title: Unified univariate and multivariate random field theory.
abstract: 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 results for all types of univariate and multivariate image data analysis. All other known univariate and multivariate random field theory results are now special cases, so these new results present a true unification of all currently known results. As an illustration, we use these results in a deformation-based morphometry (DBM) analysis to look for regions of the brain where vector deformations of nonmissile trauma patients are related to several verbal memory scores, to detect regions of changes in anatomical effective connectivity between the trauma patients and a group of age- and sex-matched controls, and to look for anatomical connectivity in cortical thickness.
authors.affiliation: Department of Mathematics and Statistics, McGill University, Montreal, Canada H3A 2K6. keith.worsely@mcgill.ca
authors.forename: Keith J
authors.initials: KJ
authors.lastname: Worsley
authors.forename: Jonathan E
authors.initials: JE
authors.lastname: Taylor
authors.forename: Francesco
authors.initials: F
authors.lastname: Tomaiuolo
authors.forename: Jason
authors.initials: J
authors.lastname: Lerch
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology; D001930:Brain Injuries / Q000473:pathology; D002540:Cerebral Cortex / Q000293:injuries / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical