We report new random field theory P values for peaks of canonical correlation SPMs for
detecting multiple contrasts in a linear model for multivariate image data. This completes�…

Functional connectivity between two voxels or regions of voxels can be measured by the
correlation between voxel measurements from either PET CBF or BOLD fMRI images in 3D�…

Determining power and sample size in neuroimaging studies is a challenging task because
of the massive multiple comparisons among tens of thousands of correlated voxels. To�…

In recent years, multiple brain MR imaging modalities have emerged; however, analysis
methodologies have mainly remained modality-specific. In addition, when comparing across�…

This chapter is an introduction to the multiple comparison problem in functional imaging, and
the way it can be solved using Random field theory (RFT). In a standard functional imaging�…

This technical report revisits the analysis of family‐wise error rates in statistical parametric
mapping—using random field theory—reported in (Eklund et al.[]: arXiv 1511.01863)�…

Statistical parametric mapping is the application of Random Field Theory to make inferences
about the topological features of statistical processes that are continuous functions of space�…

Although covarying for potential confounds or nuisance variables is common in
psychological research, relatively little is known about how the inclusion of covariates may�…

Structural covariance analysis is a widely used structural MRI analysis method which
characterises the co-relations of morphology between brain regions over a group of�…

Studies of brain-wide functional connectivity or structural covariance typically use measures
like the Pearson correlation coefficient, applied to data that have been averaged across�…