Abstract
We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function.
Highlights
Discovery of organizational principles within the structure of human cortex will advance understanding of normal and abnormal behavior
We previously found orderly spatial patterns of Network properties of cortical genetic parcellations genetic relationships between measures of brain size paralleling those seen in mouse cortex and derived two genetically-informed brain atlases based on cortical surface area (SA) (Chen et al, 2011) and cortical thickness (CT), respectively (Chen et al, 2013). (See Section Post-Processing and Image Analysis in Materials and Methods for an explanation of SA and CT parcellations.) Higherlevel patterns of organization among measures of the size of these genetically-derived parcellations remain to be discovered
This study used biometric modeling and graph theoretical approaches to examine emergent organizational principles from genetic associations among regional measures of brain size, where the boundaries of the parcellations were determined a priori based on genetic relationships. Results from these analyses suggest that (1) genetic covariances between structures in SA are less than in CT; (2) the genetic covariance between CT and SA for each structure is low; (3) the genetic covariance between SA and CT overall is low and basically negative; (4) the genetic correlations between homologous structures were the highest for SA and CT, and all positive
Summary
Discovery of organizational principles within the structure of human cortex will advance understanding of normal and abnormal behavior. Calculating regional size measures such as SA and mean CT for our genetically-derived parcellations, and examining patterns of association within a twin sample, could advance upon previous studies and improve our understanding of higher order patterning of brain structure. Two main organizational patterns might be observed when examining genetic correlation between cortical size measures, and graph theory analysis has been used to discover properties of brain structural networks. One other study has examined network properties emerging from genetic correlation matrices between structural measures within human anatomical brain regions (Schmitt et al, 2008). In this study of correlations among cortical thickness measures within sulcal/gyral regions in children and adolescents, small world properties were observed
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