title: Cortical thickness analysis examined through power analysis and a population simulation.
abstract: We have previously developed a procedure for measuring the thickness of cerebral cortex over the whole brain using 3-D MRI data and a fully automated surface-extraction (ASP) algorithm. This paper examines the precision of this algorithm, its optimal performance parameters, and the sensitivity of the method to subtle, focal changes in cortical thickness. The precision of cortical thickness measurements was studied using a simulated population study and single subject reproducibility metrics. Cortical thickness was shown to be a reliable method, reaching a sensitivity (probability of a true-positive) of 0.93. Six different cortical thickness metrics were compared. The simplest and most precise method measures the distance between corresponding vertices from the white matter to the gray matter surface. Given two groups of 25 subjects, a 0.6-mm (15%) change in thickness can be recovered after blurring with a 3-D Gaussian kernel (full-width half max = 30 mm). Smoothing across the 2-D surface manifold also improves precision; in this experiment, the optimal kernel size was 30 mm.
authors.affiliation: McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada.
authors.forename: Jason P
authors.initials: JP
authors.lastname: Lerch
authors.forename: Alan C
authors.initials: AC
authors.lastname: Evans
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002508:Cephalometry / Q000706:statistics & numerical data*; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D003196:Computer Graphics; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional; D008432:Mathematical Computing; D016011:Normal Distribution; D011338:Probability Theory; D012016:Reference Values; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013499:Surface Properties