Hierarchical multivariate group analysis of functional MRI data

This paper presents an original multivariate approach for group analysis of functional magnetic resonance imaging (fMRI) experiments. The proposed hierarchical method avoids the use of any spatial normalization. Rather, it relies on the analysis of a particular set of time series whose variations are common to all subjects. This common set of time series is extracted from the fMRI data of all subjects considered simultaneously, using a generalized fixed-effect model. Then, a multivariate regression model is applied for analyzing these time series and estimating activation maps associated with each subject. The method is illustrated by using real fMRI data.