Quantitative assessment of head movement in children
Head movement statistics for one child performing the cognitive task during the MEG recording are plotted in . On average, the mean displacement of the MEG sensors from the position at the beginning of the experiment was 12 mm (range: 3–26 mm). The changes in the head position were largest in the z (up-down) direction, and tended to increase toward the end of the experiment. The average standard deviation was 3.7 mm in the x-direction, 15 mm in the y-direction, and 23 mm in the z-direction. A planned series of paired t-tests comparing the magnitude and variance of head movement in each direction (e.g., x-direction vs. y-direction) indicated that there was more variation in head movement for the z-direction than the x- and y-directions (p < 0.01, and p < 0.05 respectively), and more variation in head movement in the y-direction than the x-direction (p < 0.01).
Figure 2 The mean (top) and standard deviation (bottom) head movement in 10-second bins for one child over the course of the entire 25-minute experiment. The left three columns describe the relative head movement in the x (right-left), y (front-back), and z (up-down) (more ...)
A likely explanation for the movement to be largest in the z
-direction (up-down) is that over the course of the experiment, most subjects begin to slouch in the seat, thereby lowering their head inside the measurement helmet. Although a particularly salient problem for children, this downward movement has also been seen in adults (Wehner, unpublished data). The second largest head motion was in the y
-direction (front-back). As a child’s head is smaller than the inside of the helmet containing the measurement sensors, there is ample opportunity for front-back head movement should the child sneeze, cough, or simply lean forward or backward. Different positioning of the head in the front-back direction with respect to the MEG sensor array has been shown to strongly affect the detection of frontal activity in adults (Marinkovic et al., 2004
); the effect is expected to be even larger in children. The amount of empty space on the sides of the head is usually less, reflected in the smallest amount of head movement in the x
-direction (side-to-side). Rotations of the head, particularly “nodding” the head forward and downward as sometimes happens when the subject is tiring, would also show head movement in the y-
-directions in our analysis but not in the x
In the present study, the subjects were sitting under the helmet-shaped MEG sensor array. One way to reduce the amount of head movements is to record MEG data in a supine position. In this case only rotational sideways (left-right) and up-down movements are expected, with the largest displacements occurring frontally, in the x- and z-directions. The supine position, however, is often less comfortable for the subject, especially when EEG electrodes are attached on the back of the scalp. Also, some subjects tend to fall asleep in the quiet conditions in the shielded room. Therefore, sitting position is preferred in most cognitive experiments.
Overall, the results indicate that a fair amount of head motion occurs over the course of a typical 25-minute MEG experiment with children. In the next section we will examine the effect of this head movement on source estimation.
Dipole localization error due to head movements
The effect of realistic head motion on the source localization error was examined using simulated dipoles distributed throughout the cerebral cortex (). The localization error introduced by the dipole fitting method in the absence of noise or head movement is shown in . The automated dipole-fitting algorithm was able to localize the majority of sources accurately in this ideal case; most of the localization errors were below 5 mm (). Largest errors typically occurred deep in the sulci and on the medial surface of the hemispheres. Inclusion of simulated EEG data in combination with the MEG data could help to lower this methodological error in these problematic locations.
Figure 3 Dipole source localization errors due to head movements. A) Locations of the sources used in the simulations (yellow dots) are shown on the inflated cortical surface of the left hemisphere for one subject. B) Dipole sources that had a localization error (more ...)
The effect of head motion on the dipole localization errors throughout the cortex is shown in . For the run with least head movement (), the mean localization errors were only slightly larger than those in the no movement case. However, for the run with the largest amount of movement (), the mean localization error was about 12 mm.
The largest localization errors induced by the head movement in the subject shown in resided in the frontal lobe. This could result from the subject rotating the head forward and down during the course of the experiment, as the points with the most movement would correspond to those farthest from the center of rotation. Similarly, if the head were to rotate around the vertical axis (e.g., from side to side), the locations on the head with the most movement would be points on the lateral temporal regions as well as those in the frontal and occipital cortices.
Movement compensation using the Signal Space Separation method
Averaged event-related magnetic fields with and without head movement compensation are illustrated in . The overall similarity of the original and transformed signals suggests that the MEG data is moderately robust against head movements that are small.
Figure 4 Event-related magnetic fields for one child. The averaged responses in the uncorrected, SSS-corrected for each run separately (compensating for head movements within but not between runs), and SSS-corrected for the entire experiment cases are superimposed. (more ...)
To quantify the effect of head movement compensation on the ECDs, we calculated the mean change (SSS corrected for the entire experiment – uncorrected, using the initial head position from the first of the five runs) in the location and the goodness-of-fit of dipoles for the N100m response in each hemisphere for all subjects. The average change in the location was 5.0 mm in the left hemisphere, and 5.6 mm in the right hemisphere. Since we do not have independent information about the true location of the sources, we cannot determine whether the ECD localization after SSS correction was actually more accurate. However, the ECD goodness of fit increased after SSS correction for 15 (of the 19) subjects in the left hemisphere and 14 in the right hemisphere. The average change in the goodness-of-fit was 1.5% for the left hemisphere and 1.0% for the right hemisphere. After the SSS-correction, the ECD goodness-of-fit was significantly greater for the left hemisphere ECDs (Wilcoxon signed-rank test, p < 0.01), but not for the right hemisphere ECDs (p > 0.1). Successful compensation of the head movement is expected to improve the ECD goodness-of-fit by reducing the modeling error: head movements are likely to smooth the spatial patterns of the averaged MEG signals such that the pattern cannot be fully explained with an ECD, even if the true brain source were focal, and as such could be well modeled with a dipole in the absence of head movement. However, for small head movements the change in the goodness-of-fit is expected to be minor, and therefore, only modest conclusions can be made from these results. To complement the goodness-of-fit analysis we will next discuss the effect of the SSS compensation on the precision of the estimated ECD locations
The spread of the locations of ECDs fit to the N100m was examined with a bootstrap approach in one subject (we only examined the dipole clusters in the right hemisphere, which showed stronger signals in this subject). In the run with the most movement (in which the mean displacement from beginning of run was 17.5 mm) the 95% confidence volume was reduced after SSS compensation: for uncorrected data the confidence limits were σ1 = 3.3 mm, σ2 = 2.8 mm, σ3 = 2.1 mm, and the confidence volume = 1240 mm3, whereas for the SSS-corrected data the values were σ1 = 3.2 mm, σ2 = 2.6 mm, σ3 = 1.9 mm, confidence volume = 1010 mm3. Note that the total error in the uncorrected case is likely to be larger than that given by the estimated confidence limits, as the results do not include a potential bias between the true location of the source and the mean location of the estimated ECDs. No improvement was found in the run with the least amount of movement (mean displacement 2.7 mm): uncorrected: σ1 = 2.1 mm, σ2 = 1.7 mm, σ3 = 1.2 mm, confidence volume = 274 mm3, SSS-corrected: σ1 = 2.2 mm, σ2 = 1.7 mm, σ3 = 1.3 mm, confidence volume = 311 mm3. This may be due to a minimal effect of the head movement in this case, compared to other sources of uncertainty in the data. In general, head movements can be considered as contributing to the measurement noise, and therefore, compensation for the movements is expected to reduce the uncertainty in the ECD parameters estimated from the data when the effective noise due to head movements is equal or larger than other sources of noise in the data.
To assess the impact of SSS on a distributed source estimate, the MNE, we examined four different levels of head movement compensation (). The location of the estimated activation was similar across the different cases, being maximal at the superior temporal regions (), consistent with sources in the auditory cortex (Reite et al., 1994
). The MNE maps for the N100m response suggest that the estimated source amplitudes were larger when increasing amounts of information about the head position were taken into account. The mean MNE amplitude within a small patch of cortex surrounding the peak response for each of the four conditions for one subject is shown in . Group data representing the peak MNE response across all subjects is shown in . A series of pairwise t
-tests indicated that the peak N100m response in the left hemisphere was significantly larger for all three conditions that were tested (uncorrected-all runs; SSS-each run; SSS-entire experiment
) relative to the uncorrected-run1
< 0.01, p
< 0.09, and p
< 0.03, respectively). There was a trend toward larger N100m amplitudes for the SSS-each run
relative to the uncorrected-all runs
, although this difference was not significant (p
= 0.14). There was, however, a significant difference (p
< 0.04) in the peak N100m amplitudes for the two SSS conditions, with larger amplitudes for the SSS-entire experiment
relative to the SSS-each run
. The right hemisphere peak N100m responses again yielded significantly larger amplitudes for the three tested conditions (p
< 0.02, p
< 0.01, p
< 0.02, respectively) relative to the uncorrected-run1
. In addition, there were significantly larger N100m amplitudes for the SSS-entire experiment
compared to the SSS-each run
< 0.04) and the uncorrected-all runs
Figure 5 Effect of SSS head movement compensation on a distributed source estimate. A) Minimum Norm Estimates (MNE) at the peak latency of the N100m response in one child, calculated using forward solutions with four different levels of head movement compensation: (more ...)
The enhanced MNE amplitude is in accordance with the hypothesis that the SSS compensation would reduce the spatial smoothing caused by the head movements to the MEG signals and subsequently derived source estimates. The averaged forward solution proposed by Uutela et al. (Uutela et al., 2001
) also provided good results. This method assumes that there are movements only between the runs; due to the linearity of the forward model, the average forward solution takes the between-run movements into account, albeit with some smoothing, as there is no actual compensation for the movement, unlike in the SSS-based approaches. Often the between-run changes in the head position are larger than within-run changes, as the subjects have a tendency to relax and stretch during the breaks between the runs. Within-run movements can be considerable, however, as suggested by the present head displacement data on children. The present analysis did not show a significant difference between the uncorrected-all runs
vs. SSS-each run
conditions, which was expected to reveal the effect of within-run head movements. The full SSS compensation, however, appears to be beneficial, taking into account effects due to head movements during the whole recording period, as well as reducing the smoothing effect due to the averaging of the forward models across runs.
In the analyses it was assumed the MNE peak amplitude will increase after compensation for the spatial smoothing of the field patterns caused by head movements; however, the relationship between the smoothing and amplitude is not straightforward, and therefore, caution is necessary in interpreting the results based on the amplitude measure.
In the present study auditory activity in the temporal lobe was chosen to be analyzed because of the high SNR of the MEG signals. However, the dipole simulation (cf. ) suggested that the largest errors were expected in the frontal lobe. Therefore, the benefits of the SSS compensation may appear modest here, as the error in the auditory N100m source estimates due to head movements was rather small to begin with. Furthermore, we did not evaluate of the SSS compensation on the overall accuracy of the source estimates for the N100m, which would include the bias in the estimated source locations due to a change is the mean position of the head; here we only evaluated the effect of spatial smoothing on the precision of the source estimates. It is worth noting that greater improvements are expected when the head movements are larger (Taulu et al., 2005
), as is likely to be the case with very young children (Cheour et al., 2004
; Pihko et al., 2004
) and patient populations such as children with attention deficit hyperactivity disorder.