The Use of Labeled Cortical Distance Maps for Quantization and Analysis of Anatomical Morphometry of Brain Tissues
E. Ceyhan, M. Hosakere, T. Nishino, J. Alexopoulos, R.D. Todd, K.N., Botteron, M.I. Miller, J.T. Ratnanather

TL;DR
This paper explores the use of Labeled Cortical Distance Maps (LCDM) to quantify and analyze brain tissue morphometry, demonstrating their effectiveness in detecting shape differences related to neuropsychiatric conditions.
Contribution
It introduces a statistical framework for analyzing LCDM distances, addressing assumptions and demonstrating their utility in identifying morphometric differences in brain tissues.
Findings
Significant shape differences in VMPFCs between depressed and control subjects.
Pooled LCDM distances effectively detect group and asymmetry differences.
Violations of normality assumptions have mild impact on test results.
Abstract
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…
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Taxonomy
TopicsFunctional Brain Connectivity Studies · Morphological variations and asymmetry · Advanced Neuroimaging Techniques and Applications
