Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain

We describe almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neu roanatomical difference between two groups of structural magnetic resonance imaging (MRI) data. Theoretical distributions under the null hypothesis ar e available for 1) global tissue class volumes; 2) standardized linear mode l [analysis of variance (ANOVA and ANCOVA)] coefficients estimated at each voxel; and 3) an area of spatially connected clusters generated by applying an arbitrary threshold to a two-dimensional (2-D) map of normal statistics at voxel level. We describe novel methods for economically ascertaining pr obability distributions under the null hypothesis, with fewer assumptions, by permutation of the observed data. Nominal Type I error control by permut ation testing is generally excellent; whereas theoretical distributions may be over conservative. permutation has the additional advantage that it can be used to test any statistic of interest, such as the sum of suprathresho ld voxel statistics in a cluster (or cluster mass), regardless of its theor etical tractability under the null hypothesis. These issues are illustrated by application to MRI data acquired from 18 adolescents with hyperkinetic disorder and 16 control subjects matched for age and gender.