A PET Study of Visuomotor Learning under Optical Rotation

NeuroImage 11, 505–516 (2000) doi:10.1006/nimg.2000.0554, available online at http://www.idealibrary.com on

A PET Study of Visuomotor Learning under Optical Rotation Kentaro Inoue,* Ryuta Kawashima,* ,† ,‡ ,1 Kazunori Satoh,* Shigeo Kinomura,* Motoaki Sugiura,* Ryoi Goto,* Masatoshi Ito,§ and Hiroshi Fukuda* ,† ,‡ *Institute of Development, Aging, and Cancer, Department of Nuclear Medicine and Radiology, ‡Center for Interdisciplinary Research, and §Cyclotron and Radioisotope Center, Tohoku University, Sendai 980-8575, Japan; and †Aoba Brain Imaging Research Center, TAO, Sendai 980-0872, Japan Received July 26, 1999

We measured the regional cerebral blood flow (rCBF) in six healthy volunteers with PET (positron emission tomography) and H 215O to identify the areas of the human brain involved in sensorimotor learning. The learning task was visually guided reaching with sensorimotor discrepancy caused by optical rotation. PET measurements were performed in the early and late stages of the adaptation to the sensorimotor perturbation. Control measurements were obtained during an eye movement task and a reaching task without optical rotation. The rCBF data of each learning stage were compared to those of both control conditions. During the early stage, rCBF increases were detected in the rostral premotor cortex bilaterally, the posterior part of the left superior parietal lobule (SPL), and the right SPL including the intraparietal sulcus (IPS). During the late stage, rCBF increases were detected in the left caudal premotor area, the left supplementary motor area proper, the left SPL, the right SPL including the IPS, and the right postcentral sulcus extending to the inferior parietal lobule. These results reveal that sensorimotor learning accompanies changes in the recruited cortical areas during different stages of the adaptation, reflecting the different functional roles of each area for different components of adaptation, from learning of new sensorimotor coordination to retention or retrieval of acquired coordination. ©

2000 Academic Press

INTRODUCTION The performance of accurate reaching movements with the arm toward a visually detected target requires complex neural transformations, from visual representation of the target location to motor commands for 1

To whom correspondence should be addressed at the Institute of Development, Aging, and Cancer, Department of Nuclear Medicine and Radiology, Tohoku University, 4-1 Seiryomachi, Aoba-ku, Sendai 980-8575, Japan. Fax: 81 (22) 717-8560. E-mail: ryuta@ idac.tohoku.ac.jp.

positioning the hand in space. This transformation can be viewed as a transformation between two different coordinate systems, from the frame of reference or coordinate system in which the target is perceived to the intrinsic or somatic frame of reference in which the brain can program the parameters of movement (Jeannerod, 1988; Soechting and Flanders, 1989; Kalaska and Crammond, 1992). Neurophysiological experiments have provided a basis for understanding this neuronal coordinate transformation system in the motor, premotor, and parietal areas of the cerebral cortex (Caminiti et al., 1991; Sakata and Kusunoki, 1992; Graziano and Gross, 1995; Winstein et al., 1997; Wise et al., 1997). Added sensory transformations have been studied using prisms or other optical devices to alter the visually derived information on both the target and the hand position. The plasticity of this visuomotor system in learning accurate movement execution under sensory perturbations has been studied extensively (Welch, 1986). Mechanisms of error correction during the learning of new sensorimotor transformation have been proposed (Roby-Brami and Burnod, 1995) based on psychophysical studies. Several brain imaging studies have revealed activation in many brain areas such as the primary and nonprimary motor areas, parietal cortex, thalamus, basal ganglia, and cerebellum in relation to visually guided finger or hand movements (Grafton et al., 1992b, 1996; Kawashima et al., 1994, 1995). Another study reported different functional roles of the areas activated during the accurate pointing task, from areas associated with the visuomotor demands of accurate aiming to areas associated with the motor execution demands of larger amplitude motor performance (Winstein et al., 1997). A recent study using the prism adaptation task reported posterior parietal activation (Clower et al., 1996). Another study on sensorimotor learning revealed activations in the parietal, prefrontal, and premotor areas and the cerebellum during the learning of externally induced force fields, as well as differences in the magnitude of acti-

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vation of various areas during different stages of learning (Shadmehr and Holcomb, 1997). These studies have partly elucidated the distinct roles of each cortical and subcortical area during visually guided limb movement and sensorimotor learning, but have not clarified brain activity during a new sensorimotor learning with visual perturbation of the hand, except one study (Clower et al., 1996), which did not focus on changes of activity in areas when the adaptation was accomplished. The purpose of this study was, first, to reveal areas involved in the ongoing learning of new sensorimotor transformation using the memory of trial-totrial correction (Roby-Brami and Burnod, 1995), which was based on the visual feedback of the terminal error during the reaching task with optical rotation. We also examined changes in activity in areas during adaptation to a sensorimotor discrepancy to address possible differences in the functional roles of these areas between the early stage during the progress of adaptation and the late stage, after accomplishment of sensorimotor learning. MATERIALS AND METHODS Subjects Six right-handed normal male volunteers (ages 19 –26 years) participated in this study. Handedness was assessed by the H. N. Handedness Inventory (Hatta and Nakatsuka, 1975). Written informed consent was obtained from each subject in accordance with the guidelines approved by Tohoku University and the Declaration of Human Rights, Helsinki. The protocol was approved by the local ethics committee of the Institute of Development, Aging, and Cancer, Tohoku University. Experimental Apparatus and Stimuli A schematic representation of the experimental setup is shown in Fig. 1. Each subject wore an individual stereotaxic fixation helmet and a head-mounted display (HMD; i-glass; Virtual I/O, Seattle, WA) during the PET measurements with a paper cover to prevent subjects from viewing outside of the HMD. A target board bearing six red light-emitting diodes (LEDs) and a green LED was set in front of the subjects. A chargecoupled device (CCD) camera (XC-999; Sony, Tokyo, Japan) was fixed on the helmet and was used to monitor the LEDs and the subject’s hand movements. The green LED and one of the six red, target LEDs were alternately switched on and off. The duration of lighting of the green LED (1 to 3 s) and the red LEDs (0.75 to 1.25 s) and the position of the target red LEDs to be illuminated were controlled in random order by a personal computer in all tasks. All visual stimuli were presented to the subjects on the HMD through the CCD camera.

FIG. 1. The subject lay supine on the bed of a PET apparatus (not shown) with his head inside the scanner wearing the HMD over the fixation helmet (not shown). A black board bearing six red LEDs aligned in a 10-cm-sided hexagon, with a green LED at the center, was set at a distance of about 50 cm in front of the subjects. A CCD camera was fixed on the helmet and tilted downward so that the LEDs could be captured at the center of its field of view (visual angle about 35 ⫻ 35). The subject was required to focus on the HMD in front of his face and could not see the target board directly. During the learning task, the CCD camera was rotated 60° clockwise.

Behavioral Tasks Four measurements of regional cerebral blood flow (rCBF) were performed for each subject. (1) Control task 1 (Saccade). The subject was asked to keep his right hand on his chest in a pointing shape and simply look at each LED as it was illuminated. (2) Control task 2 (Reach). The subject was instructed to point to the lit target LED with the right index finger and to return the hand to the resting position on the chest after each trial. To allow the subject to become accustomed to making reaching movements from the supine position in the PET scanner along with monitoring the HMD, the subject practiced for 3 min just before scans for this task were started, which was sufficient practice for performing accurate pointing movements (Welch, 1986). The subject was allowed to move his eyes to the target before hand movements. (3) Sensorimotor learning task, early and late stage. In this task, the CCD camera was rotated 60° clockwise, causing the subject’s field of view to rotate 60° counterclockwise. For this task, the subject was informed in advance about this visual rotation. He was instructed to point at the lit targets as quickly as possible and to attempt to improve the pointing accuracy by trial and error. To exclude the influence of possible learning effects for performing reaching movements in this experimental setup, this task was always performed after the Reach task, in which the maximal learning for this experimental environment is accom-

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plished. To compare the effects of this sensorimotor learning on rCBF changes during the progress of adaptation and when no further improvement in performance was observed, we defined the early stage of learning (Early) as that stage at which a reduction in pointing errors was prominent and the late stage of learning (Late) as that at which the reduction in pointing errors had reached asymptotic levels. We performed psychophysical pilot experiments using other subjects (data not shown) in advance of the PET experiments. We determined the Early condition as starting 30 s after the beginning of the reaching trials with optical rotation and determined that a 10-min interval between the Early and the Late conditions was enough to reach the asymptote of decrease in pointing errors, as we had to have an approximately 10-min interval between each PET measurement to allow for decay of radioactivity. To ensure that the subjects of the PET experiments accomplished the learning, we conducted performance analyses for these subjects in the same way as for the pilot study (see below) and show their data below. To balance the task order, the Saccade task was performed first, between the Reach and the learning tasks (the Early and Late should be continuous), or last, after all the other tasks. Performance Analysis The performance of each subject was recorded with a video recorder (30 frames/s) for offline analyses. The number of movements, reaction time (duration from the target LED lighting to the beginning of movement), and movement time (duration from the beginning of movement to pointing to the target) were determined by frame-by-frame analysis of the video recordings. We also calculated the constant errors and the variable errors (Jeannerod, 1988). Constant error was the distance between the mean endpoint location and the target LED that must be hit, and variable error was the product of ␲ ⫻ SD v ⫻ SD h, where SD v and SD h are the standard deviation of the vertical and the horizontal errors, respectively (Georgopoulos et al., 1981). The former was considered to represent a bias of the pointing positions from the target positions and the latter to represent the scatter of pointing positions. Learning of sensorimotor coordination was judged on the basis of reduction in these errors. Eye movements were not monitored since the HMD caused artifacts on the electrooculogram. We therefore performed a separate experiment using a different group of subjects in the supine position to record the eye movements under the same experimental setup, except that they lay in a supine position on a patient bed outside of the PET scanner, because we did not measure rCBF changes with these subjects. All the subjects were right-handed males (six subjects, ages

19 –29 years). In this experiment, eye movements were recorded using an infrared eye movement monitor (Takei, Tokyo, Japan) with a linear range of about ⫾20°, sampled at 62.5 Hz. The recorded data were passed through a low-pass analogue filter with a cutoff frequency of 30 Hz. The eye movement recorders were mounted on a HMD that was firmly attached to the subject’s head. The subject was instructed to perform the same tasks, which the subjects of the PET experiments were to perform, with eye movement recordings at the time periods corresponding to the PET measurement. Imaging The PET scans were performed with a SET-2400W PET scanner (Shimadzu, Kyoto, Japan) in two-dimensional mode. The physical characteristics of this scanner have been described previously (Fujiwara et al., 1997). This scanner acquires 63 slices with an interslice spacing of 3.125 mm, and the in-plane spatial resolution had a full-width at half maximum (FWHM) of 4.5 mm. Prior to the PET experiments, each subject had a catheter placed into the left antecubital vein for tracer administration and was placed comfortably in a supine position on the PET scanner wearing an individually fitted thermoplastic fixation helmets. A transmission scan with a 68Ge source was taken to correct for attenuation. The rCBF was measured after administration of a bolus injection of approximately 30 mCi (1110 MBq) H 215O about 30 s before each scan. Each PET measurement was started immediately after radioactive counts were monitored on the PET camera and was continued for 60 s. For anatomic reference, high-resolution whole-brain magnetic resonance images (MRI) for each subject were obtained on a separate occasion using a spoiled gradient echo sequence (TE ⫽ 12 ms, TR ⫽ 50 ms, flip angle 45°), with a 0.5-T Yokogawa Medical MRVectra scanner. Each MRI consisted of 96 slices with a voxel size of 1 ⫻ 1 ⫻ 1.5 mm. Data Analysis In the present study, standard anatomical structures of a human brain atlas system (Roland et al., 1994) were fitted interactively to each subject’s MRI using both linear and nonlinear parameters. These parameters were subsequently applied to transform rCBF PET images into the standard space (Talairach and Tournoux, 1988). The mean MRI was calculated from all the subject’s reformatted MRI. All subsequent data analyses were performed with statistical parametrical mapping (SPM96 software; Wellcome Department of Cognitive Neurology, London) implemented in MATLAB (Mathworks, Sherborn, MA). All PET data were smoothed with an isotropic Gaussian kernel of a FWHM of 16 mm to increase the

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signal-to-noise ratio and to compensate for individual differences in gyral anatomy. For the data analysis, the condition and subject were estimated according to the general linear model at each voxel (Friston et al., 1995), after specifying the appropriate design matrix. The design matrix included global activity as a confounding covariate and this analysis can therefore be regarded as an analysis of covariance (Friston et al., 1990). The mean global CBF was normalized to an arbitrary level of 50 ml/100 mg/min. To test the hypotheses regarding regionally specific condition effects, the estimates were compared using linear compounds or contrasts. The resultant sets of voxel values for each contrast constitute a statistical parametrical map of the t statistic SPM{t}. The SPM{t} were transformed to the unit normal distribution (SPM{Z}) and thresholded at 3.09 (P ⫽ 0.001, uncorrected). The design of our study was first to evaluate the main effect of the learning with a comparison of each learning stage, implicating sensorimotor remapping processes by trial-to-trial correction for the early stage and retaining and/or recruiting acquired sensorimotor coordinations for the late stage, to the Reach condition implicating visually guided reaching per se. To avoid detecting false-positive activations due to decreases in rCBF during the Reach condition, we performed conjunction analyses (Price and Friston, 1997), using the Saccade task as a baseline condition, of (Early ⫺ Saccade) with (Early ⫺ Reach) and of (Late ⫺ Saccade) with (Late ⫺ Reach). To reveal changes in magnitude of the activity of areas between the learning stages, we examined changes in the mean adjusted rCBF value of a voxel with the peak Z value in each area. We also evaluated the mean adjusted rCBF in areas detected by a contrast of (Reach ⫺ Saccade) to confirm activations in areas known to be active in reaching movement execution per se. The thresholded parametric maps were superimposed onto the average reformatted MRI of the same six subjects participating in this study. Anatomical localization of the areas of activation in each contrast or conjunction was made on the mean reformatted MRI. RESULTS Behavioral Data In the eye movement experiment, the mean (SD) saccade rate during the Saccade and Reach tasks and the Early and Late stages of the learning task were 40.0 (1.5), 37.8 (1.2), 38.8 (1.2), and 39.6 (1.5) per minute, respectively. A two-way analysis of variance did not show any significant effect of the condition or the subject on the mean saccade rate (P ⬎ 0.1). Representative records displaying the eye positions of a subject during each task are shown in Fig. 2. The graphs

FIG. 2. Representative records of eye position, without compensation, of a subject during the Saccade and Reach tasks and the Early and Late stages of the learning task in the experiment for eye movement monitoring. In each graph for each condition, the horizontal and vertical eye positions during 60 s, which corresponds to the duration of PET measurements, are displayed.

illustrate that the subject fixated onto targets during most of the pointing trials, but there were additional eye movements at the end of many of the pointing trials during every condition except for the Saccade task. These eye movements at the end of each pointing trial were considered to be directed to the hand or finger appearing in his field of view on the HMD. There was no definite difference in the eye movement amplitude or duration between the Reach task and the Early and Late stages of the learning task. Performance data for the subjects in the PET experiments are summarized in Table 1. The statistical significances were assessed by a one-way ANOVA and post hoc Scheffe’s tests. There were no statistically significant differences in the mean trial number among the Reach and the learning tasks [F(2,15) ⫽ 0.24; P ⬎ 0.1]. During both learning stages, the reaction time was longer [F(2,362) ⫽ 29.89; P ⬍ 0.0001] and the movement time was shorter

PET STUDY OF VISUOMOTOR LEARNING

TABLE 1 Subjects’ Performance Data

Trial number Reaction time (s) Movement time (s) Constant error (cm) Variable error (cm 2)

Reach

Early

Late

20.3 ⫾ 0.7 0.30 ⫾ 0.05 0.49 ⫾ 0.15 0.5 ⫾ 0.1 4.7 ⫾ 2.8

20.1 ⫾ 0.5 0.38 ⫾ 0.11* 0.44 ⫾ 0.11*** 3.2 ⫾ 1.9** ,†† 63.5 ⫾ 12.3* ,†

20.3 ⫾ 0.7 0.35 ⫾ 0.07* 0.43 ⫾ 0.10** 1.0 ⫾ 0.5 15.3 ⫾ 12.3

Note. All values are means ⫾ SD. Statistically significant differences (post hoc Scheffe test) are *P ⬍ 0.0001 compared with Reach, **P ⬍ 0.005 compared with Reach, ***P ⬍ 0.05 compared with Reach, †P ⬍ 0.0005 compared with Late, ††P ⬍ 0.05 compared with Late.

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rostral part of the dorsal premotor cortex (rostral PMd), possibly extending rostrally into area 8. The other activation area was detected in the posterior part of the SPL of the left hemisphere. The mean adjusted rCBF profiles of the maximally significant voxel in each area are shown in Fig. 6a. The activation in the rostral PMd bilaterally was statistically significant only during the Early stages of the learning task, not during the Late stages of the learning task relative to the Saccade and the Reach tasks. On the other hand, the activation in the SPL bilaterally was statistically significant not only during the Early stages of the learning task but also during the Late stages of the learning task.

[F(2,365) ⫽ 7.39; P ⬍ 0.001] than during the Reach task. There were no significant differences between the Early and the Late stages of the learning task in the reaction times (P ⬎ 0.1) and movement times (P ⬎ 0.5). During the Early stages of the learning task, pointed positions on the target board were biased to the counterclockwise direction, but during the Late stages of the learning task, they could reach the targets comparatively accurately (Fig. 3). Figure 4 shows reduction in mean constant and variable errors during the sensorimotor learning task and mean errors of both types during the control reaching task for comparison. The constant error was largest during the Early stages of the learning task [F(2,15) ⫽ 9.75; P ⬍ 0.005] compared with the Reach task (P ⬍ 0.005) and the Late stages of the learning task (P ⬍ 0.05). The variable error was also largest during the Early stages of the learning task [F(2,15) ⫽ 26.56; P ⬍ 0.0001] compared with the Reach task (P ⬍ 0.0001) and the Late stages of the learning task (P ⬍ 0.0005). Table 1 and Fig. 4 show the significant decrease in both types of errors during learning, and Fig. 4 shows a stable state with no further improvement in errors before the Late stages of the learning task. The Early Stage-Related Activations During the Early stages of the learning task, four regions showed statistically significant rCBF increases (Table 2, Fig. 5a) relative to the Saccade and Reach tasks. The largest activated area occupied a large part of the superior parietal lobule (SPL) of the right hemisphere and extended to the anterior part of the inferior parietal lobule (IPL) just caudal to the postcentral gyrus, including the intraparietal sulcus (IPS). Another activation was found bilaterally in the posteromedial part of the middle frontal gyrus (MFG) and included a part of the superior frontal sulcus. These areas were located rostrocaudally from the putative human frontal eye field (FEF) (Paus, 1996; Petit et al., 1996) and corresponded to the

FIG. 3. Positions of the ends of pointing movements on the target board of all subjects during the Reach, Early, and Late conditions. Abscissa and ordinate centered at the central LED; right and upper positions were applied positive values. Only pointed positions directed to the left upper target (pointed positions in open squares, target in filled square) are shown for clarity. Filled circles indicate the central LED and other five target LEDs. Note that most of the pointed positions during the Early condition biased to counterclockwise direction. During the Early and Late conditions, the upper left target was displayed on the HMD at the location of the most leftsided target.

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The Fields Involved in the Performance of Visually Guided Movements

FIG. 4. Changes in mean constant (open circle) and variable (filled circle) errors during the learning task, which was continued for about 12 min. These errors during the control reaching task (Reach) are also indicated for comparison. PET scans (indicated by bars on the abscissa line) for the early stage (Early) were taken 30 s after the learning task started and scans for the late stage (Late) were performed during the last minute of the task. These scans followed scans for those for the Reach (4 of 6 subjects) or the control eye movement task (Saccade, 2 of 6 subjects, not shown) with 10-min interval. All trials during the learning task were grouped into 12 bins comprising about 20 trials, each performed in 60 s, for analysis, and the first and the last bin almost corresponded to the early and the late condition, respectively. For clarity, the SD bars are shown in one direction only. A reduction in both types of errors with learning was initially observed with no further improvement in errors after about half of the learning course.

The Late Stage-Related Activations During the Late stages of the learning task, five areas were identified as being statistically significantly activated relative to the Saccade and Reach tasks. One of these occupied the most posterior part of the medial bank of the MFG, including the precentral sulcus of the left hemisphere. This area was located dorsal to the putative FEF (Paus, 1996; Petit et al., 1996) and was considered to correspond to the caudal part of the dorsal premotor cortex. Areas of activation in the supplementary motor area (SMA) and the posterior part of the SPL were also detected in the left hemisphere. The area in the SMA was located posterior to a vertical plane through the anterior commissural line (VCA line) (Talairach and Tournoux, 1988), thus it corresponds to the putative SMA proper (Picard and Strick, 1996). In the right hemisphere, an area including the posterior part of the SPL, extending to the IPS, and another area, extending from the postcentral sulcus (PoCS) to the anterior part of the IPL, were activated (Table 3, Fig. 5b). Figure 6b shows the rCBF increase in the left SMA proper specifically during the Late stages of the learning task. Activations in the caudal part of the PMd and the posterior SPL in the left hemisphere were not statistically significant during the Early stages of the learning task, but those in the SPL and in the PoCS/IPL of the right hemisphere were activated during the Early stages of the learning task as well as during the Late stages of the learning task.

In the contrast to (Reach ⫺ Saccade), three fields of significant activation were identified (Table 4, Fig. 5c). A large area extending from the lateral SPL to the central sulcus, the PMA, and the posterior part of the cingulate of the left hemisphere was centered on the primary sensorimotor cortex. The other two areas were located in the anterior part of the cerebellar vermis and the lateral part of the right cerebellar hemisphere. There were no statistically significant differences in rCBF change in these areas between the Reach, the Early, and the Late conditions of the sensorimotor learning task (Fig. 6c). DISCUSSION We found that a widespread network of cortical regions, including the parietal cortex, the rostral and caudal PMA, and the left SMA, was recruited to adapt sensorimotor perturbation for performing visually guided pointing movements under optical rotation. It was also shown that there were significant differences in the rCBF changes in areas between the early and the late stages of sensorimotor learning. This might reflect the prevalent role of the respective areas in the neural processing during each of these learning stages. Specifically, learning-related activations of the rostral PMA and parietal areas bilaterally were demonstrated in relation to acquisition of sensorimotor coordination and those of the caudal PMA and SMA of the left hemisphere, in relation to performance of learned motor skills (Fig. 7). The Task and the Subject’s Performances During the adaptation, there were no significant differences in trial number among the three conditions TABLE 2 Foci of Significant Increases in rCBF during the Early Stage of the Learning Task Compared with the Saccade and Reach Tasks Region

Talairach coordinates

Z score

R. SPL–IPS R. SFS–MFG (rostral PMd) L. posterior SPL L. SFS–MFG (rostral PMd)

18, ⫺60, 58 34, 14, 58 ⫺24, ⫺64, 52 ⫺36, 12, 60

4.81 3.72 4.32 4.32

Note. Anatomical localization was made for each area based on the mean MRI of the subjects and the atlas (Talairach and Tournaux, 1988). The Talairach coordinates in the order x, y, z (in millimeters) and the Z score are given for the maximally significant voxel in each area. Abbreviations: R, right hemisphere; L, left hemisphere; SPL, superior parietal lobule; IPS, intraparietal sulcus; MFG, middle frontal gyrus; SFS, superior frontal sulcus; PMd, dorsal premotor cortex.

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FIG. 5. Statistical parametric maps of statistically significant increases of regional cerebral blood flow. (a, b, and c) Each corresponds to the analysis of (Early ⫺ Saccade) with (Early ⫺ Reach), (Late ⫺ Saccade) with (Late ⫺ Reach), and (Reach ⫺ Saccade), respectively, superimposed on a mean MRI of the same 6 subjects. The coronal sections are ⫺22 mm to the anterior commissural line in Talairach coordinates, and each of the five axial sections are ⫺32, ⫺4, 54, 58, and 62 mm to the anterior–posterior commissural line. The left hemisphere is on the right side of the image.

requiring pointing movements (Reach, Early, and Late) due to the external pacing, thus the differences in the rCBF increases in brain areas cannot be attributed to differences in the number of movements. In addition, there were slight but clear differences in eye movements between the control eye movement condition (Saccade) and the three conditions requiring pointing, but there was no apparent difference in subjects’ eye movements among these pointing tasks. These analyses compared the learning conditions not only with the eye movement condition but also with the pointing condition without optical rotation; therefore, it is unlikely that areas contributing to eye movement generation per se or planning for eye movements were detected. On the other hand, during both of the stages of the learning task, the subjects took a little longer time to start the reaching movement after the target LED lighting and a shorter time to point to the target

from the start of the movement. As we could not monitor reaching trajectories in this experiment, we could not rule out possible confounding kinematics effects among the three conditions requiring pointing movements, but these differences in duration could have resulted from the prior instruction to the subjects to point at the target as quickly as possible and the allowance to point inaccurately in each trial, although they were required to improve pointing accuracy through the learning course. The slight reduction of movement time indicates that the subjects did not make online corrective movement, which would take several hundred milliseconds (Jeannerod, 1988). Rather, the slight increase of reaction time indicates that the subjects estimated the correct movement direction from the visual information of the terminal error of finger position in previous trials. This learned correction would be modified trial by trial and result in

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FIG. 6. Graphs illustrating changes of rCBF across four conditions: S, Saccade; R, Reach; E, Early; and L, Late. The mean adjusted rCBF values and SDs are given for the peak activation (specified in terms of Talairach coordinates). Black bar: statistically significantly increased (P ⬍ 0.001) compared with other three conditions. Gray bars: statistically significantly increased (P ⬍ 0.001) compared with the two controls but not significant in comparison with the other learning stage. (a) For fields activated during the early stage of learning. (b) For fields activated during the late stage. (c) For fields activated during performance of reaching movements. Top bars, SD. Abbreviations: see Tables.

a progressive improvement in command (Roby-Brami and Burnod, 1995). This would cause larger activation in areas involved in spatial information processing, sensorimotor coordinate transformation, and motor TABLE 3 Foci of Significant Increases in rCBF during the Late Stage of the Learning Task Compared with the Saccade and Reach Tasks

planning than in areas involved in the execution of corrective movements. The Premotor Area The present study revealed statistically significant rCBF increases, in the relatively rostral part of the PMd bilaterally, during the early stage of learning, and

TABLE 4 Region

Talairach coordinates

Z score

R. SPL R. PoCS–anterior IPL L. posterior SPL L. SMA proper L. caudal PMd

28, ⫺44, 62 56, ⫺22, 50 ⫺20, ⫺68, 46 ⫺12, ⫺22, 54 ⫺32, ⫺2, 54

4.06 3.95 3.92 4.2 3.64

Note. Abbreviations: PoCS, postcentral sulcus; PMd, dorsal premotor cortex; SMA, supplementary motor area; IPL, inferior parietal lobule. See Table 2 for other details.

Foci of Significant Increases in rCBF during the Reach Task Compared with the Saccade Task Region

Talairach coordinates

Z score

L. SPL–CS–posterior cingulate R. anterior lateral cerebellum Anterior vermis

⫺26, ⫺30, 58 30, ⫺52, ⫺30 6, ⫺56, ⫺8

4.81 3.74 3.63

Note. Abbreviations: CS, central sulcus. See Table 2 for details.

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FIG. 7. Activation patterns for cortical areas and contralateral cerebellar regions. Images are shown in the Talairach space (Talairach and Tournox, 1988) with the Z-coordinate label. The right side of the image corresponds to the left and the top to the frontal region. Open squares, regions significantly more activated during the early stage than during the late stage; gray squares, regions activated during both the learning stages without significant differences between stages; black squares, regions significantly more activated during the late stage than during the early stage; black circles, regions activated in relation to the reaching movement per se. a, right lateral cerebellum; b, anterior vermis; c, right SPL; d, right PoCS; e, left SPL; f, bilateral rostral PMd; g, left caudal PMd; h, left CS; i, left SNA proper. Abbreviations: see Tables.

in the caudal part of the left PMd, at the precentral sulcus, during the late stage. In brain imaging studies, an area corresponding to the dorsal premotor area in the macaque (He et al., 1993) is considered to be located in the dorsal part of area 6 on the lateral surface, near the intersection of the superior frontal sulcus and the precentral sulcus (Grafton et al., 1996; Fink et al., 1997) and this location lies behind the VCA line (Fink et al., 1997). In this article, we will refer to the dorsal premotor area rostral to the VCA line as the rostral PMd and that caudal to the VCA line as the caudal PMd in referring to the results of other studies as well as those of the present study. The rostral PMd bilaterally in this study showed the highest rCBF increase during the early stage of learning but the rCBF decreased during the late stage. In this study of visually guided sensorimotor learning, subjects had to retain the visual and proprioceptive information of each pointing trial in mind and to estimate the correct reaching direction in subsequent trials based on the error information. These processes would become relatively more load-less to the neural system during the late stage than during the early stage, as the sensorimotor remapping is accomplished, and this would be reflected in the rCBF changes in the rostral PMd. The activity of these areas did not decrease to the control level during the late stage, probably because the incompleteness of adaptation to the optical rotation (Welch, 1986) would not allow the learning to reach to completely automatic performance. Deiber et al. (1997) reported a learning-related decrease in rCBF in the rostral PMd during the learning of a conditional motor task, as well as an increase in the caudal PMd, like in the present study, and they suggested a functional

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affinity between the rostral PMd and the prefrontal cortex. Kawashima et al. (1995) also reported similar results during actual reaching with learning of locations of targets, although they regarded all those areas as being included in the premotor area. Several studies also reported rCBF changes in this area, which were higher during the execution of unlearned movement than during that of learned movement, during learning of sequential finger movements (Jenkins et al., 1994; Jueptner et al., 1997), and maze writing learning without visual information (van Mier et al., 1998). These studies, however, also reported rCBF decreases in the caudal PMd during learning so that the functional differences between the rostral and the caudal PMd were, if any, unclear. On the other hand, several recent studies have proposed a functional role for the posterior part of the superior frontal sulcus in spatial working memory (Courtney et al., 1996, 1998), although others have reported area 9/46 as being the spatial working memory area (McCarthy et al., 1994, 1996; Petit et al., 1996) or have reported activities of both areas (Jonides et al., 1993; Owen et al., 1996; Carlson et al., 1998). Grafton et al. (1995) have reported rCBF increases in the right premotor area, corresponding to the caudal PMd, as well as the right prefrontal area and the parietal area bilaterally when a spatial motor learning became explicit. Whether or not the designation of the prefrontal or premotor cortex is appropriate for the rostral PMd in this study, activation in this area is considered a result of the process of holding spatial error information to estimate the correct reaching direction. The functional role of this rostral PMd area could indeed involve spatial working memory or attention in space (Corbetta et al., 1993), but it might be more directed at information processing for planning of movement in space, than the middorsolateral prefrontal cortex (area 9/46), in which we did not find statistically significant activation. Changes in activity of the caudal PMd were reported during motor sequence learning (Jenkins et al., 1994; Jueptner et al., 1997), conditional motor learning (Deiber et al., 1997), skill learning (Seitz et al., 1994; Shadmehr and Holcomb, 1997; van Mier et al., 1998), and learning of incompatible stimulus mappings (Iacoboni et al., 1996) and after procedural learning that accompanies remapping of sensorimotor coordinations (Shadmehr and Holcomb, 1997). In several studies, those activations were found only after learning (Seitz et al., 1994; Shadmehr and Holcomb, 1997) or were greater than those during the Early stage of learning (Iacoboni et al., 1996; Deiber et al., 1997) as in the present study. In nonhuman primates, increases in the neuronal activity in the PMd were found during the progress of conditional motor learning (Mitz et al., 1991) and the PMd was considered to be involved in novel spatial transformations or the conversion of nonspatial information into spatial motor commands (Wise

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et al., 1997). Based on these findings, our results were considered to reflect the functional roles of the caudal PMd in the retrieval of established visuomotor associations (Mitz et al., 1991) more than in the learning process of pointing in the presence of visuomotor discrepancy. The Other Motor Cortices and the Cerebellum The adjusted rCBF profiles revealed a different activation pattern among the primary sensorimotor cortex, PMd (discussed above), and SMA proper in relation to learning. The putative left SMA proper (Picard and Strick, 1996) was specifically activated during the late stage of the learning task, while the left SMC was activated during the Reach task as much as during each of the learning stages. Activation in the SMA or SMA proper during the performance of learned movements has been reported by several authors (Roland et al., 1980; Grafton et al., 1992a, 1994; Jenkins et al., 1994; Schlaug et al., 1994) as well as in our study. Another report showed that disruption of the SMA using transmagnetic stimulation did not interrupt procedural learning (Pascual-Leone et al., 1996), also consistent with the results of the present study. Together, the results are considered to show the functional role of the SMA proper in the performance of previously learned motor skills without direct involvement in the promotion of procedural learning. We found large fields of activation in the lateral part of the right cerebellar hemisphere and the anterior part of the vermis in relation to the performance of visually guided reaching movements, but did not find any statistically significant activation related to the adaptation of visually guided reaching with optical rotation. Involvement of the cerebellum in prism adaptation has been shown in lesion studies and patient studies (Baizer and Glickstein, 1974; Thach et al., 1992). A recent PET study also reported rCBF increase in the right anterior cerebellum during a recall task of motor skill learning (Shadmehr and Holcomb, 1997). In another imaging study of visually guided reaching wearing displacing prisms, on the contrary, the authors reported absence of activation of the cerebellum (Clower et al., 1996). One possible reason for this could be the use of an error-correction task as the control task in the latter study so that the possible rCBF increases in relation to the process of error correction were canceled. In the present study, on the other hand, the subjects prepared for a movement and did not use online correction based on sensory feedback from the ongoing movement (described above). If the cerebellum is an organ for sensory information analysis as suggested by several authors (Gao et al., 1996; Bower, 1997), then this strategy of planning the movement and then executing the appropriate one means that the subjects did not rely on concurrent sensory feedback,

which is in agreement with our present results. Another possible reason is that the subjects could adapt to the sensorimotor perturbation within 12 min, which could be too short a time for synaptic plasticity to occur. It could also be possible that the intersubject averaging methods for statistical analyses and/or usage of a relatively large Gaussian filter (FWHM 16 mm) did not allow detection of activation. In a recent study of functional MRI using intrasubject analyses and a small filter (FWHM 3.75 mm) (Imamizu et al., 1998), activations of the cerebellum were reported to be found in different locations in the lateral part of the hemisphere depending on the rules for visuomotor transformation. Therefore, the inconsistency of our results with previous neurological findings (Baizer and Glickstein, 1974; Thach et al., 1992) could be attributed to these differences in methodologies. The Parietal Cortex The lateral SPL, extending into the IPS, bilaterally, and the most rostral part of the right IPL were activated during both early and late stages of the learning. There was no statistically significant difference in the magnitude of rCBF increase at the maximally significant voxel for each area, but the extent of activation showed a marked decrease as the learning progressed. It was also found that these learning-related activations were particularly apparent in the right hemisphere. We also found rCBF increases in the left SPL in relation to visually guided reaching with the right arm, but there were no significant differences in activation in terms of the sensorimotor learning. In nonhuman primates, recent studies suggested that the SPL plays important roles in visuospatial information processing related to visually guided reaching (Tanne et al., 1995; Caminiti et al., 1996; Johnson et al., 1996). Involvement of SPL in visually guided movements in humans was reported in a study on patients (Perenin and Vighetto, 1988) and in imaging studies (Grafton et al., 1992b, 1996; Kawashima et al., 1995; Winstein et al., 1997). It was also reported that the SPL and the anterior part of the IPS were involved in mental spatiotemporal projections of the body (Bonda et al., 1996). Thus, in the present experiment, the activity observed in the right SPL and caudal left SPL, which was increased during the learning task, compared to the control reaching task, unlike the rostral left SPL, may underlie transformations between the body-centered frame of reference and the external one altered by optical rotation. Changes in the activities of the right SPL that are significant during the early stage of learning or prelearned stage and decrease as learning proceeds were reported by several authors (Jenkins et al., 1994; Deiber et al., 1997; Jueptner et al., 1997; Seitz et al., 1997; van Mier et al., 1998). In the present experiments, subjects used only their right arm so that

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the data were not sufficient to discuss about the functional differences between the right and the left hemispheres for sensorimotor learning. However, van Mier et al. (1998) reported practice-related decreases in activity during learning that were independent of the used hand. The present results, therefore, also might suggest that the right parietal cortex plays a greater role in skill learning. CONCLUSION Two main findings emerge from the present study. The first is that the right hemisphere-dominant activation in the SPL and activation of the rostral PMd bilaterally during the early stage of learning, when transformation of sensorimotor coordinates to adjust visuomotor discrepancy, might be progressing. The second is activation of the left SMA proper and the left caudal PMd during the late stage when retrieval or retention of acquired sensorimotor coordination might be required. The differences in the areas activated compared with previous studies on procedural learning during each learning stage were considered to be due to the differences in neural processing required for the tasks adopted in each study; on the other hand, the frontoparietal activations similar to those reported in previous studies might reflect common aspects in procedural learning such as transition from attentive to automatic processes. ACKNOWLEDGMENTS This work was supported by Grants-in-Aid for Scientific Research on Priority Research from the Japanese Ministry of Education, Science, Sports, and Culture (1145204, 09207102); by Research for the Future from Japan Society for the Promotion of Science (JSPS-RFTF 97L00202); and by a grant from the Telecommunication Advancement Organization of Japan.

REFERENCES Baizer, J. S., and Glickstein, M. 1974. Proceedings: Role of cerebellum in prism adaptation. J. Physiol. 236: 34P–35P. Bonda, E., Petrides, M., Frey, S., and Evans, A. 1995. Neural correlates of mental transformations of the body-in-space. Proc. Natl. Acad. Sci. USA 92: 11180 –11184. Bower, J. M. 1997. Control of sensory data acquisition. Int. Rev. Neurobiol. 41: 489 –514. Caminiti, R., Ferraina, S., and Johnson, P. B. 1996. The sources of visual information to the primate frontal lobe: A novel role for the superior parietal lobule. Cereb. Cortex 6: 319 –328. Caminiti, R., Johnson, P. B., Galli, C., Ferraina, S., and Burnod, Y. 1991. Making arm movements within different parts of space: The premotor and motor cortical representation of a coordinate system for reaching to visual targets. J. Neurosci. 11: 1182–1197. Carlson, S., Martinkauppi, S., Rama, P., Salli, E., Korvenoja, A., and Aronen, H. J. 1998. Distribution of cortical activation during visuospatial n-back tasks as revealed by functional magnetic resonance imaging. Cereb. Cortex 8: 743–752.

515

Clower, D. M., Hoffman, J. M., Votaw, J. R., Faber, T. L., Woods, R. P., and Alexander, G. E. 1996. Role of posterior parietal cortex in the recalibration of visually guided reaching. Nature 387: 618 – 621. Corbetta, M., Miezin, F., Shulman, G., and Petersen, S. 1993. A PET study of visuospatial attention. J. Neurosci. 13: 1202–1226. Courtney, S. M., Ungerleider, L., Keil, K., and Haxby, J. 1996. Object and spatial visual working memory activate separate neural systems in human cortex. Cereb. Cortex 6: 39 – 49. Courtney, S. M., Petit, L., Maisog, J. M., Ungerleider, L. G., and Haxby, J. V. 1998. An area specialized for spatial working memory in human frontal cortex. Science 279: 1347–1351. Deiber, M. P., Wise, S. P., Honda, M., Catalan, M. J., Grafman, J., and Hallett, M. 1997. Frontal and parietal networks for conditional motor learning: A positron emission tomography study. J. Neurophysiol. 78: 977–991. Fink, G., Frackowiak, R., Pietrzyk, U., and Passingham, R. 1997. Multiple nonprimary motor areas in the human cortex. J. Neurophysiol. 77: 2164 –2174. Friston, K. J., Frith, C. D., Liddle, P. F., Dolan, R. J., Lammertsma, A. A., and Frackowiak, R. S. 1990. The relationship between global and local changes in PET scans. J. Cereb. Blood Flow Metab. 10: 458 – 466. Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. B., Frith, C. D., and Frackowiak, R. S. J. 1995. Statistical parametric maps in functional imaging: A general linear approach. Hum. Brain Mapp. 2: 189 –210. Fujiwara, T., Watanuki, S., Yamamoto, S., Miyake, M., Seo, S., Itoh, M., Ishii, K., Orihara, H., Fukuda, H., Satoh, T., Kitamura, K., Tanaka, K., and Takahashi, S. 1997. Performance evaluation of a large axial field-of view PET scanner: SET-2400W. Ann. Nucl. Med. 11: 307–313. Gao, J. H., Parsons, L. M., Bower, J. M., Xiong, J., Li, J., and Fox, P. T. 1996. Cerebellum implicated in sensory acquisition and discrimination rather than motor control. Science 272: 545–547. Georgopoulos, A. P., Kalaska, J. F., and Massey, J. T. 1981. Spatial trajectories and reaction times of aimed movements: Effects of practice, uncertainty, and change in target location. J. Neurophysiol. 46: 725–743. Grafton, S. T., Fagg, A. H., Woods, R. P., and Arbib, M. A. 1996. Functional anatomy of pointing and grasping in humans. Cereb. Cortex 6: 226 –237. Grafton, S. T., Hazeltine, E., and Ivry, R. 1995. Functional mapping of sequence learning in normal humans. J. Cognit. Neurosci. 7: 497–510. Grafton, S. T., Mazziotta, J. C., Presty, S., Friston, K. J., Frackowiak, R. S., and Phelps, M. E. 1992a. Functional anatomy of human procedural learning determined with regional cerebral blood flow and PET. J. Neurosci. 12: 2542–2548. Grafton, S. T., Mazziotta, J. C., Woods, R. P., and Phelps, M. E. 1992b. Human functional anatomy of visually guided finger movements. Brain 115: 565–587. Grafton, S. T., Woods, R. P., and Tyszka, M. 1994. Functional imaging of procedural motor learning: Relating cerebral blood flow with individual subject performance. Hum. Brain Mapp. 1: 221–234. Graziano, M. S. A., and Gross, C. G. 1995. The representation of extrapersonal space: A possible role for bimodal, visual-tactile neurons. In The Cognitive Neurosciences (M. S. Gazzaniga, Ed.), pp. 1021–1034. MIT Press, Cambridge, MA. Hatta, T., and Nakatsuka, Z. 1975. Handedness inventry. In Papers Celebrating the 63rd Birthday of Prof. Ohnishi (D. Ohno, Ed.), pp. 224 –245. Osaka City Univ., Osaka. He, S.-Q., Dum, R. P., and Strick, P. L. 1993. Topographic organization of corticospinal projections from the frontal lobe: Motor areas on the lateral surface of the hemisphere. J. Neurosci. 13: 952–980.

516

INOUE ET AL.

Imamizu, H., Miyauchi, S., Tamada, T., Sasaki, Y., Takino, R., Yoshioka, T., Putz, B., and Kawato, M. 1998. Multiple representations for visuomotor learning in the cerebellum: A functional MRI study. NeuroImage 7: S819. Jeannerod, M. 1988. The Neural and Behavioural Organization of Goal-Directed Movements. Oxford Univ. Press, Oxford. Jenkins, I. H., Brooks, D. J., Nixon, P. D., Frackowiak, R. S., and Passingham, R. E. 1994. Motor sequence learning: A study with positron emission tomography. J. Neurosci. 14: 3775–3790. Johnson, P. B., Ferraina, S., Bianchi, L., and Caminiti, R. 1996. Cortical networks for visual reaching: Physiological and anatomical organization of frontal and parietal lobe arm regions. Cereb. Cortex 6: 102–119. Jonides, J., Smith, E., Koeppe, R., Awh, E., Minoshima, S., and Mintun, M. 1993. Spatial working memory in humans as revealed by PET. Nature 363: 623– 625. Jueptner, M., Stephan, K. M., Frith, C. D., Brooks, D. J., Frackowiak, R. S., and Passingham, R. E. 1997. Anatomy of motor learning. I. Frontal cortex and attention to action. J. Neurophysiol. 77: 1313–1324. Kalaska, J. F., and Crammond, D. J. 1992. Cerebral cortical mechanisms of reaching movements. Science 255: 1517–1523. Kawashima, R., Roland, P. E., and O’Sullivan, B. T. 1994. Fields in human motor areas involved in preparation for reaching, actual reaching, and visuomotor learning: A positron emission tomography study. J. Neurosci. 14: 3462–3474. Kawashima, R., Roland, P. E., and O’Sullivan, B. T. 1995. Functional anatomy of reaching and visuomotor learning: A positron emission tomography study. Cereb. Cortex 2: 111–122. McCarthy, G., Blamire, A., Puce, A., Nobre, A., Bloch, G., Hyder, F., Goldman-Rakic, P., and Shulman, R. 1994. Functional magnetic resonance imaging of human prefrontal cortex activation during a spatial working memory task. Proc. Natl. Acad. Sci. USA 91: 8690 – 8694. McCarthy, G., Puce, A., Constable, R. T., Krystal, J. H., Gore, J. C., and Goldman-Rakic, P. 1996. Activation of human prefrontal cortex during spatial and nonspatial working memory tasks measured by functional MRI. Cereb. Cortex 6: 600 – 611. Mitz, A. R., Godschalk, M., and Wise, S. P. 1991. Learning-dependent neuronal activity in the premotor cortex: Activity during the acquisition of conditional motor associations. J. Neurosci. 11: 1855–1872. Owen, A. M., Evans, A. C., and Petrides, M. 1996. Evidence for a two-stage model of spatial working memory processing within the lateral frontal cortex: A positron emission tomography study. Cereb. Cortex 6: 31–38. Pascual-Leone, A., Wassermann, E. M., Grafman, J., and Hallett, M. 1996. The role of the dorsolateral prefrontal cortex in implicit procedural learning. Exp. Brain Res. 107: 479 – 485. Paus, T. 1996. Location and function of the human frontal eye-field: A selective review. Neuropsychologia 34: 475– 483. Perenin, M. T., and Vighetto, A. 1988. Optic ataxia: A specific disruption in visuomotor mechanisms. Brain 111: 643– 674. Petit, L., Orssaud, C., Tzourio, N., Crivello, F., Berthoz, A., and B. M. 1996. Functional anatomy of a prelearned sequence of horizontal saccades in humans. J. Neurosci. 16: 3714 –3726. Picard, N., and Strick, P. L. 1996. Motor areas of the medial wall: A review of their location and functional activation. Cereb. Cortex 6: 342–353.

Price, C. J., and Friston, K. J. 1997. Cognitive conjunction: A new approach to brain activation experiments. NeuroImage 5: 261– 270. Roby-Brami, A., and Burnod, Y. 1995. Learning a new visuomotor transformation: Error correction and generalization. Cognit. Brain Res. 2: 229 –242. Roland, P. E., Graufelds, C. J., Wahlin, J., Ingelman, L., Andersson, M., Ledberg, A., Pedersen, J., Akerman, S., Dabringhaus, A., and Zilles, K. 1994. Human brain atlas: For high-resolution functional and anatomical mapping. Hum. Brain Mapp. 1: 173–184. Roland, P. E., Larsen, B., Lassen, N. A., and Skinhoj, E. 1980. Supplementary motor area and other cortical areas in organization of voluntary movements in man. J. Neurophysiol. 43: 118 – 136. Sakata, H., and Kusunoki, M. 1992. Organization of space perception: Neural representation of three-dimensional space in the posterior parietal cortex. Curr. Opin. Neurobiol. 2: 170 –174. Schlaug, G., Knorr, U., and Seitz, R. 1994. Inter-subject variability of cerebral activations in acquiring a motor skill: A study with positron emission tomography. Exp. Brain Res. 98: 523–534. Seitz, R. J., Canavan, A. G., Yaguez, L., Herzog, H., Tellmann, L., Knorr, U., Huang, Y., and Homberg, V. 1994. Successive roles of the cerebellum and premotor cortices in trajectorial learning. NeuroReport 5: 2541–2544. Seitz, R. J., Canavan, A. G., Yaguez, L., Herzog, H., Tellmann, L., Knorr, U., Huang, Y., and Homberg, V. 1997. Representations of graphomotor trajectories in the human parietal cortex: Evidence for controlled processing and automatic performance. Eur. J. Neurosci. 9: 378 –389. Shadmehr, R., and Holcomb, H. H. 1997. Neural correlates of motor memory consolidation. Science 277: 821– 825. Soechting, J. F., and Flanders, M. 1989. Sensorimotor representations for pointing to targets in three-dimentional space. J. Neurophysiol. 62: 582–594. Talairach, J., and Tournoux, P. 1988. Co-planar Stereotaxic Atlas of the Brain. Thieme, New York. Tanne, J., Boussaoud, D., Boyer-Zeller, N., and Rouiller, E. M. 1995. Direct visual pathways for reaching movements in the macaque monkey. NeuroReport 7: 267–272. Thach, W. T., Goodkin, H. P., and Keating, J. G. 1992. The cerebellum and the adaptive coordination of movement. Annu. Rev. Neurosci. 15: 403– 442. van Mier, H., Tempel, L., Perlmutter, J., Raichle, M., and Petersen, S. 1998. Changes in brain activity during motor learning measured with PET: Effects of hand of performance and practice. J. Neurophysiol. 80: 2177–2199. Welch, R. B. 1986. Adaptation of space perception. In Handbook of Perception and Human Performance (K. R. Boff, L. Kaufman, and J. P. Thomas, Eds.). Wiley, New York. Winstein, C. J., Grafton, S. T., and Pohl, P. S. 1997. Motor task difficulty and brain activity: Investigation of goal-directed reciprocal aiming using positron emission tomography. J. Neurophysiol. 77: 1581–1594. Wise, S. P., Boussaoud, D., Johnson, P. B., and Caminiti, R. 1997. Premotor and parietal cortex: Corticocortical connectivity and combinatorial computations. Annu. Rev. Neurosci. 20: 25– 42.