Personalized Alpha-Motoneuron Pool Models Driven by Neural Data Encode the Mechanisms Controlling Rate of Force...

Area of Science:

• Neuroscience
• Motor Control
• Computational Biology

Background:

• The central nervous system uses varied motor control strategies based on task demands.
• Alpha-motoneuron (MN) pool activity is modulated by muscle force and rate of force development (RFD).
• Biophysical MN models are crucial for inferring in vivo neural processes in humans, requiring personalization due to individual neurophysiology.

Purpose of the Study:

• To investigate the mechanisms behind RFD modulation using personalized MN pool models.
• To assess changes in in vivo MN activity across different RFD and muscle force levels.
• To evaluate the role of common synaptic input and excitatory synaptic gains (∆ IF) in motor control.

Main Methods:

• Developed person-specific alpha-motoneuron pool models.
• Utilized in vivo common synaptic input estimates derived from high-density electromyography.
• Analyzed MN recruitment, rate coding, and estimated excitatory synaptic gains (∆ IF) across varying RFD and force.

Main Results:

• Identified RFD-specific changes in MN activity correlated with alterations in ∆ IF.
• Demonstrated that MN pool models with RFD-specific ∆ IFs accurately reproduced in vivo MN firing and force profiles.
• Showed modulation of MN recruitment and rate coding across the RFD spectrum.

Conclusions:

• This work advances the modeling of human force generation and personalized spinal circuitry models.
• Provides a framework for studying in vivo human neuromechanics.
• Paves the way for developing motor restoring interventions.