Motor control complexity can be dynamically simplified during gait pattern exploration using motor control-based...

Area of Science:

• Neuroscience
• Biomechanics
• Motor Control

Background:

• The central nervous system's coordination of motor outputs is extensively studied.
• It is generally accepted that a small set of synergies underlies common activities like walking.
• However, the robustness and flexibility of these synergies across diverse gait patterns remain unclear.

Purpose of the Study:

• To evaluate how motor synergies change as healthy adults explore various gait patterns using biofeedback.
• To identify factors associated with synergy modulation using machine learning.

Main Methods:

• Fourteen healthy adults explored numerous gait patterns using custom biofeedback.
• Bayesian additive regression trees were employed to analyze factors influencing synergy modulation.
• Changes in synergy recruitment and complexity were assessed in relation to gait modifications.

Main Results:

• Synergy recruitment adapted to the type and magnitude of gait modifications.
• A consistent set of synergies accommodated small gait deviations, while additional synergies emerged for larger changes.
• Synergy complexity decreased with altered gait mechanics, particularly with increased ankle dorsiflexion and knee flexion/extension moments.

Conclusions:

• The central nervous system employs a largely invariant, low-dimensional control strategy for gait but can flexibly modify it.
• Findings enhance understanding of neural control during gait and may inform interventions for motor recovery after injury.