Genetic and environmental influence on the human functional connectome: Evidence from studies of twins and unrelated individuals

Functional connections refer to intrinsically correlated activity between brain regions when individuals are not engaged in a particular task (i.e., measured during the “resting state”). Individual differences in the... [ view full abstract ]

Functional connections refer to intrinsically correlated activity between brain regions when individuals are not engaged in a particular task (i.e., measured during the “resting state”). Individual differences in the strength of functional connections predict psychological function, but the sources of connectivity strength differences are unknown. Historical attempts to estimate the genetic etiology of functional connectivity have focused on large-scale brain networks - obscuring possible heterogeneity among small network subcomponents. Detailed mapping of genetic and environmental influences on the functional connectome is made possible by using twin modelling methodology and SNP-based methods using genotype data from unrelated individuals. In this presentation, we present genetic and environmental maps at a finer-grained level of detail than prior work, which is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyze resting-state functional MRI data from two adult twin samples – 198 twins from the Colorado Longitudinal Twin Sample and 422 twins from the Human Connectome Project – and from approximately 8300 unrelated individuals from the UK Biobank dataset to examine genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35,000 functional connections; twin samples) or 51 brain regions (~1,500 functional connections; UK Biobank). Genetic analyses conducted on each connection were run as structural equation models in R through the OpenMx and UMX packages for twin samples, or using GCTA software for UK Biobank data. Results of twin and GCTA models were similar and suggest non-shared environmental influence was high across the entire connectome, with moderate heritability in approximately half of all connections. The highest heritability estimates were found for connections involving default, attention, and frontoparietal areas. Twin models revealed shared environmental influences were weak to moderate across the entire connectome, with the highest estimates for connections between subcomponents of the default network. The pattern of genetic influence across the connectome is related to a priori notions of functional brain networks, with some evidence of similar levels of genetic and environmental influence among regions of the same network. However, we also found substantial evidence of heterogeneity based on the distribution of heritability estimates. Additionally, a hierarchical clustering analysis of the genetic influence patterns of all regions revealed that the brain’s genetic organization is diverse and not as one would expect based solely on community structure evident in non-genetically informative data. We found regions clustered into two to three groups, a level more superordinate to the 7-20 classically defined resting-state networks. Notably, there was a mixed sensory/attention cluster and a cluster of higher-level cognitive regions including default and frontoparietal areas. In conclusion, our analyses reveal a novel genetic taxonomy of brain regions and suggest genetic risk factors may be limited to a subset of the connectome.