Assessment and evaluation of Hill-type muscle models for predicting in vivo force

用于预测体内力的 Hill 型肌肉模型的评估和评价

• 批准号: 7584804
• 负责人: Andrew A Biewener
• 金额: $ 37.41万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-26 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7584804
• 关键词: Address; Affect; Aging; Algorithms; Animal Model; Animals; Architecture; Basic Science; Behavior; Biomechanics; Characteristics; Clinical; Clinical assessments; Condition; Data; Development; Distal; Electric Stimulation; Electrodes; Evaluation; Exercise; Fascicle; Felis catus; Fiber; Frequencies; Gait; Genus Capra; Goals; Goat; Human; In Situ; Individual; Invasive; Laboratories; Length; Life; Limb structure; Locomotion; Measurement; Measures; Methods; Modeling; Motor; Motor output; Muscle; Muscle Fibers; Muscle function; Outcome; Output; Patients; Pattern; Performance; Physical activity; Physiological; Population; Process; Property; Prosthesis; Public Health; Range; Rate; Recruitment Activity; Rehabilitation therapy; Relative (related person); Research; Research Personnel; Signal Transduction; Simulate; Skeletal Muscle; Skin; Stroke; Surface; Techniques; Tendon force; Testing; Therapeutic Intervention; Time; Translating; Wheelchairs; Work; base; design; grasp; improved; in vivo; motor disorder; motor impairment; neuromuscular function; novel; orthotics; relating to nervous system; size; spatiotemporal

DESCRIPTION (provided by applicant): The overarching goal of the proposed research is to improve the quality of understanding and assessment of neuromotor performance that can be obtained through the use of electromyographic (EMG) recordings of muscle activity patterns and Hill-type muscle models. Muscle modeling and EMG analysis has widespread use for improving the assessment and development of rehabilitation therapies important to the treatment of motor impairment, as well as changes in muscle function associated with aging. EMG recordings, whether from indwelling electrodes or measured from the skin surface, are frequently used in combination with muscle models to simulate and evaluate motor performance to address a broad range of clinical problems and therapies that include gait rehabilitation, the evaluation and treatment of stroke, wheel chair use, and prosthetics. This work seeks to combine cutting-edge basic science analysis of muscle properties and in vivo contractile function with computational muscle models for interpreting the contractile performance of whole muscles relative to their motor recruitment patterns. The proposed work is designed to directly test and refine the models, facilitating improvements to the quality of muscle modeling that can be applied in human neuromotor studies to a range of clinical problems and conditions. By combining direct in vivo recordings of muscle force (via tendon force buckles), fascicle length change (via sonomicrometry), and neural activation (via multiple indwelling fine-wire EMG electrodes) in an animal model (goat hind limb muscles), quantitative measures of in vivo contractile performance will be used to validate and improve the fit of four different Hill-type muscle models based on muscle activation and architecture. Spatio-temporal features of the EMG signals recorded within the muscles will be analyzed using wavelets to examine patterns of motor recruitment in relation to in vivo contractile performance of select muscles. These will be used to derive and test activation patterns used as input to the muscle models. Fundamental features, such as the Henneman size-principle for orderly recruitment and changes in work output (concentric versus eccentric exercise), will be examined to test and refine the models. Sensitivity analyses will also be carried out to test model output robustness against known changes in model input parameters derived from in situ muscle measurements of activation and force development rates, F-L properties, and Vmax. The following two specific aims will be examined: Aim #1 will examine the ability of different Hill-type muscle models to characterize measured patterns of whole muscle force and work output under in vivo conditions, based on activation input derived from the fine-wire EMG recordings. Time-frequency spectra of the EMGs will be analyzed to reveal patterns of motor unit recruitment, testing the hypotheses that: (a) differential patterns of motor recruitment (between the fast and slow units) occur during goat locomotion, and (b) the faster motor units are preferentially activated, relative to slow units, for tasks that require high strain rates and high rates of force development. Measurements of intrinsic in situ muscle properties, architecture and fiber type will also test the hypothesis that a homogeneous distribution of fiber types and pennation angle within muscle regions results in uniform patterns of fascicle strain and contractile function for a given type of locomotor behavior. Aim #2 will analyze detailed spatio-temporal features of the EMG recordings made within local muscle regions of select limb muscles using wavelets to provide a quantitative time-varying evaluation of motor unit recruitment. In situ recordings of twitch force development and slack-test releases will provide estimates of the intrinsic properties of the different motor units. Wavelet analysis will be used to refine and improve algorithms developed for the activation/deactivation dynamics used in the muscle models to improve their fit to direct measurements of muscle contractile performance. Aim #2 will test the hypothesis that the time-varying patterns of whole muscle force development are better predicted by muscle model that incorporate the actual in vivo motor recruitment patterns tha models that do not. PUBLIC HEALTH RELEVANCE: The relevance of the proposed research to public health is that it will help improve the clinical assessment of neuromotor performance that can be obtained through non-invasive use of electromyographic (EMG) recordings of patient muscle activity patterns associated with particular motor functions, such as gait or manipulation and grasping. EMG recordings are commonly made from surface (skin mounted) electrodes to assess neuromuscular function in an individual. These muscle activity recordings are then interpreted to assess and develop rehabilitation therapies, important to the treatment of motor impairment, such as that which results from stroke, as well as changes in muscle function associated with aging. Muscle researchers also widely use Hill-type muscle models derived from known physiological force-velocity and force-length properties of skeletal muscle to simulate or predict the motor output of a muscle based on its measured EMG activation. The combination of non-invasive EMG recordings as input to drive muscle models for predicting biomechanical outcomes is frequently used to address a broad range of clinical problems and therapies, including functional electrical stimulation, applied to gait rehabilitation, the evaluation and treatment of stroke, and prosthetics and orthotics. However, in humans, such models of an individual's muscles cannot be tested directly. Further, most muscle models assume uniform motor unit characteristics, whereas most muscles have mixed populations of motor units that can be differentially recruited. Consequently, the proposed work seeks to combine cutting-edge basic science analysis of muscle properties and in vivo contractile function, based on novel recording and analysis methods, with computational muscle models that will allow the models' output to be assessed directly by the measurements of the muscle's contractile performance in the living animal. Goat muscle function will be assessed across a range of physical activity using methods that allow muscle force, length change, and activation to be recorded in vivo. Wavelet decomposition of regional EMGs within the muscle will allow the recruitment patterns of motor units to be identified in relation to changes in contractile performance. The proposed work is designed to facilitate the refinement of Hill-type muscle models to improve their ability to predict muscle force and work output that can be obtained from non-invasive EMG recordings of muscle, which are commonly made in the clinical laboratory setting and applied to the assessment and treatment of broad range of motor disorders and conditions.

描述（由申请人提供）：拟议研究的总体目标是通过使用肌电图（EMG）记录肌肉活动模式和hill型肌肉模型来提高对神经运动表现的理解和评估的质量。肌肉建模和肌电图分析已广泛用于改善康复疗法的评估和发展，这对治疗运动损伤以及与衰老相关的肌肉功能变化至关重要。肌电图记录，无论是通过留置电极还是从皮肤表面测量，经常与肌肉模型结合使用，以模拟和评估运动表现，以解决广泛的临床问题和治疗，包括步态康复，中风的评估和治疗，轮椅使用和假肢。这项工作旨在将肌肉特性和体内收缩功能的前沿基础科学分析与计算肌肉模型相结合，以解释与运动招募模式相关的整个肌肉的收缩性能。所提出的工作旨在直接测试和完善模型，促进肌肉建模质量的提高，可以应用于人类神经运动研究，以解决一系列临床问题和条件。通过结合动物模型（山羊后肢肌肉）的肌肉力（通过肌腱力扣）、肌束长度变化（通过声测法）和神经激活（通过多个静置细丝肌电电极）的直接体内记录，将使用体内收缩性能的定量测量来验证和改进基于肌肉激活和结构的四种不同hill型肌肉模型的拟合性。在肌肉中记录的肌电图信号的时空特征将使用小波分析，以检查与选定肌肉的体内收缩性能相关的运动招募模式。这些将用于导出和测试作为肌肉模型输入的激活模式。基本特征，如有序招聘的亨内曼尺寸原则和工作输出的变化（同心运动与偏心运动），将被检查以测试和完善模型。灵敏度分析还将进行，以测试模型输出对已知模型输入参数变化的稳健性，这些参数来自于原位肌肉测量的激活和力发展率、F-L特性和Vmax。以下两个具体目标将被检查：目标1将检查不同的hill型肌肉模型的能力，以表征体内条件下全肌肉力和功输出的测量模式，基于来自细线肌电图记录的激活输入。将分析肌电图的时频谱，以揭示运动单元募集的模式，测试以下假设：(a)山羊运动过程中运动单元募集的不同模式（在快速和慢速单元之间），以及(b)相对于慢速单元，在需要高应变率和高力发展率的任务中，更快的运动单元优先被激活。测量固有的原位肌肉特性、结构和纤维类型也将测试假设，即肌肉区域内纤维类型和插入角的均匀分布会导致给定类型的运动行为的束束应变和收缩功能的均匀模式。目标2将使用小波分析选择肢体肌肉局部肌肉区域的肌电图记录的详细时空特征，以提供运动单元招募的定量时变评估。抽搐力发展和松弛测试释放的现场记录将提供对不同运动单元内在特性的估计。小波分析将用于完善和改进用于肌肉模型中激活/失活动力学的算法，以提高其适合于肌肉收缩性能的直接测量。目标2将检验这样一个假设，即结合实际的体内运动招募模式的肌肉模型比不结合实际的体内运动招募模式的肌肉模型更能预测整个肌肉力量发展的时变模式。公共卫生相关性：拟议的研究与公共卫生的相关性在于，它将有助于改善神经运动性能的临床评估，这种评估可以通过无创使用肌电图（EMG）记录患者与特定运动功能（如步态或操作和抓握）相关的肌肉活动模式来获得。肌电图记录通常是由表面（安装在皮肤上）电极来评估个体的神经肌肉功能。然后对这些肌肉活动记录进行解释，以评估和开发康复疗法，这对运动损伤的治疗很重要，例如中风引起的运动损伤，以及与衰老相关的肌肉功能变化。肌肉研究人员还广泛使用Hill-type肌肉模型，该模型来源于已知的骨骼肌的生理力-速度和力-长度特性，基于测量的肌电图激活来模拟或预测肌肉的运动输出。非侵入性肌电图记录的组合作为驱动肌肉模型预测生物力学结果的输入，经常用于解决广泛的临床问题和治疗，包括功能电刺激，应用于步态康复，中风的评估和治疗，以及假肢和矫形器。然而，在人类中，这种个体肌肉模型不能直接进行测试。此外，大多数肌肉模型假设运动单元特征是统一的，而大多数肌肉有混合的运动单元，可以不同地招募。因此，拟议的工作旨在将基于新颖记录和分析方法的肌肉特性和体内收缩功能的前沿基础科学分析与计算肌肉模型相结合，计算肌肉模型将允许模型的输出直接通过测量活体动物的肌肉收缩性能来评估。山羊肌肉功能将通过一系列身体活动进行评估，使用允许肌肉力量，长度变化和激活的方法记录在体内。肌肉区域肌电信号的小波分解将使运动单元的招募模式与收缩性能的变化相关联。提出的工作旨在促进hill型肌肉模型的改进，以提高其预测肌肉力量和功输出的能力，这些肌肉可以从肌肉的非侵入性肌电记录中获得，这通常在临床实验室环境中进行，并应用于评估和治疗广泛的运动障碍和条件。