The Use of Labeled Cortical Distance Maps for Quantization and Analysis   of Anatomical Morphometry of Brain Tissues
  Anatomical shape differences in cortical structures in the brain can be associated with various neuropsychiatric and neuro-developmental diseases or disorders. Labeled Cortical Distance Map (LCDM), can be a powerful tool to quantize such morphometric differences. In this article, we investigate various issues regarding the analysis of LCDM distances in relation to morphometry. The length of the LCDM distance vector provides the number of voxels (approximately a multiple of volume (in mm^3)); median, mode, range, and variance of LCDM distances are all suggestive of size, thickness, and shape differences. However these measures provide a crude summary based on LCDM distances which may convey much more information about the tissue in question. To utilize more of this information, we pool (merge) the LCDM distances from subjects in the same group or condition. The statistical methodology we employ require normality and within and between sample independence. We demonstrate that the violation of these assumptions have mild influence on the tests. We specify the types of alternatives the parametric and nonparametric tests are more sensitive for. We also show that the pooled LCDM distances provide powerful results for group differences in distribution, left-right morphometric asymmetry of the tissues, and variation of LCDM distances. As an illustrative example, we use gray matter (GM) tissue of ventral medial prefrontal cortices (VMPFCs) from subjects with major depressive disorder, subjects at high risk, and control subjects. We find significant evidence that VMPFCs of subjects with depressive disorders are different in shape compared to those of normal subjects. 
E. Ceyhan, M. Hosakere, T. Nishino, J. Alexopoulos, R.D. Todd, K.N.   Botteron, M.I. Miller, J.T. Ratnanather