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Do individual differences in the distribution of activation between synergist muscles reflect individual strategies?

  • Marion Crouzier
  • François Hug
  • Sylvain Dorel
  • Thibault Deschamps
  • Kylie Tucker
  • Lilian Lacourpaille
Research Article

Abstract

Individual differences in the distribution of activation between synergist muscles have been reported during a wide variety of tasks. Whether these differences represent actual individual strategies is unknown. The aims of this study were to: (i) test the between-day reliability of the distribution of activation between synergist muscles, (ii) to determine the robustness of these strategies between tasks, and to (iii) describe the inter-individual variability of activation strategies in a large sample size. Eighty-five volunteers performed a series of single-joint isometric tasks with their dominant leg [knee extension and plantarflexion at 25% of maximal voluntary contraction (MVC)] and locomotor tasks (pedalling and walking). Of these participants, 62 performed a second experimental session that included the isometric tasks. Myoelectrical activity of six lower limb muscles (the three superficial heads of the quadriceps and the three heads of the triceps surae) was measured using surface electromyography (EMG) and normalized to that measured during MVC. When considering isometric contractions, distribution of normalized EMG amplitude among synergist muscles, considered here as activation strategies, was highly variable between individuals (15.8% < CV < 42.7%) and robust across days (0.57 < ICC < 0.82). In addition, individual strategies observed during simple single-joint tasks were correlated with those observed during locomotor tasks [0.37 < r < 0.76 for quadriceps (n = 83); 0.30 < r < 0.66 for triceps surae (n = 82); all P < 0.001]. Our results provide evidence that people who bias their activation to a particular muscle do so during multiple tasks. Even though inter-individual variability of EMG signals has been well described, it is often considered noise which complicates the interpretation of data. This study provides evidence that variability results from actual differences in activation strategies.

Keywords

Electromyography Muscle coordination Pedalling Gait 

Notes

Acknowledgements

The authors thank Killian Bouillard and Lois Boucherf (University of Nantes, France) for collecting some data of this study.

Funding

This study was supported by a grant from the Région Pays de la Loire (QUETE project, no. 2015-09035). François Hug was supported by a fellowship from the Institut Universitaire de France (IUF).

Compliance with ethical standards

Conflict of interest

The authors have no financial conflicts of interest to disclose.

References

  1. Ahn AN, Kang JK, Quitt MA, Davidson BC, Nguyen CT (2011) Variability of neural activation during walking in humans: short heels and big calves. Biol Lett 7:539–542CrossRefPubMedPubMedCentralGoogle Scholar
  2. Alessandro C, Rellinger BA, Barroso FO, Tresch MC (2018) Adaptation after vastus lateralis denervation in rats demonstrates neural regulation of joint stresses and strain. eLife 7:e38215CrossRefPubMedPubMedCentralGoogle Scholar
  3. Avrillon S, Guilhem G, Barthélémy A, Hug F (2018) Coordination of hamstring is individual specific and is related to motor performance. J Appl Physiol 125(4):1069–1079CrossRefPubMedGoogle Scholar
  4. Brochner Nielsen NP, Hug F, Guevel A, Fohanno V, Lardy J, Dorel S (2017) Motor adaptations to unilateral quadriceps fatigue during a bilateral pedaling task. Scand J Med Sci Sports 27:1724–1738CrossRefPubMedGoogle Scholar
  5. Cicchetti D, Bronen R, Spencer S, Haut S, Berg A, Oliver P, Tyrer P (2006) Rating scales, scales of measurement, issues of reliability: resolving some critical issues for clinicians and researchers. J Nerv Ment Dis 194:557–564CrossRefPubMedGoogle Scholar
  6. Craig CL, Marshall AL, Sjöström M, Bauman AE, CBooth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF et al (2003) International physical activity questionnaire: 12 country reliability and validity. Med Sci Sports Exerc 35:1381–1395CrossRefPubMedPubMedCentralGoogle Scholar
  7. Crouzier M, Lacourpaille L, Nordez A, Tucker K, Hug F (2018) Neuromechanical coupling within the human triceps surae and its consequences on individual force sharing strategies. J Exp BiolGoogle Scholar
  8. De Rugy A, Loeb GE, Carroll TJ (2012) Muscle coordination is habitual rather than optimal. J Neurosci 32:7384–7391CrossRefPubMedGoogle Scholar
  9. De Marchis C, Schmid M, Bibbo D, Bernabucci I, Conforto S (2013) Inter-individual variability of forces and modular muscle coordination in cycling: a study on untrained subjects. Hum Mov Sci 32:1480–1494CrossRefPubMedGoogle Scholar
  10. Dideriksen JL, Enoka RM, Farina D (2011) Neuromuscular adjustmens that constrain submaximal EMG amplitude at task failure of sustained isometric contractions. J Appl Physiol 111:485–494CrossRefPubMedGoogle Scholar
  11. Enoka RM, Duchateau J (2015) Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function. J Appl Physiol (1985) 119:1516–1518CrossRefGoogle Scholar
  12. Farina D, Merletti R, Enoka RM (2004) The extraction of neural strategies from the surface EMG. J Appl Physiol 96:1486–1495CrossRefPubMedGoogle Scholar
  13. Farina D, Holobar A, Merletti R, Enoka RM (2010) Decoding the neural drive to muscles from the surface electromyogram. Clin Neurophysiol 121:1616–1623CrossRefPubMedGoogle Scholar
  14. Hebenstreit F, Leibold A, Krinner S, Welsch G, Lochmann M, Eskofier BM (2015) Effect of walking speed on gait sub phase durations. Hum Mov Sci 43:118–124CrossRefPubMedGoogle Scholar
  15. Henneman E, Somjen C, Carpenter DO (1965) Excitability and inhibitability of motoneurons of different sizes. J Neurophysiol 28:599–620CrossRefPubMedGoogle Scholar
  16. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G (2000) Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 10:361–374CrossRefPubMedGoogle Scholar
  17. Hewett TE, Zazulak BT, Myer GD, Ford KR (2005) A review of electromyographic activation levels, timing differences, and increased anterior cruciate ligament injury incidence in female athletes. Br J Sports Med 39:347–350CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hodges PW, Tucker K (2011) Moving differently in pain: a new theory to explain the adaptation to pain. Pain 152:90–98CrossRefGoogle Scholar
  19. Hopkins W (2000) Measures of reliability in sports medicine and science. Sports Med 30:1–15CrossRefPubMedGoogle Scholar
  20. Hug F, Dorel S (2009) Electromyographic analysis of pedaling: a review. J Electromyogr Kinesiol 19:182–198CrossRefPubMedGoogle Scholar
  21. Hug F, Tucker K (2017) Muscle coordination and the development of musculoskeletal disorders. Exerc Sport Sci Rev 45:201–208CrossRefPubMedGoogle Scholar
  22. Hug F, Turpin NA, Guével A, Dorel S (2010) Is interindividual variability of EMG patterns in trained cyclists related to different muscle synergies? J Appl Physiol 108:1727–1736CrossRefPubMedGoogle Scholar
  23. Hug F, Goupille C, Baum D, Raiteri BJ, Hodges PW, Tucker K (2015) Nature of the coupling between neural drive and force-generating capacity in the human quadriceps muscle. Proc R Soc B 282(1819):20151908CrossRefPubMedGoogle Scholar
  24. Ivanenko YP, Grasso R, Macellari V, Lacquaniti F (2002) Control of foot trajectory in human locomotion: role of ground contact forces in simulated reduced gravity. J Neurophysiol 87:3070–3089CrossRefPubMedGoogle Scholar
  25. Keenan KG, Farina D, Maluf KS, Merletti R, Enoka RM (2005) Influence of amplitude cancellation on the stimulated surface electromyogram. J Appl Physiol 98:120–131CrossRefPubMedGoogle Scholar
  26. Kurtzer I, Herter TM, Scott SH (2005) Random change in cortical load representation suggests distinct control of posture and movement. Nat Neurosci 8:498–504CrossRefPubMedGoogle Scholar
  27. Laine CM, Martinez-Valdes E, Falla D, Mayer F, Farina D (2015) Motor neuron pools of synergists thigh muscles share most of their synaptic input. J Neurosci 35:12207–12216CrossRefPubMedGoogle Scholar
  28. Loeb GE (2012) Optimal isn’t good enough. Biol Cybern 106:757–765CrossRefPubMedGoogle Scholar
  29. Martinez Valdes E, Negro F, Falla D, De Nunzio AM, Farina D (2018) Surface EMG amplitude does not identify differences in neural drive to synergistic muscles. J Appl Physiol 124:1071–1079CrossRefPubMedGoogle Scholar
  30. Masood T, Bojsen-Moller J, Kalliokoski KK, Kirjavainen A, Aarimaa V, Magnusson SP, Finni T (2014) Differential contributions of ankle plantarflexors during submaximal isometric muscle action: a PET and EMG study. J Electromyogr Kinesiol 24:367–374CrossRefPubMedGoogle Scholar
  31. Pal S, Besier TF, Draper CE, Fredericson M, Gold GE, Beaupre GS, Delp SL (2012) Patellar tilt correlates with vastus lateralis: vastus medialis activation ratio in maltracking patellofemoral pain patients. J Orthop Res 30:927–933CrossRefPubMedGoogle Scholar
  32. Pincivero DM, Coelho AJ (2000) Activation li earity and parallelism of the superficila quadriceps across the isometric intensity spectrum. Muscle Nerve 23:393–398CrossRefPubMedGoogle Scholar
  33. Prilutsky BI, Zatsiorsky VM (2002) Optimization-based models of muscle coordination. Exerc Sport Sci Rev 30:32–38CrossRefPubMedPubMedCentralGoogle Scholar
  34. Schmitz A, Silder A, Heiderscheit B, Mahoney J, Thelen DG (2009) Differences in lower-extremity muscular activation during walking between healthy older and young adults. J Electromyogr Kinesiol 19:1085–1091CrossRefPubMedGoogle Scholar
  35. Shadmehr R (2016) Distinct neural circuits for control of movement vs. holding still. J Neurophysiol 117:1431–1460CrossRefGoogle Scholar
  36. Ting LH, Chiel HJ, Trumbower RD, Allen JL, McKay JL, Hackney ME, Kesar TM (2015) Neuromechanical principles underlying movement modularity and their implications for rehabilitation. Neuron 86:38–54CrossRefPubMedPubMedCentralGoogle Scholar
  37. Todorov E (2004) Optimality principles in sensorimotor control. Nat Neurosci 7:907–915CrossRefPubMedPubMedCentralGoogle Scholar
  38. Valero-Cuevas FJ, Cohn BA, Yngvason HF, Lawrence EL (2015) Exploring the high-dimensional structure of muscle redundancy via subject-specific and generic musculoskeletal models. J Biomech 48:2887–2896CrossRefPubMedPubMedCentralGoogle Scholar
  39. Winter DA, Yack HJ (1987) EMG profiles during normal human walking: stride-to-stride and inter-subject variability. Electroencephalogr Clin Neurophysiol 67:402–411CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Faculty of Sport Sciences, Laboratory “Movement, Interactions, Performance” (EA 4334)University of NantesNantesFrance
  2. 2.School of Health and Rehabilitation Sciences, NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and HealthThe University of QueenslandBrisbaneAustralia
  3. 3.School of Biomedical SciencesThe University of QueenslandBrisbaneAustralia
  4. 4.Institut Universitaire de France (IUF)ParisFrance

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