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This experiment also included a second nonoverlapping condition, in which the sequence of thumb press and arm movement was reversed. Therefore, in experiment 4, we could compare the overlapping condition to the average of the two nonoverlapping conditions, balancing for the influence of the sequence of the two actions. Participants performed five conditions in a block design: Pretraining in each of these condition was performed as described in experiment 2, with the order of these trainings counterbalanced between participants.
However, no spontaneous generalization and transfer test were acquired. Participants then practiced the task in a supine position for 1 h and finally underwent fMRI scanning. For functional scans, we used an echoplanar imaging sequence with sensitivity-encoded MRI Pruessmann et al. We used 31 slices 3 mm thickness; 0.
Therefore, we did not record data from the inferior aspects of the prefrontal cortex or from the anterior temporal lobes. Each scan consisted of four dummy images that were discarded and data images. The behavioral velocity and force data were up-sampled to Hz and smoothed with a Gaussian kernel of 20 ms width. Three temporal landmarks were determined for the force pulse: The functional data were analyzed using Matlab and SPM2 http: First, we corrected for the temporal offset of slice acquisition and then spatially aligned the data to the first scan using a six parameter rigid-body transform.
The time series for each voxel was modeled using multiple regression with a separate regressor for each task phase. The regressors were generated by convolving a boxcar function spanning a block of six movements with a standard hemodynamic response function. One additional regressor per scan was used to model the neural response to the instruction stimulus. To control for possible noise artifacts in the data, we used a weighted least-squares approach that down-weights the images with higher noise variance Diedrichsen and Shadmehr, The resulting coefficient estimates for each task block were then transformed into percentage of signal change by dividing the peak of the predicted response by the mean signal intensity for a given voxel.
From this, the average percentage signal for each condition was computed. Previous studies on coordination have sometimes relied on a contrast between the sum of the activity of two movement components A and B compared with a coordination condition C Ramnani et al.
From this, it becomes immediately clear that the regions with a relatively high rest activity are more likely to become significant, resulting in a potentially biased result. Indeed, regions that were found in this contrast in the above studies showed negative activations of the movement components compared with rest. For example, in a study by Ramnani et al. To avoid this bias, we relied here only on the direct comparison between the overlapping and nonoverlapping conditions. In experiment 4, we compared the overlapping condition to the average activation of the two nonoverlapping conditions.
To determine the influence of sequence effects, we also compared the two nonoverlapping conditions against each other. The arm-only and thumb-only conditions served to spatially localize the activity caused by movement components, but no direct comparisons to the overlapping and nonoverlapping conditions were made. To reduce the impact of anatomical variability across subjects, we used three different strategies to average the data for group analysis. We isolated the cerebellum and brainstem in each subject and then matched these to a newly developed atlas template for infratentorial structures Diedrichsen, The atlas template is spatially unbiased with respect to the MNI template while preserving the fine anatomical detail of the cerebellum.
We corrected for multiple comparisons over the cerebellar search volume by using corrected cluster-wise p values, derived from Gaussian field theory Friston et al. For activity in the neocortex, we reconstructed the cortical surfaces of both cerebral hemispheres for each participant and projected their functional data onto it using caret software http: Using six landmarks and spherical alignment, these surfaces were then brought into the population average landmark and surface-based atlas Van Essen, We then used custom Matlab functions to correct for multiple comparisons on the surface http: For the remaining subcortical areas, we used the nonlinear normalization to the MNI template Ashburner and Friston, For all comparisons, we restricted the search region to areas that were positively activated compared with rest in at least one of the task conditions.
Participants were trained to produce a thumb press at various time points before, during, or after a ms arm movement Fig. All groups learned to perform the task and improved over the course of six training blocks. Unilateral thumb-arm task experiment 1. A , Participants held the handle of a robotic arm and placed their thumb on a load cell.
They were instructed to reach toward a visual target by moving a cursor and to produce a thumb press. Note that during the fMRI study, the task was performed bilaterally with the right arm and the left thumb. B , Visual feedback. After each trial, feedback about movement time was provided by an arrow, indicating whether the cursor reached the target in time, too early, or too late.
A proportional transfer test T1 and an absolute transfer test T2 were separated by an interposed training block. E , State- and time-dependent control can be distinguished in how the skill generalizes to a slower movement. To determine whether participants learned to produce the thumb press based on a representation of time or of arm state, we examined how the skill generalized when participants were instructed to make a slower arm movement.
In the spontaneous generalization phase Fig. When using the state-dependent control, the brain learns to coordinate the two components by making the motor commands to the thumb depend on the state of the arm i. Because both position and peak velocity of the movement changed in the generalization phase, we hypothesized that participants would use a high-level combined representation that represents the percentage of movement completed.
In this case, two specific changes for the thumb press during spontaneous generalization can be predicted. Second, because the onset and offset of the thumb press would also be produced at a certain state of the arm, the duration of the thumb press, a feature of the response that was unconstrained and about which no feedback was given, should also increase proportionally with the arm movement time Fig.
Results for the two control groups see text are shown in white. The gray line indicates the predicted change under the state-dependent control hypothesis, and the dashed line predicted the change under the time-dependent control hypothesis. Error bars indicate SEM. In contrast, when using a time-dependent control, the brain learns the task by accurately representing the absolute time between the onsets of the components.
Our results suggest a transition from state- to time-dependent control as a function of the temporal overlap between the two task components. When the components overlapped, we observed that changing the speed of the arm movement affected the timing of the thumb press. In the , , and ms groups Fig. Additionally, the duration of the thumb press Fig. Also, the change in the duration of the thumb press Fig. Apart from this transition area, however, the results are clear. When the two movement components overlapped, participants learned state-dependent control; temporal scaling of one movement resulted in temporal scaling of the other.
When the two movement components were separated by even a small temporal gap, they learned time-dependent control, regulating the time between the onsets or offsets of each component. We considered alternative explanations for the finding that participants used time-dependent control for nonoverlapping components. First, the feedback in the main experiment was based on the absolute time between peak force of the thumb press and the start of the arm movement. This may have biased participants toward using the time-dependent control. Despite this manipulation, which favored a state-dependent mode of control, the generalization pattern showed time-dependent control.
Second, it is possible that the order of movement components rather than their temporal overlap determined the pattern of results. That is, perhaps the thumb press would scale with the arm movement if it were performed after the arm movement. To address this possibility, a second control group was trained to produce a thumb press ms after the end of the movement.
In this case, feedback was based on the time between the end of the arm movement and the time of maximal force. The results of this control condition Fig. Thus, the overlap of the two movement components rather than their order was the determining factor in whether control was time or state dependent. If participants had learned state-dependent control during training Fig. The variable error Fig. In conclusion, the results from the explicit transfer corroborated the results for the spontaneous generalization test.
Participants learned to coordinate movement components using a state estimate of the arm when the two components overlapped. However, when the components were separated by a temporal gap, the coordination depended on an internal measure of time. For the transfer test Fig. Therefore, regardless of whether the task was unilateral or bilateral, temporal overlapping of task components led to the state-dependent control, whereas nonoverlapping led to the time-dependent control. Result of the bilateral coordination task experiment 2.
In experiments 3 and 4, we looked for the neural correlates of time- and state-dependent control. In experiment 3, the volunteers used their right arm for reaching and the left thumb for pressing. All participants performed the overlapping and the nonoverlapping conditions, as well as the individual actions, in isolation. By using a bilateral task, we could assess the exact role of the cerebellum in state-dependent coordination. If the cerebellum was involved in producing an accurate state estimate of the arm, then coordination-related activity during the overlapping condition should be found in the right cerebellum, ipsilateral to the arm movement.
If, in contrast, the cerebellum received a state estimate of the arm and adjusted the motor commands to the thumb accordingly, then this activity should be found in the left cerebellum, ipsilateral to the thumb press. As in all fMRI studies of motor control, it is important that the comparisons are made between conditions that are comparable in movement parameters because small changes in movement speed or force can drastically alter the BOLD signal in movement-related areas Seidler et al.
Note, however, that the visual error that participants saw on the screen was matched as a result of the different scaling of the visual feedback signal in the different conditions. No evidence of mirror movements was detected when only one limb moved. Participants were instructed to keep central fixation, and a control study confirmed that eye movements were rather infrequent with only small differences between the conditions experiment 3, see Materials and Methods. In the thumb-only and the arm-only conditions, we observed activity ipsilateral to the moving arm or thumb, respectively, in the anterior lobe lobule V Fig.
Both of these regions have reciprocal connections with the primary motor cortex Kelly and Strick, and contain a representation of the ipsilateral upper extremity Grodd et al. Bilateral activity in both conditions was also observed in the hemispheric portion of lobule VI. The activity caused by the arm movement was stronger than the activity caused by the thumb press, consistent with the increased demands on muscle recruitment and feedback control during reaching.
Cerebellar activity in experiments 3 and 4. A , Thumb-only task compared with rest.
B , Arm-only task compared with rest. C , D , Contrast between state- and time-dependent control. Areas that were more activated during state-dependent control are shown in blue and areas more activated during time-dependent control are shown in red. Data are shown on coronal section of a high-resolution cerebellar atlas template Diedrichsen, E , F , Bar graphs show the percentage of signal change in each of the four conditions thumb-only, arm-only, time-dependent control, state-dependent versus rest for left and right lobule V and lobule VI.
The hemisphere ipsilateral to the thumb movement is shown in red, the hemisphere ipsilateral to the arm movement in blue. For the contrast between time- and state-dependent control, we limited our analysis to areas that were more active during any of the four tasks when compared with rest. We found more activity in the overlapping condition state-dependent control in the right anterior lobe of the cerebellum Fig.
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This activity was quite pronounced: The result suggested the involvement of the anterior cerebellum in the state-dependent control and, more precisely, in the estimation of the state of the arm. We also found a small region in the right hemisphere of lobule VI that was significantly more active in the nonoverlapping condition time-dependent control Fig. At lower thresholds, a small cluster could also be seen in a symmetric location in the left lobule VI.
Although these results suggested an anatomical dissociation between state- and time-dependent control in the cerebellum, there were two potential problems. First, in the nonoverlapping condition, the thumb movement preceded the arm movement, whereas the two were executed simultaneously in the overlapping condition. Therefore, it was possible that the sequence of actions rather than the requirements of control could explain the differences in BOLD signal between these two conditions.