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The main and largest component of the human brain is the cerebrum. The cerebrum consists of two main parts: the cerebral cortex, an outer layer with wrinkles or folds known as gyri and shallow grooves called sulci, and a deeper region beneath it. The cerebrum divides into two distinct hemispheres and contains five different lobes: the frontal, parietal, temporal, occipital, and insula. The central sulcus separates the frontal and parietal lobes and two functionally important gyri — the...

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Related Experiment Video

Updated: Jun 16, 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

A computational model of cerebral cortex folding.

Jingxin Nie¹, Lei Guo, Gang Li

¹School of Automation, Northwestern Polytechnical University, Xi'an, China.

Journal of Theoretical Biology

|February 20, 2010

Summary

This summary is machine-generated.

This study models human cerebral cortex folding using computational methods. Simulations suggest skull constraints and varying cell growth rates significantly influence cortical folding patterns.

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Area of Science:

Neuroscience
Computational Biology
Biophysics

Background:

The human cerebral cortex exhibits complex geometric variability.
Understanding cortical folding mechanisms is a key research objective.

Purpose of the Study:

To develop a computational 3D geometric model of cerebral cortex folding.
To investigate the mechanisms driving cortical folding using simulations.

Main Methods:

Initialized a 3D geometric model with fetal brain MRI data.
Applied a partial differential equation to simulate cortical growth and deformation.
Varied simulation parameters to observe folding dynamics.

Main Results:

The model successfully generated folding convolutions and shape dynamics.
Simulations supported hypotheses regarding skull constraints and growth rates.
Cortical folding patterns were shown to depend on global and regional cell growth rates.
Initial cortical geometry was identified as a determinant of folding patterns.

Conclusions:

Computational modeling provides experimental support for hypotheses on cortical folding.
Skull mechanics and differential growth rates are crucial factors in shaping the cerebral cortex.
The model offers insights into the developmental processes underlying cortical complexity.