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Related Experiment Video

Updated: Jun 20, 2026

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

Chemically based mathematical model for development of cerebral cortical folding patterns.

Deborah A Striegel¹, Monica K Hurdal

¹Department of Mathematics, Florida State University, Tallahassee, Florida, USA. dsmith@math.fsu.edu

Plos Computational Biology

|September 26, 2009

Summary

This study introduces a mathematical model for cortical folding, linking brain structure to ventricle size and shape. It explains species-specific patterns and individual consistency in brain development.

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Last Updated: Jun 20, 2026

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Area of Science:

Neuroscience
Developmental Biology
Mathematical Modeling

Background:

Cortical folding patterns are crucial for brain function but their formation mechanisms remain unclear.
Existing models often focus on local interactions, like the intermediate progenitor (IP) model, which links progenitor cell proliferation to gyrification.
A comprehensive model explaining both local and global features of cortical development is needed.

Purpose of the Study:

To develop a novel mathematical model for cortical folding that incorporates global characteristics.
To investigate the relationship between the ventricular zone's geometry and the resulting cortical folding patterns.
To explain species-specific variations and individual consistency in cortical patterns.

Main Methods:

Utilizing a prolate spheroidal surface to model the ventricular zone.
Applying prolate spheroidal harmonics to a Turing reaction-diffusion system for a chemically based framework.
Integrating features of the intermediate progenitor (IP) model with global geometric considerations.

Main Results:

Demonstrated a direct correlation between cortical pattern formation and the lateral ventricle's size and shape.
Explained the placement and directionality of sulci based on the model's parameters.
Addressed the relationship between domain scaling and the elaboration of cortical patterns.

Conclusions:

The developed model provides a chemically based framework for understanding cortical folding.
It elucidates the consistency of cortical patterns within species and variability between species.
This work offers a significant contribution to understanding the complex puzzle of cortical pattern formation.