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.
Spatial modeling of brain connectivity data
Emanuele Aliverti
2017
Abstract
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.Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.