Motor units are recruited in a task-dependent fashion during locomotion
Structure and Motion Laboratory, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, Hertfordshire, AL9 7TA, UK
e-mail: jwakeling{at}rvc.ac.uk
Accepted 3 August 2004
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Summary |
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Key words: muscle fibre recruitment, myoelectric signal, principal component analysis, PC loading, hysteresis, task dependence
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Introduction |
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In synergistic muscles with different contractile properties, the faster
muscle may be selectively used for faster tasks, and this has been
demonstrated for the paw-shake in the cat
(Smith et al., 1980;
Fowler et al., 1988
) and
swimming in the blue gilled sunfish (Jayne
and Lauder, 1994
). However, little is known about how mammals
recruit their motor units for different movement tasks within a mixed muscle.
There are mechanical (Rome et al.,
1988
) and energetic reasons
(Woledge et al., 1985
) to
suppose that the faster motor units within a mixed muscle should be
selectively used for faster tasks. Indeed, jumping in the bushbaby involves
the selective use of the faster muscle fibres in the mixed muscles of the
vastus lateralis and gastrocnemius
(Gillespie et al., 1974
). The
mechanical benefits of using faster motor units for faster activities should
hold true across a range of locomotor speeds and for different dynamic tasks
within each stride, but such task-dependent recruitment has not yet been
observed. Thus, the purpose of the present study was to investigate whether
patterns of motor unit recruitment varied within a stride at a range of
locomotor speeds.
The myoelectric signals that are emitted from an active muscle contain
information about the muscle fibre types that generated the signal. Faster
muscles generate higher frequencies within the myoelectric spectra than do
slow muscles (Elert et al.,
1992; Gerdle et al.,
1988
; Kupa et al.,
1995
; Moritani et al.,
1985
; Solomonow et al.,
1990
), and distinct high and low frequency bands have recently
been identified that characterize activity from faster and slower muscle
fibres, respectively, in rainbow trout, cats, rats and humans
(Wakeling et al., 2002
;
Wakeling and Syme, 2002
;
Wakeling and Rozitis, 2004
).
Myoelectric signals with different frequencies but the same power indicate the
activity of different motor units. Therefore, in the present study, we tested
the hypothesis that myoelectric bursts with distinct frequencies but the same
power would occur at different times within each stride during walking and
running.
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Materials and methods |
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EMG measurement
Myoelectric activity was measured from the muscle bellies of the vastus
medialis, rectus femoris, vastus lateralis, lateral gastrocnemius, medial
gastrocnemius, soleus, biceps femoris, semitendinosus and tibialis anterior
muscles of the right leg using round bipolar surface electrodes (Ag/AgCl; 10
mm diameter, 22 mm spacing). A ground electrode was placed on the fibular
head. The EMGs were preamplified at source (bandwidth 10500 Hz, 3 dB;
Biovision, Wehrheim, Germany) and recorded at 3600 Hz (DAQCard-6062E; National
Instruments, Austin, TX, USA). Myoelectric activity was recorded for 20
consecutive steps in the last 30 s of each running trial. An accelerometer
mounted on the right shoe measured the time of heel-strike.
EMG analysis
The myoelectric signals (Fig.
1A) were resolved into their myoelectric intensities in
timefrequency space using wavelet techniques
(von Tscharner, 2000). The
intensity is a close approximation of the power of the signal contained within
a given frequency band, and the intensity spectrum is equivalent to the power
spectrum from the myoelectric signal.
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Strides were demarked by the time of heel-strike, and further subdivided into 20 equal time-windows. Stride times were thus normalized to a time of 100% and began at heel-strike. The mean intensity spectrum was calculated for each time-window (Fig. 1B,C). The first wavelet covered a frequency band of 212 Hz, which is typically associated with movement artefacts. If the myoelectric intensity resolved by the first wavelet was greater than the maximum intensity resolved by the higher wavelets then the data from that trial were considered noisy and not analyzed further. Intensities resolved by the first wavelet were then ignored, so the final analysis considered the total frequency band of 10524 Hz).
The mean myoelectric intensity for each muscle and subject for the 4.5 m
s1 trials was calculated and used to normalize the spectra
for the respective muscles and subjects. One matrix of spectra was compiled
from the normalized spectra for all muscles and all subjects. The principal
components (PCs) were calculated from the covariance matrix of the matrix of
spectra (Wakeling and Rozitis,
2004). The PCs were calculated with no prior subtraction of the
mean data and so describe the components of the entire signal
(Wakeling and Rozitis,
2004
).
Each measured spectrum can be reconstructed from the vector product of the
PC weightings and the PC loading scores (e.g.
Fig. 2C). The majority of the
signal for any given myoelectric spectrum is defined by the first two PCs, and
the relative PC I and PC II loading scores give a measure of the frequency of
the myoelectric signal. The angle was thus defined by ArcTan (PC I
score/PC II score) and used as a measure of the myoelectric frequency for each
spectra (Fig. 2;
Wakeling and Rozitis,
2004
).
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A multivariate analysis of covariance (MANCOVA) was used to test the
hypothesis that the myoelectric frequency differed at different times within
each stride (Minitab Inc., State College, PA, USA). The response variable was
, the measure of myoelectric frequency. The subject code, time-window
and locomotor velocity were used as factors in the test, and the myoelectric
intensity used as the covariate. The MANCOVA was repeated for each muscle
tested.
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Results |
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The first two principal components of the myoelectric intensity spectra
described 82.8% of the signal. The weighting for PC I had a shape similar to
that for a myoelectric intensity spectrum
(Fig. 2A). The myoelectric
intensity for each of the time-windows correlated with the PC I score with a
correlation coefficient of r=0.98. The weighting for PC II had a
negative region at frequencies below 60 Hz and a positive region at
frequencies above 60 Hz (Fig.
2B). Intensity spectra could be reconstructed from the vector
product of the PC weightings and the PC loading scores. Reconstructed spectra
with positive intensities across all frequencies (a physiological constraint)
occur for a range of and result in two extreme spectra with mean
frequencies of 54.7 Hz and 125.9 Hz (Fig.
2C).
The stride durations during walking and running at 1.5, 3.0 and 4.5 m s1 were 1050±1, 746±1 and 694±1 ms, respectively. As the running velocity increased, the myoelectric intensity increased for all muscles tested, and this is illustrated by the greater PC I loading scores in Figs 3, 4, 5.
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Each burst of muscle activity can be visualized by a loop of PC IPC
II loading scores in the PC IPC II scoring plane (Figs
3,
4,
5). For instance, during
walking, the biceps femoris showed a single burst of activity from
approximately 85% of one stride to 5% of the next. When the velocity increased
to a run at 3.0 m s1, the biceps femoris showed a pause in
myoelectric activity at heel-strike, and the activity extended into two
bursts: the first burst from 75% to heel-strike and then a second burst from
heel-strike to 30% of the stride (Fig.
3A). In some cases, for instance the tibialis anterior activity
during walking (Fig. 3C), the
paths of PC IPC II loading scores showed little hysteresis and lay on a
vector projecting from the origin. These paths indicate that the myoelectric
frequency remained steady throughout the burst of activity. In other cases,
for instance the tibialis anterior activity during running at 4.5 m
s1 (Fig. 3C),
the loops of PC IPC II loading scores showed a marked hysteresis. These
loops indicate that the myoelectric frequency changed during the burst of
activity. In some cases, PC IPC II loading scores followed
anticlockwise loops, for instance the rectus femoris activity during running
at 3.0 m s1 (Fig.
4B), and these loops indicated a gradual decrease in the
myoelectric frequency as the burst of activity progressed. In other cases, PC
IPC II loading scores followed clockwise loops, for instance the medial
and lateral gastrocnemius activity during running at 3.0 m
s1 (Fig.
5A,B), and these loops indicated a gradual increase in the
myoelectric frequency as the burst of activity progressed. The PC loading
scores from between 20 880 and 34 560 spectra were included in each MANCOVA,
and for all muscles there was a significant effect of the time-window on the
angle (P<0.001).
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Discussion |
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The myoelectric signals change in frequency during each stride, as shown by
the relative PC IPC II loading scores and the angle . Decreases
in myoelectric frequency can occur during fatiguing contractions
(Brody et al., 1991
) and with
decreases in muscle temperature (Petrofsky
and Lind, 1980
). However, in the present study, the randomized
block design minimized such bias. Furthermore, the intra-stride cycling of
myoelectric frequency occurs at a time scale too short to be significantly
affected by levels of fatigue or temperature. The different myoelectric
frequencies in this study thus represent the signals from different units
within each muscle. Motor units form the functional contractile unit within
the muscles (Sherrington,
1929
) and can generate different myoelectric frequencies
(Wakeling et al., 2002
;
Wakeling and Syme, 2002
;
Wakeling and Rozitis, 2004
).
The patterns of PC loading scores and the shape of the PC weightings were
similar to those distinguished from faster and slower motor unit activity in
humans (Fig. 2;
Wakeling and Rozitis, 2004
).
In the present study, the PC I and PC II explain 83% of the measured spectra,
with the remaining 17% explained by PC III to PC XI. Variations in myoelectric
frequency between muscles are accounted for by some of the loading scores of
PC III to PC XI. The changes in
in this study should thus be
considered as a relative measure of the motor unit recruitment patterns rather
than an absolute measure of frequency from the individual muscles.
Nonetheless, the changes in myoelectric frequency observed within each stride
in this study are most likely due to changes of the motor unit recruitment
patterns.
The results from this study thus indicate that the motor unit recruitment
patterns change through each stride, between locomotor speeds and gaits, and
are also different between muscles (Figs
3,
4,
5). The recruitment of the
motor units can be considered to start with a basic plan of orderly
recruitment determined by the excitabilities of the -motoneurons
(Henneman et al., 1974
). The
excitability of the motoneurons can be modulated by input from higher centres,
for instance via the corticospinal tract. Superimposed on this plan,
inhibitory interneurons within the spinal cord can modify the recruitment
pattern, and, in particular, the Renshaw calls can cause reversals of the
recruitment order (e.g. Ryall et al.,
1972
; Friedman et al.,
1981
; Broman et al.,
1985
). Furthermore, the sensitivity of the muscle spindles, which
provide monosynaptic input to the
-motoneurons, is modulated by the
fusimotor drive of the gamma efferents, which in turn cycles during each
stride (Loeb, 1985
). Motor
unit recruitment is thus the result of complex neural integration that is able
to shape the recruitment to different movement tasks.
Locomotion can place large energetic demands on an animal, and, indeed,
during vigorous activity the metabolic rate of mammals can range from six
times the resting rate in small mammals
(Pasquis et al., 1970) to
20 times the resting rate in larger athletic animals
(Young et al., 1959
). A large
portion of this additional metabolic energy expenditure during movement
results from muscular contractions, and thus recruiting the most appropriate
motor unit to maximize power output or contractile efficiency may result in
considerable energetic savings to the animal. Faster muscle fibres have higher
rates of force development and relaxation than do slower fibres
(Burke et al., 1971
), and the
maximum mechanical power output and efficiency during steady contractions
occurs at 2030% of their maximum shortening velocity,
Vmax (Hill,
1964
; Kushermick and Davies,
1969
). The faster motor units contain faster muscle fibres with
higher Vmax and so generate maximum power output and
efficiency at higher shortening velocities than do slow fibres. Therefore,
there is scope for a muscle to optimize its power production and efficiency by
recruiting the most appropriate motor units for each contractile task.
Matching the recruitment patterns to contractile requirements has the
potential for reducing the metabolic energy expenditure for locomotion, and
these patterns have been observed across anatomically distinct synergistic
muscles during cat paw-shakes (Smith et
al., 1980
; Fowler et al.,
1988
) and swimming fish (Jayne
and Lauder, 1994
). Observations within mixed mammalian muscle have
been limited to a study on the bushbaby in which selective recruitment of the
faster motor units for jumping as opposed to running has been demonstrated
within the mixed muscles of the vastus lateralis and gastrocnemius
(Gillespie et al., 1974
). The
mechanical arguments discussed above, however, suggest that there should be a
general pattern of matching contractile tasks to the recruited motor units
that holds true across all locomotor speeds and gaits and also for the
different movement tasks that occur within each stride.
Cycling of recruitment patterns within each stride may be a general feature
of mammalian locomotion. It is likely that the motor units are recruited to
match their contractile properties to the mechanical requirements for the
motion, and these requirements change between gaits, speeds and within each
stride. The demands on the muscle depend on its requirement to generate force
during lengthening, shortening or isometric phases of each stride, and
furthermore the demands of cross-bridge cycling may be utilized to dissipate
mechanical energy during soft-tissue vibrations
(Wilson et al., 2001). It is
not known which movement tasks place selective demands on specific populations
of motor unit, but this will be a necessary step in understanding the
complexity of motor recruitment within mixed muscle during locomotion.
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Acknowledgments |
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