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: 7927041
• 负责人: Andrew A Biewener
• 金额: $ 50.45万
• 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/7927041
• 关键词: Address; Affect; Aging; Algorithms; Animal Model; Animals; Architecture; Basic Science; Behavior; Biomechanics; Characteristics; Clinical; Clinical assessments; Data; Development; Distal; Electric Stimulation; Electrodes; Evaluation; Exercise; Fascicle; Felis catus; Fiber; Frequencies; Gait; Goals; Goat; Human; In Situ; Individual; 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; 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; public health relevance; relating to nervous system; 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提高对神经运动性能的理解质量和评估，这可以通过使用肌肉活动模式和山丘型肌肉模型的肌电图（EMG）记录来获得。肌肉建模和EMG分析已广泛用于改善对治疗运动障碍至关重要的康复疗法的评估和开发，以及与衰老相关的肌肉功能的变化。 EMG记录，无论是从留置电极还是从皮肤表面测量的电极，经常与肌肉模型结合使用，以模拟和评估运动性能，以解决各种临床问题和疗法，包括步态康复，中风，轮椅的评估和治疗，使用轮椅，使用轮椅和前提。这项工作旨在将肌肉特性和体内收缩功能的尖端基础科学分析与计算肌肉模型相结合，以解释整个肌肉相对于其运动募集模式的收缩性能。拟议的工作旨在直接测试和完善模型，从而促进可以在人类神经运动研究中应用于一系列临床问题和条件的肌肉建模质量的改进。通过结合肌肉力的直接体内记录（通过肌腱力扣），筋膜长度的变化（通过体学法）和神经激活（通过动物模型中的多个居住细网emg电极）（山羊后肢肌肉）中的神经激活（通过多个嵌入式EMG电极）（基于山go的肌肉），基于体内收缩性能的定量度量将基于体内效果和相同的群体的效果。将使用小波来分析记录肌肉中记录的EMG信号的时空特征，以检查与某些肌肉的体内收缩性能相关的运动募集模式。这些将用于得出和测试激活模式，用作肌肉模型的输入。将检查基本特征，例如用于有序招募和工作输出变化的汉尼曼尺寸原则（同心与偏心运动），以测试和完善模型。灵敏度分析还将进行测试模型输出鲁棒性，以与从原位肌肉测量和力发育速率，F-L特性和VMAX得出的模型输入参数的已知变化。将检查以下两个具体目标：AIM＃1将根据根据源自细线EMG记录得出的激活输入来研究不同山丘型肌肉模型在体内条件下表征整个肌肉力量和工作输出模式的能力。将分析EMG的时频光谱，以揭示运动单元募集的模式，并测试以下假设：（a）在山羊运动过程中发生运动型差异模式（在快速和慢速单位之间），并且（b）相对于慢速单元，较高的运动率和高率的速率是高速率，而较高的电动机则优先激活速度。固有原位肌肉特性，结构和纤维类型的测量还将检验以下假设：肌肉区域内纤维类型的均匀分布以及肌肉区域内的串联角度导致筋膜菌株和收缩功能的均匀模式。 AIM＃2将分析使用小波的当地肌肉区域内的EMG记录的详细时空特征，以提供对运动单位募集的定量时间变化评估。 Twitch力开发和松弛测试释放的原位记录将提供不同电机单元的内在特性的估计。小波分析将用于完善和改善用于肌肉模型中使用的激活/失活动力学开发的算法，以改善其合适性，以直接测量肌肉收缩性能。 AIM＃2将检验以下假设：整个肌肉力的时间变化模式可以通过肌肉模型更好地预测，肌肉模型融合了实际的体内运动募集模式。公共卫生相关性：拟议的研究与公共卫生的相关性是，它将有助于改善神经运动性能的临床评估，这可以通过对患者肌肉活动模式的非侵入性使用（EMG）记录获得与特定运动功能相关的患者（例如步态或操纵和操纵和握把）获得的。 EMG记录通常是由表面（皮肤安装）电极制成的，以评估个体中的神经肌肉功能。然后将这些肌肉活动记录解释为评估和开发康复疗法，这对于治疗运动障碍很重要，例如中风引起的疾病以及与衰老相关的肌肉功能的变化。肌肉研究人员还广泛使用山型肌肉模型，这些肌肉模型源自骨骼肌的已知生理力 - 长度和力长度特性，以根据其测得的EMG激活来模拟或预测肌肉的运动输出。非侵入性EMG记录作为驱动肌肉模型的输入，以预测生物力学结局，通常用于解决广泛的临床问题和疗法，包括功能性电刺激，应用于步态康复，中风的评估和治疗方法，以及pros脚和前提和矫正。但是，在人类中，不能直接测试个人肌肉的这种模型。此外，大多数肌肉模型都采用统一的运动单元特性，而大多数肌肉的运动单元种群混合了可以差异募集的运动单元。因此，拟议的工作旨在将肌肉特性的尖端基础科学分析和基于新颖记录和分析方法的体内收缩功能与计算肌肉模型相结合，这些肌肉模型将允许模型的输出直接通过测量活动物中肌肉的收缩性能进行测量。将使用允许肌肉力，长度变化和激活记录在体内的方法上评估山羊肌肉功能。肌肉内区域EMG的小波分解将允许与收缩性能变化相关的运动单位的募集模式。拟议的工作旨在促进山型肌肉模型的完善，以提高其预测肌肉力量和工作输出的能力，这些能力可以从非侵入性的肌肉记录中获得，这些肌肉的记录通常是在临床实验室环境中进行的，并应用于运动灾害和状况的评估和处理。