DEALING WITH SPATIAL NORMALIZATION ERRORS IN fMRI GROUP INFERENCE USING HIERARCHICAL MODELING

An important challenge in neuroimaging multi-subject studies is to take into account that different brains cannot be aligned perfectly. To this end, we extend the classical mass univariate model for group analysis to incorporate uncer- tainty on localization by introducing, for each subject, a spatial "jitter" variable to be marginalized out. We derive a Bayes factor to test for the mean popula- tion effect's sign in each voxel of a search volume, and discuss a Gibbs sampler to compute it. This Bayes factor, which generalizes the classical t-statistic, may be combined with a permutation test in order to control the frequentist false positive rate. Results on both simulated and experimental data suggest that this test may outperform conventional mass univariate tests in terms of detection power, while limiting the problem of overestimating the size of activity clusters.

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