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

Abstract

We describe almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neuroanatomical difference between two groups of structural magnetic resonance imaging (MRI) data. Theoretical distributions under the null hypothesis are available for 1) global tissue class volumes; 2) standardized linear model [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 probability distributions under the null hypothesis, with fewer assumptions, by permutation of the observed data. Nominal Type I error control by permutation 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 suprathreshold voxel statistics in a cluster (or cluster mass), regardless of its theoretical 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.

Real‐time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder

“…The individual and group‐level analysis methods are described in detail elsewhere [Brammer et al, 1997; Bullmore et al, 1999a; Bullmore et al, 1999b] and in the Supporting Information.…”

Functional brain network centrality is related to APOE genotype in cognitively normal elderly

“…Significant differences between the APOE4+ and APOE4− groups were computed in a group‐level general linear model (GLM) whose design included gender, age, NGMV, and Framingham index as covariates. Significance was computed based on permutation testing of group labels, using cluster mass as a test statistic (Bullmore et al., 1999). A cluster‐forming threshold was automatically determined to maximize the number of suprathreshold clusters in the null distribution.…”

Structural and functional hyperconnectivity within the sensorimotor system in xenomelia

“…It is important to note that in all analyses reported, we controlled the alpha error probability ( p  < .05) for multiple comparisons using 5,000 permutations of the group label. The permutation testing method implemented in NBS (Zalesky et al., 2010) is not new and is synonymous with conventional cluster‐based thresholding procedures of statistical parametric maps (Bullmore et al., 1999; Hayasaka & Nichols, 2004; Nichols & Holmes, 2002). p ‐Values for the network‐based measures are reported one‐tailed due to the directed contrasts.…”