Distributed neural networks for controlling human locomotion: lessons from normal and SCI subjects

Area of Science:

• Neuroscience
• Biomechanics
• Rehabilitation Science

Background:

• Human locomotion is controlled by complex brain-spinal networks and muscle synergies.
• Motor output for locomotion is adaptable, with conserved temporal but variable spatial muscle activation patterns.
• Spinal cord injury (SCI) can lead to altered motor patterns and compensatory strategies.

Purpose of the Study:

• To explore the adaptability and plasticity in human locomotion control.
• To investigate motor control strategies in spinal cord injury (SCI) patients.
• To evaluate the effectiveness of kinematics-based versus muscle-activation-based rehabilitation.

Main Methods:

• Analysis of muscle activation patterns (EMG) and leg kinematics during locomotion.
• Comparison of motor control in healthy individuals and SCI patients.
• Review of recent findings on plasticity and robotic-assisted training.

Main Results:

• Locomotor output exhibits non-linear changes in muscle activation with varying conditions, yet leg kinematics remain consistent.
• SCI patients may develop new motor patterns rather than restoring old ones, with potential for compensatory solutions.
• Despite altered neural activity maps, SCI patients can achieve near-normal foot kinematics.

Conclusions:

• Human locomotion control relies on adaptable, distributed neural networks demonstrating plasticity.
• Kinematics training may be more effective for SCI rehabilitation than focusing solely on muscle activation patterns.
• Robotic devices can leverage motor plasticity by enabling active movement generation and correction for rehabilitation.