Construction of Multiplex Muscle Network for Precision Pinch Force Control^.

Area of Science:

• Neuroscience
• Biomechanics
• Systems Biology

Background:

• Muscle synergy is crucial for motor control, but its role in low-level precision pinch is understudied.
• Traditional analysis methods struggle with nonlinear electromyographic signals, especially at low forces.
• Understanding muscle coordination during precision tasks is vital for rehabilitation and assistive technologies.

Purpose of the Study:

• To introduce a novel 'multiplex muscle network' method for analyzing muscle coordination during precision pinch.
• To investigate dynamical muscle interactions at low-level force production (1% and 10% MVC).
• To assess the effectiveness of network metrics in differentiating force control states.

Main Methods:

• Constructed multiplex muscle networks using the multiplex limited penetrable horizontal visibility graph (MLPHVG) method.
• Recorded surface electromyography (sEMG) from seven forearm and hand muscles in eight healthy subjects.
• Analyzed data using network metrics: interlayer mutual information (I), average edge overlap (ω), weighted clustering coefficient (C^W), and weighted characteristic path length (L^W).

Main Results:

• Significant differences in network metrics (I, ω, C^W, L^W) were observed between higher (10% MVC) and lower (1% MVC) force levels.
• Higher force levels showed increased I, ω, C^W, and decreased L^W, indicating enhanced information processing efficiency.
• The average edge overlap (ω) achieved 82.21% accuracy in classifying different force control states.

Conclusions:

• The novel multiplex muscle network method effectively reveals alterations in functional muscle interactions during precision pinch.
• Dynamical changes in muscle coordination are evident between different force production levels.
• This approach offers new insights into muscle synergies and motor control mechanisms in precision tasks.