Learning partially directed functional networks from meta-analysis imaging data

Jane Neumann, Peter T. Fox, Robert Turner, Gabriele Lohmann

Research output: Contribution to journalArticle

18 Scopus citations


We propose a new exploratory method for the discovery of partially directed functional networks from fMRI meta-analysis data. The method performs structure learning of Bayesian networks in search of directed probabilistic dependencies between brain regions. Learning is based on the co-activation of brain regions observed across several independent imaging experiments. In a series of simulations, we first demonstrate the reliability of the method. We then present the application of our approach in an extensive meta-analysis including several thousand activation coordinates from more than 500 imaging studies. Results show that our method is able to automatically infer Bayesian networks that capture both directed and undirected probabilistic dependencies between a number of brain regions, including regions that are frequently observed in motor-related and cognitive control tasks.

Original languageEnglish (US)
Pages (from-to)1372-1384
Number of pages13
Issue number2
Publication statusPublished - Jan 15 2010


ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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