Biophysical modeling of anatomically realistic prenatal cortical folding development

Area of Science:

• Neuroscience
• Developmental Biology
• Computational Biology

Background:

• Cortical folding is crucial for human cognition, but its developmental mechanisms are not fully understood.
• Existing biophysical models lack anatomical realism and fail to capture dynamic aspects of human brain development.
• Understanding fetal brain development is key to addressing neurodevelopmental disorders.

Purpose of the Study:

• To introduce a novel whole-brain developmental framework for modeling cortical morphogenesis.
• To integrate region-specific growth laws with accurate cortical geometry for realistic simulations.
• To investigate the mechanisms underlying typical and atypical fetal brain development.

Main Methods:

• Developed a computational framework integrating data-driven growth laws with precise anatomical geometry.
• Utilized large-scale prenatal magnetic resonance imaging data to derive region-specific growth fields.
• Performed systematic perturbations to identify control parameters for cortical folding.

Main Results:

• The model successfully generated anatomically faithful folding patterns matching human brain imaging data.
• Simulations replicated key qualitative landmarks and quantitative morphometrics of cortical development.
• The framework reproduced atypical brain phenotypes associated with lissencephaly, pachygyria, and polymicrogyria.

Conclusions:

• The novel framework provides a realistic and biologically interpretable model of cortical morphogenesis.
• It elucidates mechanisms controlling typical and atypical fetal brain development.
• The model can generate synthetic brain data to advance AI-driven neuroscience and clinical applications.