Cerebral Cortex Advance Access originally published online on May 13, 2004
Cerebral Cortex 2004 14(10):1153-1163; doi:10.1093/cercor/bhh075
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© Oxford University Press 2004
Article |
Parieto-premotor Areas Mediate Directional Interference During Bimanual Movements
1 Motor Control Lab, Group Biomedical Sciences, KU Leuven, Belgium, 2 Magnetic Resonance Research Center, Group Biomedical Sciences, KU Leuven, Belgium
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Abstract |
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In bimanual movements, interference emerges when limbs are moved simultaneously along incompatible directions. The neural substrate and mechanisms underlying this phenomenon are largely unknown. We used functional magnetic resonance imaging to compare brain activation during directional incompatible versus compatible bimanual movements. Our main results were that directional interference emerges primarily within superior parietal, intraparietal and dorsal premotor areas of the right hemisphere. The same areas were also activated when the unimanual subtasks were executed in isolation. In light of previous findings in monkeys and humans, we conclude that directional interference activates a parieto-premotor circuit that is involved in the control of goal-directed movements under somatosensory guidance. Moreover, our data suggest that the parietal cortex might represent an important locus for integrating spatial aspects of the limbs’ movements into a common action. It is hypothesized to be the candidate structure from where interference arises when directionally incompatible movements are performed. We discuss the possibility that interference emerges when computational resources in these parietal areas are insufficient to code two incompatible movement directions independently from each other.
Key Words: interlimb coordination, interhemispheric integration, monkey–human analogy, MIP, PEip
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionSupplementary MaterialNotesReferences |
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Doing two incompatible tasks at once often results in interference at the behavioural level, reflecting the central nervous system’s limitations in controlling different streams of action in parallel (Pashler, 1994
). In particular, interference can emerge from the simultaneous execution of two motor tasks, such as ‘patting the head while rubbing the stomach’. In this and related bimanual skills requiring concurrent left and right limb movements with incompatible spatial features, mutual assimilation effects between limb trajectories can be observed. As a consequence, one limb’s movement direction is systematically biased towards the other. For discrete and cyclical bimanual tasks it was shown that interference is absent when limbs are moved in mirror-symmetry, but increases dramatically when the required limb trajectories deviate from symmetry, primarily when trajectories are orthogonal (Franz et al., 1991, 1996; Franz, 1997; Swinnen et al., 2001, 2002; Swinnen, 2002; Wenderoth et al., 2003).
At the cortical level, the neural substrate mediating directional interference is largely unknown. Sporadic hints have been derived from studies in split-brain patients (Eliassen et al., 1999; Franz et al., 2000; Franz and Ramachandran, 1998; Kennerley et al., 2002), showing that spatial assimilation effects during non-symmetric bimanual movements are abolished when the corpus callosum is transected. Therefore, interhemispheric information flow seems to be a prerequisite for the emergence of directional interference (Eliassen et al., 1999; Franz et al., 2000; Kennerley et al., 2002). Interestingly, the directional bias observed during the simultaneous execution of orthogonal movements seems to be primarily mediated by the posterior callosal fibers that connect the parietal cortices of both hemispheres (Eliassen et al., 1999).
Current behavioural models assume that interlimb interference emerges as a result of cortical cross-talk (Cardoso de Oliveira, 2002; Swinnen, 2002). The cortical cross-talk model proposes that important movement parameters, which are encoded within the hemisphere contralateral to each moving limb, spread to the other hemisphere due to interhemispheric cross-coupling (Heuer, 1993). This causes some degree of neural irradiation, evoking either reinforcement if movement parameters are compatible or interference if movement parameters are incompatible. Alternatively, interference can be assumed to emerge when tasks performed simultaneously compete for the same specialized brain areas. The latter hypothesis has gained some support from other types of dual-task paradigms in which interference has been hypothesized to arise when two different tasks engage common cortical fields (Passingham, 1996; Roland and Zilles, 1998). Such cortical fields represent specialized cell populations that constitute a functional entity to control one specific aspect of the required behaviour (Roland and Zilles, 1998). In this case, interference is thought to emerge when the available computational resources within a cortical field are insufficient to comply with the concurrent demands of two incompatible tasks. This idea has been supported by brain imaging findings in dual-task experiments that used mainly competing working memory tasks (Passingham, 1996; Klingberg and Roland, 1997; Klingberg, 1998; Adcock et al., 2000; Bunge et al., 2000; Gruber and von Cramon, 2003). Even though this has not been explored yet, it is conceivable that similar mechanisms cause interference when bimanual movements with differential directional requirements are performed. More specifically, two motor tasks may access common brain areas involved in the directional tuning of movement, resulting in neural interference that becomes evident in directionally biased behaviour.
In the present study, we investigated the neural substrate as well as the mechanisms mediating directional interference during cyclical bimanual movements. Therefore, we specifically designed a functional magnetic resonance imaging (fMRI) experiment requiring rhythmical uni- and bimanual drawing movements with different levels of spatial incompatibility between the limbs’ trajectories. All movements were executed solely under somatosensory guidance and confounding eye movements were avoided. This experimental setup allowed us first, to isolate a network reflecting bimanual directional interference and second, to determine whether areas of this interference network were commonly accessed by each of the single limb tasks.
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Materials and Methods |
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Ten subjects (5 male, 5 female, aged 25 ± 5 years) participated in the experiment. All were right-handed (Oldfield, 1971
) and naive with respect to the task. None had a history of neurological or psychiatric disease or exhibited overt sensorimotor deficits. The study was approved by the local ethical committee of KU Leuven and subjects gave written informed consent in accordance with the Helsinki Declaration.
Subjects laid supine in the scanner with their upper arms next to the body and the forearms nearly vertical, i.e. with an elbow angle between 90° and 135°. In this position they operated with each wrist a two-dimensional joystick (Fig. 1). The joysticks were manufactured in-house, each utilizing two fMRI-compatible optical encoders (Hewlett-Packard, Malaysia; spatial resolution 0.18°) to register movements along the vertical and horizontal dimension with a sampling rate of 100 Hz. The behavioural task consisted of cyclical drawing movements, which were paced by a metronome at 2.4 Hz. During the ‘line drawing’ task, subjects maintained a vertical line at all times (required orientation angle 
= 90°). During star drawing, subjects had to change movement direction after completing eight full movement cycles (corresponding to an interval of 3.34 s) by 45°, resulting in movements along four principal orientations with = 90°, 45°, 0° and 135°. The switching between orientations was indicated by a stressed beep of the metronome.
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Before scanning, subjects were trained to perform the following conditions: (i) unimanual line drawing with the left wrist (leftLine); (ii) unimanual star drawing with the right wrist (rightStar); (iii) line drawing with the left, while star drawing with the right wrist (LineStar); (iv) star drawing with the left, while line drawing with the right wrist (StarLine); (v) mirror-symmetrical star drawing with both wrists (StarStar); (vi) line drawing with both wrists (LineLine); and (vii) rest without any movements (Rest). All conditions lasted 26.6 s, requiring the subjects to visit each principal orientation of the star twice. Vision of the hands was obstructed and no other visual information was provided, so that all movements were executed under somatosensory guidance only. Additionally, subjects were trained to look at a fixation cross, displayed in front of them to avoid confounding eye-movements. During scanning, the upcoming condition as well as the start orientation of the star drawing (i.e.
= 0°, 45°, 90° 135°) were indicated visually by a template appearing 1.5 s prior to initiation and remaining visible for 3s. The fMRI measurements were accomplished on a 1.5 T MR Siemens Sonata scanner with a quadrature head coil. For anatomical details, a three-dimensional high-resolution T1-weighted image was obtained first (MPRAGE, TR/TE = 11.4/4.4 ms, TI = 300 ms, field of view = 256 mm, matrix = 256 x 256 mm2, slab thickness = 160 mm, 160 slices). Then, subjects performed eight scanning runs, each containing 201 gradient-echo echoplanar T2-weighted functional images (TR/TE = 2840/50, field of view = 192 mm, matrix = 64 x 64 mm2, slice thickness = 4 mm, 36 sagittal slices). During one run, three blocks of the seven conditions were performed, each condition lasting 9.4 scans. Conditions as well as the start orientation for the star-drawing were randomized between runs and subjects.
Kinematic Analysis
For the star drawing hand, the required orientation angle changed every 3.34 s, corresponding to the imposed time to complete eight movement cycles along one orientation. Since each orientation was visited twice, each trial was subsequently subdivided into eight intervals (Fig. 2b,d,f). Using interactive software (Matlab 5.3), for each of the eight intervals, orientation angles were calculated as = arctan[(y2 – y1)/(x2 – x1)], with 
[required – 90, required + 90] and (x1,y1), (x2,y2) indicating the coordinates of two consecutive turning points. From these data, mean as well as the standard deviation (SD) of were determined and the orientation error (error) was calculated as the absolute difference between the required and produced mean . Subsequently, for intervals requiring the same principal orientations (i.e. the 1st and the 5th, 2nd and 6th etc.), error and SD were averaged between the star and the line task, representing the overall performance of both limbs. These values were subjected to an analysis of variance (ANOVA) with repeated measures on the within-subjects factors Task Category [unimanual, bimanual compatible, bimanual incompatible], Orientation [ = 90°, 45°, 0°, 135°] and Run [1...8]. Significant interactions were further inspected by means of Tukey a posteriori tests. The level of significance was set to 0.05.
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Imaging Preprocessing
Imaging data were analysed with the Statistical Parametric Mapping software (SPM99; http://www.fil.ion.ucl.ac.uk/spm; Wellcome Department of Cognitive Neurology, London) (Friston et al., 1995a,b). The functional images were realigned to the first volume of each run to correct for head movements. Slice timing was applied to correct for differences in acquisition time during scanning. After co-registering the functional images to the anatomical image, they were spatially normalized into a standard reference system (Talairach and Tournoux, 1988), using a representative brain (MNI, Montreal Neurological Institute) as a template. All functional images were subsampled to a voxel size of 2 x 2 x 2 mm and smoothed with a Gaussian kernel of 8 mm full width at half maximum (FWHM).
Epoch-related Subtraction Analysis
For the first level analysis, a general linear model was used, containing for each condition a delayed boxcar function convolved with the standard SPM99 haemodynamic response function. Additionally, movement parameters derived from realignment (translation and rotation in x, y, z dimension) were added as covariates of no interest. Comparisons of interest were calculated as linear contrasts for each subject and run individually. Subsequently, these contrasts were entered into a second-level mixed effects analysis. We report all areas reaching significance on cluster level (P < 0.005, after correction for multiple comparisons), with a cluster size (k) larger than 10 voxels. However, to allow a more accurate localization, for large clusters, we additionally report MNI-coordinates of significant activation maxima on voxel level (P < 0.05, after correction for multiple comparisons). To determine the interference network (StarLine+LineStar–2xStarStar), our analysis was restricted to those areas responding more strongly to the incompatible than to the compatible conditions (StarLine+LineStar–StarStar–LineLine) by applying a masking procedure followed by a small volume correction.
Event-related Parametric Analysis
For the StarLine and LineStar conditions, spatial incompatibility varied from low (Line = 90, Star = 90) to intermediate (Line = 90, Star = 45°/135°) to high (Line = 90°, Star = 0°). As such, a separate event-related analysis could be performed to investigate whether some regions changed their activity in accordance to the direction traced by the star drawing wrist. Therefore, the onset of each orientation interval (Star = 90°, 45°, 0° or 135°) was determined from the kinematics. During the first level analysis, each onset was convolved with the standard SPM99 haemodynamic response function. Non-linear interactions between successive events (interval between two successive onsets = 3.34 s) were eliminated by including a Volterra series in the model. Note that the interval between two successive onsets was different from the TR (2.84s), resulting in a jittered estimation of the subjects’ haemodynamic response. For each subject and run, we determined with a parametric design in which areas activity was either linearly or quadraticly modulated for Star = 90°, 45°, 0° and 135°. Subsequently, these contrasts were subjected to a second level, mixed-effects analysis to reveal the group results. This analysis was restricted to the interference network by a masking procedure.
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Results |
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Behavioural Results
Figure 2 depicts exemplary kinematic data of the joystick displacements (Fig. 2a,c,e) as well as the continuous orientation angle (Fig. 2b,d,f) for a unimanual (leftLine and rightStar), bimanual incompatible (LineStar) and bimanual compatible (StarStar) trial of a typical subject. While subjects traced the required orientations accurately when either the Line or the Star task were executed unimanually (Fig. 2a,b), performance decreased substantially when both tasks had to be executed simultaneously (Fig. 2c,d). Directional assimilation effects became particularly prominent for the Line subtask, so that the orientation was substantially biased towards that of the star drawing hand when the horizontal ( = 0°) or the left diagonal ( = 135°) was traced. By contrast, when hands continuously produced symmetrical movements during the StarStar task (Fig. 2e,f), bimanual performance differed only slightly from the unimanual performance. To quantify directional interference during the different conditions on group level, variability (SD) as well as the mean error (error) of the produced movement direction for each of the four principal orientations of the star drawing was analysed (Fig. 2h,g). Directional interference was minimal for the bimanual compatible conditions (LineLine/StarStar), such that SD and error hardly differed from unimanual performance (P > 0.9). By contrast, for the bimanual incompatible tasks (LineStar/StarLine), error and SD were significantly increased as compared to the bimanual compatible or the unimanual conditions (ANOVA, P < 0.005), indicating a substantially higher degree of interference (Fig. 2g,h). Moreover, within each StarLine/LineStar condition, directional incompatibility varied from low, when both wrists were moved symmetrically along the vertical direction (Line = 90°, Star = 90°) to high, when orthogonal directions (Line = 90°, Star = 0°) had to be traced. SD changed accordingly, with low values when the movement directions were parallel, intermediate values when movement directions differed by 45° and high values, when movement directions were orthogonal (Fig. 2h). error also increased when the star orientation changed from vertical (Line = 90°, Star = 90°) to the first diagonal (Line = 90°, Star = 45°) and to the horizontal (Line = 90°, Star = 0°). However, subjects exhibited the largest directional errors when the star drawing wrist traced the Star = 135° orientation, which differed significantly from error found for the other diagonal (Line = 90°, Star = 45°) (P < 0.05, Tukey post hoc test) (Fig. 2g). Taken together, SD was related to the level of directional incompatibility and changed for Star = 90°, 45°, 0°, 135° and Line = 90° at all times in an inverse quadratic way, as tested by an additional regression (goodness of fit r2 = 0.95). By contrast, error was related to the direction of the star drawing wrist and changed in a linear way over the four principal orientations (r2 = 0.95).
Finally, secondary movement parameters such as movement amplitude, cycle duration as well as the temporal lag between the hands did not differ significantly (P > 0.05) between bimanual compatible and bimanual incompatible conditions.
Imaging Results
Interference Network
To investigate which areas reflect directional interference, we first determined the regions responding more strongly to the directionally incompatible tasks (LineStar/StarLine) than to the fully compatible tasks (StarStar/LineLine). We identified a network (Table 1) containing bilateral activation of the superior parietal cortex (Brodmann area [BA] 2/5/7) extending to the primary sensory cortex SI (BA 2), the caudal supplementary motor area (SMA proper), the caudal part of the lateral dorsal premotor cortex (PMd proper) and the cerebellum. Additionally, we found unilateral activation within right supramarginal gyrus (BA 40), right anterior superior temporal gyrus (BA 38), right ventral premotor (BA 6) and the cerebellar hemispheres.
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However, the spatial complexity of the LineLine drawing was substantially lower than for the other bimanual tasks, since no switching between different directions was required. Therefore, it is possible that some of the aforementioned areas do not primarily reflect directional interference, but are related to unspecific differences in general spatial complexity. To overcome this potential confound, we used the above network as a mask and calculated which of the included areas responded more strongly to the directionally incompatible tasks (LineStar/StarLine) than to the StarStar task only. Note that the StarStar drawing differed from the combined Star/Line tasks only with respect to the observed directional interference, while other features, such as moving along several orientations as well as switching between them were the same. As such, it is a more strict contrast to identify an ‘interference network’, which is shown in Figure 3 and Table 2. Surprisingly, the identified interference network was nearly exclusively located within the right hemisphere, which contained a large, significantly activated cluster that consisted of superior parietal and premotor areas. More specifically, we found significant activation maxima along the right postcentral sulcus at the border of BA 2/BA 5 and at the junction of the postcentral and the intraparietal sulcus, right SI (BA 2), as well as bilateral SMA proper and the right PMd proper (both BA 6). Significantly activated clusters were additionally yielded for the right supramarginal gyrus (BA 40), right anterior superior temporal gyrus (BA 38) as well as the cerebellar vermis. Within the left cortex, a single local activation maximum was found within PMd proper (BA 6), reaching significance only without correction for multiple comparisons (P < 0.0001, Z = 3.87). To increase the sensitivity of our statistical analysis, another 12 subjects were added who performed identical StarLine, LineStar and StarStar conditions in an accompanying study, that addressed the influence of different types of feedback (N. Wenderoth et al., unpublished data). However, this more powerful analysis (n = 22) revealed basically the same cortical network that was only slightly more extended. In particular, the activation of the left premotor cortex reached significance also after correction for multiple comparisons underscoring the tendency of a bilateral activation within PMd. By contrast, no significant activation was observed in the left parietal regions, indicating a strong functional lateralization of this area.
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Figure 3 displays a bar plot for each significantly activated cluster, presenting the brain activation for the StarStar, LineStar and StarLine drawing. Comparing the two latter conditions, it can be seen that activation was larger for the StarLine drawing than for the reversed task assignment across all regions. These differences were particularly pronounced in the parietal areas, while premotor regions were activated to a similar degree for both incompatible tasks. (Note that no significant differences were observed for the behavioural measurements, yielding for the StarLine condition
error = 9.80°, SD = 11.42° and the LineStar condition error = 9.23°, SD = 10.19°)
Within the interference network, we scrutinized whether the haemodynamic response was modulated by the different levels of interference observed during StarLine/LineStar drawing. Therefore, we performed a separate analysis whereby each orientation interval of the star drawing was modeled as an event. Subsequently, those areas were determined, that were modulated either in accordance to error or in accordance to SD, i.e. exhibiting either a linear or an inverse quadratic change of activity across the orientation intervals Star = 90°, 45°, 0° and 135°, respectively. Figure 4a,b shows exemplary haemodynamic responses of a typical subject. Figure 4a shows an error-related voxel, with its peak haemodynamic response (marked by the dotted black line) increasing linearly from Star = 90° to Star = 135°. In contrast, Figure 4b shows the activation of an SD-related voxel (peak haemodynamic response marked by dotted grey line), exhibiting the lowest response to Star = 90°, an intermediate response to Star = 45°/135° and the largest response to Star = 0°. On group level, this analysis revealed two subcircuits of the interference network (Fig. 4c,d, white) that presumably serve different functions: First, a parieto-premotor-temporal circuit (Fig. 4c,d, black areas) within the right hemisphere was strongly related to error. This circuit consisted of a superior parietal cluster along the postcentral sulcus (BA 2/5), including the junction of the postcentral and the intraparietal sulcus, an inferior parietal cluster (BA 40), a cluster within PMd proper and the cluster within the posterior superior temporal gyrus. This suggests that within the parieto-premotor-temporal network, activity was related to directional interference but varied as a function of the produced orientation of the star drawing. No areas were inversely related to error. Secondly, activation of a premotor circuit consisting of SMA proper and PMd proper (both BA 6) (Fig. 4c,d, grey areas), was modulated analogous to SD, i.e. activity was lowest when both limbs moved in parallel along fully compatible orientations and highest when limbs moved orthogonal along maximal incompatible orientations. No areas were inversely related to SD. Thus, activity in the different circuits showed a unique association with different measures of the behavioural response, i.e. directional accuracy and variability.
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Overlapping Activity Evoked by the Unimanual Tasks
To identify which areas were commonly involved in the execution of the unimanual tasks (only line drawing with the left wrist or only star drawing with the right wrist), a mutual masking procedure was used to identify the overlap between those clusters that were significantly activated during the leftLine and rightStar condition (Table 3). We found overlapping activity within the right superior parietal cortex along the postcentral sulcus (BA 2/5/7/40), bilateral SMA proper, right PMd proper, left inferior parietal cortex (BA 42), left middle temporal gyrus (BA 39), right primary auditory cortex (BA 41), right Putamen and cerebellar vermis. Interestingly, most of the parietal areas, particularly along the postcentral sulcus, and the premotor clusters were either part of or were located in close proximity to the interference network. This is visualized in Figure 4d where areas with overlapping activity (white borders) are displayed on top of the interference network and its subcircuits.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionSupplementary MaterialNotesReferences |
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In the present study we identified for the first time the neural structures mediating directional interference that emerges when limbs are moved simultaneously along incompatible trajectories. The identified interference network consisted of superior parietal, inferior parietal, dorsal premotor, medial premotor (SMA) and superior temporal areas, and was nearly exclusively located within the right hemisphere. All regions were more strongly activated during the directionally incompatible StarLine/LineStar tasks than during the fully compatible StarStar task. Whereas all tasks were endowed with identical spatial complexity, the latter did not evoke directional interference
Within the general interference network, we identified two subnetworks: (i) a parieto-premotor-temporal network in which activity was modulated in parallel with error; and (ii) a lateral–medial premotor network whose activity changed in parallel with SD. Even though both subnetworks are related to directional interference, we hypothesize that they serve different functions during the control of bimanual movements.
The Parieto-premotor-temporal Subnetwork
We identified a network including superior parietal and intraparietal regions (BA 5/2) as well as a frontal area located within the caudal part of the dorsal premotor cortex (PMd proper, BA 6). Moreover, the network contained the supramarginal gyrus (BA 40) of the inferior parietal cortex and the anterior part of the superior temporal lobe (BA 38). Moving along directionally incompatible directions, these areas increased their activation linearly for Star = 90°, 45°, 0° and 135°, respectively. Based on previous findings, it can be assumed that biomechanical (e.g. different inertial requirements) and also coordinative demands increased for movements along the four different orientations (Levin et al., 2001). This indicates that the parieto-premotor-temporal areas are involved in higher-order control of spatial movement aspects.
Superior Parietal and Dorsal Premotor Activity
The superior parietal cortex together with the dorsal premotor cortex have previously shown activation in different bimanual tasks (de Jong et al., 2002de Jong et al., 2002; Ullen et al., 2003; Debaere et al., 2004). Our present results extend this knowledge by defining the more specific role of these areas in bimanual coordination. In particular, the functional requirements of our paradigm suggest that the superior parietal and premotor cortex play a crucial role in the control of spatial aspects of bimanual movements. This view is supported by neuroimaging results in humans that identified similar activation spots during the execution of goal-directed motor actions such as reaching and pointing movements (Kawashima et al., 1996; Lacquaniti et al., 1997; Connolly et al., 2000; Culham and Kanwisher, 2001; Simon et al., 2002). Additionally, work in monkeys has shown that superior parietal and frontal areas are highly interconnected, building functional circuits that make use of several sensory modalities to plan and execute goal-directed arm movements (Johnson et al., 1996; Kalaska et al., 1997; Wise et al., 1997; Caminiti et al., 1998; Lacquaniti and Caminiti, 1998; Rizzolatti et al., 1998; Buneo et al., 2002; Rizzolatti and Matelli, 2003; for a review see Battaglia-Mayer et al., 2003). In our study, we identified superior parietal areas along the postcentral sulcus (BA2/5) as well as around the junction of the postcentral/intraparietal sulcus, which probably represent the human analogue of monkeys dorsal area 5 and the intraparietal regions PEip and perhaps also the anterior part of the medial intraparietal area (MIP), respectively. Within the frontal cortex, we identified PMd proper that corresponds to monkeys area F2 (Grefkes et al., 2001; Picard and Strick, 2001). In monkeys, the parietal and premotor areas were shown to be activated during goal-directed arm movements and their neurons exhibit directional tuning reflecting the intended behaviour (Johnson et al., 1996; Kalaska et al., 1997). Neurons of area 5 process mainly proprioceptive information to encode arm postures and movements within a body-centered reference frame and project to the primary motor cortex and the caudal part of F2 (Johnson et al., 1996; Lacquaniti et al., 1995) as well as to other parietal areas (Pandya and Seltzer, 1982). Interestingly, area 5 receives many interhemispheric projections via the corpus callosum that terminate along the border of area 5 and 2 (Caminiti and Sbriccoli, 1985; Iwamura et al., 2001). Accordingly, this area around the area 5/2 border contains a higher percentage of finger-, wrist- or arm-related neurons with bilateral receptive fields (Iwamura et al., 1994, 2002). As such, anterior area 5 might play an important role in the interhemispheric integration of somatosensory information.
Similar to area 5, also the intraparietal area PEip contains predominantly unimodal neurons responding to somatosensory stimuli (Iwamura and Tanaka, 1996), whereas MIP is a truly bimodal area containing neurons with somatosensory, visual and bimodal receptive fields (Colby and Duhamel, 1991; Johnson et al., 1996). Both areas project to F2 (Johnson et al., 1996; Marconi et al., 2001), which contains neurons that encode the required movement direction, particularly during movement preparation and execution (Kalaska and Crammond, 1995). Predominantly MIP, but probably also Peip, contribute to the control of visually guided movements by integrating visual and somatosensory information (Johnson et al., 1996). However, both areas and, particularly, the PEip–F2 projections might provide a substantial contribution to the performance of goal-directed actions under somatosensory guidance only (Rushworth et al., 1997; Rizzolatti et al., 1998). In conclusion, the above results in humans and monkeys indicate that directional interference during bimanual movements activates a superior parietal–premotor circuit that is most likely involved in the control of goal-directed actions under somatosensory guidance. Moreover, it can be assumed that the identified parietal areas located at the border of the human BA 5 and 2 play a crucial role in the coupling of left and right hand actions. These findings underscore the importance of the superior parietal cortex for the interhemispheric integration of spatial aspects during bimanual control, as suggested in previous work (Kermadi et al., 2000; Serrien et al., 2001).
Inferior Parietal Activity
We also identified interference-related activation within the right inferior parietal cortex, more specifically in the supramarginal gyrus (BA 40). In humans, the right inferior parietal cortex seems to be part of a network involved in the storage of spatial information (for an overview, see Smith and Jonides, 1998). Studies concerning spatial perception indicate that this function is most likely lateralized to the right hemisphere (Driver and Vuilleumier, 2001), while the left supramarginal gyrus is activated during the covert orienting of attention (Rushworth et al., 2001). Therefore, it can be concluded that either spatial working memory or spatial perceptual demands were higher during the incompatible than the compatible (StarStar) conditions.
Superior Temporal Activity
Unexpectedly, directional interference was also reflected by higher activation levels in the anterior part of the superior temporal gyrus (BA 38). Activation in this area emerged previously when unimanual (Rao et al., 1997; Jantzen et al., 2002) or non-symmetrical bimanual movements (Meyer-Lindenberg et al., 2002; Ullen et al., 2003) were performed in accordance with a fixed rhythm. The human BA 38 has been suggested to be the functional correlate of monkey’s polysensory area, located in the superior temporal lobe (Vaina et al., 2001), that integrates somatosensory, visual and auditory information (e.g. Schroeder and Foxe, 2002). In the present study the superior temporal cluster was more strongly activated during the execution of directionally incompatible than directionally compatible movements. This indicates that a common ‘temporal’ framework might become more important for synchronizing the single limb actions when a common ‘spatial’ framework is less salient.
The Premotor Subnetwork
We identified a large premotor cluster including primarily right PMd proper, SMA proper and to a smaller extent also left PMd proper. Activation in these areas varied as a function of incompatibility between the orientations traced by the left and right wrist. Several imaging studies using interlimb coordination tasks, reported activation of SMA and right PMd proper whenever the required movement pattern deviated from mirror-symmetry (Sadato et al., 1997; de Jong et al., 2002; Meyer-Lindenberg et al., 2002; Ullen et al., 2003), pointing to a rather general function in interlimb coordination. Both regions have dense interhemispheric connections via the anterior part of the corpus callosum (Marconi et al., 2003). However, transecting this part while keeping the posterior fibers intact, does not affect spatial interference (Eliassen et al., 1999). We conclude therefore, that the SMA proper and PMd proper are not primarily involved in interhemispheric conveyance of directional information. Instead, these areas are hypothesized to suppress the ‘default’ coupling of homologous muscles (Sadato et al., 1997). Supportive evidence for this hypothesis was yielded by a TMS experiment, showing that disrupting PMd activity during bimanual, anti-phase movements induced an involuntary transition to the symmetric in-phase movement pattern (Meyer-Lindenberg et al., 2002). Similar effects, even though less pronounced, were also found for the SMA. In our study, we found the highest activity level when nonhomologous muscles (Line = 90°, Star = 0°), intermediate activity levels when partly homologous muscles (Line = 90°, Star = 45/135°) and the lowest activity level when solely homologous muscles (Line = 90°, Star = 90°) were used. Thus, we conclude that by suppressing muscle homology, the SMA proper–PMd proper circuit allowed the execution of movements deviating from mirror symmetry.
Recently, the afferent versus efferent locus of bimanual coupling has received increasing attention (Mechsner et al., 2001; Li et al., 2003; Swinnen et al., 2003). Note that neither the parietal nor the premotor cortex are strictly sensory or motor. However, our data suggest that interference is reflected at several levels of information processing (e.g. perception versus action or planning versus execution), since the parietal cortex relies strongly on afferent information that is subsequently used to plan appropriate motor responses (Snyder et al., 2000) while premotor cortex activity is more strongly related to the selection and execution of the final motor action (Boussaoud et al., 1995).
How Can Directional Interference Emerge at the Cortical Level? A Working Hypothesis
It has been proposed that neurons of the parietal, premotor and primary motor cortex represent directions by means of population vectors, yielded in accordance to the weighted sum of the activity of directionally tuned neurons (Georgopoulos et al., 1982; Kalaska et al., 1983). Applied to bimanual movements, the right hemisphere (e.g. the parietal area 5) is assumed to encode the intended direction of the left wrist trajectory by population vectors while bilateral neurons might simultaneously represent the intended direction of the right hand. It can be assumed that when the left and right wrist are moving symmetrically (i.e. along the same directions in joint coordinates), trajectories are represented by identical population vectors, reinforcing each other during averaging. Conversely, when the left and right wrist are moved along incompatible directions, the concurrent population vectors differ from each other and averaging now results in mutual assimilation. Consequently, the represented movement direction of one limb is systematically biased towards the direction of the other limb. Such assimilation effects could occur within parietal, premotor and primary motor areas. In particular, for the primary motor cortex, recent results suggest an analogous mechanism, such that successful limb movements along incompatible directions require that interhemispheric information flow is suppressed by local inhibitory processes. When this inhibition fails, the population vector predicting the upcoming movement of the contralateral hand would be biased towards the planned direction, coded by the other hemisphere (Rokni et al., 2003).
Even though such a mechanism could take place at several cortical sites, we hypothesize that directional interference originates predominantly from the parietal areas. This view is strongly supported by findings in split-brain patients showing that a transection of the posterior corpus callosum, connecting the parietal cortices, abolishes directional interference during bimanual drawing movements (Eliassen et al., 1999; Franz et al., 1996). By contrast, when only the anterior corpus callosum, connecting the frontal areas, is transected, directional interference persists (Eliassen et al., 1999).
Hemispheric Asymmetry for Directional Coding of Movement
We found interference effects nearly exclusively in the right hemisphere. This is consistent with earlier imaging studies, which showed that right parietal and right premotor areas are more strongly activated during bimanual anti-phase than in-phase movements (de Jong et al., 2002; Sadato et al., 1997). This obvious asymmetry hints at possible mechanisms mediating directional interference. First, it can be assumed that interference was caused by unbalanced neural cross-talk, such that information spread predominantly from the left hemisphere (controlling the dominant hand) to the right hemisphere (controlling the non-dominant hand) (Viviani et al., 1998). Secondly, the right hemisphere could be specialized for processing spatial information, such that both single tasks (performed with only the left or only the right wrist) relied on the same, right-hemispheric parietal areas. The second mechanism is suggested by our data, indicating that the same right-hemispheric parietal and premotor regions were engaged when each task was performed in isolation. Moreover, findings concerning the representation of egocentric space in hemi-neglect patients (Vallar, 1997) and healthy subjects (Galati et al., 2000; de Jong et al., 2001) also revealed a higher involvement of the right hemisphere. Based on the cortical field hypothesis, Roland and Zilles (1998) proposed that interference arises when two tasks engage the same cortical field(s), as, for example, during dual-working memory tasks (Klingberg and Roland, 1997; Adcock et al., 2000; Bunge et al., 2000). Transposed to our study, this implies that directional interference emerged because cell populations, specialized in the coding of movement direction, disposed of insufficient computational resources to encode two different movement directions at the same time. This possible neural resource deficit, however, seems to be specific either to motor execution or to the bimanual nature of our task, since the premotor cortex can apparently encode several potential movement directions in parallel during the preparation of unimanual movements (Cisek et al., 2003).
Taken together, our study indicates a functional asymmetry between hemispheres, such that the superior parietal cortex together with the premotor cortex of the right hemisphere play a prominent role in the interhemispheric integration of spatial information during bimanual coordination.
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionSupplementary MaterialNotesReferences |
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Supplementary material can be found at: http://www.cercor.oupjournals.org/
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionSupplementary MaterialNotesReferences |
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We are indebted to Dr I. Toni for his comments on an earlier draft of this paper. We thank M. Beirinckx, P. Meugens and C. Ballestrin for invaluable assistance in developing the hardware and software for data acquisition and analysis. This study was supported by the Flanders Fund for Scientific Research (FWO Project G.0460.04 ) and the Research Fund of K.U.Leuven (OT/03/61).
Address correspondence to Nicole Wenderoth, Motor Control Lab, KU Leuven, Tervuursevest 101, 3001 Leuven, Belgium. Email: nicole.wenderoth{at}flok.kuleuven.ac.be
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