The procedure used in constructing maps of interhemispheric correlation is illustrated in , where the value at each voxel represents the correlation coefficient between the time series at that voxel and the corresponding voxel in the opposite hemisphere. Such interhemispheric correlation maps showed reproducible patterns of interhemispheric correlation at the single subject level. These patterns are shown in , in which Z-transformed maps of interhemispheric correlation were averaged from 39 control subjects, overlaid on a canonical MNI-normalized MP-RAGE image.
Interhemispheric correlation averaged over 39 control subjects. Scale bar shows Fisher-transformed correlation (Z-score).
Interhemispheric Correlation in Typical Development
We first characterized the spatial distribution of voxelwise interhemispheric correlation in the control population. The group map of interhemispheric correlation in control subjects shows spatial heterogeneity, consistent with the ROI technique for interhemispheric correlation observed in a prior study (Stark et al. 2008
) and similar to results obtained using a related voxelwise technique to study age-related changes in interhemispheric correlation (Zuo et al. 2010). First, interhemispheric correlations appear higher among gray matter voxels than white matter voxels, as would be expected if interhemispheric correlation is a measure of synchronized underlying neural activity in areas of relatively higher anatomic connectivity. It is also possible that this reflects our postprocessing strategy of CSF and white matter regression because mean brain signal or gray matter signal was not regressed, but this is considered unlikely because a similar pattern was seen in interhemispheric correlation results from data before they were subjected to the regression postprocessing technique.
Interhemispheric correlation appears higher for voxels closer to the midline. Relatively higher values of correlation are seen in the frontal pole, occipital cortex and medial parietal lobe, deep gray nuclei, and cerebellum, all of which are relatively close to the midline. The trend toward higher connectivity near the midline does not apply uniformly, however. Areas of lateral sensorimotor cortex, visual cortex, primary auditory cortex, and the anterior insula show, for example, greater correlation than surrounding brain structures of similar distance to the midline. Higher connectivity in sensorimotor areas might be expected given known strong thalamocortical contributions in these areas, with shared inputs from sensory and motor signals that exhibit left/right symmetry. Common inputs from the thalamus might be expected to produce greater synchronization in activity. Given these patterns, interhemispheric correlation appears consistent with known underlying anatomical connectivity. Similar spatial heterogeneity was observed in the group map of interhemispheric correlations in the autism group.
Differences in Interhemispheric Correlation in Autism
Interhemispheric correlation shows a trend toward lower values in autism throughout the brain, but some areas are affected more than others. shows regions where control subjects showed significantly higher interhemispheric correlation than autistic subjects, with all clusters significant at an acceptable false discovery rate q < 0.001. Peak coordinates for significant clusters are listed in . No voxels showed significantly higher correlation for autistic subjects than control subjects. Regions showing higher interhemispheric correlation for control subjects include sensorimotor cortex, frontal insula, and superior parietal lobule extending from the parietooccipital junction to the intraparietal sulcus.
Figure 3. Control > autism interhemispheric correlation. Regions of greater interhemispheric correlation for 39 controls than in 53 autism subjects. All clusters were significant at q < 0.001, false discovery rate. No voxels showed significantly (more ...)
Peak MNI coordinates of control > autism interhemispheric correlation
Differences in interhemispheric correlation in autism do not merely occur in areas of highest interhemispheric correlation in the control population. Rather, many areas with high correlation, such as visual cortex, medial frontal lobes, and striatum, do not show significant differences between autism and control samples. Moreover, the effect is also not well described by changes only to voxels of intermediate connectivity, as might be seen if the effect were due to a greater dynamic range among subjects in voxels with intermediate correlation. Many areas in this range do not show group differences, such as dorsolateral prefrontal cortex and caudate nuclei, among others. Differences in interhemispheric connectivity also show poor match to spatial distribution of physiological confounds, such as cerebral blood volume and respiratory variation (Birn et al. 2006
Covariates and Interhemispheric Correlation
To account for factors other than diagnosis that might underlie these regional differences in interhemispheric correlation, we included in a general linear model covariates of age, vIQ, pIQ, language total score (CELF-3), SRS, ADOS-G algorithm score, and handedness. Regions showing control greater than autism interhemispheric correlation remained significant when these factors were included as regressors in the model. Each covariate was analyzed separately with diagnosis as well as in a combined general linear model with all of the covariates. In each case, the only covariate that showed significant associations with interhemispheric correlation was age, shown in . Younger subjects showed higher interhemispheric correlation near the midline, particularly in the supplementary motor area, precuneus, and occipital lobe.
Figure 4. Increased interhemispheric correlation associated with younger age. All clusters were significant at q < 0.001, false discovery rate. No voxels showed significantly higher interhemispheric correlation with older age or higher or lower vIQ, pIQ, (more ...)
Mean Interhemispheric Correlation and Age
We obtained an estimate of mean interhemispheric correlation by averaging overall gray matter voxels within the interhemispheric correlation images for each subject. Mean gray matter interhemispheric correlation was reduced in the autism sample relative to the control sample (control 0.253 ± 0.071 standard deviation [SD], autism 0.232 ± 0.071 SD) To evaluate the significance of this difference, we included age as a covariate because a significant relationship was seen in the voxelwise analysis of . Using a general linear model with diagnosis and age as regressors and including an interaction term, we confirmed the hypothesis that control subjects showed significantly higher interhemispheric correlation than autism subjects (P = 0.023, one-tailed t-statistics), with a significant interaction between age and diagnosis (P = 0.043). This is illustrated in , showing greater decreases in mean interhemispheric correlation with age among control subjects than among autism subjects.
Figure 5. Relationship of mean gray matter interhemispheric correlation with subject age. (A) Each point represents mean gray matter interhemispheric correlation for 1 of 39 control subjects (above), with best straight line fit through the data. (B) The same is (more ...)
The changes seen in interhemispheric correlation appear most significant among the younger subjects. We divided our autism and control samples into subjects younger than or equal to age 20 and subjects older than 20. In the younger control group, the correlation with age was significant (r = −0.48, P = 0.019, one-tailed t-test). In the older group, the correlation was not significant (r = −0.09, P = 0.36). In the autism sample, neither the younger group (r = 0.03, P = 0.43) nor the older group (r = −0.22, P = 0.15) showed a significant correlation between mean interhemispheric correlation and age. In the combined autism and control sample, no voxels showed a significant relationship with one-tailed t-tests between mean interhemispheric correlation and neuropsychological metrics of vIQ (r = −0.09, P = 0.19), pIQ (r = 0.05, P = 0.31), handedness (r = −0.11, P = 0.16), Autism Diagnostic Observation Schedule-Social (r = −0.03, P = 0.40), Autism Diagnostic Observation Schedule-Communication (r = −0.09, P = 0.23), or language function testing (r = −0.01, P = 0.45) in our data. SRS scores showed a strong trend toward decreased social impairment with higher mean interhemispheric correlation (r = −0.18, P = 0.058).
Corpus Callosum Volume and Interhemispheric Correlation
Corpus callosum mean volume was significantly reduced in the autism sample relative to the control sample (control 3523 ± 450 SD, autism 3271 ± 600, one-tailed t-test, P = 0.015). In order to evaluate the relationship of functional interhemispheric correlation with callosal volume, we compared the mean gray matter interhemispheric correlation values for each subject with total corpus callosal volume and found no significant correlation (r = −0.009, P = 0.93). No significant correlation was seen in either the autism or control sample between gray matter functional interhemispheric correlation and corpus callosal volume when samples were analyzed separately. These data are shown in .
Interhemispheric correlation does not vary with corpus callosal volume. Mean interhemispheric correlation of gray matter voxels is compared with corpus callosal volume from the MP-RAGE scan for each subject.
Interhemispheric Correlation and Distance from Midline
Voxelwise data showed significant differences in interhemispheric correlation in autism were lateral to the midline. To evaluate whether interhemispheric correlation in autism is related to a voxel’s position on the medial–lateral axis, we computed the difference in mean interhemispheric correlation for each voxel between the control and autism samples and averaged this value for all gray matter voxels in each sagittal slice, shown in . There is increasing interhemispheric correlation for control subjects relative to autism subjects with distance from the midline with a peak at 60 mm. Greater than one SD of gray matter voxels at this distance show greater interhemispheric correlation for control subjects, while essentially no difference in interhemispheric correlation is seen at the midline.
Figure 7. Differences in interhemispheric correlation increase with distance from the midline. The difference of mean interhemispheric correlation from gray matter voxels between control and autism subjects is shown for each sagittal slice. Error bars represent (more ...)
Alternate Method: Regional Parcellation
Another method for calculating interhemispheric correlation was used in addition to the voxelwise technique described above. The supratentorial brain was parcellated into 90 regions using the AAL brain atlas (Tzourio-Mazoyer et al. 2002
; Maldjian et al. 2003
). These consist of 45 left/right homologous regions. For each region, the mean BOLD time course was extracted for each subject, and correlation was compared with the homologous region in the opposite hemisphere, similar to a method previously used to measure interhemispheric correlation (Stark et al. 2008
). While this method has lower spatial resolution, it might also be less sensitive to noise present in the voxelwise method.
Mean control and autism population values for each region are shown in . Forty of the 45 regions showed greater interhemispheric correlation for controls. This was statistically significant across subjects after multiple comparison correction in 7 regions: rolandic operculum, insula, superior and mid occipital, fusiform, postcentral, and superior temporal. These are largely the same regions seen in the voxelwise analysis of . Strong trends in the fusiform gyrus and superior temporal gyrus were also seen in the voxelwise data. When the distance from midline was measured from the centroid of each region, there was again a significant trend toward greater interhemispheric correlation in controls with distance from the midline (r = 0.35, P = 0.009).
Figure 8. Effect of distance between regions on interhemispheric correlation. (A) The supratentorial brain was parcellated into 45 pairs of left/right homologous regions. Each point shows the mean interhemispheric correlation between left and right homologues for (more ...)