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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
09:57

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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
PubMed
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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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.