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Compensating cortical thickness for cortical folding-related variation.

Nagehan Demirci1, Timothy S Coalson2, Maria A Holland3

  • 1Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States.

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|January 29, 2026
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Summary
This summary is machine-generated.

This study introduces a new method to accurately measure brain cortical thickness by removing folding variations. This improves the reliability of brain imaging biomarkers for health and disease research.

Keywords:
Human Connectome Projectcortical atrophycortical foldingcortical thicknesscurvatureindividual variability

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Area of Science:

  • Neuroimaging
  • Brain Morphology
  • Computational Neuroscience

Background:

  • Cortical thickness is a key brain biomarker, but is influenced by local cortical folding.
  • Existing methods for folding compensation are incomplete or reduce spatial precision.
  • Individual variations in cortical folding introduce noise into thickness measurements.

Purpose of the Study:

  • To develop and validate a novel method for folding-compensated cortical thickness estimation.
  • To provide a more biologically interpretable measure of cortical architecture.
  • To enhance the utility of cortical thickness as a structural phenotype.

Main Methods:

  • Developed a novel method using nonlinear local multiple regression with five folding measures.
  • Modeled and removed folding-related variance from cortical thickness measurements.
  • Applied the method to Human Connectome Project (HCP-YA and HCA) datasets.

Main Results:

  • Demonstrated substantial reductions in intra-areal and inter-individual variability.
  • Increased standardized effect sizes of age on cortical thickness by 41%.
  • Preserved neurobiologically expected patterns and spatial precision, unlike spatial smoothing.

Conclusions:

  • The new method provides a more accurate and interpretable measure of cortical thickness.
  • This technique enhances cortical thickness as a biomarker for brain health and disease.
  • The method is integrated into HCP pipelines for broader application in neuroscience research.