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The Journal of Neuroscience, 1999, 19:RC1:1-6
RAPID COMMUNICATION
Presupplementary Motor Area Activation during Sequence
Learning Reflects Visuo-Motor Association
Katsuyuki
Sakai1, 2,
Okihide
Hikosaka1,
Satoru
Miyauchi3,
Yuka
Sasaki3,
Norio
Fujimaki3, and
Benno
Pütz4
1 Department of Physiology, Juntendo University School
of Medicine, Tokyo 113, Japan, 2 Department of Neurology,
Division of Neuroscience, Graduate School of Medicine, University of
Tokyo, Tokyo 113, Japan, 3 Communications Research
Laboratory, Kobe 651-24, Japan, and 4 Exploratory Research
for Advanced Technology, Japan Science and Technology Corporation,
Kyoto 619-02, Japan
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ABSTRACT |
In preceding studies (Hikosaka et al., 1996 ; Sakai et al., 1998) we
have shown that the presupplementary motor area (pre-SMA), an anterior
part of the medial premotor cortex, is active during visuo-motor
sequence learning. However, the paradigm required the subjects first to
acquire correct visuo-motor association and then to acquire correct
sequence, and it was still unknown which of the two processes the
pre-SMA is involved in. To further characterize the role of pre-SMA, we
have conducted another series of functional magnetic resonance
imaging experiments using three learning paradigms. The three
were the same in that they involved a visuo-motor association
component, but they differed in terms of the involvement of sequential
components; one involved no sequence learning, whereas the other two
involved learning of motor sequence or perceptual sequence. Comparison
of the learning conditions with the any-order button press condition
revealed pre-SMA activation in all three paradigms. The pre-SMA
activation remained unchanged during learning of visuo-motor
associations but decreased during learning of sequences, suggesting
that the pre-SMA is related to visuo-motor association rather than
sequence. The decrease of pre-SMA activation in the sequential
paradigms may reflect the process by which individual visuo-motor
associations were replaced by the formation of sequential procedural
memory, which occurs outside the pre-SMA. Thus activation of the
pre-SMA was related to the extent to which the task performance
depended on conscious visuo-motor associations.
Key words:
presupplementary motor area; visuo-motor association; sequence; learning; functional magnetic resonance imaging; premotor
cortex
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INTRODUCTION |
The
presupplementary motor area (pre-SMA) is an area located in the
anterior part of the medial premotor cortex originally demonstrated in
the monkey (for review, see Tanji, 1996). Subsequent functional imaging
studies revealed the human homolog of the pre-SMA, which is located
anterior to the coronal plane passing the anterior commissure (VCA),
and have shown its involvement in higher-order aspects of motor control
(Picard and Strick, 1996). Consistent with this idea, we found, both in
monkeys and humans, that the pre-SMA was active in visuo-motor
sequence-learning tasks (Hikosaka et al., 1996; Nakamura et al., 1998;
Sakai et al., 1998). However, because the learning paradigm involved
both visuo-motor association and sequence, it remained unresolved which
components the pre-SMA is associated with. In addition, the paradigm
comprised two sequential components: motor sequence and perceptual
sequence. To determine whether the pre-SMA activation is related to
visuo-motor association or sequence (motor or perceptual), we used
three learning paradigms. These paradigms involved the same visuo-motor
association components but differed in their sequential components:
motor sequence, perceptual sequence without motor sequence, and no sequence.
| |
MATERIALS AND METHODS |
Subjects. Six normal human subjects participated in
the study (five males and one female, ages 29-50 years, all
right-handed). Informed consents were obtained from all the subjects
before the study. The experimental protocol was approved by the ethics
committee of our institute.
Task procedures. The subjects, lying supine in the magnetic
resonance imaging (MRI) scanner, saw four white rectangles arranged in
a 2 × 2 matrix in which two circles in different colors (four possible colors) appeared simultaneously in different positions (four
possible positions) (colors and positions together called "set"; Fig. 1a,b).
They had to press the buttons on a plate that corresponded to the
circles using the index and middle fingers of both hands (Fig.
1b).
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Figure 1.
a, The subjects lay supine in the
MRI scanner and saw the screen through the mirror. They held a button
plate on which four button switches were attached. b, On
the screen were presented 2 × 2 matrices in which two color
circles appeared. The subjects had to press the two of the four buttons
corresponding to the circles, using the same finger for each one of the
four positions.
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During the test conditions, the subjects had to find the predetermined
correct order of pressing the two buttons for a consecutive number of
sets by trial and error (called "hyperset," 6 or 10 sets; for
details, see Sakai et al., 1998). The rate of button presses was paced
by tone stimuli at 1 set/sec. Three learning paradigms were
used; position sequence task (Pos-Seq), color sequence task (Col-Seq),
and color mapping task (Col-Map) (Fig.
2a).
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Figure 2.
a, Examples of two successive
trials for position sequence (Pos-Seq), color sequence
(Col-Seq), and color mapping tasks
(Col-Map). The subjects had to find the correct order of
button presses (shown on the right), which was
predetermined by the position (Pos-Seq) or color
(Col-Seq and Col-Map) of the circles. In
Pos-Seq, for example, the correct order of button presses was
bottom left  top right for the first
set and top left bottom left for the
second set. In Col-Seq, the correct order was red blue for the first set and yellow red for the second set. In Col-Map, the six sets (i.e.,
6 possible color combinations) were presented in random order. For
example, the correct order of button presses for the pair of
red and blue circles was
red blue, but this pair appeared as
the first set in the top trial, whereas it appeared as
the third set in the bottom trial. Also note that the
patterns of finger movements were fixed in Pos-Seq but were varied in
Col-Seq and Col-Map. b, The three tasks were the same in
that they involved learning of visuo-motor association (to find the
correct order of button presses for each set), whereas they differed in
terms of the involvement of sequential (perceptual and motor)
components.
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Pos-Seq is the same as used in the previous studies (Hikosaka et al.,
1996; Sakai et al., 1998). The number of sets in a hyperset was 10, and
the 10 sets were presented in a fixed order. The position of the two
circles within 2 × 2 matrices for each set was the same across
trials, whereas their color was changed from trial to trial. The
correct order of button presses was determined by the position of the
circles in each set, and the subjects were asked to use the same finger
for each button corresponding to one of the four positions of the
circles (Fig. 1b). Therefore, the correct order of finger
movements was fixed in Pos-Seq for every trial of a hyperset. In other
words, the subjects learned both the motor and perceptual (position) sequences.
In Col-Seq, 10 sets were presented also in a fixed order in a hyperset.
However, the correct order of button presses was determined by the
color of the circles for each set, which was the same across trials. In
contrast, the position of the circles was changed from trial to trial.
Therefore, the correct order of finger movements was also changed for
every trial of a hyperset. Thus, the subjects learned the perceptual
(color) sequence but not the motor sequence.
In Col-Map, the correct order of button presses was also determined by
the color of the circles. However, unlike in Col-Seq, the number of
sets in a hyperset was six, and the six sets, having different
combinations of colors, were presented in a random order for each
trial. The correct order of button presses was determined uniquely for
each combination of colors. Therefore, no sequence learning was
involved in Col-Map, but each combination of colors had to be correctly
mapped to the order of button presses.
In short, the three paradigms were the same in that the subjects had to
find the correct order of button presses for each set (learning of
visuo-motor association), whereas they differed such that Pos-Seq
involved both the motor and perceptual sequence, Col-Seq involved only
the perceptual sequence, and Col-Map involved no sequence (Fig.
2b).
During the control conditions, the subject pressed the buttons for the
two circles in any order, and the computer determined randomly whether
the button press was correct. The error rate was programmed to match
that of the test conditions. Thus the subjects experienced the same
sensorimotor processes as in the test conditions, but no learning was
involved (called "pseudo-learning"; see Sakai et al., 1998). Thus,
comparison of the neural activities between the two conditions would
selectively reveal learning-related activation.
Functional MRI experiments. We used a 1.5 tesla whole-body
scanner (Siemens Vision, Erlagen, Germany) equipped with a
circular-polarized head coil. First, T1-weighted sagittal anatomical
brain images [FLASH; repetition time (TR), echo time (TE), and
inversion time (TI), 2800, 4, and 300 msec, respectively; flip angle
(FA), 15°; matrix, 256 × 256, field of view (FOV),
256 × 256 mm; and slice thickness, 1 mm] were obtained for each
subject to determine the anatomical landmarks. Subsequently, three
functional MRI (fMRI) experiments were conducted, each using one of the
three learning paradigms as a test task and pseudo-learning as a
control task. The order of the three experiments was counterbalanced
across the six subjects. Each experiment consisted of eight alternating blocks of the test and control conditions, each one of them lasting 42 sec, and, between the two conditions, instruction was presented on the
screen for 6 sec, indicating the next condition. Each subject continued
to learn the same hyperset throughout the eight test blocks. In the
meantime, a time series of 128 scans separated by 6 sec was performed
for each experiment. In each scan, a set of 10 axial T2*-weighted
gradient-echo echo-planar images (TR, TE, and TI, 6000, 66, and 300 msec, respectively; FA, 90°; FOV, 220 × 220 mm; matrix, 128 × 128; and slice thickness, 5 mm) was collected parallel to the anterior
commissure-posterior commissure line.
Data analysis. After motion correction (AIR 3.0; Woods et
al., 1992) and spatial smoothing with a Gaussian filter (4.5 mm full
width, half-maximum), the time series of signal intensity (SI)
data for each experiment was cross-correlated with an idealized boxcar
reference function shifted for one data point to account for the
hemodynamic delay. If the correlation coefficient calculated for a
pixel was >0.35, the pixel was determined to be significantly more
active (corresponding to p < 0.0001, uncorrected).
Then, for each individual subject, we determined the region of interest (ROI; Fig. 3a, region encircled by the green
lines) for the pre-SMA to include all the pixels active in either
of the three experiments within the medial premotor cortex above the
cingulate sulcus and anterior to the VCA line (Fig. 3a, yellow
lines). Thus we compared the activity of the identical brain areas
for the three experiments.
The relative SI increase for each of the eight test blocks from the
preceding and following control blocks ( SI) was calculated for this
ROI of the pre-SMA (for details, see Sakai et al., 1998). To account
for the difference in the speed of learning among the subjects and also
among the tasks, we classified each of the eight test blocks into one
of the four learning levels based on the rate of correct performance.
The correct performance rate was calculated by dividing the number of
correctly performed sets by the total number of performed sets (usually
42) and was expressed as a percentage. Blocks with a correct
performance rate <70% were categorized into level 1, those with
70-80% into level 2, those with 80-90% into level 3, and those
>90% into level 4. Subsequently, the mean SI was calculated for
each learning level for each subject. Thus the time course of the
pre-SMA activation can be shown as the mean SI expressed as a
function of the learning level.
To test the difference in the time course of the pre-SMA activation
across the three experiments, repeated measurements of ANOVA
were performed on the SI using two within-subjects factors: learning
level (Level) and task paradigm (Task).
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RESULTS |
All the subjects showed improvement in performance from level 1 to
level 4 during a single run of the experiment for all three learning
paradigms. Although the number of sets in a hyperset was different
among the three tasks (10 for Pos-Seq and Col-Seq, 6 for Col-Map), the
learning performance as measured by the increase of the correct
performance rate for the eight test blocks was not different (Fig.
3c). ANOVA applied to the
correct performance rate of the six subjects revealed that both the
main effect of Task and Task × Block interaction were not
significant (F(2,10) = 0.968; p > 0.05; and F(14,70) = 1.81; p > 0.05, respectively).
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Figure 3.
a, Activation maps from a
representative single subject. The right hemisphere is shown on the
left. The active pixels were determined as the ones with
correlation coefficients >0.35 and were color-coded in
orange-red according to the color bar on
the right. In each subject, the ROI for the pre-SMA,
encircled by the green line, was placed on the medial
premotor cortex anterior to the VCA line (yellow
line) to include all the pixels active in any of the three
experiments. b, Time course of the mean signal intensity
for the pre-SMA ROI during the three experiments. The eight blocks for
the test condition are shown in gray. Note the
difference between the three experiments. c, The
performance of the subject was expressed by the correct performance
rate (the number of correctly performed sets/the number of performed
sets) plotted against the eight blocks.
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For all three learning paradigms, significant activation was observed
in the medial premotor cortex, lateral prefrontal cortex, and posterior
parietal cortices (Sakai et al., 1998). The difference of activation
pattern was observed in the location of the prefrontal cortex activity;
its ventral part was more active in Col-Seq and Col-Map compared with
Pos-Seq. An additional difference was found in the extent of activation
for all the areas mentioned above; in general, wider areas were active
in Col-Map compared with the other two.
The present study focused on the activation of the medial premotor
cortex, which was nearly identical across the three experiments (Fig.
3a). The area was located above the cingulate sulcus and anterior to the VCA (Fig. 3a, yellow line), was thus
identified as the pre-SMA. As shown in Figure 3b, the SIs
for the pre-SMA were higher in the test conditions (gray
areas) than in the control conditions, the difference of which
(SI) reflected the cognitive components required for learning.
However the SI decreased gradually across the eight blocks in
Pos-Seq and Col-Seq, whereas it remained unchanged in Col-Map,
indicating the difference in the time course of pre-SMA activation.
Because the improvement in performance was similar across the three
tasks (Fig. 3c), the difference in the time course of
pre-SMA activation would be attributable to the task requirements, not
the difference in the learning performance.
To confirm this idea, the pre-SMA activation was calculated
respectively for each one of the four learning levels (mean SI). ANOVA applied to the mean SI showed significant Task × Level interaction (F(6,30) = 11.7; p < 0.01), again indicating the difference in the time course of pre-SMA
activation between the tasks. Post hoc testing (Tukey's
honestly significant difference method) revealed that Level
significantly affected SI in Pos-Seq and Col-Seq
(p < 0.01) but did not in Col-Map
(p > 0.05). As shown in Figure 4, the mean SI in Pos-Seq and Col-Seq
decreased along with the progress in the learning level, whereas that
in Col-Map remained unchanged.
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Figure 4.
Time course of the relative SI increase (SI)
shown as a function of the learning level (mean of 6 subjects; error
bar indicates SE). Level 1 indicates the early phase of
learning, and Level 4 indicates the advanced phase. A
decrease of SI along with the progress in learning was observed in
Pos-Seq and Col-Seq but not in Col-Map.
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DISCUSSION |
The present study used three learning paradigms to answer the
question, is the pre-SMA related to visuo-motor association or
sequence? As shown in Figure 2b, Col-Map required the
acquisition of visuo-motor associations, whereas Pos-Seq and Col-Seq
required the acquisition of a sequence as well. The pre-SMA activation was commonly observed in all three paradigms, suggesting that this area
is related to visuo-motor association rather than motor or perceptual
sequence. Interestingly, the activity of the pre-SMA decreased
gradually in Pos-Seq and Col-Seq. This may indicate that, in Pos-Seq
and Col-Seq, the visuo-motor association processes operating on
individual sets were taken over during learning by the sequence process
operating on a whole hyperset. After acquisition of the sequential
procedure, it was no longer necessary to consciously retrieve the
visuo-motor association at each set, and therefore the pre-SMA
activation was not required. By contrast, in Col-Map, the subjects had
to retrieve the visuo-motor association at each set even at the late
stage of learning, resulting in the persistent pre-SMA activation. This
persistent pre-SMA activity despite the decrease in the number of
errors would exclude the possibility that the activity merely reflected
arousal or error correction.
The present results are consistent with the pre-SMA neuronal activities
in monkeys performing a task similar to Pos-Seq (Nakamura et al.,
1998): many pre-SMA neurons behaved in the same manner as in the
present study, showing prominent discharge during new sequence
learning, which decreased along with the progress in learning
performance. Interestingly, at the initial phase of learning, most of
the pre-SMA neurons discharged at every set (Nakamura et al., 1998,
their Fig. 4), and the activity was usually observed between the
stimulus presentation and the first button press during which the
subjects tried to associate the visual stimuli with the correct motor
responses. The fact that a single neuron discharged for any set would
suggest that each pre-SMA neuron is not specialized for a particular
type of visuo-motor transformation but is related, more generally, to
the control of visuo-motor transformation (Rizzolatti et al., 1996).
This idea might be supported by the anatomical finding (Luppino et al.,
1993) showing that the pre-SMA receives inputs from the prefrontal and
cingulate cortex and sends projections to the lateral premotor cortex,
which is shown to be involved in visuomotor transformation (Wise et
al., 1992).
Learning-related modulations of neuronal activity were also found in
the supplementary eye field (SEF) of the monkey during a conditional
oculomotor association task (Chen and Wise 1995). In the present tasks,
although the subject had to press the two buttons in response to each
visual stimulus, the second button to be pressed was automatically
determined once the first button was selected. Therefore, the Col-Map
can be thought to require essentially the same cognitive operations as
the traditional conditional motor learning. Because we did not ask the
subjects to fixate their eyes during learning, the activation of the
medial premotor cortex observed in the present study might include the
activity of the SEF in addition to that of the pre-SMA.
It is interesting that the pre-SMA activity decreased also in Col-Seq,
in which the subjects had to change the pattern of movements for each
trial. This suggests that perceptual sequence alone contributes to the
procedural memory formation, as has been demonstrated by Fendrich et
al. (1991).
As discussed, we consider that learning occurs in two stages, first
acquisition of visuo-motor associations and then acquisition of the
whole sequence, and the pre-SMA, which was active in the former stage,
is related to visuo-motor association components. The sequential
components may be subserved by the supplementary motor area proper
(Tanji and Shima 1994) and basal ganglia (Kermadi and Joseph, 1995;
Mushiake and Strick, 1995; Miyachi et al., 1997; Rauch et al.,
1997) and possibly by the precuneus (Ghaem et al., 1997; Maguire et
al., 1997; Sakai et al., 1998).
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FOOTNOTES |
Received Oct. 7, 1998; revised Jan. 7, 1999; accepted Jan. 12, 1999.
Correspondence should be addressed to: Okihide Hikosaka, Department of
Physiology, Juntendo University, School of Medicine, 2-1-1 Hongo,
Bunkyo-ku, Tokyo 113, Japan.
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N. Laaris and A. Keller
Functional Independence of Layer IV Barrels
J Neurophysiol,
February 1, 2002;
87(2):
1028 - 1034.
[Abstract]
[Full Text]
[PDF]
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D. H. Smith and D. F. Meaney
Axonal Damage in Traumatic Brain Injury
Neuroscientist,
December 1, 2000;
6(6):
483 - 495.
[Abstract]
[PDF]
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K. Kurata, T. Tsuji, S. Naraki, M. Seino, and Y. Abe
Activation of the Dorsal Premotor Cortex and Pre-Supplementary Motor Area of Humans During an Auditory Conditional Motor Task
J Neurophysiol,
September 1, 2000;
84(3):
1667 - 1672.
[Abstract]
[Full Text]
[PDF]
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K. Sakai, O. Hikosaka, R. Takino, S. Miyauchi, M. Nielsen, and T. Tamada
What and When: Parallel and Convergent Processing in Motor Control
J. Neurosci.,
April 1, 2000;
20(7):
2691 - 2700.
[Abstract]
[Full Text]
[PDF]
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K. D. Sims, D. J. Straff, and M. B. Robinson
Platelet-derived Growth Factor Rapidly Increases Activity and Cell Surface Expression of the EAAC1 Subtype of Glutamate Transporter through Activation of Phosphatidylinositol 3-Kinase
J. Biol. Chem.,
February 18, 2000;
275(7):
5228 - 5237.
[Abstract]
[Full Text]
[PDF]
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TOP
ABSTRACT
INTRODUCTION
