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.
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.
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