Neuromusculoskeletal Modeling and Force Prediction: Verification Through Experimental Neuromuscular Dynamics

Abstract

Purpose Neuromusculoskeletal (NMS) function is influenced by the interactions between neural and musculoskeletal systems. Age-related changes in motor unit morphology contribute to changes in motor control and force production with advancing age; however, a better understanding of the underlying mechanisms between force production and motor unit reorganization and their interrelationships is needed to develop targeted therapies and interventions to age-related changes. Direct experimental measurement of these neuromuscular changes is challenging due to ethical and logistical constraints and the complexity of isolating individual motor unit contributions in vivo, particularly across time. Computational modeling provides a complementary approach which can help bridge this gap. The objective of this study is to develop a computational framework for predicting dorsiflexion force profiles through the translation of experimental motor unit recordings into simulated musculoskeletal responses. Methods This study presents the development of a combined NMS model that integrates experimental motor unit recordings into a musculoskeletal simulation framework. Specifically, the NMS model predicts dorsiflexion force profiles by translating experimental data from high-density electromyography recordings into simulated subject-specific motor unit discharge characteristics and simulated muscle responses. The NMS model incorporates a detailed motor neuron pool simulation and a finite element musculoskeletal model, allowing for physiologically accurate representation of motor unit discharge characteristics, muscle force generation, and force variability. Results The accuracy of the simulated force profiles in predicting the experimental force were 10.25 N and 0.95, respectively, for average root mean square error and R 2 values. Results demonstrate strong agreement between simulated and experimental force profiles and motor unit recordings. Conclusion By bridging the gap between computational and experimental approaches, this study aims to enhance understanding of NMS dynamics and support the development of personalized treatment strategies for neurodegenerative disease patients.

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