Skip to Main content Skip to Navigation
Journal articles

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

Abstract : 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.
Complete list of metadatas

https://hal-cea.archives-ouvertes.fr/cea-00333625
Contributor : Alexis Roche <>
Submitted on : Thursday, October 23, 2008 - 4:41:48 PM
Last modification on : Monday, February 10, 2020 - 6:12:50 PM

Identifiers

Collections

Citation

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

Share

Metrics

Record views

187