Consistent with our previous findings, the current results showed that genetic influences play a large role in individual differences in both surface area and cortical thickness. Using vertex-based measures of areal expansion, we created heritability maps that revealed only modest regional variation. Unadjusted cortical thickness heritabilities were generally lower than for areal expansion. This is consistent with one previous study that reported heritabilities for both surface area and cortical thickness as measured with sulcal-based regions of interest (Winkler et al., 2010
) and with our own study that reported lobar heritabilities for both measures in the same sample (Panizzon et al., 2009
When examining the relationship of regional to global measures of surface area, we found large genetic correlations between total and regional surface area across the entire cortex. Examination of maps of the region-specific genetic contributions to local areal expansion after accounting for shared genetic variance with total surface area reveal that heritability was moderate overall, and slightly more variable across the cortex than seen in unadjusted maps. These findings of a strong genetic overlap between global and regional surface area measures are similar to what has been observed in previous analyses using sulcal-based regions of interest (Eyler et al., 2011
; Winkler et al., 2010
). For cortical thickness, the genetic correlations with total mean thickness were weaker, so adjustment for total thickness made less of a difference to the regional heritabilities. This is consistent with our reported lobar findings (Eyler et al., 2011
) and similar to findings from a family study (Winkler et al., 2010
), although adjustment was for both total surface area and total cortical thickness in that analysis.
The large heritability estimates we observed for both regional surface area and cortical thickness using a vertex-based approach contrasts with the more moderate values reported in previous studies by our group (Eyler et al., 2011
; Kremen et al., 2010
) and by others (Schmitt et al., 2009
; Winkler et al., 2010
). Greater heritability for vertex-based results may be due to the degree of spatial averaging applied in the vertex-based approach. In the present study, we directly compared the average vertex-based heritability estimate within each region to the heritability estimate calculated in the traditional ROI approach. Inter-regional differences in heritability were greater in the ROI-based approach, and the degree of discrepancy between the approaches was dependent on the ROI size. Specifically, the curvilinear form of this relationship was consistent with the function expected due to an effect of degree of spatial averaging (which varies in the ROI-based approach and is constant and large in the vertex-based approach) on measurement error.
These results suggest that relative levels of spatial averaging, both by initial image processing and by methods that average data points on the surface maps, should be considered when interpreting inter-regional and inter-study differences in magnitudes of heritability. For very small regions, heritability is likely to be underestimated by an ROI-based approach. When the size of regions varies substantially, regional differences in heritability are likely to be overestimated. As predicted, we did also find a somewhat stronger association of heritability with ROI size for surface area than for cortical thickness. As suggested, this difference is consistent with the fact that inaccuracy of boundary placement will have a far greater effect on heritability estimates for surface area than on heritability estimates for cortical thickness.
Our results have implications for investigators who seek brain phenotypes that are likely to be associated with genetic polymorphisms. First, it is clear that investigators should determine whether they are searching for genes that are important only for determining the size of particular regions or for genes whose influences may be both regional and global. Given the high genetic correlation between areal expansion measures and total surface area, this issue is of particular relevance for surface area investigations. While in certain regions this distinction may not matter much, a number of regions appear to have relatively fewer unique genetic contributions and might not be good candidate phenotypes for association studies seeking to identify regionally-specific genes that determine surface area.
Also, if there is an interest in examining genetic associations or influences on particular regions from a given parcellation system, it appears that calculating heritabilities or associations at each point and then summarizing into mean values within a region may be preferable, at least for small regions. There are, however, some limitations to averaging heritabilities. There would be subsequent difficulties calculating confidence intervals, but there still may be instances where the vertex-based approach is preferred for understanding regional genetic contributions. At the least, interpretations of how heritability of surface area and thickness varies from region to region should be made while keeping in mind the possible effects of spatial averaging and boundary inaccuracy. For measures of area, the difference between the sensitivity of ROI-based and vertex-based approaches to variation in region size may be particularly striking because the placement of the boundaries between regions in each individual will have a direct and strong impact on the measured surface area of the region in the ROI-based approach.
There were some minor limitations to our study that should be noted. First, our sample consisted of only middle-aged men, so the results may not generalize to women or other age groups. Second, although our vertex-based approach gave good qualitative information about patterns of heritability, we were not able to determine which observed regional variations were statistically meaningful. As a guide for future studies and generation of hypotheses, however, maps have the advantage of not being restricted by a priori boundaries that may not be genetically meaningful. Future studies will use patterns of genetic covariation in measures of surface area and cortical thickness to determine parcellation systems that are most relevant for genetic investigations.
In conclusion, use of spatially-unconstrained maps of areal expansion and cortical thickness is a powerful method to examine spatial patterns of the contribution of genetic and environmental influences. Even if particular regions based on functional or anatomical characteristics are of interest, a vertex-based approach may be the preferred first step for genetic studies. Further investigation of genetic patterning for areal expansion and cortical thickness using measures of genetic correlation among vertices is warranted, and will inform the search for particular genes or particular environmental conditions that influence cortical structure and the effect of development and disease on these measures.