title: Learning partially directed functional networks from meta-analysis imaging data.
abstract: 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.
authors.affiliation: Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, D-04103, Leipzig, Germany. neumann@cbs.mpg.de
authors.forename: Jane
authors.initials: J
authors.lastname: Neumann
authors.forename: Peter T
authors.initials: PT
authors.lastname: Fox
authors.forename: Robert
authors.initials: R
authors.lastname: Turner
authors.forename: Gabriele
authors.initials: G
authors.lastname: Lohmann
mesh_terms: D001185:Artificial Intelligence; D001331:Automation; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015201:Meta-Analysis as Topic; D009434:Neural Pathways / Q000502:physiology; D011336:Probability