title: Bayesian Community Detection in the Space of Group-Level Functional Differences.
abstract: We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.
authors.forename: Archana
authors.initials: A
authors.lastname: Venkataraman
authors.forename: Daniel Y-J
authors.initials: DY
authors.lastname: Yang
authors.forename: Kevin A
authors.initials: KA
authors.lastname: Pelphrey
authors.forename: James S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D001321:Autistic Disorder; D001499:Bayes Theorem; D001921:Brain; D006801:Humans; D008279:Magnetic Resonance Imaging