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Published on: September 24, 2014
Force estimation from ensembles of Golgi tendon organs
1Department of Biomedical Engineering, Alfred E. Mann Institute for Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
Journal of Neural Engineering
|April 16, 2009
Summary
Golgi tendon organ (GTO) activity accurately reflects muscle tension, especially at lower forces. This encoding varies significantly under pathological conditions and electrical stimulation compared to natural movements.
Area of Science:
- Neuroscience
- Biomechanics
- Motor Control
Background:
- Golgi tendon organs (GTOs) in skeletal muscles sense muscle tension, providing crucial sensory feedback to the central nervous system.
- The collective activity of GTOs is hypothesized to represent whole muscle force, but its precision and accuracy remain largely unquantified due to recording limitations.
Purpose of the Study:
- To investigate the accuracy and precision of ensemble GTO activity in encoding muscle force under various physiological and pathological conditions.
- To model the relationship between GTO firing and muscle tension using computational approaches.
Main Methods:
- Developed a novel mathematical model for force sampling and transduction in individual GTOs.
- Integrated the GTO model with diverse motor unit (MU) recruitment and organization models.
- Simulated muscle activity across normal, pathological, and neural prosthetic conditions.
Main Results:
- Ensemble GTO activity accurately encodes muscle force via a nonlinear, monotonic relationship, steepest at low forces and saturating at high forces.
- Under pathological conditions, GTO-force relationships differed from intact muscles, particularly with slow activation or sustained contraction.
- Simulated functional electrical stimulation revealed substantial deviations in GTO activity encoding.
Conclusions:
- Ensemble GTO activity provides a robust, albeit nonlinear, representation of muscle force in intact muscles.
- Altered muscle activation patterns and pathological states significantly impact the fidelity of GTO-based force encoding.
- These findings have implications for understanding sensory feedback in motor control and developing neural prosthetics.
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