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1Montreal Neurological Institute, Montreal, Quebec; and 2Rotman Research Institute at Baycrest Centre, Toronto, Ontario, Canada
Submitted 21 September 2004; accepted in final form 24 November 2004
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ABSTRACT |
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INTRODUCTION |
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). Mastering these skills requires extensive training, which is generally accompanied by long-lasting changes in neuronal firing rate (Rioult-Pedotti et al. 2000), synaptogenesis (Kleim et al. 2002), and the topographical arrangement of the primary motor cortex (Nudo et al. 1996). In the last 10 years, a substantial number of neuroimaging studies have shown that the brain regions activated during the acquisition of a motor skill vary with practice (Doyon et al. 1996; Friston et al. 1992; Grafton et al. 1992; Inoue et al. 2000; Karni et al. 1995; Krakauer et al. 2004). Most of these studies, however, have examined changes in brain activity occurring within one training session, whereas the time course of changes in performance (Brashers-Krug et al. 1996; Korman et al. 2003; Shadmehr and Brashers-Krug 1997; Walker et al. 2003) and topography (Kleim et al. 1998; Nudo et al. 1996) operate on a larger time scale.
A few neuroimaging studies have examined the time course of neural changes associated with the acquisition of a motor skill beyond one training session. Some of them have used sequencing learning tasks where the sequence is first learned explicitly (Doyon et al. 2002; Karni et al. 1995; Tracy et al. 2003). Given that the neural substrates of explicit and implicit learning differ remarkably (Grafton et al. 1995; Hazeltine et al. 1997; Pascual-Leone et al. 1994), changes in the pattern of brain activity associated with performance in this type of tasks may not reflect the course of memory formation but a switch from explicit to implicit learning. In contrast, other studies have used certain paradigms of which the procedures are unavailable to the subject and hence the task is learned implicitly. The results from three PET studies using the pursuit rotor task (Grafton et al. 1992, 1994) and force-field adaptation (Nezafat et al. 2001; Shadmehr and Holcomb 1997), point to motor-execution areas as the regions supporting procedural motor learning. Grafton and collaborators (1994) have shown that, during early stages of learning the pursuit rotor task, activity increased in the contralateral sensorimotor cortex and anterior cerebellum; increments in activity 2 days later were observed in contralateral sensorimotor cortex, bilateral putamen, bilateral parietal cortex, and premotor cortex. On the other hand, adaptation to a force field while reaching to visual targets was initially associated with greater activity in prefrontal cortices, followed by activity increases in contralateral sensorimotor cortex, anterior cerebellum, and superior parietal cortex during the second (last) day of training (Shadmehr and Holcomb 1997). Using the same experimental paradigm, Nezafat and Shadmehr (Nezafat et al. 2001) further showed that activity in the anterior cerebellum increased during the first training session but decreased when subjects were tested 15 and 29 days after adaptation, with no concomitant changes in performance. Given that the control used in this study was a random—unpredictable—force field, associated with a much larger motor error, the latter result should be considered with caution.
Here, we used PET to further study the time course of changes in brain activity as subjects adapted to a visuomotor distortion over the course of 1 week of training. With the aim of understanding the neural interactions underlying learning-related changes in activity, we also examined the effect of practice on the functional connectivity of sensorimotor regions. In neuroimaging, functional connectivity refers to the degree of association between two regions, which can be easily assessed by computing their correlation (Horwitz 1989). Consistent changes in the functional connectivity of specific regions have been observed in association with associative learning acquired within one training session (Buchel et al. 1999; McIntosh et al. 1999). However, the persistence of changes in functional connectivity after long-term training has not been examined in humans. To address this goal, we first analyzed the PET images obtained during adaptation using a multivariate statistical technique that allowed the identification of a pattern of brain regions specific to learning based on interindividual differences in performance. Subsequently, we examined the progress of the functional connectivity across these learning-specific regions using correlation analysis. Based on the fact that changes in functional connectivity may drive regional changes in activity (McIntosh et al. 1999) and considering the literature discussed above, we predicted that the functional connectivity between regions involved in attention would increase during initial stages of adaptation. In addition, based on the current (Grafton et al. 1994; Nezafat et al. 2001; Shadmehr and Holcomb 1997) and additional literature (Willingham 1998) pointing to motor areas as those implicated in motor learning, we predicted that only the functional connections between motor-execution areas would remain strong once adaptation is achieved. Our findings suggest that the acquisition of a motor skill is not only reflected in the time course of a region's activity but also in a persistent change in the strength of specific functional connections.
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METHODS |
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Twenty right-handed healthy male subjects (age range = 18–35 yr) were recruited for the experiment. Subjects were screened to ensure none suffered from medical, neurological, or psychiatric disorders and were informed of the risks of the experimental procedure before giving written consent. Participants were paid for participation.
The time course of changes in brain activity during visuomotor adaptation was assessed by scanning subjects before, during and after adaptation to a spatial transformation that distorted visual feedback. Subjects used their right (dominant) hand to track a target moving randomly on a screen with a joystick. A cursor represented the direction of movement of the hand on the screen. The spatial transformation, which is illustrated in Fig. 1, consisted in rotating the cursor by a certain angle, which varied depending on the hand's movement direction. For example, when the hand moved 90° relative to the vertical axis of the joystick, the position of the cursor on the screen was rotated 180 to –90°, and when the hand moved 45° relative to the vertical axis of the joystick the cursor was rotated 90 to –45°. Thus the direction of movement on the screen was the mirror image of the hand's movement direction. This distortion, combined with the target's high velocity (0.3 pixels/ms), was chosen to increase task difficulty and to prevent subjects from becoming aware of the transformation.
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To assess the long-term retention of the motor memory, experimental subjects were re-tested 30 days later on the RVT task. No practice occurred during this period.
Imaging
Blood flow was measured with a Scanditronix/GEMS PC 2048-15B PET Scanner using 15O-water. Sixty-second scans (in-plane resolution: 5–6 mm, axial FOV ranged from –28 to +48 mm) were acquired; measurements began when the bolus tracer arrived to the head. Head movements were minimized with a custom-fitted thermoplastic facemask. Six PET scans were obtained after a bolus injection of 40 mCi [15O] H2O per scan: five during the first scanning session on day 2 of the study and one during the second scanning session on day 7. The inter-scan interval varied between 12 and 15 min. The spatial coordinates of each participant in the scanner, and their facemask were kept and used during the second scanning session to relocate them in the same position.
To minimize the occurrence of artifacts due to mismatches in head position between scanning sessions, subjects' PET images were aligned within scanning session using SPM99 (Wellcome Department of Cognitive Neurology, London, UK). Images were spatially transformed into the standard stereotaxic space of the Talairach andTournoux atlas (Talairach and Tournoux 1988) and smoothed with a 10-mm isotropic Gaussian filter using SPM99 software. Voxel-by-voxel proportional scaling (i.e., the ratio between each voxels' value and the scan mean) was applied to remove differences in global activity between scans.
Data analysis
PERFORMANCE.
The distance between target and cursor, sampled at 200 Hz, was used as an index of error. For each subject, the error was divided by the baseline obtained on day 1. A two-way repeated-measures ANOVA with time and task as factors was conducted on the relative mean distance between target and cursor to assess statistical differences in performance with the level of significance set at 
= 0.05. Time to adaptation, assessed using Tukey's honest significant difference (HSD) test, was defined as the first block which error did not differ statistically from the baseline at an alpha level of 0.1.
PET IMAGES. The analysis of PET images was conducted in two stages. In the first stage, a behavior partial least squares (PLS) analysis, based on the covariance between blood flow and performance, was conducted to identify the brain regions of which the activity varied as a function of learning. In the second stage, learning-related changes in the functional connectivity between these regions were investigated using correlation analysis.
PARTIAL LEAST SQUARES.
A full description of behavior PLS can be found elsewhere (McIntosh et al. 1996) and is summarized here. Behavioral PLS analysis was used in this study to identify patterns of brain areas which relation with performance differed across groups. Based on the covariance between regional cerebral blood flow (rCBF) and performance across subjects, PLS extracts a discrete number of latent variables (LVs) that best reflects the brain-behavior relationship. This procedure comprises three steps. First, correlation between behavior and rCBF values is computed across subjects for each voxel of each scan. This yields one vector per scan, the columns of which represent the brain voxels. Second, the correlation vectors are combined to form a cross-correlation matrix. Singular value decomposition of this correlation matrix produces orthogonal LV, each one consisting of a singular image, a singular profile and a singular value. Singular images contain a weighted linear combination of voxels that, as a whole, covary with performance. The numerical weights within the images are called saliences and can be positive or negative. Third, multiplication of the singular image by the raw images (dot-product) for each subject results in individual brain scores. The correlation between behavioral performance and brain scores for each scan produces the scan profile, which is a graphical representation of the contribution of each experimental condition to the observed spatial pattern. The scan profile is used to interpret the singular image. Indeed, the brain pattern shown in each singular image represents the brain voxels of which the correlation with subject's performance follows the relation described by the scan profiles. Positive saliences in the singular image correlate positively with the scan profile, whereas negative saliences correlate negatively with the scan profile. If, for example, the scan profile was similar across tasks or groups, salient areas in the singular image would relate similarly with performance. Conversely, if the scan profile differed between tasks or groups, then the singular image would reflect a task or group difference in the brain-behavior relationship. Finally, the singular value (similar to an eigenvalue) indicates the strength of the covariance between brain activity and behavior for each LV.
Statistical significance was evaluated using a permutation test for the singular values, aimed at assessing whether or not the patterns represented by each LV were obtained by chance (McIntosh and Gonzalez-Lima 1998). The reliability of the spatial pattern identified in the singular images was assessed by bootstrap estimation of the standard error (Efron and Tibshirani 1986). In interpreting the singular images, voxels were considered reliable if they had a ratio of salience to SE > 2.5.
FUNCTIONAL CONNECTIVITY.
In neuroimaging, functional connectivity refers to the degree of association between two regions that can be easily assessed by computing their correlation within scan (Horwitz 1989). The correlation between those brain regions identified with the behavior PLS were examined across subjects to determine whether the functional connectivity of the brain pattern also changed with learning.
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RESULTS |
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Behavioral results are displayed in Fig. 2, which depicts the relative distance to target averaged within each 14-min block ± SE throughout adaptation. The inset in Fig. 2 depicts the averaged error corresponding to each of the six scans. A two-way repeated-measures ANOVA yielded significant main effects of group (P < 0.001) and time (P < 0.001) and a significant group-by-time interaction (P < 0.001). Post hoc tests indicated that visuomotor adaptation was achieved during the first block of the fourth day (P = 0.691).
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). Aftereffects were present in the experimental group (indicated in Fig. 2 by *, P < 0.001). Figure 3 illustrates the time course of aftereffects for one participant of the experimental group obtained during VT2. The fact that the Pearson correlation computed between the average performance obtained during the baseline period (VT1) and that obtained during VT2 yielded a significant positive correlation (r = 0.877; P = 0.001) suggests that the size of the aftereffect depended on the subject's skill level. Assessment of performance on the RVT task 30 days after the experiment finished showed no significant differences when compared with that of RVT5 (P > 0.1), suggesting a long-term memory for the transformation.
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Neuroimaging results
A behavioral PLS analysis was conducted to examine how brain activity changed with performance as subjects learned to adapt to the visuomotor distortion. The analysis was conducted using the behavioral measure corresponding to the six scans (Fig. 2, inset). To facilitate interpretation of the results, the reciprocal of the relative distance between target and cursor was used in the behavior PLS. The analysis revealed two significant LVs of which LV2 represented our effect of interest, that is, a group by time (scan) interaction (P = 0.016). The shift in the brain-behavior relationship was only evident in the experimental group suggesting that the brain pattern identified by LV2 was specific to adaptation.
Figure 4 shows the results corresponding to LV2. The figure is composed of a singular image, which depicts the saliences for the brain regions, and a scan profile, which is a graphical representation of the contribution of each experimental condition to the spatial pattern observed in the singular image. The ordinate of the singular profile represents the correlation between the brain scores (a measure of each individual's contribution to the pattern of the singular image) and performance. Talairach and Tournoux (1988) coordinates for foci >20 contiguous voxels with a bootstrap ratio >2.5 are presented in Table 2. With the introduction of the experimental manipulation (RVT1, i.e., scan 2 in Fig. 4), rCBF of regions colored in red such as the left inferior frontal gyrus (BA 47), different areas of the dorsolateral prefrontal cortex of both hemispheres [middle frontal gyrus (BA 9/46), (BA 9/10), BA (9/11)], a bilateral region of the superior frontal gyrus compatible with the location of the inferior frontal eye fields [(FEF) x = –48, y = 2, z = 44 and x = 54, y = –2, z = 36, BA 6] and a small bilateral extrastriate area corresponding to a region of the dorsal visual stream (x = 40, y = –70, z = 12 and x = –38, y = –70, z = 12; BA 19/37) became positively correlated with performance (correlation displayed in the singular profile, r = 0.6193). However, this relationship was reversed during RVT2 (r = –0.3822) and became even stronger as adaptation progressed, i.e., from RVT3 to RVT5 [RVT3 (scan 4) = –0.828; RVT4 (scan 5) = –0.8473; RVT5 (scan 6) = –0.8012]. Conversely, activity of brain regions colored in blue such as bilateral anterior cerebellum, left sensorimotor cortex, middle cingulate gyrus (corresponding to the cingulate motor area), left middle temporal gyrus, and right putamen correlated negatively with the scan profile. Namely, rCBF was negatively correlated with performance on the introduction of the manipulation (RVT1) but became gradually positively correlated with performance from RVT2 to RVT5 as adaptation progressed.
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DISCUSSION |
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). Second, subjects performed the task equally well 1 month after the completion of the experiment, indicating a long-term memory for the skill. The retention of the visuomotor skill over a long period of time suggests that physiological processes such as long-lasting changes in synaptic efficacy (Rioult-Pedotti et al. 2000) and/or synaptogenesis (Kleim et al. 2002) may have taken place. Visuomotor adaptation was associated with a shift in the relationship between brain activity and performance during the first scanning session. Specifically, during early stages of adaptation, improvements in performance were associated with greater activity in dorso- and ventrolateral prefrontal cortices, FEFs, and extrastriate areas of the dorsal visual pathway. These regions were activated bilaterally. However, during later stages of adaptation, improvements in performance were associated with lower activity in these areas but greater activity in the left (contralateral) sensorimotor cortex, left cingulate motor area, left middle temporal gyrus, anterior cerebellum, and right putamen. This pattern persisted during the second scanning session, i.e., once adaptation was achieved, suggesting that this network may have supported the formation of a motor memory.
To explore this possibility, we examined the time course of changes in the functional connectivity of this pattern as a function of adaptation. The analysis revealed two major outcomes. First, the strengthening in the functional connections within the attentional and the motor networks increased toward the last two scans of the first scanning session, when the correlation between the brain pattern and performance became stronger. Interestingly, at this point, the functional connectivity between the two networks also increased. The fact that the two sets of regions were negatively correlated with each other is indeed compatible with their differential recruitment during the course of learning, namely, the recruitment of the attentional network during early stages of adaptation and the recruitment of the motor network during later stages of adaptation. This possibility is supported by subject's debriefing information, which indicated a significant decrease in task difficulty and greater "automaticity" toward the last two scans of the first session. Second, once adaptation was achieved, only the functional connections between the anterior cerebellum, the left sensorimotor cortex and the left middle temporal gyrus of the motor network remained strong, suggesting that the interaction between these areas may be necessary to preserve a memory for the visuomotor transformation.
Comparison to other studies
There is substantial experimental evidence from neuroimaging studies indicating that early stages in the acquisition of a motor skill are associated with activation of the prefrontal cortex (PFC). Several studies have used finger-sequencing tasks in which subjects rehearse the sequence explicitly either before (Doyon et al. 2002; Penhune and Doyon 2002) or during the experiment (Jenkins et al. 1994; Jueptner et al. 1997a, b; Toni et al. 1998). In these cases, the involvement of PFC, generally dorsolateral PFC, is likely associated with the declarative component of the task, i.e., working memory. This was unlikely in the present case because the target's trajectory was unpredictable. Recruitment of PFC has also been reported during early stages of procedural learning such as the serial reaction time task (Grafton et al. 1995), or during adaptation to a new stationary force-field (Shadmehr and Holcomb 1997, 1999). In these cases, greater activity of the PFC may be associated with inhibiting the execution of movements triggered by the old—unaltered—visuomotor mapping. We think that the positive correlation between PFC activity and performance observed in our study during early stages of adaptation is consistent with the latter possibility. That is, subjects exhibiting greater PFC activation may have been more efficient at inhibiting the sequence of movements associated with the old visuomotor mapping than those showing lower levels of PFC activity. On the other hand, the reversal of this correlation unveiled during RVT3, suggests that activation of the PFC during later stages of adaptation may have simply reflected individual differences in cognitive control of processing (Ridderinkhof et al. 2004).
In addition to prefrontal cortices, two areas involved in the visual processing of movement were highly correlated with performance at the introduction of the experimental manipulation. A region consistent with the FEFs was located to the posterior wall of the precentral sulcus. The second area was located to a region between the temporal and the occipital lobes, which may be the human homologue of MT. Activation of both FEF and the homologue of MT has been described during ocular pursuit (Berman et al. 1999; Petit and Haxby 1999; Rosano et al. 2002). Although eye movements were not measured in the present study, the positive correlation between FEF and performance observed during early stages of adaptation may reflect an increment in the rate of eye movements, necessary to track the position of the cursor while still following the target brought apart by the perturbation. Thus, it is possible that those subjects who showed greater activity performed better because they were more efficient at estimating the magnitude of the distortion in visual feedback, crucial to the execution of corrective hand movements.
Improvements in performance during later stages of adaptation were associated with less activity in these regions but with greater activity in left sensorimotor cortex, bilateral anterior cerebellum, right putamen, and the left middle temporal gyrus. Experimental evidence from neuroimaging and TMS studies suggests that the sensorimotor cortex is involved in the acquisition of motor learning over one or two practice sessions (Grafton et al. 1994, 1995; Muellbacher et al. 2002; Pascual-Leone et al. 1994; Shadmehr and Holcomb 1997). In addition, the effect of prolonged practice on the extent of activation of the sensorimotor cortex has been demonstrated by Karni and collaborators (1995) after 3 weeks of training in a finger-sequencing task. In our study, the subjects who performed better exhibited greater activity in the left sensorimotor cortex, whereas those who performed worse exhibited less activity in this area from RVT2 on. One could argue that the positive correlation between motor cortex and performance may have originated in higher movement rate or/and hand/wrist force associated with the remapping of the eye-hand relationship. Given that movement rate was not monitored in this study, this possibility cannot be ruled out. However, the fact that activity in this region remained positively correlated with performance even after adaptation was achieved (at which point the error rate was undistinguishable from that of the baseline), suggests that recruitment of the sensorimotor cortex was specific to learning. The persistence of strong functional connections between this region and the anterior cerebellum during the second scanning session further supports this possibility.
Like the sensorimotor cortex, activity in the cerebellum became positively correlated with performance as learning progressed and throughout the end of the experiment. Among other theories of cerebellar function, it has been proposed that this structure has a general role in movement error detection (Holmes 1939). Substantial neuroimaging evidence showing an initial increment in cerebellar activity during early stages of motor learning followed by a reduction as the error rate declines, support this theory (Friston et al. 1992; Imamizu et al. 2000; Jenkins et al. 1994). On the other hand, it has been argued that the cerebellum is where motor memories are formed (Ito 2002). The cerebellar theory of motor learning has found support in neuroimaging studies using visuomotor adaptation paradigms (Imamizu et al. 2000; Nezafat et al. 2001; Shadmehr and Holcomb 1997).
Our findings are partially consistent with both theories of cerebellar function. During early stages of adaptation, when the error was maximal, activity in the cerebellum was greater for subjects who made more errors. However, as adaptation progressed from RVT3 to RVT5, subjects who made more errors showed lower cerebellar activity than those who made fewer errors. Our results are consistent with those of two recent fMRI studies. Using a control condition with a comparable error rate as that of the experimental condition, Imamizu and collaborators (2000) were able to demonstrate that activity in some cerebellar regions, initially proportional to the error rate, remained high once subjects adapted to a rotational transformation. Likewise, Miall and collaborators (2001) have shown that cerebellar activity does not vary monotonically with performance. Using a visuomotor tracking task with different degrees of eye-hand coordination, the authors demonstrated that cerebellar activity does not always reflect higher error rates. Greater cerebellar activity was obtained in two very different situations: when a condition where eye and hand movements were independent and errors were high—was compared with another one where eye and hand were coordinated and when activity measured during the eye-hand coordinated condition—and low error rate—was compared with a third condition in which a temporal offset was introduced between the movement of the eye and that of the hand. Altogether, the results from these studies indicate that large visuomotor tracking errors may hide smaller activity increments associated with the improvement of visuomotor coordination. In agreement with Imamizu's and Mialls' findings, our results suggest that the cerebellum is not only necessary for eye-hand coordination (i.e., error correction) but also for learning to coordinate eye and hand movements in the presence of spatial transformations.
Among the areas integrating the motor network, a large region including the anterior portions of the middle and superior temporal gyri was identified in the left hemisphere. The exact role of this region in visuomotor adaptation is unclear to us. The extent of the activation lies in the proximity of other temporal areas associated with the processing of biological motion (Bonda et al. 1996) and tool motion (Beauchamp et al. 2002), although more anterior. It is also in the vicinity of a region localized to the posterior part of the superior temporal sulcus, which has been associated with the consolidation of learning a visuomotor tracking task with a predictable trajectory (Maquet et al. 2003). However, given that the stimulus used in our study was nonbiological and moved randomly, interpreting our results in the context of these studies may be misleading. Further research is needed to clarify the role of this region in visuomotor adaptation.
Finally, previous work has shown that adaptation to rotational transformations is associated with activation of the posterior parietal cortex (Ghilardi et al. 2000; Inoue et al. 1997, 2000; Krakauer et al. 2004). Experimental evidence indicating activation of posterior parietal cortex during the retrieval of a newly learned visuomotor skill after long-term training suggests that this region may also form an internal model of the sensorimotor transformation (Grafton et al. 1994; Sekiyama et al. 2000; Shadmehr and Holcomb 1997). In the current study, however, the posterior parietal cortex (PPC) did not contribute substantially to the pattern identified by LV2. Indeed, only a small region localized to the anterior and inferior portion of PPC was identified by the PLS analysis. This finding suggests that the correlation between activity in the PPC and performance did not shift as adaptation progressed. However, this result does not necessary imply that this area was not activated at any stage during adaptation. Previous work carried out in humans suggests that PPC is necessary when a movement needs to be updated as it occurs when altering the position of the visual target during a pointing movement (Desmurget et al. 1999, 2001). Thus it is possible that activity in the PPC may have been positively correlated with performance since the introduction of the visuomotor distortion and throughout the first scanning session, until the error decreased. However, given that the field of view of the PET scanner was limited (z ranged from –28 to +48)—and hence a large portion of the superior parietal lobule was not scanned—this possibility cannot be assessed.
Functional connectivity
Although the time course of the functional connectivity has been explored in associative learning and declarative memory paradigms (Buchel et al. 1999; Cabeza et al. 1997; Della-Maggiore et al. 2000; Grady et al. 1995; McIntosh and Gonzalez-Lima 1998; McIntosh et al. 1999), little is known about the functional connectivity associated with motor skill learning. In a recent study, Maquet and collaborators (2003) have shown that training on a visuomotor tracking task where the target moved predictably in one direction was associated with an increase in the functional connectivity between a region of the superior temporal sulcus (STS) and the dentate nucleus of the cerebellum. Our results are in agreement with their findings. However, although the role of the STS in their study is consistent with learning a sequence of movements for the target, the role of the middle/superior temporal area identified in our study is unclear.
To our knowledge, our study is the first to show the persistence of a stronger functional connectivity between the anterior cerebellum and the primary motor cortex after long-term training in a visuomotor adaptation paradigm. Given that we examined functional rather than effective connectivity (Della-Maggiore et al. 2000; Friston 1994), we are unable to determine whether the association of these areas is direct or mediated through a third area. This does not, however, detract from the observation of a persistent functional association between these regions as a result of motor learning.
In conclusion, we have shown that long-term training on a visuomotor adaptation paradigm is associated with the recruitment of an attentional network during early stages of learning, followed by the recruitment of a motor network as performance improves and the task becomes "automatic." The persistence of this pattern 3 days after the achievement of adaptation together with the results from the functional connectivity analysis, suggest that long-term learning may not only be reflected in changes in regional activity but also in the functional connections between specific motor regions.
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GRANTS |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: V. Della-Maggiore, Cognitive Neuroscience Unit, Montreal Neurological Institute, 3801 University St., Montreal, PQ H3A 2B4, Canada (E-mail: valeria{at}bic.mni.mcgill.ca)
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ABSTRACT
INTRODUCTION
) and 10 control subjects (
) as a function of time. The experiment was 7 days long. 
, blocks where PET scans were obtained. *, aftereffect observed after the transformation was removed on day 7. The last data point represents the error measured 30 days after the completion of the experiment. Inset: means ± SE for the error corresponding to the 6 scans only. For experimental subjects: scan 1 = task 1 VT1, scan 2 = RVT1, scan 3 = RVT2, scan 4 = RVT3, scan 5 = RVT4, scan 6 = RVT5. For control subjects: scans 1–6 = VT1–VT6.
) on day 7. Aftereffects are indicated by the triangles.
