Search for more papers by this author. Behavioral deficits in visuomotor planning and control exhibited by children with developmental coordination disorder DCD have been extensively reported. With respect to motor performance, typically developing TD children exhibited age-related improvements in key aspects of motor planning and control. Despite overall similarities in performance, engagement of cortical resources in the children with DCD was markedly different from that in their TD counterparts.
While the patterns of activation are stable in TD children across the age range, the young children with DCD exhibited less engagement of motor cortical brain areas and the older children with DCD exhibited greater engagement of motor cortical brain areas than their TD peers. These results suggest that older children with DCD may employ a compensatory strategy in which increased engagement of relevant motor resources allows these children to perform comparably to their TD peers.
Moreover, the magnitude of activation was related to several kinematic measures, particularly in children with DCD, suggesting that greater engagement in motor resources may underlie better behavioral performance. This motor learning disorder is characterized by marked impairment in the performance of activities of daily living requiring movement coordination and interferes with the child's academic achievement American Psychiatric Association In particular, children with DCD exhibit marked deficits in movement planning Smyth et al.
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Although the motor performance of children with DCD has been extensively studied, the neurophysiological mechanisms underlying these functional deficits are not well known.
Kaplan and colleagues Kaplan et al.
To address this knowledge gap, the present study not only characterized the cortical dynamics underlying motor planning and control in children with DCD by electroencephalography EEG but also investigated the relationship between cortical dynamics and movement kinematics in children with and without DCD.
The temporal sensitivity of EEG makes it an ideal tool to study preparatory and ongoing cortical processes during the performance of motor tasks. In particular, movement-related cortical potentials MRCPs obtained from scalp locations overlying the supplementary motor area, premotor cortex, and primary motor cortex have revealed age-related differences in typically developing TD children and adults Chiarenza ; Chiarenza et al.
Specifically, whereas adults exhibit larger negative-going waveforms in these relevant motor planning and control areas prior to and immediately after movement onset, this pattern of activity is attenuated or absent in children.
A complementary approach to time-domain analyses i. Many studies have reported task-related spectral power changes desynchrony in the alpha and beta frequency bands in adults during the preparation of motor tasks Gerloff et al. More recently, these analyses have been applied to developmental data and have led to two important findings. First, young children lack the characteristic task-related changes decreases in alpha power reflecting a relative lack of movement preparation compared with older children and adults Bender et al.
Second, young children exhibit a relative increase in frontal cortical areas compared with motor cortical areas during motor planning, which may reflect the greater effort or attention needed for young children to plan and control arm movements Pangelinan et al.
As the motor performance of children with DCD is often more similar to that of much younger TD children, it follows that children with DCD may also exhibit a lack of alpha desynchrony over motor cortical brain areas while exhibiting a relative increase in frontal desynchrony during motor planning.
The present study examined movement kinematics as well as time- and frequency-domain analyses of EEG during the performance of a center-out drawing task to determine whether children with DCD exhibit a different developmental trajectory in behavioral and cortical dynamics compared with TD children.
We also examined whether on average children with DCD differ from TD children after any age-related differences are accounted for. It was expected that children with DCD would exhibit EEG patterns that reflect a relative lack of engagement of relevant cortical motor resources and greater cortical activation from compensatory frontal brain regions. The results from this study provide insights into the neural mechanisms underlying visuomotor performance in children with DCD.
Children were recruited from the local university area. Table 1 provides details for the two groups.
Fourteen children with DCD 8 girls, 6 boys; age range: 6. Three additional 6- to 7-year olds with DCD were recruited for the study but were unable to complete the task and are not included in the analysis. Prior to participation, parents and children provided informed consent and assent, respectively. For their participation, the children received a modest monetary compensation and a choice of an age-appropriate prize.
DCD, developmental coordination disorder; TD, typically developing. Parents completed a pediatric health questionnaire to provide details about their child's overall development. This questionnaire also inquired about the diagnosis of any general medical conditions and developmental learning disabilities i. This questionnaire also asked whether motor coordination difficulties interfered with common activities of daily living, including tying shoes, buttoning clothing, taking notes in class, completing tests or school assignments, and participating in playground activities.
The children completed a item handedness test Fagard and Corroyer to ensure right-hand dominance and the Movement Assessment Battery for Children, Second Edition MABC2; Henderson and Sugden to characterize their motor skill ability in the areas of manual dexterity, ball skills, and balance.
Table 1 provides summary percentile scores median and range on the MABC2 for the two groups. The data collection procedures were similar to previous studies in our lab Contreras-Vidal and Kerick ; Pangelinan et al. The experimental setup is depicted in Fig. Participants were seated at a table facing a computer monitor 21 in. A chin rest was used to stabilize and maintain the participant's head position, and the heights of the chair and chin rest were adjusted for each participant.
Experiment setup. Top : the monitor displayed the center circle and the two peripheral targets. Bottom : the participants were seated at a desk with their head stabilized with a chin rest.
The participants made self-selected and self-initiated center-out drawing movements with a digitized pen on a digitizing tablet for each of 60 trials. The sampling rate of the digitizing tablet was Hz.
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The monitor provided real-time visual feedback of pen movement. The OASIS program also generated event markers that were synchronized with the EEG data collection indicating the beginning of a trial, target appearance, movement onset, target acquisition, and the end of a trial. Eye movement artifacts were recorded from electrodes placed superior and inferior to the left eye and on the lateral canthi of the left and right eyes.
Average mastoids served as the common reference, and FPz served as the common ground. These sites were not included in the final analysis. The participants completed 12 practice trials to become familiar with the digital pen, tablet, and computer display. For some of the young children, an additional 12 trials were provided if the participant did not demonstrate an understanding of the task after the first practice set.
Figure 1 , top , depicts the behavioral task as presented on the computer monitor. The participants began a trial by moving the digital pen into a central home position indicated by a circle 0.
Upon entering the home position, two target circles each 0. The purpose of this hold period was to provide the participants with sufficient time for target selection and movement planning, and to allow ample time for electrophysiological data acquisition during this phase of the task.
After the hold period, the participants made one fast and straight movement with the digitizing pen from the home position to the target. The participants were able to see the pen trace displayed on the computer screen in real time. Once the pen reached the target position, the targets and pen trace disappeared and the participant returned the pen to the home position to begin the next trial.
On average both groups of children met this requirement mean DCD: Behavioral data analyses were consistent with previously reported studies conducted in our lab King et al. The time series of x and y positions for each trial were filtered with an 8th-order dual-pass Butterworth filter cutoff frequency 10 Hz. Peak velocity was the maximum velocity between the onset and offset. MT was the total time between movement onset and offset. ML was the total distance of the movement trajectory.
The following filters were applied to the data.
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Data were baseline corrected with a ms time window prior to the start of the epoch ms prior to movement onset. These data were visually inspected for excessive movement and ocular artifacts. For the time-domain analyses or MRCPs, the 60 trials were averaged in time for each electrode site.
For the spectral analysis, fast Fourier transforms FFTs were applied to data from the behavioral task as well as 1,ms epochs from the resting eyes open baseline condition. Power spectra were segmented into the alpha 8—12 Hz and beta 13—30 Hz bands.
February 16, 1907, Vol. 84, No. 2173
These bands were selected for their relevance to motor tasks Gerloff et al. Spectral data were then log-transformed to meet the requirements for the statistical analysis homogeneity of residuals. SAS 9. Intercept terms were excluded from the discussion of the results because the value of these parameter estimates i. This statistical approach was employed by King and colleagues King et al.
Note that for the EEG dependent measures, regression models were created for each electrode of interest. Second, to examine mean group differences after accounting for the effect of age, each dependent measure behavioral and EEG was analyzed by analysis of covariance ANCOVA with age as the covariate.
For the analysis of movement-related cortical potentials, the following electrodes were examined before and after movement onset: Fz, C3, Cz, and C4.
These electrode sites were selected for their relevance to motor planning and were found to be sensitive to age-related differences in children in our previous work Pangelinan et al. For TRSpec, separate analyses were examined corresponding to the frontal, central, and parietal regions for each of the two frequency bands of interest alpha and beta. Pearson's correlations between all behavioral and EEG measures were used to determine brain-behavior relationships across all children.
Table 2 provides the beta coefficients, standard error, and significance level for the slope parameters in the regression analyses used to assess differences in the developmental trajectories for the performance measures.
Table 2. Regression coefficients and standard error for kinematic analysis. Values in parentheses are SE. The slope coefficients for RMSE and peak velocity were not significant for either group.
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Moreover, no group differences were revealed for the slope and intercept terms for any dependent measure. Movement kinematics with respect to age and group: movement time MT; top left , movement length ML; top right , variability of initial directional error VIDE; middle left , normalized jerk NJ; middle right , peak velocity PV; bottom left , and root mean squared error RMSE; bottom right. Regression for children with developmental coordination disorder DCD is indicated by the dashed red line; regression for typically developing TD children is indicated by the solid black line.
To assess mean group differences after accounting for the effects of age, each behavioral variable was assessed by ANCOVA with age as the covariate. Taken together, in contrast to our hypotheses, these behavioral results suggest that the behavioral developmental trajectories of the two groups did not differ for any measure.
Moreover, only after the effect of age was accounted for was a group mean difference found for one of the six behavioral dependent measures. Thus the behavioral performance was, by and large, similar across the two groups.
Below the mesh grid is a contour map to highlight peaks positive and negative in the grid. The characteristic MRCP waveform consists of an increasingly negative amplitude leading to and immediately following movement onset depicted as cool colors in Figs.
Top : Fz. Bottom : C3 left , Cz center , and C4 right.