Statistical Analysis of Cortical Morphometrics Using Pooled Distances Based on Labeled Cortical Distance Maps

• Published in: Journal of mathematical imaging and vision Vol. 40; no. 1; pp. 20 - 35
• Main Authors: Ceyhan, E., Hosakere, M., Nishino, T., Alexopoulos, J., Todd, R. D., Botteron, K. N., Miller, M. I., Ratnanather, J. T.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.05.2011
• Subjects: Applications of Mathematics; Computer Science; Image Processing and Computer Vision; Mathematical Methods in Physics; Signal,Image and Speech Processing; Depression; Ventral medial prefrontal cortex; Morphometry; Computational anatomy; Laminar cortical mantle
• ISSN: 0924-9907; 1573-7683
• DOI: 10.1007/s10851-010-0240-4

Neuropsychiatric disorders have been demonstrated to manifest shape differences in cortical structures. Labeled Cortical Distance Mapping (LCDM) is a powerful tool in quantifying such morphometric differences and characterizes the morphometry of the laminar cortical mantle of cortical structures. Specifically, LCDM data are distances of labeled gray matter (GM) voxels with respect to the gray/white matter cortical surface. Volumes and descriptive measures (such as means and variances for each subject) based on LCDM distances provide descriptive summary information on some of the shape characteristics. However, additional morphometrics are contained in the data and their analysis may provide additional clues to underlying differences in cortical characteristics. To use more of this information, we pool (merge) LCDM distances from subjects in the same group. These pooled distances can help detect morphometric differences between groups, but do not provide information about the locations of such differences in the tissue in question. In this article, we check for the influence of the assumption violations on the analysis of pooled LCDM distances. We demonstrate that the classical parametric tests are robust to the non-normality and within sample dependence of LCDM distances and nonparametric tests are robust to within sample dependence of LCDM distances. We specify the types of alternatives for which the tests are more sensitive. We also show that the pooled LCDM distances provide powerful results for group differences in distribution of LCDM distances. As an illustrative example, we use GM in the ventral medial prefrontal cortex (VMPFC) in subjects with major depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy subjects. Significant morphometric differences were found in VMPFC due to MDD or being at HR. In particular, the analysis indicated that distances in left and right VMPFCs tend to decrease due to MDD or being at HR, possibly as a result of thinning. The methodology can also be applied to other cortical structures.