title: High level group analysis of FMRI data based on Dirichlet process mixture models.
abstract: Inferring the position of functionally active regions from a multi-subject fMRI dataset involves the comparison of the individual data and the inference of a common activity model. While voxel-based analyzes, e.g. Random Effect statistics, are widely used, they do not model each individual activation pattern. Here, we develop a new procedure that extracts structures individually and compares them at the group level. For inference about spatial locations of interest, a Dirichlet Process Mixture Model is used. Finally, inter-subject correspondences are computed with Bayesian Network models. We show the power of the technique on both simulated and real datasets and compare it with standard inference techniques.
authors.affiliation: INRIA Futurs, Neurospin, Gif-sur-Yvette cedex, France. bertrand.thirion@inria.fr
authors.forename: Bertrand
authors.initials: B
authors.lastname: Thirion
authors.forename: Alan
authors.initials: A
authors.lastname: Tucholka
authors.forename: Merlin
authors.initials: M
authors.lastname: Keller
authors.forename: Philippe
authors.initials: P
authors.lastname: Pinel
authors.forename: Alexis
authors.initials: A
authors.lastname: Roche
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Mangin
authors.forename: Jean-Baptiste
authors.initials: JB
authors.lastname: Poline
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological