adaptação neural e de força em pessoas treinadas e nao treinadas

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J Bro,,,eeho,,,cs Vol 26, Suppl I, pp 95-107, 1993 
Printed in Great Bntain 
0021.9290/93 56.00+.00 
Perpmon Press Lid 
NEUROMUSCULAR ADAPTATIONS DURING THE ACQUISITION OF 
MUSCLE STRENGTH, POWER AND MOTOR TASKS 
TOSHIO MORITANI 
Laboratory of Applied Physiology, The Graduate School of Human and Environmental Studies, Kyoto University, Sakyo-ku, 
Kyoto 606, Japan 
Abstract-Neuromuscular performance is determined not only by the size of the involved muscles, but also 
by the ability of the nervous system to appropriately activate the muscles. Adaptive changes in the nervous 
system in response to training are referred to as neural adaptation. This article briefly reviews current 
evidence regarding the neural adaptations during the acquisition of muscle strength, power and motor tasks 
and will be organized under four main topics, namely: (i) muscle strength gain: neural factors versus 
hypcrtrophy, (ii) neural adaptations during power training, (iii) neuromuscular adaptations during the 
acquisition of a motor task, and (iv) neuromuscular adaptations during a ballistic movement. 
INTRODUCTION 
Before describing neuromuscular adaptations during 
the acquisition of muscle strength, power and motor 
tasks, a brief review of neuromuscular physiology will 
be provided. A motor unit (MU) consists of a moto- 
neuron in the spinal cord and the muscle fibers it 
innervates (Burke, 1981). The number of MUs per 
muscle in humans may range from about 100 for a 
small hand muscle to 1000 or more for large limb 
muscles (Henneman and Mendell, 1981). It has also 
been shown that different MUs vary greatly in force 
generating capacity, i.e., a lOO-fold or more difference 
in twitch force (Garnett et at., 1979; Stephens and 
IJsherwood. 1977). In voluntary contractions, force is 
modulated by a combination of MU recruitment and 
changes in MU activation frequency (rate coding) 
(Kukulka and Cramann, 1981; Milner-Brown et al., 
1973; Moritani and Muro, 1987). The greater the 
number of MUs recruited and their discharge frequen- 
cy, the greater the force will be. During MU recruit- 
ment the muscle force, when activated at any constant 
discharge frequency, is approximately 2-5 kg/cm2, and 
in general is relatively independent of species, gender, 
age and training status (Alway et al., 1990; Close, 
1972; Ikai and Fukunaga, 1968). 
The electrical activity in a muscle is determined by 
the number of MUs recruited and their mean discharge 
frequency of excitation, i.e., the same factors that 
determine muscle force (Bigland-Ritchie, 1981; 
Moritani and Muro, 1987; Moritani et al., 1986a). 
Thus, direct proportionality between electromyogram 
(EMG) and force might be expected. Under certain 
experimental conditions, these proportionalities can be 
well demonstrated by recording the smoothed rectified 
or integrated EMG (IEMG) (devries, 1968; Milner- 
Brown and Stein, 1975: Moritani and devries, 1978, 
1979; Seyfert and Kunkel, 1974) and reproducibility 
of EMG recordings are remarkably high, e.g. the test- 
retest correlation ranging from 0.97 to 0.99 (Komi and 
Buskirk, 1970, 1972; Moritani and devries, 1978, 
1979). However, the change in the surface EMG 
should not be automatically attributed to changes in 
either MU recruitment or excitation frequencies as the 
EMG signal amplitude is further influenced by the 
individual muscle fiber potential, degree of MU dis- 
charge synchronization, muscle training and fatigue 
(Bigland-Ritchie, 1981; Bigland-Ritchie et al., 1979; 
Jessop and Lippold, 1977; Milner-Brown et al., 1975; 
Moritani er al.. 1985, 1986b). Nonetheless, carefully 
controlled studies have successfully employed surface 
EMG recording techniques and demonstrated the 
usefulness of iEMG as a measure of muscle activation 
level under a variety of experimental conditions 
(Hakkinen and Komi, 1985; Hakkinen er al., 1987; 
Komi et al.. 1978; Moritani and deVries, 1979, 1980; 
Moritani et al., 1987; Sale, 1988). 
MUSCLE STRENGTH GAIN: 
NEURAL FACTORS WI-SW HYPERTROPHY 
It is a common observation that repeated testing of 
the strength of skeletal muscles results in increasing 
test scores in the absence of measurable muscle hyper- 
trophy (Bowers, 1966; Coleman, 1969; devries, 1968). 
Such increasing test scores are typically seen in daily 
or even weekly retesting at the inception of a muscle 
strength training regimen. In some cases. several 
weeks of intensive weight training resulted in signifi- 
cant improvement in strength without a measurable 
change in girth (devries, 1968; Komi et al., 1978). It 
has also been shown that when only one limb is 
trained, the paired untrained limb improves signifi- 
cantly in subsequent retests of strength but without 
evidence of hypertrophy (Coleman, 1969; Ikai and 
Fukunaga, 1970: Moritani and deVries. 1979, 1980). 
Rasch and Morehouse (1957) demonstrated strength 
gains from six weeks of training in tests when muscles 
were employed in a familiar way, but little or no gain 
in strength was observed when unfamiliar test proced- 
ures were employed. These data suggest that the high- 
er scores in strength tests resulting from the training 
95 
96 T. MORITANI 
programs reflected largely the acquisition of skill and 
training-induced alterations in antagonist muscle ac- 
tivity, i.e., enhanced reciprocal inhibition that contri- 
bute to greater net force production, reduced energy 
expenditure and more efficient coordination (Kamen 
and Gormley, 1968; Rutherford and Jones, 1986). 
All of the above findings support the importance of 
‘neural factors’, which although not yet well defined, 
certainly contribute to the display of maximal muscle 
force which we call strength. On the other hand, a 
strong relationship has been demonstrated both be- 
tween absolute strength and the cross-sectional area of 
the muscle (Rodahl and Horvath, 1962; Close, 1972) 
and between strength gain and increase in muscle 
girth or cross-sectional area (Ikai and Fukunaga, 
1970). It is quite clear, therefore, that human volun- 
tary strength is determined not only by the quantity 
(muscle cross sectional area) and quality (muscle fiber 
types) of the involved muscle mass, but also by the 
extent to which the muscle mass has been activated 
(neural factors). 
Earlier studies (Kawakami, 1955; Cracraft and 
Petajan, 1977) regarding the neural factors involved in 
muscle training demonstrated that specific exercise 
programs (high intensity, short duration static exercise 
vs. low intensity, long duration dynamic exercise) can 
effectively produce changes in the firing patterns of 
single motor units and the expected direction of that 
change can be predicted based on the type of exercise 
(static or dynamic). Our own experimental results that 
showed an increase in iEMG after weight training is 
illustrated in Fig. 1. In the trained arm, the increase in 
strength was associated with both an increase in iEMG 
and an increase in muscle size. The contralateral 
untrained arm also showed an increase in strength but 
this was associated only with an increase in iEMG, 
indicating that the so-called ‘cross education’ or 
‘cross-training’ effect was the result of neural adapta- 
tion, In this case, there was no change in force per 
given muscle activation level (E/F ratio). When hy- 
pertrophy of muscle fibers took place with training, 
the motor unit activation required to produce a given 
force decreased. 
Figure 2 illustrates the time course of strength gain 
with respect to the calculated percent contributions of 
neural factors and hypertrophy during the course of 8 
weeks of strength training of the arm flexors. The 
results clearly demonstrate that the neural factors 
played a major role in strength development at early 
stages of strength gain for both young and old men 
and then hypertrophic factors graduallydominated 
over the neural factors for the young subjects in the 
contribution to the further strength gain (see Moritani 
and deVries, 1979; 1980 for more detail). The strength 
gain seen for the untrained contralateral arm flexors 
provide further support for the concept of cross educa- 
tion. It is reasonable to assume that the nature of this 
cross education effect may entirely rest on the neural 
factors presumably acting at various levels of the 
nervous system which could result in increasing the 
NEURAL FACTORS HYPERTROPHY 
I 
- BEFORE 
.-.-. AFTER 
I 
IMPROVED E/F RATIO 
FORCE 
%CONTRIBUTlONS OF NEURAL 
FACTORS[hV.l vs HYPERTR0PHYhf.H.~ 
%M.H. = f=$ x 100 
‘2 
w c-B Xl00 %N.F. =- ._ c--A 
FORCE 
Fig. 1. Schema for evaluation of percent contributions of 
neural factors and hypertrophy to the gain of strength. If 
strength gain is brought about by ‘neural factors’ such as 
learning to disinhibit, then we would expect to see increases 
in maximal activation without any change in force per fiber 
or motor units innervated as shown in Fig. top left. On the 
other hand, if strength gain were entirely attributable to 
muscle hypertrophy, then we would expect the results shown 
in Fig. top right. Here the force per fiber (or per unit activa- 
tion) is increased by virtue of the hypettrophy but there is no 
change in maximal iEMG. Fig. below shows our method for 
evaluation of the percent contributions of the components 
when both factors may be operative in the course of strength 
training. [Based on Moritani and deVries (1979)]. 
maximal level of muscle activation. Subsequent stud- 
ies (Davies et al., 1985; Davies et al., 1988; H&kinen 
et al., 1981, 1985, 1987; Houston et al., 1983; Ishida 
et al., 1990; Jones and Rutherford, 1987; Komi, 1986; 
Narici et al., 1989) have confirmed these observations 
and provided evidence for the concept that in strength 
training the increase in voluntary neural drive ac- 
counts for the larger proportion of the initial strength 
increment and thereafter both neural adaptation and 
hypertrophy takes place for further increase in 
strength, with hypertrophy becoming the dominant 
factor after the first 3 to 5 weeks (Moritani and 
deVries, 1979; Htlkkinen et al., 1981). 
NEURAL ADAPTATIONS DURING POWER TRAINING 
The development of muscular power is of great 
importance in sports events requiring a high level of 
force and speed. Significant correlations have been 
demonstrated among the force-velocity characteristics, 
muscle mechanical power and muscle fiber compo- 
sition in human knee extensor muscles (Thorstensson 
Neuromuscular adaptations 97 
et At., 1976; Tihanyi ef al., 1982). Faulkner et al. 
(1986) have studied the contractile properties of 
bundles of fibers from human skeletal muscles. It was 
found that the peak power output of fast-twitch fibers 
was fourfold that of slow-twitch fibers due to a greater 
shortening velocity for a given afterload. When the 
composite power curve for the mixed muscle was 
studied, the fast-twitch fibers contributed 2.5 times 
more than the slow-twitch fibers to the total power. 
The training effect of different loads on the force- 
velocity relationship and mechanical power output in 
human muscles has been extensively studied by 
Kaneko and his coReagues (Kaneko, 1970, 1974; 
3 
m 
OLD 
WEEKS OF TRAINING 
Fig. 2. The ttme course of strength gain showing the percent 
contributions of neural factors and hypertrophy in the trained 
and contralateral untrained arms of the young and old 
subjects. [Based on Moritani and deVries (1980)]. 
q(m/sec) w, 250 
(30 a,. Fo) 
Kaneko ct at., 1983). For example, Kaneko (1974) 
studied the time course of changes in the force-veloci- 
ty and mechanical power output of the elbow flexors 
with respect to different training intensities [e.g. 0, 30, 
60, 100% F,, (maximal strength), IO times/day, 6 
times/week] for a period of 20 weeks. This study 
showed significantly large initial improvements in the 
force-velocity curve and corresponding mechanical 
power outputs as a result of muscle power training 
(Fig. 3). Koneko ef ai. (1983) also demonstrated the 
‘specificity’ of muscle power training effect; i.e., that 
training by maximal contractions with 0% F,, (no load) 
was found to be most effective for improving the 
maximal velocity tested with no external load, while 
100% F,, training improved maximal strength most. It 
was concluded that different training loads could bring 
about specific modifications of the force-velocity 
relationship, and that the load 30% F,, was most effec- 
tive in improving maxima1 mechanical power output. 
In these and the other studies (Caiozzo et al., 1981; 
Coyle et al., 1981; Moffroid and Whipple, 1970), no 
EMG recording has been made so that it was not 
possible to determine the effects of muscle power 
training on maximal muscle activation level and other 
possible neural adaptations. 
We have recently investigated the effects of short- 
term 30% F,, muscle power training upon the force- 
velocity, power and electrophysiological parameters 
(Moritani err 01.. 1987). The right biceps brachii 
muscle was trained by pulling the load equivalent to 
30% F,, with maximal effort, 30 times/day, three 
times/week for a period of two weeks. The surface 
and intramuscular EMGs from the long and short 
heads were recorded simultaneously and analyzed by 
means of frequency power spectrum and MU ampli- 
tude-frequency histogram techniques, respectively 
(Moritani el al.. 1985. 1986b). Figure 4 represents a 
typical set of computer outputs showing the raw EMG 
signals recorded from the biceps brachii long and 
short head muscles and the corresponding power 
spectral parameters obtained at the initial and at the 
__.__... BEFORE 
1 
_c 20WKS 
(loo */oFa) 
FORCE (kg) 
Fig. 7. The time course of changes in the force-velocity (concave) and force-power (convex) relationships during 
muscle power training with different loads. [Based on Kaneko (1974)). 
98 T. MORITANI 
BEFORE (SHORT) 
flPF: 112 Hz 
BEFORE (LONG) 
HPF: 93.2 Hz 
RtlS: SE9 pV 
0 100 200 300 400 see 
‘ml I- la MPF: 81.9 Hz 
-0 100 200 300 400 500 
FREQUENCY 
3 
0 
-3 
100 
E 
:: 
a se 
z 
5 
RFTER (LONG) 
MPF: 76.5 Hz 
RMS: 903 pi’ 
El 
0 100 200 300 400 500 
FREQUENCY 
Fig. 4. A typical set of computer outputs showing the raw EMGs and corresponding frequency power spectra 
observed for the biceps brachii short (left) and long head (right) muscles before (above) and after (below) 
training. [Based on Mortiani et al. 19871. 
end of the training. It was found that the level of 
muscle activation as determined by RMS (root mean 
square EMG amplitude) values increased dramatically 
at any given load after training. On the other hand, 
MPF (mean power frequency) which reflects the 
frequency component of the recorded action potentials, 
markedly shifted toward lower frequency bands as a 
result of large, low-frequency EMG oscillations due 
possibly to better summation (synchronization) of the 
underlying action potentials. 
To further elucidate the possibility of synchronous 
muscle activation patterns or association in the time 
and frequency domains, cross power spectra and cross 
correlation coefficients were obtained between the 
action potentials recorded from the short and long 
head muscles at the pre- and post-training periods. 
Figures 5(a) and 5(b) represent the typical changes 
observed. It seems apparent that two action potential 
waveforms have little association in the amplitude and 
waveform patterns at the pre-training, revealing a 
maximal cross correlation coefficient (R,,) of 0.40 
[see Fig. 5(a)]. However, very similar action potential 
waveforms with much higher amplitude were obtained 
after thetraining which increased R,, to 0.91 [Fig. 
5(b)]. This suggests a greater muscle activation and 
more synchronous MU activities or similar MU dis- 
charge rates after training (Mimer-Brown et al., 1975). 
This may lead to an increased oscillation in the sur- 
face EMG which would theoretically approach to- 
wards the area of the maximal evoked M waves (mass 
action potential), indicating that all MUs are now fully 
synchronized (Bigland-Ritchie, 1981). Group data 
indicated that there were highly significant increases 
in the maximal power output, RMS and R,, together 
with the significant decrease in MPF after the training 
in all load conditions (Fig. 6). These data strongly 
suggest that the short-term training-induced shifts in 
force-velocity relationship and the resultant mechani- 
cal power output might have been brought about by 
the neural adaptations in terms of greater muscle 
activation levels and more synchronous activation 
patterns. 
NEUROMUSCULAR ADAPTATIONS DURING THE 
ACQUISITION OF A MOTOR TASK 
We have recently conducted a series of studies in 
an attempt to investigate the possible neurophysio- 
logical adaptations during a variety of different motor 
tasks (Yamashita and Moritani, 1989; Moritani et al., 
1989; Yamashita et al., 1990; Moritani and Mimasa, 
1990; Moritani et al., 1990, 1991a, 199lb). In our 
Neuromuscular adaptations 
A 
3500 r BEFORE (SHORT) 
99 
BEFORE (LONG) 
0 25 50 75 IEl0 125 
TIME(mr 1 
CROSS SPECTRUM CROSS CORRELATION 
100 - 1 r Rxy- .402 0 1 mt 
w" 
z 
.s - 
IL 50 - 
E -.5 - 
S 
0 6s I -1 L , I 
0 100 200 300 400 500 0 ES S0 75 I00 12s 
FRE0UENCY~l-k) TIMEtms 1 
AFTER (SHORT) 
RFTER (LONG) 
0 2s s0 7s 100 12s 
TIME(mt 1 
CROSS SPECTRUM CROSS CORRELATION 
100 r- I Rxy- .913 C 1 ms 
H .s - 
2 
z g 0- 
-.s - 
S 
OAdd I I -I 8 I 
0 100 200 300 400 500 0 25 50 75 100 12s 
FREQUENCY (Hz 1 TIMEtms 1 
Fig. 5. A typical set of action potential recordings from the biceps brachii short and long head muscles and the 
corresponding cross spectra and cross correlation coeffkients obtained before (A) and after (B) training. [Based 
on Moritani et al. (1987)J. 
100 T. MORITANI 
BI CEPS SHORT BICEPS LONG 
p” T 500 
1200 
uJ 800 
x 
~ 600 
300 
1200 
Ln 900 
Ix 
~ 600 
300 
J 0 
I before 
IIIIIII13 after 
BICEPS SHORT BICEPS LONG 
T 
125 HZ 
100 
LL 75 
a 
x s0 
2s 
0 
CROSS CORRELATIONS 
1 
.El 
.6 
x 
lz .4 
.2 
0 
before 
after 
l- 
Fig. 6. Group data on the cross correlation coefficients (mean + SE), RMS, and MPF obtained at different loads 
before aad after training. [Based on Moritani et al. (1987)]. 
Neuromuscular adaptations 101 
earlier attempt, we studied the effects of extended 
practice on the parameters of motor output variability 
such as force variability, maximal rate of force devel- 
opment, contraction time interval and accuracy during 
force-varying isometric muscular contractions with 
respect to the variability in neural outputs as deter- 
mined by surface EMG power spectral characteristics. 
Subjects were instructed to produce ‘shots’ of force- 
varying isometric contractions corresponding to 20 and 
60% of maximal voluntary contraction of the biceps 
brachii muscle. They attempted 10 ‘shots’ for each 
trial as rhythmically as possible as the target dot 
crossed the screen of the oscilloscope. All the subjects 
returned to the laboratory for 1500 extended practice 
trials (a total of 15,000 ‘shots’) over a one-week pe- 
riod of time. The force data were processed by com- 
puter so as to determine motor output variability such 
as force variability, maximal rate of force develop- 
ment (dF/dt), contraction time interval and accuracy 
[constant error, CE (average algebraic error); absolute 
error, AE (average absolute error); and variable error, 
VE (standard deviation of error)] (for more detail, see 
Poulton, 1981). 
Results indicated that all of the motor output pa- 
rameters showed significant improvements after the 
extended practice in terms of accuracy (AE, CE, and 
VE) and variability in dF/dt and contraction time 
interval for both 20 and 60% MVC trials (e.g. Fig. 7 
and 8). These changes were accompanied by signifi- 
cant reductions in the neural output variability as 
evidenced by significantly smaller coefficients of 
variation in the MPF and RMS (see Moritani and 
Mimasa, 1990 for more detail). Interestingly, when 20 
and 60% MVC trials were compared after the extend- 
ed practice, significantly greater improvements in 
accuracy and less variability in the neural output 
parameters were found for the 60% MVC trials (see 
Fig. 9). These data strongly support the findings of 
Sherwood and Schmidt (1980) who have demonstrated 
the limitation of Fitts’ Law (Fitts, 1954) for rapid 
movements. Our data and those reported by Sherwood 
and Schmidt (1980) seem to be consistent with well- 
established neurophysiological evidence that motor 
unit (MU) recruitment is the primary factor in increas- 
ing muscular force at low force levels, while rate 
coding (MU firing frequency modulation) becomes 
significant and predominant at intermediate and high 
force levels (Kukulka and Clamann, 1981; Milner- 
Brown er al., 1973; Moritani and Muro, 1987; 
Moritani et al.. 1986a). Because the rate coding would 
bring about much smoother force regulation through 
temporal summation than MU recruitment, in which a 
small ‘error’ would cause recruitment of ‘high thres- 
hold motoneurons’ innervating fast-twitch fibers (type 
IIa and IIb) capable of producing strong contractile 
force, one can thus expect much less mechanical and 
neural output variability during the 60% MVC trials as 
most of the motor units are probably recruited. 
Considering the relationship between surface EMG 
power spectra (e.g. MPF) and underlying MU activi- 
ties (Moritani et al., 1986a; Moritani and Muro, 
1987), the observed significant increases in MPF (20% 
MVC: from 90.1 at 6.3 to 101.0 f 6.7 Hz, p<O.Ol and 
60% MVC: from 102.8 + 6.9 to 111.9 f 7.6 Hz, 
p<O.Ol and dF/dt (20% MVC: from 1037 f 155 to 
1233 + 80 N.s.‘, p<O.Ol and 60% MVC: from 2670 f 
435 to 3280 f 280 N.s.’ p<O.Ol) after the extended 
practice thus may indicate the possible modification in 
MU activities such that a preferential recruitment of 
high threshold MUs with fast-twitch fibers might have 
taken place to meet the demands of rapid alternating 
forceful motor activities. This may also be a result of 
training-induced alterations in antagonist muscle ac- 
tivity, i.e., enhanced reciprocal inhibition that 
contribute to greater net force production, reduced 
energy expenditure and more efficient coordination 
(Kamen and Gormley, 1968: Rutherford and Jones, 
1986). 
Available experimental results suggest that MU 
recruitment patterns are not stereotyped motor pat- 
terns, but can be specifically modulated for different 
functional requirements in animals (Smith et al.. 1980: 
Hodgson, 1983) and in humans (Nardone ef al.. 1988; 
Nardone and Schippati, 1989; Moritani et al., 1990). 
Interestingly. Capaday and Stein (1987) have demon- 
strated that H-reflex amplitude (largely reflecting 
monosynaptic reflex excitability) of the soleus increas- 
es progressively during the stance phase and reaches 
its peak amplitude late in the stance phase during 
walking. During running, however, the H-reflex is 
found to be significantly smaller than during walking, 
suggesting a modified spinal reflex gain for the differ- 
ent functional requirements of the motor behaviour. 
This modulation may occur in relation to different 
phases of motor learning process as well as to a vary- 
ing degree in fast- and slow-twitch fibers, depending 
on the demands of force and speed of the motor ac- 
tivity (Capadayand Stein, 1987; Stein and Capaday, 
1988; Moritani and Mimasa, 1990; Moritani et al.. 
1990, 1991a, 199lb). 
NEUROMUSCULAR ADAPTATIONS DURING A BALLISTIC 
MOVEMENT 
Previous studies attempting to analyze central 
mechanisms for the initiation and execution of ballis- 
tic movements have mainly dealt with qualitative and 
quantitative aspects of the early EMG bursts of the 
agonist muscles (Hallett and Marsden. 1979; 
Lestienne, 1979). Considerable attention has also been 
given to the triphasic activation pattern of agonist and 
antagonist muscles during rapid movements (Garland 
and Angel, 1971; Sanes and Jennings, 1984). It has, 
however, been observed that the earliest manifestation 
of rapid movements is not an activation, but rather a 
depression or silencing of EMG activity (called pre- 
movement silent period, SP), which has been de- 
scribed for both antagonist and agonist muscles (Ikai, 
1955; Yabe, 1976; Conrad et al., 1983; Kawahatsu 
and Miyashita, 1983; Mortimer er al., 1987; Aoki et 
102 T. MORITANI 
FORCE-EMG ANALYSIS 
SUBJECT: BK 
AE: 18.5 N 
CE:-12.4 N 
VE: 23.2 N 
DRTE: B/25 
dFdT: 2010 +- 750 N/s 
T-INT: 716.7 +- 00.6 msec 
!I II A EMG POWER SPECTRR MPF: 127 +- 15 Hz RHS: 112 +- 43 UV 
0 I 4 16’ 80 100 200 300 
IME FREQUENCY (Hz > 
Fig. 7. Computer analysis results showing force curve and corresponding EMG together with mechanical (error, 
dF/dt and contraction time interval) as well as neural (MPF and RMS) parameters obtained at the beginning of 
practice session. [Based on Moritani and Mimasa (1990)], 
FORCE-EMG ANALYSIS 
SUBJECT: BK 
RE: 9.96 N 
CE:-2.46 N 
VE: 9.86 N 
DATE: 9/5 
dFdT: 3540 +- 329 N/s 
T-INT: 906.9 +- 59.1 msec 
300 
240 
180 
120 
60 
0 
.5 
0 
nnn EMG POWER SPECTRA MPF: 130 +- 14 Hz RMS: 95 +- 26 UV 
-. I 0 2 4 ‘6 8 -0 100 200 300 
TIME FREQUENCY (Hz> 
Fig. 8. Computer analysis results obtained after the end of extended practice session (total of 15,OOO shots). 
[Based on Moritani and Mimasa (1990)]. 
Neuromuscular adaptations 
20% MVC ERROR 60% MVC ERROR 
30 N r 15 TN 
20 % 
r 
VE 
j---l PRE 
20% MVC ERROR 
POST 20 
r 
x 
60% MVC ERROR 
RE VE 
Fig. 9. Group data (means + SE, N=9) on absolute (AE) and variable (VE) errors expressed in absolute and 
normalized (relative to II-C target force levels) units. [Based on Monrani and Mimosa (1991)I. 
lm- N FORCE SP 
56 -- 
0- 
1 
1 
mV TA 
0 -’ 
2. 
t. : 
-1 f ms , 
0 200 400 600 800 1000 
Fig. 10. A typical set of data showing force curve, intramuscular and surface EMG recordings from soleus (SOL) 
muscle and antagonisr tibialis anterior (TA) muscle during a ballistic plantar flexion. 
I04 T. MORITANI 
al., 1989). The definite functional role of the SP and 
its neurophysiological mechanisms remains to be 
determined. It has been suggested that in high speed 
movements where a maximal number of motor units 
have to be recruited, those motoneurons which are 
already tonically active have to be released from tonic 
activity for optimal synchrony (Conrad et al., 1983). 
In this regard, it may be worth noticing that top world 
athletes (sprinter and high jumpers) demonstrated con- 
siderably shorter SP duration than a group of physical 
education students (Kawahatsu, 1981). Furthermore, 
in the movement of shooting an arrow, Nishizono et 
al. (1984) observed a SP prior to release in world- 
class archers and the appearance rate of SP was also 
found to be significantly higher in the group of highly 
skilled archers than that of less skilled archers. 
We have recently investigated the possible 
neurophysiological mechanisms of SP preceding a 
ballistic voluntary movement in 10 male subjects 
(Shibata and Moritani, 1991). The subjects were asked 
to respond to a flashing light signal by performing a 
plantar flexion as strongly and quickly as possible. 
The EMG signals from agonists (lateral gastroc- 
nemius, LG and soleus, SOL) and antagonist (tibialis 
anterior, TA) were simultaneously recorded together 
with the force signal (Fig. 10). The excitability of 
00- N FORCE 
40--r I I , I , , , , , , , , , , , , , , , , , , , I 
O-IIIIIIIIIIIIIIIIIIIII I 
.01- mV 
, . . . . . . . . . . 
LG 
. . . . . . . . . ...* . . . *. *. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
.02- A” 
::: :: :: :::::: I I , . I 
IIIIITIII~ 
. . . . . *. . ...*. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
0- . . . .* . . *..... 
. 14- mv SOL 
I , , 
I I’l’l l-77-Y-7 I I I 
I- mV 
.s-. 
TA 
0- 
60 
40 
20 
0 
LG 
SOL 
40 - 
20 - 
0 ““““’ 
-200 -150 -100 -50 
Fig. 11. Group data (mean 2 SE, n=5) on force, rectified EMG mean amplitude for lateral gastrocnemius (LG). 
soleus (SOL) and tibialis anterior (TA) (top four traces) together with H-reflex amplitude changes for LG and 
SOL during ballistic plantar flexion accompanying promotion EMG silent period. H-reflex elicited at different 
phases of movement were grouped in 10 ms-bins and averaged with reference to the onset of force production 
denoted as 0 time. 
Neuromuscular adaptations 105 
spinal alpha motoneuron pools by means of H-reflex 
analysis was also determined at various phases of the 
movement. Our results indicated that: (1) SP occurred 
on some, but not all, trials within single subjects and 
had a variable duration from trial to trial, (2) the 
maximal rate of force development (dF/dt) was sig- 
nificantly greater in the trials with SP than without 
SP. and (3) the significant decrease in H-wave ampli- 
tude was observed approximately 40 ms prior to the 
appearance of SP which precedes the force develop- 
ment by about 50 to 60 ms (Fig. 11). 
Several physiological mechanisms that may explain 
the occurrence of SP have been suggested by 
Mot-timer et al. (1987). namely (i) inhibition by supra- 
spinal centers producing disfacilitation of tonically 
active motoneurons, (ii) postsynaptic inhibition by 
spinal interneurons, and (iii) presynaptic inhibition by 
primary afferent depolarization. Reciprocal inhibition 
could not be responsible since SP occurs in the ab- 
sence of any EMG burst in the antagonist. Further- 
more. SP latencies are much shorter than the fastest 
premotor times in pre-tensed muscles (Ward, 1978) 
which argues against postsynaptic inhibition via spinal 
interneurons activated in parallel with the moto- 
neurons. One may, on the other hand, speculate that 
PS could serve to increase the synchrony of the 
motoneuron pool. Many of the tonically active 
motoneurons would be refractory when the command 
of rapid contraction reaches this motoneuron pool. On 
this basis, Conrad et al. (1983) have suggested that SP 
would bring all motoneurons into a non-refractory 
state, enabling all available motoneurons to be ready 
to fire at the same time. This could be achieved, for 
example, by inhibition of the alpha motoneuron via 
spinal inhibitory interneurons known to be activated 
monosynaptically by the cortico-spinal tract. Our 
findings of decreased H-reflex amplitude and complete 
disappearance of motor unit firings during SP thus 
seem to support this hypothesis, although the possible 
inhibitory mechanisms acting on supraspinal centers 
disfacilitating tonic activity could not be ruled out. 
The fact that SP manifests a variable duration from 
trial to trail and that some subjects appear to be more 
capable of producing SP than others, suggests that SP 
may be a learned motor response rather than an auto- 
matic component of the movement program. 
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