Correlation of average muscle fiber conduction ...

Journal of Electromyography and Kinesiology 17 (2007) 393–400 www.elsevier.com/locate/jelekin

Correlation of average muscle fiber conduction velocity measured during cycling exercise with myosin heavy chain composition, lactate threshold, and VO2max Dario Farina a

a,*

, Richard A. Ferguson b, Andrea Macaluso b, Giuseppe De Vito

b,c

Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Fredrik Bajers Vej 7 D-3, Aalborg University, DK-9220 Aalborg, Denmark b Department of Applied Physiology, University of Strathclyde, Glasgow, UK c Department of Human Movement and Sport Science, Istituto Universitario di Scienze Motorie (IUSM), Rome, Italy Received 15 October 2005; received in revised form 11 March 2006; accepted 13 March 2006

Abstract The aim of the study was to investigate the correlation between myosin heavy chain (MHC) composition, lactate threshold (LT), maximal oxygen uptake VO2max, and average muscle fiber conduction velocity (MFCV) measured from surface electromyographic (EMG) signals during cycling exercise. Ten healthy male subjects participated in the study. MHC isoforms were identified from a sample of the vastus lateralis muscle and characterized as type I, IIA, and IIX. At least three days after a measure of LT and VO2max, the subjects performed a 2-min cycling exercise at 90 revolutions per minute and power output corresponding to LT, during which surface EMG signals were recorded from the vastus lateralis muscle with an adhesive electrode array. MFCV and instantaneous mean power spectral frequency of the surface EMG were estimated at the maximal instantaneous knee angular speed. Output power corresponding to LT and VO2max were correlated with percentage of MHC I (R2 = 0.77; and 0.42, respectively; P < 0.05). MFCV was positively correlated with percentage of MHC I, power corresponding to LT and to VO2max (R2 = 0.84; 0.74; 0.53, respectively; P < 0.05). Instantaneous mean power spectral frequency was not correlated with any of these variables or with MFCV, thus questioning the use of surface EMG spectral analysis for indirect estimation of MFCV in dynamic contractions. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Dynamic exercise; Surface EMG; Electrode arrays; Muscle fiber types

1. Introduction Muscle fiber conduction velocity (MFCV) provides important information on motor control. It is a size principle parameter (Andreassen and Arendt-Nielsen, 1987; Gantchev et al., 1992), and thus indicative of motor unit recruitment strategies (Farina et al., 2004c). Measures of MFCV have been traditionally performed during isometric contractions (e.g., Arendt-Nielsen and Mills, 1985; Bigland-Ritchie et al., 1981) while indirect assessments of MFCV from single-channel surface EMG spectral analysis *

Corresponding author. Tel.: +4596358821; fax: +4598154008. E-mail address: [email protected] (D. Farina).

1050-6411/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2006.03.003

have been adopted in dynamic conditions (Balestra et al., 2001; Karlsson et al., 2000; Knaflitz and Molinari, 2003; Wakeling et al., 2002). Although EMG spectral analysis may provide indications of the relative changes in MFCV for a motor unit population, the elucidation of spectral variables with motor unit recruitment/derecruitment is not straightforward and may lead to ambiguous interpretations of the results (Farina et al., 2004b). Farina et al. (2004a) recently proposed a technique for direct assessment of MFCV from surface EMG signals during fast dynamic tasks. The method is based on multi-channel EMG signal detection by the use of adhesive electrode arrays and has been applied to investigate motor unit recruitment during cycling exercise (Farina et al., 2004c).

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D. Farina et al. / Journal of Electromyography and Kinesiology 17 (2007) 393–400

who had trained for an average of 8 years. This enabled us to investigate a continuum of performance levels rather than discrete groups. The study was conducted in accordance with the Declaration of Helsinki, approved by the Local Ethics Committee, and written informed consent was obtained from all participants prior to inclusion.

Within muscle fibers, the primary determinant of contractile and metabolic properties, such as force–velocity, power generating characteristics, energy turnover and efficiency, is the myosin heavy chain (MHC) (Bottinelli et al., 1996; Larsson and Moss, 1993; He et al., 2000), of which there are three main isoforms, type I, IIA and IIX. Since MHC composition is an important factor in determining the properties of skeletal muscle, in vivo locomotory performance would also be influenced. For example, there are strong relationships between the histochemically determined proportion of type I fibres and lactate threshold and VO2max (Ivy et al., 1980). Studies that have investigated the interrelationship between fibre composition and EMG profiles have employed histochemical analysis that characterises the individual fibres into two discrete types. However, this is limited in that although histochemical staining patterns from mATPase activity have been found to correlate to MHC (e.g., Staron et al., 1991; Staron and Pette, 1986, 1993), human fibres are often hybrids of more that one MHC which may assume the histochemical staining profile of the dominant isoform. A more accurate analysis would be to establish the MHC profile of the skeletal muscle. Therefore, the main aim of this study was to investigate the correlation between LT, VO2max, MHC composition, and MFCV in a population of subjects with a wide range of LT and VO2max values. The measure of MFCV was performed during cycling at power output corresponding to the individual LT at which point the contribution from type II fibers starts to become significant (e.g., Greig et al., 1986; Krustrup et al., 2004). A further aim was to investigate the relationship between MFCV and instantaneous mean power spectral frequency of the surface EMG signal, which is usually adopted as an indirect measure of MFCV (Balestra et al., 2001).

2.2. Muscle sampling and myosin heavy chain analysis A single muscle sample was obtained from all subjects at rest at least two weeks before the experimental trial. Under local anaesthesia (2% xylocaine) the sample was obtained from the medial part of the right vastus lateralis muscle using the needle biopsy technique (Bergstrom, 1962). The samples were immediately frozen in liquid nitrogen, freeze-dried, and stored at 80 °C before subsequent analysis. MHC composition was determined by sodium dodecylsulphate gel electrophoresis (SDS-PAGE) using a method based on that described by Fauteck and Kandarian (1995). Samples were homogenised in sample buffer (1 mg muscle tissue per ml buffer) consisting of 10% glycerol, 5% 2-mercaptoethanol, 2.3% SDS and 62.5 mM Tris (pH 6.8). These were then heated for 10 min at 70 °C and centrifuged at 4000 rpm for 10 min at 4 °C. Up to 10 ll of sample buffer were loaded onto 8 cm long gels (BioRad Mini-Protean). The resolving gel consisted of 30% glycerol, 6% acrylamide (2.7% bis-acrylamide cross-linking). The stacking gel consisted of 30% glycerol, 4% acrylamide (2.7% bis-acrylamide cross-linking). Gels were electrophoresed for 24 h at 4 °C and constant voltage (70 V). After electrophoresis, gels were fixed and silver stained using a method modified from Oakley et al. (1980). Quantification of the protein bands was made by densitometry (BioRad GS8000 calibrated densitometer) and each band was expressed as a percentage of the total MHC content of the corresponding line. MHC isoforms were identified according to migration rates compared with molecular weight standards and characterized as type I, IIA and IIX.

2. Methods 2.1. Subjects

2.3. Lactate threshold and VO2max Ten healthy male subjects participated in the study (Table 1). The subject’s participation in physical activity ranged from recreational to well trained endurance cyclists

At least three days before the main experimental session, the participants undertook a standardized incremental

Table 1 Subject characteristics, LT, VO2max, and myosin heavy chain composition Subject

1 2 3 4 5 6 7 8 9 10

Age (year)

28 30 20 32 20 22 24 28 23 23

Height (m)

1.71 1.86 1.83 1.81 1.78 1.70 1.96 1.69 1.85 1.82

Body mass (kg)

73 73 76 79 74 68 104 64 90 71

LT, lactate threshold; MHC, myosin heavy chain.

LT

VO2max

W

ml kg

200 239 221 266 138 137 200 165 161 149

42 52 39 46 26 29 25 36 25 31

1

min

1

% VO2max

W

ml kg

69 79 63 79 60 59 64 63 63 62

385 420 420 420 270 295 385 295 385 320

61 66 62 58 43 49 39 57 40 50

MHC 1

min

1

I (%)

IIA (%)

IIX (%)

46 55 52 66 32 20 24 37 25 23

33 45 48 34 45 41 67 50 47 60

21 0 0 0 23 39 9 13 28 17


exercise test on an electronically braked cycle ergometer (Excalibur Sport, Lode, The Netherlands), for the measurement of LT and VO2max, using a method modified from a treadmill test described by Jones (1998). Subjects cycled on the ergometer at an initial power output of 70 W which was increased every 3 min by 35 W. At the end of each stage, a capillary blood sample for the determination of blood lactate concentration (Lactate Pro, Arkray, Japan) was obtained. When lactate concentration had increased by more than 1 mmol l 1, at least three further stages were performed, after which a 10 min recovery period was allowed. Following this, VO2max was determined with the subjects cycling, beginning at the power output of the penultimate stage, with power being increased by 35 W every minute until the subject reached volitional exhaustion. Pulmonary oxygen uptake and carbon dioxide production was measured continuously during the exercise via an online gas analysis system (Oxycongamma, Mijnhardt, Netherlands), which had been calibrated with known values of O2, CO2 and volume. Data points for blood lactate concentration were plotted against power output. Two distinct portions of data points which could be fit by a regression line were identified. LT was determined as the intersection point of the two best fitting lines (Farina et al., 2004c).

395

2.4. Experimental protocol Subjects attended the laboratory on a single occasion where they were prepared for surface EMG measurements. A warm-up of 5–10 min cycling was performed at a freely chosen pedal rate on the cycle ergometer, after which the electrodes for surface EMG recording were placed on the right vastus lateralis muscle, as described below. The subject then performed a 2-min cycling exercise at an average pedal rate of 90 rpm and power output corresponding to LT. The knee joint angle was recorded with an electronic goniometer (model GONA2C03144, DEM, Torino, Italy). The acquisition of surface EMG signals started when the subject reached the target pedal rate and power output. The subject was provided with a visual feedback of the average pedal rate. 2.5. Surface EMG recordings and analysis Multi-channel surface EMG signals were detected from the right vastus lateralis muscle with a linear adhesive array (model ELSCH008, SPES Medica, Salerno, Italy) consisting of eight electrodes with 5 mm inter-electrode distance, in bipolar configuration (Farina et al., 2004a). The procedures for electrode placement were described in detail in

Bipolar EMG signals

0.6 mV

Knee joint angle

45° 0

2

4

6

8

10

12

14

16

18

20

Time (s)

A Bipolar EMG signals

0.6 mV

Knee joint angle

45° 18.9

B

19.0

19.1

19.2

Time (s)

Fig. 1. (A) 20-s long portion of multi-channel surface EMG signals and joint angle recorded from a representative subject. The black circles indicate the time instants selected for estimation of MFCV, corresponding to maximum instantaneous knee angular speed. (B) One of the surface EMG signal bursts shown in A. The Gaussian window applied to the mean square error to localize the MFCV estimation is representatively shown superimposed with the first surface EMG channel (dashed line).

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previous work (Farina et al., 2004c). EMG signals were amplified (16 channel surface-EMG amplifier, EMG-16, LISiN – Prima Biomedical & Sport, Treviso, Italy), band-pass filtered ( 3 dB bandwidth, 10 Hz to 500 Hz, 40 dB/decade), and sampled at 2 kHz. Analysis of surface EMG was performed off-line. The goniometer signal was low-pass filtered (anti-causal Butterworth filter of order 4, cut-off frequency 10 Hz) and differentiated for estimating the instantaneous knee angular speed. Muscle fiber conduction velocity was estimated from the surface EMG signals with the method proposed by Farina et al. (2004a) and previously applied for analyzing a similar exercise (Farina et al., 2004c). Briefly, the method is based on the estimation of the delay between the signals detected by the array. The delay is assessed from the mean square error between the aligned signals with a Gaussian window (with standard deviation 30 ms in this study) weighting the contributions of the action potentials. Since it has been shown that MFCV significantly changes within a single pedal revolution during cycling (Farina et al., 2004c), indicating motor unit recruitment/derecruitment, it was important to standardize the instantaneous angular speed at which MFCV was measured. Thus, MFCV was estimated at the maximum angular speed in each revolution (Fig. 1). Instantaneous mean power spectral frequency of the EMG signal was estimated at the same time instants as MFCV from the Choi-Williams time–frequency representation (Choi and Williams, 1989), which provides the most accurate estimates of instantaneous frequency from surface EMG over a number of other Cohen’s class time–frequency distributions (Bonato et al., 1996). The parameter r in the kernel of the time-frequency representation was set to 1, as suggested previously (Bonato et al., 1996). Moreover, to reduce the variance of estimation, instantaneous mean frequency estimates were averaged over a 30ms interval centered at the time instant used for MFCV estimation, as described in Bonato et al. (2001). Conduction velocity and instantaneous mean frequency were assessed for each revolution at the maximal instantaneous speed, thus a total of 180 values were obtained during the 2-min exercise. These values were fit with a

regression line. The intercept of the regression line at time t = 0 indicated the initial variable value while its slope revealed the rate of change over time. 2.6. Statistical analysis Data are presented as mean and standard deviation (SD). Pearson correlation coefficient was computed to assess linear relations between variables together with the corresponding coefficient of determination (R2). Statistical significance was set at P < 0.05. 3. Results 3.1. Lactate threshold, VO2max and MHC composition Lactate threshold (expressed either as the power output at which LT occurred, or as ml kg 1min 1, or as a percentage of VO2max), VO2max (expressed in ml kg 1 min 1 or as the power output at VO2max), and MHC composition are reported for each subject in Table 1. Percentage of MHC I was positively correlated with all measures of LT and VO2max (Table 2). 3.2. Trends over time of muscle fiber conduction velocity and instantaneous mean frequency The coefficient of variation of estimates of MFCV and instantaneous mean power spectral frequency with respect to the regression line was 6.8 ± 1.5% and 11.9 ± 4.1%, respectively. Neither MFCV nor instantaneous mean frequency had slope significantly different from zero in the 2-min exercise. Thus, the initial values of MFCV and instantaneous mean frequency were assumed as representative of the contraction. 3.3. Correlation between MFCV, MHC composition, LT, and VO2max MFCV measured during cycling was positively correlated with percentage of MHC I, LT, and VO2max (Fig. 2). On the contrary, instantaneous mean frequency

Table 2 Coefficient of determination (R2) among percentage of MHC I, LT, VO2max, and instantaneous mean frequency estimated from the surface EMG LT (ml kg LT (W) LT (ml kg 1min 1) LT (% VO2max) VO2max (W) VO2max (ml kg 1 min 1) MHC %Type I

0.61

IMNF, instantaneous mean frequency. * P < 0.05. à P < 0.001. P < 0.01.

1

min 1)

LT (% VO2max)

0.76 0.73

VO2max (W) à

0.76 0.33 0.49*

VO2max (ml kg 0.37 0.87à 0.37 0.18

1

min 1)

MHC %Type I à

0.77 0.78à 0.67 0.42* 0.62

IMNF 0.16 0.04 0.10 0.11 0.07 0.13

5.5 R2=0.74†

5.5 MFCV (m/s)

MFCV (m/s)


5.0 4.5 200 LT (W)

30 40 50 LT (ml kg-1 min-1)

5.5

R2=0.47*

5.0

4.5 65 70 75 LT (%VO2max)

5.0

80

R2=0.42*

5.0

4.5

280 320 360 400 VO2max (W)

MFCV (m/s)

MFCV (m/s)

R2=0.53*

4.5 60

5.5

5.0

250

MFCV (m/s)

MFCV (m/s)

5.5

showed a significant decrease in MFCV during cycling for 4 min at 150 W and 60 rpm in sedentary subjects. Although LT was not measured by Farina et al. (2004a) and thus a direct comparison with the present study is not possible, it is likely that a power output of 150 W was above LT in most of the sedentary subjects measured in Farina et al. (2004a). Above LT, lactate accumulation in the muscle modifies pH (Stringer et al., 1994) which will affect MFCV (Brody et al., 1991). At power output corresponding to LT or below, the modifications of fiber membrane properties may have been minor and not detectable. This is in agreement with the findings of Knaflitz and Molinari (2003) who did not observe changes in instantaneous mean frequency of rectus femoris, biceps femoris, and gastrocnemius muscles during cycling at very low power for 30 min (mechanical work 10 kJ).

R2=0.52*

4.5 150

5.5 R2=0.84‡

4.3. Average muscle fiber conduction velocity and MHC composition

5.0

4.5 40 50 60 70 VO2max (ml kg-1 min-1)

20

30 40 50 60 MHC %Type I

397

70

Fig. 2. Scatter plots of MFCV, myosin heavy chain composition, LT, and VO2max. LT: lactate threshold expressed as power output (W), oxygen uptake (ml kg 1 min 1), or percent of VO2max; MHC: myosin heavy chain. The coefficient of determination (R2) and significance levels are reported for each plot. * P < 0.05; P < 0.01; à P < 0.001.

was not correlated to any of these variables (Table 2). Moreover, instantaneous mean frequency was not significantly correlated with MFCV (R2 = 0.22). 4. Discussion The main finding of this study was that, during cycling at LT, MFCV correlated with percentage of MHC I, LT, and VO2max. On the other hand, instantaneous mean power spectral frequency was not correlated to any of the variables investigated. 4.1. MHC composition, LT, and VO2max The relationships between MHC composition and LT, VO2max (Table 2) have been demonstrated previously using histochemical analysis (e.g., Ivy et al., 1980). Our results confirm that MHC composition is an important determinant of LT and VO2max, reflecting the close association between MHC composition, contractile and metabolic properties of the muscle and in vivo muscle performance. 4.2. Trends over time of conduction velocity and instantaneous mean frequency Neither MFCV nor mean frequency changed in the 2min exercise. In a previous study, Farina et al. (2004a)

MFCV was measured at an external power output corresponding to LT in order to obtain exercise intensity comparable among subjects in the attempt to reduce the variability in the physiological and metabolic response, as suggested by Meyer et al. (1999). Thus, the reported MFCV values are representative, across the subject sample, of the recruitment of the entire pool of type I muscle fibers just before the contribution from type II fibers starts to become significant (see, e.g., Krustrup et al., 2004). The observed correlation between MFCV and proportion of MHC I may appear in contrast to previous work which demonstrated a relationship between MFCV and proportion of type II muscle fibres (e.g., Sadoyama et al., 1988). However, these previous studies were done during intense isometric exercise when it is likely that all muscle fibres were active and force development is dominated by type II motor units. The unique aspect of the present study is that it was performed during sub-maximal dynamic exercise when mostly type I fibres (containing predominantly MHC I isoform) were active. The observed positive relation between MFCV and the relative proportion of MHC I may be explained by the possibility that the average diameter of fibers that express MHC I increases in parallel with the overall proportion of MHC I (Howald et al., 1985). MFCV is indeed proportional to muscle fiber diameter (Plonsey and Barr, 2000). This interpretation is supported by the finding that the elevated proportion of MHC I that can occur with endurance training is accompanied with an increase in muscle fiber cross-sectional area (Ricoy et al., 1998; Rodriguez et al., 2002). For example, Rodriguez et al. (2002) found an increase in the proportion of MHC I and fiber cross-sectional area of approximately 30% and 25% in well trained cyclists with respect to sedentary subjects. For the same type of fiber, MFCV may be different depending on the average fiber diameter which is consistent with the fact that muscle fibers present a distribution, rather than a single value, of conduction velocities which

398


may change depending on training, pathology, or ageing (Farina and Merletti, 2004). The present results also underline problems with attempts to indirectly estimate the proportion of different fiber types from MFCV or other surface EMG derived variables (e.g., Gerdle et al., 1991). The association between muscle fiber type and MFCV is indirect and follows the relation between fiber diameter and fiber type, i.e., type I fibers usually presents smaller diameter, and thus conduction velocity, than type II fibers. This study, however, indicates that MFCV of the pool of type I fibers may be very different across subjects. Thus, the rationale of analyzing MFCV from the entire motor unit pool with high-force contractions and attributing lower average MFCV values simply to larger proportion of type I fibers (Gerdle et al., 1988, 1991; Sadoyama et al., 1988; Wretling et al., 1987) may be questioned. The concomitant increase in average muscle fiber diameter and proportion of type I muscle fibers may mask the expected decrease in average MFCV of the whole motor unit pool when the proportion of type I fibers increases. 4.4. Spectral analysis for inferring muscle fiber properties from surface EMG Surface EMG spectral features are often used as indirect measure of MFCV (Balestra et al., 2001; Bernardi et al., 1997; Wakeling et al., 2002). However, in this study we observed that instantaneous mean power spectral frequency was not correlated with MFCV, LT, VO2max, or MHC proportion. The use of spectral frequencies instead of the direct estimation of MFCV is practically convenient since the technical requirements for spectral analysis are less strict than those for MFCV assessment. A single pair of electrodes is sufficient for extracting the characteristic spectral frequencies of the surface EMG while an array of electrodes is required for accurate MFCV estimation (Farina and Merletti, 2004). The indirect estimation of MFCV from surface EMG power spectrum is based on the observation that in ideal conditions changes in MFCV of single motor units reflect the same relative changes in spectral variables of the detected action potentials (Lindstrom and Magnusson, 1977). This relation is not valid in general due to the effect of the intracellular action potential shape changes and generation and extinction of the potential at the end-plate and tendons (Arabadzhiev et al., 2005). The relation between spectral frequencies and conduction velocity may be approximately valid in specific conditions when relative changes are analyzed, i.e., when the same active motor unit pool is analyzed over time. This condition occurs, for example, in sustained, isometric contractions at high, constant force. In this case, the relative decrease in MFCV can be approximately estimated from the relative decrease in mean or median power spectral frequencies (Stulen and DeLuca, 1981), although the percent changes in spectral frequencies are usually larger than those in conduction velocity (Broman et al., 1985). How-

ever, when absolute rather than relative differences are investigated, the correlation between spectral variables and MFCV may be disrupted. This is the case of isometric, non-constant force contractions (Farina et al., 2002) or dynamic contractions (Merlo et al., 2005). Only one previous study has compared, during explosive dynamic contractions, MFCV estimates and instantaneous mean frequency estimated from surface EMG signals (Merlo et al., 2005). In the present study we were able to compare MFCV and instantaneous mean frequency in relation to their association with MHC proportion, LT, and VO2max. According to previous results (Merlo et al., 2005), there was no correlation between MFCV and mean frequency across the subject sample. These variables are affected differently by the parameters of the surface EMG generation system (Farina et al., 2004b). While MFCV correlated with proportion of MHC I, LT, and VO2max, instantaneous mean frequency did not correlate to any of these variables. In the dynamic task analyzed, spectral analysis failed to provide a reliable assessment of muscle properties. Therefore, data on EMG spectral analysis obtained during cycling (e.g., Viitasalo et al., 1985) should be considered carefully. The present results underline that estimation of MFCV in dynamic exercises provides a unique window into the peripheral properties of the neuromuscular system. 4.5. Limitations Estimates of MFCV are affected by many methodological factors. Estimates may be biased due to non-propagating signal components arising from the generation and extinction of the action potentials at the end-plates and tendons (Arabadzhiev et al., 2005). The relative weight of these components depends on factors such as fiber length, location of the electrodes, thickness of the subcutaneous tissue. These factors may be different in different subjects, which may partly affect the comparison across subjects. Moreover, during movement the relative location of the electrodes with respect to the muscle fibers may shift, affecting the MFCV estimates. Although these factors cannot be totally excluded and probably partly affected the estimates, their influence on the results shown was minimized by electrode location in the middle between the innervazion zone and tendon, where non-propagating components are lower. In addition, the location of the innervation zone was identified at different joint angles in preliminary test contractions to assure that it did not correspond to electrode location at any joint angle, as described in Farina et al. (2004a). 5. Conclusion This study shows for the first time that MFCV estimated from vastus lateralis during cycling at LT is correlated to the relative proportion of MHC I, LT, and VO2max. It is concluded that MFCV measured during dynamic exercise provides important information on muscle fiber properties


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Stulen FB, DeLuca CJ. Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans Biomed Eng 1981;28:515–23. Stringer WW, Wasserman K, Casaburi R, Porszasz J, Maehara K, French W. Lactic acidosis as a facilitator of oxyhemoglobin dissociation during exercise. J Appl Physiol 1994;76:1462–7. Viitasalo JT, Luhtanen P, Rahkila P, Rusko H. Electromyographic activity related to aerobic and anaerobic threshold in ergometer bicycling. Acta Physiol Scand 1985;124:287–93. Wakeling JM, Pascual SA, Nigg BM. Altering muscle activity in the lower extremities by running with different shoes. Med Sci Sports Exerc 2002;34:1529–32. Wretling ML, Gerdle B, Henriksson-Larsen K. EMG: a non-invasive method for determination of fibre type proportion. Acta Physiol Scand 1987;131:627–8. Dario Farina graduated summa cum laude in Electronics Engineering from Politecnico di Torino, Torino, Italy, in February 1998. During 1998, he was a Fellow of the Laboratory for Neuromuscular System Engineering in Torino. In 2001 and 2002, he obtained the PhD degree in Automatic Control and Computer Science and in Electronics and Communications Engineering from the Ecole Centrale de Nantes, Nantes, France, and Politecnico di Torino, respectively. In 1999–2004, he taught courses in Electronics and Mathematics at Politecnico di Torino and in 2002–2004, he was Research Assistant Professor at the same University. Since 2004, he is Associate Professor in Biomedical Engineering at the Faculty of Engineering and Science, Department of Health Science and Technology of Aalborg University, Aalborg, Denmark, where he teaches courses on biomedical signal processing, modeling, and neuromuscular physiology. He regularly acts as referee for approximately 20 scientific International Journals, is an Associate Editor of IEEE Transactions on Biomedical Engineering, is on the Editorial Board of the Journal of Electromyography and Kinesiology and of the Journal of Neuroscience Methods, and member of the Council ISEK (International Society of Electrophysiology and Kinesiology). His main research interests are in the areas of signal processing applied to biomedical signals, modeling of biological systems, basic and applied physiology of the neuromuscular system, and brain–computer interfaces. Dr. Farina is a Registered Professional Engineer in Italy. Richard Ferguson has a BSc (1994) and MPhil (1997) in Sport and Exercise Science from University of Birmingham and received a PhD in Skeletal Muscle Energetics from Manchester Metropolitan University in 2000. He is currently a Lecturer in the Division of Physiology and Pharmacology at the Strathclyde Institute of Pharmacy and Biomedical Sciences. As part of the Integrative Mammalian Biology Research Group his interests include energetics and efficiency of human locomotory muscle and human muscle fibre recruitment characteristics.

Andrea Macaluso was born in Rome, Italy, in 1967. He received his degree in Medicine at the University of Rome ‘‘La Sapienza’’, Italy, in 1991 and specialised in Sports Medicine in 1995. Since 1997 he has been working as a researcher within the Applied Physiology Group at the University of Strathclyde in Glasgow, UK, towards his Ph.D. degree, which was awarded in 2003. Since 2001 he has also served as a Lecturer (Senior Lecturer since 2005) in the same University. He has authored and co-authored relevant papers in the area of neuromuscular control and adaptation to physical exercise, especially in older populations. Giuseppe De Vito was born in 1958. He is currently Associate Professor in Human and Exercise Physiology in the Department of Human Movement and Sport Science at the ‘‘Istituto Universitario di Scienze Motorie’’ of Rome (Italy). He received his degree in Medicine in 1986, specialized in Sports Medicine in 1989 and in 1994 obtained his PhD degree in Exercise Physiology. All three courses were completed at the University La Sapienza of Rome Italy. From 1994 to 1996, he served as physician/physiologist to the Italian Olympic sailing team. From 1996 to 2005, he worked at the University of Strathclyde in Glasgow (UK), firstly as a Lecturer (1996–1999) and then as Reader (up to 2005) in Exercise Physiology within the Strathclyde Institute for Biomedical Sciences. His primary area of teaching is human physiology and exercise physiology with special attention to ageing. His research interests involve mainly two areas: muscle function and adaptations and autonomic cardiovascular control in health and disease. He is ordinary member of both British and Italian Physiological Societies and the European College of Sport Science. He serves as a manuscript reviewer for several scientific journals and he is member of the editorial board of the Journal of Electromyography and Kinesiology and of the Journal of Physical Activity and Ageing.