Patterns of regional brain activation associated with different forms of motor learning

Brain Research 871 (2000) 127–145 www.elsevier.com / locate / bres

Research report

Patterns of regional brain activation associated with different forms of motor learning a,b ,

a

c

d

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Maria-Felice Ghilardi *, Claude Ghez , Vijay Dhawan , James Moeller , Marc Mentis , Toshitaka Nakamura c , Angelo Antonini c , David Eidelberg c a

Center for Neurobiology and Behavior, Center for Neurobiology and Behavior, NYS Psychiatric Institute, Columbia College of Physicians and Surgeons, PI Annex Room 819, New York, NY 10032, USA b I.N.B.-C.N.R., I. S. San Raffaele, Milano, Italy c Department of Neurology, North Shore University Hospital, Manhasset, New York and New York University School of Medicine, New York, NY, USA d Department of Psychiatry, Columbia College of Physicians and Surgeons, New York, NY 10032, USA Accepted 4 April 2000

Abstract To examine the variations in regional cerebral blood flow during execution and learning of reaching movements, we employed a family of kinematically and dynamically controlled motor tasks in which cognitive, mnemonic and executive features of performance were differentiated and characterized quantitatively. During 15 O-labeled water positron emission tomography (PET) scans, twelve right-handed subjects moved their dominant hand on a digitizing tablet from a central location to equidistant targets displayed with a cursor on a computer screen in synchrony with a tone. In the preceding week, all subjects practiced three motor tasks: 1) movements to a predictable sequence of targets; 2) learning of new visuomotor transformations in which screen cursor motion was rotated by 308–608; 3) learning new target sequences by trial and error, by using previously acquired routines in a task placing heavy load on spatial working memory. The control condition was observing screen and audio displays. Subtraction images were analyzed with Statistical Parametric Mapping to identify significant brain activation foci. Execution of predictable sequences was characterized by a modest decrease in movement time and spatial error. The underlying pattern of activation involved primary motor and sensory areas, cerebellum, basal ganglia. Adaptation to a rotated reference frame, a form of procedural learning, was associated with decrease in the imposed directional bias. This task was associated with activation in the right posterior parietal cortex. New sequences were learned explicitly. Significant activation was found in dorsolateral prefrontal and anterior cingulate cortices. In this study, we have introduced a series of flexible motor tasks with similar kinematic characteristics and different spatial attributes. These tasks can be used to assess specific aspects of motor learning with imaging in health and disease.  2000 Elsevier Science B.V. All rights reserved. Theme: Motor system and sensorimotor integration Topic: Control of posture and movement Keywords: Motor learning; Automaticity; Reaching movements; Procedural learning

1. Introduction Successful motor performance depends critically on both implicit and explicit learning using visual and proprioceptive signals. Even the simple act of reaching for an object requires the learning of both sensorimotor representations of external space and of internal models of the dynamic *Corresponding author. E-mail address: [email protected] (M.-F. Ghilardi)

properties of the musculoskeletal system. Changes in head or body posture or the use of spectacles alter the reference frame and / or the scaling of visual errors. Hand held objects modify the inertial properties of the limb and require learning new models of effector dynamics, which depends largely on proprioception. The learning of novel sensorimotor transforms occurs implicitly, without conscious awareness [8,86,89]; subjects cannot describe the individual feedback events, the precise sequence of motor responses or the nature of the learned behavior. By

0006-8993 / 00 / $ – see front matter  2000 Elsevier Science B.V. All rights reserved. PII: S0006-8993( 00 )02365-9

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contrast, the learning of ordered sequences of events or of required movements are examples of explicit learning: subjects are aware of errors, which can be communicated verbally or through other sign systems [8,67,86,89]. Whereas in sensorimotor transformations errors are signaled by a continuous stream of visual and proprioceptive information, in explicit learning of a motor sequence feedback is typically discrete and available only at the completion of individual responses or of the entire sequence. With the advent of functional neuroimaging, many studies have begun to investigate the changes associated with the learning of various motor tasks such as rotary pursuit, prism adaptation and the learning of movement sequences. It is often difficult, however, to compare the patterns of brain activity in different reports because of differences in motor tasks with different kinematic features and different performance criteria. The present study using positron emission tomography (PET) addresses this issue seeking to identify the cortical and subcortical regions involved in different forms of motor learning. It introduces a new family of tasks in which subjects reach for visual targets making kinematically similar reaching movements in different tasks using a computer display of targets and hand movements. The learning of new sensorimotor transformations is accomplished by altering the relationship between the subjects’ hand movements and the displayed hand paths. In the experiments reported here we used 15 O-labeled water (H 15 2 O) PET to characterize the changes in regional cerebral blood flow (rCBF) during two forms of learning from a motor reference condition: the learning of a visuomotor transformation, a form of implicit learning, and the learning of a new target sequence involving explicit learning. We find marked differences in the patterns of rCBF between the two learning tasks.

2. Methods

screen with the tablet placed at waist height. During scanning, they were supine and moved their hand on the digitizing tablet supported over their chest. In the week prior to imaging, all subjects learned to perform all the tasks during four to five training sessions. A typical training session (one per day) lasted for about 1 h. Subjects achieved stable levels of accuracy by the second training session. Training sessions allowed us to assess the degree of subject performance, to insure its stability and to equate the level of difficulty for the learning tests performed during PET scanning (see below). PET recordings were always performed the day after the last training session.

2.2. General characteristics of the motor tasks All motor tasks required out and back movements of the hand from a central start area to one of eight radial targets (458 apart, distance from the center 9.6 cm). The start area and target locations on the screen were displayed on a white background as 2 cm diameter circles (Fig. 1A). Upon target presentation (1 per second) one circle turned black, in synchrony with a 160 ms tone. All trial blocks lasted 90 s. At the start of a trial block, subjects positioned the screen cursor within the central start area and a series of 3 tones were sounded at 1 Hz to provide the required tempo of the movements to follow. With the fourth and subsequent tones, successive targets turned black and subjects were instructed to move their hand smoothly out and back to each target without corrections and with sharp reversal synchronized with the tone and the target’s appearance. As illustrated in Fig. 1B, if the movement reached the target within a time window of 250 ms prior to and after each tone, the target turned gray, signaling a successful hit. The number of hits was displayed to the subjects on the screen after each block of trials.

2.3. Motor reference task: execution of a predictable sequence ( MPRED )

2.1. Subjects and apparatus We studied twelve right-handed neurologically normal individuals (9 men and 3 women, mean age 35.6 years; range from 22 to 59 years). All subjects were made aware of the scope and hazard of the study and signed informed consent forms. The motor tasks required subjects to move a cursor with their dominant hand on a 1293189 digitizing tablet (Numonics Corporation) while their hand position and target locations were displayed on a computer screen with a cursor indicating hand position on the tablet (gain:1:1). A Macintosh computer (Apple Computer) controlled the experiment, generated screen displays and acquired kinematic data from the digitizing tablet at 200 Hz. During practice, subjects sat facing a vertical computer

For this motor task, subjects moved to the targets according to a predetermined repeating sequence of 8 elements. Each target appeared only once in a cycle of 8 movements. Subjects had learned and extensively practiced the sequence both during the days preceding the scans and earlier in the same day of PET recording. For all subjects, hit rates for the prelearned sequence in the training blocks were always higher than 95%.

2.4. Learning of visuomotor transformations ( MROT ) In this condition, the target sequence remained predictable and was the same as in the M PRED . However, the direction of cursor movement on the screen was rotated clockwise or counterclockwise by 30–608 relative to the

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Fig. 1. A. Target array and hand-paths to three targets. Movement of the hand forward moved the cursor upward on the screen and movements to the right moved the screen cursor to the right; 4 cm on the screen corresponded to 9.6 cm on the tablet. Linear error and directional error are shown for the movement to the first target. B. Temporal profiles of the three hand paths in A are plotted as a function of target distance. Tone occurrence is indicated in the first line, while hits and misses are indicated in the bottom line. The striped area indicates the time window for successful movements (see methods). The first and the third movements were ‘misses’ because movement reversals occurred either outside target area (1) or before time window (3). The second movement was a ‘hit’.

direction of the hand movement itself. This rotation was imposed after the initial alignment period while the subjects maintained the cursor in the start area. To extinguish after effects, rotation blocks were always followed by one or more blocks of trials in which the relationship of cursor-to-hand movements was returned to the control condition. During training sessions, all subjects experienced rotation equal to or less than 308. Four subjects experienced 308 rotations during PET recordings. However, since the remaining subjects were especially proficient at this task during testing, we increased the degree of rotation to 408, 508 and 608 for them in order to maintain equivalent degree of difficulty across subjects.

2.5. Learning of spatial sequences ( MSEQ ) In this paradigm, a subset of the 8 target locations was displayed on the screen. However, targets did not turn black with each tone as for the previous tasks: instead, subjects were to discover the correct order of a repeating sequence by trial and error. A successful hit was indicated by the target area turning gray. The number of targets on the screen corresponded to the number of items in the sequence. Thus, each target in a sequence appeared only once and the order was varied pseudo randomly for each trial block. Task difficulty increased with the number of targets in the sequence. To learn sequences, subjects were trained to adopt a

stereotyped strategy in the first training session. First, they were to select a specific target and reach for it in time with successive tones. The eventual graying of the target area indicated that this was the ‘first’ target in the sequence. Then, they were to identify another target and, by counting the number of successive tones, to return to their first target with the appropriate tone. If subjects lost the order of the sequence in the course of the trial, they were instructed to restart the same procedure from the ‘first’ target. This strategy was learned in the first day of training: in the trials run during the second and subsequent training days, most subjects were able to identify a sequence of 6 targets in 40–45 s, by acquiring on average a new target per cycle. In the course of each training session, the number of targets per sequence was gradually increased from three to seven. The number of targets selected for testing during PET recording was based upon each subject’s performance during the last training session: this number was increased until subjects discovered and remembered the correct order of all targets within the first 60 s of the 90 s trial block.

2.6. Sensory reference ( S) In this condition, used only during scanning, subjects remained immobile but experienced the same visual and auditory stimuli as during the motor activation task. Screen targets, cursor images and tones were presented to the subjects asynchronously and irregularly in equal numbers

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to those used in the motor tasks. Subjects were instructed to attend to the screen and to see which of the targets did not turn gray. This occurred randomly to one of the eight targets.

2.7. Data acquisition and analysis of motor performance Tangential velocities and accelerations were computed from path data. Automatic routines identified the time and position at movement onsets, peak velocities and reversals, and assigned each movement to the appropriate target. Cursor positions at direction reversals were taken as the outer end points for each movement while those at zero crosses, calculated backwards from peak velocities, were defined as the onsets. Movement times were calculated from the corresponding bin times. Using x, y and t values for each point and the task criteria set for the learning tasks, we then derived measures of task performance. For each trial we computed averages of the following parameters (Fig. 1B): (1) Linear error was taken as the shortest distance of the reversal point from the center of the target; (2) Directional error was taken as the difference in degrees between the vector from the starting point to the target and the direction of the vector from the starting point to the movement endpoint; (3) Movement time was taken as the time from the onset of the outward motion to the reversal point. For M PRED and M ROT we computed means and variabilities (standard deviations and coefficients of variation) across the entire trial block and for each complete cycle of 8 movements. For M SEQ , where error feedback consisted of discrete hits or misses, performance was assessed by the mean hit rate per trial as well as by the mean and variability in hit rate per each complete cycle of either 5, 6, or 7 targets. In addition, for each target of a sequence we assessed the time and the cycle of the first acquisition and, by adding the acquisition of new targets during the 90 s trial, we computed a cumulative index of first ‘encoding’ across cycles for each trial block. All targets were ‘encoded’ when this index was equal to 100%. The successive hits for each target were considered ‘retrieved’ and a retrieval index was computed for each cycle by averaging the number of ‘retrieved’ targets. All targets were ‘retrieved’ when this index was equal to 100%. Analysis of variance (ANOVA) and regression analysis were performed on all psychophysical data across subjects and cycles, using STATVIEW 4.5 (Abacus Concepts, Inc).

2.8. Positron emission tomography ( PET) Motor tasks were performed with the right arm and an intravenous catheter was placed in the left arm for administration of H 15 2 O. In a single session, all subjects repeated the three motor tasks, M PRED , M ROT , M SEQ , and the sensory condition, S, three times for a total of 12 scans

per subject. PET studies were performed using the GE Advance [Milwaukee, WI, USA] tomograph at North Shore University Hospital. The performance characteristics have been described elsewhere [14]. This 8-ring bismuth germanate scanner provided 35 two-dimensional image planes with an axial field of view of 14.5 cm and transaxial resolution of 3.8 mm (FWHM) at the center. To minimize head movement during the scan, subjects were positioned in a Laitinin stereoadapter [38]. Reconstructed PET images were corrected for random coincidences, electronic dead time and tissue attenuation by transmission scans, and single scalar correction was used to compensate for scatter effects [16]. Relative rCBF was estimated using a modification of the slow bolus method of Silbersweig and colleagues [85] in which 10 mCi of H 15 2 O in 4 ml saline was injected by automatic pump in 16 s (15 ml / min) followed by a manual 3 ml saline flush. Using this injection protocol there was a time delay of approximately 17 s before onset of brain radioactivity, and the time from onset to peak count rate was 45–50 s. The timing of task initiation was individually adjusted so that the arrival of radioactivity would occur approximately 10 s after the start of each task. PET data acquisition began at the time of radioactivity arrival in the brain and continued for 80 s. The end of the task thus coincided with the end of data acquisition. The interval between successive H 15 2 O administrations was 12 min.

2.9. Data acquisition and analysis of PET The scans were registered, normalized into the stereotaxic space of Talairach and Tournoux [87], and smoothed with a filter of 10, 10, 10 in the X, Y, and Z axes (X, left–right; Y, posterior–anterior; and Z, inferior–superior) using Statistical Parametric Mapping (SPM 96, Wellcome Department of Cognitive Neurology, London, UK) [22]. Voxel-by-voxel proportional scaling was applied to remove between-subject differences in global counts. That is, the count value of each voxel was divided by the global count for the image and then multiplied by 50, to give an adjusted value within the range of normal blood flow. To identify voxels activated by the tasks, voxel-by-voxel ttests were performed contrasting each motor condition with the appropriate control condition. In all comparisons, voxels that exceeded a P,0.001 two-tailed threshold were identified. In addition, statistical significance of each cluster of voxels was tested by calculating the probability that an activation of that spatial extent would occur by chance [23]. The statistical significance of the clusters was set at P,0.05, two-tailed.

3. Results Subjects performed all tasks as instructed making relatively straight movements with sharp direction reversals

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and overlapping outgoing and return segments. Hand tangential velocity rose and fell monotonically during the outgoing and return phases and showed sharp minima at direction reversals. Thus, subjects relied primarily on feedforward mechanisms to control trajectories. The number of movements and blocks of movements was the same for all conditions.

3.1. Performance characteristics and learning 3.1.1. Predictable motor sequence during PET recording During PET, trial blocks of movements to predictable targets had mean hit rates of 98.260.8%. Nevertheless, linear error and movement times constantly decreased during each 90 s trial block. Reductions in error and in movement time had different time courses: linear error decreased linearly (r50.96) (Fig. 2A), while movement time dropped rapidly in the first 2 to 3 cycles and more slowly thereafter (Fig. 2B). Data were fit by a double exponential function (P,0.001). The same trends present for grouped data of Fig. 2 were also evident in all subjects individually. Comparing the last to the first two cycles of eight targets across subjects, mean values of linear error decreased by 1.8 mm and movement time decreased by 35.2 ms from the first to the last cycle. The different time course of the changes in accuracy and movement time suggested that speed–accuracy tradeoff functions might change across trials. Although it is necessary to examine movements over a range of target distances to fully characterize speed–accuracy tradeoff functions, observations indicate that practice and learning are critical in determining the form of the underlying function [12]. While linear error was inversely related to movement duration during the first two cycles (P,0.01) in all subjects, this correlation was never present for the last 16 movements (P.0.05). ANOVAs showed significant differences in slope (P50.0002) and correlation coefficients (P,0.0001) (Fig. 2C) between the first and the last pairs of cycles. 3.1.2. Learning visuo-motor transformations during PET recording When the direction of screen cursor movement was rotated unexpectedly, subject’s first movements were deviated by approximately the same amount, consistent with movement direction being largely preprogrammed. This directional bias decreased progressively with successive movements as the subjects learned to use this information effectively. The drop was most rapid over the first few cycles and slower thereafter. For each subject, data points were consistently fitted with a double exponential function (r 2 .0.80) as shown in Fig. 3A for the pooled data of 4 subjects learning a 308 rotation. In mean, subjects compensated for about 73% of the imposed rotation (range 40 to 98%): adaptation was therefore not complete. Comparing the first and last pair of cycles, however, mean directional

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error decreased significantly in all subjects (Fig. 3B). Similarly, directional variability, which increased dramatically after the imposed rotation, decreased with practice but did not to return to the control levels of the motor reference task at the end of the trial block (Fig. 3B). Moreover, it should be noted that movement time increased immediately with the imposed rotation and remained higher than in M PRED at the end of the trial block. Fig. 3C shows the comparison of the mean movement time and its variability between the first two and last two cycles.

3.1.3. New sequence learning 3.1.3.1. Pre-PET results. In the second training session, subjects showed consistently reproduceable results in learning sequences by trial and error. The mean data for all subjects in Fig. 4A show that the mean percentage of hits significantly decreased with the number of targets per sequence (P,0.0001). Regression analysis showed that 72.25% of the variance in this performance index was accounted for by number of targets; 12.4% was accounted for by subject age. As noted in Section 2 some subjects had greater difficulty than others did in this task and, thus, during scanning we used different numbers of targets per sequence in an attempt to equalize task difficulty: 9 subjects learned 6 target sequences, one 5, and two subjects 7. 3.1.3.2. During PET recording. In these trials, mean hit rate and coefficient of variation were not significantly different for sequences of 5, 6, and 7 targets (Fig. 4B and C). With one exception requiring the entire 90 s, all subjects discovered the correct sequence within the first two third of the trial: the averaged data in Fig. 5A show that the process of discovering targets’ order was virtually over within 60 s for sequences of either 5, 6 and 7 targets. The rate of this ‘first encoding’ paralleled the rate of retrieving the target order as well as the percentage of hits per cycle, which increased progressively along the trial block in all subjects. This trend is illustrated in Fig. 5B for the pooled data of all subjects performing with 6 target sequences. As in the case of adaptation to rotated displays, the time course of learning this discrete task was well fit by a double exponential function. However, the percentage of hits did not increase monotonically in each cycle. As Fig. 5C shows for one subject, performance degraded transiently following the first completely identified cycle, as though the subject ‘forgot’ parts of the sequence. As discussed further below, several mechanisms, including lapses in attention and parallel encoding and retrieval mechanisms, might account for this effect. Individual subjects differed not only in the rate at which they identified the complete order of the target sequence successfully, but also in the stability of this learning. These two aspects of performance were assessed by the mean hit

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Fig. 2. Movements to a prelearned sequence (M PRED ) during PET recording. (A) Mean linear error (top) and its variability (bottom) for 12 subjects across cycles of 8 movement each. Notice that these variables decrease linearly. (B) Mean movement time (top) and its variability (bottom) for 12 subjects across cycles of 8 movements each. The decrements of these variables are best fitted by double exponential curves. (C) Correlation between movement time and linear error for the first and the last 16 movements of three trials during PET in 12 subjects. The resulting mean slope1S.D. (left) and mean r correlation coefficients1S.D. (right) are shown for the first and the last 16 movements. Notice the significant decrease of both variables across trial duration (F(1,11)541.5, P50.0002) and correlation coefficients (F(1,11)574.4, P,0.0001).

rate and the coefficient of variation of the hit rate during the entire trial block, as shown in Fig. 4B and C. As in the other motor tasks, movement time decreased as the sequence was acquired. In contrast to M ROT , in M SEQ movement time and its variability were significantly reduced by the end of the trial block (see Fig. 4D).

3.2. Changes in rCBF with motor execution and learning 3.2.1. Movement execution In order to isolate the patterns of brain activation related to the motor events from those related to the sensory

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Fig. 3. Movements to rotated displays (M ROT ). (A) Mean directional error6S.E. for 11 cycles of 8 movements each in 4 subjects (total of 12 trials) learning 308 rotation (M ROT : solid line and filled circles) and in other 4 subjects learning 408 rotation (dashed gray line and gray empty circles). The dotted black line and empty circles show mean directional errors for the same subjects performing pre-learned sequence (M PRED ). Double exponential curves fitted the 308 (r50.91, P,0.001) and 408 (r50.99; P,0.001) M ROT data points. (B) Right: Mean directional error 6S.D. for the first (white column) and the last (gray column) 16 movements of 4 subjects learning a 308 rotation (M ROT ). Mean 6S.D. of these variables for pre-learned sequence (M PRED ) in the same subjects are shown as bold lines and white boxes. Repeated measure ANOVA found significant difference between M PRED and M ROT tasks (F(1,20)5196.2, P,0.0001) and between First and Last 16 movements (F 5100.2, P,0.0001), but not significant interaction. Significant comparisons of post-hoc tests (Bonferroni) are shown by the asterisk. Left: Mean variability of directional error. Significant difference were found between M PRED and M ROT tasks (F(1,20)525.2, P,0.0001) and between First and Last 16 movements (F 531.7, P,0.0001). (C) Right: Mean movement time in 7 subjects learning rotation (M ROT ). Legend as per Fig. 3B. Significant differences were found between M PRED and M ROT tasks (F(1,42)55.0, P50.03) and between First and Last 16 movements (F 514.8, P50.0004). Notice that post hoc analysis showed significant difference between Fist and Last 16 movements for MPRED , but not for MROT . Left: Mean variability of movement time. Significant differences were found between MPRED and MROT tasks (F(1,42)55.4, P50.02) and between First and Last 16 movements (F 531.6, P,0.0001).

events we subtracted S from M PRED images. Fig. 6 shows the SPM of increases in rCBF in 12 subjects. The spatial coordinates of areas of peak activity are reported in Table 1. The main cerebral areas with increased activity contralateral to the arm movement during were: the left primary sensorimotor corresponding to Brodmann’s areas (BA) 3, 2, 1 and 4, dorsal pre-motor cortex (PMC) and supplementary motor area (SMA) in BA 6, posterior cingulate (BA 23), parietal (BA 5, 7 and 40), and visual areas BA 7

and 18. Subcortically, the putamen, globus pallidus and thalamus were also active. Within the sensorimotor cortex the highest levels of activity were located in the arm representation, but the entire motor representation showed increased activity. Ipsilateral to arm movement, the right primary sensorimotor areas showed increased activity but this increase was less and more circumscribed than contralaterally. There were also significant increases in the right PMC, parietal (BA 5 and 7, but not 40) and visual cortical areas.

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Fig. 4. Sequence Learning (M SEQ ). (A) Mean hit rate (in percentage) for 12 subjects for sequences of 3, 4, 5, 6, and 7 targets. These data were collected before PET scanning in the second training session. Mean Hit rate showed a significant effect for the number of targets in the sequence (F(4,11)585.5, P,0.0001). The bar represents the mean across subjects and the circles the mean of 4 trials for each subject. (B and C) Mean Hit rate (B) and Coefficient of Variation (C) for the trials performed during PET recording plotted as a function of target number. The bar represents the mean per target number and the circles the individual trials. No statistical effect was found for number of targets in the sequence for either variable. Coefficient of variation was the ratio of the S.D. of Hit percentage across cycles and their mean. (D) Right: Mean movement time in 12 subjects learning sequences. Legend as per Fig. 3B. Significant differences were found between M PRED and M SEQ tasks (F(1,45)513.0, P50.0008) and between First and Last 16 movements (F 54.4, P50.04). Post hoc analysis showed significant difference between First and Last 16 movements for M SEQ . Left: Mean variability of movement time. Variability was not statistically different at the beginning and at the end of the trials.

In BA 7 and 18 increases covered a smaller extent than on the left. The cerebellum showed a large focus of increased activity in the anterior and posterior vermis and laterally in

the right hemisphere extending to the vicinity of the dentate nucleus and in the paraflocculus. Smaller but significant foci were also present in the left cerebellar hemisphere and left pontine tegmentum.

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Fig. 5. Sequence Learning (M SEQ ) during PET recording. (A) Cumulative index of target encoding in percentage for all sequences of 5 (s), 6 (d), and 7 (triangles) targets plotted as a function of time. Each point represents the mean across trials per each complete cycle of either 5, 6, or 7 targets. The time of first encoding for each target was the cycle in which the subject encountered that specific target for the first time. Notice that virtually all targets were first ‘encoded’ within the first minute. (B) Mean percentage of hits (filled black circles and thick black line), encoded (filled gray circles and gray line), and successfully retrieved (empty circles and thin black line) targets for all sequences of 6 targets plotted as a function of time. Each point represents the mean across trials per each complete cycle of 6 targets. Double exponential curves fit the data points (r 2 .0.90). (C) Number of hits per each complete cycle of 6 targets are plotted for three consecutive trial blocks for subject RB while learning a 6 target sequence.

3.2.2. Learning visuomotor transformations To define the patterns of activation related to the added processing during learning new visuomotor transformations we subtracted M PRED images from those obtained in the M ROT condition (Fig. 7). We identified two distinct areas of activation in the right parietal lobe: one in the right inferior parietal region, corresponding to BA 40 (coordinates: 52, 234, 38), and another peak in BA 7 (coordinates: 14, 244, 62). 3.2.3. Learning new sequences The patterns of activation associated with the learning of target and movement sequences were determined by subtracting M PRED from M SEQ images (Fig. 8). The structures with significant increases in rCBF are listed in

Table 2. Several areas showed significantly increased activation bilaterally; however, the spatial extent of activation was often greater in the right hemisphere, as seen in BA 46 and in anterior cingulate BA 23 and 32. Similar increases were observed bilaterally in the dorsolateral part of BA 6 (in an area rostral to that seen in M PRED ) and in the tip of the frontal cortex (BA 10) and in the lateral cerebellum. Additional areas of increased activation on the right were found in posterior parietal BA 7 and in occipital BA 19. In the left, increases were seen in BA 7 (precuneus). In order to compare the activation of selected brain regions across all tasks, we computed the increases in normalized blood flow in each of the 4 conditions for different brain regions. The bar plots of Fig. 9 show the

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Fig. 6. Brain areas significantly activated by movement execution (M PRED .S). To achieve significance, voxels were thresholded at P,0.001 two tailed, and spatial extent of activation was set at P,0.05 two tailed.

M.-F. Ghilardi et al. / Brain Research 871 (2000) 127 – 145 Table 1 Brain areas significantly activated by movement execution (M PRED .S) X

Y

Z

z-Value

BA

232 252 230 240 220 212 28 26 218 24 214 246 220 228 220

236 218 222 216 218 210 220 288 294 24 256 224 248 10 212

66 48 70 56 72 66 56 6 26 42 66 24 220 6 8

7.68 6.8 7.56 6.87 7.55 7.43 7.24 6.4 4.54 6.16 6.53 4.25 4.76 3.74 4.88

Left 1,2 1,2,3 4 4 6 6 6 17 17 24 7 40 Cerebellum Anterior putamen Putamen / thalamus

6 24 28 38 22 10 18

260 244 238 216 212 224 268

216 224 62 50 60 48 52

7.47 7.2 5.66 4.78 5.65 4.74 4.41

Right Cerebellum Cerebellum 1,2 4 6 6 7

main findings. Peak activity in the right lateral cerebellum was high in the three motor conditions and significantly lower in the sensory condition. Both parietal BA 7 and 40 on the right were significantly more active during the M ROT than in other tasks. However, while in sequence learning, area 7 was significantly more elevated than in the S, this was not the case either in M PRED or M ROT . On the right, the anterior cingulate and dorsolateral pre-frontal cortex (DLPFC) were significantly more active in M SEQ compared to the other motor tasks and S. However, left DLPFC was most active in the sensory reference, followed closely by M SEQ . This may be attributed to attentional or memory demands resulting from the instruction given to subjects to pay special attention to the number of occurrences. The lowest activity in both BA 46 and anterior cingulate was found in M PRED .

4. Discussion

4.1. Motor execution of a predictable sequence 4.1.1. Continued improvements in accuracy and movement time optimization after extensive practice Our motor reference task, in which all movements were anticipatory as in timed response paradigm [31,43], was designed to require neither stimulus response translation nor substantial new learning. Indeed movements were all initiated before target appearance. Nevertheless, continued learning, in the form of modest reductions in linear error

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and movement time, did occur during the 90 second scanning interval. The fact that the two variables decay with different time courses suggests that they reflect different processes. Since there was no explicit requirement for learning and since subjects were unaware of either change, this learning was ‘implicit’ in nature. Moreover, as noted in the introduction, reaching movements reflect sensorimotor transformations including one to map visual space to a vectorial ‘motor-error’ space and another to map direction and extent to an intrinsic coordinate space coding muscle and / or joint torques. It appears plausible that accuracy improvements should reflect optimization in these mapping functions over time [30]. Additionally, improvement could reflect adaptation to consistent postural conditions prior to movement, since both kinematic and dynamic transformations depend on the state of the limb. While visual feedback is necessary to improve spatial accuracy, the reduction in movement time might have occurred without it and involve a different optimization. Indeed, we have noted similar reduction in movement time in another set of studies in which the screen cursor was blanked during movement. Thus, this progressive reduction appears to be a distinct optimization process as seen in many motor tasks [12]. The different time course of accuracy and movement time optimization led to a different relationship between the two variables at the beginning and at the end of the trial: the significant speed accuracy tradeoff seen initially was no longer evident by the end of the trial. Speed–accuracy tradeoffs are ubiquitous and almost universal characteristics of motor performance in a wide variety of motor tasks, reflecting both feedforward and feedback mechanisms. The latter are better understood: greater accuracy is achieved with longer movement time in which an initial ‘ballistic’ movement may be combined with a larger number of corrective submovements [62–64]. Speed–accuracy functions become shallower with practice, a phenomenon perhaps due to a progressive reduction in overt [62–64] or covert trajectory updates [34]. Presumably, movements become more dependent on feedforward control and less dependent on visual feedback. A reduced dependence on feedback is also a feature of automatization, through which the attentional demands decrease allowing subjects to perform other tasks concurrently. Attending to a particular stimulus or contextual cue is then thought to be sufficient to retrieve from memory an associated specific motor solution or sensorimotor transform [29,32,57–59]. The execution of a pre-learned sequence such as in M PRED demands engagement of automatic routines, which are also required in the two learning tasks presented in this paper. For instance, one of the characteristics common to all motor tasks was the pacing of movements which were initiated in a timed-response paradigm before (and not after, like in RT paradigms) the 1 per second tone and target appearance. Moreover, as for M PRED , the movement time decrement in the course of both M ROT and M SEQ ,

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Fig. 7. Brain areas significantly activated by visuo-motor transformation. To achieve significance, voxels were thresholded at P,0.001 two tailed, and spatial extent of activation was set at P,0.05 two tailed.

which paralleled the learning of task-specific requirements, may reflect optimization of kinematic parameters and, thus, engagements of automatic routines.

4.1.2. Neural activation profile The motor networks underlying the performance of sequential movements to spatial targets was identified by the M PRED –S contrast and showed peak activities mostly in posterior areas of the brain with extensive activation of the left sensorimotor and premotor areas, the posterior parietal lobe, the basal ganglia, the thalamus and the right cerebellum. Our results confirm the data reported by similar activation studies of humans performing finger or hand move-

ments. However, although different motor paradigms have been used to define the anatomical bases of motor execution [24,36,37,49,81,82], only few PET studies had investigated cerebral activation involved in pre-learned sequences extensively practiced for days before the scanning. Notably, Jenkins and colleagues [49] comparing PET images obtained during execution of prelearned spatial sequences with a joy-stick to an uncontrolled rest condition, found significant peak activations in the sensorimotor, premotor cortex and parietal area 40, as well as SMA with adjacent cingulate area 24. These results were replicated with finger movements [51,52], in a study demonstrating also an important activation of basal ganglia [52]. This activation was present in our data as well, consistent with the part

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Fig. 8. Brain areas significantly activated by sequence learning (M SEQ .M PRED ). To achieve significance, voxels were thresholded at P,0.001 two tailed, and spatial extent of activation was set at P,0.05 two tailed.

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Table 2 Brain areas significantly activated by sequence learning (M SEQ .M PRED ) X

Y

Z

z-Value

BA

222 250 234 226 244

270 250 46 4 266

50 48 8 56 232

6.24 4.96 4.21 5.28 4.45

Left 7 40 46 / 10 6 Lateral cerebellum

38 46 42 22 30 6 36

276 256 32 42 6 28 254

30 42 24 12 52 30 238

6.82 5.86 4.73 3.84 5.45 3.99 4.44

Right 19 40 46 10 6 32 Lateral cerebellum

played by the basal ganglia in the acquisition and retention of motor skills [88], the automatic execution of motor plans [6], the internal generation of movement [80] and, possibly, the storage and retrieval of long-term motor memories [44]. Ipsilateral cerebellum is involved in limb movements [24,36,37,49,81,82]: whereas ipsilateral dentate nucleus, which is activated in M PRED , is likely used to store or retrieve implicit memories [44], activation of both anterior and posterior vermis is probably related to executive control in ongoing motor execution [5]. As expected, we also found activation of the caudal supplementary motor area, which is thought to be more active during the automatic performance of learned movements [24,36,37,49,81,82], together with primary motor cortex and thalamus [36,37]. Execution of prelearned sequences produced also significant increases in the ipsilateral sensorimotor, prefrontal and posterior parietal areas as well as left cerebellum. However, activation in BA4, 3 and 2 on the right was of lesser extent and confined to the representation of proximal limbs. Although – in the absence of EMG recordings – we cannot exclude contamination by movements of the left shoulder, it is possible that activation spread from the contralateral areas through callosal connections. In monkeys and cats callosal projections are found between proximal sensory motor areas only, as demonstrated by anatomical studies [48,69,70]. Nevertheless, activation of the ipsilateral sensorimotor and posterior parietal cortexes has been reported in motor activation studies [27,28,84]. In addition, electroencephalographic recordings during finger movements have confirmed these results by showing functional coupling between right and left primary sensorimotor cortexes mostly during internally paced movements [27,28].

4.2. MROT : a type of implicit learning 4.2.1. Adaptation to a rotated display is a form of implicit learning In general, implicit learning is acquired through extend-

ed practice; when a direct condition–action association is established, the task performance becomes fast, effortless, precise, autonomous [57,58,74,79] and, in some cases, unavailable to conscious awareness [7]. This process requires first, successful memory encoding and then, obligatory memory retrieval, the second operation being strictly dependent upon the first [59]. The learning of many skills can be mathematically described by the power law, where the time to perform the task or the error decreases exponentially reaching an asymptotic time [59]. Adaptation to a rotated display is a type of implicit learning which occurs in two main phases, as indicated by the time course of directional errors decay. In the first phase, which lasted usually three to four cycles, directional error fell rapidly, as attested by the decay constant of the first exponent. The second phase was characterized by slower decrement in mean error, accompanied by variability reduction [73]. However, mean directional error did not reach baseline values in the course of one 90 s trial. As we have previously shown [30], complete learning of a 308 imposed rotation can take more than three blocks of 90 movements each, because, at this point, error variability may be still high. It is possible that the long time course of adaptation reflects specificity of learning, in that each target direction needs to be learned as a separate and specific entity. In fact, the rate of learning is faster for one or two target directions and is much slower for four and eight target directions. Moreover, generalization of adaptation occurs promptly when targets are close together while it may not occur for targets far apart [30]. Thus, adapting to rotation involves establishing a new reference axis, and errors of successive movements in eight directions must be stored and interpreted, making substantial demands on spatial working memory. Indeed, this form of visuospatial learning has implicit attributes: subjects are not aware of the type of distortion introduced and, within one cycle, their performance is such that they do not notice any interference with their movements. Additionally, the kinematic learning of an imposed rotation is also reflected by a trend toward reduction in movement time and its variability with the parallel decrease in directional error.

4.2.2. Neural activation profile By analyzing M ROT and M PRED image pairs, we reduced the effects associated with motor execution as discussed in the previous paragraphs and thus isolated the areas involved in visuomotor learning. Blood flow increased in right BA 40 and 7. Both of the activation loci play important roles in the sensory processes for spatial attention [10,11], exploration and representation [42]. In particular, BA 40 receives converging proprioceptive, visual and vestibular inputs [4,18,46]. Additionally, BA 7, which is also involved in the integration of multisensory inputs, represents a crucial station in the dorsal visual pathways emanating from the striate cortex [33]. Different studies have dealt with visuomotor learning

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Fig. 9. Changes of normalized rCBF (mean6S.E.) across the four conditions. The values are given for the peak activation specified in terms of Talairach coordinates. Significant differences between tasks are shown by horizontal lines.

[9,36,47,53], but only few had contrasted adaptation to a rotated visual display with automatic execution of similar movements. In a study similar to ours, Clower and colleagues [9] reported activation of the posterior parietal region contralateral to the reaching hand in the course of prism-adaptation tasks. Learning to make movements with a rotated display shares some features with reaching during adaptation to displacing prisms: in both, motor errors generated by distortion in visual presentation are abolished while reaching to targets. However, adaptation to displacing prisms is faster and is confined to the prism-exposed limb without generalization or transfer to other extremities [39]. On the contrary, adaptation imposed by M ROT is slower and transferable to the contralateral limb [77]. In the study by Clower and colleagues, the parietal site of

activation was located in area 40 on the lateral bank of the intraparietal sulcus, a transition zone between the superior and the inferior parietal lobe. In our study, activation peak occurred ipsilaterally in a corresponding area 12 mm anteriorly – with 90 contiguous pixels versus 38 in Clower and colleagues [9] – and also in right area 7, corresponding to the precuneus. Despite similarity of the two tasks, there are major differences in the analysis and methodology which could account for the divergent activation patterns. First, in their analysis the search regions were restricted to the left sensorimotor, premotor, and posterior parietal regions as well as to the right cerebellum. The contralateral regions were excluded from the analysis. Second, their task design was different: since prisms adaptation was complete within 5 movements, to maintain

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subjects in a state of on-going adaptation, the direction of the prismatic displacement was switched every four movements by alternating the ‘adapting’ eye for a total of 21–22 movements per scan [9]. Finally, our results for adaptation to a rotated visual display is in agreement with the dominant role attributed to the right hemisphere in spatial tasks: regions of the right hemisphere are particularly involved in tasks of spatial working memory [50], in face encoding and retrieval [40], in object and location identification [40,41,60] and in global attention paradigms [19]. Interestingly, BA 40 on the right is usually larger architectonically to subserve this specialization [17]. This role is further supported by our results for sequence learning as discussed in following paragraphs.

4.3. MSEQ : explicit learning of sequences by trial and error 4.3.1. Many learning processes are embedded in MSEQ In this task, subjects used a previously acquired procedure to discover and memorize a spatial sequence of targets, while reaching for them in a short time window around 1 / s pacing tones. This strategy was taught and learned in the first day of training. By applying a stereotyped routine, in the ensuing sessions subjects were consistently able to identify a 6 elements sequence within 6 to 7 cycles. Therefore, the learning of the procedure was not part of the task studied in PET experiments. The changes obtained at that time were instead related to the explicit learning of the spatial sequence: at the end of each 90 s block subjects were able to identify the target sequence verbally. Once the procedure was learned, the total hit rate and the number of movements used to discover each sequence mainly depended upon the number of spatial locations in the sequence, which accounted for 73% of hit rate variance. Because the trial duration is constant, hit rate is highly dependent upon encoding: the more successful and shorter the encoding process, the higher the hit rate. Although with different temporal courses, here encoding and retrieval are mostly parallel processes: encoding a sequence takes on average one cycle for each target, it dominates the first 30–50 s and has lesser relevance afterwards. Retrieval starts after the first encounter with the first target and is predominant later on once the order of targets is discovered. In this group of subjects, age was also a significant determinant of hit rate accounting for more than 12% of the variance. In general, for memory and learning-related tasks, reaction time and accuracy decline with age [3,45,68], with impairment of both encoding and retrieval processes. A study of working memory for faces showed that worse performance is associated with increased rCBF mainly in the left DLPFC compared to young controls, suggesting that different and more extensive processes are needed to maintain memory representation with aging [35]. As in the other two tasks, movement time in M SEQ

steadily decreased in the course of learning, although its value did not reach the range of M PRED . Decrease in movement time was parallel to increase in hit rate, suggesting that explicit knowledge of a sequence may have beneficial effect on kinematic – thus, implicit – aspects of motor performance.

4.3.2. Neural activation profile As expected, since spatial learning was involved in this task, the M SEQ 2M PRED subtraction image revealed increased activation mostly in regions of the right hemisphere, namely, occipito-parietal (BA 19 and 40), prefrontal (BA 46, 9, 10) and the anterior cingulate (BA 32) areas. Contralaterally, BA 6, BA 7 and cerebellum were significantly activated. Activation in parietal areas has been reported when subjects attend to spatial location as shown by study of visual attention [10,11,75] or during motor performance like in the present study, even when the tasks are repetitive and automatic. DLPFC, which in our study was uniquely activated by M SEQ , is instead involved in the first stages of different types of learning in association with anterior cingulate areas [49,51]. In general, as for parietal regions, tests of spatial working memory, including face recognition, activate more extensively right prefrontal areas [19,36,40,50,60,66], while semantic tasks activate more the left hemisphere [54,66]. In addition, right prefrontal areas are preferentially activated in tasks based on internally driven selection [72,90]. Interestingly, the majority of sequence learning paradigms [24,36,49,51,52,81], including ours, identified task-related activation of left DLPFC. In light of the Tulving’s ‘hemispheric encoding / retrieval asymmetry’ model, where right DLPFC subserves retrieval and the left encoding [20,21,72,83,90], the predominant activity in the right DLPFC could reflect a major role of retrieval efforts required by our task. In fact, by the first 8 movements (i.e., within 10 s post-injection), all subjects had identified at least two targets’ order, thus initiating the process of retrieval which increases in the course of the trial. In agreement with this hypothesis, Sakai and colleagues [78] have found that left DLPFC is especially activated in the very early stages of learning, while right DLPFC and either right or left pre-SMA are activated during the early and intermediate stages. Their fMRI study also showed that parietal BA 40 and 7 were active during retrieval of learned sequences. Thus, it is possible that inferior parietal areas are active when performance is sufficiently automatic and conscious processing of visual signal is no longer required. These conclusions are supported by studies of sequence and sensory learning [36,61]. Extensive activation of cingulate cortex, in particular area 32 and 24, it is very often associated with activity in the DLPFC. It has been reported for a variety of tasks involving novelty, attention and selection of responses [15,25,49,51,71,76] and it is related to the number of targets to be detected [75] and the demands and difficulty

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of the task [13]. Therefore, the significant activation of cingulate cortex in M SEQ is likely due to the multiple and concurrent demands of this task, including attentiveness and working memory. In summary, our present results and those of others [36,61,78] suggest that when subjects are required to concentrate on solving new problems, or consciously learning new motor sequences, prefrontal and anterior cingulate activation is obligatory. When the task becomes automatic or the sequence has been practiced for many trials, activation shifts from DLPFC to premotor and / or sensory associative areas of the parietal lobe. The cerebellum is involved in a variety of motor and non-motor functions, including self-paced movements [84], motor sequence learning [52], sensory discrimination [26], attention [1], working memory [56], verbal learning [2] and problem solving [55]. We have found that in M PRED –S the major activation site was located in the right anterior lobe with foci into the deep cerebellar nuclei and the paraflocculus, as well as in left pontine nuclei. This activation pattern is likely related to the motor requirements of this task in agreement with studies showing that cerebellar activity during movements primarily occurs in the anterior ipsilateral cerebellum [49,52], whereas in tasks requiring attention activation is spread to the left posterior cerebellum. Indeed, in all four tasks there is an attentional component: in the sensory task, subjects pay attention to the visual display in order to answer questions about it at the end of session. In M PRED , attention was needed at least at the beginning of the testing, before engaging automatic processes [1]. Nevertheless, attentional requirements are greatest during spatial learning as in M SEQ , where a significant activation of posterior lobe in the left lateral cerebellum was accompanied by activation in the right dentate. Since the left lateral neocerebellum sends output to the right DLPFC [65], a highly active area during M SEQ , our data support the hypotheses that, first, the lateral cerebellum is part of networks involved in attentional, learning and cognitive routines and, second, different cerebellar areas are involved in motor and attentional tasks.

5. Conclusions The tasks described in this report possess controlled spatial and kinematic attributes. They can be used to study and dissociate different aspects of motor learning and, thus, to identify the underlying cerebral networks both in health and disease. First, visual presentation of the targets and monitoring of movement performance can be dissociated from each other and / or from kinesthetic input, making it possible to investigate separately different aspects of motor learning. Second, for all these tasks it is possible to study the effect of different levels of difficulty: In M PRED it is possible to vary and study the effect of movement rate and extent; in M ROT , the amount and direction of rotation, as well as the effect of short and long

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term adaptation [30]; and in M SEQ , the number of targets in a sequence, thereby altering the cognitive load. The flexibility of these tasks makes them suitable for studying the different aspects of motor learning in aging and different patient populations.

Acknowledgements Supported by Grants: NS KO8 01961, NS RO1 22715, NS RO1 35069 and NS K24 02101. The authors wish to thank Dr. Thomas Chaly for radiochemistry support and Dr. Ylong Ma for PET data analysis. We acknowledge the valuable technical support provided by Mr. Claude Margouleff and Dr. Abdel Belakhleff in the PET studies.

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