Ravi Duggirala

The default-mode network, a coherent resting-state brain network, is thought to characterize basal neural activity. Aberrant default-mode connectivity has been reported in a host of neurological and psychiatric illnesses and in persons at genetic risk for such illnesses. Whereas the neurophysiologic mechanisms that regulate default-mode connectivity are unclear, there is growing evidence that genetic factors play a role. In this report, we estimate the importance of genetic effects on the default-mode network by examining covariation patterns in functional connectivity among 333 individuals from 29 randomly selected extended pedigrees. Heritability for default-mode functional connectivity was 0.424 +/- 0.17 (P = 0.0046). Although neuroanatomic variation in this network was also heritable, the genetic factors that influence default-mode functional connectivity and gray-matter density seem to be distinct, suggesting that unique genes influence the structure and function of the network. In contrast, significant genetic correlations between regions within the network provide evidence that the same genetic factors contribute to variation in functional connectivity throughout the default mode. Specifically, the left parahippocampal region was genetically correlated with all other network regions. In addition, the posterior cingulate/precuneus region, medial prefrontal cortex, and right cerebellum seem to form a subnetwork. Default-mode functional connectivity is influenced by genetic factors that cannot be attributed to anatomic variation or a single region within the network. By establishing the heritability of default-mode functional connectivity, this experiment provides the obligatory evidence required before these measures can be considered as endophenotypes for psychiatric or neurological illnesses or to identify genes influencing intrinsic brain function.

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).