Disentangling Genetic Contributions to Human Brain Connectivity Using an Efficient Estimator of Variance Components in Multivariate Random Effects Models

Music City Center 

Magnetic resonance imaging has significantly improved our understanding of the connectivity patterns within the human brain by enabling measurement of the strength of anatomical connections between brain regions through white matter fibers (structural connectivity) and the degree of coactivation of brain regions (functional connectivity). Heritability analyses of connectivity have established that genetics account for a considerable portion of the observed intersubject variability. However, such analyses typically ignore the multidimensional nature of functional and structural connectomes. In this work, we model observed brain connectivity as the sum of multidimensional latent genetic and environmental contributions and introduce a novel constrained estimator for the covariance matrices of the genetic and environmental components. Our estimator is several orders of magnitude faster than existing methods without sacrificing estimation accuracy. The proposed covariance estimate provides a summary statistic which can be used to estimate the parameters of a novel regression analysis that enables us to characterize the relationship between the latent genetic components of structural and functional connectomes. Our analysis suggests that the genetic component of functional connectomes is highly predictable from the genetic component of structural connectomes, suggesting a close relationship at the genetic level that is attenuated by distinct environmental factors.