Neuromusculoskeletal Modeling and Force Prediction: Verification Through Experimental Neuromuscular Dynamics.

Area of Science:

• Neuromuscular science
• Computational modeling
• Biomechanics

Background:

• Neuromuscular (NMS) function relies on neural and musculoskeletal system interactions.
• Aging alters motor unit morphology, affecting motor control and force production.
• Understanding these age-related changes is crucial for developing targeted therapies.

Purpose of the Study:

• To create a computational framework for predicting dorsiflexion force profiles.
• To translate experimental motor unit recordings into simulated musculoskeletal responses.
• To bridge the gap between experimental measurements and computational predictions of NMS function.

Main Methods:

• Developed a combined NMS model integrating experimental motor unit recordings.
• Translated high-density electromyography data into subject-specific motor unit discharge characteristics.
• Utilized a detailed motor neuron pool simulation and a finite element musculoskeletal model.

Main Results:

• The computational model accurately predicted experimental dorsiflexion force profiles.
• Achieved strong agreement between simulated and experimental force profiles (R² = 0.95).
• Demonstrated high accuracy in predicting experimental forces with low root mean square error (10.25 N).

Conclusions:

• The developed computational framework enhances understanding of NMS dynamics.
• This approach supports the development of personalized treatment strategies for neurodegenerative diseases.
• Bridging computational and experimental methods offers a powerful tool for NMS research.