A reaction-diffusion model of human brain development.

Area of Science:

• Computational Neuroscience
• Developmental Biology
• Mathematical Modeling

Background:

• Cortical folding, essential for brain function, displays both consistent patterns and individual variations.
• Existing hypotheses for brain convolution often overlook the variability in fold geometry and topology.

Purpose of the Study:

• To develop a phenomenological model simulating cortical folding using reaction-diffusion mechanisms.
• To investigate the reproducibility and variability of cortical folding patterns computationally.
• To explore potential links between model parameters and developmental brain pathologies.

Main Methods:

• A reaction-diffusion model with Turing morphogens was employed to simulate differential growth of sulci and gyri.
• A finite element approach computed morphogen evolution and surface deformation iteratively.
• Multiple model simulations were run from noisy initial conditions to assess reproducibility.

Main Results:

• The model successfully mimicked progressive cortical folding during simulated fetal development.
• Consistent patterns of reproducibility were observed across multiple model runs.
• Variability in fold topology was noted, with similar folds exhibiting different topological properties.
• Model parameters generated patterns resembling developmental pathologies like polymicrogyria and lissencephaly.

Conclusions:

• The proposed model offers a computational framework for understanding the reproducibility and variability in cortical folding.
• The findings support theories like the sulcal roots theory, linking initial conditions to folding organization.
• The model's ability to simulate pathologies highlights its potential for studying neurodevelopmental disorders.