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Updated: Aug 29, 2026
CMAP Scan MUNE (MScan) - A Novel Motor Unit Number Estimation (MUNE) Method
Published on: June 7, 2018
Reliability and Agreement of CMAP Scan-Derived MUNE Algorithms in Healthy Individuals
Objective:
CMAP scan-based motor unit number estimation (MUNE) methods offer non-invasive, physiologically meaningful measures of motor unit integrity. This study evaluated and compared the test-retest measurement properties of three algorithms: STEPIX, CDIX, and StairFit, with comparison to previously published MScanFit values.
Methods:
MUNE was estimated from the abductor pollicis brevis (APB), abductor digiti minimi (ADM), and tibialis anterior (TA) muscles in 148 healthy adults recruited across 15 international sites. Relative reliability was assessed using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Agreement was evaluated using Bland-Altman analysis and estimation statistics. Absolute measurement error was quantified using the standard error of measurement (SEM), SEM%, and smallest detectable changes (SDC). Sensitivity analyses were performed after excluding site- or participant-level data that exceeded quality control thresholds.
Results:
Across all muscles and algorithms, relative reliability was poor-to-moderate (ICC: 0.29-0.76), with SEM% values of 12-29%. CDIX demonstrated the lowest reliability and highest measurement error, while StairFit and STEPIX performed comparably. CMAP peak amplitude showed moderate-to-good relative reliability (ICC: 0.73-0.82) with SEM% values of 12-16%. Sensitivity analyses improved CMAP peak reliability, but produced only modest changes in MUNE measurement properties. Percent-change analyses indicated that visit-to-visit CMAP peak variability contributed to MUNE variability, but the strength of this association differed across algorithms.
Conclusion:
CMAP scan MUNE methods showed limited test-retest measurement properties in healthy individuals. Variability reflected contributions from both CMAP acquisition and algorithm-based estimation.
Significance:
No algorithm consistently achieved the combined reliability, precision, and SDC values needed for tracking individual-level change under the conditions studied. Future studies should evaluate whether measurement properties improve in clinical populations with motor unit loss.
