%0 Journal Article %T Mixed-effect statistics for group analysis in fMRI: a nonparametric maximum likelihood approach. %+ Service NEUROSPIN (NEUROSPIN) %A Roche, Alexis %A Mériaux, Sébastien %A Keller, Merlin %A Thirion, Bertrand %< avec comité de lecture %@ 1053-8119 %J NeuroImage %I Elsevier %V 38 %N 3 %P 501-10 %8 2007-11-15 %D 2007 %R 10.1016/j.neuroimage.2007.06.043 %M 17890108 %Z Computer Science [cs]/Signal and Image Processing %Z Cognitive science/Neuroscience %Z Statistics [stat]/Methodology [stat.ME] %Z Life Sciences [q-bio]/Bioengineering %Z Engineering Sciences [physics]/Signal and Image processingJournal articles %X 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. %G English %L cea-00333625 %U https://cea.hal.science/cea-00333625 %~ CEA %~ JOLIOT %~ CEA-DRF %~ NEUROSPIN