Mixed-effect statistics for group analysis in fMRI: a nonparametric maximum likelihood approach.
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.
Roche
Alexis
A
CEA, Neurospin, Gif-sur-Yvette, France. alexis.roche@cea.fr
Mériaux
Sébastien
S
Keller
Merlin
M
Thirion
Bertrand
B
D000465:Algorithms; D001699:Biometry; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D006801:Humans; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D018709:Statistics, Nonparametric