J Appl Physiol 99: 1558 –1568, 2005.
First published May 12, 2005; doi:10.1152/japplphysiol.01408.2004.
Motor skill training and strength training are associated with different plastic
changes in the central nervous system
Jesper Lundbye Jensen, Peter C. D. Marstrand, and Jens B. Nielsen
Department of Medical Physiology and Institute of Exercise and Sport Sciences,
Panum Institute, University of Copenhagen, Copenhagen, Denmark
Submitted 22 December 2004; accepted in final form 4 May 2005
disciplines require some degree of
both strength and motor skill for the athlete to be successful,
and athletes often use a combination of resistance training and
skill learning to optimize their performance. Whereas plastic
changes in the central nervous system are well documented in
relation to acquisition of new skills (59, 61), such neural
adaptations are generally reported mainly to take place in the
initial stages of strength training (21), and their significance for
the increased strength compared with the well-documented
muscular changes is still debated (8, 16, 18, 20, 36).
There is now increasing evidence suggesting that plastic
changes in the primary motor cortex play an important role in
skill acquisition. Motor skill learning has thus been demonstrated to be associated with anatomical and physiological
changes within the primary motor cortex in primates and
nonprimate animals (31–33, 41, 53, 65). In humans, neuroimaging techniques and transcranial magnetic stimulation (TMS)
have demonstrated that motor skill training induces changes in
the organization of movement representations in the primary
motor cortex in the form of expansion and increased excitability of the cortical representation of specific muscles (or movements) involved in the tasks (10, 11, 17, 27, 30, 35, 43– 45,
47, 48).
In contrast to skill acquisition, nonskill training or passive
motor training is mainly reported to elicit no or only minor
changes in excitability (35, 45). For instance, Plautz et al. (50)
demonstrated in squirrel monkeys that movement repetition in
the absence of motor skill acquisition was not sufficient to
produce changes in cortical representational organization. On
the basis of these findings, learning thus seems to be a prerequisite or an important factor in driving cortical representational
plasticity related to motor experience. To fully acknowledge
this hypothesis, however, it remains to be elucidated which
aspects of motor experience relate to motor learning or how
motor learning is to be defined.
Little is known about the neuronal mechanisms involved in
the increased neuronal drive in the early stages of strength
training, although it has been suggested that increased cortical
drive to the spinal motoneurons may be of importance (1).
Strength increments arise as a consequence of numerous factors, but in many ways it would make sense to consider
strength training as a kind of motor learning process. As
reviewed by Carroll et al. (8), strength training relates to motor
learning because of the fact that athletes learn to produce
muscle recruitment patterns associated with optimal performance of the specific task. It is thus likely that strength training
in parallel with motor learning can lead to improved muscular
coordination. Considering this, it would be reasonable to assume that similar plastic changes in the primary motor cortex
as reported for acquisition of new motor tasks are also involved
in the improved ability to generate force in the early stages of
strength training.
Remple et al. (53) demonstrated in the rat that training of
skilled reaching movements involving either a progressive
increase of the maximal load (strength) or a control condition
induced a similar degree of plastic changes in the motor
Address for reprint requests and other correspondence: J. B. Nielsen, Dept.
of Medical Physiology, Panum Institute, Univ. of Copenhagen, Blegdamsvej 3,
2200 Copenhagen N, Denmark (e-mail: [email protected]).
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
plasticity; corticospinal tract; transcranial magnetic stimulation; skill
learning; detraining
IN THE WORLD OF SPORTS MOST
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8750-7587/05 $8.00 Copyright © 2005 the American Physiological Society
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Jensen, Jesper Lundbye, Peter C. D. Marstrand, and Jens B.
Nielsen. Motor skill training and strength training are associated
with different plastic changes in the central nervous system. J Appl
Physiol 99: 1558 –1568, 2005. First published May 12, 2005;
doi:10.1152/japplphysiol.01408.2004.—Changes in corticospinal
excitability induced by 4 wk of heavy strength training or visuomotor skill learning were investigated in 24 healthy human subjects. Measurements of the input-output relation for biceps brachii
motor evoked potentials (MEPs) elicited by transcranial magnetic
stimulation were obtained at rest and during voluntary contraction
in the course of the training. The training paradigms induced
specific changes in the motor performance capacity of the subjects.
The strength training group increased maximal dynamic and isometric muscle strength by 31% (P ⬍ 0.001) and 12.5% (P ⫽
0.045), respectively. The skill learning group improved skill performance significantly (P ⬍ 0.001). With one training bout, the
only significant change in transcranial magnetic stimulation parameters was an increase in skill learning group maximal MEP
level (MEPmax) at rest (P ⫽ 0.02) for subjects performing skill
training. With repeated skill training three times per week for 4 wk,
MEPmax increased and the minimal stimulation intensity required
to elicit MEPs decreased significantly at rest and during contraction (P ⬍ 0.05). In contrast, MEPmax and the slope of the inputoutput relation both decreased significantly at rest but not during
contraction in the strength-trained subjects (P ⱕ 0.01). No significant changes were observed in a control group. A significant
correlation between changes in neurophysiological parameters and
motor performance was observed for skill learning but not strength
training. The data show that increased corticospinal excitability
may develop over several weeks of skill training and indicate that
these changes may be of importance for task acquisition. Because
strength training was not accompanied by similar changes, the data
suggest that different adaptive changes are involved in neural
adaptation to strength training.
MOTOR SKILL TRAINING AND STRENGTH TRAINING
METHODS
Participants
The experiments were performed on 24 healthy volunteers (11
women, 13 men) with an average age of 25 ⫾ 5 yr. All subjects gave
their written, informed consent to the experimental procedures, which
were approved by the local ethics committee. The study was performed in accordance with the Declaration of Helsinki. All subjects
were right-handed according to the Edinburgh Handedness Inventory
(42), and no volunteers had any history of neurological disease.
The subjects were randomly allocated to three groups, two different
motor training groups and a control group (n ⫽ 8, 5 men and 3
women) that did not train but participated in all testing procedures.
Motor training consisted of either strength training (n ⫽ 8, 4 men and
4 women) or visuomotor skill learning (n ⫽ 8, 4 men and 4 women).
There were no age differences between the three groups.
General Organization of the Study
Thirteen training sessions were performed by the participants over
a 4-wk training period. At the beginning of the training period, after
2 wk, and at the end of the training period, each volunteer participated
in a longer lasting experimental session. These experimental sessions
involved 1) strength tests evaluating the maximal voluntary dynamic
and isometric elbow flexor muscle strength of the subjects, 2) an
electrophysiological testing procedure involving peripheral electrical
nerve stimulation and TMS at rest and during tonic contraction, 3) one
training session of either motor skill training or strength training, and
J Appl Physiol • VOL
4) repeated measures of TMS and peripheral electrical nerve stimulation after training. This experimental design aimed at investigating
short-term adaptations to training defined as the effect of a single
training session as well as long-term adaptations to training defined as
the effect of training 2– 4 wk. For every testing and training procedure
involved in the study, subjects were familiarized with the equipment
and the measuring procedures on separate occasions before data
sampling. Six months after completion of the training, it was possible
to retest four of the subjects in the skill learning group to investigate
reversibility of the training-induced phenomena. At this occasion,
TMS and peripheral electrical nerve stimulation were applied.
Strength Tests
At the beginning of the testing procedure, the subjects’ maximal
dynamic muscle strength was determined as one-repetition maximum
(1 RM) biceps curl and the maximal isometric muscle strength was
determined as the peak torque of a maximal voluntary contraction
(MVC). For the 1 RM test, the subjects were standing in a standardized position at a custom-made biceps curl bench. Before the test,
subjects performed a warm-up procedure and received instructions in
how to perform unilateral biceps curl. During the test, the subject was
handed a submaximal weight and performed one extension-flexion
cycle of the elbow joint with the forearm supinated. As this task was
completed, the load increased progressively until failure of the biceps
curl occurred. 1 RM was determined as the highest load at which the
task was fulfilled. In all tests, the subject performed 5– 8 trials with
increasing load depending on maximal strength.
Because of a large similarity between the 1 RM test and the
strength training paradigm, it was hypothesized that the 1 RM test
could be influenced by effects of learning (28, 58). Therefore, in
addition to the 1 RM test, a MVC test was used as a control to validate
any training-induced alterations in the maximal strength of the subjects. For the MVC test, subjects were seated in a custom-built rigid
chair and firmly strapped to an upright backrest. The elbow was flexed
to 90° and the forearm was supinated and rested on a table. A
nonelastic strap around the wrist was connected to a strain-gauge
transducer. Subjects were instructed to perform a maximal contraction
of the right arm elbow flexors by increasing the torque to maximum
within a few seconds and then to exert maximal torque for 2 s, while
maintaining the standardized position. Verbal encouragement and
visual feedback of the torque exerted were provided. Typically four or
five successive trials were performed until the peak torque did not
increase any further. The peak torque recorded in either of the trials
was taken as the MVC. Strength measurements were only obtained
during the first and the last of the three testing sessions.
Electrophysiological Testing Procedures
After completing the strength tests, subjects were seated in an
armchair for the electrophysiological testing procedure involving
TMS and peripheral electrical nerve stimulation. Subjects were positioned with the head supported and the examined right arm fixed on a
cushioned arm support, the shoulder joint flexed 45° and the elbow
joint almost fully extended.
Data recording. Surface electrodes were used for electrical nerve
stimulation and recording of electromyographic activity (EMG). EMG
activity was recorded from the BB and triceps brachii muscle by
nonpolarizable bipolar Ag-AgCl electrodes (1 cm2, interelectrode
distance 1 cm). The amplified EMG signals were filtered (band-pass,
25 Hz to 1 kHz), sampled at 2 kHz, and stored on a personal computer
for offline analysis. Furthermore, the EMG was full-wave rectified,
integrated, and displayed to the subject as visual feedback during tonic
contraction.
TMS. Motor evoked potentials (MEPs) were evoked by TMS of the
left hemisphere (contralateral) motor cortical arm area at the hot spot
for activation of BB by using a magnetic stimulator (Magstim 200,
Magstim) with the capability to deliver a magnetic field of 2 T for 100
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cortical movement representations. This finding indicated that
the observed plasticity related to the development of skilled
movements rather than increased muscle strength per se (64).
Electron microscopy of the ventral spinal cord showed that
rats training power reaching had a significantly greater density
of synapses onto spinal motor neurons than both a normal
reaching group and a nonreaching control group, which led to
the suggestion that strength training is supported by spinal cord
synaptogenesis (64).
The only study that has addressed the issue of supraspinal
adaptations to strength training in human subjects observed a
decrease rather than an increase in corticospinal excitability in
relation to strength training index finger abduction (7). Isolated
finger abduction may, however, have little relevance to normal
strength training, which often involves complex exercises involving large proximal muscle groups in combination with
distal muscle groups, as is seen in biceps curl. We thus
speculate that “normal” strength training involving more complex muscle recruitment patterns and a more prominent role of
muscular coordination may have the potential to induce learning-related phenomena in the central nervous system.
In the present study, we used TMS to investigate whether 4
wk of strength training of the biceps brachii (BB) muscle is
associated with increased excitability of corticospinal projections to the muscle. Similar measurements were also made in
relation to the acquisition of a difficult motor task requiring
precise control of elbow joint movements. We found that
increased corticospinal excitability was only observed in relation to acquisition of the difficult motor task, whereas 4 wk of
strength training that increased the dynamical strength of the
biceps muscle by 31% caused a depression of corticospinal
excitability at rest. A significant correlation between the
changes in corticospinal excitability and motor performance
was only observed for the skill-trained subjects.
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response curve with a well-defined MEPthreshold, slope, and maximal
level (MEPmax) had been obtained. Figure 1 illustrates an example of
the obtained BB MEPs, their increase with stimulus intensity, and the
creation of a stimulus-response curve.
After a pretraining stimulus-response curve was obtained at rest,
the maximal amplitude of the integrated BB EMG was determined
and a TMS stimulus-response curve was obtained during tonic contraction of BB corresponding to 5% of maximal amplitude of the
integrated EMG. This procedure was followed by a training session.
Immediately after training, two additional stimulus-response curves
were generated during tonic contraction and at rest.
Peripheral Electrical Nerve Stimulation
Before generation of a stimulus-response curve, maximal compound muscle action potentials of BB (maximal M-waves or Mmax)
were elicited by bipolar surface electrical stimulation of the musculocutaneus nerve. A custom-built stimulator applied current to the
nerve via ball-shaped electrodes fixed in the axilla with an interelectrode distance of 4 cm. The intensity of stimulation was increased
from a subliminal level until there was no further increase in the
peak-to-peak amplitude of the M-wave with increasing intensity.
Mmax was determined by using this procedure before the generation of
Fig. 1. Transcranial magnetic stimulation (TMS) procedure and generation of stimulus-response curves. When transcranial magnetic stimulation is applied
over the motor cortex, contraction of contralateral muscles may be elicited because of activation of corticospinal cells and spinal motoneurons (A). B:
typical surface EMG recordings of motor evoked potentials (MEPs) of the biceps brachii (BB) obtained in a subject resting (left) and exerting a voluntary
tonic contraction of 5% of maximum voluntary contraction integrated EMG (right). Responses to stimuli of increasing strength are aligned. For each
stimulating intensity, a sequence of 10 stimuli (⬍0.25 Hz) was delivered and the peak-to-peak amplitudes of the elicited MEPs were averaged and
normalized to the corresponding maximal M-wave (Mmax). When all mean MEP amplitudes are plotted against stimulation intensity, a stimulus-response
curve is obtained (C). Each stimulus-response curve is characterized by a set of parameters including minimal stimulation intensity required to elicit MEPs
(MEPthreshold), peak slope, maximum level of MEP amplitude (MEPmax), and stimulus intensity at which the MEP amplitude size is 50% of MEPmax (S50).
Before training, 2 stimulus-response curves were obtained at rest and during a tonic contraction, and immediately after training 2 additional stimulus
response curves were obtained during tonic contraction and at rest. For comparison, all stimulation intensities were normalized to the individual
pretraining motor threshold of the baseline test.
J Appl Physiol • VOL
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␮s through the figure-of-eight coil (loop diameter, 9 cm; type no.
8106). The MEPs were recorded from BB and triceps brachii EMG.
Before TMS stimulation, a cap with a coordinate system marked on it
was placed on the subject’s head and the hot spot for activation of BB
was identified through a motor cortical mapping procedure. The hot
spot was identified as the coordinates in which the lowest intensity of
magnetic stimulation was required to evoke a MEP of 50 ␮V peakto-peak amplitude in at least three of five consecutive trials (55).
The coil was oriented and positioned with the handle of the coil
pointing backward to induce posterior to anterior current flow across
the primary motor cortex, and the coil was secured to ensure that the
same area of the cortex was stimulated throughout the experiment.
Single pulse stimuli were delivered at an interstimulus interval of 4 s.
During the experiment, MEPs were displayed and averaged online for
visual inspection as well as stored on a computer for offline analysis.
At first, TMS was applied at rest. Magnetic stimuli were applied at
10 –15 different stimulation intensities from 0.6 –2.0 of the minimal
stimulation intensity required to elicit MEPs (MEPthreshold) with 10
stimulations at each intensity. The sequence of intensities was randomly varied. Responses were measured as the peak-to-peak amplitude and expressed as a percentage of the corresponding maximal
M-wave (Mmax). For each stimulation intensity, responses were averaged and the peak-to-peak amplitude was plotted until a stimulus-
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MOTOR SKILL TRAINING AND STRENGTH TRAINING
every stimulus-response curve (54) by application of TMS. The
purpose of this procedure was to normalize the TMS data to the
corresponding individual Mmax, thereby making it possible to compare the different test sessions.
Strength Training
The strength training group performed heavy-load strength training
of the dominant right arm elbow flexors three times per week for 4 wk.
Training sessions never took place on consecutive days. The subjects
performed standing unilateral biceps curl using a curl bench supporting both arms in a position of 20° shoulder flexion. Biceps curl was
performed by doing flexion-extension movements of the elbow joint
with the forearm supinated and the left hand placed on the right
shoulder. After a warm-up procedure, the subjects performed five sets
of 10 – 6 repetitions maximum. The sets were separated by a few
minutes of rest, and the load was progressively adjusted throughout
the training period to maximize the training response. For this purpose, all training sessions were also monitored by a supervisor.
The subjects in the skill learning group also performed motor
training of the right arm elbow flexors three times a week for 4 wk and
training sessions never took place on consecutive days. During training, subjects were seated in an armchair with the right arm positioned
on an arm support, the shoulder joint flexed, and the forearm supinated. This position was chosen to match the strength training paradigm anatomically and kinematically in the sense that only simple
elbow flexion-extension movements were allowed. Because of the
setup, flexion was caused by concentric contraction of the elbow
flexors whereas extension primarily was caused by eccentric contraction of the same muscles.
For the skill training, a purpose-build computer program was used.
The position of the elbow joint was measured by a SG110 twin axis
elbow goniometer (Biometrics) and displayed as a circular cursor on
a computer screen in front of the subject. On the screen, a series of six
figures were presented in a randomized order, each of them sketching
a different series of combinations of flexion and extension movements. The cursor automatically moved from the left to the right at a
velocity that was predetermined for each screen paradigm. Subjects
were able to control the vertical movement of the cursor by varying
the position of the elbow joint, thereby tracking the presented figures
as precisely as possible. During extension of the elbow, the cursor
moved to the bottom of the screen, whereas during flexion the cursor
moved to the top of the screen. Figure 2 illustrates the different screen
figures presented to the subjects. Time over the screen varied between
1.88 and 3.14 s, and the range of movement varied between the six
different figures as well. After 2 wk of training, two additional screen
figures were introduced to ensure maximal attention to the task. For
this purpose, all training sessions were also monitored by a supervisor.
Each training session consisted of four sets of 4-min continuous
tracking with 1 min of rest in between the sets.
During the first, fifth, ninth and thirteenth training session, goniometer data were digitally sampled at 2,000 Hz with a QNX real-time
analog-to-digital capturing system for calculation of performance. For
each set of 4 min, training goniometer data were averaged for each
screen figure and superimposed on the optimal track. Motor performance was quantified as the total distance between the performed and
the optimal track in 10 different points.
Data Processing
Measures of corticospinal excitability include, among other parameters, MEP amplitudes, MEPthreshold/motor threshold (55), and stimulus-response curves (14, 54). To characterize the stimulus-response
function, the stimulus-response curve data were quantified through
several procedures. The data of each curve were fitted with a threeparameter sigmoid function
J Appl Physiol • VOL
Fig. 2. Motor skill training. A: general setup during skill training. B: training
paradigm. During the first 2 wk of training, events 1– 6 were presented to the
subjects in a randomized sequence. During the last 2 wk of training, events 7
and 8 were added. The cursor speed varied between the events so time over
screen was 1.88 –3.14 s. The cursor trajectory (goniometer data) was sampled
during training and motor performance (error) was calculated for each of the
4 sets of the 1st, 5th, 9th, and 13th training sessions.
MEP(s) ⫽
MEPmax
1 ⫹ em共S50⫺S兲
where S is stimulus intensity, MEPmax represents the maximum MEP
defined by the function, and m is the slope parameter of the function.
S50 is the stimulus intensity at which the MEP amplitude size is 50%
of MEPmax. This equation has previously been referred to as an analog
of the Boltzmann equation and has been used to fit data points by the
Levenberg-Marquard algorithm (6, 7, 14, 29, 54). From this analysis,
the maximal amplitude of the stimulus-response curve MEPmax was
obtained. Furthermore, every curve was characterized by the slope
parameter of the function and the MEPthreshold. The slope was calculated for the steepest part of the curve (i.e., at S50), indicating the
maximal increase of MEP amplitude with increasing stimulator intensity. Because the MEPthreshold is not an explicit parameter of the
equation and cannot be directly derived, it was calculated by using
linear regression analysis. The data points on the steepest part of the
curve were fitted by a straight-line regression formula (y ⫽ a ⫹ bx)
and the baseline activity ⫾ 1 SD were included in another linear
regression. MEPthreshold was then calculated as the intercept between
these two regression lines.
For comparison and to be able to pool group data, all stimulus
intensities were normalized to the resting or tonic MEPthreshold of the
individual stimulus-response curves obtained pretraining on the day of
the first training session. Normalization to MEPthresholds of the initial
test was preferred because an analysis based on this procedure could
detect whether stimulus-response curves were shifted left or right as a
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Skill Learning
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consequence of training. It has previously been demonstrated that the
sigmoidal function parameters can be obtained reliably in testing
sessions conducted on different days (9).
Statistics
RESULTS
Changes in Muscle Strength and Motor Performance
At the end of the training period, the strength training group
displayed a significant improvement in both maximal isomeric
and dynamic muscle strength. After 4 wk of strength training,
the group average maximal dynamic strength increased significantly by 31.2% from 10.5 ⫾ 2 to 13.8 ⫾ 1.8 kg (P ⬍ 0.001*).
MVC also increased significantly by 12.5% from 21.9 ⫾ 2.7 to
24.8 ⫾ 2.3 N䡠m (P ⫽ 0.045*). The maximal dynamic as well
as isometric muscle strength of the skill learning group and the
control group remained unaltered. Skill learning group mean 1
RM was 12.9 ⫾ 1.7 before and 12.9 ⫾ 1.9 kg after the training
period, whereas MVC was 25.1 ⫾ 2.5 before and 24.7 ⫾ 2.6
N䡠m after training. 1 RM in the control group decreased
slightly from 12.4 ⫾ 1.6 to 12.2 ⫾ 1.6 kg. MVC in the control
group was 21.6 ⫾ 2.5 N䡠 m before training and 21.7 ⫾ 2.2 N䡠m
after the training. These results imply that the strength training
paradigm caused significant improvements of the subjects’
maximal muscle strength. Neither the skill learning paradigm
nor the experimental procedures induced changes in the maximal strength of the subjects.
The level of performance was tested in the subjects in the
skill learning group during the first, fifth, ninth, and thirteenth
of the training sessions, and the performance was quantified as
the mean deviation from the optimal track for each of the four
training sets in the individual sessions (Fig. 3). The skill
training group improved mean tracking performance significantly during the first training session from (mean ⫾ SD)
162.8 ⫾ 14.5 mm deviation to 142.6 ⫾ 10.2 mm (P ⬍ 0.001*).
During the fifth training session, the mean deviation decreased
from 101 ⫾ 13 to 91.7 ⫾ 18.3 mm (P ⫽ 0.092). During the
ninth training session, the mean deviation decreased significantly from 46.2 ⫾ 11.3 to 37.8 ⫾ 19.6 mm (P ⫽ 0.037*), and
during the thirteenth (last) training session deviation decreased
from 30.1 ⫾ 16.6 to 27.3 ⫾ 17.8 mm (P ⫽ 0.052). It follows
from this marked improvement of motor performance during
the individual training sessions that the long-term improvement
J Appl Physiol • VOL
Fig. 3. Strength tests. Measures of maximal dynamic muscle strength (1 RM)
and maximal isometric muscle strength (MVC) before (shaded bars) and after
(solid bars) the 4-wk motor training period (means ⫾ SE). Left: MVC group
mean peak torque (N 䡠 m) for the strength training group, the skill learning
group, and the control group. Right: group mean 1 RM data (kg) for the 3
groups. *P ⬍ 0.05.
of motor performance capacity over the 4 wk was highly
significant (P ⬍ 0.001*), which is also evident from Fig. 4.
TMS Measurements
The TMS measurement aimed at investigating both shortterm and long-term adaptations to the motor training paradigms. None of the control group measurements showed any
significant changes during the whole period. The measurements from the control group will therefore not be considered
further.
Short-term adaptations to training. Figure 5 illustrates measurement before and after one training session at rest and
during tonic contraction on the day of the first, the seventh, and
the final training session. Because only the skill learning group
subjects exhibited any significant short-term changes in response to single training sessions, only the results of this group
are illustrated.
In the skill learning group, there was a significant effect of
the first training session. At rest, the MEPs were generally
facilitated after training, and this was reflected in a significant
increase of MEPmax (pretraining ⫽ 3.89 ⫾ 0.8% of Mmax to
posttraining ⫽ 6.03 ⫾ 0.91% of Mmax; P ⫽ 0.02*). As an
effect of training, the MEPthreshold seemed to decline and the
slope seemed to increase; none of these alterations were,
however, significant. S50 remained unchanged. The same pattern of changes was seen on the day of the seventh and the last
training sessions (Fig. 5, B and C). However, none of these
changes were significant. The short-term effect of training thus
seemed to be largest in response to the first training session.
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Before statistical comparison, all data sets were tested for normal
distribution by a Kolmogorov-Smirnoff test. Changes in maximal
strength and motor skill performance were tested by using paired
t-tests for each of the three groups.
The stimulus-response curve parameters MEPthreshold, MEPmax,
slope, and S50 were analyzed by comparing pre- and posttraining
values in each of the three testing sessions. Resting and contraction
values were analyzed separately for each of the three groups by paired
t-tests, and a criterion of P ⬍ 0.05 was used. Significant P values are
marked with an asterisk.
Long-term adaptations to training were investigated by comparing
pretraining data from the three testing sessions with repeated-measures ANOVA. For multiple-comparison analysis, Tukey’s test was
used for all pairwise comparisons between the group mean responses.
Data are presented as means ⫾ SE unless reported otherwise. Correlation between changes in the neurophysiological parameters and
changes in motor performance capacity was tested using the Pearson’s
product-moment correlation test.
MOTOR SKILL TRAINING AND STRENGTH TRAINING
The same tendencies as those seen at rest were evident
during tonic contraction. As shown in Fig. 5, D, E, and F,
MEPmax also tended to increase after training during tonic
contraction. However, none of the changes after training were
significant in any of the three testing sessions. Strength training
did not induce any significant short-term changes in the TMS
stimulus-response curves.
Long-term adaptations to training. The long-term adaptations to training are defined as the differences that occur when
comparing the pretraining values obtained in the baseline test,
the 2-wk test, and the 4-wk test. The adaptations that occurred
after 2 and 4 wk of training are illustrated in Fig. 6.
For the skill learning group, MEPmax at rest increased from
3.9 ⫾ 0.8 to 6.9 ⫾ 1.5% of Mmax after 2 wk (P ⫽ 0.04*) and
6.8 ⫾ 1.1% of Mmax after 4 wk of training (P ⫽ 0.046*).
MEPthreshold decreased from 48.7 ⫾ 4.8% of maximal stimulator output in the baseline test to 42.5 ⫾ 5% after 2 wk (P ⫽
0.07) and 41.2 ⫾ 5.1% after 4 wk (P ⫽ 0.03*). No other
parameters exhibited any significant changes.
MEPmax also increased during tonic contraction in response
to training [from 33.2 ⫾ 4.3% of Mmax to 52.5 ⫾ 10.6% (P ⫽
0.04*)] after 2 wk and 50.28 ⫾ 10% after 4 wk of training (P ⫽
0.07). MEPthreshold decreased from 32.7 ⫾ 1.4% of maximal
stimulator output initially to 29.5 ⫾ 2% after 2 wk (P ⫽ 0.16)
of training and 28.1 ⫾ 1.5% after 4 wk of training (P ⫽ 0.04*).
No other parameters exhibited any significant changes during
tonic contraction.
For the strength training group, there was no change of
MEPmax at rest after the first 2 wk of training. After 4 wk of
training, however, MEPmax decreased significantly from the
initial 6.5 ⫾ 1.4 to 3.8 ⫾ 1.5% of Mmax (P ⫽ 0.01*). The slope
of the stimulus-response curves decreased from 0.24 ⫾ 0.07 to
0.17 ⫾ 0.06 after 2 wk of training (P ⫽ 0.11) and to 0.11 ⫾
0.04 after 4 wk of strength training (P ⬍ 0.01*). Similar
Fig. 5. TMS results: short-term effects of
training. The figure illustrates pooled TMS
data from the stimulus-response curves of
the subjects of the skill learning group on the
day of the 1st, 7th (after 2 wk), and 13th
(after 4 wk) training sessions. Measurements
were obtained at rest (A–C) and during tonic
contraction (D–F) before (F) and after (E)
each of the 3 training sessions. The abscissa
of each graph represents intensity of stimulation normalized to the individual pretraining MEPthreshold of the baseline test and the
ordinate represents MEP amplitudes (group
mean ⫾ SE) normalized to the corresponding individual Mmax. The characteristics of
the stimulus-response curves reflect the
short-term effects of the individual training
session when comparing data obtained before training and data after training.
J Appl Physiol • VOL
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Fig. 4. Skill learning motor performance. On the day of the 1st, 5th, 9th, and
13th (last) training sessions, the motor performance of the subjects in the skill
learning group was quantified. Each session consisted of 4 times 4 min of
training, and the individual motor performance was calculated as mean
performance for each set of 4-min tracking. The performance (ordinate) is
quantified as the deviation (error) from the optimal track to the actual cursor
trajectory (group mean ⫾ SE). The figure illustrates the improvement of motor
performance in the skill learning group over the complete training period of 4
wk (52 sets). Statistical comparisons are based on the Wilcoxon’s signed rank
test.
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MOTOR SKILL TRAINING AND STRENGTH TRAINING
changes were observed during tonic contraction; these changes
did not, however, reach a statistically significant level.
Detraining
It was possible to test four of the eight subjects in the skill
training group again 6 mo after they had completed the training
period. The results from the four subjects are illustrated in Fig.
7. As can be seen, MEPmax, MEPthreshold, and the slope of the
recruitment curve were almost similar to the measurements
before the training. Because of the small number of subjects
the data were not subjected to a statistical analysis. The
material was also too limited to determine a difference in the
performance of the task at the three occasions (before training,
after training, and 6 mo after training).
Correlation Analysis
The correlation analysis using the Pearson product moment
correlation test showed a significant correlation between the
long-term changes in the skill learning group TMS parameters
and the motor performance (skill) of the subjects. The correlation analysis of the measurements obtained at rest including
MEPmax and skill performance (error) showed a correlation
coefficient of R ⫽ 0.356 (R2 ⫽ 0.127) and P ⫽ 0.021* whereas
the analysis with MEPthreshold and skill performance showed
that R ⫽ 0.431 (R2 ⫽ 0.186) and P ⫽ 0.006*. For the
measurements obtained during tonic contraction, the correJ Appl Physiol • VOL
sponding analysis showed that R ⫽ 0.369 (R2 ⫽ 0.136) and
P ⫽ 0.026* for skill and MEPmax whereas the analysis of skill
and MEPthreshold showed a correlation coefficient of R ⫽ 0.486
(R2 ⫽ 0.236) and P ⫽ 0.001*. In contrast to skill learning,
there was no correlation between the neurophysiological
changes and the increase of maximal strength observed in the
strength training group.
DISCUSSION
In this study, we have demonstrated that acquisition of a
visuomotor skill is associated with increased corticospinal
excitability over several weeks. Strength training for a similar
amount of time was in contrast associated with decreased
corticospinal excitability.
Increased Corticospinal Excitability in Relation to
Acquisition of a Visuomotor Task
Increased corticospinal excitability and expansion of the
cortical representation of hand and finger muscles in relation to
the acquisition of motor tasks is well documented (45, 46, 48).
The present data demonstrate that similar changes also take
place for proximal arm muscles in relation to acquisition of a
visuomotor tracking task requiring precise control of the elbow
flexor muscles. The excitability changes are thus not restricted
to distal finger muscles, which are generally believed to receive
a more significant corticospinal control than proximal muscle
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Fig. 6. TMS results: long-term effects of training. Pooled TMS
data from the skill learning group, the strength training group,
and the control group. The figure illustrates measurements
obtained before motor training on the day of the first training
session (F), after 2 wk (shaded circles), and after 4 wk of
training (E). Measurements were obtained at rest (A, C, E) and
during tonic contraction (B, D, F). The intensity of stimulation
(abscissa) is normalized to the individual MEPthreshold of the
day of the 1st training session. The MEP amplitude is normalized to the corresponding individual Mmax amplitude.
MOTOR SKILL TRAINING AND STRENGTH TRAINING
1565
Fig. 7. Retest: effect of detraining. Retest after 6 mo of detraining (n ⫽ 4).
Group mean responses to TMS normalized to the corresponding individual
Mmax of the 4 subjects. Intensity of stimulation is normalized to the individual
MEPthreshold of the baseline test.
groups (52). This is of importance in relation to many forms of
sports in which large proximal muscle groups are more important for the performance than the smaller distal muscle groups.
It is not possible from our study to determine the underlying
physiological mechanisms responsible for the changes in corticospinal excitability. Changes in the spinal motoneurons,
corticospinal neurons, subcortical neurons contacted by corticospinal tract fibers and projecting to the spinal motoneurons,
as well as intracortical inhibitory and/or excitatory interneurons may be involved. However, previous studies have demonstrated that changes within the primary motor cortex are
involved in the expansion of the cortical representation of the
muscles as well as the increased corticospinal excitability
changes demonstrated by recording of the input-output relation
for the MEP as in the present study (4, 5, 9, 12–14, 23, 49, 52,
56, 57, 61). Experiments in primates and rats give strong
support to this (15, 32, 40, 41, 60, 61).
J Appl Physiol • VOL
Changes in Corticospinal Excitability in Relation to
Strength Training
Several studies have suggested that strength training is
associated with increased neuronal drive to the muscles: 1)
Significant increases in strength precede muscular hypertrophy
in the course of a strength training program (25, 28, 34, 39, 51).
2) “Cross education,” whereby movements contralateral to the
trained limb exhibit increased strength, has been observed in a
number of studies (19, 26, 37, 39). 3) Subjects who train
imaginary muscle contractions have been shown to exhibit
significant MVC increases (Refs. 66, 67; see, however, Ref.
24). 4) Several studies have reported increased maximal EMG
recorded from the trained muscle after a period of training and
used this to infer an increased neuronal drive to the muscles (2,
24, 37, 39).
A few studies have also argued against any changes in
neuronal drive in relation to strength training. Using twitch
interpolation to evaluate the voluntary drive to the muscle has
thus generally shown that subjects are able to voluntarily
activate the muscle almost to its maximal capacity before
training and that no or only minor changes occur in relation to
strength training (3, 22, 24, 62).
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Pascual-Leone et al. (45) demonstrated changes in the representation of hand muscles in the course of a 5-day training
program involving acquisition of a finger motor skill, and
Pascual-Leone et al. (46) added information to this study by
including measurements during 28 days of training. The results
demonstrated that the main improvement in the performance
occurred during the first week of (quite intense) training after
which the subjects continued to perform the task at a high level
in the rest of the training period. The expansion of the cortical
representation of the tested muscle was also mainly seen in the
first week of training after which it gradually declined. We
similarly found that the corticospinal excitability mainly increased within the first 2 wk of training, which was also the
period where the main improvement in the performance of the
task was observed. Some improvement of the performance of
the task was still observed between the second and fourth
weeks, but this was not accompanied by any changes in
corticospinal excitability. This is in line with the idea that the
increased corticospinal excitability is involved in the early
acquisition of the visuomotor skill is not necessary for the
skilled performance of the task as such. Convincing evidence
of a crucial role of the motor cortex in early acquisition of
motor skills has also been provided by Muellbacher et al. (38),
who observed that the retention of motor learning could be
blocked by repetitive TMS over the primary motor cortex in
the early stages of learning. The exact time course of the
changes in corticospinal excitability probably reflects the complexity of the task and the intensity of the training. This likely
explains why the corticospinal excitability declined in the
study by Pascual-Leone et al. (46) in the third and fourth weeks
of training, whereas we observed no change. Evidence for a
continuous reorganization in the primary motor cortex over
several weeks has also been obtained in relation to the acquisition of a finger sequence learning task using brain imaging
(30, 63).
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MOTOR SKILL TRAINING AND STRENGTH TRAINING
J Appl Physiol • VOL
Is the Ability to Generate Large Force Not a Skill?
A 4-wk strength training program is usually considered to be
too short for any structural muscular changes to take place, but
we cannot fully exclude that some changes did take place in the
muscles in our study and thus explain at least partly the
increased muscle strength in the subjects. Nevertheless, most
studies agree that increased neuronal drive is manifest very
soon after the onset of strength training and is responsible for
the main part of the initial gain in muscle strength. Then why
did we not see similar changes in the MEPs as during the
visuomotor skill training? Does this imply that the initial
strength gain during strength training is not explained by the
neuronal adaptations involved in learning and optimizing a
skill? We do not think so. There are several factors that
distinguish learning the visuomotor task and “learning” to
generate maximal force: 1) novelty of the task, 2) visual
feedback, 3) complexity of the task, and 4) pattern of somatosensory feedback related to the training. We find it likely that
changes in the MEP and expansion of the cortical area might be
involved when subjects are forced to use visual feedback to
improve their ability of generating force as quickly and possibly also as precisely as possible. This would be of clear
relevance to most sports activities, where it is not only important to be able to generate large force, but also to do it at a
precise time and with maximal precision.
In conclusion, these experiments have demonstrated that
increased corticospinal excitability occurs over the course of
several weeks of skill learning and that these changes seem to
be closely related to the acquisition of new visuomotor skills.
Such changes do not occur in relation to strength training. In
contrast, strength training was associated with decreased corticospinal excitability at rest, which was not correlated to the
increased muscle strength. The findings in the study thus
emphasize the role of plastic changes in the corticospinal
pathway in the acquisition of new motor skills but question
whether similar changes play a role in possible neuronal
adaptations to muscle strength training.
GRANTS
This study was supported by The Danish Ministry of Culture (Sports
Research Foundation).
REFERENCES
1. Aagaard P, Simonsen EB, Andersen JL, Magnusson P, and DyhrePoulsen P. Neural adaptation to resistance training: changes in evoked
V-wave and H-reflex responses. J Appl Physiol 92: 2309 –2318, 2002.
2. Aagaard P, Simonsen EB, Andersen JL, Magnusson SP, BojsenMoller F, and Dyhre-Poulsen P. Antagonist muscle coactivation during
isokinetic knee extension. Scand J Med Sci Sports 10: 58 – 67, 2000.
3. Allen GM, Gandevia SC, and McKenzie DK. Reliability of measurements of muscle strength and voluntary activation using twitch interpolation. Muscle Nerve 18: 593– 600, 1995.
4. Brasil-Neto JP, Valls-Sole J, Pascual-Leone A, Cammarota A, Amassian VE, Cracco R, Maccabee P, Cracco J, Hallett M, and Cohen LG.
Rapid modulation of human cortical motor outputs following ischaemic
nerve block. Brain 116: 511–525, 1993.
5. Buonomano DV and Merzenich MM. Cortical plasticity: from synapses
to maps. Annu Rev Neurosci 21: 149 –186, 1998.
6. Capaday C. Neurophysiological methods for studies of the motor system
in freely moving human subjects. J Neurosci Methods 74: 201–218, 1997.
7. Carroll TJ, Riek S, and Carson RG. The sites of neural adaptation
induced by resistance training in humans. J Physiol 544: 641– 652, 2002.
99 • OCTOBER 2005 •
www.jap.org
Downloaded from http://jap.physiology.org/ by 10.220.32.247 on January 11, 2017
A number of studies have reported various adaptations in the
central nervous system in relation to strength training. Aagaard
et al. (1) demonstrated significant increases of evoked H-reflex
and V-wave responses during maximal contraction after 14 wk
of strength training and suggested that this reflected increases
in descending motor drive from higher centers leading to
increased ␣-motoneuronal excitability. However, in the present
study we found no evidence of increased corticospinal excitability either at rest or during voluntary contraction of the
muscle. In line with a recent study by Carroll et al. (7), we
actually observed a decrease of corticospinal excitability. In
rats, Remple et al. (53) have also found that reorganization of
the movement representation within the motor cortex is similar, whether the rats perform the movements against a low or a
high load. There is thus no evidence that stronger muscles are
related to a larger representation of the muscles in the primary
motor cortex. From our study, we cannot decide whether the
observed decrease in MEPmax and the slope of the input-output
relation is explained by changes at a cortical or subcortical
level. In the study by Carroll et al., MEPs evoked by transcranial electrical stimulation (TES) showed similar changes after
strength training as MEPs evoked by TMS. Because TES is
assumed to be only little influenced by cortical excitability
changes, this observation favors a subcortical mechanism,
although it should be pointed out that the low sensitivity of
MEPs evoked by TES to cortical excitability changes mainly
applies for small MEPs, whereas the changes in the MEP after
strength training was mainly seen for large MEPs in the study
by Carroll et al. (7) as well as in the present study. In the
present study, a significant depression of the MEPs after
strength training was only observed at rest.
Carroll et al. (7) observed no changes in the MEPs at rest,
but only during stronger contractions, whereas we only found
significant changes at rest (a decrease in MEPmax was observed
also during voluntary contraction, but it did not reach a significant level; this is in all likelihood explained by the higher
variability of the recordings during voluntary contraction compared with rest). This discrepancy may be explained either by
the different muscles being studied (Carroll et al. studied the
first dorsal interosseus muscle), differences in the training
design (the subjects in the study by Carroll et al. performed 4
times 6 abduction-adduction movements against external loads
3 times per week), or the MEP measurements (Carroll et al.
investigated contraction levels up to 60% of MVC). Carroll et
al. suggested that the decrease in the MEP size in their study
was most easily explained by changes in the firing rate of the
spinal motoneurons and/or their intrinsic firing properties. The
findings in the present study do not exclude that such changes
may occur in relation to strength training but suggest that other
changes, such as changes in the excitability of cortical and/or
spinal neurons and the transmission across synaptic connections between corticospinal fibers and spinal neurons, may also
occur.
We did not observe any correlation between the changes in
MEPmax and the increased muscle strength in the subjects.
Although this negative finding should not be overinterpreted, it
does question whether changes in corticospinal excitability
have any functional significance for the increased muscle
strength.
MOTOR SKILL TRAINING AND STRENGTH TRAINING
J Appl Physiol • VOL
33. Kleim JA, Lussnig E, Schwarz ER, Comery TA, and Greenough WT.
Synaptogenesis and Fos expression in the motor cortex of the adult rat
after motor skill learning. J Neurosci 16: 4529 – 4535, 1996.
34. Komi PV. Training of muscle strength and power: interaction of neuromotoric, hypertrophic, and mechanical factors. Int J Sports Med 7, Suppl
1: 10 –15, 1986.
35. Lotze M, Braun C, Birbaumer N, Anders S, and Cohen LG. Motor
learning elicited by voluntary drive. Brain 126: 866 – 872, 2003.
36. Moritani T. Neuromuscular adaptations during the acquisition of muscle
strength, power and motor tasks. J Biomech 26, Suppl 1: 95–107, 1993.
37. Moritani T and deVries HA. Neural factors versus hypertrophy in the
time course of muscle strength gain. Am J Phys Med 58: 115–130, 1979.
38. Muellbacher W, Ziemann U, Wissel J, Dang N, Kofler M, Facchini S,
Boroojerdi B, Poewe W, and Hallett M. Early consolidation in human
primary motor cortex. Nature 415: 640 – 644, 2002.
39. Narici MV, Roi GS, Landoni L, Minetti AE, and Cerretelli P. Changes
in force, cross-sectional area and neural activation during strength training
and detraining of the human quadriceps. Eur J Appl Physiol 59: 310 –319,
1989.
40. Nudo RJ and Milliken GW. Reorganization of movement representations in primary motor cortex following focal ischemic infarcts in adult
squirrel monkeys. J Neurophysiol 75: 2144 –2149, 1996.
41. Nudo RJ, Milliken GW, Jenkins WM, and Merzenich MM. Usedependent alterations of movement representations in primary motor
cortex of adult squirrel monkeys. J Neurosci 16: 785– 807, 1996.
42. Oldfield RC. The assessment and analysis of handedness: the Edinburgh
inventory. Neuropsychologia 9: 97–113, 1971.
43. Pascual-Leone A, Cammarota A, Wassermann EM, Brasil-Neto JP,
Cohen LG, and Hallett M. Modulation of motor cortical outputs to the
reading hand of braille readers. Ann Neurol 34: 33–37, 1993.
44. Pascual-Leone A, Grafman J, and Hallett M. Modulation of cortical
motor output maps during development of implicit and explicit knowledge. Science 263: 1287–1289, 1994.
45. Pascual-Leone A, Nguyet D, Cohen LG, Brasil-Neto JP, Cammarota
A, and Hallett M. Modulation of muscle responses evoked by transcranial
magnetic stimulation during the acquisition of new fine motor skills.
J Neurophysiol 74: 1037–1045, 1995.
46. Pascual-Leone A, Tarazona F, and Catala MD. Applications of transcranial magnetic stimulation in studies on motor learning. Electroencephalogr Clin Neurophysiol Suppl 51: 157–161, 1999.
47. Pascual-Leone A and Torres F. Plasticity of the sensorimotor cortex
representation of the reading finger in Braille readers. Brain 116: 39 –52,
1993.
48. Pascual-Leone A, Wassermann EM, Sadato N, and Hallett M. The role
of reading activity on the modulation of motor cortical outputs to the
reading hand in Braille readers. Ann Neurol 38: 910 –915, 1995.
49. Perez MA, Lungholt BK, Nyborg K, and Nielsen JB. Motor skill
training induces changes in the excitability of the leg cortical area in
healthy humans. Exp Brain Res 159: 197–205, 2004.
50. Plautz EJ, Milliken GW, and Nudo RJ. Effects of repetitive motor
training on movement representations in adult squirrel monkeys: role of
use versus learning. Neurobiol Learn Mem 74: 27–55, 2000.
51. Ploutz LL, Tesch PA, Biro RL, and Dudley GA. Effect of resistance
training on muscle use during exercise. J Appl Physiol 76: 1675–1681,
1994.
52. Porter R and Lemon R. Corticospinal Function and Voluntary Movement. Oxford, UK: Clarendon, 1993.
53. Remple MS, Bruneau RM, VandenBerg PM, Goertzen C, and Kleim
JA. Sensitivity of cortical movement representations to motor experience:
evidence that skill learning but not strength training induces cortical
reorganization. Behav Brain Res 123: 133–141, 2001.
54. Ridding MC and Rothwell JC. Stimulus/response curves as a method of
measuring motor cortical excitability in man. Electroencephalogr Clin
Neurophysiol 105: 340 –344, 1997.
55. Rossini PM, Barker AT, Berardelli A, Caramia MD, Caruso G,
Cracco RQ, Dimitrijevic MR, Hallett M, Katayama Y, and Lucking
CH. Non-invasive electrical and magnetic stimulation of the brain, spinal
cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee. Electroencephalogr Clin Neurophysiol 91: 79 –92, 1994.
56. Rothwell JC. Techniques and mechanisms of action of transcranial
stimulation of the human motor cortex. J Neurosci Methods 74: 113–122,
1997.
99 • OCTOBER 2005 •
www.jap.org
Downloaded from http://jap.physiology.org/ by 10.220.32.247 on January 11, 2017
8. Carroll TJ, Riek S, and Carson RG. Neural adaptations to resistance
training: implications for movement control. Sports Med 31: 829 – 840,
2001.
9. Carroll TJ, Riek S, and Carson RG. Reliability of the input-output
properties of the cortico-spinal pathway obtained from transcranial magnetic and electrical stimulation. J Neurosci Methods 112: 193–202, 2001.
10. Classen J, Liepert J, Hallett M, and Cohen L. Plasticity of movement
representation in the human motor cortex. Electroencephalogr Clin Neurophysiol Suppl 51: 162–173, 1999.
11. Classen J, Liepert J, Wise SP, Hallett M, and Cohen LG. Rapid
plasticity of human cortical movement representation induced by practice.
J Neurophysiol 79: 1117–1123, 1998.
12. Datta AK, Harrison LM, and Stephens JA. Task-dependent changes in
the size of response to magnetic brain stimulation in human first dorsal
interosseous muscle. J Physiol 418: 13–23, 1989.
13. Day BL, Dressler D, Maertens de NA, Marsden CD, Nakashima K,
Rothwell JC, and Thompson PD. Electric and magnetic stimulation of
human motor cortex: surface EMG and single motor unit responses.
J Physiol 412: 449 – 473, 1989.
14. Devanne H, Lavoie BA, and Capaday C. Input-output properties and
gain changes in the human corticospinal pathway. Exp Brain Res 114:
329 –338, 1997.
15. Donoghue JP, Suner S, and Sanes JN. Dynamic organization of primary
motor cortex output to target muscles in adult rats. II. Rapid reorganization
following motor nerve lesions. Exp Brain Res 79: 492–503, 1990.
16. Duchateau J and Enoka RM. Neural adaptations with chronic activity
patterns in able-bodied humans. Am J Phys Med Rehabil 81: S17–S27,
2002.
17. Elbert T, Pantev C, Wienbruch C, Rockstroh B, and Taub E. Increased cortical representation of the fingers of the left hand in string
players. Science 270: 305–307, 1995.
18. Enoka RM. Neural adaptations with chronic physical activity. J Biomech
30: 447– 455, 1997.
19. Enoka RM. Muscle strength and its development. New perspectives.
Sports Med 6: 146 –168, 1988.
20. Gandevia SC. Spinal and supraspinal factors in human muscle fatigue.
Physiol Rev 81: 1725–1789, 2001.
21. Hakkinen K and Komi PV. Electromyographic changes during strength
training and detraining. Med Sci Sports Exerc 15: 455– 460, 1983.
22. Harridge SD, Kryger A, and Stensgaard A. Knee extensor strength,
activation, and size in very elderly people following strength training.
Muscle Nerve 22: 831– 839, 1999.
23. Hauptmann B, Skrotzki A, and Hummelsheim H. Facilitation of motor
evoked potentials after repetitive voluntary hand movements depends on
the type of motor activity. Electroencephalogr Clin Neurophysiol 105:
357–364, 1997.
24. Herbert RD, Dean C, and Gandevia SC. Effects of real and imagined
training on voluntary muscle activation during maximal isometric contractions. Acta Physiol Scand 163: 361–368, 1998.
25. Hickson RC, Hidaka K, Foster C, Falduto MT, and Chatterton RT Jr.
Successive time courses of strength development and steroid hormone
responses to heavy-resistance training. J Appl Physiol 76: 663– 670, 1994.
26. Hortobagyi T, Lambert NJ, and Hill JP. Greater cross education
following training with muscle lengthening than shortening. Med Sci
Sports Exerc 29: 107–112, 1997.
27. Hund-Georgiadis M and von Cramon DY. Motor-learning-related
changes in piano players and non-musicians revealed by functional magnetic-resonance signals. Exp Brain Res 125: 417– 425, 1999.
28. Jones DA and Rutherford OM. Human muscle strength training: the
effects of three different regimens and the nature of the resultant changes.
J Physiol 391: 1–11, 1987.
29. Kaelin-Lang A and Cohen LG. Enhancing the quality of studies using
transcranial magnetic and electrical stimulation with a new computercontrolled system. J Neurosci Methods 102: 81– 89, 2000.
30. Karni A, Meyer G, Jezzard P, Adams MM, Turner R, and Ungerleider LG. Functional MRI evidence for adult motor cortex plasticity during
motor skill learning. Nature 377: 155–158, 1995.
31. Kleim JA, Barbay S, Cooper NR, Hogg TM, Reidel CN, Remple MS,
and Nudo RJ. Motor learning-dependent synaptogenesis is localized to
functionally reorganized motor cortex. Neurobiol Learn Mem 77: 63–77,
2002.
32. Kleim JA, Barbay S, and Nudo RJ. Functional reorganization of the rat
motor cortex following motor skill learning. J Neurophysiol 80: 3321–
3325, 1998.
1567
1568
MOTOR SKILL TRAINING AND STRENGTH TRAINING
57. Rothwell JC, Thompson PD, Day BL, Boyd S, and Marsden CD.
Stimulation of the human motor cortex through the scalp. Exp Physiol 76:
159 –200, 1991.
58. Rutherford OM and Jones DA. The role of learning and coordination in
strength training. Eur J Appl Physiol 55: 100 –105, 1986.
59. Sanes JN. Motor cortex rules for learning and memory. Curr Biol 10:
R495–R497, 2000.
60. Sanes JN and Donoghue JP. Static and dynamic organization of motor
cortex. Adv Neurol 73: 277–296, 1997.
61. Sanes JN and Donoghue JP. Plasticity and primary motor cortex. Annu
Rev Neurosci 23: 393– 415, 2000.
62. Scaglioni G, Ferri A, Minetti AE, Martin A, Van HJ, Capodaglio P,
Sartorio A, and Narici MV. Plantar flexor activation capacity and H
reflex in older adults: adaptations to strength training. J Appl Physiol 92:
2292–2302, 2002.
63. Ungerleider LG, Doyon J, and Karni A. Imaging brain plasticity during
motor skill learning. Neurobiol Learn Mem 78: 553–564, 2002.
64. VandenBerg P, Bruneau R, Remple M, Soroka N, Cooper N, and
Kleim JA. Strength vs skill: differential patterns of plasticity within the rat
motor system. Soc Neurosci Abstr, Vol. 27, Program No. 572.14, 2001.
65. VandenBerg PM, Hogg TM, Kleim JA, and Whishaw IQ. Long-Evans
rats have a larger cortical topographic representation of movement than
Fischer-344 rats: a microstimulation study of motor cortex in naive and
skilled reaching-trained rats. Brain Res Bull 59: 197–203, 2002.
66. Yue G and Cole KJ. Strength increases from the motor program:
comparison of training with maximal voluntary and imagined muscle
contractions. J Neurophysiol 67: 1114 –1123, 1992.
67. Zijdewind I, Toering ST, Bessem B, Van Der LO, and Diercks RL.
Effects of imagery motor training on torque production of ankle plantar
flexor muscles. Muscle Nerve 28: 168 –173, 2003.
Downloaded from http://jap.physiology.org/ by 10.220.32.247 on January 11, 2017
J Appl Physiol • VOL
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