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1Neuropediatric Unit, Department of Woman and Child Health, Karolinska Institutet, Stockholm, Sweden; 2Department of Hand and Plastic Surgery, Norrlands University Hospital, Umeå, Sweden; and 3Wellcome Department of Imaging Neuroscience, Institute of Neurology, London, United Kingdom
Submitted 8 March 2004; accepted in final form 15 September 2004
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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). Anticipatory parameter control is used in advance to specify the appropriate motor commands. It is based on sensorimotor memory representations that store critical properties of the object acquired during previous manipulatory experience (see Fig. 1) (Johansson 1998; Johansson and Cole 1992). The information stored is used to set the parameters of the motor program that generates distributed muscle commands to the muscles producing the fingertip forces. The importance of such sensorimotor memory representations for the retrieval of parameters relevant to motor commands has been demonstrated regarding the prediction of object's weight (Johansson and Westling 1984, 1988; Westling and Johansson 1987), shape (Jenmalm and Johansson 1997; Jenmalm et al. 2000), and frictional characteristics (Edin et al. 1992; Fagergren et al. 2003; Johansson and Westling 1984). At the same time, the motor system uses an efference copy of the motor command (Von Holst and Mittelstædt 1950) produced when the movement is made to predict the sensory inputs of the motor act (Wolpert et al. 1995) and compares it with the sensory input produced by the movement (Fig. 1).
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) together with an update of the sensorimotor memory representations of the object physical properties used in anticipatory parameter control (Fig. 1).
While the neural substrate for several control functions involved in the performance of precision grip-lift tasks in humans have been identified (Ehrsson et al. 2000, 2001, 2003; Kinoshita et al. 2000; Kuhtz-Buschbeck et al. 2001), little is known about the neuronal counterparts of the sensorimotor memory representations of hand-object interactions proposed in the theoretical models of dexterous manipulation. In the present study, we examine two sensorimotor events required in the performance of a precision grip that, according to the theoretical model outlined in the preceding text, should be associated with particularly extensive neural computation. We designed a prototypical lifting task and used functional magnetic resonance imaging (fMRI) to register the blood-oxygenation level-dependent (BOLD) signal as an index of local increases in synaptic activity in the brain (Logothetis et al. 2001). We compared three lifting situations where the possibility of referring to a sensorimotor memory representation to use an anticipatory parameter control to target the object weight varied. When the subjects lifted an object with identical weight in consecutive lifts (constant condition), according to the model shown on Fig. 1 (Johansson and Westling 1988), the same sensorimotor representation should be used to program the forces applied to the object. When the object weight was changed after each lift regularly between a heavy and a light weight (regular condition), because the sequence was known, the subjects were expected to use the serial order strategy (light-heavy-light-...) to switch between two sensorimotor memory representations and correctly parameterize in advance the forces to apply to the object. Last, when the weight of the object was changed at random in an unknown sequence (irregular condition), we expected the motor command to be targeted erroneously to the previous weight. The mismatch that would result from the comparison of the predicted sensory input and the actual sensory input was expected to trigger an update of the engaged sensorimotor representations.
We hypothesized that the regions activated during regular (predictable) and irregular (unpredictable) weight changes would be part of the cerebral and subcortical network previously shown to be used for precision grip lifts (see Ehrsson et al. 2000, 2001, 2003; Kinoshita et al. 2000). This network includes the primary motor cortex (M1), the bilateral dorsal and ventral premotor cortices, area 44, the supplementary motor area, the cingulate motor area, and several parietal areas in both hemispheres (parietal operculum, supramarginal cortex and intraparietal cortex). In the subcortical areas, the bilateral hemispheres and right vermis of the cerebellum, left basal ganglia and thalamus are also recruited (Kinoshita et al. 2000). Further, we hypothesized that some of these areas would be more active in the regular and irregular conditions because these conditions involved a switch of the sensorimotor object representation and a strong mismatch, respectively, that would result in corrective reactions and an update of the memory representation. Finally, we expected some of these areas to be more active during the irregular condition than during the regular condition because of the larger errors occurring in the programmed fingertip forces during unexpected weight changes.
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METHODS |
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Twelve right-handed subjects (mean age: 29 ± 3 yr; 7 male, 5 female) participated in the study. All gave their written informed consent, and the study was approved by the Ethical Committee of Karolinska Hospital. During the experiments, the subjects rested comfortably in a supine position in the MR scanner. A plastic bite bar was used to restrict head movements. The extended right arm, used for the experiments was oriented parallel to the trunk. The subjects were asked to use the tips of the index finger and thumb in a precision grip to lift a test object 
5 cm above a support. The lifting movement was produced by a radial flexion of the wrist. We used supports to minimize movements in proximal joints. The subjects were blindfolded, and they wore headphones to reduce the noise from the scanner and to give auditory instructions relating to the task.
Apparatus
The test object, shown in Fig. 2, had a nonmagnetic instrumented handle with vertical flat parallel contact surfaces spaced 30 mm apart (35 x 35 mm) that were covered with sandpaper (No. 320). The handle was connected by a taut string to a dish located outside the MR-scanner to which an extra weight of 600 g could be easily added and removed between trials by the experimenter. The total weight of the object could have one of two values, 230 (light) or 830 g (heavy). The grip force, perpendicular to the surfaces, and the vertical load force, tangential to the surface, were registered continuously by force transducers in a fiber optics system. The vertical position of the object was measured with a fiber optics transducer.
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The subjects lifted the object, following the pace set by an auditory metronome that made a sound of 1-s duration every 2 s (0.5 Hz). Subjects performed three different lifting conditions that differed from each other only with regard to the sequence in which the two weights were presented; a rest condition was also included. In the first condition (constant), the weight of the object was kept the same during consecutive lifts. This condition included either 18 lifts with the heavy weight (constantheavy) or 18 lifts with the light weight (constantlight). In the second lifting condition (regular), the heavy and light weights were exchanged between every lifts so that the subjects knew the sequence of lifts. During the last lifting condition (irregular), the weight of the object changed unpredictably between heavy and light. In this condition, there were also 18 lifts, and the weight was changed 10 times, i.e., the same weight was used for one to three lifts before it was changed. Figure 3 illustrates 10 lifts from the three different lifting conditions. During the resting condition (baseline; not shown), the subjects relaxed their hand without applying a grip force. The metronome was played through the headphones exactly as it had in the different lifting conditions. Before each condition, a verbal instruction of 4-s duration indicated to the subject the beginning of a new condition. The order of presentation of the different lifting conditions and the resting condition was varied across subjects. Before scanning, subjects were trained to correctly lift the object in accordance with the instructions during 10 min. They were asked not to apply an excessive grip force.
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A flexible data-acquisition and analysis system (SC/ZOOM, Department of Physiology, Umeå University, Umeå, Sweden) was used to sample signals from the force and the position sensors (400 samples/s) time locked with the MR scanner. The grip and load forces were measured and the mean values taken for the thumb and the index finger. Touch was determined by an off-line inspection of the grip force trace and was defined as the moment at which the grip force started to increase. For each lifting trial, the vertical position, the static grip, and load forces were calculated as the value of the force 1 s after touch when the object was held steadily in the air. The peak rate of grip force increase was assessed from the first time derivative of the grip force signal using a ±5 points numerical differentiation, i.e., the force rate was calculated within a window of ±12.5 ms. Repeated-measures ANOVAs were used to evaluate the influence of lifting condition (constant, regular, irregular) and object weight (230 and 830 g) on the vertical position, static grip force, and the maximum grip force rate during the load phase. During the irregular condition, the previous weight (230 and 830 g) was analyzed with repeated-measures ANOVA on the same dependent variables. The level of probability selected as statistically significant was P < 0.05. The values reported in the figures for data pooled across trials for all subjects refer to means ± SD.
Brain imaging
Functional MRI was performed on a 1.5 T scanner (Sigma Horizon Echospeed, General Electric Medical Systems) equipped with a head-coil. A high-resolution T1-weighted anatomical image volume of the whole brain (3D-SPGR), and functional gradient-echo, echo-planar (EPI) T2*-weighted echo planar images with BOLD contrast were collected [with settings: field of view (FOV) = 22 cm; matrix size = 64 x 64; pixel size = 3.4 x 3.4 mm; echo time (TE) = 50 ms; and flip angle = 90° for the BOLD images]. Each functional image volume comprised 30 slices of 5.4-mm thickness that covered the whole brain (including the cerebellum). Two runs were collected per subject. Each run, lasting 608 s [repetition time (TR) = 4 s], included a total of 152 functional volumes. In every run, each of the lifting conditions and the resting condition were performed three times using a block design. Each block lasted 40 s. To allow time for the equilibration of T1, six volumes were recorded and discarded before each run.
Images were processed and analyzed using the Statistical Parametric Mapping software (SPM99; http//:www.fil.ion.ucl.ac.uk/spm/; Wellcome Department of Cognitive Neurology, London). The functional images were realigned to correct for head movements, coregistered to each individual anatomical T1-weighted image and normalized (transformed by nonlinear transformations) to the standard coordinate system of Talairach and Tournoux (1988) and the Montreal Neurological Institute (MNI) standard space. The functional images were spatially smoothed with an 8-mm full-width at half-maximum (FWHM) isotropic Gaussian filter.
INDIVIDUAL SUBJECT ANALYSIS (LEVEL 1). We applied a general linear model to the functional data, using covariates for the five conditions: constantheavy, constantlight, regular, irregular, and rest. We also included the head-movement parameters as regressors in the modal to eliminate any activity that might correspond to head movement artifacts. The covariates were convolved using a canonical hemodynamic response function. We used the movement parameters estimated during the realignment preprocessing to model potential movement-related artifacts to eliminate movement-related activity in the data. Parameter estimates and variance were derived for each covariate in a subject-specific fixed effects model. We computed the statistical images corresponding to the contrasts: (regular –constant), (irregular –constant), (irregular –regular) and each lifting task minus the resting baseline. Note that when defining these contrasts we "pooled" the data from constantheavy and constantlight (constant = constantheavy + constantlight). The rationale for this was to match the mean object weight in the constant, regular, and irregular conditions.
RANDOM-EFFECT ANALYSIS (LEVEL 2). To accommodate intersubject variability in group analysis, the contrast images obtained from level 1 were entered into a second level t-test, to create an SPM {t} map. A one-sample t-test was used (11 df). Voxels were identified at P < 0.05 after correction for multiple comparisons. We restricted the search space for our analysis to the sensory motor system that is known to be active during precision grip tasks. For the most important contrasts, i.e., (regular –constant), (irregular –constant), and (irregular –regular), we used a region of interest mask that corresponded to the main effect contrast of all lift tasks minus rest (regular –rest + irregular –rest + constant –rest). This contrast is orthogonal to the pair-wise contrasts in the preceding text (i.e., statistically independent) and can thus be used for this purpose. The region-of-interest mask included bilaterally the premotor cortex, inferior frontal gyrus, thalamus, putamen, and cerebellum. In the left hemisphere, the primary sensori-motor cortex, supplementary motor area (SMA) and parietal operculum were included, together with the intraparietal cortex and supramarginal gyrus in the right hemisphere. In addition, we report activations detected anywhere in the brain at P < 0.001 uncorrected because the random effect approach is so conservative. Last, a conjunction analysis was conducted to detect those areas that were activated in both the contrasts (regular –constant1) and (irregular –constant2) (P = 0.05 corrected using the region of interest mask defined above). Because we had collected twice as many image volumes during constant as compared with the other two conditions, we could divide these volumes into two identical, but statistically independent, constant conditions, constant1 and constant2, which is required in the conjunction analysis.
Anatomical localizations
The anatomical localization of the activations was determined according to the major sulci and gyri (Duvernoy 1999) distinguishable in a mean standardized anatomical MRI obtained from six subjects. We used the cytoarchitectonic maps in stereotaxic space available on-line (http://www.bic.mni.mcgill.ca/cytoarchitectonics/) that were produced by a collaboration between the Medical Institute of the Research Centre Juelich and the Montreal Neurological Institute to relate our inferior frontal activation to the cytoarchitectonically defined area 44 (Amunts et al. 1999). For the cerebellum, we used the terminology of the Schmahmann Atlas (Schmahmann et al. 2000).
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RESULTS |
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The performance of the different lifting conditions was consistent across subjects. There was no effect of condition on the vertical position of the lifted object (P > 0.05). All subjects used a stronger static grip force for the heavy weight in all three lifting conditions (P < 0.001, Fig. 4A ; see also Fig. 3). The static grip force for the light weight was slightly higher during the irregular condition than it was for the constant and the regular conditions (P < 0.001).
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; Johansson and Westling 1988). When the previous weight was heavy, the grip force rate was larger than when the previous weight was light, regardless of whether the present weight was heavy or light (Fig. 4B, P < 0.001). Thus in this condition, the internal representations were updated after each lift and the anticipatory parameter control influenced the grip force rate in subsequent lifts. In addition, the force output was updated after lift-off (see Fig. 4A). Brain activation: all lifting conditions minus rest
The brain regions showing stronger activity when the subjects performed the three lift conditions as compared with the resting condition are shown in Table 1 and Fig. 5. All conditions were associated with a significant increase in activity in the contralateral M1 and primary somatosensory cortex (S1), the SMA, the cingulate sulcus (CMA), and the bilateral cerebellum, (P < 0.05 corrected; Fig. 5, A–C, Table 1). Peaks of activation were observed in the left parietal operculum in the constant and regular conditions, and in the irregular condition, a cluster of active voxels extended into this area. Furthermore, peaks of activation were observed in the right inferior frontal gyrus in the regular and the irregular conditions, and in the constant condition, a cluster of active voxel also extended into this area (P < 0.001 uncorrected). We related the location of the inferior frontal activation to the cytoarchitectonic maps of this area in MNI space published on http://www.bic.mni.mcgill.ca/cytoarchitectonics/. Our peak of activation was located at a voxel in standard space where 30–40% of the brains have area 44 located. Thus it is likely that the activation in the right inferior frontal gyrus corresponds to area 44. We also observed peaks of activation in the left lateral fissure (at P < 0.001 uncorrected for the constant condition) in all lifting conditions. The clusters of active voxels extended superiorly to the inferior frontal gyrus pars opercularis. Using the results of the single-subject analysis superimposed on the individual anatomical pictures, we found that in eight of the subjects, the activity was located around the left lateral fissure and extended both into the inferior frontal gyrus and in the superior temporal gyrus. In three subjects, the activity was found in the superior temporal gyrus only, and in one subject, the activity was found in the inferior frontal gyrus only. Significant activations (at P < 0.05 corrected or P < 0.001 uncorrected) were also observed in the right supramarginal gyrus and the left ventral thalamus in all three lifting conditions. In the primary sensorimotor area, a large cluster of active voxels extended into the precentral gyrus and the dorsal premotor cortex (PMD) in all conditions. In the left SMA, the cluster of active voxels was located mainly posterior to the vertical plane at y = 0, which corresponds to SMA proper according to Picard and Strick (1996). These clusters extended rostrally into the putative pre-SMA and ventrally into the CMA. In the cerebellum all conditions engaged the cerebellar hemispheres bilaterally.
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The comparison regular minus constant revealed an increased activation in the left postcentral sulcus, the left cerebellar hemisphere, the left thalamus, and in the left parietal operculum (P < 0.05 corrected; Table 2 and Fig. 6A ). At P < 0.001 uncorrected, an activation was observed in the right inferior frontal gyrus pars opercularis (area 44) and the left inferior part of the precentral sulcus.
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A conjunction analysis was made to determine the cortical areas that were more strongly activated in both regular and irregular as compared with constant. The result of this analysis was consistent with the results present in the preceding text. Activations were observed in the left parietal operculum, the right supramarginal gyrus, the right inferior frontal gyrus pars opercularis (area 44), and the left thalamus (P < 0.05 corrected; Table 3 and Fig. 7 ). At P < 0.001 uncorrected activations in the left lateral and medial cerebellum, the left inferior part of the precentral sulcus (border zone between areas 44 and 6) and the left supramarginal cortex were also found. Thus the engagement of the inferior frontal cortex and supramarginal cortex appears to be bilateral. The BOLD signals in these, and some other relevant areas, are plotted in Fig. 8. Stronger activation was observed in irregular and regular as compared with constant in the left parietal operculum, left thalamus, and right supramarginal cortex. In addition, there was a step-wise increase in activity in the right inferior frontal gyrus when going from constant to regular, and regular to irregular. As a control, no BOLD signal change was found in a peak situated in the central sulcus, equally activated throughout the three experimental conditions.
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The right inferior frontal gyrus pars opercularis was more strongly activated in the irregular condition than in the regular condition (Table 2 and Fig. 6C). In contrast, the right precentral gyrus (PMD) showed increased activity when we contrasted regular minus irregular (Table 2).
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DISCUSSION |
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Mechanisms for object manipulation
The behavioral results obtained during the MR scanning corresponded well with the model of precision grip manipulation presented in the introduction (Fig. 1). During the constant condition, when the same weight was lifted in consecutive lifts, subjects generated a well-programmed motor output that was targeted to the weight of the object. Thus there was no mismatch between the predicted and afferent sensory information. As such, there was no need for corrective adjustments to be made and no need to update the sensorimotor memory representation. During the regular condition, the subjects programmed the force output close to the heavy and light target, respectively. Thus they shifted from one object representation (heavy) to the other (light) after each lift. But as the scaling effect of the peak force rate to the object rate was smaller than for the constant condition, there was a slight mismatch that generated small corrective force adjustments. During the irregular condition, subjects could not predict the weight changes. Most of the subjects adopted a strategy in which they programmed the motor output between the two weights with a clear bias toward the heavy weight. A similar safety strategy for randomly presented weights has been described previously (Gordon et al. 1991). In both heavy and light lifts, subjects performed an inappropriate programming, which resulted in either the lift off occurring sooner than expected (when the weight was lighter), or no lift off taking place when expected (because the object was heavier than predicted) (see Gordon et al. 1993). Both of these unexpected events are known to trigger prestructured reactive motor commands that result in either a fast termination or a brisk increase of the force output within 100 ms (Johansson and Westling 1988; Westling and Johansson 1987). The memory of the previous lift has previously been shown to be a powerful factor that influences the anticipatory scaling of fingertip forces in precision grip lifting tasks (Edin et al. 1992; Jenmalm and Johansson 1997; Johansson and Westling 1988). At present, only sparse knowledge exists about the time characteristics of the memory representations of object properties that are used to set the parameters in the grip-lift motor program (see Gordon et al. 1993). Yet we know that these memory representations can be swiftly updated between subsequent lifts (Johansson and Westling 1988). Although most subjects in the present study adopted a safety strategy in which they programmed the forces close to those appropriate for the heavy weight, the anticipatory parameter control was used in the irregular series to update the internal representation between each lift. This was evident by the small differences depending on which weight had just been lifted (Fig. 4B).
In summary, the behavioral analysis validated the design of our experiment. In the first constant condition, there was a well-programmed force output with no need for corrections to be made or for updating of the sensorimotor memory representation of the object. In the regular condition, there was a change in the sensorimotor representation between each lift attributed to updating. In spite of this, there was still a small error in the programming of the lift, generating a small sensory mismatch and some corrective force adjustments. Finally, in the irregular condition, the motor output was erroneously programmed in most lifts, generating a large mismatch, significant corrective force adjustments, and updating of the sensorimotor memory representation of the object's weight.
Fronto-parietal activity associated with sensorimotor representations
When compared with the resting condition, the three lifting conditions activated a bilateral fronto-parietal network that was similar to the active networks observed in previous precision grip (Ehrsson et al. 2000, 2001, 2003) and tactile exploration studies (Binkofski et al. 1999; Seitz et al. 1991). The key novel finding in the present study is that some regions within the bilateral fronto-parietal network were more active in the regular and irregular conditions as compared with the constant condition. These differences could not be due to differences in the amplitude of the force, the number of lifts or the frequency of lifts because these were similar in all three conditions. According to the behavioral analysis, the regular and irregular conditions engaged the anticipatory parameter control and the sensory driven control to a greater extent than the constant condition. Hence, the changes in neural activity in the right inferior frontal gyrus, the right supramarginal gyrus, the left parietal operculum, and the left thalamus seem to be specifically involved in the sensorimotor processes leading to corrective reactions and updating of the memory representation of the object's weight. That the inferior frontal gyrus takes part in the sensory-driven control policy is further strengthened by the fact that it was more active during the irregular condition than during the regular one.
Inferior frontal cortex
The increased activity in the inferior frontal gyrus during the irregular condition probably reflects the discrepancy between the predicted and the actual sensory input and/or the subsequent neural processing to correct the ongoing movement and update the sensorimotor memory representation. The inferior frontal cortex might influence the fingertip force output via its extensive anatomical connections to the primary motor cortex (Godschalk et al. 1984; Kurata 1991; Matelli et al. 1986) or via direct cortico-spinal projections (Dum and Strick 1991). It can also receive tactile and kinesthetic information about the weight of the object and slips at the finger-object interface via projections from somatosensory areas in the parietal lobes, including the parietal operculum (Cipolloni and Pandya 1999; Ghosh and Gattera 1995; Preuss and Goldman-Rakic 1989). Of interest is the specific location of the frontal activity to the most ventral part of area 44 (see RESULTS). At P < 0.001 uncorrected, an activation was also observed in the left inferior frontal cortex, suggesting a bilateral engagement of this region. The human inferior frontal cortex is known to be active during object manipulation with the precision grip (Ehrsson et al. 2000, 2001) and during manual exploration (Binkofski et al. 1999). Likewise, in macaque monkeys a similar region is active when they grasp objects using precision grip (Rizzolatti et al. 1988). The inferior frontal cortex thus seems to contain a representation of skilled finger actions in both human and non-human primates. This representation does probably not represent lower level aspects of the finger movement such as muscles and forces but rather higher-order aspects of the movement such as grasp gesture (precision or power grip) (Ehrsson et al. 2000; Rizzolatti et al. 1988), the spatial pattern of the movement (Kakei et al. 2003; Schwartz et al. 2004), and action goal (Rizolatti et al. 1988). Neuronal populations in the inferior frontal gyrus are also activated when monkeys (Rizzolatti et al. 1996) and humans (Buccino et al. 2001; Heiser et al. 2003; Iacoboni et al. 1999; Nishitani and Hari 2000) observe goal directed hand actions and when humans imagine hand actions (Binkofski et al. 2000; Decety et al. 1994; Ehrsson et al. 2003); this is consistent with the view that the action representation in the inferior frontal cortex reflects higher-order aspects of movement. In the present study, predictable and unpredictable weight changes of an object during a lifting task were associated with increased synaptic activity in the inferior frontal gyrus. This might indicate that the role of the inferior frontal cortex is to process and store information about the physical properties of external objects and use this information to set the parameters of the motor programs used for object manipulation.
Parietal lobe
The lateral part of the left (contralateral) parietal operculum showed increased activity during the two conditions in which the weight of the object changed in the lifting series as compared with the constant lift. Previous studies have shown that the parietal operculum contributes to somatosensory processing in humans (Burton et al. 1993; Disbrow et al. 2000; Ledberg et al. 1995). In non-human primates, several somatosensory fields have been described in this region (Krubitzer et al. 1995; Robinson and Burton 1980a, b). The parietal operculum is activated in various manipulative tasks including precision grip (Ehrsson et al. 2000, 2001, 2003; Kuhtz-Bushbeck et al. 2001) and manual exploration of objects (Seitz et al. 1991). One cause of the increased activity in the parietal operculum could be increased afferent somatosensory inputs related to slips occurring during erroneous grip force programming. However, our force recordings could reveal no slips either in the regular or in the irregular condition. Yet small corrective force adjustments took place both in the irregular and regular conditions, whereas no such adjustments occurred in the constant condition. The somatosensory signals associated with these adjustments could be one source of the increased parietal activity. However, there was no activation (even when the threshold was lowered; P < 0.01 uncorrected) detected in the primary somatosensory or motor cortices; this would be expected if the activation in the parietal operculum was due to an increased afferent somatosensory input. Another explanation for the parietal opercular activation, getting some support from previous studies, is that it reflects somatosensory information about the weight change of the object. An earlier fMRI study has shown that activity in the lateral inferior parietal cortex reflects unpredictable changes in tactile stimulation (Downar et al. 2000), and neuronal recordings in monkeys have shown that cells in SII has the capacity to maintain somatosensory information over several seconds to facilitate the discrimination between two sequential stimuli (Romo et al. 2002). The parietal operculum has neuroanatomical connections with a wide number of both primary and nonprimary sensorimotor areas including the inferior frontal cortex and the inferior parietal cortex (area 7b, which might correspond to the supramarginal gyrus in humans) (Cipolloni and Pandya 1999; Disbrow et al. 2003; Ghosh and Gattera 1995; Matelli et al. 1986; Pandya and Kuypers 1969; Preuss and Goldman-Rakic 1989). Thus the parietal operculum might convey the critical somatosensory feedback from the fingertips to a fronto-parietal network including the inferior frontal and the supramarginal cortices that control the fingertip forces during object manipulation.
In a previous study, we found activity in the right intraparietal cortex that seemed to reflect the predictive grip force increase providing grasp stability during an isometric grip-lift task (Ehrsson et al. 2003). Unexpectedly, we did not observe an activation of this section of the right intraparietal cortex when we contrasted the lifting tasks with rest in the present study (P < 0.01 uncorrected). Instead we found parietal activity in the right supramarginal cortex and in the most anterior part of the left intraparietal cortex (P < 0.001 uncorrected). This difference could be due to the fact that different sections of the frontal, parietal and cerebellar areas are used for different skilled hand actions (e.g., Ehrsson et al. 2000; Imamizu et al. 2003), reflecting the various sensorimotor transformations involved.
Complementary role of the cerebellum
We found cerebellar activations in the bilateral hemispheres and right vermis during all lifting conditions, well in agreement with the central role of the cerebellum in the control of skilled motor actions (see also Kinoshita et al. 2000). In our previous fMRI experiments, exploring brain activity during precision grip (Ehrsson et al. 2000, 2001, 2003), the field of view of the scanner was restricted, which means that the cerebellum was not scanned.
A key function of the cerebellum is to predict the sensory consequences of movement (Blakemore et al. 2001). Patients with cerebellar atrophy show changes in the timing and magnitude of the applied grip forces during object manipulation that support the role of cerebellum in anticipatory control (Babin-Ratté et al. 1999; Nowak et al. 2002). Convergent observations have suggested that the cerebellum, together with the parietal cortex, also may play a role in the process of on-line correction (for review, see Desmurget and Grafton 2000). If the cerebellar activations were reflecting the rapid corrective grip force corrections that occurred in the irregular condition, we would have expected the strongest activation in this condition. However, there was no significant difference between the irregular and regular conditions in this region. Depending on whether the weight is lighter or heavier than expected, the corrections are not the same and they are probably not subserved by the same areas. Because both events (lighter than predicted, or heavier than predicted) were mixed in the irregular condition, our design did not allow this distinction.
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FOOTNOTES |
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Present address and address for reprint requests and other correspondence: C. Schmitz, Neuropediatrics Unit, Dept. of Women and Child Health, Karolinska Institutet, SE-17177 Stockholm, Sweden (E-mail: Christina.Schmitz{at}kbh.ki.se)
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