研究在两个方面具有变革性。首先，鉴于中风对个人施加的神经控制限制，这将是第一项能够预测个人理论上能够实现的步态模式的计算模拟技术。其次，它可能是神经康复治疗设计的范式转变，因为它将把一个主观的、定性的过程转变为一个客观的、定量的过程。

1159735FreglyStroke is the leading cause of serious long term disability in adults worldwide. Over 795,000 strokes occur in the United States each year. Walking dysfunction is one of the greatest stroke-related physical limitations. While approximately two-thirds of persons who suffer a stroke regain ambulatory function, their gait is slow, asymmetrical, and metabolically inefficient. Despite recognition of the problem, there is limited evidence that rehabilitation produces meaningful changes in walking function. These findings underscore a significant knowledge gap regarding the capacity for locomotor recovery and represent an urgent unmet need obstructing development of interventions to promote recovery and restoration of locomotor function in persons post-stroke.The long term goal is to improve walking function in persons post-stroke. The objective of this proposal is to develop computational simulation technology that can predict best achievable gait patterns by individuals who have had a stroke. The technology will account for both subject-specific neural control limitations caused by the stroke and remaining neural control capabilities, as well as subject-specific musculoskeletal anatomy. The simulations will target two key aspects of normal walking function - speed and bilateral symmetry - when seeking to predict the gait patterns that a hemiparetic individual is theoretically capable of achieving. Differences between current and predicted muscle excitation patterns, joint kinematics, and joint kinetics will be used in a future project as the basis for selecting, on an individual subject basis, the neurorehabilitation treatment protocol most likely to restore normal gait speed and symmetry.Intellectual Merit: The intellectual merit of the proposed project is development of novel neuromechanical modeling methods that will permit the prediction of best achievable gait patterns by individuals who have had a stroke. The novel technical aspects are three-fold. First, a new technique called "statistical moment estimation" will be developed that allows the transformation of measured muscle electrical activity signals directly into joint moments. The method uses a statistical over-determined system of equations rather than a geometric under-determined system of equations to calibrate the necessary model parameter values. Second, existing muscle synergy analysis techniques will be extended to quantify subject-specific neural control limitations and constrain gait motion predictions. Third, statistical moment estimation and muscle synergy analysis will be incorporated into an existing computational framework for predictive gait optimization. Whereas existing neuromechanical modeling methods are only descriptive of situations for which experimental data exist, our enhanced computational framework will be predictive of situations for which no experimental data yet exist.Broader Impact: The broader impact of the proposed project is the development of computational simulation technology that can add objectivity to the design and selection of neurorehabilitation treatments for stroke. Current treatment design paradigms are highly subjective, being based primarily on clinician experience. Thus, there is often no clear rationale for selecting one treatment approach over another or for selecting the specific quantities to target within a selected treatment approach. Objective prediction of the gait patterns that a patient is theoretically capable of achieving could provide clinicians with valuable new information to improve the efficacy of the treatment design process.Transformative Nature: The proposed research is transformative in two ways. First, it would be the first computational simulation technology capable of predicting gait patterns that an individual is theoretically capable of achieving, given the neural control limitations imposed on the individual by a stroke. Second, it could be a paradigm shift in neurorehabilitation treatment design, since it would transform a subjective, qualitative process into an objective, quantitative one.