A computational approach for understanding locomotor learning post-stroke

理解中风后运动学习的计算方法

• 批准号: 9246583
• 负责人: Gelsy Torres-Oviedo
• 金额: $ 22.44万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9246583
• 关键词: Address; Affect; Age; Arthralgia; Award; Biomechanics; Biomedical Engineering; Biometry; Brain; Clinical; Computer Simulation; Data; Dependency; Development; Doctor of Philosophy; Ensure; Equilibrium; Fall prevention; Funding; Gait; Goals; Human; Impairment; Individual; Intervention; Knowledge; Learning; Leg; Life; Mathematics; Mediating; Mentors; Mentorship; Mission; Modeling; Motion; Movement; Neurology; Neuropsychology; Neurosciences; One-Step dentin bonding system; Outcome; Output; Patient Education; Patients; Physical activity; Physics; Positioning Attribute; Public Health; Quality of life; Rehabilitation therapy; Research; Research Personnel; Rest; Specific qualifier value; Speed; Stroke; Techniques; Testing; Time; United States National Institutes of Health; Update; Walking; Work; base; career; career development; computer framework; experience; experimental study; foot; gait rehabilitation; improved; indexing; innovation; interest; kinematics; mathematical model; motor control; motor learning; musculoskeletal injury; nervous system disorder; post stroke; public health relevance; stroke rehabilitation; stroke survivor; treadmill; visual feedback; walking speed

 DESCRIPTION (provided by applicant): Step asymmetry post-stroke (i.e., limp) substantially affects the quality of life of stroke survivors because it impairs patients' mobility, thereby limiing their daily activities and increasing their dependency on others. Consequently, a primary interest for patients, clinicians, and researchers is to correct the step asymmetry in stroke survivors. Promising studies show that patients can re-learn to walk symmetrically if their step asymmetry is exaggerated with a split-belt treadmill that moves the legs at different speeds. While these results are encouraging, gait improvements are highly contextual and do not persist when walking over ground. To address this critical issue for gait rehabilitation, the PI is proposing a combination of computational and experimental approaches to identify key factors regulating the generalization of locomotor learning after stroke. The PI's central hypothesis is that inherent features from one's movement (e.g., kinematic errors and walking speed) regulate the generalization of locomotor learning. This hypothesis was formulated on the basis of the PI's preliminary data showing more generalization of treadmill learning to over ground walking when kinematic errors or walking speed during split-belt adaptation are similar to those naturally experienced. In the proposed computational approach, model inputs are errors that subjects experience during split-belt walking (for example, unexpected leg motions disturbing one's balance), model outputs are actions to correct these errors (for example, a larger step to prevent falling). The mathematical relationship between inputs and outputs is used to predict the effect of error size (Aim 1) and walking speed (Aim 2) on the generalization of learning in an individual basis. Once factors mediating the generalization of learning are identified, they can be harnessed to develop interventions that improve the gait of stroke survivors during real-life situations. PI qualifications: the PI is a prolific and creative bioengineer. Her first class trainng in physics, biomechanics, and neuroscience, in addition to her strong interest in rehabilitation make her the adequate individual for doing the proposed work. Her studies in human motor control are well recognized (>700 citations; h-index 11) in a relatively short, but highly productive academic career. Through this award she will receive mentorship from two extraordinary investigators with complementary expertise: Michael Boninger, MD, PhD. (clinical rehabilitation) and Reza Shadmehr, PhD (computational motor control). They will serve as primary co-mentors. In addition the PI will receive mentorship from an expert panel of collaborators including Dr. Subashan Perera (biostatistics), Dr. Steven Graham (neurology), Dr. Julie Fiez (neuropsychology) and Dr. Skidmore (post-stroke rehabilitation). Thus, this award will provide the mentorship and career development allowing the PI to become an independent researcher able to compete for R01-level funding to study gait deficits post-stroke through computational modeling.

 描述(申请人提供)：卒中后台阶不对称(即跛行)严重影响中风幸存者的生活质量，因为它损害了患者的行动能力，从而限制了他们的日常活动，并增加了他们对他人的依赖。因此，患者、临床医生和研究人员的主要兴趣是纠正卒中幸存者的台阶不对称。有希望的研究表明，如果用分体式跑步机以不同的速度移动双腿，夸大患者的步幅不对称性，患者可以重新学习对称行走。虽然这些结果令人鼓舞，但步态的改善是高度相关的，在地面上行走时不会持续下去。为了解决步态康复的这一关键问题，PI提出了一种计算和实验相结合的方法，以确定调节中风后运动学习泛化的关键因素。PI的中心假设是，一个人的运动固有特征(例如，运动学错误和行走速度)调节着运动学习的概括性。这一假说是基于PI的初步数据提出的，该数据显示，当分带适应过程中的运动学错误或步行速度与自然经历的运动错误或步行速度相似时，跑步机学习更普遍地适用于地面步行。在提出的计算方法中，模型输入是受试者在分带行走过程中经历的错误(例如，意外的腿部运动扰乱了一个人的平衡)，模型输出是纠正这些错误的行动(例如，防止跌倒的更大步骤)。输入和输出之间的数学关系被用来预测误差大小(目标1)和行走速度(目标2)对个体基础上的学习泛化的影响。一旦确定了调节学习泛化的因素，它们就可以 开发干预措施，改善中风幸存者在现实生活中的步态。PI资质：PI是一位多产且富有创造力的生物工程师。她在物理、生物力学和神经科学方面的第一堂课训练，再加上她对康复的浓厚兴趣，使她成为做这项拟议工作的合适人选。她在人类运动控制方面的研究在相对较短但富有成效的学术生涯中得到了很好的认可(&gt；700篇引用；h-index 11)。通过这个奖项，她将得到两位具有互补专业知识的非凡研究人员的指导：迈克尔·博宁格，医学博士，博士。Reza Shadmehr博士(计算机运动控制)。他们将担任主要的共同导师。此外，PI还将接受专家小组的指导，合作者包括Subashan Perera博士(生物统计学)、Steven Graham博士(神经学)、Julie Fiez博士(神经心理学)和Skinmore博士(中风后康复)。因此，该奖项将提供指导和职业发展，使PI成为一名独立研究人员，能够竞争R01级别的资金，通过计算建模研究中风后的步态缺陷。