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This thesis is dedicated to the statistical analysis of multi-sub ject fMRIdata, with the purpose of identifying bain structures involved in certaincognitive or sensori-motor tasks, in a reproducible way across sub jects. Toovercome certain limitations of standard voxel-based testing methods, asimplemented in the Statistical Parametric Mapping (SPM) software, we introducea Bayesian model selection approach to this problem, meaning that the mostprobable model of cerebral activity given the data is selected from apre-defined collection of possible models. Based on a parcellation of the brainvolume into functionally homogeneous regions, each model corresponds to apartition of the regions into those involved in the task under study and thoseinactive. This allows to incorporate prior information, and avoids thedependence of the SPM-like approach on an arbitrary threshold, called thecluster- forming threshold, to define active regions. By controlling a Bayesianrisk, our approach balances false positive and false negative risk control.Furthermore, it is based on a generative model that accounts for the spatialuncertainty on the localization of individual effects, due to spatialnormalization errors. On both simulated and real fMRI datasets, we show thatthis new paradigm corrects several biases of the SPM-like approach, whicheither swells or misses the different active regions, depending on the choiceof a cluster-forming threshold.

Selection of a Model of Cerebral Activity for fMRI Group Data Analysis