Abstract
Morphometric correlation networks of cortical thickness, surface area, and gray matter volume have statistically different structural topology. However, there is no report directly describing their correlation patterns in view of interregional covariance. Here, we examined the characteristics of the correlation patterns in three morphometric networks of cortical thickness, surface area, and gray matter volume using a Venn diagram concept across 314 normal subjects. We found that over 60% of all nonoverlapping correlation patterns emerged with divergent unique patterns, while there were 10% of all common edges in ipsilateral and homotopic regions among the three morphometric correlation networks. It was also found that the network parameters of the three networks were different. Our findings showed that correlation patterns of the network itself can provide complementary information when compared with network properties. We demonstrate that morphometric correlation networks of distinct structural phenotypes have different correlation patterns and different network properties. This finding implies that the topology of each morphometric correlation network may reflect different aspects of each morphometric descriptor.
Highlights
Mechanisms as the result of a distinct genetic origin
VTAV is the set of the common edges of the three networks; VTA is the set of the shared edges of thickness and surface area networks; VTV is the set of the shared edges of thickness and GM volume networks; VAV is the set of the shared edges of surface area and GM volume networks; VTO is the set of the exclusive edges in thickness networks; VAO is the set of the exclusive edges in surface area networks; and VVO is the set of the exclusive edges in GM volume networks
We examined the characteristics of correlation patterns to determine the difference in structural topology between the three MCNs of cortical thickness, surface area, and GM volume
Summary
Mechanisms as the result of a distinct genetic origin. Different genetic factors influence patterns of structural covariance most strongly within different brain networks; that is, there seem to be network-specific genetic influences. The distinct structural phenotypes could result in different interregional correlation patterns, which in turn have an effect on the network properties. The quantitative evaluation of the similarity of the correlation patterns between the network topology of distinct structural phenotypes including cortical thickness, surface area, and GM volume has not been considered in detail. The similarity of the correlation patterns can be evaluated by the overlap ratio (OR) of the spatially distributed edges and the types of edge. The number of edges belonging to each connection type in seven partitions respectively yields quantitative information about the correlation patterns of MCNs. In this study, we partitioned the spatially distributed edges into seven parts and classified them into four different edge types to characterize the various correlation patterns of cortical volume, thickness, and surface area networks. We calculated network parameters to explore whether the characteristics of correlation pattern have an effect on the network parameters
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