School of Medicine Publications and Presentations
Document Type
Article
Publication Date
8-1-2020
Abstract
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 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.
Recommended Citation
Alberton, B. A., Nichols, T. E., Gamba, H. R., & Winkler, A. M. (2020). Multiple testing correction over contrasts for brain imaging. NeuroImage, 216, 116760. https://doi.org/10.1016/j.neuroimage.2020.116760
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
First Page
116760
Publication Title
NeuroImage
DOI
10.1016/j.neuroimage.2020.116760
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
Under a Creative Commons license