-1-

Accepted manuscript
Imamizu H, Kawato M (in press) Cerebellar Internal Models: Implications for the
Dexterous Use of Tools. Cerebellum
Request for a reprint PDF to imamizu@gmail.com
5

Cerebellar internal models: implications for dexterous use of tools

10

Hiroshi Imamizu 1, 2 and Mitsuo Kawato 2

1. National Institute of Information and Communications Technology (NICT)
2. Advanced Telecommunications Research Institute International (ATR)

15

Address correspondence to Hiroshi Imamizu
20

2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan
Telephone: +81 774 95 1220, Fax: +81 774 95 1236
e-mail: imamizu@gmail.com

-2-

Conflict of interest statement
The authors declare that they have no potential conflict of interests.

-3-

Abstract
Humans have remarkable abilities in the dexterous use of tools to extend their physicalcapabilities. Although previous neuropsychological and functional neuroimaging
studies have mainly focused on the contribution of frontal-parietal cerebral networks to
5skills for tool-use, dense anatomical and functional connections are known to exist
between the frontal-parietal regions and the lateral cerebellum, suggesting that the
cerebellum also supports the information processing necessary for dexterous use oftools. In this article, we review functional and structural imaging studies reporting thatthe cerebellum is related to learning acquisition of neural mechanisms representing

10

input-output properties of controlled objects, including tools. These studies also suggestthat such mechanisms are modularly organized in the cerebellum corresponding todifferent properties of objects such as kinematic or dynamic properties and types oftools, and that they enable humans to flexibly cope with discrete changes in objects andenvironments by reducing interference and combining acquired modules. Based on

15

these studies, we propose a hypothesis that the cerebellum contributes to skillful use oftools by representing the input-output properties of tools and providing information on
prediction of sensory consequences of manipulation done with the parietal regions,which are related to multisensory processing, and information on the necessary control
of tools with the premotor regions, which contribute to the control of hand movements.

20

-4-

Introduction
Humans are unique in having remarkable abilities of manual dexterity and conceptual
knowledge of tool use in comparison to other animals. Many neuropsychologicalstudies of patients with brain lesions have suggested two distinct types of functional
5

components in human faculties for tool use: One is the skill necessary for manipulating
tools, and the other is the semantic/conceptual knowledge about tools representing therelationships between tools and their functions [1, 2] (for a recent review, see [3]).
Although brain regions related to each type of component cannot be uniquelydetermined, a meta-analysis of a large number of functional neuroimaging studies (3510studies including 64 paradigms) has shown that the brain network consisting of the
premotor and parietal regions supports skills for tool use while the network consistingof the inferior frontal gyrus and the middle temporal gyrus contribute tosemantic/conceptual knowledge about tools [4]. Neurophysiological studies in monkeys
found activity changes in parietal neurons after acquisition of skills to use a rake to15

retrieve food rewards (for a recent review, see [5]). The front-parietal network is known
to be involved in generating and mediating skilled movements associated with tool use
as well as other behaviors that do not involve external objects (e.g., gestures). Althoughmany neuropsychological and neuroimaging studies on tool use have focused on
neocortical regions, some works have proposed the existence of dense anatomical and20functional connections between the premotor or the parietal regions and the lateral
cerebellum [6-8], suggesting that the cerebellum also contributes to skilled behaviors

-5-using tools.
It has been suggested that neural mechanisms representing input-output propertiesof controlled objects, including our own bodies, enable us to skillfully manipulate
objects in a predictive fashion, and such mechanisms are called internal models. The
5central nervous system (CNS) is thought to use two forms of internal models: an inverse
internal model and a forward internal model. In the context of arm-reaching movements,
forward models transform efference copies of motor commands into the resultant
trajectory or sensorimotor feedback [9-11]. Inverse models transform an intended
motion of the arm into the motor commands used to realize the motion [9].

10Although inverse and forward models were originally proposed in studies oncontrol of muscle-skeletal systems such as arms and the eye-movement system, many
behavioral studies have suggested that the CNS applies such models to manipulation of
external objects [12] as well as tools [13]. In the case of external objects, forward
models predict how the object will moves from ones manipulation of the object while15

the inverse models predict manipulation necessary for realizing intended motion of theobject. For example, skilled manipulation of a computer mouse requires the ability to
predict how the cursor will move on the screen if the mouse is moved in a particular
direction (forward model) and how the mouse should be moved in order to move thecursor to a particular position on the screen (inverse model). Namely, we have to learn

20

input-output properties of tools for skillful use of them. Learning such properties isnecessary for use of more common and simple tools than a computer mouse such as

-6-scissors. The movement direction of the grips of scissors is different from that of the
cutting point (Fig. 1A, blue and red arrows) as well as the movement distance of thegrips is different from that of the cutting point. Therefore, we have to learn the
kinematic relationship (input-output property) between the motion of the grips and that
5of the cutting point for skillful use of scissors. Of course, learning kinematic propertiesof tools is unlikely a unique component for skillful use of tools, but it is an importantcomponent for various kinds of tools.
We have been involved in functional imaging studies on how internal models ofsuch kinematic properties of tools are newly acquired in the cerebellum as well as

10

studies on the models’ modular organization, which is important for flexible use of tools.
We will review a series of our studies on the cerebellum and issues related to learning
kinematic and dynamic properties of tools.

Cerebellar activity during acquisition of an internal model for a new tool
We examined whether activities reflecting an internal model of kinematic properties of15

a new tool and its acquisition process could be visualized in the human cerebellum byfunctional magnetic resonance imaging (fMRI)[14]. Subjects manipulated a computer
mouse in an MR scanner so that the corresponding cursor followed a randomly moving
target on a screen (Fig. 1B). By changing the relationship between the mouse motion
and the corresponding cursor motion, we made subjects learn a novel input-output

20

property of the mouse in a test condition. That is, the cursor appeared in a position that

-7-

was rotated 120˚ around the center of the screen (a rotated mouse; Fig. 1C):,
where (X,Y ) is a cursor position in the screen coordinates and (x, y) is a mouseposition in the hand coordinates. This novel relationship between the mouse and cursor
5

is defined in external coordinates (the screen coordinates and the hand coordinates) thatare different from intrinsic (body) coordinates such as joint angles of the wrist and theelbow. Thus, subjects are expected to learn this relationship as external but not intrinsic
one. Actually, our previous behavioral study investigated how learning use of therotated mouse using one hand affects use of the same mouse using the other hand

10(inter-manual transfer of learning), and suggested that subjects learn this property asthat of an external object rather than that of their own body [15].
A baseline condition, where the mouse was not rotated (normal mouse), was also
tested. Each subject’s performance was measured by tracking errors, i.e., the distance
between the cursor and the target. We subtracted cerebellar activity in the baseline

15

condition from that in the test condition and found that the subtracted activity became
smaller as the tracking error in the test condition decreased (Fig. 2A). Based on thisresult and previous functional imaging studies reporting decreasing cerebellar activity
with learning [16, 17], we hypothesized that the activity was evoked by the error. To
test this hypothesis, we conducted a control experiment in which target velocity was20

increased in the baseline condition so as to equalize the error in this condition to that in

-8-

the test condition (an error-equalized experiment: Fig. 2B). This experiment was done
after training sessions (Fig. 2A). We could still identify significant activity when we
subtracted the baseline-condition activity from the test-condition activity in theerror-equalized experiment, suggesting that it was evoked not only by the error but also
5

by other factors.
Therefore, we investigated how activity changed during learning in 1) regions
where significant activity was identified in the error-equalized experiment (regions
enclosed by blue lines in Figs. 2B and 2C), and 2) regions where great activity wasobserved in the initial stage of the learning (regions enclosed by red lines in Figs. 2A10

and 2C). We also conducted correlation analysis between tracking error and brain
activity during learning, and found that regions where significant correlation was
identified was almost the same as those where great activity was observed in the initialstage (regions enclosed by red lines). The activity in these regions markedly decreased
as learning proceeded (middle panel of Fig. 2C). However, activity in the former

15

regions did not decrease so drastically (left panel) in comparison to the activity in thelatter regions, and its correlation with the error was not statistically significant,confirming that the former regions contain activity evoked by factors other than theerror. By subtracting an activity time course in the latter regions from that in the former
regions, we found that the activity unrelated to the error increased at the beginning and20

remained high during the training sessions (right panel). This activity was thought toreflect the acquired internal model representing the input-output property of the rotated-9-mouse. The remaining activity was mainly found around the posterior superior fissure(broken lines in Figs. 2B and 2C) in the lateral cerebellum.
Physiological correspondences to learning acquisition of internal models
Many studies have suggested that Purkinje cells play an important role in motor
5learning. Purkinje receive major inputs from parallel fibers and climbing fibers and thensend the output signals from the cerebellar cortex. A computational theory proposes thatthe cerebellum represents inverse internal models of musculoskeletal systems, whichcalculate the necessary feedforward motor commands from the desired trajectory’s
information [18, 19]. In this theory, climbing fiber inputs carry motor-command errors,

10

which are derived from the performance errors between the desired and actual
movements, and guide the learning acquisition of internal models by changing synaptic
efficacy. Purkinje cells are thought to learn to transform parallel fiber inputs into
appropriate feedforward motor commands [20, 21]. Thus, the complex spike of Purkinjecells activated by climbing fiber inputs reflects error signals, while the simple spike

15

activated by parallel fiber inputs reflects motor-command outputs [22, 23]. In light of
the uniform structure of neuronal circuitry in the cerebellum, we speculate that theabove learning schema also supports learning acquisition of internal models for tools
and external objects.
A hypothesis about physiological correspondence to changes in fMRI signals

20

during learning

-10-

The brain’s almost exclusive source of energy is glucose. Since most of the glucose isoxidatively metabolized, local oxygen consumption is proportional to local brain
activity, and increases local cerebral blood flow, which is reflected in increase in fMRI
signal [24-27]. Several studies indicated that the most energy-demanding process in the5cerebellar cortex is the restoration of ionic equilibrium after a complex spike with wideplateau potentials [28, 29]. Therefore, strong activity in the broad cerebellar regions at
the early stage of learning (middle panel of Fig. 2C) may reflect local energy
consumption due to a complex spike activated by climbing fiber inputs.
On the other hand, the increase in fMRI activity with learning (right panel of Fig.

102C) may reflect processes inducing various types of synaptic plasticity related tolearning acquisition of internal models. Long-term depression (LTD) of efficacy of theparallel fiber-Purkinje cell synapse is well known as a form of synaptic plasticity [30].
Intracellular calcium ion (Ca2+) and influx of Ca2+ into Purkinje cells in response toclimbing fiber input have been suggested to play an important role in inducing LTD [31,1532]. However, it is important for cells to maintain low concentration of Ca2+ for proper
cell signaling. Therefore, it is necessary to employ calcium pumps to remove the Ca2+,
and the pumps need energy consumption, which is expected to increases local oxygen
consumption, blood flow and fMRI signals. It has also been suggested that sustained
activation of intracellular signaling pathways is required for LTD: A theoretical [33]20

and an empirical [34] study indicated that a positive feedback loop, in which
mitogen-activated protein kinase and protein kinase C mutually activate each other,

-11-plays a pivotal role in LTD (see a reference [35] for review). Such sustained activationof intracellular pathways may be another factor of energy consumption related tosynaptic plasticity.
The other types of synaptic plasticity than LTD are also known such as a5long-term potentiation (LTP) [36, 37] and rebound potentiation [38]. Recently,
plasticity at parallel fiber-stellate/basket cell synapses leads to change in feedforward
inhibition onto Purkinje cells, and decreases excitatory postsynaptic potentials of
Purkinje cells evoked by parallel fiber inputs [39, 40]. A study indicated that a pair ofclimbing fiber input and inhibitory input from interneurons induces LTP. This study

10

suggests that a balance between excitatory inputs from parallel fibers and inhibitory
inputs from interneurons plays a crucial role in change in outputs from Purkinje cells[41]. So far the most detailed studies, including bioinformatics, have been conducted onmechanisms for LTD. However, the other types of synaptic plasticity, as above
mentioned, are probably related to intense energy-consumption processes such as influx

15

and pumping-out of Ca2+, activation of intracellular signaling pathways, gene
expressions and morphological changes. Such energy-consumption may be related toincrease in fMRI signals as learning proceeds.
Ogasawara and colleagues pointed out that local concentration of nitric oxide(NO) is critical for induction of LTD and for its input specificity [42]. They suggested

20

that the NO concentration contributes to regional specificity of internal models in the
cerebellum depending on context of behavior. On the other hand, Akgoren and-12-colleagues found that NO dilates blood vessels and increases blood flow in the
cerebellum [43]. At the late stage of learning, NO is expected to concentrate in
restricted regions where appropriate internal models are acquired and updated.
Therefore, blood flow and thus fMRI signal increase in those regions.

5

The above hypothesis about physiological and chemical correlates for changes infMRI signals during learning is based on results from various studies using different
animals, experimental methods including computer simulations. Therefore, it should be
ideally examined in a unified experimental setup that can simultaneously measure fMRI,
electrophysiological and chemical signals of animals during learning.

10

Modular organization of internal models for kinematic properties of tools
We further examined whether the remaining activity reflects the input-output propertiesof controlled objects by investigating whether the activity pattern changes depending on
the properties [44]. We asked subjects to learn another relationship between the cursor
motion and the mouse motion. The relationship was also kinematic, but the cursor

15

velocity

was proportional to the mouse position (a velocity-control mouse):.
Here, k is a constant value determined so as to equalize the difficulty of manipulation,measured by tracking errors, to that of the rotated mouse. Newly recruited subjects
sufficiently learned manipulation of both the rotated mouse and the velocity-control

-13-

mouse. Then, we measured their cerebellar activity when they manipulated each mouse(test condition) in comparison to when they manipulated the normal mouse (baseline
condition). Tracking error in the baseline condition was equalized to that in the test
condition by increasing the target velocity. By subtracting the activity in the baseline
5

condition from that in the test condition, we derived a map specific to each type ofmouse. Figure 3 shows activity map averaged across subjects for the rotated mouse(magenta regions) and that for the velocity-control mouse (cyan regions). Both types ofactivity were found near the superior posterior fissure, but the rotated-mouse activations
tend to be located more anteriorly and laterally than the velocity-control mouse10

activations. Because Figure 3 shows activity averaged across subject, a significant
overlap between the two types activity can be observed. However, the overlapping
volume was only 2.1% of the total activated volume when we measured the volume ineach subject’s activity map and then averaged. This suggests that the different tools
evoked activities in distinct locations, demonstrating that the activity pattern changes

15

depending on the input-output properties of controlled objects. By showing that distinct
regions in the lateral cerebellum help to represent different input-output properties, thisresult suggests the modularity and multiplicity of internal models [45, 46].

Modular organization of cerebellar internal models: kinematics versus dynamics
Although learning kinematic properties is important for dexterous use of tools, dynamic
20

properties such as mass, moment of inertia, and rigidity should be learned for use ofcommon tools such as scissors and a wrench, which dynamically interact with external

-14-

objects. To explore brain activity related to internal models that contribute tomanipulation of an object with complex dynamics, we measured activity when subjects
grasped a weighted flexible ruler and balanced it in an unstable equilibrium position(Fig. 4A) as an archetype of grasping an object with complex dynamics [47]. In a5baseline condition, subjects squeezed a foam ball (Fig. 4B) as an archetype of grasping
an object with simple dynamics. Subjects were instructed to compress the ball to a point
where the force was equal to the force applied to the ruler. This amounted tocompressing the ball until the thumb was close the fingers. By measuring
electromyogram, muscle activation was carefully matched between the two conditions

10

in amplitude and frequency. Extensive training was not required to perform these tasks
successfully. All subjects were given sufficient practice with each condition prior to theexperiment to master the control of each object. First, we contrasted activity whensubjects grasped an object (with complex or simple dynamics) with that in a rest
condition and found activity in the primary motor cortex, Brodmann area 6, and the15

cerebellum among motor related regions. Then, we investigated the difference inintensity of activation in these regions between the two grasping conditions. We foundstatistically greater activation in the complex condition than in the simple condition
only in the cerebellum, suggesting that the cerebellum represents an internal dynamics
model necessary for balancing the flexible ruler.

20

The cerebellar activity was mainly found in the anterior and posterior lobules of
the ipsilateral hemisphere to the grasping hand. More specifically, activated regions in

-15-

the anterior lobule were near the primary fissure (lobules V/VI) and more anterior to theposterior superior fissure around which activity related to internal models for the
kinematic properties were found. The activated region in the posterior lobule is theinferior part of the cerebellum (lobule VIII), and also significantly different from5regions related to internal kinematic models. These results suggest that distinct regionsin the cerebellum contribute to representing internal kinematic or dynamic models.
Cerebellar activity in the anterior lobule associated with learning dynamic
properties was also found in previous studies as follows. Adaptation to force field aswell as rotated (displaced) visual feedback is often used to investigate mechanisms for10

sensorimotor learning. Using positron emission tomography and a robot manipulandum
that exerts force to subjects’ hands during reaching movements, Shadmehr andHolcomb [48] investigated how brain activity changes as learning proceeds. They foundthat activity shifts from prefrontal regions to the premotor, posterior parietal andcerebellar regions when skills for interacting with the manipulandum become

15

consolidated and less fragile with respect to behavioral interference. The cerebellar
activity reported by their study was also near the primary fissure in the anterior part ofthe cerebellum ipsilateral to the moving hands.
Recently, a neuropsychological study [49] investigated the relationships between
regions of atrophy in a large number of patients with cerebellar degeneration and their20

performance during adaptation to kinematic perturbation (visuomotor rotation ofhand-position feedback) and dynamic perturbation (force field). Their results indicated-16-that atrophy in the anterior lobule is correlated more with impairment in adaptation to
dynamic perturbation while that in the posterior lobule is correlated more withadaptation deficit in kinematic perturbation, suggesting that adaptation to kinematic ordynamic perturbation is processed independently and relies on different cerebellar
5structures. Consistent with the above findings, a behavioral study has reported thathumans can independently learn dynamic properties of their own arms altered byweights and kinematic properties altered by the rotation of visual feedback of their hand
position, with little interference of each other [50, 51].

Modular organization of cerebellar internal models: common tools

10

We measured cerebellar activity of subjects using sixteen common tools (scissors, ahammer, chopsticks and so on) to investigate internal models for those tools [52].
Because it was impossible to create baseline conditions in which muscle activation was
precisely matched with that during manipulation of each tool, we asked subjects tomentally imagine using the tools without actual hand movements. Subjects lied in

15

supine position in an MR scanner and looked at their own hand and the tool through an
arrangement of two mirrors. In an actual-use condition, subjects were asked to use oneof the sixteen common tools along with an appropriate object (e.g., using scissors to cut
a sheet of paper). In an imaginary-use condition, subjects were asked to imagine using atool without making actual hand movements but while holding the tool. In a rest20

condition, subjects held the tool in their hand and looked at the object without moving
the hand or imagining using the tool.

-17-

Activity in the actual use condition in comparison to the rest condition was foundin both the anterior and the posterior lobules of the cerebellum across the tools but
activity in the anterior lobule was generally stronger than that in the posterior lobule. By
contrast, activity in the imaginary-use condition in comparison to the rest condition was
5observed laterally in the posterior lobule. That is, the activity in the actual use condition
was overlapped with that in the imaginary condition in the posterior lobule. Strong
activities found in the anterior lobule are probably evoked by activities of limb muscles
and sensory feedbacks. This speculation is consistent with the existence of somatotopic
representations in the anterior lobule (e.g., [53]). By contrast, activities observed mainly

10

in the posterior lobule during the imaginary-use conditions may reflect internal models
for use of the tools.
To characterize distributions of activity, we calculated t-value-weight centroids ofactivation for individual tools in both actual-use and imaginary-use conditions. Figure 4
shows the calculated centroids when subjects actually used individual tools (Fig. 4A) or

15

when they imagined using the tools (Fig. 4B) in comparison to the rest condition. Someof the centroids during the imaginary use are also found in the anterior lobule (e.g.,wrenches and a pencil). However, the centroids in the imaginary-use condition tend to
distribute more posteriorly and laterally than those in the actual-use condition. It wasdifficult to find how locations of the centroids are organized in the cerebellum but

20

distribution of the centroids in the imaginary-use condition suggest that internal models
contributing to skillful use of common tools are modularly organized, that is, different

-18-

parts of the lateral cerebellum contribute to the use of different tools.

Combination of acquired internal models
A benefit of modular organization of internal models is that it facilitates initial learningof objects and environments by combining stored modules. Many situations that we
5encounter are derived from combinations of previously experienced contexts. By
modulating the contribution of the outputs of the internal models to the final motor
command, an enormous repertoire of behaviors can be generated. In our previous
behavioral study, we made subjects learn a kinematic perturbation (visuomotor rotation),a dynamic perturbation (force field), and a combination of these perturbations [51].

10

When the subjects learned the combined transformation, reaching errors were smaller if
the subject first learned the separate kinematic and dynamic transformations. This result
suggests the ability of humans to combine internal models as needed, depending on thesituation.
Our functional imaging study [54] also suggests the ability of the CNS to combine

15

output signals from internal models. Subjects sufficiently learned to use 60º and 160º
rotated joysticks, where the cursor appeared in a position rotated 60º or 160º around thecenter of the screen. Then we investigated brain activity when subjects learned to use a
110º rotated joystick (an intermediate angle between 60º and 160º). In the early and latestages of learning the 110º joystick, we measured cerebellar activity specific to the 60º,

20

110º or 160º joystick according to the same method we used when measuring activity

-19-

specific to the rotated mouse or the velocity-control mouse (see above).
We hypothesized that the CNS combined output signals from internal models for
60º and 160º joysticks to cope with the novel 110° joystick in the early stage, while in
the late stage an internal model for the 110º joystick had been acquired, and activity of5internal models for 60º and 160º joysticks decreased due to the decreased necessity forthese models. We investigated activated volumes in the lateral cerebellum separately for
60º, 110º and 160º conditions across subjects (Fig. 6). Consequently, volumes for both
60º and 160º conditions were larger than that for the 110º condition in the early stage,suggesting that internal models for 60º and 160º joysticks largely contribute to subjects’

10

performance. However, the volumes for 60º and 160º conditions in the late stage
became small in comparison to the initial stage, and the volume for the 110º condition
became the largest. These results support the above hypothesis and suggest that the
CNS may be able to combine acquired internal models according to the degree ofacquisition of a new internal model.

15

We confirmed that performance levels of subjects, which were measured by a
tracking error, in the 60º and 160º conditions did not significantly change after the
training for 110º condition from those before the training despite decreased activity forthe 60º and 160º conditions. This may suggest that processing for the 60º and the 160ºconditions became more effective and need smaller regions after 110º training. Such20

reorganization of internal models is necessary for effective use of limited volumes of
the cerebellum but mechanisms for the reorganization are unknown and need further

-20-

studies.

Discussion
We comprehensively reviewed functional neuroimaging studies investigating how
internal models for tools are acquired in the cerebellum. First, we succeeded in
5visualizing changes in cerebellar activity during the learning acquisition of a new tool.
Our findings were consistent with a learning schema supported by physiological studies
on changes in synaptic efficacy of Purkinje cells. Then, we summarized studies on
differences in cerebellar regions corresponding to distinct input-output properties ofcontrolled objects and tools. These studies suggested multiplicity and modularity of

10internal models from several aspects: differences in kinematic properties, whether
properties are kinematic or dynamic, and types of common tools used. We believe such
multiplicity and modularity enable humans to flexibly cope with discrete changes inobjects and environments by reducing interference and combining acquired internal
models. Our behavioral and fMRI studies have found evidence indicating that the CNS

15

can combine acquired internal models depending on the situation.
In this review article, we focused on changes in cerebellar activity based on
previous neurophysiological and computational studies. Many studies have reportedchanges in whole-brain activity with sensorimotor learning when a force field alters
limb dynamics [48] or when a screen controlled by a computer program kinematically20

alters visual feedback of hand position [55] or joystick position [56]. These studies

-21-

identified different cerebral regions related to learning as a consequence of thedifferences in experimental methods (e.g. adaptation to a force field or altered visualfeedback; different effectors such as the arm, a computer mouse and a joystick; tracking
a continuously moving target or aiming at a static target). However, their results are
5

consistent with ours in that significant activity is found in the lateral cerebellum after
learning. These results suggest that the cerebellum is one of the regions where internal
models representing input-output properties of controlled objects are likely acquired.
Recent advances in structural imaging of the brain support a contribution of thecerebellum to dexterous use of tools. Using MRI and voxel-based morphometry, Quallo

10

and colleagues [57] investigated changes in brain structure in Japanese macaquemonkeys trained for six weeks to use a rake to retrieve food rewards. Their analysis
revealed increases in gray matter with improved rake performance of the monkeys. The
effects were significant in the superior temporal sulcus, second somatosensory area, andintraparietal sulcus in the right hemisphere. They also found significant bilateral

15

increases in the white matter of the lateral cerebellum (lobule VI near the posterior
superior fissure), in which our functional imaging studies on humans reported activity
related to kinematic internal models of new tools. Another structural imaging study
found correlation between rate of learning the use of a new tool and white-matter
microstructure in the human cerebellum: Della-Maggiore and colleagues [58] used20

diffusion-weighted MRI to estimate the fractional anisotropy (FA) reflecting
myelination of axons, axonal diameter, and packing density of axons. According to their

-22-

results, FA in regions near the posterior superior fissure positively correlated withindividual rate of adaptation to visuomotor rotation. They also found the same pattern ofcorrelation in the superior cerebellar peduncle that contains fibers connecting the
cerebellar cortex with the motor and premotor cortices.

5

As mentioned in the Introduction, many neuropsychological and functional
imaging studies have suggested that a brain network including the premotor and parietal
regions supports the skills needed for tool use. We speculate that primary skills are
acquired in the premotor-parietal network at an early stage of learning tool use.
However, after intensive training, humans gain the ability to use a tool very rapidly and10

precisely in a predictive fashion. At this stage, neural mechanisms predicting control ofa tool to realize desired performance and the sensory consequences of that control play
crucial roles in skillful use of tools. This is because sensory feedbacks of movements
are inevitably delayed by many factors, including delay for transmission of motor
commands from the brain to muscles, mechanical delay of a tool, and time for

15

processing sensory information. The cerebellum contributes to the skillful use of tools
by providing information about prediction of sensory consequences with the parietal
regions, which are related to multisensory processing, and about the necessary control
of tools by the premotor regions, which contribute to control of movements.
However, the above hypothesis about global networks contributing to skillful use

20

of tools is mainly based on studies of ours and others investigating of correlation
between behavioral and neuroimaging data. Such studies have limitations in-23-

examination of information represented in specific brain regions. Recent advances inmachine-learning techniques made it possible to non-invasively extract informationfrom various regions (e.g., [59-61]). Future studies that can extract neural information
simultaneously from multiple regions will enable us to elucidate the outlines of the5brain network supporting skillful use of tools as well as the functional differencesamong the regions, and to examine validity of the above hypothesis.

Acknowledgements
We would like to thank Miho Onizuka and Nicolas Schweighofer for helpful comments
10

on an earlier version of the manuscript.

-24-

References
1.

De Renzi E & Lucchelli F (1988) Ideational apraxia. Brain 111:1173–1185.

2.

Ochipa C, Rothi LJ, & Heilman KM (1992) Conceptual apraxia in Alzheimer's
disease. Brain 115 ( Pt 4):1061-1071.

5

3.

Johnson-Frey SH (2004) The neural bases of complex tool use in humans. Trends
Cogn Sci 8:71-78.

4.

Lewis JW (2006) Cortical networks related to human use of tools. Neuroscientist
12:211-231.

5.

Iriki A (2006) The neural origins and implications of imitation, mirror neurons andtool use. Current Opinion in Neurobiology 16:660-667.

10

6.

Clower DM, West RA, Lynch JC, & Strick PL (2001) The inferior parietal lobule isthe target of output from the superior colliculus, hippocampus, and cerebellum. J
Neurosci 21:6283-6291.

7.

Dum RP & Strick PL (2003) An unfolded map of the cerebellar dentate nucleus andits projections to the cerebral cortex. J Neurophysiol 89:634-639.

158.

Ramnani N (2006) The primate cortico-cerebellar system: anatomy and function.
Nat Rev Neurosci 7:511-522.

9.

Kawato M, Furukawa K, & Suzuki R (1987) A hierarchical neural-network model
for control and learning of voluntary movement. Biol Cybern 57:169-185.

20

10. Miall RC, Weir DJ, Wolpert DM, & Stein JF (1993) Is the cerebellum a Smith
predictor? Journal of Motor Behavior 25:203-216.
11. Wolpert DM, Ghahramani Z, & Jordan MI (1995) An internal model for
sensorimotor integration. Science 269:1880-1882.
12. Flanagan JR & Wing AM (1997) The role of internal models in motion planning25

and control: evidence from grip force adjustments during movements of hand-heldloads. J Neurosci 17:1519-1528.
13. Mehta B & Schaal S (2002) Forward models in visuomotor control. J Neurophysiol
88:942-953.
14. Imamizu H, et al. (2000) Human cerebellar activity reflecting an acquired internal

30

model of a new tool. Nature 403:192-195.
15. Imamizu H & Shimojo S (1995) The locus of visual-motor learning at the task ormanipulator level: implications from intermanual transfer. J Exp Psychol Hum-25-

Percept Perform 21:719-733.
16. Raichle ME, et al. (1994) Practice-related changes in human brain functional
anatomy during nonmotor learning. Cereb Cortex 4:8-26.
17. Flament D, Ellermann JM, Kim SG, Ugurbil K, & Ebner TJ (1996) Functional
5magnetic resonance imaging of cerebellar activation during the learning of avisuomotor dissociation task. Human Brain Mapping, 4:210-226.
18. Kawato M & Gomi H (1992) The cerebellum and VOR/OKR learning models [seecomments]. Trends Neurosci 15:445-453.
19. Kawato M & Gomi H (1992) A computational model of four regions of the10

cerebellum based on feedback-error learning. Biol Cybern 68:95-103.
20. Shidara M, Kawano K, Gomi H, & Kawato M (1993) Inverse-dynamics model eye
movement control by Purkinje cells in the cerebellum. Nature 365:50-52.
21. Gomi H, et al. (1998) Temporal firing patterns of purkinje cells in the cerebellar
ventral paraflocculus during ocular following responses in monkeys I. Simple

15

spikes. J Neurophysiol 80:818-831.
22. Kitazawa S, Kimura T, & Yin PB (1998) Cerebellar complex spikes encode both
destinations and errors in arm movements. Nature 392:494-497.
23. Kobayashi Y, et al. (1998) Temporal firing patterns of purkinje cells in the
cerebellar ventral paraflocculus during ocular following responses in monkeys II.

20

Complex spikes. J Neurophysiol 80:832-848.
24. Moonen C & Bandettini P (2000) Functional MRI (Springer-Verlag, NY).
25. Smith AJ, et al. (2002) Cerebral energetics and spiking frequency: theneurophysiological basis of fMRI. Proc Natl Acad Sci U S A 99:10765-10770.
26. Aubert A & Costalat R (2002) A model of the coupling between brain electrical25

activity, metabolism, and hemodynamics: application to the interpretation offunctional neuroimaging. Neuroimage 17:1162-1181.
27. Kida I & Hyder F (2006) Physiology of functional magnetic resonance imaging:energetics and function. Methods Mol Med 124:175-195.
28. Hounsgaard J & Yamamoto C (1979) Dendritic spikes in Purkinje cells of the30

guinea pig cerebellum studied in vitro. Exp Brain Res 37:387-398.
29. Hockberger PE, Tseng HY, & Connor JA (1989) Fura-2 measurements of cultured
rat Purkinje neurons show dendritic localization of Ca2+ influx. J Neurosci
9:2272-2284.

-26-

30. Ito M, Sakurai M, & Tongroach P (1982) Climbing fibre induced depression ofboth mossy fibre responsiveness and glutamate sensitivity of cerebellar Purkinjecells. J Physiol (Lond) 324:113-134.
31. Doi
5T,

KurodaS,

MichikawaT,&

KawatoM(2005)

Inositol

1,4,5-trisphosphate-dependent Ca2+ threshold dynamics detect spike timing in
cerebellar Purkinje cells. J Neurosci 25:950-961.
32. Tanaka K, et al. (2007) Ca2+ requirements for cerebellar long-term synaptic
depression: role for a postsynaptic leaky integrator. Neuron 54:787-800.
33. Kuroda S, Schweighofer N, & Kawato M (2001) Exploration of signal transduction

10

pathways in cerebellar long-term depression by kinetic simulation. J Neurosci
21:5693-5702.
34. Tanaka K & Augustine GJ (2008) A Positive Feedback Signal Transduction Loop
Determines Timing of Cerebellar Long-Term Depression. Neuron 59:608-620.
35. Ogasawara H & Kawato M (2009) Bistable Switches for Synaptic Plasticity.

15

Science Signaling 2:pe7-pe7.
36. Sakurai M (1987) Synaptic modification of parallel fibre-Purkinje cell transmission
in in vitro guinea-pig cerebellar slices. J Physiol (Lond) 394:463-480.
37. Wang X, Chen G, Gao W, & Ebner T (2009) Long-term potentiation of the
responses to parallel fiber stimulation in mouse cerebellar cortex in vivo.

20

Neuroscience 162:713-722.
38. Kano M, Rexhausen U, Dreessen J, & Konnerth A (1992) Synaptic excitation
produces a long-lasting rebound potentiation of inhibitory synaptic signals in
cerebellar Purkinje cells. Nature 356:601-604.
39. Mittmann W (2004) Feed-forward inhibition shapes the spike output of cerebellar

25

Purkinje cells. The Journal of Physiology 563:369-378.
40. Brunel N, Hakim V, Isope P, Nadal J-P, & Barbour B (2004) Optimal Information
Storage and the Distribution of Synaptic WeightsPerceptron versus Purkinje Cell.
Neuron 43:745-757.
41. Mittmann W & Hausser M (2007) Linking Synaptic Plasticity and Spike Output at

30

Excitatory and Inhibitory Synapses onto Cerebellar Purkinje Cells. Journal of
Neuroscience 27:5559-5570.
42. Ogasawara H, Doi T, Doya K, & Kawato M (2007) Nitric oxide regulates input
specificity of long-term depression and context dependence of cerebellar learning.

-27-

PLoS Comput Biol 3:e179.
43. Akgoren N, Fabricius M, & Lauritzen M (1994) Importance of nitric oxide for local
increases of blood flow in rat cerebellar cortex during electrical stimulation. Proc
Natl Acad Sci U S A 91:5903-5907.
544. Imamizu H, Kuroda T, Miyauchi S, Yoshioka T, & Kawato M (2003) Modularorganization of internal models of tools in the human cerebellum. Proc Natl Acad
Sci U S A 100:5461-5466.
45. Wolpert DM & Kawato M (1998) Multiple paired forward and inverse models formotor control. Neural Networks 11:1317-1329.

10

46. Haruno M, Wolpert DM, & Kawato M (2001) Mosaic model for sensorimotor
learning and control. Neural Comput 13:2201-2220.
47. Milner TE, Franklin DW, Imamizu H, & Kawato M (2007) Central control ofgrasp: manipulation of objects with complex and simple dynamics. NeuroImage
36:388-395.

15

48. Shadmehr R & Holcomb HH (1997) Neural correlates of motor memory
consolidation. Science 277:821-825.
49. Rabe K, et al. (2009) Adaptation to visuomotor rotation and force field perturbationis correlated to different brain areas in patients with cerebellar degeneration. J
Neurophysiol 101:1961-1971.

20

50. Krakauer JW, Ghilardi MF, & Ghez C (1999) Independent learning of internal
models for kinematic and dynamic control of reaching. Nat Neurosci 2:1026-1031.
51. Flanagan JR, et al. (1999) Composition and decomposition of internal models inmotor learning under altered kinematic and dynamic environments. J Neurosci
19:RC34.

25

52. Higuchi S, Imamizu H, & Kawato M (2007) Cerebellar activity evoked by common
tool-use execution and imagery tasks: an fMRI study. Cortex 43:350-358.
53. Grodd W, Hulsmann E, Lotze M, Wildgruber D, & Erb M (2001) Sensorimotor
mapping of the human cerebellum: fMRI evidence of somatotopic organization.
Hum Brain Mapp 13:55-73.

30

54. Imamizu H, Higuchi S, Toda A, & Kawato M (2007) Reorganization of brain
activity for multiple internal models after short but intensive training. Cortex
43:338-349.
55. Krakauer JW, et al. (2004) Differential cortical and subcortical activations in-28-

learning rotations and gains for reaching: a PET study. J Neurophysiol 91:924-933.
56. Graydon FX, Friston KJ, Thomas CG, Brooks VB, & Menon RS (2005)
Learning-related fMRI activation associated with a rotational visuo-motortransformation. Brain Res Cogn Brain Res 22:373-383.
5

57. Quallo MM, et al. (2009) Gray and white matter changes associated with tool-use
learning in macaque monkeys. Proc Natl Acad Sci U S A 106:18379-18384.
58. Della-Maggiore V, Scholz J, Johansen-Berg H, & Paus T (2009) The rate ofvisuomotor adaptation correlates with cerebellar white-matter microstructure. Hum
Brain Mapp 30:4048-4053.

1059. Kamitani Y & Tong F (2005) Decoding the visual and subjective contents of thehuman brain. Nat Neurosci 8:679-685.
60. Miyawaki Y, et al. (2008) Visual image reconstruction from human brain activity
using a combination of multiscale local image decoders. Neuron 60:915-929.
61. Toda A, Imamizu H, Kawato M, & Sato M-a (2010) Reconstruction of15

two-dimensional movement trajectories from selected magnetoencephalography
cortical currents by combined sparse Bayesian methods. Neuroimage.

-29-

Figure legends
Figure 1(A) Movement directions of grips (blue arrows) and the cutting point (red arrow) of
5

scissors. (B) Posture of subjects during a tracking task using a computer mouse in themagnetic resonance (MR) scanner (scanner is not drawn in the figure). Subjects moved
a mouse using their right hand while viewing a screen through a mirror. A target and amouse cursor were projected onto the screen. (C: upper panel) Examples of target(small red circles) trajectories, whose vertical and horizontal components were the sum

10of sinusoids with different amplitude and frequency, and cursor (small open circles)
trajectories in the screen coordinates (X, Y) for 1.5 s. Circles indicate positions of thetarget and cursor every 16.7 ms (60 Hz). Blue arrows indicate moving direction. (C:lower panel) A mouse trajectory in the hand coordinates (x, y) corresponding to theabove cursor trajectory. Data from REF. [14].

15

Figure 2(A) The upper panel shows tracking error (mean ± SD) averaged across subjects as afunction of number of training sessions. The lower panels show the activation maps
when activity in the baseline periods was subtracted from that in the test periods (test baseline). (B) The upper panel indicates tracking error (mean + SD) in the20

error-equalized session (see main text). The upper lower shows the activation map when
activity in the error-equalized baseline periods was subtracted from that in the test

-30-

periods in the same session (test - baseline). (C) The left panel shows change in activity(test - baseline) across training session averaged within regions enclosed by blue lines
in Fig. 2B. The middle panel shows change in activity (test - baseline) across training
sessions averaged within the regions where strong activity was observed in the initial
5

stage of the learning (regions enclosed by red lines in Fig. 2A). Right panel shows thesubtraction of the activity change in the middle panel from that in the left panel. Eachcurve indicates the exponential function fitted to the circles. Broken lines in the activity
maps indicate the posterior superior fissure. Figure modified, with permission, from
REF. [14] © (2000) Macmillan Magazines Ltd.

10

Figure 3
Cerebellar activity averaged across subjects in transverse sections in the order from thesuperior (top panels) to inferior (bottom panels) sections. Magenta and cyan regions
were significantly activated when subjects manipulated a rotated mouse and velocity
mouse, respectively, in comparison to when subjects manipulated the normal mouse15(error equalized). White lines in the bottom-right figure indicate vertical levels of thesections in the sagittal plane whose sagittal position is indicated by white vertical lines
in the left figures. Data from REF. [44].
Figure 4
The objects chosen as archetypes of complex and simple dynamics are illustrated.

20

Arrows indicate directions of forces generated by subjects. Figure modified, with

-31-

permission, from REF. [47] © (2007) Elsevier.
Figure 5Distribution of t-value-weighted centroid of activation coordinates when subjects
actually used common tools (A) or when they imagined use of the tools (B). Centroids5

are projected to transverse (upper panels) or coronal (lower panels) planes as indicatedby the left anatomical images. Subjects used 16 tools but one of the tools (saw) could
not evoke significant activation (P < 0.001, uncorrected for multiple comparisons in
random effect analysis). Thus, the number of centroids is 15. Broken lines indicate theprimary fissure. Data from REF. [52].

10Figure 6
Volumes (across-subjects mean) in the cerebellar regions that were more activated whensubjects manipulated 60º, 110º or 160º rotated joystick than when subjects manipulated
normal joystick (error equalized). (A) Volumes measured in the initial stage of learning
how to use 110º joystick just after intensive training for use of 60º and 160º joysticks.

15(B) Volumes measured in the late stage of learning to use 110º joystick. Diameter of
each circle is proportional to total activated volume. Threshold for activation was t >3.08, p < .05 uncorrected for multiple comparisons. Data from REF. [54].

-32-

Figure 1

-33-

5

Figure 2

-34-

Figure 3
5

-35-

5

Figure 4

-36-

5

Figure 5

-37-

5

Figure 610