SPECIAL ISSUE: ORIGINAL ARTICLE
REORGANIZATION OF BRAIN ACTIVITY FOR MULTIPLE INTERNAL MODELSAFTER SHORT BUT INTENSIVE TRAINING
Hiroshi Imamizu1, Satomi Higuchi1,2, Akihiro Toda3 and Mitsuo Kawato1(1ATR Computational Neuroscience Laboratories, Kyoto, Japan; 2Nara Institute of Science and Technology, Nara, Japan;
3Nagaoka University of Technology, Niigata, Japan)

ABSTRACTInternal models are neural mechanisms that can mimic the input-output properties of controlled objects. Our studies
have shown that: 1) an internal model for a novel tool is acquired in the cerebellum (Imamizu et al., 2000); 2) internal
models are modularly organized in the cerebellum (Imamizu et al., 2003); 3) their outputs are sent to the premotor regions
after learning (Tamada et al., 1999); and 4) the prefrontal and parietal regions contribute to the blending of the outputs(Imamizu et al., 2004). Here, we investigated changes in global neural networks resulting from the acquisition of a newinternal model. Human subjects manipulated three types of rotating joystick whose cursor appeared at a position rotated60°, 110°, or 160° around the screen’s center. In a pre-test after long-term training (5 days) for the 60° and 160° joysticks,
brain activation was scanned during manipulation of the three joysticks. The subjects were then trained for the 110° for
only 25 min. In a post-test, activation was scanned using the same method as the pre-test. Comparisons of the post-test to
the pre-test revealed that the volume of activation decreased in most of the regions where activation for the three rotations
was observed. However, there was an increase in volume at a marginally significant level (p < .08) only in the inferiorlateral cerebellum and only for the 110° joystick. In the cerebral cortex, activation related to 110° decreased in the prefrontal
and parietal regions but increased in the premotor and supplementary motor area (SMA) regions. These results can be
explained by a model in which outputs of the 60° and 160° internal models are blended by prefrontal and parietal regions
to cope with the novel 110° joystick before the 25-minute training; after the acquisition within the cerebellum of an internal
model for the 110°, output is directly sent to the premotor and SMA regions, and activation in these regions increases.
Key words:

INTRODUCTION
It is common knowledge that corticalrepresentations in sensory-motor areas are not fixed
but are continuously modified by experience (seeBuonomano and Merzenich, 1998). When a lesion
is made such that an adult animal loses sensationfrom a particular area, the region of the cortex
innervated by the missing nerves loses inputs.
However, after several weeks, this region becomes
active again, being innervated by other axons
adjacent to the lesion. Changes in corticalrepresentation also occur as a result of training fortasks that produce specific, differential patterns ofactivity in cortical sectors. Skill learning or
repetitive tactile stimulation expands digit
representations in the somatosensory cortex ofmonkeys (Jenkins et al., 1990) and humans (Elbert
et al., 1995), and in the human primary motor
cortex (Karni et al., 1995, 1998). Reorganization is
also known in the cerebellum regarding
phylogenetically older sensory-motor parts. That is,a fractured tactile map reorganizes after
deafferentation of a particular body part in adultrats (Shumway et al., 1999).
In the past decade, a number of studies haverevealed that the cerebellum contributes to higher
cognitive functions. Some of the first examples ofa clearly cognitive task were reports of language

processing and activation in the right lateral
cerebellum (Petersen et al., 1989; Raichle et al.,1994). Since then, other studies (see Thach, 1996;
Desmond and Fiez, 1998) have also pointed tocerebellar involvement in such cognitive tasks asworking memory (Desmond et al., 1997) andproblem solving (Grafman et al., 1992; Kim et al.,1994). Many studies (Desmond et al., 1997; Sakai
et al., 1998; van Mier et al., 1998) havedemonstrated that cognitive processing exists in thelateral part of the cerebellum. However, little is
known about whether the organization of the lateral
cerebellum and cerebro-cerebellar communication
loop changes due to experience.
A recent positron emission tomography (PET)
study on monkeys revealed that tool use activates acerebro-cerebellar communication loop including
the intraparietal sulcus (IPS) regions, thesupplementary motor area (SMA), the premotor
cortex, and the lateral cerebellum (Obayashi et al.,2001). Our previous study (Imamizu et al., 2000)
demonstrated that an internal model for a novel tool(a rotated mouse whose cursor appeared in aposition 120° rotated around the center of a screen)is acquired in lateral parts of the human cerebellum.
Tamada et al. (1999) have shown that functional
connectivity between the lateral parts and thepremotor regions increased after acquisition of theinternal model, suggesting that the internal model

Cortex, papers in press published online
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sends outputs to the premotor regions. We
investigated cerebellar activity after subjects
learned how to use two novel tools (the rotatedmouse and a velocity mouse whose cursor velocity
was proportional to the mouse position) (Imamizu
et al., 1998, 2003). The brain locations activatedwhile using the two different tools were spatiallysegregated with a small overlap, suggesting thatmultiple internal models are acquired in a modular
fashion. Our study (Imamizu et al., 2004) suggested
that the dorsolateral prefrontal cortex, the insulaand parietal regions contribute to the switching andselection of multiple internal models as “central
executives” or “responsibility estimators” in a
MOdular Selection And Identification for Control(MOSAIC) model proposed for the learning andswitching of internal models (Wolpert and Kawato,
1998; Kawato and Wolpert, 1998a).
Here, using our experimental paradigm, weinvestigated the reorganization of internal models
and changes in neural networks between the
cerebellum and the frontal and parietal regions dueto the acquisition of a new internal model. After
long-term training with the two novel tools,
subjects were briefly but intensively trained on theuse of a third novel tool. Brain activities for thethree types of tools were scanned before and after
training for the third tool and compared.
A computer program mapping the joystick
position to the cursor position makes the joysticks
functionally different, and there is no difference intheir form or fundamental manipulation. Although
there might be some argument about whether we
can consider them as entirely different “tools”, we
can find common tools that have similar form but
require a different skill. For example, scissors are
similar to pliers in form and essential mechanism.
However, pliers need a grip force in a direction
parallel to the handle movements, while scissors
need not only the grip force but also a force toantagonistically push the two edges. Each joystickin our experiment requires a different skill tocontrol the cursor, depending on different mapping
between the cursor movement and the joystick
movement. Thus, our experimental paradigm canprobe into how different skills for tool use areorganized in the brain.
METHODS
Subjects
Fourteen neurologically normal subjects (fivefemales and nine males from 21 to 39 years ofage) participated in the experiments. All wereright-handed according to the Edinburgh
handedness inventory (Oldfield, 1971). Informed
written consent was obtained from each subject.
The test protocols were approved by the ATR
ethics committee.

Tasks
Basic task designs are the same as those used in
our previous studies (Imamizu et al., 2000, 2003,
2004). The subjects moved a small crosshair cursor
on the screen with a joystick and continuously
tracked a moving target. They controlled thejoystick with their right hand. The target was asmall white square on a dark background andmoved around within a square area subtending
horizontal and vertical visual angles of 7.33°. The
x-and y-components of the target trajectory weresums of sinusoids whose amplitude and frequency
were pseudo-randomly determined. A portion of thetarget trajectory is shown by small rectangles in
Figure 3.
The relationship between cursor position andthe joystick angle was x = 0.12  cos θ sin θ   p y
 – sin θ cos θ  q 
where (x, y) denotes the cursor’s screen
coordinates (visual angle: °) and (p, q) denotes thedeviational angles of the joystick from the verticalaxis in the left-right and the back-forth directions.
6 was a rotational angle of 0°, 60°, 110°, or 160°.
Figure 1 illustrates the correspondence between the
direction of joystick movement and that of cursor
movement under each rotation.
Procedures
All subjects underwent the following: 1) fivedays of long-term training for the 60° and the 160°
joysticks; 2) a pre-test; 3) 25 min of short-term
training for the 110° joystick; and 4) a post-test.
Brain activity was scanned in the pre- and posttests.
Five Days of Long-Term Training for the 60°
and the 160° Joysticks
The subjects were trained for the tracking task
under 60° and 160° conditions. During the five-day
training sessions, brain activity was not scanned
but the subjects performed the tasks lying on a bed
just as they would later do in the magnetic
resonance imaging (MRI) scanner. Within eachsession, a tracking task period (1.6 min) and a rest
period (18 sec) were alternately repeated threetimes without changing the joystick type. As shownin Figure 2a, the subjects used the 0° joystick in
the first session each morning and were thentrained for either the 60° or 160° joystick in thesubsequent eight sessions. Breaks between sessions
lasted 5-10 min. After a 1-2 hour lunch break, thesubjects used the 0° joystick in the first session ofeach afternoon, and were subsequently trained forthe other 60° or 160° joystick in eight sessions.
The accumulated tracking time over the five days
was 192 min for the 60° and 160° joysticks.

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Reorganization of internal models

3Fig. 1 – Relationship between the direction of joystick movements and that of cursor movements on the screen under each rotation.
Black arrows indicate the direction in which subjects moved the joystick, while white arrows indicate the direction of the resulting cursor
movement. Numbers indicate correspondence between the black and the white arrows.

Fig. 2 – A graphic representation of the procedures in the present experiment.

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Fig. 3 – Examples of target trajectories (white rectangles) and joystick trajectories (filled circles) manipulated by a subject for 4 sec. a, b,c and d show trajectories when rotation angle was 0°, 60°, 110°, and 160°, respectively. Upper and lower panels show trajectories in thescreen (visual angle: °) and hand coordinates (deviational angle of the joystick from the vertical axis: °). Marker positions are 40 msec apart.

Pre-Test
In a pre-test, the subjects used the 0°, 60°,
110°, and 160° joysticks in one session. The orderof the tracking periods using these joysticks
was counterbalanced between the sessions. An
example of the session was three repetitionsof [rest → 60° → 0° → 110° → 0° → 160° → 0°]
as illustrated in Figure 2b. The rest period
lasted 24 sec, while the other periods lasted 48 sec.
When the subjects used the 0° joystick, thetarget velocity was adjusted so that the tracking
error was equalized to the error for the joystickused in the preceding period (see Equalization of
Tracking Error). The subjects underwent four
sessions.
25 Minutes of Short-Term Training for 110° Joystick
One session (7.2 min) was comprised of two
repetitions of [rest → 110° → 0° → 110° → 0°] asshown in Figure 2c. The rest periods lasted 24 sec,
while each tracking period lasted 48 sec. The
subjects underwent eight sessions with 5-10-minutebreaks between sessions. The accumulated tracking
time for 110° joystick was short (25 min) in

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Reorganization of internal models

comparison to that for 60° or 160° in the five daysof long-term training (192 min).
Post-Test
The subjects underwent the same sessions as in
the pre-test.
Analysis of Behavioral Data
The cursor , the target , and the joystick angles
were sampled at 1 kHz. The distance between
the cursor and the target at each sampling point( ( xt – xc )2 + ( yt – yc )2 ) was accumulated over 5.3
sec (position tracking error). A velocity tracking
error ( ( xt – xc )2 + ( yt – yc )2 ) was also accumulated
over 5.3 sec. Because the results obtained from thevelocity error were similar to those from position
tracking error, we only present the position
tracking error in this article.
Equalization of Tracking Error
In our previous study (Imamizu et al., 2000),
two types of activities related to learning wereobserved in the cerebellum. One reflected error
signals that guide the learning acquisition ofinternal models and the other reflected an acquired
internal model. The former activity was so strongly
distributed over the cerebellum that it also blurred
the latter activity. Following the procedure used in
the previous study, tracking error during
manipulation of the normal (0°) joystick wasequalized to that during the rotated joystick using alinear relationship between tracking error and target
velocity. This procedure allowed us to distinguishinternal model activity for the novel tools from
activity reflecting the error signal.
Before functional imaging, the subjects
performed the tracking task using the 0° joystick atvarious target velocities (V0) that ranged from
.0427 to .0783°/second at .0021 intervals. A linear
relationship between V0 and the tracking error (E)was derived by a least squares method for eachsubject:
E = aV0 + b.
In the pre- and post-tests, the target velocity
was adjusted using the estimated values a and b in
the 0° baseline condition, so that the error was
equal to the mean error (E) in the preceding 60°,
110°, or 160° test periods:
V0 = E – ba
The averaged target velocity was .0652°/secondin the pre-test and .0543°/second in the post-test.
MRI Acquisition
A 1.5 T MRI scanner (Shimadzu-Marconi) wasused to obtain blood oxygen level dependent

5(BOLD) contrast functional images. Images
weighted with the apparent transverse relaxation
time were obtained with an echo-planar imaging
sequence (repetition time = 5.3 sec, echo time = 65
msec, flip angle = 90˚). A total of 158 sequential
whole brain volumes (64 × 64 × 44 voxels at 3.4-mm
isotropic resolution) were acquired in each session.
High resolution anatomical images of all subjects
were also acquired with a T1 weighted sequence.
MRI Analysis
We used Statistical Parametric Mapping 99 (SPM
99) software (http://www.fil.ion.ucl.ac.uk/spm/) for
image processing and analysis. The first twovolumes of images were discarded to allow for T1
equilibration while the remaining 156 image
volumes were realigned to a reference volume. The
first volume was chosen as the reference to minimize
the difference between the realigned functional
images and the anatomical image that was acquired
immediately after subjects entered the MRI scanner.
The realigned images were normalized to theMontreal Neurological Institute (MNI) reference
brain. The data were spatially smoothed with aGaussian kernel with a 7 mm full-width halfmaximum (FWHM). The voxel time series were
temporally smoothed with a Gaussian filter (FWHM
of 4 sec).
We conducted a multiple regression analysis tofind regions related to the manipulation of the three
types of joysticks.
vik = αiw k + βix k + γi y k + δi zk + ei
Here, vik denotes the functional magnetic
resonance imaging (fMRI) signal at the i-th voxelin the k-th scan, and w, x, y and z are explanatory
variables corresponding to the use of the 0° (error
equalized), 60°, 110°, and 160° joysticks,
respectively. They were assigned 1 if the scancorresponded to their joystick type and 0 otherwise.
We performed a random effect analysis
comprising two stages. In the first stage analysis,
parameters were estimated for the differences ofinterest (β – α, γ – α and δ – α) for each subject.
They were then entered into a second-level analysis
to test whether parameters for the 60°, 110°, or 160°
joysticks were significantly larger than parameters
for the error-equalized 0° joystick (β – α > 0, γ – α> 0 and δ – α > 0), using a one-sample t-test acrosssubjects [t(13) > 3.85, p < .05 uncorrected].
Quantitative Analysis of Activity Change
from Pre- to Post-Test
We searched for voxels in which activation was
significantly associated with all three types of
joystick. Then we identified the anatomical volume
of interest (VOI) including the voxel according to(Tzourio-Mazoyer et al., 2002) and investigated thechange in activated volume separately for each

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Fig. 4 – a. Change in tracking error (across-subjects mean ± SD) during training sessions. Red and blue curves represent errors in 60°
and 160° conditions, respectively. b. Pre-test, the 110° training, and post-test errors. The green curves represent errors in 110° condition.

VOI. The voxels were found in the premotor cortex
and regions near the IPS. The premotor VOI was
defined as Broadmann area 6, excluding the medialcortex but including both the ventral (PMv) anddorsal (PMd) parts. The parietal VOI comprised thesuperior parietal gyrus, the angular gyrus, thesupramarginal gyrus, and the precuneus. The
cerebellum was divided into upper and lower parts
by a horizontal plane (z = – 40) that roughly
corresponded to the horizontal fissure.
RESULTS
Behavioral Data
Figure 3 shows a representative target andcursor trajectory from a subject’s data while using
each of the rotated angles. Although the target

trajectories were randomly determined and thus
different for the four angles, the relationshipbetween the target motion and the corresponding
cursor motion was qualitatively the same.
Figure 4a shows how the tracking errors
changed during long-term training for the 60° (red)and 160° (blue) joysticks before scanning. The
errors for both joysticks decreased as theaccumulated tracking time increased. A repeatedmeasures analysis of variance (ANOVA) for errors
indicated a significant time effect for the 60°
joystick [F (1, 39) = 1.76, p < .005] and for the160° joystick [F (1, 39) = 3.25, p < .0001],
suggesting that learning had occurred.
Figure 4b shows errors during the pre-test, theshort-term training for the 110° joystick, and thepost-test. Using a paired t-test, we examined
whether there was a significant difference between
the errors in the pre- and post-tests. No significant

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Reorganization of internal models

7Fig. 5 – Brain activity related to 60° (red), 110° (green) and 160° (blue) conditions in pre-test (a and b) and post-test (c and d).
Activity in error-equalized 0° condition was subtracted [random effect model, t(13) > 3.85, p < .05 uncorrected, cluster size > 10
voxels]. Activities are shown in left-superior-posterior view (a and c) and in right-superior-anterior view (b and d). The cerebrum andthe cerebellum are separated into the upper and lower parts of each panel.

difference was observed for the 60° or 160°
joystick [60°: t(13) = .17, 160°: t(13) = 1.57] but asignificant decrease was observed for the 110°
joystick [t(13) = 3.83, p < .001]. Thus, a significant
effect of short-term training for the 110° training
could be identified.
In the pre- and post-tests, target velocity was
adjusted during the use of the 0° joystick toequalize tracking error to the error during use of
the 60°, 110° and 160° joysticks (see Equalizationof Tracking Error in Methods). There was no
significant difference between these two equalized
errors during both the pre-test and post-test foreach type of joystick [60°: t(13) = .64, 110°: t(13)= 1.19, 160°: t(13) = .97 in the pre-test; 60°: t(13)= .24, 110°: t(13) = 1.55, 160°: t(13) = .57 in the
post-test], suggesting that error-equalization
succeeded. The activations shown below wereobtained by subtracting the corresponding 0°
activation from the 60°, 110°, or 160° activation as
explained in the MRI analysis section.
fMRI Data
Change of Three Types of Activation from Pre- to
Post-Test
Figures 5a and 5b show the 60° (red), 110°(green), and 160° (blue) activations in the pre-test[t(13) > 3.85, p < .05 uncorrected, cluster size > 10

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Hiroshi Imamizu and Others
TABLE I

Regions activated in 110° condition and compared to error-equalized 0° condition
Pre-test
Anatomical description(Broadmann area)
Frontal lobe (Lateral Surface)
Superior frontal gyrus
Middle frontal gyrus
Inferior frontal gyrus,
Opercular Part
Frontal lobe (Medial Surface)Supplementary motor area
Central region
Precentral gyrus

R/L

Post-test

T-value
Coordinates

(6)
(6)
(8)
(45)
(46)

R/L
VoxelsR(22, 0, 52)

7.26

403

L
R
L

(– 22, 8, 56)(48, 42, 22)(– 38, 52, 24)

5.14
6.28
4.61

42
121
10

T-value
Coordinates

VoxelsRR(20, – 12, 58)(24, – 14, 74)

5.51
4.48

149
25

(44)

R

(62, 12, 26)

4.97

14

(6)

L

(– 2, – 14, 66)

4.46

12

L
R

(– 28, – 8, 58)(54, 10, 4)

7.59
4.97

346
54

(6)
(6)
(48)

L
L

(– 60, 10, 26)(– 24, 14, 50)

5.91
5.79

37
229

(40)
(48)
(48)
(6)
(2)
(40)
(40)
(40)
(7)
(7)

R
R
R
L
L
L
L
L
R
R

(40, – 36, 42)(58, – 22, 28)(58, – 34, 28)(– 58, – 30, 36)(– 44, – 32, 38)(– 40, – 42, 50)(– 48, – 48, 56)(– 42, – 40, 36)(16, – 74, 50)(16, – 66, 58)

6.38
5.83
5.31
5.44
4.72
5.10
4.88
4.18
5.86
4.52

214
33
72
51
13
32
12
12
50
29

R
R

(64, – 36, 32)(56, – 22, 28)

5.24
5.82

27
31

L

(-34,-36,42)

4.91

11

(6)

R

(8, – 6, 48)

6.39

214

R

(8, 10, 44)

5.85

169

(48)
(48)
(48)

L
L
R

(– 34, 16, 6)(– 40, 14, – 4)(42, 14, 0)

4.68
4.93
4.17

14
11
15

L

(– 28, 18, 10)

9.44

70

R
R
L
L

(18, – 6, 10)(20, – 18, 2)(– 14, – 8, 10)(– 8, 0, 2)

4.49
5.97
5.44
5.52

12
217
33
20

R

(16, – 12, 0)

7.62

291

L

(– 14, – 14, 8)

7.76

140

(– 36, – 46, – 36)(– 30, – 70, – 30)(24, – 70, – 30)(44, – 50, – 36)(– 6, – 26, – 18)(– 2, – 44, – 8)(– 20, – 26, – 28)(28, – 44, – 32)(– 16, – 74, – 46)

8.62
5.91
5.05
4.73
6.54
5.42
4.55
6.82
4.78

369
85
20
19
138
26
13
125
15

L

(– 48, – 56, – 40)

4.42

10

Lobule 6
Lobule 7b

L
L
R
R
L
L
L
R
L

Lobule 8

L(– 24, – 56, – 54)

6.67

181

Lobule 9
Vermis 9R(12, – 62, – 54)

6.17

29

R
L
L
L
R
R
R
R

(34, – 34, – 34)(– 14, – 74, – 46)(– 32, – 46, – 40)(– 14, – 58, – 54)(16, – 64, – 32)(28, – 46, – 56)(10, – 34, – 38)(10, – 54, – 32)

5.21
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Rolandic operculum
Parietal lobe
Supramarginal gyrus
Supramarginal gyrus
Inferior parietal gyrus

Precuneus
Limbic lobe
Median cingulate andparacingulate gyri
Insula lobe
InsulaSubcortical gray nuclei
ThalamusLentricular nucleus pallidum
Cerebellum
Crus 1

Lobule 3
Lobule 4-5

voxels]. Figures 5c and 5d show the activations in
the post-test. The red (60°) regions decreased in the
post-test in various regions, especially in the left
and right intraparietal regions and the cerebellum.
The green (110°) regions decreased in the cerebral
cortex but increased in the inferior parts of thecerebellum. In the right ventral premotor cortex, inthe pre-test the dominant color was blue (160°) but
it was green (110°) in the post-test.
We searched for regions where the three typesof activation (for 60°, 110° and 160°) overlappedin the pre-test and found them in the left premotor
region, the left and right intraparietal regions, andthe superior left and inferior right cerebellum. The

t-value weighted centers of gravity of thoseoverlapping regions were (– 27, – 5, 51), (– 37,– 42, 45), (38, – 35, 41), (– 23, – 66, – 25) and (14,– 61, – 51), respectively. The number of voxels
included in the regions were 6, 4, 19, 12 and 3.
These regions are assumed to be closely related tothe manipulation of the rotated joysticks.
Change of 110° Activity from Pre- to Post-Test
Table I lists the 110° activation peaks and peakt-values. A number of cerebral regions wereactivated in the pre-test but not all weresignificantly activated in the post-test. A decrease

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Reorganization of internal models

9Fig. 6 – The same activities as in Figure 5 are shown in the transverse (a-d) or coronal sections (e). Sections include the prefrontal
regions (areas 45 and 46) (a), the dorsal premotor cortex (b), the ventral premotor cortex (c), the intraparietal regions (d), and the
cerebellum (e). Lines in e indicate the horizontal level at z = – 40.

of activation was prominent in the prefrontal
regions (area 6 in the superior frontal gyrus; areas
8, 45, and 46 in the middle frontal gyrus) and in
the parietal regions. In contrast, an increase ofactivity was observed in the premotor (area 44 in
the opercular part of the inferior frontal gyrus andarea 6 in the superior frontal gyrus at z = 74 level)
and in the SMA. The total number of voxels did
not change drastically in the cerebellum. However,activation shifted from the superior parts (crus 1
and lobules 3-6) to the inferior parts (lobules 7b-9).

Figures 6a-6e show representative slices fromthe above regions. The panels in Figure 6a show
areas 45 and 46 in the prefrontal regions where
significant activation was observed in the pre-test
but not in the post-test. Figures 6b and 6c include
the dorsal and ventral premotor regions,
respectively. Activation in the dorsal and ventral
premotor regions increased in the post-test. Figure
6d shows the intraparietal regions where the 60°(red) and 110° (green) activations decreased in the
post-test. Figure 6e shows the cerebellar activity in

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Fig. 7 – Activated volumes (across-subjects mean + SE) in anatomical regions where overlap among the 60°, 110°, and 160°
activations was found in the pre-test. Threshold for activation was t > 3.08, p < .05 uncorrected (fixed effect model). Filled bars andopen bars correspond to pre- and post-tests. * indicates p < .05, and # p < .10 according to a paired t-test.

the coronal sections. The 60° activation decreasedin the superior parts, and the 110° activation
increased in the inferior parts.
Quantitative Analysis of Change in Activity
We compared activated volume in the pre-test tothat of the post-test for each subject for anatomical
regions including the overlap among the three typesof activation (Figure 7). The activated volume wasmeasured in activation map of each subject (p <.001 uncorrected). This analysis was conducted not
only in the regions where the overlap was foundbut also in the homologous region on the other sideof the hemisphere. Below, we will describe
increases or decreases thresholded at p < .10.

Although marginally significant (not significant in
the strict sense) results pass this threshold, we willreport them to indicate trends in change of activity.
Regarding the premotor regions, a significant
volume decrease was found in the left premotor[t(13) = 1.89, p < .04, paired t-test] for the 60°
activation. In the left and right intraparietal regions,a significant or marginally significant decrease was
identified for the 60° activation [left: t(13) = 1.45,
p < .09; right: t(13) = 1.71, p < .06] and the 110°
activation [left: t(13) = 1.85, p < .05; right: t(13) =1.63, p < .06]. The decrease in the 110° activationin the parietal regions was consistent with Table I.
In the superior part of the cerebellum, the volume
for the 60° activation decreased in the post-test[left: t(13) = 1.92, p < .04; right: t(13) = 1.54, p <Imamizu - 3/2007 - 1LT

Reorganization of internal models.08] but the volume for the 110° activation
increased [left: t(13) = 1.65, p < .06, right: t(13) =1.69, p < .06]. Thus, an increase of activity at p <.10 level was only identified in the cerebellum.
We confirmed the above increase or decrease ofvolume for 110° activity in the post-test separately
for each subject. When the activated volume wasmeasured in the activation map of each subject (p <.01 uncorrected), the volume for 110° activity
increased in the left or the right inferior cerebellumin 11 of the 14 subjects. Regarding those 11 subjects,
we investigated change of the volume in the otherregions of interest. The volume for the 110° activity
increased in the left or right premotor region of six
subjects and the SMA region of six subjects. The
volume decreased in the left or right intraparietal
region of eight subjects, the area-45 region of ten
subjects, and the area-46 region in nine subjects.
DISCUSSIONIn summary, the 110° activation in the frontal
and parietal regions decreased in the post-test
whereas it increased in the premotor (in theprecentral gyrus and the opercular part of theinferior frontal gyrus) regions, in the SMA, and in
the inferior part of the cerebellum. The 60°
activation decreased in most of the brain regions.
Regarding 160° activation, no significant change inactivity was observed.
As mentioned in the Introduction, we
previously investigated cerebellar activity on
subjects who learned to use a 120° rotated mouseand identified activity reflecting an internal model
separate from activity reflecting performance error.
We used the same method to equalize tracking
error as in the current experiment (Imamizu et al.,2000). We also previously investigated cerebellar
activity after learning the use of two novel tools (arotated mouse and a velocity mouse whose cursor
velocity was proportional to the mouse’s position).
The activities of the two different tools were
spatially segregated with slight overlap, suggestingthat multiple internal models are acquired in amodular fashion (Imamizu et al., 2003).
The first advantage of possessing multiple
internal models is the reduction of interference
between different learning epochs, which enables
the rapid switching of skilled behaviors. The second
advantage is being able to cope with an entirely
new environment by adaptively mixing pre-existing
motor primitives as multiple internal models.
However, the central nervous system (CNS) mustsolve a selection problem: deciding which internal
models are appropriate for usage and learning undera given context of sensory-motor behaviors.
MOSAIC models have been proposed for thelearning and switching of internal models (Wolpert
and Kawato, 1998; Kawato and Wolpert, 1998b;
Wolpert et al., 1998, 2003; Wolpert and

11

Ghahramani, 2000; Haruno et al., 2001; Doya et al.,2002). In this model, output from multiple internal
models are mixed in proportion to theirappropriateness for the current context. Therefore,
an enormous repertoire of behavior can begenerated even if the number of internal models is
limited, and many situations that we encounter may
be derived from a combination of previously
experienced situations. A recent study (Imamizu etal., 2004) suggests that the dorsolateral prefrontal(area 46) and the parietal regions contribute to theselection and combination (‘blending’) of multiple
internal models. A PET study on monkeys also
found that fronto-cerebellar interaction contributes
to switching tools when monkeys use several tools
simultaneously (Obayashi et al., 2002). A recent
neuroanatomical study (Kelly and Strick, 2003)
found loop connections between area 46 and the
cerebellum, which is consistent with these results.
Based on a series of previous studies that wehave conducted, the results of our current study canbe interpreted as follows. Before scanning brain
activity, an internal model for the 60° joystick andone for the 160° joystick were acquired in the
cerebellum as a result of long-term training. In the
pre-test, it was assumed that the CNS copes with
the new 110° joystick by blending the output fromthe 60° and the 160° internal models. Activation inarea 46 and the parietal regions contributed to thisblending process. An increase in 110° activity wasobserved in the premotor regions, in the SMA, andin the inferior part of the cerebellum in the post-test
in comparison to pre-tests, suggesting that aninternal model for the 110° joystick was acquired inthese regions. For the following reasons, it isassumed that internal models are acquired in theinferior part of the cerebellum. First, group analysis(random effect analysis) indicated that the volume
increase was the largest in lobule 7b of the inferior
cerebellum (from 15 to 686 + 11 voxels, Table I).
Second, a marginally significant increase ofactivation was identified only in the inferior
cerebellum among the regions where thequantitative analysis was conducted (Figure 7). As
mentioned above, a previous study that we
conducted indicated that functional connectivity
between the lateral parts of the cerebellum and thepremotor regions increased after acquisition of theinternal model, which suggests that the acquired
internal models send outputs to the premotor
regions (Tamada et al., 1999). Electrophysiologicalstudies (e.g., Sasaki et al., 1977) andneuroanatomical studies (Middleton and Strick,
1994; Kelly and Strick, 1998; Dum and Strick,2003) have revealed reciprocal connections between
the cerebellar and premotor regions. It is supposedthat internal models for the 60° and 160° joysticks
had already been acquired in the pre-test and thatthese models sent outputs to the premotor regions.
The connectivity probably further increased in the
post-test after acquisition of the 110° internal

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model. Our study suggested that the SMA also
receives outputs from the acquired internal models.
The 60° and the 110° internal models contributed tomanipulation of the 110° joystick in the pre-test,
but it is unknown whether these models contributed
to learning the 110° internal models.
The results of our current experiments do notcontradictthefollowing
computational
interpretations. When encountering a new tool, the
CNS attempts to cope with it by blending outputs
from internal models previously acquired. The
prefrontal and parietal regions receive outputs fromthe internal models and contribute to the blending.
However, after the acquisition of a proper internal
model, the output of the internal model is directlysent to the premotor regions. Our findings suggestthat the acquisition of a new cognitive function
causes reorganization in the lateral cerebellum andchanges global information flow in the cerebrocerebellar communication loop.
Acknowledgements. This research was supported inpart by the National Institute of Information andCommunications Technology.
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Hiroshi Imamizu, 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan.
e-mail: imamizu@atr.jp

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