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Related Experiment Video

Updated: Sep 15, 2025

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

Compensating Cortical Thickness for Cortical Folding-Related Variation.

Nagehan Demirci¹, Timothy S Coalson², Maria A Holland³

¹Department of Radiology, Washington University Medical School, St. Louis, MO, USA.

Biorxiv : the Preprint Server for Biology

|July 14, 2025

Summary

This summary is machine-generated.

We developed a new method to accurately measure cortical thickness by removing folding variations. This improves brain imaging analysis for better understanding brain health and disease.

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Last Updated: Sep 15, 2025

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Area of Science:

Neuroimaging
Brain Morphology
Computational Neuroscience

Background:

Cortical thickness is a key brain health biomarker.
Cortical folding introduces variability, obscuring biological insights.
Existing methods for folding compensation are insufficient and reduce spatial precision.

Purpose of the Study:

To develop a novel method for folding-compensated cortical thickness estimation.
To provide a more biologically interpretable measure of cortical architecture.
To address limitations of current methods in handling folding-related variance.

Main Methods:

Utilized nonlinear local multiple regression with five folding measures.
Modeled and removed folding-related variance from cortical thickness.
Estimated cortical thickness in the absence of local cortical folding.

Main Results:

Demonstrated substantial reductions in intra-areal and inter-individual variability.
Significantly increased effect sizes of age on cortical thickness.
Preserved neurobiologically expected patterns and spatial precision, unlike traditional smoothing methods.

Conclusions:

The novel method enhances cortical thickness as a structural phenotype.
This technique may improve cortical parcellation, longitudinal tracking, and biomarker discovery.
The method has been integrated into Human Connectome Project pipelines for broader application.