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Experimental Brain Research

, Volume 236, Issue 11, pp 3065–3075 | Cite as

Beta, gamma band, and high-frequency coherence of EMGs of vasti muscles caused by clustering of motor units

  • Vinzenz von TscharnerEmail author
  • Martin Ullrich
  • Maurice Mohr
  • Daniel Comaduran Marquez
  • Benno M. Nigg
Research Article
  • 136 Downloads

Abstract

The vasti muscles stabilize the knee joint during the running movement. This requires some motor units to synchronize. Test the hypothesis that EMGs from the vasti muscles (VM and VL) are coherent in four frequency bands, one below 30 Hz, the 40 Hz (30–45 Hz), the middle band up to 120 Hz, and the high-frequency band (135–280 Hz). Because the VM during one step and the VL during another step contain common EMG signal parts the inter-step coherence at low frequencies does not disappear when the coherence is computed between the EMGVM obtained from one step and the EMGVL of the previous step. Twelve participants ran on a treadmill at 2.9 m/s for 15 min. EMGs were recorded from the vasti muscles using bipolar current amplifiers. Ordinary coherence was computed between the EMGVM and EMGVL and for the inter-step-condition. Significant coherence was observed in all frequency bands. In the mid- and high-frequency range, coherence disappears for the inter-step condition, whereas the low-frequency coherence is still present. Four frequency bands must be considered. It was proposed that coherence at low frequencies reflects cortico-muscular interactions. However, the clustering of motor unit action potentials is sufficient to generate the low-frequency coherence as well. There is a low-frequency coherence resulting from EMGs of the vasti muscles that are similar in different steps. Therefore, at least these three effects must be considered to draw conclusions from the coherence of the vasti muscles at low frequencies that occur while running.

Keywords

Running Synchronization of vasti muscles Current amplifier Clustering of motor units Power spectral density of electromyograms 

Notes

Acknowledgements

There was no Grant supporting this project. The project was supported by Biomechanigg Sport and Health Research Inc. (BSHR) who provided support in the form of salaries for authors MM and BMN. The National Council of Science and Technology of Mexico (CONACYT) provided support in the form of salary for DCM. The funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. MU was a visiting student from Friedrich Alexander University Erlangen-Nuremberg, Germany, supervised by VVT and was supported by the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Asmussen MJ, von Tscharner V, Nigg BM (2018) Motor unit action potential clustering—theoretical consideration for muscle activation during a motor task. Front Hum Neurosci.  https://doi.org/10.3389/fnhum.2018.00015 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Boonstra TW (2010) The nature of periodic input to the muscles. J Neurophysiol 104:576–576.  https://doi.org/10.1152/jn.00258.2010 CrossRefPubMedGoogle Scholar
  3. Boonstra TW, Daffertshofer A, van Ditshuizen JC et al (2008) Fatigue-related changes in motor-unit synchronization of quadriceps muscles within and across legs. J Electromyogr Kinesiol 18:717–731.  https://doi.org/10.1016/j.jelekin.2007.03.005 CrossRefPubMedGoogle Scholar
  4. Brown P (2000) Cortical drives to human muscle: the Piper and related rhythms. Prog Neurobiol 60:97–108.  https://doi.org/10.1016/S0301-0082(99)00029-5 CrossRefPubMedGoogle Scholar
  5. Brown P, Salenius S, Rothwell JC, Hari R (1998) Cortical correlate of the Piper rhythm in humans. J Neurophysiol 80:2911–2917CrossRefGoogle Scholar
  6. Clark DJ, Kautz SA, Bauer AR et al (2013) Synchronous EMG activity in the piper frequency band reveals the corticospinal demand of walking tasks. Ann Biomed Eng 41:1778–1786.  https://doi.org/10.1007/s10439-013-0832-4 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Datta AK, Farmer SF, Stephens JA (1991) Central nervous pathways underlying synchronization of human motor unit firing studied during voluntary contractions. J Physiol 432:401–425CrossRefGoogle Scholar
  8. Farina D (2006) Interpretation of the surface electromyogram in dynamic contractions. Exerc Sport Sci Rev 34:121–127.  https://doi.org/10.1249/00003677-200607000-00006 CrossRefPubMedGoogle Scholar
  9. Farina D (2008) Point: Counterpoint: spectral properties of the surface EMG do not provide information about motor unit recruitment and muscle fiber type. J Appl Physiol 105:1683.  https://doi.org/10.1152/japplphysiol.90598.2008a CrossRefPubMedGoogle Scholar
  10. Farina D, Negro F (2015) Common synaptic input to motor neurons, motor unit synchronization, and force control. Exerc Sport Sci Rev 43:23–33.  https://doi.org/10.1249/JES.0000000000000032 CrossRefPubMedGoogle Scholar
  11. Farina D, Merletti R, Enoka RM (2004) The extraction of neural strategies from the surface EMG. J Appl Physiol 96:1486–1495.  https://doi.org/10.1152/japplphysiol.01070.2003 CrossRefPubMedGoogle Scholar
  12. Farina D, Negro F, Muceli S, Enoka RM (2015) Principles of motor unit physiology evolve with advances in technology. Physiology 31:83–94.  https://doi.org/10.1152/physiol.00040.2015 CrossRefGoogle Scholar
  13. Farmer SF, Bremner FD, Halliday DM, Rosenberg JR, Stephens JA (1993) The frequency content of common synaptic inputs to motoneurones studied during voluntary isometric contraction in man. J Physiol 470:127–155.  https://doi.org/10.1113/jphysiol.1993.sp019851 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Fattorini L, Felici F, Filligoi GC et al (2005) Influence of high motor unit synchronization levels on non-linear and spectral variables of the surface EMG. J Neurosci Methods 143:133–139.  https://doi.org/10.1016/j.jneumeth.2004.09.018 CrossRefPubMedGoogle Scholar
  15. Grosse P, Cassidy MJ, Brown P (2002) EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: physiological principles and clinical applications. Clin Neurophysiol 113:1523–1531.  https://doi.org/10.1016/S1388-2457(02)00223-7 CrossRefPubMedGoogle Scholar
  16. Halliday DM (2002) Functional coupling of motor units is modulated during walking in human subjects. J Neurophysiol 89:960–968.  https://doi.org/10.1152/jn.00844.2002 CrossRefGoogle Scholar
  17. Hermens J, Freriks B, Merletti R (1999) SENIAM 8: European recommendations for surface electromyography, 2nd edn. Roessingh Research and Development, NetherlandsGoogle Scholar
  18. Keenan KG, Farina D, Maluf KS et al (2005) Influence of amplitude cancellation on the simulated surface electromyogram. J Appl Physiol 98:120–131.  https://doi.org/10.1152/japplphysiol.00894.2004 CrossRefPubMedGoogle Scholar
  19. Maurer C, Von Tscharner V, Nigg BM (2013) Speed-dependent variation in the Piper rhythm. J Electromyogr Kinesiol 23:673–678.  https://doi.org/10.1016/j.jelekin.2013.01.007 CrossRefPubMedGoogle Scholar
  20. Mellor R, Hodges P (2005) Motor unit synchronization between medial and lateral vasti muscles. Clin Neurophysiol 116:1585–1595.  https://doi.org/10.1016/j.clinph.2005.04.004 CrossRefPubMedGoogle Scholar
  21. Merletti R, Conte LR (1997) Surface EMG signal processing during isometric contractions. J Electromyogr Kinesiol 7:241–250.  https://doi.org/10.1016/S1050-6411(97)00010-2 CrossRefPubMedGoogle Scholar
  22. Mohr M, Nann M, Von Tscharner V et al (2015) Task-dependent intermuscular motor unit synchronization between medial and lateral vastii muscles during dynamic and isometric squats. PLoS One 10:1–18.  https://doi.org/10.1371/journal.pone.0142048 CrossRefGoogle Scholar
  23. Mohr M, Schön T, Von Tscharner V, Nigg B (2018) Intermuscular coherence between surface EMG signals is higher for monopolar compared to bipolar electrode configurations. Front Physiol 9:566.  https://doi.org/10.3389/FPHYS.2018.00566 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Oppenheim AV, Lim JS (1981) The importance of phase in signals. Proc IEEE 69:529–541.  https://doi.org/10.1109/PROC.1981.12022 CrossRefGoogle Scholar
  25. Piper H (1907) Über den willkürlichen Muskeltetanus. Pflüger Arch.  https://doi.org/10.1007/BF01678075 CrossRefGoogle Scholar
  26. Pozzo M, Merlo E, Farina D et al (2004) Muscle-fiber conduction velocity estimated from surface emg signals during explosive dynamic contractions. Muscle Nerve 29:823–833.  https://doi.org/10.1002/mus.20049 CrossRefPubMedGoogle Scholar
  27. Rosenberg J, Amjad A, Breeze P et al (1989) The fourier approach to the identification of functional coupling between neuronal spike trains. Prog Biophys Mol Biol 53:1–31CrossRefGoogle Scholar
  28. Shin K, Hammond JK (2008) Fundamentals of signal processing for sound and vibration engineers. Wiley, ChichesterGoogle Scholar
  29. van Boxtel A, Schomaker LRB (1984) Influence of motor unit firing statistics on the median frequency of the EMG power spectrum. Eur J Appl Physiol Occup Physiol 52:207–213.  https://doi.org/10.1007/BF00433394 CrossRefPubMedGoogle Scholar
  30. von Tscharner V (2010) Amplitude cancellations in surface EMG signals. J Electromyogr Kinesiol 20:1021–1022.  https://doi.org/10.1016/j.jelekin.2010.04.002 CrossRefGoogle Scholar
  31. Von Tscharner V, Nigg BM, Farina D (2008) Spectral properties of the surface EMG can characterize/do not provide information about motor unit recruitment strategies and muscle fiber type. J Appl Physiol.  https://doi.org/10.1152/japplphysiol.90598.2008 CrossRefGoogle Scholar
  32. von Tscharner V, Maurer C, Ruf F, Nigg BM (2013) Comparison of electromyographic signals from monopolar current and potential amplifiers derived from a penniform muscle, the gastrocnemius medialis. J Electromyogr Kinesiol 23:1044–1051.  https://doi.org/10.1016/j.jelekin.2013.07.011 CrossRefGoogle Scholar
  33. von Tscharner V, Ullrich M, Mohr M et al (2018) A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns. PLoS One 13:e0195125.  https://doi.org/10.1371/journal.pone.0195125 CrossRefGoogle Scholar
  34. Wakeling JM, Rozitis AI (2004) Spectral properties of myoelectric signals from different motor units in the leg extensor muscles. J Exp Biol 207:2519–2528.  https://doi.org/10.1242/jeb.01042 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Kinesiology, Human Performance LaboratoryUniversity of CalgaryCalgaryCanada
  2. 2.Machine Learning and Data Analytics Lab, Department of Computer ScienceFriedrich-Alexander-University Erlangen-Nürnberg (FAU)ErlangenGermany
  3. 3.ICT-417, Integrated Circuits and Optical Imaging LabSchulich School of Engineering, University of CalgaryCalgaryCanada

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