Quantifying Spatial Activation Patterns of Motor Units in Finger Extensor Muscles

Area of Science:

• Neuroscience
• Biomechanics
• Motor Control

Background:

• Hand dexterity relies on precise finger control during object manipulation.
• Macroscopic muscle activation patterns (sEMG) are well-studied, but microscopic motor unit (MU) behaviors during distinct finger movements remain underexplored.
• Understanding MU spatial activation is crucial for elucidating the neural mechanisms of hand function.

Purpose of the Study:

• To quantify the spatial activation patterns of individual MUs during distinct finger extension movements (index, middle, ring, little).
• To evaluate the separability and classification accuracy of MU spatial activation patterns across different fingers.

Main Methods:

• High-density surface electromyography (HD-sEMG) was recorded during isolated finger extension contractions.
• Motor unit action potentials (MUAPs) were decomposed from HD-sEMG signals.
• 2-D root-mean-square (RMS) maps quantified MU spatial activation patterns.
• Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA) was used for MU-finger classification.

Main Results:

• Distinct spatial activation patterns of MUs were observed for each finger, primarily varying along the distal-proximal axis with some overlap.
• High classification accuracy (mean 88.98% across 12 subjects) was achieved in associating individual MUs with their corresponding finger movements.
• This study provides the first quantification of MU spatial activation patterns during distinct finger movements.

Conclusions:

• Quantifiable and distinct spatial activation patterns exist for individual MUs during different finger movements.
• These findings demonstrate the feasibility of classifying MUs based on their spatial activation patterns.
• The results offer valuable insights into the neural control of hand dexterity and can inform future studies on motor unit behavior.