Motor unit number estimation of facial muscles using the M Scan-Fit method

Area of Science:

• Neurology
• Biomedical Engineering
• Electromyography

Background:

• Motor Unit Number Estimation (MUNE) is crucial for diagnosing neuromuscular disorders.
• Assessing facial muscles presents unique challenges due to their small size and complex innervation.

Purpose of the Study:

• To evaluate the M Scan-Fit automated method for MUNE in facial muscles.
• To determine the reliability and accuracy of M Scan-Fit in the nasalis and depressor anguli oris (DAO) muscles.

Main Methods:

• Healthy volunteers underwent repeated M Scan recordings from nasalis and DAO muscles.
• Data were fitted to a probabilistic model for automated motor unit number estimation.
• Inter-rater and intra-class correlation coefficients were calculated to assess reliability.

Main Results:

• M Scan-Fit demonstrated high test-retest reliability for the nasalis muscle (ICC=0.88).
• The method showed good inter-rater reliability for both nasalis (r=0.96) and DAO (r=0.87) muscles.
• DAO muscle MUNE showed moderate test-retest reliability (ICC=0.39), with a significant difference between recordings (P=.04).

Conclusions:

• M Scan-Fit is a reliable and accurate automated method for estimating motor unit number and size in facial muscles.
• The technique shows promise for clinical application in assessing facial neuromuscular integrity.