Computational Neuromechanics for Stroke Rehabilitation

中风康复的计算神经力学

• 批准号: 1159735
• 负责人: Benjamin Fregly
• 金额: $ 33万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1159735&HistoricalAwards=false
• 关键词: Computational; Neuromechanics; Stroke; Rehabilitation

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.

1159735中风是全球成年人严重长期残疾的主要原因。美国每年有超过79.5万例中风病例。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可以恢复行走功能，但他们的步态缓慢、不对称且代谢效率低下。尽管认识到这个问题，但康复对行走功能产生有意义的改变的证据有限。这些发现强调了关于运动功能恢复能力的重大知识缺口，并代表了一个迫切的未满足的需求，阻碍了促进中风后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行走功能。这项提议的目的是开发计算模拟技术，可以预测中风患者的最佳步态模式。该技术将考虑到中风引起的特定受试者的神经控制限制和剩余的神经控制能力，以及特定受试者的肌肉骨骼解剖。模拟将针对正常行走功能的两个关键方面——速度和双侧对称性——试图预测偏瘫患者理论上能够实现的步态模式。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在未来的项目中作为选择的基础，在个体受试者的基础上，最有可能恢复正常的步态速度和对称性的神经康复治疗方案。智力优势：该项目的智力优势在于开发了新的神经力学建模方法，可以预测中风患者的最佳步态模式。新的技术方面有三个方面。首先，将开发一种称为“统计力矩估计”的新技术，该技术可以将测量到的肌肉电活动信号直接转换为关节力矩。该方法使用统计上的过定方程组而不是几何上的欠定方程组来校准必要的模型参数值。其次，现有的肌肉协同分析技术将被扩展到量化受试者特定的神经控制限制和约束步态运动预测。第三，将统计矩估计和肌肉协同分析结合到现有的预测步态优化计算框架中。鉴于现有的神经力学建模方法只能描述有实验数据存在的情况，我们增强的计算框架将预测没有实验数据存在的情况。更广泛的影响：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选择一种治疗方法而不是另一种治疗方法，或者在选定的治疗方法中选择特定数量的目标。客观预测患者理论上能够实现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：提议的研究在两个方面具有变革性。首先，考虑到中风对个人的神经控制限制，这将是第一个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，因为它将把主观的、定性的过程转变为客观的、定量的过程。