Lower-Limb Non-Parametric Functional Muscle Network: Test-Retest Reliability Analysis

Abstract

Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects. Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated. The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures. This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.

Keywords

• Functional muscle connectivity
• network analysis
• surface electromyography
• fatigue