Abstract

Objective. The brain can be represented as a network, where anatomical regions are nodes and relations between regions are edges. Within a network, the co-existence of co-operative and competitive relationships between different nodes is called co-opetition. Inter-regional genetic influences on morphological phenotypes (thickness, surface area) of the cerebral cortex display such co-opetitive relationships. However, whether these co-operative and competitive genetic influences are organized similarly has remained elusive. How the collective organization of the co-operative and competitive genetic influences is related to the inter-individual variations of cortical morphological phenotypes has also remained unexplored. Approach. We constructed inter-regional genetic influence networks underlying the morphologies (thickness, surface area) of the human cerebral cortex combining the T1 weighted MRI of genetically confirmed 593 siblings and twin-study design. Graph theory was used to characterize the genetic influence networks and the collective organizations of genetic influences were characterized using the theory of structural balance. Principal component (PC) analysis was used to estimate the principal modes of morphological phenotype variations. Main results. The inter-regional co-operative genetic influences are assortative, while competitive influences are disassortative. Co-operative genetic influences are more cohesive and less diverse than the competitive influences. The collective organization of co-opetitive genetic influences partially explains the fifth principal modes of inter-individual variation of cortical morphological phenotypes. Other principal modes were not significantly associated with collective genetic influences. Significance. Our study furnishes fundamental insight regarding the organization of co-opetitive genetic influences underlying the morphologies of the human cerebral cortex. In future studies, investigation of the alterations of co-opetitive genetic network properties in brain disorders may furnish disorder-specific insight that may be associated with the disease state or lead to vulnerability to those conditions.

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