Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1016/j.neuroimage.2004.07.045

Type

Journal article

Journal

Neuroimage

Publication Date

01/01/2005

Volume

24

Pages

163 - 173

Keywords

Algorithms, Artificial Intelligence, Cephalometry, Cerebral Cortex, Computer Graphics, Computer Simulation, Humans, Imaging, Three-Dimensional, Mathematical Computing, Normal Distribution, Probability Theory, Reference Values, Reproducibility of Results, Signal Processing, Computer-Assisted, Surface Properties