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Analysis of cortical morphometric variability using labeled cortical distance maps.

E Ceyhan1, T Nishino2, K N Botteron2,3

  • 1Dept. of Mathematics, Koç University, 34450, Sarıyer, Istanbul, Turkey.

Statistics and Its Interface
|July 21, 2023
PubMed
Summary
This summary is machine-generated.

Labeled Cortical Distance Maps (LCDM) analyze brain morphometry. New methods using homogeneity of variance tests reveal significant variations in cortical structures, aiding neurodevelopmental disorder research.

Keywords:
62-0762P10Brown-Forsythe testCensoringComputational anatomyHomogeneity of variancePooled distancesPrimary 62H35Simultaneous inferencesecondary 62F03

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Morphometric differences in cortical structures are linked to neurodevelopmental and neuropsychiatric disorders.
  • Labeled Cortical Distance Map (LCDM) is a tool to quantify shape and size variations.
  • Existing methods may not fully capture the extent of morphometric variability.

Purpose of the Study:

  • To introduce and validate a novel method for analyzing morphometric variability using LCDM distances.
  • To enhance information extraction from LCDM data through pooling and censoring.
  • To assess the robustness of the proposed statistical approach.

Main Methods:

  • Application of pooling and censoring techniques to LCDM distances.
  • Utilizing the Brown-Forsythe (BF) test for homogeneity of variance (HOV) on pooled and censored distances.
  • Evaluating the robustness of the BF HOV test against assumption violations like non-normality and within-sample dependence.

Main Results:

  • HOV analysis of pooled distances provides an overall assessment of morphometric variability.
  • HOV analysis of censored distances identifies specific locations of significant variation from the gray/white matter surface.
  • The BF HOV test demonstrates robustness to assumption violations and data aggregation.

Conclusions:

  • HOV analysis of LCDM distances is a valuable complementary tool for understanding morphometric variability.
  • The methodology is robust and applicable to various cortical structures, including the VMPFC in relation to depression risk.
  • This approach enhances the detection and localization of structural brain differences associated with disorders.