A0732
Title: Genetic regression analysis of structure-function connectome coupling
Authors: Eardi Lila - University of Washington (United States) [presenting]
Abstract: Magnetic resonance imaging has significantly improved the 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. Observed brain connectivity is modeled as the sum of multidimensional latent genetic and environmental contributions, and a novel constrained estimator is introduced for the covariance matrices of the genetic and environmental components. The estimator is several orders of magnitude faster than existing methods without sacrificing estimation accuracy. The proposed covariance estimate provides a summary statistic that can be used to estimate the parameters of a novel regression analysis that enables the characterization of the relationship between the latent genetic components of structural and functional connectomes. The analysis suggests that the genetic component of functional connectomes is more highly predictable from the genetic component of structural connectomes, suggesting a close relationship at the genetic level that is attenuated by distinct environmental factors.
