Spatial modeling of brain connectivity data

Brain network data—measuring anatomical interconnections among a common set of brain regions—are increasingly collected for multiple individuals, and recent studies provide additional information on the brain regions of interest. These predictors typically include the 3-dimensional anatomical coordinates of the brain re- gions, and their membership to hemispheres and lobes. Although recent studies have explored the spatial effects underlying brain networks, there is still a lack of statistical analyses on the net connectivity topology, after controlling for spatial constraints. We answer this question via a latent space model for network data, obtaining a meaningful representation for the net connectivity architecture via a set of latent positions, which capture brain network topologies not explained by closeness in the anatomical space.