High level group analysis of FMRI data based on dirichlet process mixture models
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
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fMRI group studies are usually based on the computation of a mean signal for each voxel across subjects (Random Effects Analyzes), assuming that all subjects are in the same anatomical space (Talairach space). Although this is the standard approach, it lacks efficiency because its underlying hypotheses are often violated. We present here a new framework that detects structures of interest from each subject's data, then searches for correspondences across subjects and outlines the most reproducible activation in the group studied. This framework enables a strict control on the number of false positives. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyzes compared to standard methods. Moreover, it directly provides information on the activated regions spatial position correspondence or variability across subjects, which is difficult to obtain in standard voxel-based analyzes.