Improving the reproducibility of motor unit number index

Evaluation method of motor unit number index based on optimal muscle strength combination

Repeatability is an important attribute of motor unit number index (MUNIX) technology. This paper proposes an optimal contraction force combination for MUNIX calculation in an effort to improve the repeatability of this technology. In this study, the surface electromyography (EMG) signals of the biceps brachii muscle of eight healthy subjects were initially recorded with high-density surface electrodes, and the contraction strength was the maximum voluntary contraction force of nine progressive levels. Then, by traversing and comparing the repeatability of MUNIX under various combinations of contraction force, the optimal combination of muscle strength is determined. Finally, calculate MUNIX using the high-density optimal muscle strength weighted average method. The correlation coefficient and the coefficient of variation are utilized to assess repeatability. The results show that when the muscle strength combination is 10, 20, 50 and 70% of the maximum voluntary contraction force, the repeatability of MUNIX is greatest, and the correlation between MUNIX calculated using this combination of muscle strength and conventional methods is high (PCC > 0.99), the repeatability of the MUNIX method improved by 11.5-23.8%. The results indicate that the repeatability of MUNIX differs for various combinations of muscle strength and that MUNIX, which is measured with a smaller number and lower-level contractility, has greater repeatability.

The utility of motor unit number index: A systematic review

The need for a valid biomarker for assessing disease progression and for use in clinical trials on amyotrophic lateral sclerosis (ALS) has stimulated the study of methods that could measure the number of motor units. Motor unit number index (MUNIX) is a newly developed neurophysiological technique that was demonstrated to have a good correlation with the number of motor units in a given muscle, even though it does not necessarily accurately express the actual number of viable motor neurons. Several studies demonstrated the technique is reproducible and capable of following motor neuron loss in patients with ALS and peripheral polyneuropathies. The main goal of this review was to conduct an extensive review of the literature using MUNIX. We conducted a systematic search in English medical literature published in two databases (PubMed and SCOPUS). In this review, we aimed to answer the following queries: Comparison of MUNIX with other MUNE techniques; the reproducibility of MUNIX; the utility of MUNIX in ALS and preclinical muscles, peripheral neuropathies, and other neurological disorders.

Why averaging multiple MUNIX measures in the longitudinal assessment of patients with ALS?

OBJECTIVE

To assess the impact of averaging multiple MUNIX trials on the follow-up of patients with amyotrophic lateral sclerosis (ALS).

METHODS

We determined the percent relative change (%RC) of MUNIX, in healthy subjects and patients with ALS, by subtracting the MUNIX value in the second visit from the first. Both the mean of a set of three MUNIX (mean-MUNIX) and the first MUNIX sample (single-MUNIX) were evaluated. Then, we studied the sensitivity to detect relative changes over time and the statistical dispersion of the %RC from these two parameters.

RESULTS

We found that the mean-MUNIX %RC has lower mean coefficient of variation than the single-MUNIX %RC in all muscles. The mean-MUNIX also resulted in more ALS patients with significant %RC, i.e., outside reference limits.

CONCLUSION

The mean-MUNIX resulted in less dispersed values of %RC in patients with ALS and thus, increased the precision of the technique. The mean-MUNIX resulted also in an increase in the sensitivity to track changes over time in these patients.

SIGNIFICANCE

The mean-MUNIX should be considered in any ALS follow-up study as a more reliable approach and as a way of potentially reducing the sample size needed for the study.

Improving the repeatability of Motor Unit Number Index (MUNIX) by introducing additional epochs at low contraction levels

OBJECTIVE

To evaluate the repeatability of (Motor Unit Number Index) MUNIX under repeatability conditions, specify the origin of variations and provide strategies for quality control.

METHODS

MUNIX calculations were performed on the bicep brachii muscles of eight healthy subjects. Negative effect of suboptimal electrode positions on MUNIX accuracy was eliminated by employing the high-density surface electromyography technique. MUNIX procedures that utilized a variety of surface interferential pattern (SIP) epoch recruitment strategies (including the original MUNIX procedure, two proposed improvement strategies and their combinations) were described. For each MUNIX procedure, ten thousands of different SIP pools were constructed by randomly recruiting necessary SIP epochs from a large SIP epoch pool (3 datasets, 9 independent electromyography recordings at different contraction levels per dataset and 10 SIP epochs per recording) and implemented for MUNIX calculation. The repeatability of each MUNIX procedure was assessed by summarizing the resulting MUNIX distribution and compared to investigate the effect of SIP epoch selection strategy on repeatability performance.

RESULTS

SIP epochs selected at lower contraction levels have a stronger influence on the repeatability of MUNIX than those selected at higher contraction levels. MUNIX under repeatability conditions follows a normal distribution and the standard deviation can be significantly reduced by introducing more epochs near the MUNIX definition line.

CONCLUSIONS

The MUNIX technique shows an inherent variation attributable to SIP epochs at low contraction levels. It is recommended that more epochs should be sampled at these low contraction levels to improve the repeatability.

SIGNIFICANCE

The present study thoroughly documented the inherent variation of MUNIX and the causes, and offered practical solutions to improve the repeatability of MUNIX.