SPECIAL ISSUE: ORIGINAL ARTICLECEREBELLAR ACTIVITY EVOKED BY COMMON TOOL-USE EXECUTION
AND IMAGERY TASKS: AN fMRI STUDY
Satomi Higuchi1,2, Hiroshi Imamizu2 and Mitsuo Kawato2(1Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan; 2ATR,
Computational Neuroscience Laboratories, Kyoto, Japan)

ABSTRACTThe purpose of this study is to identify the functional brain networks activated in relation to actual tool-use in humans.
Although previous studies have identified brain activity related to tool-use gestures (Moll et al., 2000), they did notinvestigate the brain activity involved in such tool-use. We investigated brain activity using functional magnetic resonanceimaging (fMRI) while human subjects mentally imagined using sixteen common tools and while they actually used them.
Brain activity for both actual and imagined tool-use was found in the posterior part of the parietal cortex, in the
supplementary motor area, and in the cerebellum. Under imagined tool-use conditions, we found brain activity in the
premotor and right pars opercularis. Under actual tool-use conditions, we found it in the primary motor area, in thethalamus, and in the left pars opercularis. Our precise analysis in the cerebellum indicated that activity evoked by imagery
was located significantly more lateral to that evoked by actual use. We found a relationship between activity in the tool
imagery and execution conditions by comparing their t-value-weighted centroid of activation coordinates. Moreover, forhalf of the subjects the spatial distribution pattern for each tool was similar, suggesting that neural mechanisms contributing
to skillful tool-use are modularly organized in the cerebellum.
Key words: tool-use, imagery, fMRI, cerebellum

INTRODUCTION
Such intelligent animals as humans andchimpanzees can use tools. To acquire the ability touse particular tools, for example, scissors or
chopsticks, humans and chimpanzees require muchpractice; however, once such ability is acquired, itcannot be forgotten. According to computational
neuroscience, such abilities are carried out by
inverse models that calculate the motor commandto achieve task goals and forward internal models
that predict the sensory feed back from theefference copy of the motor command. Based onthese models humans predict or simulate the inputoutput properties of tool-use (Kawato et al., 1987).
By internally representing the relationship between
tool manipulation and the outcome of tool-use
behavior, the predictive control of tools becomes
possible, enabling us to use them quickly andsmoothly.
Computational neuroscience fields haveproposed that internal models exist in the
cerebellum (Kawato, 1999). Recent functional
neuroimaging studies (Imamizu et al., 2000) havefound cerebellar activity that reflects an internal
model of a novel tool, indicating that internal
models of novel tools are modularly organized(Imamizu et al., 2003). We investigated whether
the internal models of such common tools as
chopsticks and scissors are also modularlyorganized in the cerebellum.
On the other hand, in neurological fields
numerous works have suggested that the parietal
Cortex, (2007) 43, 350-358

cortex is one location candidate that stores internal
models (Blakemore et al., 2003; Sirigu et al.,2004). Furthermore, many studies have indicatedthat the parietal cortex is the most important partof a tool-use network. A neuropsychologicalinvestigation demonstrated that apraxic patientswith a left parietal lesion without paralysis oraphasia make spatial and temporal errors in tooluse as well as when pantomiming such gestures(Heilman et al., 1986). Pantomime has been
considered an effective test for apraxia since some
reports have shown a strong correlation between
pantomime and actual tool-use (Goldenberg et al.,
1998; De Renzi and Lucchelli, 1988). In functional
magnetic resonance imaging (fMRI) experimentson healthy subjects, Moll et al. (2000) and Choi etal. (2001) compared activity evoked by tool-use
pantomimes to that evoked by a hand movementtask devoid of any tool-use connotation by
matching the intrinsic properties of the movements
employed in the tool-use pantomime task. The
involvement of the intraparietal sulcus (IPS) was
shown in tool-use skill as well as cognitive
knowledge of tool-use. In positron emission
tomography (PET) experiments on healthy subjects,
Rumiati et al. (2004) compared activity evoked byimitating observed pantomimed action andpantomiming the use of an observed object, finding
activity in the dorso-lateral prefrontal cortex andthe dorsal and ventral parietal lobules. We knowthat pantomime is a useful test for apraxia.
However an apraxic patient with Alzheimer’s
disease had more difficulties in a pantomime task

Tool-use execution and imagery of tool-use

than in an actual tool-use task (Kato et al., 2001).
Some patients showed the opposite case; they
could pantomime but had difficulty in actual tooluse (Watson et al., 1986; Motomura et al., 1994).
As an example of a non-common tool-use
experiment using PET, Inoue et al. (2001) asked
subjects to manipulate a small graspable object
with a pair of tongs and found activity in theposterior parietal cortex ipsilateral to the handused. Their study excluded the cerebellum fromanalysis because it was outside of the transaxial
field of view. An activation study using PET inmonkeys (Obayashi et al., 2001) with an actual
tool-use task revealed IPS, the supplementary
motor area (SMA), the ventral part of the premotor,
the cerebellum, and some other areas of the brain.
However, no actual and common tool-use studies
with human subjects have been conducted.
Therefore, to understand the mechanisms ofactual tool-use we must determine the brain
activity involved when subjects actually use tools.
The aim of the present study is to investigate brain
activity related to the actual use of various
common tools and to infer the underlying neuronalmechanisms. The task for actual tool-use not only
evokes brain activity related to the cognitive
knowledge of tool-use and skills for tool-use but
also to muscle activity. Therefore, we investigatedand compared brain activity related to actual tooluse tasks with activity related to tool-use motor
imagery to isolate the cognitive components ofcommon tool-use.
MATERIALS AND METHODS
Subjects
Eight neurologically normal subjects (23-39
years old; three females and five males)
participated in the experiment. Each subject
received explanation of the experiment and signed
an agreement. The protocol was approved by the
Ethics Committee of ATR. All participants wereright-handed according to the Edinburgh
Handedness Inventory (Oldfield, 1971) and hadnormal or corrected-to-normal vision.

351
TABLE I

Tool and manipulation for tools
Tool

Manipulation

Brush
Chopsticks
Paper clip
Comb
Screwdriver
Fork

Brushing something hair-likePicking up a bead using chopsticks
Clipping papers together with a clip
Combing out something hair-like
Turning a screw
Threading beads using a fork like rollingup spaghetti
Hammering a nailCutting clay with a knife
Adjusting a nut and turning it
Drawing something on a piece of paper
Turning a nut with pliers
Mimicking the sawing of a board by
making a little cut
Cutting a sheet of paper
Catching beads using a spoon
Cleaning a small hole
Turning a nut with a wrench

Hammer
Knife
Monkey wrench
PencilPliersSaw
Scissors
Spoon
Toothbrush
Wrenchsame way it was used in the tool-use execution
condition. In this condition, the subjects did notactually manipulate the tools but held them andlooked at the target (e.g., paper) in the samemanner as in the execution condition. In the third
condition, a control condition, subjects were asked
to hold the tool without imagining its use while
keeping their eyes open and maintaining the same
hand and arm posture.
Figure 1 shows an instance of tool-use in an
execution task. Subjects lied on the bed of the
fMRI scanner and looked at their own hand andthe tool through two mirrors that were combined toremove all reversal or inversion of the visual
image.
Each condition lasted 30 seconds. The
transitions and type of conditions were indicatedby a sound stimulus of different frequency
presented using Presentation Version 0.42 from
Neurobehavioral System Execution and imagery
conditions were always followed by the control
condition. Each condition was repeated nine times
per session (eighteen minutes). The order of theexecution and imagery conditions was randomized.
At the beginning of the session the subject was

Tasks
There were three conditions in this experiment.
In the execution condition, subjects were asked touse one of sixteen common tools along with anappropriate object with visual feedback (e.g., using
scissors to cut a sheet of paper). All tools andtarget objects for the tools are given in Table I.
Subjects were instructed to use all tools with theright hand and taught the operation of each tools;
then they practiced with each tool a few times
before fMRI scanning. In the imagery condition,subjects were asked to imagine using a tool theFig. 1 – Example of a task situation (chopsticks).

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Fig. 2 – Comparison of brain activity between tool-use
motor imagery (yellow) and actual use (blue). Images a, b, c,and d show sagital sections at the black lines in left panels; x, y,
and z in the figure indicate MNI coordinates. The typicalstructural image of one of the eight subjects was used.

handed a tool that did not change during thesession. Thus, the subjects underwent 16 sessions
for the 16 tools. The sessions were conducted overa few days, and the order of tools was
randomized.
MRI Acquisition
The 1.5T MRI scanner (Shimadzu-Marconi
Magnex ECLIPSE 1.5T) of the ATR brain imaging
center was used to obtain blood oxygen level
dependent (BOLD) contrast functional images.
Images weighted with the apparent transverse
relaxation time (T*2) were obtained with an echoplanar imaging sequence (repetition time = 6 sec,echo time = 50 msec, flip angle = 90°). We
acquired 182 sequential whole-brain volumes (64 ×
64 × 64 voxels at 3 × 3 mm isotropic resolution) ineach session. High-resolution anatomical images ofall subjects were also acquired with a T1-weighted
sequence.
MRI Analysis
We used Statistical Parametric Mapping 99(SPM99) software for image processing andstatistical analysis. The first two volumes of images
were discarded to allow for T1 equilibration, while
the remaining 180 image volumes were realigned
to the first volume. T2 and T1 structural images
were coregistered to the average realigned echoplanar imaging (EPI) image and then spatiallynormalized to the Montreal Neurological Institute(MNI; Montreal, Canada) reference brain. The
typical structural image of one of the eight subjects
was used in Figure 2. The structural images used in
Figure 3 were constructed from the normalized T1

Fig. 3 – Distribution of t-value-weighted centroid ofactivation coordinates in tool-use (blue) and imagery (yellow)
conditions. Right figure shows expansion of square area in left
figure and coordinates.

averaged over all subjects. The normalized EPI
images were spatially smoothed with a Gaussian
kernel with a 6 mm full width at half maximum(FWHM). To remove the drift component of thesignal change carried with time, high-pass filtering
was applied. The voxel time series was temporallysmoothed with a Gaussian filter (FWHM of 4
seconds). Parameter estimates for the imaging andexecution trials were determined by fixed effectanalysis for each subject using a boxcar function
convolved with the hemodynamic response
function (HRF). The model was designed toestimate the main effects of each condition (16tools of execution and imagery).
ANALYSIS
Analysis of Execution and Imagery Conditions
Using fixed effect analysis, we investigatedactivity related to the execution and imagery
conditions for each subject across the sixteen tools.
Then the main effect of the imagery and execution
condition data were independently used for arandom effect analysis to investigate activities
common to all subjects (random effect analysis p <.001 uncorrected).
Analysis of Activity Positionin Right Upper Cerebellum
To investigate activity specific to each tool, we
conducted subtraction analysis (imagery minus
control and execution minus control) across the
subjects separately for each tool (fixed effectmodel p < .05 corrected). Because we did notdetect any significant activity, we excluded theSAW tool from this analysis. We limited the region

Tool-use execution and imagery of tool-use

353

TABLE II

Coordinates and t-values of activation peaks for imagery and execution condition as revealed by random effect analysis(p < .001 uncorrected). Regions of ten highest t-value peaks are listed
Anatomical description

Laterality

x

y

z

t-value

Precentral
Precentral
Postcentral
Supplementary motor area (SMA)
Supplementary motor area (SMA)
Pars opercularis
Posterior part of intraparietal sulcus (IPS)
Posterior part of intraparietal sulcus (IPS)
Angular gyrus
Lobule VI

L
R
L
L
L
R
L
L
L
R

– 38
48
– 46
– 12
–8
56
– 26
– 26
– 50
32

0
10
– 28
4
–6
12
– 72
– 58
– 64
– 62

50
48
52
66
74
–6
54
52
42
– 28

8.37
6.18
7.17
7.55
5.99
7.60
8.37
6.07
7.96
6.18

Precentral
Posterior part of IPS
Inferior parietal
Supramarginal
Middle occipital
Inferior occipital
Inferior temporal
PutamenLobule IV, V
Lobule VI

L
R
R
R
L
R
R
L
R
L

– 30
26
40
58
– 46
36
52
– 30
18
– 20

– 12
– 66
– 48
– 22
– 76
– 92
– 64
– 20
– 48
– 60

66
52
54
26
–2
– 10
–4
0
– 24
– 32

17.12
8.63
12.74
9.00
9.70
8.36
14.12
9.03
14.87
10.30

Imagery
Central region
Frontal lobe
Parietal lobe
Cerebellum
Execution
Central region
Parietal lobe
Occipital lobe
Temporal lobe
Subcortical area
Cerebellumof interest to the right cerebellum superior to thehorizontal fissure, since we consistently foundactivities in this area over all tool conditions.
Based on these results, we calculated a t-valueweighted centroid of activation for each tool andinvestigated the centroid distributions.
RESULTS
Activation Differences between Imagery
and Execution Conditions
Figure 2 shows significant activation (t > 4.79,
p < .001) related to the execution condition (blueregions) and the imagery condition (yellow
regions). Pink circles indicate a blue and yellow
mosaic, i.e., overlap between the imagery andexecution conditions.
Regions activated only in the imagery condition
were found in the left premotor area and right pars
opercularis, while regions activated only in theexecution condition were found in the primary
motor area, the left sensory area, the thalamus, thepars opercularis, and bilaterally in the cerebellum.
Overlap between the imagery and execution
conditions was found in regions of the left motorarea, the SMA, the parietal area, prominently inthe posterior IPS, and in the right cerebellum(Figure 2e).
In the left panel of Figure 2e, the redline indicates the primary fissure in the
cerebellum. Activation related to the execution
condition was only located anteriorly to theprimary fissure, whereas the overlap was locatedposteriorly to the fissure. All coordinates are listed
in Table II.

Positional Relationship between Imagery
and Execution Conditions
For all tools and subjects, control condition
activation was subtracted from the activation of thetool-use imagery and execution conditions to assess
the brain activity of imagery and execution,
respectively (p < .001 uncorrected). Figure 3 shows
the t-value-weighted centroid of activation for eachtool projected to horizontal and coronal planes.
Yellow and blue markers indicate the centers
for the imagery and execution conditions,
respectively. Corresponding markers are connectedby a gradation line that is colored yellow and blueon the imagery and execution sides, respectively.
As shown in Figure 3, imagery activation is
located more laterally and posterior relative to the
execution activation, which is located more
medially and anterior.
Coordinate Distribution of the Upper Right Partof Cerebellar Activity
First, we calculated the coordinates of the tvalue-weighted centroid of activation. Then, we
calculated the distance between these coordinates
and the fourth ventricle, located in the middle ofthe cerebellum, and averaged these distances over
all tools separately for each subject. Finally, weused a signed rank test to examine for significant
difference in the distances averaged across subjects
between the imagery and the execution conditions.
Figure 4 shows the distance of the imagery
condition (left) and the execution condition (right)
from the forth ventricle located in the center of thecerebellum. According to the signed rank test, thedistance in the imagery condition was significantly

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Satomi Higuchi and Others

[min]
60
p < .05
50
40
30
20
10
0

Imagery

Execution

Fig. 4 – Distance from fourth ventricle to coordinates of tvalue-weighted centroid of activation coordinates in tool-use
execution and imagery tasks.

larger than in the execution condition (p < .05),
which is consistent with observations that, on
average, imagery activation is located morelaterally and posteriorly.

Fig. 5 – Relationship between t-values-weighted centroid ofactivation coordinates in tool-use execution and imagery
conditions.

Analysis of Relation between Imagery
and Execution Conditions

Positional Relationship of Tools in the Right
Upper Cerebellum

Activation for each tool across subjects wasinvestigated in the right upper part of thecerebellum using the same method as in section“Analysis of activity position in the right upper
cerebellum”. In this limited area, the first principal
component of all coordinates from both tasks foreach tool was calculated. Then all coordinates were
projected to the first principal component, and the
distances between a given point and projected
coordinates were calculated. These distance data
were categorized into the execution and theimagery conditions. The coordinates in theexecution condition group and in the imagery
condition are plotted on the x and y axes of Figure
5, respectively. If each pair of tool coordinates
is located along the primary fissure boundaryline, the first principle component for thesecoordinates will be located near the primaryfissure. If execution and imagery condition
coordinates are located at a similar location, they
will be projected to a closer position and plotted on
a 45° sloped line.
Figure 5 shows the plots of the projected
coordinates of the t-value-weighted centroid ofactivation in the tool-use imagery task as a valueof the x axis and those in the tool-use execution
task as the value of the y axis. The dotted line in
the figure indicates a 45° slope, and the solid line
indicates the principal component vector. The
correlation coefficient was .5497, which issignificantly larger than zero (p < .05). The
coordinates of the t-weighed centroid of activationin the imagery and execution conditions were thus
located spatially close to each other.

In this section, we show the analysis results ofthe distribution of activities by tool in the rightupper cerebellum when subjects imagined tool-use.
First, we examined brain activity separately for all
tools and subjects (fixed effect model p < .001
uncorrected). The distances of t-value-weighted
centroid of activation were calculated from thefourth ventricle to these coordinates. Second, weinvestigated the order of distance in each subject
and compared it to the order obtained from thegroup-analysis activation map, as shown in Figure
4. In this case, since there was no significant
activation evoked by the SAW tool, it was
excluded from the analysis. Correlation between
individual and group order was examined by
Spearman rank correlation coefficient. Four of theeight subjects showed marginally significant
correlation between the order of the distance fromthe fourth ventricle for each tool (p < .10),
suggesting similarity in the spatial distribution of
the tools.
DISCUSSION
Comparison with Previous Tool-Use Studies
Action or grasping related brain circuits havebeen studied in macaque monkeys. The
connectivity between the parietal cortex andinferior frontal areas has been well-studied (Matelli
and Luppino, 2001). We also found activity in theparietal cortex especially in the posterior IPS andin the inferior frontal region in the case of tool-use.

Tool-use execution and imagery of tool-use

Activity in the inferior frontal region was found in
the left hemisphere only during execution condition
and in the right hemisphere only during imagery
condition (Figures 1c and 1d). However, with alower threshold (p < .005 uncorrected), bilateral
activities can be found in both imagery andexecution conditions. While (Johnson-Frey et al.,2003) have shown the importance of this region forgrasping, our experiments showed that this region
was more activated during test conditions than thecontrol condition of grasping. Therefore, we
believe this region is involved in tool-use functions
and not just grasping. Tamada et al. (1999)
investigated the learning of a new tool (a rotatedcomputer mouse) and found activity in the inferior
frontal region when a rotated computer mousecondition was contrasted with a normal computer
mouse condition. In Tamada et al.’s (1999) and ourexperiments, both the test and control conditions
had a grasping component, so that inferior frontal
region activity needs to be interpreted as acognitive function, such as knowledge of how thetool works. This cognitive function cooperates with
tool-use in the absence of motor components;
simply imaging the use of a tool is sufficient toactivate the inferior frontal region. Moreover,cortical activity in the left inferior frontal region
found during the execution condition in the present
study was localized laterally in comparison to theactivities reported by Tamada et al. (1999),
suggesting that spatial localization may be different
for novel and common tools.
As shown in Figures 2a and 2b, activation near
the left IPS of the parietal lobule is consistent withfMRI studies that showed left IPS activation in apantomiming task (Moll et al., 2000). Similarly inanother study, the left IPS was activated both inleft-hand and right-hand tool-use pantomime (Choiet al., 2001). As in a PET study, the dorsal lateralparietal cortex was activated during thepantomiming of object use (Rumiati et al., 2004).
The present result is consistent with studies
conducted on nonhuman primates (macaquemonkeys) using complex tools (Obayashi et al.,2002) and intermanual transfer of tool-use(Obayashi et al., 2003), which reported significant
activities in the left IPS and cerebellum. As shownin Figure 2e, we found activity in the cerebellum.
The cerebellum has not been studied as much as
the cerebral cortex because of such technicalproblems as the limited transaxial field of view of
PET. In addition, it is difficult to control muscle
activity and sensory input. To differentiate activity
from muscle and sensory input and activity fromtool-use, our imagery condition had no changes ofmuscle movement during the task. Recent studies
have shown that the cerebellum is also important
for tool-use (Obayashi et al., 2001; Imamizu et al.,2000, 2003), and other experimental data and theclinical studies have shown that the lateral part ofthe cerebellum has various cognitive functions

355(Grafman et al., 1992; Parsons et al., 1995). The
one of reports related to cerebellar activity, Parsons
et al. (1995) showed parietal, premotor andcerebellar activities in their paradigm which
involves deciding whether a hand is left or right.
This decision is accomplished by the subject only
by “thinking” of that hand as it was his/her own
hand. It is by all means a simulated motor act, andcould be done without actually employing tools inthe scanner. The activity shown by Parsons et al.
(1995) may relate to the internal model of themental rotation of the hand which calculates input
and output of the hand movement. On the other
hand, our experiment showed that the cerebellum isalso activated by imagery of tool-use. The activity
in the cerebellum could be related with tool-use as
other previous studies (Obayashi et al., 2001;
Imamizu et al., 2000, 2003) or imagery of the hand
movement as shown in Parsons et al. (1995). Thus
not only parietal, premotor, primary motor areas
but also cerebellum may be involved in both
“imagery of object usage” and “object usage”.
Activity reported from a PET experiment on
using new tools was found in the right IPS area
(Inoue et al., 2001), which is inconsistent withother pantomime and novel tool-use experiments aswell as this experiment. We believe that the left
IPS is an important region for tool-use, and theirfinding of right IPS activity might be caused bydifferent experimental conditions, such as a noncommon tool-use experiment.
In our tasks, the control condition was toolholding, which might have implicitly activated thesame brain regions as tool-use imagery. We cannot
reject this possibility. But even if the intended andimplicit imagery of the control condition share thesame brain functions, as Grezes et al. (2003) haveshown, we found significantly larger activity intool-use imagery tasks compared with tool-holding
tasks. Furthermore, the subjects were asked tosimply hold the object and no imagery was
required during the tool-holding condition, so toolholding was a suitable control for tool-use imagery.
Therefore, most of the difference between theimagery and control conditions was imagined or
not imagined tool-use.
Pantomime is a traditional test for Ideomotor
Apraxia. The left posterior parietal lesions,
including the intra parietal sulcus, often lead
Ideomotor Apraxia (Heilman et al., 1986, 1997;
Goldenberg, 2003). However, left parietal stroke
patients showed improvements in actual tool
performances compared to pantomiming use (Wadaet al., 1999). In apraxia patients, the dissociation of
automatic and voluntary tool-use is known as
automatic voluntary dissociation. The result that a
subject who has damage to the left parietal cortex
can still use the actual tool indicates that the leftparietal cortex is not always involved in the actual
tool-use process. Pantomime and actual tool-use
appear to involve different mechanisms.

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Satomi Higuchi and Others

Pantomime requires a more intentional process
while actual tool-use can also be carried out atleast partly by an automatic process.
Consciously accessing knowledge about tooluse might be distinct from the automatic skills
related to tool-use, as suggested by a hypothesis of
the automatic-voluntary dissociation first describedas Baillarger-Jackson principle (Jackson, 1884). He
showed a patient who could not make the sign ofthe cross at the clinic but did so automaticallywhen entering a church. In this case, the patient
had an internal model for signing the cross, but
could not voluntarily retrieve it in an arbitrary
situation and could only automatically retrieve it.
Sirigu et al. (2004) proposed that internal models
for voluntary (self-initiated) actions exist in theparietal cortex. Signing the cross at the clinic is anintentional and voluntary action, and thus thisfunction might be located in the parietal cortex.
Hanakawa et al. (2003) reported the relationshipbetween the accuracy of motor imagery andactivity in the left IPS and superior precentral
regions. Since such accuracy may relate to howmuch the subject intentionally concentrates on
doing so, it could be strongly related to intensity ofintentional efforts. Accordingly, Sirigu et al.’s(2004) internal model could be closely related toconscious actions, which at least partiallynecessitates intentional processes. In general, thisintentional process might also increase theintentional visual (imagery) process more than theautomatic processes. Recent vision research
revealed IPS activity when a voluntary saccades
task was contrasted with a reflexive (automatic)
saccades task using fMRI (Mort et al., 2003).
When contrasting reflexive with voluntary tasks,
IPS was not activated. So, we speculate that theparietal cortex could be more related to internal
models of intentional processing than to those of
automatic processing.
Distribution of t-Value-Weighted Centroidof Activation Coordinates
Figure 3 suggests that tool-use execution
activation is processed in the superior part of thecerebellum and that tool-use imagery tends to belocated in the more inferior and lateral parts. The
distance from the fourth ventricle in the imagery
condition was larger than the execution condition(Figure 4). Furthermore, we found significant
correlation in the distances of the projected pointon the principle vector of the t-value weighed
centroid coordinates between imagery andexecution conditions (Figure 5). These results
support the hypothesis that the cerebellum is
involved in non-motor functions. Relative to the
cerebellar study, the brain activity in both
movement and imagery processing were located in
the posterolateral part of the cerebellum (Naito etal., 2002). This study also suggests that a positionalcorrelation exists between the imagery andexecution activation sites. Regarding the distancefrom the fourth ventricle for each tool, similarity
was found in the order of tools among the subjects,
as revealed by Spearman rank correlation
coefficient. This similarity of distances wasprominent in near and far coordinates for half ofthe subjects. These results suggest that tool-use
skills and/or cognitive knowledge of tool-use are
acquired in similar distribution, even though there
is a large difference between subjects who havedifferent familiarity of tool-use or different skill
acquisition.
We used an extended tied mixture model torepresent the spatial distribution of the brain
activity that showed that cerebellar activity can be
explained with six clusters (Furukawa et al., 2002).
In Grodd et al. (2001), most of the clusters showedsimilar somatotopic topography of hand and wrist
muscle control while two other clusters werelocated in the bilateral region near the posterior
superior fissure, which are not included in Grodd
et al.’s (2001) topography map. Therefore, thesetwo clusters were considered to be related to theinternal model of tool-use rather than simple
muscle control. One cluster related to the internal
model in the right cerebellum was located moreposterior and more lateral to the cluster for thehand muscle control area. In Figure 5, “chopsticks”
and “scissors”, which require complex finger
controls, were projected in a closer region. At thesame time, “screwdriver” and “pliers”, which
require wrist controls, were projected in a closer
region. In the horizontal section shown in Figure 3,“screwdriver” and “pliers” were located moreanterior than “chopsticks” and “scissors”. These
position relationships are compatible with Grodd etal.’s (2001) topography map that showed that thewrist is located more anterior than the finger
control center. So we speculate that internal models
are engaged for the related muscle control areas of
the hand, the fingers, and the wrist, and they will
be shifted near the location of the hand muscle
control center in the cerebellum. This could be
one reason why activity related to the internal
model was similar among subjects: the muscles
used should be identical between subjects for eachtool.
On the other hand, the organization of internal
models in the cerebellum seems also related tofamiliarity because cerebellar activity was
associated with novel tool-use manipulation(rotated computer mouse manipulation) by hand
(Imamizu et al., 2000). This novel tool internal
model activates more lateral area than activity
associated with common tool-use in the present
experiment. Therefore, a newer internal model
might be acquired in more lateral parts of thecerebellum and then gradually move to the more
medial and anterior area where the action- or
movement-related muscle control centers exist.

Tool-use execution and imagery of tool-use

As mentioned above, some studies exist which
examine cerebellar function related to tool-use, but
generally the cerebellum has not been considered
an important site for tool-use especially in clinicalstudies. Clinical studies of tool-use have often
examined apraxic patients, where apraxia is definedby tool-use difficulty without paralysis. However,most apraxic patients have damage to the leftparietal cortex, while cerebellar-associated apraxia
is rare or non-existent Because the internal models
for tool-use are located close to the muscle control
center in the cerebellum, it would be highly
unlikely to have selective damage to the tool-use
internal models in the cerebellum without also
damage to the general motor control areas. This
would result in more severe motor deficits and nolonger considered apraxia. Thus, it is reallydifficult to find an apraxia patient with cerebellum
damage. Another reason for the lack of clinicalstudies relating the cerebellum to tool-use, is that
if the cerebellum has a particular role inautomatic tool-use (see discussion section),then patients may still be able to use tools
intentionally via the parietal pathway. Therefore,
because the behavior at the clinic is mostly
intentional action for patients, previous patientstudies may have ignored any deficit in automatic
tool-use.
Network Related to Tool-Use in Brains
Tamada et al. (1999) reported functional
connectivity between the cerebellum and pars
opercularis or pars triangularis in tasks that require
the acquisition of internal models. We also foundactivity in the pars opercularis or pars triangularisand the cerebellum, so these activities may also befunctionally connected, as in Tamada et al.’s (1999)
study.
It seems that both parietal and cerebellum
networks exist for tool-use. Parietal activity
confirms the neuropsychological findings from
apraxia, and cerebellum activity confirms otherexperiments related to tool-use. Perhaps theexistence of parallel networks could explain
automatic-voluntary dissociation in apraxia (i.e.,parietal automatic and cerebellar intentional
networks). During tool-use, even when a subject
automatically controls the tool, at some point
he/she begins to control that tool intentionally,making slight adaptations to the surrounding
environment. We don’t know if the parietal andcerebellum regions actually work cooperatively or
simply in parallel but at least it seem there are two
ways for tool-use. Interestingly, the existence of abrain network connection between the cerebellum
andparietalregionswasreportedinneurophysiological and anatomical studies (Clower
et al., 2001, 2005). These input-output relationships
between parietal and cerebellum regions could be
used in tool-use mechanisms.

357

Acknowledgments. This research was supported in partby a contract with the National Institute of Information
and Communications Technology (NICT) entitled“Research on Human Communication”. We would like tothank Dr. Thierry Chaminade for his perspicacious
comments and ideas.
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(Received 6 October 2004; accepted 25 April 2005)

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Satomi Higuchi, Department of Psychology, Lancaster University, Lancaster, LA1
4YF, UK. e-mail: s.higuchi@lancaster.ac.uk