Mixed-effect statistics for group analysis in fMRI: a nonparametric maximum likelihood approach.

Alexis Roche (1) , Sébastien Mériaux , Merlin Keller , Bertrand Thirion

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This technical note describes a collection of test statistics accounting for estimation uncertainties at the within-subject level, that can be used as alternatives to the standard t statistic in one-sample random-effect analyses, i.e. when testing the mean effect of a population. We build such test statistics by estimating the across-subject distribution of the effects using maximum likelihood under a nonparametric mixed-effect model. For inference purposes, the statistics are calibrated using permutation tests to achieve exact false positive control under a symmetry assumption regarding the across-subject distribution. The new tests are implemented in a freely available toolbox for SPM called Distance.

Domains: Signal and Image Processing; Neuroscience; Methodology [stat.ME]; Bioengineering; Signal and Image processing

Alexis Roche, Sébastien Mériaux, Merlin Keller, Bertrand Thirion. Mixed-effect statistics for group analysis in fMRI: a nonparametric maximum likelihood approach.. NeuroImage, 2007, 38 (3), pp.501-10. ⟨10.1016/j.neuroimage.2007.06.043⟩. ⟨cea-00333625⟩