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Myofascial trigger points alter the modular control during the execution of a reaching task: a pilot study.

Tommaso Geri | Leonardo Gizzi | Anna Di Marco | Marco Testa
Scientific reports | 2019

Myofascial trigger points (TP) constitute a conundrum in research and clinical practice as their etiopathogenesis is debated. Several studies investigating one or few muscles have shown that both active and latent TP causes an increased muscle activity, however the influence of TP on modular motor control during a reaching task is still unclear. Electromyographic signals, recorded from the muscles of the shoulder girdle and upper arm during a reaching task, were decomposed with Non-Negative Matrix Factorization algorithm. The extracted matrices of motor modules and activation signals were used to label the muscles condition as dominant or non-dominant. The presence of latent and active TP was detected in each muscle with manual examination. Despite a similar muscle activity was observed, we found that muscles with active TP had increased weighting coefficients when labeled in the dominant condition. No influences were found when muscles were in the non-dominant condition. These findings suggest that TP altered the motor control without co-contraction patterns. As a preliminary evidence, the present results suggest that the increased weighting coefficients in presence of TPs are associated with an alteration of the modular motor control without affecting the dimensionality of motor modules for each individual and reciprocal inhibition.

Pubmed ID: 31690799

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MATLAB (tool)

RRID:SCR_001622

Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.

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R package: lmerTest (tool)

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Source code of statistical tests for linear mixed effects models as implemented in lme4. The package also provides the calculation of population means for fixed factors with confidence intervals and corresponding plots.

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