Bayesian modeling for brain networks
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 regions 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 Bayesian latent space model for network data, obtaining a meaningful representation for the net connectivity architecture via a set of latent positions which are assigned mixtures of Gaussian priors.
This allows flexible inference on brain network structures not explained by closeness in the anatomical space and facilitates clustering among brain regions according to their latent positions.
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