Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

This chapter focuses on the theory and practicalities of multiple-comparisons nonparametric randomization and permutation tests for functional neuroimaging experiments and this has been illustrated with worked examples. The permutation approach offers various advantages. The methodology is intuitive, flexible and accessible. While the traditional approach to multiple comparisons controls the family-wise error rate, the chance of any false positives, another perspective has recently been introduced. The new approach controls the false discovery rate (FDR), the fraction of false positives among all detected voxels. This chapter considers the family-wise error rate and proposes permutation approach to FDR. It begins with an introduction to nonparametric permutation testing, reviews experimental design and hypothesis testing issues, and illustrates the theory by considering testing a functional neuroimaging dataset at a single voxel. The problem of searching the brain volume for significant activations is then considered, and the extension of the permutation methods to the multiple-comparisons problem of simultaneously testing at all voxels is described. © 2004 Elsevier Inc. All rights reserved.

Original publication





Book title

Human Brain Function: Second Edition

Publication Date



887 - 910