The multiple testing problem arises not only when there are many voxels or vertices in an
image representation of the brain, but also when multiple contrasts of parameter estimates
(that represent hypotheses) are tested in the same general linear model. We argue that a
correction for this multiplicity must be performed to avoid excess of false positives. Various
methods for correction have been proposed in the literature, but few have been applied to
brain imaging. Here we discuss and compare different methods to make such correction in …