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 Previous Article
The Journal of Neuroscience, April 15, 2001, 21(8):2919-2928
Functional Anatomy of Nonvisual Feedback Loops during Reaching: A
Positron Emission Tomography Study
Michel
Desmurget1, 2,
Helena
Gréa1, 2,
Jeff
S.
Grethe3,
Claude
Prablanc2,
Garret E.
Alexander1, and
Scott T.
Grafton3, 4
1 Emory University School of Medicine, Department of
Neurology, Atlanta, Georgia 30322, 2 Institut National de
la Santé et de la Recherche Médicale U534, Espace et
Action, 69500 Bron, France, and 3 Center for Cognitive
Neuroscience and 4 Department of Psychological and Brain
Science, Dartmouth College, Hanover, New Hampshire 03755
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ABSTRACT |
Reaching movements performed without vision of the moving limb are
continuously monitored, during their execution, by feedback loops
(designated nonvisual). In this study, we investigated the functional
anatomy of these nonvisual loops using positron emission tomography
(PET). Seven subjects had to "look at" (eye) or "look and point
to" (eye-arm) visual targets whose location either remained stationary or changed undetectably during the ocular saccade (when vision is suppressed). Slightly changing the target location during gaze shift causes an increase in the amount of correction to be generated. Functional anatomy of nonvisual feedback loops was identified by comparing the reaching condition involving large corrections (jump) with the reaching condition involving small corrections (stationary), after subtracting the activations associated with saccadic movements and hand movement planning [(eye-arm-jumping minus eye-jumping) minus (eye-arm-stationary minus
eye-stationary)]. Behavioral data confirmed that the subjects were
both accurate at reaching to the stationary targets and able to update
their movement smoothly and early in response to the target jump. PET difference images showed that these corrections were mediated by a
restricted network involving the left posterior parietal cortex, the
right anterior intermediate cerebellum, and the left primary motor
cortex. These results are consistent with our knowledge of the
functional properties of these areas and more generally with models
emphasizing parietal-cerebellar circuits for processing a dynamic
motor error signal.
Key words:
error correction; feedback; reaching; cerebellum; parietal; double step; PET; human
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INTRODUCTION |
Imaging studies using positron
emission tomography (PET) have identified a large set of cortical and
subcortical areas activated during the execution of goal-directed
movements. During the last decade, several attempts have been made to
partition this extended reach related network into separate functional
subcircuits mediating, for instance, visuomotor adaptation (Clower et
al., 1996 ) or the control of movement velocity (Turner et al., 1998).
The present study is in line with this approach. Our goal was to
identify the functional anatomy of nonvisual feedback loops, i.e., of
feedback loops that do not rely on the vision of the moving limb (for
review, see Desmurget and Grafton, 2000). The existence of these loops was demonstrated initially in simple psychophysical experiments showing
that reaching movements performed without vision of the moving limb
were significantly less accurate when the target was turned off after
hand movement onset (Prablanc et al., 1986). Further evidence was
provided by subliminal target displacement experiments (Goodale et al.,
1986; Prablanc and Martin, 1992; Desmurget et al., 1999a). In these
"double-step" experiments, subjects were required to "look and
point," in the dark, to visual targets displayed in the peripheral
visual field. During saccadic gaze displacement (when vision is
suppressed) the target location was slightly modified. This
modification triggered a change in hand trajectory that deviated early
and smoothly from its initial path to reach the new target location.
The occurrence and characteristics of these deviations were identical
whether or not vision of the moving limb was allowed, indicating that
nonvisual feedback loops represent the key mechanism for early hand
trajectory control, even when vision of the moving limb is available
(Prablanc and Martin, 1992).
Functionally, subliminal double-step paradigms do mimic the
organization of single-step movements directed at stationary targets (Desmurget et al., 1999a; Desmurget and Grafton, 2000). Indeed, when a
subject is required to point "quickly and accurately" to a
stationary target located in the peripheral visual field, muscle activation starts nearly simultaneously for eyes and arm (Biguer et
al., 1982), indicating that the motor command initially sent to the
upper limb is based on the initial peripheral visual signal. As
reported in several studies, this signal is not entirely accurate (Prablanc et al., 1979; Bock, 1993). At the end of the ocular saccade,
which roughly corresponds to hand movement onset (Prablanc and Martin,
1992), the target location is recomputed on the basis of perifoveal
information. The updated visual signal is then used by the nervous
system to adjust the ongoing trajectory (Prablanc et al., 1986).
Modifying slightly the target location during gaze shift simply
increases an error that is already present in the system. In this
study, we took advantage of this functional similarity to investigate
the motor network mediating nonvisual feedback loops during reaching movements.
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MATERIALS AND METHODS |
Subjects
Seven right-handed naive subjects (one female, six males)
ranging from 19 to 37 years (mean, 25.4; SD, 6.6) participated in the
study. All subjects gave informed consent, and the study was approved
by the institutional Human Investigation Committee of Emory University.
All subjects underwent a brief neurological examination to ensure they
were healthy and devoid of visual deficits.
Apparatus
Throughout the study, subjects were supine in the scanner. Their
heads were immobilized with a thermoplastic mask. The experimental apparatus was similar to one used in previous psychophysical studies (Desmurget et al., 1999a,b) (Fig.
1A). It consisted of a
pointing board placed in front of the subjects. An array of
light-emitting diodes (LEDs) was arranged orthogonally to the pointing
board. A half-reflecting mirror was positioned at an angle of 45°
with respect to both the pointing board and the array of LEDs. The subjects saw the virtual images of the LEDs through the mirror, in the
plane of the board. Consequently, the reaching hand could not occlude
the virtual image of the LEDs, which prevented the subjects from
gaining an indirect feedback of their reaching accuracy. The targets
were located on a circle centered on the hand starting point (S;
radius, 25 cm). A direct orthogonal reference frame was defined for
data analysis and target location definition (Fig. 1B). S was the origin of this reference frame. The
z-axis was orthogonal to the pointing board and oriented
toward the subject. The x-axis was horizontal and oriented
rightward. The y was orthogonal to x-z
and oriented upward. Nine targets were used in the present experiment.
One green diode was located in the left hemispace at minus30° (with
respect to the y-axis). Eight red diodes were located in the
right hemispace with a 5° increment from 10 to 45°. The orientation
of the pointing board was adjusted so that the z-axis passed
through the subject cyclopean eye (center of mass of the two eye
balls). The distance between the cyclopean eye and the hand starting
point was 45 cm. During the experiment, movement of an infrared
emitting diode located on the subject's index fingertip was recorded
with an Elite motion analysis system at a sampling frequency of 100 Hz.
Eye movements were recorded binocularly using DC electro-oculography
(EOG).
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Figure 1.
A, Schematic representation of the
experimental apparatus. Subjects were supine with their head
immobilized in the scanner. A pointing board was placed in front of
them. An array of LEDs and a half-reflecting mirror were suspended over
the pointing board. The subjects saw the virtual image of the LEDs
(targets) through the mirror, in the plane of the board.
B, Schematic representation of the pointing board. Nine
LEDs were used in the present experiment. They were located on a circle
(radius, 250 mm; center, the hand starting point). One green diode
(white circle) was located in the left hemispace at
 30° (visual fixation point). Eight red diodes (black
circles) were located in the right hemispace with a 5°
increment from 10 to 45° (targets).
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Experimental conditions
Light was turned off at injection time (i.e., 10 sec before the
start of the scan; see below), and all scans were recorded in a totally
dark room. Light was turned on between scans. The protocol resulted
from the combination of two experimental variables. The first variable
was related to the instruction given to the subject before the session.
Subjects were instructed either to "look and point to the target"
(eye + arm: eye-arm), or to only "look at the targets, without
pointing" (eye). The second variable was related to the type of trial
defined by the target response. In half of the sessions, the target
remained stationary during the whole trial (stationary). In the other
half, the target location was modified during the saccadic response
(jump). The combination of these two variables resulted in four
experimental conditions: eye-arm-stationary, eye-arm-jump,
eye-stationary, and eye-jump. Each of these four conditions was
replicated three times leading to a total number of 12 scans per
subject (four conditions, three replications). The different conditions
and different replications were randomly ordered across subjects. The
sequence of target presentation was strictly balanced with respect to
the effector factor (for a given subject, the three sequences of target
presentation used for the three repetitions of the eye condition were
also used for the eye-arm condition). For the perturbation factor
(jump vs stationary) the sequence of target presentation was balanced with respect to the movement final location. In the stationary condition the subjects pointed 12 times to each target. In the jump
condition the same targets were presented after either a leftward or a
rightward jump. The 10° target (extreme left) was always presented
after a leftward jump (15°  10°). The 45° target (extreme
right) was always presented after a rightward jump (40° 45°).
The other targets were presented after either a leftward (six times) or
a rightward jump (six times).
All scans involved the same sequence of events. (1) The green visual
fixation point (left LED) was turned on for 1.4 sec. (2) Visual
fixation was turned off while one of the red targets (right LEDs) was
simultaneously turned on. (3) Depending on the experimental
instruction, the subjects had to "look at" (eye) or "look and
point to" (eye-arm) the target. (4) The target location either
remained stationary or was modified during the ocular saccade (because
of saccadic suppression this displacement was not consciously detected
by the subjects). The target presentation phase lasted 1.4 sec. (5) The
green target was turned on again for visual fixation. In the eye-arm
conditions, the subjects used proprioceptive information from the
contralateral left hand to return the right hand quickly to the
starting point. Their left index finger was placed, as a tactile mark,
just below the hand starting location while their left arm rested
comfortably on a large pillow placed on their abdomen. In the reaching
condition when the right hand was at the starting point, the elbow was
slightly flexed (~140° if 180 describes a fully extended arm) with
the plane of the arm (wrist-elbow-shoulder) making an angle of
~30° with respect to the sagittal plane. In the eye condition both
the right and left hands rested passively on the pillow.
During the experiment, eye velocity was extracted on-line from the
position signal, using a two-point central difference derivative algorithm (Bahill and McDonald, 1983). The change in target location occurred, in the jump conditions, when eye velocity reached a level
roughly equal to half of its the peak value. The threshold for target
jump was set manually on an oscilloscope at the beginning of the
experiment while the subject was required to perform a series of
saccades. It was adjusted during the scans if necessary.
Behavioral analyses
For arm movements, the x, y, and
z position signals were filtered at 10 Hz with a
second-order Butterworth dual pass filter. Movement velocity was
computed from the filtered position signal, using a least square
second-order polynomial method (window ± 4 points). The same
method was used to compute the acceleration of the hand from the
velocity signal. The main arm-related parameters analyzed in this
experiment were the hand reaction time (RThand), the hand movement duration (MDhand), the index
finger final location, and the hand path linearity. The index finger
final location was defined by the x and y
coordinates of the index fingertip location at the end of the trial.
Hand path linearity was defined as the ratio of the largest deviation
of arm trajectory from the line connecting the start and end points of
the movement to the length of this line (Atkeson and Hollerbach, 1985).
It accounted for the global movement curvature (Desmurget et al.,
1999b). The hand path linearity index is equal to 0 when the movement
is perfectly straight and to 0.5 when the movement is semicircular. In
addition to the previous parameters, we also determined the movement
direction (Mdir) at the time of peak acceleration and time of peak
velocity. Mdir was determined by computing the azimuth and elevation
angles of the tangential velocity vector. As shown by earlier studies (Prablanc and Martin, 1992; Desmurget and Prablanc, 1997), Mdir is the
most accurate indicator of the motor reaction time to the perturbation.
The onset and the end of the movements were computed automatically
using the following thresholds: hand velocity = 80 mm/sec and hand
acceleration = 150 mm/sec2.
Two-way ANOVA (perturbation × target location) was used to
determine significant differences between experimental conditions for
arm movement parameters. Two different ANOVA were conducted to contrast
the stationary condition with each of the jump conditions (leftward or
rightward). This was done because rightward and leftward target jumps
are expected to have opposite kinematic effects and because the
experimental design was not complete with respect to the initial target
location factor (the extreme left target was never the initial target
location for the leftward jump condition; the extreme right target was
never the initial target location for the rightward jump condition).
For the comparisons involving leftward jumps, the 10° target was
removed from the stationary dataset. For the comparisons involving the
rightward jumps, the 45° target was removed from the stationary
dataset. Bidimensional parameters such as the index final location
(x and y coordinates) were compared using two way
MANOVAs. In this case, the F value was determined from the
Wilk's lambda, using Rao's approximation (Maxwell and Delaney,
1990).
The calibration of the EOG signal was performed in two steps.
First, the eccentricity of the different targets was redefined with
respect to the cyclopean eye (target eccentricity was initially defined
with respect to the hand starting point). When expressed in
eye-centered coordinates, the target eccentricities were: 15.5° (30° fixation point/sec), 5.5° (10°/sec), 8.2° (15°/sec),
10.8° (20°/sec), 13.2° (25°/sec), 15.5° (30°/sec), 17.7°
(35°/sec), 19.7° (40°/sec), and 21.4° (45°/sec). Second, the
EOG signal was measured while the subject looked at the different
targets. A calibration curve was then computed from these measurements
by fitting a polynomial through the data. This curve was used to transform the EOG signal into a calibrated eye position signal. Once
calibrated, the eye position signal was numerically filtered at 30 Hz
with a second-order Butterworth dual pass filter. The velocity signal
was computed from the filtered position signal, using a least square
second-order polynomial method (window ± 4 points). The main
saccadic parameters analyzed in this experiment were the eye reaction
time (RTeye), the eye movement duration (MDeye), and the amplitude of the primary
saccade. The latter parameter was expressed as a percentage of the
initial required displacement. The beginning and the end of the primary
saccade were automatically detected using a velocity threshold
procedure (20°/sec). The results of this procedure were checked
off-line and corrected if necessary.
Three-way ANOVA was used to determine significant differences between
experimental conditions for eye movements [effector (eye alone vs
eye-arm); perturbation; target location]. As for arm movements (see
above), we conducted two different ANOVAs to contrast the stationary
condition with each of the jump conditions (leftward or rightward).
Because the primary saccadic response is known to be unmodified on-line
on the basis of peripheral visual information (Deubel et al., 1986;
Desmurget et al. 2000), we also conducted a single three way ANOVA in
which the saccades related to leftward or rightward trials were
averaged together. Results were identical in both analyses. As a
consequence, only the first one will be reported here for the sake of consistency.
The statistical threshold was set at p = 0.05 for all
behavioral analyses.
Imaging
Imaging methods have been described in previous publications
(Desmurget et al., 1998, 2000). In brief, regional cerebral blood flow
(rCBF) images were acquired with a Siemens ECAT Exact scanner, by using
a modified autoradiographic method in three-dimensional mode. Ninety
second scans were recorded every 8 min. The series of scans was made,
from each subject, using bolus intravenous injections of
H215O (25 mCi) that were
delivered into the left arm 10 sec before the start of the scan.
Performance of the designated task began at the same time as the
scanning. Images were reconstructed by using calculated attenuation correction.
Image processing was performed on a SunSparc5 workstation. For spatial
normalization, a within-subject alignment of PET scans was performed by
using an automated registration algorithm (Woods et al., 1998a,b). For
each subject, the mean PET image was then coregistered to a
population-based PET reference atlas centered in Talairach coordinates
(Talairach and Tournoux 1988), using affine and nonlinear transforms
with 60 degrees of freedom (Woods et al., 1998a,b). Coregistered PET
images were smoothed to a final isotropic resolution of 15 mm full
width at half maximum and normalized to each other by using
proportionate global scaling. ANOVA for a randomized complete
block design was used, for all contrasts, to identify significant task
effects (Neter et al., 1990; Woods et al., 1996). The effects (and
source of variance) in the statistical model were subject, task, and
repetition. Given the performance consistency of the behavioral
paradigm under investigation and the randomization procedure,
repetition could be treated as replication, resulting in a two-way
ANOVA (Turner et al., 1998). For all the contrasts evaluated in this
study, the statistical threshold was initially set at p = 0.005. The following five contrasts were evaluated.
Overall hand-reaching effect. [(eye-arm-jump plus
eye-arm-stationary) minus (eye-jump plus eye-stationary)] (df = 70; t  2.65). This contrast allows removal of the
brain activation specifically related to the visual capture of the
target. As a consequence it should primarily identify the areas
involved in movement planning and movement control. However, one may
not exclude the possibility that areas associated specifically with
motor correction (response to the jump) or eye-hand coordination
(interaction) contribute to the overall effect observed. No a
priori hypothesis was formulated about the areas that might be
activated in this contrast (unplanned contrast). To adjust for multiple
comparisons, the t statistic image was corrected using the
method developed by Friston et al. (1994). This method takes into
account the size of the activation (359 resolving elements), the search
volume, and the degree of image smoothness. Correction for multiple
comparison was conducted at a final certainty of p < 0.01.
Strict hand-reaching effect. [eye-arm-stationary minus
eye-stationary] (df = 28; t 2.76). This
contrast is similar to the previous one except that only the movements
to stationary targets were taken into account. If it is correct that
the jump and no jump trials involve similar functional mechanisms, the
strict and overall reaching contrast should give comparable results. No
a priori hypothesis was formulated about the areas that
might be activated in this contrast (unplanned contrast). As a
consequence, t statistic image was corrected for multiple
comparisons (359 resolving elements) to a final certainty of
p < 0.01 using the method developed by Friston et al.
(1994).
Overall jump effect. [(eye-arm-jump plus eye-jump) minus
(eye-arm-stationary plus eye-stationary)] (df = 56;
t 2.67). In this contrast we determined the effect
of jumping the target location, irrespective of the effector. The
overall jump effect should give information about the global network
activated when the estimation of the target location by the peripheral
retina is erroneous (see introductory remarks). No a priori
hypothesis was formulated about the areas that might be activated in
this contrast (unplanned contrast). As a consequence, t
statistic image was corrected for multiple comparisons (359 resolving
elements) to a final certainty of p < 0.01 using the
method developed by Friston et al. (1994).
Eye error correction effect. [eye-jump minus
eye-stationary] (df = 56; t 2.67). In this
contrast we determined the effect of jumping the target location on the
oculomotor system. This contrast should give information about the
network activated when the initial saccadic response is incorrect. No
a priori hypothesis was formulated about the areas that
might be activated in this contrast (unplanned contrast). As a
consequence, t statistic image was corrected for multiple
comparisons (359 resolving elements) to a final certainty of
p < 0.01 using the method developed by Friston et al.
(1994).
Hand error correction effect. [(eye-arm-jump minus
eye-jump) minus (eye-arm-stationary minus eye-stationary)]
(df = 56; t 2.67). In this contrast we
determined the subcircuit mediating on-line hand trajectory
adjustments. To this end, we contrasted the jump and stationary
conditions after subtraction of the oculomotor-related activity. It is
worth emphasizing that this double difference amounts, in fact, to an
interaction. The areas identified by this contrast are the areas that
increase their responsiveness when larger corrections have to be
performed. As already emphasized, what we compare in this experiment is
a condition involving small corrections (stationary) with a condition
involving large corrections (jump). Consequently, the interpretation of
the present contrast can be framed in terms of modulation of the
underlying executive system by a perturbation that increases the error
processing. A potential difficulty with this design, and more precisely
with the fact that similar feedback loops are engaged in both the jump and stationary conditions, is that the hand error correction effect might be very subtle and hard to detect. Stringent statistical procedures involving strict corrections for multiple comparisons might
be excessively conservative in this context. At the same time, however,
increasing the statistical p value or abolishing corrections
for multiple comparisons might abnormally increase the risk of type I
errors (declaring significant an activation that is not). To
accommodate these contradictory exigencies, (i.e., increasing
statistical sensitivity while minimizing type I errors) a two-step
analysis was conducted. First, a nonplanned contrast was evaluated. For
this contrast, no a priori hypothesis was formulated about
the areas that might be activated. As a consequence, the t
statistic image was corrected for multiple comparisons (359 resolving
elements) to a final certainty of p < 0.01 using the method developed by Friston et al. (1994). Second, a planned contrast was evaluated. For this contrast, the search volume was restricted a priori to the structures showing an unequivocal reach
related effect, i.e., to the structures activated in the strict
hand-reaching contrast. No correction for multiple comparisons was
applied within this restricted set of functionally plausible
structures. To avoid an overly conservative restriction of the search
volume "the reach-related network" was defined at a relaxed
threshold, with no corrections for multiple comparisons (strict
hand-reaching effect at p < 0.01; 39 resolving elements).
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RESULTS |
Behavioral observations: saccade characteristics
For both the stationary and jump conditions, the saccadic response
consisted of two phases (Fig.
2A): an initial saccade
undershooting the initial target position and covering on average 96%
(±3.7) of the initially required displacement and a corrective saccade achieving accurate target acquisition. The amplitude of the primary saccade did not vary significantly as a function of the perturbation [stationary, 96.3% (±3.9); rightward jump, 95.1% (±3.8); leftward jump, 95.7% (±4.1); p > 0.10] or effector factors
[eye, 95.6% (±4.1); eye-arm, 95.9% (±3.9); p > 0.35]. The number of trials involving more than one corrective saccade
was marginal, even in the jump condition. This latter observation was
expected considering the modest amplitude of the target jump (between
1.7° and 2.7°; see Materials and Methods) (Desmurget et al.,
2000).
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Figure 2.
Individual trials performed by one subject in the
eye-arm condition to stationary and jumping targets (stationary 25°,
continuous line; jumping 25° 20°, dotted
line; jumping 25° 30°, dashed line).
A, Eye velocity signals. B, Hand velocity
signals. C, Cartesian hand paths. A and
B show that hand movement starts around the end of the
main saccade. C shows, for the perturbed trials, that
hand movement is initially directed to the first target location before
diverging toward the second target location. As shown by the hand
velocity profiles, hand paths updating was performed smoothly without
interrupting the ongoing movement.
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RTeye was equal to 212 msec (±36). This
parameter did not vary significantly as a function of the perturbation
[stationary, 215 msec (±43); rightward jump, 209 msec (±31);
leftward jump, 211 msec (±31); p > 0.35] or effector
factors [eye, 215 msec (±36); eye-arm, 209 msec (±35);
p > 0.25]. MDeye was equal to
88 msec (±15). It was not affected by the perturbation factor
[stationary, 87 msec (±14); rightward jump, 85 msec (±14); leftward
jump, 92 msec (±16); p > 0.10] but was found to be
significantly shorter in eye-arm than in eye [86 msec (±13) versus
90 msec (±16); p < 0.01]. The meaning of this
heretofore undescribed effect is unclear. On the one hand, this slight
difference may represent a false positive inference. On the other hand,
it may reflect the functional capacity of the motor system to update
the target location more quickly to allow early hand path adjustments.
None of the subjects reported the existence of a change in target
position during the saccadic response, even when questioned explicitly
at the end of the study. The absence of conscious perception of the
target jump is coherent with the absence of significant variation of
the kinematic characteristics of the primary saccadic response as a
function of the perturbation factor.
Behavioral observations: arm movement characteristics
RThand was independent of the perturbation
factor [stationary, 277 msec (±50); rightward jump, 269 msec (±48);
leftward jump, 263 msec (±40); p > 0.25]. On average
RThand was 58 msec longer than
RTeye [270 msec (±46) vs 212 msec (±36)].
This indicates that arm movement started around the end of the ocular
saccade (for eye-arm, RTeye + MDeye = 300 msec; Fig. 2A,B),
or in other words that the arm motor command was issued on the basis of
a peripheral retinal input. If one considers that the onset of the agonist muscle contraction occurs 50-100 msec before the actual motion
for reaching movements (Biguer et al., 1982; Turner et al., 1995), the
latencies observed in this study are compatible with previous
observations showing that arm muscle contraction is synchronous with
eye movement onset during fast reaching movements directed at
peripheral targets (Biguer et al., 1982).
Peak hand acceleration occurred on average at 125 msec (±22 msec) and
was independent of the perturbation factor (p > 0.30). No significant variations of the movement direction were
observed at the time of peak hand acceleration
(p > 0.90), supporting the idea that the
subjects did not develop a specific strategy in the jump sessions. Peak
hand velocity occurred on average at 251 msec (±37), i.e., at 41% of
the total movement duration. Like peak hand acceleration, peak hand
velocity did not vary significantly with the perturbation factor
(p > 0.50). Interestingly, significant variations of Mdir were observed at the time of peak hand velocity (p < 0.04). At this instant, Mdir was found to
be rotated to the left by 2.7° on average in the leftward jump
condition and rotated to the right by 3.3° on average in the
rightward jump condition. This result demonstrates the existence of
early path corrections in response to the target jump.
Arm trajectory amendments were clearly visible in the hand path
linearity index, which varied significantly as a function of the
perturbation factor [stationary, 0.120 (±0.021); rightward jump,
0.130 (±0.024); leftward jump, 0.108 (±0.019); p < 0.005] (Fig. 2C). They were also reflected in the index
fingertip final location. This parameter was significantly different in
the control trials directed to a given target and in the jump trials
initially directed to the same target (p < 0.001). Interestingly, when the control trials directed to a given
target T were contrasted with the jump trials for which T was the final
target, no significant difference was observed for the final hand
location (p > 0.20). This result indicates that
the path corrections observed in the jump condition were nearly
complete. Trajectory amendments occurred without significant increase
of the mean movement duration [stationary, 618 msec (±64); rightward
jump, 609 msec (±81); leftward jump, 627 msec (±88);
p > 0.60], as observed in previous reports (Goodale et al., 1986; Desmurget et al., 1999a).
PET image: overall hand-reaching effect
The overall hand-reaching contrast revealed a large set of
motor-related areas (Table 1, Fig.
3). Congruent with earlier studies, the
main cortical site of activation was observed in a wide portion of
cortex surrounding the left central sulcus (contralateral to the used
arm). The activated area encompassed the primary motor and the premotor
cortices, the primary somatosensory cortex, the superior and inferior
parietal lobules, the intraparietal sulcus, the central and parietal
operculum, and the insula. A focal activation was also observed in the
left occipitotemporal region. Strikingly, no cortical activation was
observed in the supplementary motor area or the right hemisphere
(ipsilateral to the used arm). At a noncortical level, a very large
bilateral activation was observed in the cerebellum. Other significant
responses were found in the thalamus (bilaterally), the left lenticular
nuclei, and the brainstem.
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Table 1.
Coordinates of local maxima of significant rCBF increase
observed in the overall reaching contrast (p < 0.005; corrected for multiple comparisons)
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Figure 3.
Horizontal difference images representing the
overall reaching effect corrected for the eye kinematics effect
(p < 0.005; adjusted for multiple
comparisons). Activations are shown superimposed on a mean magnetic
resonance image (MRI) in Talairach coordinates. The anatomic right side
is shown on the left side of the figure. The first
section (top left) is 49 mm below the anteroposterior
commissural line (Z = 49). The last section
(bottom right) is 65 mm above the anteroposterior
commissural line (Z = +65). Sections are presented
every 6 mm.
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PET image: strict hand-reaching effect
The strict hand-reaching contrast revealed a pattern of activation
that was globally similar to the one observed for the overall hand-reaching contrast (Table 2, Fig.
4). The main cortical site of activation
was observed in a wide portion of cortex surrounding the left central
sulcus. The activated area encompassed the primary motor and the
premotor cortices, the primary somatosensory cortex, the superior and
inferior parietal lobules, the intraparietal sulcus, the central and
parietal operculum, and the insula. A focal activation was also
observed in the left occipitotemporal region. No cortical activation
was observed in the right hemisphere. At a subcortical level, a very
large bilateral activation was observed in the cerebellum. Other
significant responses were found in the thalamus (bilaterally) and the
brainstem. As shown in Figures 3 and 4, the main difference between the
overall and strict hand-reaching contrasts was that activations were a
little bit broader in the former than in the latter contrast. Two
nonexclusive factors may account for this result: (1) some areas may
exhibit an enhanced activity in the jump trials with respect to the
stationary trials; (2) the overall reaching contrast is statistically
more sensitive inasmuch as it involves a larger number of degrees of
freedom (see Materials and Methods). Theoretically, the close
similarity between the overall and strict contrasts is coherent with
the behavioral results, suggesting that movements directed at jumping and stationary targets involve similar functional processes (see introductory remarks). Further argument supporting this view will be
presented in the next section.
View this table:
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Table 2.
Significant activation observed in the strict reaching
contrast (p < 0.005; corrected for multiple
comparisons)
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Figure 4.
Horizontal difference images representing the
strict reaching effect corrected for the eye kinematics effect
(p < 0.005; adjusted for multiple
comparisons). Same conventions as Figure 3.
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PET image: overall jump effect, eye error correction effect, and
hand error correction effect
These three contrasts failed to reveal any significant activation
at either the cortical or subcortical levels after correction for
multiple comparisons. These results confirm and extend the conclusions
of a previous study showing that jumping the target location randomly
during the course of the saccade does not generate any significant
activation in the main oculomotor areas with respect to a condition in
which the target location remains stationary (Desmurget et al., 2000).
The present negative finding shows that the metabolic response induced
by the target jump is not substantial enough to be identified with
stringent nonplanned statistical procedures.
PET image: hand error correction effect and planned analysis
This analysis revealed a restricted motor network engaging three
areas previously postulated to have a role in nonvisual feedback loops
(see Discussion), namely the parietal cortex, the frontal cortex, and
the cerebellum (Table 3, Fig.
5). The parietal activation was located
in the left intraparietal sulcus, in a region that is generally
considered the rostral part of the posterior parietal cortex (PPC). The
cerebellar activation was found in the right anterior parasagittal
cerebellar cortex, in a region associated with the production of arm
movements. The frontal activation was located in the arm-related area
of the primary motor cortex. Figure 6
displays rCBF values for these three regions of interest, in each
experimental condition.
View this table:
[in this window]
[in a new window]
|
Table 3.
Significant activation observed in the hand error
correction contrast (p < 0.005; uncorrected
for multiple comparisons)
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[in a new window]
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Figure 5.
Horizontal, sagittal, and coronal difference
images representing the hand error correction effect corrected for the
eye kinematics and hand movement planning effects
(p < 0.005; planned comparison).
Activations are shown superimposed on a mean MRI in Talairach
coordinates. On the horizontal and coronal images, the anatomic
right side is shown on the left side. On
the sagittal images, positive values of x designate the
right hemisphere (ipsilateral to the reaching arm), and negative values
designate the left hemisphere (contralateral to the reaching arm). The
top row is centered on the cerebellar activation site
(11, 45, 20). The middle row is centered on the
posterior parietal activation site (41, 44, 58). The bottom
row is centered on the precentral activation site (30, 26,
57).
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Figure 6.
rCBF mean values (SD vertical bars), in each
experimental condition, for the three cerebral regions showing
significant activation in the hand error correction contrast
(E&A, eye and arm pointing; E, eye alone;
statio, stationary trial; jump, jump
trial). These regions are the primary motor cortex (black
triangles, left curve; Talairach coordinates:
30, 26, 57), the posterior parietal cortex (black
circles, middle curve; 41, 44, 58), and the
anterior parasagittal cerebellar cortex (black squares, right
curve; 11, 45, 20).
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DISCUSSION |
We identified a distributed cerebral network activated during
visually directed movements performed without vision of the moving
limb. Then, we describe within this network, for the first time, a
restricted subset of areas specifically involved in on-line movement guidance.
Reaching in the dark
Although the reach-related cerebral network observed in the
present study is generally coherent with previous observations performed with and without vision of the moving limb (Colebatch et al.,
1991; Grafton et al., 1992, 1996; Deiber et al., 1996; Lacquaniti et
al., 1997; Inoue et al., 1998; Turner et al., 1998; Winstein et
al., 1997), our results differ from earlier reports in two ways. First,
we observed a larger noncortical contribution, especially within the
cerebellum and pontine nuclei. Second, we noted a less distributed
cortical activation. In particular, we did not identify any activation
within the motor, premotor, and parietal cortices of the hemisphere
ispilateral to the moving arm. Similarly, we did not observe any rCBF
increase within the supplementary motor area or occipital visual areas.
These differences may be related to the fact that the motor reaching
task we investigated here was more "rudimentary" than the tasks
considered in earlier experiments. The present study differs, indeed,
from earlier studies by at least one of the following aspects: (1)
vision of the moving limb was never allowed, preventing visual feedback
loops from operating; (2) no estimation of the reaching error was
provided during or at the end of trial, prohibiting motor learning
(Jordan 1990; Redding and Wallace 1996); (3) the subjects reached to
the target directly without the mediation of a manipulandum or a
joystick, avoiding the need for complex visuomotor transformations; (4) targets were seen in binocular vision and not through a virtual display
system that provides conflicting vergence and accommodation signals,
thus requiring adaptive behavior by preventing real depth perception;
(5) oculomotor activity was strictly controlled allowing precise
evaluation of arm reaching-related changes in rCBF. Our data suggest
that basic reaching movements performed without visual guidance involve
a less distributed cortical network and rely more consistently on
cerebellar structures than the visually more complex motor tasks
usually studied.
Functional anatomy of movement guidance
Functional anatomy of nonvisual feedback loops was determined,
within the reach-related motor network, by identifying the brain areas
that increase their responsiveness when the error to be corrected is
larger. Because this planned analysis was performed without stringent
corrections for multiple comparisons (see Materials and Methods), one
might wonder whether the areas we identified may represent a series of
false-positive inferences. Although this possibility cannot be rejected
categorically it seems very unlikely, for at least two reasons. First,
the statistical threshold used in the planned analysis appears rather
conservative, considering that the search volume was limited to a very
plausible set of motor areas. Several studies have used a comparable
threshold for nonplanned investigations in which no correction for
multiple comparisons was applied (Inoue et al., 1998; Turner et al.,
1998). Second, our statistical query is supported by our knowledge of the functional properties of the cerebellum, the parietal cortex, and
the motor cortex. Convergent observations gathered during the last
decades have suggested that these areas may play a critical role in the
process of on-line error correction (for review, see Desmurget and
Grafton, 2000).
Anatomically, the PPC lies between the postcentral sulcus anteriorly,
the subparietal sulcus on the medial wall of the hemisphere, the
parieto-occipital sulcus posteriorly, and laterally the posteromedial part of the superior temporal sulcus and the posterior part of the
lateral sulcus (Stein, 1989). Numerous studies have shown, in monkey
and human, that the PPC is a highly differentiated structure with many
functional subdivisions (Andersen et al., 1997; Colby, 1998; Milner and
Dijkerman, 1998). Although the role of many of these subdivisions is
not entirely known yet, several observations have suggested that the
region of parietal cortex identified in the present study
(intraparietal sulcus and its surrounding cortex) may be involved in
on-line movement guidance in humans (Clower et al., 1996; Desmurget and
Grafton, 2000). Among these observations, the most compelling was
provided recently by our group in a transcranial magnetic stimulation
study (Desmurget et al., 1999a). Subjects pointed to visual targets
with their right hand. Vision of the arm was not allowed during the
movement. In some trials the target location was displaced during the
saccadic response, whereas in other trials it remained stationary. As
observed in the present study, the target jump elicited a smooth and
progressive adjustment of the hand path. Strikingly, when a single
transcranial magnetic stimulation pulse was applied, at hand movement
onset, over the PPC, these path corrections were disrupted, and the
subject pointed to the first target location. This result was recently
replicated in a clinical study involving a patient presenting with
bilateral ischemic lesions of the PPC (Pisella et al., 2000). Although
this patient was able to accurately point to stationary targets, she presented a dramatic inability to correct her ongoing movements when
the target location was slightly modified at movement onset.
Functionally, it has been suggested that a major role of PPC in
movement guidance is to determine whether and to what extent the
current motor response is inadequate (Desmurget et al., 1999a; Desmurget and Grafton, 2000). This hypothesis is based on the observation that PPC displays three main properties that would be
expected from an error detection module. First, it has access to a
representation of the target and current hand location through afferent
information coming from many sensory modalities (visual, proprioceptive, vestibular), and the main motor structures (Andersen et
al., 1997; Brodal and Bjaalie, 1997). Second, it is critical for
establishing stable relationships between heterogeneous information, i.e., for merging arm and target-related signals into a common frame of
reference (Clower et al., 1996; Carey et al., 1997; Colby, 1998;
Binkofski et al., 1999; Xing and Andersen, 2000). Third, it modulates
its neural activity as the hand approaches the target, i.e., as the
motor error varies (MacKay, 1992). In a recent paper, Desmurget and
Grafton (2000) have suggested that dynamic error detection was achieved
by the PPC through forward modeling. According to this view, a forward
model of the arm's dynamics is generated during the movement. This
forward model, which requires integration of both afferent and efferent
information, allows prediction of the movement end point. When a
discrepancy is detected between the predicted movement final location
and the target location, an error signal is generated. This error
signal has then to be transformed into an actual motor command. The
cerebellum is a primary candidate for this task.
Like PPC, the cerebellum has long been associated with feedback control
(Miall et al., 1993; Stein, 1986). As shown in several studies
involving reaching (Day et al., 1998) and tracking tasks (Miall et al.,
1993; Haggard et al., 1995), cerebellar lesions do not prevent on-line
trajectory adjustments from occurring. However, the motor corrections
generated by patients presenting with a lesion of the cerebellum are
characterized by excessive deviations and ill-tuned muscle activation
patterns. Anatomically, it was shown that the cerebellum receives
abundant input from PPC via the pontine nuclei (Brodal and Bjaalie,
1997; Middleton and Strick, 1998). Functionally, it was suggested that
one of the main contributions of the cerebellum to movement control is to perform the inverse computations allowing transformation of a
desired displacement into an actual muscle command. In support of this
idea, it was demonstrated that inverse models are represented within
the cerebellum (Wolpert et al., 1998; Kawato, 1999; Imamizu et al.,
2000) and that patients with cerebellar lesions display a chronic
inability to accurately define the pattern of muscle activation
required to direct the hand along a specific path (Bastian et al.,
1996; Day et al., 1998).
The previous observations suggest that the cerebellar contribution to
on-line movement guidance may be to convert the dynamic motor error
signal computed by PPC into an appropriate corrective command. Within
this context, the precentral gyrus activation we observed, concurrently
with the cerebellar activation, may be accounted for by assuming that
the cerebellar signal influence the ongoing motor command by modulating
the neural signal issued by the primary motor cortex. In agreement with
this view, it has been shown that the primary motor cortex receives
substantial input from the cerebellum via the ventrolateral thalamus
(Asanuma et al., 1983; Brodal and Bjaalie, 1997; Hoover and Strick,
1999). Also, it has been suggested that the motor system is organized in a relative hierarchy such that the primary motor cortex is mainly
involved in the low-level aspects of motor control. Consistent with
this idea, it was shown that purely kinematic and dynamical aspects of
the movement are more commonly represented in the motor cortex than,
for instance, in the parietal or premotor areas that seem to encode
more abstract variables (Alexander and Crutcher, 1990; Scott et al.,
1997; Shen and Alexander, 1997; Turner et al., 1998). The activation of
M1 during error correction may appear to be in contradiction with a
previous transcranial magnetic stimulation (TMS) study in which
we observed no effect, on movement correction, when the motor cortex
was stimulated (Desmurget et al., 1999). However, the level of
stimulation we used in our previous TMS study was not high enough to
generate any EMG response in the primary arm movers. This indicates
that the stimulation did not interfere with the ongoing movement. It is
likely that higher level of stimulation would have resulted in more
dramatic effects.
In conclusion, the present experiment provides new evidence in support
of the hypothesis that parietal-cerebellar circuits are critical for
hand movement guidance. We have shown that nonvisual feedback loops
involve a limited network including the motor cortex, the cerebellum,
and the PPC. Based on recent neurophysiological and clinical
observations, we hypothesize that PPC computes a dynamic motor error by
comparing the updated location of the visual target and the estimated
movement end point. This dynamic motor error is sent to the cerebellum,
which converts it into a corrective motor command. The corrective
signal influence finally the ongoing motor command by modulating the
neural signal issued by the primary motor cortex.
| |
FOOTNOTES |
Received Nov. 6, 2000; revised Jan. 25, 2001; accepted Feb. 5, 2001.
This project was supported by National Institutes of Health
Grant NS 33504 to S.G. We thank Michael White and Delicia Votaw for
their technical assistance and Roger Woods for providing image analysis
software. We also thank Miranda Lim for her contribution during data
collection and Laura Payne for editing this manuscript.
Correspondence should be addressed to Scott T. Grafton, Center for
Cognitive Neuroscience, 6162 Moore Hall, Dartmouth College, Hanover, NH
03755. E-mail: Scott.T.Grafton{at}dartmouth.edu.
| |
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N. Gosselin-Kessiby, J. Messier, and J. F. Kalaska
Evidence for Automatic On-Line Adjustments of Hand Orientation During Natural Reaching Movements to Stationary Targets
J Neurophysiol,
April 1, 2008;
99(4):
1653 - 1671.
[Abstract]
[Full Text]
[PDF]
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M. Desmurget and R. S. Turner
Testing Basal Ganglia Motor Functions Through Reversible Inactivations in the Posterior Internal Globus Pallidus
J Neurophysiol,
March 1, 2008;
99(3):
1057 - 1076.
[Abstract]
[Full Text]
[PDF]
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J. A. Taylor and K. A. Thoroughman
Divided Attention Impairs Human Motor Adaptation But Not Feedback Control
J Neurophysiol,
July 1, 2007;
98(1):
317 - 326.
[Abstract]
[Full Text]
[PDF]
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S. Clavagnier, J. Prado, H. Kennedy, and M.-T. Perenin
How Humans Reach: Distinct Cortical Systems for Central and Peripheral Vision
Neuroscientist,
February 1, 2007;
13(1):
22 - 27.
[Abstract]
[PDF]
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A. J. Suminski, S. M. Rao, K. M. Mosier, and R. A. Scheidt
Neural and Electromyographic Correlates of Wrist Posture Control
J Neurophysiol,
February 1, 2007;
97(2):
1527 - 1545.
[Abstract]
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[PDF]
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S. M. Beurze, F. P. de Lange, I. Toni, and W. P. Medendorp
Integration of Target and Effector Information in the Human Brain During Reach Planning
J Neurophysiol,
January 1, 2007;
97(1):
188 - 199.
[Abstract]
[Full Text]
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D. E. Vaillancourt, M. A. Mayka, and D. M. Corcos
Intermittent Visuomotor Processing in the Human Cerebellum, Parietal Cortex, and Premotor Cortex
J Neurophysiol,
February 1, 2006;
95(2):
922 - 931.
[Abstract]
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J. Diedrichsen, Y. Hashambhoy, T. Rane, and R. Shadmehr
Neural Correlates of Reach Errors
J. Neurosci.,
October 26, 2005;
25(43):
9919 - 9931.
[Abstract]
[Full Text]
[PDF]
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H.-O. Karnath and M.-T. Perenin
Cortical Control of Visually Guided Reaching: Evidence from Patients with Optic Ataxia
Cereb Cortex,
October 1, 2005;
15(10):
1561 - 1569.
[Abstract]
[Full Text]
[PDF]
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V. Della-Maggiore and A. R. McIntosh
Time Course of Changes in Brain Activity and Functional Connectivity Associated With Long-Term Adaptation to a Rotational Transformation
J Neurophysiol,
April 1, 2005;
93(4):
2254 - 2262.
[Abstract]
[Full Text]
[PDF]
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V. Della-Maggiore, N. Malfait, D. J. Ostry, and T. Paus
Stimulation of the Posterior Parietal Cortex Interferes with Arm Trajectory Adjustments during the Learning of New Dynamics
J. Neurosci.,
November 3, 2004;
24(44):
9971 - 9976.
[Abstract]
[Full Text]
[PDF]
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M. Desmurget, V. Gaveau, P. Vindras, R. S. Turner, E. Broussolle, and S. Thobois
On-line motor control in patients with Parkinson's disease
Brain,
August 1, 2004;
127(8):
1755 - 1773.
[Abstract]
[Full Text]
[PDF]
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J. W. Krakauer, M.-F. Ghilardi, M. Mentis, A. Barnes, M. Veytsman, D. Eidelberg, and C. Ghez
Differential Cortical and Subcortical Activations in Learning Rotations and Gains for Reaching: A PET Study
J Neurophysiol,
February 1, 2004;
91(2):
924 - 933.
[Abstract]
[Full Text]
[PDF]
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R. S. Turner, M. Desmurget, J. Grethe, M. D. Crutcher, and S. T. Grafton
Motor Subcircuits Mediating the Control of Movement Extent and Speed
J Neurophysiol,
December 1, 2003;
90(6):
3958 - 3966.
[Abstract]
[Full Text]
[PDF]
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A. Rodriguez-Fornells, A. R. Kurzbuch, and T. F. Munte
Time Course of Error Detection and Correction in Humans: Neurophysiological Evidence
J. Neurosci.,
November 15, 2002;
22(22):
9990 - 9996.
[Abstract]
[Full Text]
[PDF]
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TOP
ABSTRACT
INTRODUCTION
