Multiple testing correction over contrasts for brain imaging [article]

Bianca Alessandra Visineski Alberton, Thomas E. Nichols, Humberto R. Gamba, Anderson M. Winkler
2019 bioRxiv   pre-print
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 is, hypotheses) are tested in the same general linear model. Here we argue that a correction for this multiplicity must be performed to avoid excess of false positives. Various methods have been proposed in the literature, but few have been applied in brain imaging. Here we discuss and compare different methods
more » ... to make such correction in different scenarios, showing that one classical and well known method is invalid, and argue that permutation is the best option to perform such correction due to its exactness and flexibility to handle a variety of common imaging situations.
doi:10.1101/775106 fatcat:drewqx7wdrhwxfwmjhoijjfn6q