title: Anatomically informed bayesian model selection for fMRI group data analysis.
abstract: A new approach for fMRI group data analysis is introduced to overcome the limitations of standard voxel-based testing methods, such as Statistical Parametric Mapping (SPM). Using a Bayesian model selection framework, the functional network associated with a certain cognitive task is selected according to the posterior probabilities of mean region activations, given a pre-defined anatomical parcellation of the brain. This approach enables us to control a Bayesian risk that balances false positives and false negatives, unlike the SPM-like approach, which only controls false positives. On data from a mental calculation experiment, it detected the functional network known to be involved in number processing, whereas the SPM-like approach either swelled or missed the different activation regions.
authors.affiliation: LNAO, Neurospin, CEA, F-91191 Gif-sur-Yvette, France. merlin.keller@cea.fr
authors.forename: Merlin
authors.initials: M
authors.lastname: Keller
authors.forename: Marc
authors.initials: M
authors.lastname: Lavielle
authors.forename: Matthieu
authors.initials: M
authors.lastname: Perrot
authors.forename: Alexis
authors.initials: A
authors.lastname: Roche
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005071:Evoked Potentials / Q000502:physiology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique