Current Biology 16, 2440–2445, December 19, 2006 ª2006 Elsevier Ltd All rights reserved

DOI 10.1016/j.cub.2006.10.051

ReportNeural Correlatesof Internal-Model Loading
Lulu L.C.D. Bursztyn,1,4 G. Ganesh,2,3,4
Hiroshi Imamizu,3 Mitsuo Kawato,3and J. Randall Flanagan1,*
1
Department of Psychology and Centre
for Neuroscience Studies
Queen’s University
Kingston, Ontario K7L 3N6
Canada2
National Institute of Information
and Communications Technology
Tokyo 184-8795
Japan
3
Advanced Telecommunications Research InstituteInternational
Computational Neuroscience Laboratories
2-2-2 Hikaridai
Seika-cho, Soraku-gun
Kyoto 619-0288
Japan

Summary
Skilled object manipulation requires knowledge, orinternal models, of object dynamics relating applied
force to motion [1–4], and our ability to handle myriad
objects [5–6] indicates that the brain maintains multiple models [7–9]. Recent behavioral studies haveshown that once learned, an internal model of an object with novel dynamics can be rapidly recruited andderecruited as the object is grasped and released[10–12]. We used event-related fMRI to investigate
neural activity linked to grasping an object with recently learned dynamics in preparation for moving it
after a delay. Subjects also performed two control
tasks in which they either moved without the object
in hand or applied isometric forces to the object. In alltrials, subjects received a cue indicating which task to
perform in response to a go signal delivered 5–10 slater. We examined BOLD responses during the interval between the cue and go and assessed the conjunction of the two contrasts formed by comparing the
primary task to each control. The analysis revealed
significant activity in the ipsilateral cerebellum andthe contralateral and supplementary motor areas.
We propose that these regions are involved in internal-model recruitment in preparation for movement
execution.
Results
While in the MRI scanner, subjects performed a random
sequence of three tasks. In the primary MOVE task, subjects moved an object (cylindrical handle), attached to a

*Correspondence: flanagan@post.queensu.ca
4These authors contributed equally to this work.

one-degree-of-freedom MR-compatible manipulandum[13], from a start position to a target by flexing the wrist(Figure 1A). The FREE task involved the same hand
movement but without the object. There were two variations of the isometric force task, each performed byhalf the subjects. In the ISO-GRIP task, subjects simply
squeezed the object. In the ISO-TORQUE task, theyused their wrist to generate a phasic torque that was
similar, in both magnitude and duration, to that observed in the MOVE task. In all trials, simultaneous visualand haptic cues indicated the task to be performed, andthese cues were followed by a random 5–10 s delay before the presentation of a go signal (Figure 1B). All tasks
were practiced before scanning took place so that accurate performance could be ensured.
In order to control the object in the MOVE task, subjects first generated a small and brief wrist flexor torqueto overcome the initial inertia of the object, then a large
extensor torque to brake the movement, and finally agradually decreasing extensor torque to maintain theobject on target after the movement terminated (Figure 2). Overall, the six subjects who performed the ISOTORQUE task generated similar time-varying torqueprofiles in the MOVE and ISO tasks (Figure 2B). Repeated measures ANOVAs, based on these subjects,
revealed no reliable differences between tasks in termsof peak torque (F1,5 = 017; p = 0.69) or the SD of peak torques computed for each subject (F1,5 = 3.87; p = 0.11). In
addition, there was a strong positive correlation acrosssubjects between average peak torques in the ISO and
MOVE tasks (r = 0.97; p < 0.001), indicating that subjects
who exerted a large force during the MOVE task also exerted a large force during the ISO task. However, it may
be noted that the brief flexion torque, characteristic of
the MOVE task, was not produced in the ISO task.
The time period of interest for imaging was the interval
between the presentation of visual and haptic cues andthe go signal. The data were analyzed as an event occurring at the moment of the cue onset. This approach was
based on our assumption that recruiting or loading an internal model is a discrete event that, in our task, occurs
as soon as the appropriate cue is provided so that the
subject is ready to act when the go signal is presented.
By comparing the MOVE task with the FREE and ISO
tasks, we thought to isolate loading of the internal modelof the object dynamics. The control tasks were designed
to include all major events in the MOVE task except for
loading of the internal model. The FREE task controlled
for the displacement of the hand during the movement,
the two ISO tasks controlled for the tactile cue, and all
three tasks controlled for the visual cue, posture of thehand, and preparatory attention-related activity before
the onset of motor output. Whereas the ISO-GRIP task
provided a general control for grip force applied to theobject, the ISO-TORQUE task provided additional control for wrist torque production and tactile sensation
closely resembling the MOVE condition. Importantly,
there was no significant difference in neural activity

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Figure 1. Tasks and Timeline(A) In the MOVE task, subjects grasped the handle of a MR compatible robotic manipulandum and made 60 wrist flexion movements.
In the ISO task, the handle was fixed at the start (15 wrist extension)and subjects either squeezed the handle or generated an isometricextensor torque. In the FREE task, subjects made the same 60
movement but without the handle in hand.
(B) All three tasks followed the same time course. To initiate eachtrial, simultaneous visual and haptic cues were presented indicating
the task to be performed. After a random delay of 5–10 s, a visual go
signal was given. Subjects were required to complete the movement
within 800 ms and remain at the end position for a random intervalof 1–10 s before a beep indicated they should return to the start
position.

between the two ISO tasks even at a low threshold (p <0.001, uncorrected), and we therefore combined thesubject groups together for analysis. Theoretically, theconjunction of MOVE–FREE with MOVE–ISO should
leave only the HRF related to loading the internal model.
This conjunction analysis revealed significant BOLD
activity (p < 0.05 corrected) in the contralateral primarysensory-motor region including Brodmann area 4 (M1)and Brodmann area 3 (S1), bilateral supplementary
motor area (SMA), cingulate sulcus, and the ipsilateral
cerebellum (Figure 3 and Table 1).
Discussion
Our ability to skillfully manipulate familiar objects assoon as we grasp them indicates that the brain stores internal models of their dynamics and can rapidly recruit
them as needed [2, 5, 6, 10–12, 14]. Evidence from imaging, neurophysiological, and lesion studies suggeststhat the cerebellum plays an important role in storing internal models [4, 15–17]. Shadmehr and Holcomb [18]examined a task in which subjects, by using their righthand, first learned to move an object with complex dynamics and then, after a 6 hr delay, performed the task
again. By using PET, they compared changes in rCBF
from late learning to recall (where performance levels
were similar) and found increased rCBF in the right anterior cerebellar cortex as well as the left dorsal premotor
and posterior parietal cortices. They suggested that,shortly after initial learning, the internal model is stored

in the cerebellum and that premotor and parietal structures are involved in linking the internal model to contextual cues during recall. From a computational perspective, the cerebellum is well suited for internal-modelstorage because it contains the required inputs and outputs to support supervised or error-feedback learning,which is the most effective means of learning internal
models [4, 19].
Precentral gyrus (M1), postcentral gyrus (S1), SMA,
and the cingulate sulcus were also preferentially activated by the cue onset in the MOVE task. Studies in human and nonhuman primates have demonstrated thatthe motor cortex is involved in early motor learning aswell as consolidation (e.g., [20]). The SMA, along withthe putamen body and caudate nucleus, has been identified as part of the motor loop for well-learned movements ([21], see [22] for a review). Furthermore, SMA,
S1, and cingulate regions have been shown to be involved in precision grip tasks involving object manipulation [23]. The activity in these regions was found to besignificantly less in the ISO tasks even though thehand posture and forces applied by the hand were similar. This argues against the possibility that the greater
activity in the MOVE task compared to the ISO condition
is related to higher muscle activity or a difference in tactile and kinesthetic sensory feedback. The activation
pattern thus seems to suggest that loading of an internal
model, possibly in the cerebellum, is accompanied byinteractions with M1 and S1 for sensory-motor preparation and with SMA and cingulate regions for grasp control. All of these interactions may be required for a successful completion of the MOVE task.
Imamizu et al. [24] studied switching between internal
models by using a task in which subjects performed
right-handed reaches by using one normal computer
mouse and two with novel visuomotor mappings. At
the moment of transition between mouse type, signaled
by visual feedback, neural activity related to switching
was observed in the anterior parietal region, left insula,
right cerebellum, and right prefrontal region. This activity was sustained throughout the use of a novel mouse in
the cerebellum and parietal regions, suggesting an association with the use, rather than the switching, of internal models. The lack of anterior parietal cortex involvement in the current study could presumably be
explained by the difference in tasks between the twostudies. Whereas the above study involved the learningof kinematics, the current study involves the learning ofnovel dynamics.
In all three of our tasks, we might expect to observe
neural activity related to what has been termed the readiness potential: cortical activity that ramps up as movement onset approaches and that occurs prior to andlinked to voluntary movement [25]. If there are differences in movement parameters across tasks, it is possible that there may be differences in the readiness potential that could contribute to the effects we observed inour conjunction analysis. We have two arguments
against this possibility. First, when we modeled theneural response either as a ramp from the cue signal
to the go signal or from the cue signal to 5 s later andused these models as regressors in the SPM conjunction analysis, we failed to observe significant activation (p < 0.1, FDR corrected, 0 voxel threshold). This

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Figure 2. Movement Torque Profiles(A) Average torque from all trials plottedagainst wrist angular position from the go signal to the end of movement (800 ms).
(B) Mean torque profiles for the MOVE and
ISO-TORQUE tasks. Each thin curve represents the average of 40 trials from a single
subject, aligned at the go signal. The thickcurves represent the means of these average
curves, also aligned at the go signal.

indicates that any activity related to the readinesspotential was similar in the MOVE and control tasks.
Second, we attempted to preempt this issue in our experimental design by equating movement parametersin the different tasks. Specifically, we sought to equate
extensor torques in the MOVE and ISO-TORQUE tasks
and movement amplitude and duration in the MOVE
and FREE tasks. Although no visual feedback was provided about torque in the ISO-TORQUE task, subjects
successfully generated torque profiles that were similar
to those observed in the MOVE tasks.
We have suggested that the activation yielded by our
conjunction analysis is due to recruitment or recall of aninternal model of the object in the MOVE task. An alternative possibility is that subjects recall a representationof the kinematics or kinetics of the task [26–28]. However, if the areas we have identified are active in relation
to kinematics or kinetics, they would show vast differences between our two control tasks. The ISO-TORQUE
task involved relatively large torque but no hand motion,
whereas the FREE task involved low torque and significant hand motion. Both contrasts of ISO–FREE and
FREE–ISO at the cue onset show no activity (p > 0.1,
FDR corrected), suggesting that no systematic planningfor either torque production or hand movement occurs
in response to the cue presentation. (Recall that there

were no activity differences between the two ISO tasks
and that these were therefore grouped together.)
We have previously argued that moving a hand-heldload requires internal models of the dynamics of boththe object and arm [2, 4]. Although an internal model ofthe arm is presumably recruited during the FREE task,such recruitment would also occur during the MOVE
and ISO tasks, and therefore differences between the
MOVE and FREE tasks could only be due to recruitmentof an internal model of the object and not the arm. One
could also argue that generating a torque against a rigid
object (as in our ISO task) also involves recruiting an internal model of the load. It is not clear whether the mechanics of rigid objects (described by statics rather
than dynamics) are represented in the same way as objects that move when forces are applied. However, assuming that people do maintain internal models of rigid
loads, the mechanical properties of rigid objects areboth highly familiar and simple, unlike the complex andnovel dynamics of the object used in the MOVE condition. Thus, it is possible that an internal model of the rigid
object is recruited during the ISO condition but, because
the load is familiar and simple, far less activation is seen.
If so, our results suggest that the activation we observed
is linked to the recruitment of an internal model of an object with complex and relatively unfamiliar (i.e., recently

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Figure 3. Conjunction of MOVE–ISO and MOVE–FREE(A) The second level conjunction at p < 0.05, FDR corrected, gave activity in sensory-motor regions including (B) area 3 (postcentral gyrus) andarea 4 (precentral gyrus), (C) the cerebellum, (D) the rostral cluster involving the cingulate and the pre-SMA, and (E) the caudal cluster involving
the cingulate and the SMA. The maximum probability maps [33] of Area 3b (blue), Area 4a (green), Area 4p (orange), and Area 6 (red) have been
overlaid in (B), (D), and (E) for showing possible anatomic locations of the observed activations in the sensory-motor cortex.

learned) dynamics. This activation was elicited in thetime period between the cue presentation and the go
signal, suggesting that the model was recruited in response to the haptic and visual cues and was not simply
maintained throughout the entire experiment. We

therefore suggest that the observed activity is related
to the recruitment of an internal model associated with
the MOVE task. This internal model may contain information both about the dynamics of the manipulated
object as well as the target obtainment task to be

Table 1. Location of Significant Clusters of Activation
ClusterBrain Region

Sizea

Primary sensory-motor cluster

110

Caudal cingulate/SMA cluster

Rostral cingulate/pre-SMA cluster

Cerebellum cluster (lobule 4–5)
aPeak
Cytoarchitectonic
Composition (voxels)b

33
18
17
9

Left area 4p
Left area 6
Left area 4a
Left area 3b

10Left area 6

17

10

3

MNI Coordinatesxyz

T Value

235

232

60

6.30

Cytoarchitectonic
Probabilitiesc

Area 4p 40%—
Area 4a 50%
Area 3b 40%
211

214

48

5.74

0

11

44

4.77

Area 6 50%

2Left area 6

Area 6 10%

1

Right area 6

Area 6 10%
21

253

220

4.56

Number of 3 mm isotropic voxels in cluster.
Number of voxels assigned to cytoarchitectonic areas in the maximum probability maps [33]. Note that the summation is not equal to the cluster
size because the maps do not cover the entire cortical surfaces.
c
Probability of peak location belonging to each area. Note that the summation can be over 100% because a probability map for each area was
independently determined.
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performed. This putative recruitment is associated withclear activity in contralateral M1, S1, SMA, and the cingulate and ipsilateral anterior cerebellar cortex.
Experimental Procedures
Subjects
Twelve healthy right-handed subjects (nine males and three females) between 22 and 35 years old participated in the experiment
after giving informed consent. The procedure was approved by a local ethics committee. All subjects performed three different trial
conditions: MOVE, ISO, and FREE.
Apparatus and Procedure
All experiments were performed with the one-degree-of-freedom
MR-compatible manipulandum [13] mounted outside an MRI scanner. To minimize head motion caused by arm movement, we fitted
each subject with a bite bar to stabilize his or her head. In the
MOVE task, subjects held an object (or handle) attached to the robot
during movement. Visual feedback of the positions of the object,
start location, and target were displayed on a screen in the scanner.
The MOVE task involved a 60 6 5 wrist flexion for moving theobject from the start position (5 wide). The following equation
describes the nonlinear dynamics of the object controlled by the
manipulandum [29]:1€ + Dw_ + Kðpﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ_ + jw = T
Im w
Þ + Fr jw 2 wfr jsignðwÞ
jw 2 wkr j + 0:01
where Im = 0.009 Kgm2 represents the inertia of the manipulandum€ represent the angular position, velocity,_ and w
handle and w; w,
and acceleration of the object in radians. The damping constant(D = 0.246 Nms/rad), nonlinear inverse spring stiffness (K = 20.283
NmOrad), and the spring resting length (wkr = 0.2 rad) were tuned
for achieving a difficult but learnable task. The system was observed
to have a residual position-dependent static friction approximatedby a piecewise linear function with the constant Fr = 0.036 Nm/rad,_position of minimum friction wfr = 0.2 rad, and the signðwÞfunction
coding the direction of motion. Finally, jw represents the natural
wrist dynamics of the subject, and T is the joint torque applied by
the subject.
All subjects completed an initial training on the MOVE task in amock setup of the fMRI apparatus in advance of scanning. Subjects
had to initiate the movement within 400 ms of the go signal andcomplete the movement to the target, without overshooting or undershooting, within 800 ms of the go signal. Feedback for a successor a specific error message (too slow, too short, and too long) was
given after each trial. Subjects practiced the task (in blocks of 50
trials) until they achieved at least eight successes within a moving
window of ten trials. Thereafter, subjects practiced the sequenceof tasks they would perform in the scanner.
The ISO task involved either squeezing the handle (ISO-GRIP; six
subjects) or isometric torque production in the wrist extension direction while holding the handle (ISO-TORQUE; six subjects). In the
ISO-TORQUE task, subjects were asked to produce a maximum torque similar to that observed during training in the MOVE task andwere given several practice trials just before scanning, in which
they received visual feedback of their torque and a target they
were asked to match. The target was the mean maximum torquethey had applied during training on the MOVE task. Grip force was
not measured in the ISO-GRIP task. In both ISO tasks, the durationof torque production was matched to the duration of hand movement in the MOVE (and FREE) tasks.
In the FREE task, subjects made the same motion and used thesame hand posture as in the MOVE task but without grasping andmoving the object. The start position was the same as in the othertwo conditions (15 wrist extension) but with the object positioned
at the endpoint (45 wrist flexion) and covered in soft padding that
contacted the knuckles at the completion of the movement.
Twenty repetitions of each task were randomly interspersed
within a session. Each condition had the same time course, shownin Figure 1B, whereby subjects were simultaneously presented
with a visual color cue (color of the target circle) and a haptic cue(hand touches object or not). At the start position, the hand wasopen with the fingers extended and the back of the hand contacting
a block of foam. As subjects waited in this position, the object waseither moved toward the hand to gently rest on the palm (MOVEand ISO tasks), or away from the hand to mark the endpoint (FREEtask). After a random interval between 5 and 10 s, a go signal (bluering around the start circle) appeared, indicating to the subjects
that they should begin the task specified by the preceding cues. After the movement in the MOVE and FREE tasks, subjects kept the
cursor (and hand) at the end position for a random interval between
1 and 10 s until a beep indicated they should return their hand to thestart position. Toni et al. [30] have demonstrated that the neural signals from sensory and motor events occurring close together can be
distinguished by randomly varying the time period between them.
Similarly, the random delays between the cue, go, and return signals
allowed us to distinguish the hemodynamic response function (HRF)
associated with the cue signal from the actual movement.
fMRI Acquisition
A 1.5 T MRI scanner (Shimadzu-Marconi) was used for obtaining
blood-oxygen-level-dependent (BOLD) contrast functional images.
Images weighted with the apparent transverse relaxation time
were obtained with an echoplanar imaging sequence (repetition
time: 3.5 s; echo time: 50 ms; flip angle: 90 ). Two sessions were conducted where 330 sequential whole-brain volumes (64 3 64 3 35
voxels of 3.5 3 3.5 3 4 mm resolution and 224 mm field of view)
were acquired in each session.
fMRI Analysis
We used SPM2 software (http://www.fil.ion.ucl.ac.uk/spm/) for image processing and analysis. We discarded the first four volumesof images to allow for T1 equilibration. The remaining 326 image volumes were realigned to the first volume so that head motion overtime could be corrected; the volumes were spatially normalized to
the Montreal Neurological Institute (MNI; Montreal, Canada) reference brain and resliced to 3 mm isotropic voxel size. The data
were smoothed spatially with a Gaussian kernel with an 8 mm fullwidth at half maximum (FWHM).
Statistical analyses were performed in two stages of a mixedeffects model. In the first stage (fixed effect) individual analysis,
six types of events were defined: cue and go events in the MOVE,
ISO, and FREE conditions. Each event was modeled by an impulse
function locked at the onset of the cue or the go signal. The impulse
functions were convolved with the canonical hemodynamic response function in SPM2 for yielding regressors in a general linear
model. A parameter was estimated for each regressor for each voxelby the least-squares method. The two images of particular interest
were derived from pair-wise contrasts between the estimatedparameters for the cue events MOVE-FREE and MOVE-ISO. In thesecond stage (random effect) group analysis, we applied a onesample t test to contrast images from each individual to obtaint static maps for MOVE-FREE and MOVE-ISO at the onset of thecue. To examine activation common to both contrasts, we calculated a minimum t statistic conjunction map by taking the minimumt statistic from the two maps. A threshold was applied to the conjunction map such that both contrasts were individually significant(testing the conjunction null hypothesis as described in [31]). We
used a height threshold of Tn > 4.15 (p < 0.05, corrected for multiple
comparisons based on the false discovery rate) and an extent
threshold of three voxels. The Automated Anatomical Labeling [32]toolbox for SPM2 was used for identifying global anatomical regions, and the SPM Anatomy toolbox [33] was used for identifying
fine cytoarchitectonic regions near the central sulcus.
Acknowledgments
This study was supported by a grant from the Human Frontier Science Program, the Canadian Institutes for Health Research (CIHR)
and in part by the National Institute of Information and Communications Technology (NICT-KARC) and International Collaborative
Research Project (ICORP) Japan Society and Technology Agency.
L.L.C.D.B.’s visit to the Advanced Telecommunications ResearchInstitute International (ATR) was supported by a Japan Society for
the Promotion of Science Summer Program Scholarship.

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Received: August 29, 2006
Revised: October 11, 2006
Accepted: October 13, 2006
Published: December 18, 2006
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