Changes in brain activation during the acquisition of a new bimanual coordination task

Neuropsychologia 42 (2004) 855–867

Changes in brain activation during the acquisition of a new bimanual coordination task F. Debaere a , N. Wenderoth a , S. Sunaert b , P. Van Hecke b , S. P. Swinnen a,∗ a

Motor Control Laboratory, Department of Kinesiology, F.L.O.K. Group Biomedical Sciences, K.U. Leuven, Tervuurse Vest 101, 3001 Heverlee, Belgium b Department of Radiology, Group Biomedical Sciences, Magnetic Resonance Research Centre, K.U. Leuven, 3001 Heverlee, Belgium Received 14 May 2003; accepted 10 December 2003

Abstract Motor skill acquisition is associated with the development of automaticity and induces neuroplastic changes in the brain. Using functional magnetic resonance imaging (fMRI), the present study traced learning-related activation changes during the acquisition of a new complex bimanual skill, requiring a difficult spatio-temporal relationship between the limbs, i.e., cyclical flexion–extension movements of both hands with a phase offset of 90◦ . Subjects were scanned during initial learning and after the coordination pattern was established. Kinematics of the movements were accurately registered and showed that the new skill was acquired well. Learning-related decreases in activation were found in right dorsolateral prefrontal cortex (DLPFC), right premotor, bilateral superior parietal cortex, and left cerebellar lobule VI. Conversely, learning-related increases in activation were observed in bilateral primary motor cortex, bilateral superior temporal gyrus, bilateral cingulate motor cortex (CMC), left premotor cortex, cerebellar dentate nuclei/lobule III/IV/Crus I, putamen/globus pallidus and thalamus. Accordingly, bimanual skill learning was associated with a shift in activation among cortico-subcortical regions, providing further evidence for the existence of differential cortico-subcortical circuits preferentially involved during the early and advanced stages of learning. The observed activation changes account for the transition from highly attention-demanding task performance, involving processing of sensory information and corrective action planning, to automatic performance based on memory representations and forward control. © 2004 Elsevier Ltd. All rights reserved. Keywords: FMRI; Motor skill learning; Cortico-subcortical circuits; Cerebellum; Basal ganglia; Cortical motor areas; Bimanual

1. Introduction A hallmark of learning new motor skills, such as driving a car or playing the piano, is that one progresses from an initial stage that is highly attention-demanding to an advanced stage whereby the skill runs off automatically. Various neurophysiological as well as imaging studies have shown that this is accompanied by neuroplastic changes in the brain. However, opinions currently still diverge with respect to where in the brain these plastic changes occur and what their temporal evolution is across learning. This divergence can probably be accounted for by differences in paradigms used and/or the time window across which learning has been addressed. Despite the seeming heterogeneity among previously reported results, theoretical models have recently been proposed that formalize the neural architecture of motor learning. Dependent on the stage of learning and the type of task (i.e., motor sequence learning or motor ∗

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0028-3932/$ – see front matter © 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2003.12.010

adaptation), involvement of differential cortico-striatal and cortico-cerebellar routes has been hypothesized (Doyon, Penhune, & Ungerleider, 2003; Hikosaka et al., 1999). Association cortices (particularly prefrontal–parietal) are predominantly engaged during early learning, whereas the motor cortices (including primary and supplementary motor areas (SMAs)) become more involved when a task is well learned (Doyon et al., 2003; Hikosaka et al., 1999). The stage-dependent contribution of subcortical regions (basal ganglia, cerebellum) depends more on the type of task. Whereas activation of the cerebellum precedes activation of the striatum during sequence learning, the reverse pattern is observed during motor adaptation (Doyon et al., 2003). However, specific subregions of the cerebellum or striatum can also preferentially participate in either early or late learning, forming parallel interconnected loop circuits with the aforementioned cortical areas (Hikosaka et al., 1999). The majority of previous neuro-imaging studies addressed unimanual movement tasks involving predominantly the learning of finger sequencing (Doyon, Owen, Petrides, Sziklas, & Evans, 1996; Doyon et al., 2002;

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Grafton, Hazeltine, & Ivry, 1995; Hikosaka et al., 1996; Honda et al., 1998; Jenkins, Brooks, Nixon, Frackowiak, & Passingham, 1994; Jueptner, Frith, Brooks, Frackowiak, & Passingham, 1997; Jueptner, Stephan, Frith, Brooks, & Frackowiak, 1997; Karni et al., 1995; Sakai et al., 1998, 1999; Seitz & Roland, 1992; Toni & Passingham, 1999; Toni, Krams, Turner, & Passingham, 1998) or other tasks in which no coordination between limbs was required (Deiber et al., 1997; Ghilardi et al., 2000; Grafton et al., 1992; Grafton, Salidis, & Willingham, 2001; Inoue et al., 2000; Petit et al., 1996; Shadmehr & Holcomb, 1997; Van Mier, Tempel, Perlmutter, Raichle, & Petersen, 1998). These studies have provided unique information about changes in activation patterns during unimanual skill learning (cfr. supra). However, it still remains to be investigated whether the obtained insights can be generalized to the learning of bimanual or interlimb coordination tasks in general. In this respect, behavioral studies have provided evidence that laws and principles governing single-limb tasks do not unequivocally generalize to performing multi-limb coordination tasks (Swinnen, 2002). Moreover, previous work primarily quantified the behavioral correlates of learning in terms of general performance indicators (e.g., a decreasing number of errors and shorter reaction or movement times), whereas, only very few studies recorded the underlying movement kinematics (Ghilardi et al., 2000; Shadmehr & Holcomb, 1997). Therefore, the effect of potential confounds associated with changing movement kinematics as a result of learning, was often not systematically dealt with. Using functional magnetic resonance imaging (fMRI), the present study addressed learning-related changes in activation during the acquisition of a new bimanual coordination pattern, while controlling for movement kinematics. Cerebral activation patterns were assessed during initial learning and following practice, i.e., when the pattern was performed with a high degree of stability and consistency. Previous imaging work on interlimb coordination primarily focused on the involvement of different brain areas during the production of basic in- and anti-phase coordination modes (Debaere et al., 2001; Goerres, Samuel, Jenkins, & Brooks, 1998; Immisch, Waldvogel, Gelderen, & Hallet, 2001; Jancke et al., 2000; Sadato, Yonekura, Waki, Yamada, & Ishii, 1997; Stephan, Binkofski, Halsband et al., 1999; Stephan, Binkofski, Posse, Seitz, & Freund, 1999; Swinnen, 2002; Tracy et al., 2001; Toyokura, Muro, Komiya, & Obara, 1999). These modes are ‘intrinsic’ to the motor system and can be performed easily without learning (Kelso & Jeka, 1992; Swinnen, Jardin, Meulenbroek, Dounskaia, & Hofkens-Van Den Brandt, 1997). In contrast, coordination modes that deviate from these pre-existing patterns are much more difficult to produce and often require extensive practice before stable performance can be reached (Lee, Swinnen, & Verschueren, 1995; Swinnen, Lee, Verschueren, Serrien, & Bogaerts, 1997; Zanone & Kelso, 1992). Here we chose a task that involved the acquisition of a new bimanual coordination pattern in which both wrists had to be

rhythmically flexed and extended with a 90◦ phase off-set. The complexity of this task does not arise from the movement of each limb but from the difficult spatio-temporal relationship that has to be established between the limbs. Brain activation changes in the latter pattern were studied relative to those observed in the in-phase pattern that was subjected to equal amounts of practice. In view of the intrinsic nature of the in-phase pattern, no changes in brain activation across learning were predicted to occur. To summarize, this study investigates changes in brain activation associated with the acquisition of a new bimanual cyclical coordination task that was composed of sequential and simultaneous components, categorized as an explicit procedural learning task. In accordance with the proposed models for motor learning, we hypothesized that acquisition of this task would be accompanied by a decreased involvement of cerebellar, parietal, and prefrontal regions together with an increased involvement of basal ganglia and supplementary motor regions, with increasing automaticity (Doyon et al., 2003; Hikosaka et al., 1999).

2. Material and methods 2.1. Subjects Twenty subjects (10 males and 10 females) participated in the present study. Their age ranged from 21 to 29 years. They were all right-handed (Bryden, 1977) and had no history of neurological or psychiatric disease. The study was approved by the local ethical committee of K.U. Leuven and subjects provided written informed consent in accordance with the Helsinki declaration. 2.2. Experimental design 2.2.1. Task procedures The experimental design included two age–gender matched groups, i.e., a learning (n = 12) and a non-learning (control) group (n = 8). Both groups were involved in the same experimental procedures and differed only with respect to the coordination task they practiced. The learning group practiced the 90◦ out-of-phase pattern, consisting of cyclical flexion–extension movements whereby one hand leads the other with a quarter-cycle (i.e., 90◦ ) (Fig. 1A). This new spatio-temporal relationship is not intrinsic to the human motor system and can only be gradually performed following intensive practice (Lee et al., 1995; Zanone & Kelso, 1992). The control group performed the in-phase pattern with both hands flexing or extending simultaneously. This mirror-symmetrical pattern (Fig. 1B) is intrinsic to the motor system and does not require learning (Zanone & Kelso, 1992). Learning-related effects were addressed by comparing both experimental groups across two separate scanning sessions obtained at the beginning and end of practice. The rationale behind adopting this design was to

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Fig. 1. Angular displacement signals across time for a representative trial of the 90◦ out-of-phase pattern (A), the mirror-symmetrical or in-phase pattern (B) (left side), and their relative motion plots (right side). The latter plots conform with the real-time feedback signal provided to the subjects. Both movements differ with respect to the relative phasing between the limbs, i.e., a 90◦ vs. 0◦ phase offset. (C) Experimental setup.

maintain the number of experimental conditions and total scanning duration for each subject within acceptable limits, such that fatigue effects were minimized and attention could be sustained throughout the scanning sessions. Furthermore, it allowed control for non-specific effects of time and sessions (administered on separate days). Each subject was tested across four different days, which involved two scanning and two practice sessions. The first scanning session (initial learning session, PRE) was performed on Day 1, followed by two practice sessions (Days

2–3) inside the scanner to preserve environmental conditions but without actual scanning. The final scanning session (POST) was performed on Day 4 and was identical to Day 1. During scanning, participants were involved in four different conditions, discussed next. (1) Execution of the required bimanual coordination pattern with availability of on-line visual feedback about ongoing movement (MFb). The provided visual feedback signal consisted of an orthogonal plot of the right and left limb displacements. The relative motion pattern (Lissajous figure) of a correct 90◦ out-of-phase mode

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resulted in a circle configuration, whereas a correct in-phase mode gave rise to a diagonal configuration on the screen (see Fig. 1A and B). Obtaining a circle configuration was the target for the learning group. Provision of such visual feedback sources has previously shown to support the acquisition of difficult coordination patterns (Lee et al., 1995; Swinnen, Lee et al., 1997). (2) Execution of the required bimanual coordination pattern with the eyes closed, i.e., without on-line visual feedback (MN). This condition allowed us to assess performance in the absence of augmented Lissajous feedback on the basis of internal movement generation. (3) A visual control condition: viewing a feedback signal without performing any movement (VI). Here, the presented visual feedback signal had the same characteristics as during the movement conditions (luminance, speed, color, etc.) but did not represent an actual movement pattern in order to exclude movement imagery (e.g., a square configuration). (4) A baseline rest-condition in which subjects did not move and closed their eyes (R). All conditions were metronome paced at 1 Hz. During the practice sessions, subject performed the coordination task in the presence of augmented feedback across two blocks of 20 trials (30 s duration per trial) on two consecutive days, resulting in a total of 80 practice trials. Participants were instructed to produce a complete cycle (flexion–extension) per beat of the metronome. 2.2.2. Scanning procedure Subjects lay supine inside the scanner with their forearms supported by a cushion. Head movements were restricted by a bite-bar. In this position, subjects watched the real-time visual feedback of the movements, displayed by means of an LCD projector (Barco 6300, 1280 × 1024 pixels) and projected onto a mirror (at a distance of 30 cm from the head). Both wrists were positioned in a wrist-hand orthosis, restricting the movements to flexion–extension in the sagittal plane (Fig. 1C). Angular displacements of the limbs were registered by means of non-ferromagnetic high precision shaft encoders (HP, 2048 pulses per revolution; sampling frequency, 150 Hz) fixed to the axis of the orthosis (aligned with the joint axis) (Fig. 1A and B). During the MFb condition, the displacement signals were used to control a cursor on the screen, showing a trace representing the ongoing movements (cfr. supra). The displacement signals were also subsequently used to analyze various movement parameters (relative phase, amplitude, and cycle duration). Prior to initiation of the first scanning session, subjects practised the different tasks for 5 min (two trials of each condition) to familiarize themselves with the task requirements and scanning environment. The fMRI measurements were executed on a 1.5T MR scanner (Siemens, Sonata, Erlangen, Germany) using a quadrature head coil. Each scanning session began with the acquisition of a 3D high-resolution T1-weighted image (MPRAGE, TR/TE = 11.4 ms/4.4 ms, TI = 300 ms, field of view = 256 mm, matrix = 256 mm × 256 mm, slab thickness = 160 mm, and 128 slices) for

anatomical detail. Subsequently, eight functional time series, each consisting of 124 whole brain gradient-echo (GE) echoplanar scans (EPI), were acquired every 3.1 s, (TR/TE = 3104 ms/40 ms, field of view = 200 mm, matrix = 64 mm× 64 mm, slice thickness = 4 mm, interslice gap = 1 mm, and 32 transversal slices). Each time series consisted of three blocks of four conditions. Each condition lasted for 31 s (corresponding to 10 whole brain images, TR = 3.1 s) and was triggered by an auditory command. The different conditions were randomized within a balanced design across and within time series. Between the eight time series, a rest period of 3 min was provided. 2.3. Data analyses 2.3.1. Kinematic analysis The coordination between the limb segments was assessed by means of a relative phase measure, i.e., the subtraction of the phase angles of each limb segment according to the formula Φ = θ1 − θ2 = tan−1 [(dX1 /dt)/X1 ] − tan−1 [(dX2 /dt)/X2 ], whereby 1 and 2 denotes the right and left limb, respectively. θ refers to the phase of the wrist movement at each sample, X is the position of the wrist after rescaling to the interval [−1, 1] for each cycle of oscillation, and dX/dt is the normalized instantaneous velocity. Subsequently, the absolute deviations from the target relative phase (i.e., 90◦ for the learning group and 0◦ for the control group) were calculated to obtain a measure of relative phase accuracy. The standard deviation (S.D.) of relative phase was used as an estimate of movement pattern stability. In addition to the relative phase measures, cycle duration and amplitude of the limb movements were quantified. Cycle duration was defined as the time that elapsed between successive peak extension positions. The average cycle duration was computed across each trial. The spatial measure consisted of the absolute value of the peak-to-peak amplitude for each individual cycle. This measure was also averaged across each trial. The statistical analysis consisted of ANOVAs with the factors group (learning, control), session (PRE, POST), condition (MFb, MN), and additionally side (right, left) for the amplitude and cycle duration measurements. Post-hoc Tukey tests were executed when necessary for significant interactions. The α-level of significance was set at 0.01. 2.3.2. Imaging analysis Data were analyzed with SPM99 (Wellcome Department of Cognitive Neurology, London, UK). For each subject, all EPI volumes were realigned to the first volume of the first time series and a mean image was created of the realigned volumes. Then, the anatomical image was co-registered with this mean image. After co-registration the structural image was spatially normalized into a reference system (Talairach & Tournoux, 1988), using a representative brain (MNI, Montreal Neurological Institute) as a template. Normalization was done using an affine and non-linear transformation, mapping the anatomical scan to the template. The

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normalization parameters were subsequently applied to the functional images. Finally, EPI images were subsampled to a voxel size of 2 mm × 2 mm × 2 mm and smoothed with a Gaussian kernel of 10 mm full width at half maximum (FWHM) for purposes of group analysis. First level statistical data analysis consisted of modeling the different conditions using a boxcar function convolved with the haemodynamic response function in the context of the general linear model (Friston, Jezzard, & Turner, 1994; Friston et al., 1995). Global changes of the BOLD signal were adjusted by proportional scaling and an appropriate high-pass filter removed low frequency drifts. Additionally, to correct for confounding effects induced by head movements, realignment parameters (three rotation and three translation parameters) were included in the design matrix as covariates of no interest. Statistical parametric maps were then generated, testing for the effects of interest by applying appropriate linear contrasts to the parameter estimates of each condition. For the group analysis (mixed effects), a multiple regression model was applied which allowed us to test for simple main effects and interactions across both experimental groups (learning group, control group) and scanning sessions (PRE, POST). First, the general network involved in the execution of the bimanual coordination tasks was identified (contrast [MFb + MN]–[VI + R]), taking into account all areas that showed significant activation during the movement conditions in both sessions (PRE and POST)

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and both groups (learning and control). Second, specific learning-related activation changes were addressed by focussing on the interaction effects between groups (learning and control) and sessions (PRE and POST) for the contrast [MFb + MN]–[VI + R]. Third, a possible differential effect of visual control during learning was addressed by comparing MFb–VI and MN–R across sessions (PRE–POST) and between groups (learning and control). For the latter two questions, analysis was restricted to those voxels showing a main effect by means of an inclusive masking procedure (see SPM99). This allowed us to relax the threshold level to P < 0.001 (uncorrected for multiple comparisons) as the interaction effects were only likely to occur for those regions previously identified in the main effect. However, in most cases a threshold of P < 0.05 (corrected for multiple comparisons) could be applied. To increase regional sensitivity, the threshold for spatial extent was set at 10 voxels.

3. Results 3.1. Kinematic data 3.1.1. Coordination between the limbs: relative phase Fig. 2 displays measures of the quality of coordination (e.g., mean relative phase errors and S.D. scores) across scanning and practice sessions in both experimental groups. Each data point represents the average error and S.D. score

Fig. 2. Mean absolute error (AE) and standard deviation (S.D.) of relative phase across scanning and practice blocks for the learning and control group. Each data point represents the average AE and S.D. score of 20 trials (performed with feedback of the movements).

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of 20 trials. Phase errors were initially high for the learning group but decreased significantly with practice and approximated the required relative phase well. The same pattern was observed for the S.D. scores. Conversely, these parameters did not show any change across both scanning and practice sessions in the control group. This was confirmed by a significant group × session interaction: F(1, 18) = 11.00, P < 0.01 for AE and F(1, 18) = 7.08, P < 0.015 for S.D. Overall, the control group showed lower mean error and S.D. scores of relative phase than the learning group [group effect; mean F(1, 18) = 151.53, P < 0.01; S.D. F(1, 18) = 355.03, P < 0.01]. This effect remained evident following extensive practice and is a consequence of the higher complexity of the 90◦ out-of-phase relative to the in-phase pattern. 3.1.2. Individual limb motions: amplitude and cycle duration Comparable movement amplitudes (ML = 25.22◦ , MC = 22.81◦ ) and cycle durations (ML = 976 ms, MC = 995 ms) (P > 0.01) were observed for both experimental groups. These measures also did not differ between the hands (amplitude: Mright = 23.51◦ , Mleft = 25.00◦ ; cycle duration: Mright = 984 ms, Mleft = 984 ms) (P > 0.01), the movement conditions (MFb–MN) (amplitude: MMFb = 23.84◦ , MMN = 24.67◦ ; cycle duration: MMFb = 982 ms, MMN = 985 ms) (P > 0.01) or both scanning sessions (amplitude: MPRE = 25.23◦ , MPOST = 23.28◦ ; cycle duration: MPRE = 980 ms, MPOST = 986 ms) (P > 0.01). 3.2. Imaging data 3.2.1. General network Prior to identifying changes in activation across learning, the general network for producing the coordination patterns was established across sessions and experimental groups (Table 1). These included the primary sensory-motor hand area along the central sulcus (M1/S1), the supplementary motor (SMA) and cingulate motor area (CMC) on the medial wall of the superior frontal gyrus and cingulate gyrus, the dorsal and ventral premotor regions along the precentral gyrus/sulcus, the dorsolateral prefrontal cortex (DLPFC) located in the middle frontal gyrus, the frontal and parietal opercular regions, the superior and inferior parietal areas, corresponding to BA5–7 and BA40, respectively, the middle temporal gyrus corresponding to hMT/V5+, the thalamus, the putamen/globus pallidus and the cerebellum, both in the vermis and hemisphere regions. All these areas were involved bilaterally (see Table 1). 3.2.2. Learning-related decreases in activation (PRE > POST): group × session interaction Areas showing a significant decrease in activation from PRE to POST in the learning group relative to the control group, as revealed by a significant group × session interaction, are presented in Table 2. Significant effects were

Table 1 Z-scores and localizations of peak activations (MNI-coordinates) for the areas involved in the execution of the coordination tasks (general movement related network) Brain region

Stereotactic coordinates

Z-values

x

y

34 −32

−32 −28

64 64

>8.21 >8.21

Supplementary motor area Superior frontal gyrus

0

−10

65

>8.21

Cingulate motor cortex Cingulate sulcus/gyrus

Primary sensory-motor cortex Central sulcus (R) (L)

z

0

−6

54

>8.21

Dorsal premotor Superior precentral gyrus (R) (L)

38 −42

−6 −10

54 56

>8.21 >8.21

Ventral premotor Inferior precentral gyrus (R) (L)

56 −56

8 10

26 26

>8.21 >8.21

Dorsolateral prefrontal cortex Middle frontal gyrus (R) (L)

38 −38

42 42

26 24

>8.21 >8.21

Frontal operculum (R) (L)

56 −56

12 12

−6 −4

>8.21 >8.21

Parietal operculum (R) (L)

50 −56

−26 −24

16 14

>8.21 >8.21

Inferior parietal cortex (BA40) Marginal gyrus (R) (L)

52 −56

−28 −26

36 40

>8.21 >8.21

Superior parietal cortex (BA5-7) Postcentral–intraparietal sulcus (R) (L)

30 −26

−56 −56

58 58

>8.21 >8.21

hMT/V5+ Middle temporal gyrus (R) (L)

54 −50

−68 −66

−2 2

>8.21 >8.21

Thalamus (R) (L)

14 −12

−18 −18

4 6

>8.21 >8.21

Putamen/globus pallidus (R) (L)

26 −24

−6 −8

4 2

>8.21 >8.21

Cerebellum Vermis Hemisphere (R) (L)

2 24 −18

−62 −52 −48

−16 −26 −24

>8.21 >8.21 >8.21

P < 0.05 corrected for multiple comparisons.

found in the superior parietal cortex around the postcentral sulcus on the right side and around the intraparietal sulcus on both sides (Fig. 3A). In these regions, activation decreased with learning, whereas it stayed relatively constant in controls. For the intraparietal regions activation was finally lower than in controls (Fig. 3A, lower left panel), whereas for the postcentral region, activation dropped to the same level as in controls (Fig. 3A, lower right panel). Similar effects were seen in the right superior precentral gyrus,

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Table 2 Z-scores and localizations of activation peaks (MNI-coordinates) for areas showing a learning-related decrease in activation (PRE > POST), resulting from a significant group × session interaction

Table 3 Z-scores and localizations of activation peaks (MNI-coordinates) for areas showing a learning-related increase in activation (POST > PRE), resulting from a significant group × session interaction

Brain region

Brain region

Stereotactic coordinates x

y

Z-values

z

Ventral premotor Precentral sulcus (R)

54

10

32

6.17

Dorsal premotor Superior frontal gyrus (R)

24

−14

70

6.87

Superior parietal Postcentral sulcus (R) Intraparietal sulcus (R) (L) Dorsolateral prefrontal cortex Middle frontal gyrus (R) Cerebellum Hemisphere lobule VI (L) Vermis lobule VI (L)

Stereotactic coordinates

Z-values

x

y

z

Dorsal premotor Precentral sulcus/gyrus (L) Superior frontal sulcus (L)

−48 −30

−14 0

40 62

6.59 6.35

Sensorimotor cortex Central sulcus (L) (R)

−18 16

−28 −26

66 70

5.33 6.15

14 26 14 −16

−40 −48 −64 −62

75 56 58 58

4.49 3.80∗ 3.85∗ 3.34∗

Cingulate motor area Cingulate sulcus/gyrus (L) (R)

−6 8

−8 −16

46 46

4.94 4.64∗

38

36

30

3.51∗

Superior temporal gyrus (L) (R)

−50 62

−38 −22

12 4

7.45 5.23

−22 −4

−60 −78

−16 −18

4.80 4.06∗

Putamen/globus pallidus (L) (R)

−16 28

2 −2

8 2

5.76 6.12

12

−16

−2

5.26

−20 −8 −14 26

−72 −36 −54 −50

−30 −26 −34 −40

4.86 6.23 5.77 5.51

P < 0.05 corrected for multiple comparisons; ∗ P < 0.001 uncorrected for multiple comparisons.

probably corresponding to the dorsal premotor cortex (PMd) (Geyer, Matelli, Luppino, & Zilles, 2000; Picard & Strick, 2001) (Fig. 3B). With learning, activation in this region decreased and reached a level similar to that observed in controls (Fig. 3B, lower panel). A significant learning-related decrease in activation was also observed in the right middle frontal gyrus, corresponding to the DLPFC (Fig. 3C). For a cluster within the inferior precentral gyrus at the junction of the inferior frontal sulcus with the inferior precentral sulcus on the right side (Fig. 3D), corresponding to the ventral premotor cortex (PMv) (Geyer et al., 2000; Picard & Strick, 2001), activation decreased with learning and reached a lower level than in controls (Fig. 3D, lower panel). Finally, within the cerebellum two separate clusters showed a significant effect, one within the hemisphere and one within the vermal portion of the left lobule VI (Schmahman, Doyon, Toga, Petrides, & Evans, 2000) (Fig. 3E). It is important to note that for cortical regions showing a decrease in activation with learning, the effects were asymmetric. With exception of the activation in the intraparietal sulcus, all effects were found within the right hemisphere, whereas the effects for the cerebellum were all on the left side. 3.2.3. Learning-related increases in activation (POST > PRE): group × session interaction Areas showing a significant increase in activation from PRE to POST in the learning group but not in the control group, as revealed by a significant group × session interaction, are presented in Table 3. Effects were found for two clusters within the left premotor cortex, both within the PMd (Geyer et al., 2000; Picard & Strick, 2001) (Fig. 4A). Within these regions, activation increased with learning and

Thalamus (R) Cerebellum Hemisphere Crus I (L) Vermis lobule III/IV (L) Dentate Nc (L) (R)

P < 0.05 corrected for multiple comparisons; ∗ P < 0.001 uncorrected for multiple comparisons.

reached higher levels than in controls (Fig. 4A, lower right and upper left panel). Within the central sulcus, somewhat medial from the original location of the hand area (see Table 1), activation was found to increase bilaterally with learning (Fig. 4B). Again activation reached a higher level than in controls (Fig. 4B, lower panel). Similar effects were also observed within the cingulate sulcus/gyrus and within the superior temporal gyrus bilaterally (Fig. 4C and F). Subcortically, activation increased for the basal ganglia, thalamus, and cerebellum. For the basal ganglia, effects were found in the putamen/globus pallidus bilaterally. Activation in the thalamus only increased significantly with learning on the right side (Fig. 4D). For these regions, identical activation profiles were observed as described above. For the cerebellum, the learning-related increases in activation were found for clusters within the dentate nucleus bilaterally (Fig. 4E), left hemisphere Crus I and left vermal portion of lobule III/IV (Schmahman et al., 2000). 3.2.4. Specific changes for the MFb and MN conditions Indirect comparison of the MFb–VI and MN–R conditions across sessions (PRE versus POST, POST versus PRE) and groups (learning versus control) revealed some additional changes specific for the MFb condition that differed from the MN condition and that were not identified in the main contrast (discussed above). These included activations in the cerebellum and hMT/V5+. For the cerebellum, two

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Fig. 3. Anatomical localization of learning-related decreases in activation (PRE > POST) as inferred from significant group × session interactions. Each box shows an overlay of the activation on a representative normalized brain and a plot of the interaction effect for the most significant voxel in this area, i.e., the size of the effect (contrasts of parameter estimates) in both experimental groups (learning group (blue line), control group (pink line)) across both scanning sessions (PRE, POST). (A) Superior parietal cortex ((1) postcentral sulcus; (2) intraparietal sulcus), (B) right dorsal premotor cortex, (C) right dorsolateral prefrontal cortex (D) right ventral premotor cortex, and (E) left cerebellar lobule VI ((1) hemisphere; (2) vermis). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

separate clusters within the right lobule VI (x = 32, y = −82, z = −20, Z-value: 5.95; x = 34, y = −52, z = −26, Z-value = 3.99) showed a decrease in activation with learning. Also within the middle occipital gyrus, corresponding to hMT/V5+, activation decreased with learning. No specific effects for the MN condition that were dissociable from the MFb condition could be observed.

4. Discussion A “new” spatio-temporal relationship between both hands was acquired in the present study, reaching a performance plateau by the end of practice. Amplitude and cycle duration of the single limb motions did not change. Therefore, the observed activation changes reflect learning-related

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Fig. 4. Anatomical localization of learning-related increases in activation (POST > PRE) as inferred from significant group × session interactions. Each box shows an overlay of the activation on a representative normalized brain and a plot of the interaction effect for the most significant voxel in this area, i.e., the size of the effect (contrasts of parameter estimates) in both experimental groups (learning group (blue line), control group (pink line)) across both scanning sessions (PRE, POST). (A) Left dorsal premotor cortex, (B) primary motor cortex, (C) cingulate motor cortex, (D) (1) putamen/globus pallidus; (2) thalamus, (E) cerebellum dentate nuclei, and (F) superior temporal gyrus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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neuroplasticity rather than simply variations of secondary motor output parameters, which have been shown to be associated with brain activation levels, such as movement amplitude (Shadmehr & Holcomb, 1997), movement rate (Blinkenberg et al., 1996; Sadato, Ibanez et al., 1997), or force production (Dettmers et al., 1995). It is also important to note that the two groups did not differ with respect to movement amplitude or cycle duration but only with respect to the spatio-temporal relation between the limbs, suggesting excellent control for non-specific changes in activation that might have occurred across the two sessions. As revealed by the interaction between the factors group and session, the present study showed learning-related activation changes for different cortical as well as subcortical structures. Decreases in activation from initial learning to a stage where stable performance was reached were observed for the right DLPFC, the right PMd, the right PMv, the bilateral superior parietal cortex, and left cerebellar lobule VI. Increases in activation were observed for bilateral primary motor cortex, bilateral superior temporal gyrus, bilateral cingulate motor cortex, left premotor cortex, cerebellar dentate nuclei/lobule III/IV/Crus I, putamen/globus pallidus, and thalamus. DLPFC activation has been shown in different types of motor learning involving unilateral tasks (Deiber et al., 1997; Ghilardi et al., 2000; Jenkins et al., 1994; Jueptner, Stephan et al., 1997; Sakai et al., 1998; Shadmehr & Holcomb, 1997; Toni & Passingham, 1999; Toni et al., 1998). In these studies, activation was evident during the initial stage of learning and decreased when learning progressed. This is similar to the present study, in which activation was observed in the learning group at initiation of practice and subsequently dropped when the pattern was acquired. For the control group, this area only showed very weak (below conventional threshold) activation. This indicates that the DLPFC was important for learning the new bimanual coordination pattern but was no longer necessary once the task was performed skillfully. This is consistent with the view that DLPFC is related to ‘attention to action’ (Jueptner, Stephan et al., 1997; Toni & Passingham, 1999; Toni, Ramnani, Josephs, Ashburner, & Passingham, 2001), i.e., the activity decreases when the task becomes automatic and attention demands are reduced. Furthermore, the DLPFC might also be involved in the formation of action-oriented representations on the basis of processing sensory information (Sakai, Ramnani, & Passingham, 2002). In this context, it can be hypothesized that this working-memory area (Goldman-Rakic, 1998) is involved in monitoring sensory information of the ongoing actions during initial learning to update motor plans towards the correct spatio-temporal relation between the limbs. The right premotor and superior parietal areas showed a decreased activation when the task was learned. With exception of the intraparietal region, activation for these regions equalled levels similar to those observed for controls after the new skill was established. This is in good agreement with previous motor learning studies reporting activation changes in parietal and premotor regions during the

early learning of sequences, of visuo-motor transformations and associations (Deiber et al., 1997; Ghilardi et al., 2000; Inoue et al., 2000; Jenkins et al., 1994; Jueptner, Frith et al., 1997; Jueptner, Stephan et al., 1997; Toni & Passingham, 1999; Toni et al., 1998), or during unskilled performance of a maze drawing task (Van Mier et al., 1998). The similar activation profile of the premotor and superior parietal areas is not surprising given their direct interconnections as part of the neural network subserving the sensory guidance of movements (Rizzolatti, Luppino, & Matelli, 1998; Wise, Boussaoud, Johnson, & Caminiti, 1997). In our task, the on-line control and correction of the ongoing movements was predominant during the early learning phase but became less prominent once the new pattern was established. Therefore, together with the DLPFC, which is densely connected with parietal–premotor regions (Barbas & Pandya, 1987; Cavada & Goldman-Rakic, 1989), the premotor and parietal cortex might serve as the neural substrate to accomplish this function. However, with respect to the premotor regions, it should be noted that activation was asymmetric, i.e., right premotor regions showed a decrease and left premotor regions an increase in activation with learning of the task. For both primary motor cortex and cingulate motor cortex, an increase in activation was observed when the pattern was acquired. With respect to the primary motor cortex, enlarged activation has previously been observed following 3 weeks of practice on a finger sequence task (Karni et al., 1995, 1998). This activation, however, did not extend beyond the hand representation itself. The differential effect was rather accounted for by a subpopulation of voxels in the hand area that showed a significant response to the trained but little or no response to an untrained sequence (Karni et al., 1995). Similar findings were obtained in our study following intensive practice. A learning-related effect was found bilaterally for voxels located somewhat more medially than the original hand representation. These voxels probably also represent a subgroup at the border of the hand region that exhibited higher activation specifically in association with the learned task. The functional significance of this activation probably refers to a more specific representation of the skilled function in M1 with learning (Karni et al., 1998; Nudo, Milliken, Jenkins, & Merzenich, 1996). In our case, this would reflect a more optimal spatio-temporal ordering of the executive commands to the muscles of both limbs, as required for smooth execution of the task. This function might be fulfilled together with the motor areas of the medial wall to which the primary motor cortex is strongly connected (Dum & Strick, 1991), and which have already been assigned with such functions (e.g., Tanji & Shima, 1994). Furthermore, activation of both the supplementary motor area and the cingulate motor cortex (located directly underneath the SMA) have clearly been shown to play an important role in bimanual coordinated movements (Goerres et al., 1998; Immisch et al., 2001; Jancke et al., 2000; Sadato, Yonekura et al., 1997; Stephan, Binkofski, Halsband et al., 1999; Stephan,

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Binkofski, Posse et al., 1999; Swinnen, 2002; Toyokura et al., 1999). In our study, high activation levels in these regions were found across both scanning sessions and for both groups. However, a cluster located around the cingulate sulcus also showed a specific learning-related effect. Such an effect has been reported frequently in the SMA-proper or dorsal cingulate motor cortex, with the activation becoming more prominent after the task was learned (Deiber et al., 1997; Grafton et al., 1992; Inoue et al., 2000; Jenkins et al., 1994; Jueptner, Frith et al., 1997; Jueptner, Stephan et al., 1997; Seitz & Roland, 1992; Toni et al., 1998; Van Mier et al., 1998). In addition, the observed activation around the cingulate sulcus reached a higher level than that observed in controls, presumably related to the higher complexity of the 90◦ out-of-phase as compared to the in-phase (control) task. Previous imaging studies on bimanual coordination already reported higher activation in this region when anti-phase movements were compared to in-phase movements (Goerres et al., 1998; Immisch et al., 2001; Jancke et al., 2000; Sadato, Yonekura et al., 1997; Stephan, Binkofski, Halsband et al., 1999; Stephan, Binkofski, Posse et al., 1999; Swinnen, 2002; Toyokura et al., 1999). In concert with changes in activation of motor cortical regions, we also observed substantial cerebellar and basal ganglia changes. A decrease in activation was found for the left hemisphere and vermal portion of lobule VI. However, other regions in the cerebellum were found to show an increase with learning, i.e., bilateral dentate Nc, left lobule III/IV, and left Crus I. These increases occurred simultaneously with increases in putamen/globus pallidus and thalamus. Generally, both the cerebellum and basal ganglia have been assigned an important function in motor skill learning (Graybiel, 1995; Salmon & Butters, 1995; Thach, 1996). Furthermore, it has been argued that both structures make a differential contribution depending on the stage of learning (Doyon et al., 2003; Hikosaka et al., 1999). Doyon et al. (2003) proposed that the cerebellum is more important during the early phases of learning and the basal ganglia during the later phases. Accordingly, a shift from a cerebello-cortical to a striato-cortical network would occur as learning progresses (Doyon et al., 2002, 2003; Penhune & Doyon, 2002). Alternatively, the model of Hikosaka et al. (1999) indicates two independent cortico-subcortical systems preferentially involved in the early and late learning stages, i.e., prefrontal (association) cortices, posterior cerebellum, and the anterior part of the basal ganglia would predominate during the early phase, whereas, motor cortices (M1 and SMA), cerebellar dentate nuclei (anterior cerebellum), and the middle part of basal ganglia (putamen) would predominate in the late phase (Hikosaka et al., 1999; Sakai et al., 1998). These subcortico-cortical circuits that are differentially involved during the early and late stage of learning, reflect the transition of high attention-demanding task performance involving sensory information processing to automatic performance based on memory representations and anticipatory control. The present results fit partially

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with these models of cortico-subcortical interactions during motor sequence learning, as we observed higher involvement of prefrontal–premotor–parietal and cerebellar lobule VI during the early learning phase, whereas, primary motor, cingulate motor, premotor and basal ganglia, together with cerebellar dentate nuclei, became more involved when the coordination pattern was well acquired. Our findings might therefore extend the relevance of these models to cyclical bimanual motor tasks. The present findings also further underscore that basal ganglia as well as the cerebellum, through their distinct interconnections with cortical regions (Middleton & Strick, 2000), are crucial for motor learning processes, and probably represent sites involved in the formation of long-term motor memory or consolidation of motor programs (Hikosaka et al., 1999; Thach, 1996). It remains to be established, however, whether activation in these regions would further change with more extensive practice. Finally, an interesting asymmetry in the activation of the cortical regions with learning was observed in the present study. Cortical regions that decreased their activation with learning were mainly located within the right hemisphere, whereas areas with increasing activation tended to be more localized within the left hemisphere. Similarly, right hemisphere activation dominance of cortical regions during the early stage of learning and more left hemispheric activation during the late stage have been reported previously for various movement tasks (Deiber et al., 1997; Inoue et al., 2000; Shadmehr & Holcomb, 1997). However, these studies used unimanual tasks, thereby complicating the interpretation of possible left–right hemispheric differences during skill learning, as lateralized activation can simply be a concomitant of moving with only one limb. Even though the present findings require further confirmation, we would like to entertain the hypothesis that bimanual paradigms can possibly provide more convincing evidence for a higher involvement of the right hemisphere during initial learning and for a more prominent role of the left hemisphere during late learning. The present findings are also consistent with imaging studies, showing a higher involvement of the hemisphere contralateral to the dominant (right) limb during bimanual performance (Jancke et al., 2000; Viviani, Perani, Grassi, Bettinardi, & Fazio, 1998), and with lesion studies pointing to a left hemisphere dominance for motor functions, including bimanual skills (Wyke, 1971a, 1971b).

Acknowledgements Support for the present study was provided through a grant from the Research Council of K.U. Leuven, Belgium (contract no. OT/03/61) and the Research Program of the Fund for Scientific Research—Flanders (FWO-Vlaanderen #G.0105.00 and G.0460.04). F. Debaere was supported by a scholarship from FWO-Vlaanderen. The authors are indebted to professor R. Carson for his critical comments on an earlier draft of this manuscript.

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