Neuromuscular activation during isometric ramp contractions performed at different speeds

NEUROMUSCULAR ACTIVATION DURING ISOMETRIC RAMP CONTRACTIONS PERFORMED AT DIFFERENT SPEEDS I. Bazzucchi1, P. Sbriccoli1,2, A. Rosponi1,2, F. Felici1,2 1University Institute of Motor Sciences (IUSM), Rome, Italy 2Post Graduate School in Sport Medicine, Faculty of Medicine, University “La Sapienza”, Rome, Italy The term “motor unit activation” refers to the combination of recruitment and discharge properties of motor units (MUs) within muscles (Sale 1987). The relative role of MU recruitment and increased firing rate as mechanisms for increasing force exertion has been debated since the early seventies (Clamann, 1970; Milner Brown et al, 1973). Solomonow et al. (1990) experimentally reproduced, in cat muscle, MU recruitment according to the size principle proposed by Henneman et al (1965), using electrical stimulation. It has been shown that the median frequency (MDF) of the EMG power spectrum increased linearly with orderly recruitment (stimulation) of MUs until complete recruitment was obtained. The additional force gain was achieved by increasing MU firing rate, as revealed by an increase in the root mean square (RMS) value. The MDF is related, among other factors, to the average muscle fibre conduction velocity (Bigland-Ritchie et al, 1981; Lindstrom et al, 1970 among others). Solomonow et al (1990) studied the contributions of MU recruitment and firing rate to the sEMG MDF. Since firing rate of the active MUs did not affect the MDF, these researchers concluded that the increase in CV during recruitment is the major contributor to variations in MDF. Bernardi et al (1996, 1999) observed that during voluntary isometric linearly increasing contractions of the elbow flexors (from 0 to 100% of the maximal voluntary force - MVC - in 3s), full MU recruitment occurs between 65 and 85%MVC, as revealed by the MDF increase, depending upon the individual skill in performing the exercise. Several studies have been carried out as regards sEMG power spectrum changes during contractions performed in a ramp fashion (Moritani and Muro, 1987; Seki et al, 1991). To our knowledge, the effect of different speeds of contraction on sEMG changes has not yet been fully investigated. It is known that the graduation of force implies a continuous rearrangement in MU activation. Muscles involved in small and precise movements find in the firing rate modulation the major factor in force graduation, whilst large muscles can produce smooth contractions with a greater role for recruitment, because each unit would add a much smaller relative increment to the total force (De Luca et al, 1982). The purpose of the present study was to extend the studies from Bernardi et al (1999) investigating the dependency of sEMG time and frequency domain parameters on the speed of contraction. Five subjects participated in this study. They were seated comfortably, and performed isometric contractions with their right biceps brachii (BB) using an anatomic device that allowed the elbow angle to be fixed at 90°. The hand was maintained halfway between pronation and supination. A 4 array electrode (interelectrode distance: 10mm) was positioned over the longitudinal axis of the BB quite between the motor point and the distal tendon. The BB motor point was identified by electrical stimulation of the skin overlying the muscle, according to the procedure described in Farina et al (2000); a reference electrode was placed over the olecranon. In a preliminary phase, subjects were accustomed in performing ramp contractions 0 to 100%MVC (ramp phase) at different speeds (5, 10 and 20%MVC s-1) following a force trace displayed on a PC screen. The experimental sessions started only when subjects became acquainted in performing the exercise. Different sessions were carried out and during each session three ramp contractions were performed at randomly presented speeds. After measuring the Maximal Voluntary Contraction (MVC), the sEMG was recorded from BB muscle during linearly increasing isometric contractions from 0 to 100%MVC (ramp phase), and during the subsequent 10s of sustained MVC (constant phase). The sEMG signals were detected in a single differential mode (SD1, SD2, SD3) using the configuration described in Fig.1. Two double differential sEMG signals (DD1-DD2) were then computed and used for the sEMG analysis and CV computing. Force and sEMG were A/D sampled at 2048 points per second at 12 bit resolution (DAQ card AI-16XE-50, National Instruments) and the raw signals were stored on a PC for further analyses. For each subject 5 trials were accepted at each speed of contraction according to the criteria proposed by Bernardi et al (1996-1999). In particular, trials where no correspondence was obtained between the maximum sEMG RMS and maximum strength, were not accepted. Furthermore, trials were rejected either when motor performance was different from the target, or when the individual force did not reach the 100% MVC. A time (RMS) and frequency (MDF) domains analysis was performed over sEMG signals. Spectral sEMG analysis was performed on subsequent epochs of 256 samples (125 ms) overlapped one another by half their length. Such windowing was chosen in order to obtain an EMG signal that could be considered stationary and therefore to allow the use of FFT (Bilodeau et al. 1993). Epochs were windowed with a Hanning window and zero padded by 1792 points to obtain a frequency resolution of 1 Hz (Bernardi et al, 1999). The MDF is defined as where P(f) is the power spectrum density. The RMS is defined as: where x(t) is the sEMG signal and T is the acquisition time. The muscle fibre action potential CV was estimated using the EMG cross-correlation function technique between the two double differential sEMG signals (DD1- DD2). This method assumes that the time delay between two similar but not identical signals is the amount of time shift that must be applied to one of the signals to minimise the mean square error with the other (Sollie et al, 1985). This time shift is the same that maximises the cross-correlation between the two signals. According to Felici et al (2001) the cross correlation Rxy(t) of the signals x(t) and y(t) is defined as: where the symbol  denotes correlation. Estimates of CV were accepted only when the EMG cross-correlation function values were higher than 0.6. CV was estimated during the ramp phase (all speeds) and during the constant force phase. Windowing was appropriately chosen in each ramp speed (wider for slower ramp, narrower for faster ramp contractions). The average RMS, MDF values obtained from the five ramp contractions at each force level were used for the statistical analysis and graphical representation of the data. Two-way repeated measure analyses of variance (ANOVAs) were used to assess differences ( =0.05) in the values of RMS, MDF across the three different speeds (5, 10, and 20%MVCs-1). At 5%MVC s-1 the RMS continuously increased during the ramp phase and up to the very start of the constant force phase. The MDF reached its peak at about 50% of the ramp phase (50%MVC). It must be noted that as the MDF stopped increasing, the RMS slope became steeper (Figure 1). At 20%MVC s-1 the RMS increased during the ramp phase. No relevant changes in RMS were observed during the constant force phase. MDF increased up to about 85-90%MVC and then decreased during the resting ramp phase and during the constant force phase (Figure 2). These findings suggest that MU recruitment strategies are significantly related to the speed of contraction. MDF data indicated that during slower ramp contractions MU recruitment was complete at a lower percentage of MVC (50% compared to 85-90%). Although this fact could also be also associated to neuromuscular fatigue, an interesting finding was the relevant increase in the firing rate of active MU (as expressed by the RMS increase). It appears that the firing rate mechanism itself is able to complete the force demand even from 50 to 100 %MVC. Figure 1 REFERENCES 1. Bernardi M, Felici F, Marchetti M, Montellanico F, Piacentini M.F, Solomonow M. J Electromyogr Kinesiol 9: 121-130, 1999. 2. Bernardi M, Solomonow M, Nguyen G, Smith A, Baratta R. Eur J Appl Physiol 74:52-9, 1996. 3. Bilodeau M, Arsenault AB, Gravel D, Bourbonnais D. EMG power spectra of elbow extensors during ramp and step isometric contractions. Eur J Appl Physiol 63: 24-28, 1991 4. Clamann HP. Neurology, Minneapolis, 20: 117-128, 1970 5. De Luca CJ, Le Fever RS, Mc Cue MP, Xenakis AP. J Physiol 329: 113-128, 1982 6. Farina D, Fortunato E, Merletti R. IEEE Trans Biomed Eng 47(3): 380-388, 2000 7. Felici F, …….………Eur J Appl Physiol 84: 337-342, 2001 8. Henneman E, Somjen G, Carpenter DO. J Neurophysiol 28: 560-580, 1965 9. Milner-Brown HS, Stein RB, Yemm R. J Physiol 230: 371-390, 1973 10. Moritani T and Muro M. Eur J Appl Physiol 56: 260-265, 1987 11. Sachs L. Applied statistics. A Handbook of Techniques. 2nd ed. Springer Verlag, 1984. 12. Sale DG. Exerc Sport Sci Rev. 15: 95-151, 1987 13. Seki K, Mijazaki Y, Watanabe M, Nagata A, Narusawa M. Eur J Appl Physiol 63: 165-172, 1991. 14. Sollie G, Hermens H, Boon KL, Wallinga-De-Longe W, Zivold G. Electromyogr Clin Neurophysiol 25: 193-204, 1985 15. Solomonow M, Baten C, Smit J, Baratta R, Hermens H, D’Ambrosia R, Shoji H. J Appl Physiol 68(3): 1177-1185, 1990.