# Biophysical modeling of anatomically realistic prenatal cortical folding development

## TL;DR

This paper introduces a new biophysical model that realistically simulates how the human fetal brain develops its complex folded structure during gestation.

## Contribution

The novel framework integrates data-driven growth laws with anatomically accurate geometry to model cortical folding with unprecedented realism.

## Key findings

- Region-specific growth fields derived from prenatal MRI data produce realistic cortical folding patterns.
- The model replicates clinically atypical brain phenotypes like lissencephaly and polymicrogyria through controlled perturbations.
- The framework enables high-fidelity synthetic brain datasets for AI-driven developmental neuroscience.

## Abstract

Cortical folds encode the architecture of human cognition, yet the mechanisms that transform the smooth fetal cortex into its convoluted geometry remain elusive. Biophysical modeling enables mechanistic insight into cortical morphogenesis, but existing models often lack anatomical realism and fail to capture key hallmarks and morphometrics of dynamic cortical folding in the developing human brain. Here, we introduce a novel whole-brain developmental framework that integrates region-specific, data-driven growth laws with anatomically accurate cortical geometry to enable realistic and biologically interpretable modeling of cortical morphogenesis during gestation. Growth fields derived from large-scale prenatal magnetic resonance imaging data capture spatiotemporal variations in cortical expansion and thickness across parcellated regions. Incorporating this heterogeneous growth yields anatomically faithful folding patterns that closely match qualitative landmarks and quantitative morphometrics from human imaging. Systematic perturbations of geometry and growth attributes delineate control parameters that produce realistic morphological variability and replicate clinically atypical brain phenotypes consistent with lissencephaly, pachygyria, and polymicrogyria. This framework provides a quantitative foundation for elucidating the mechanisms of typical and atypical fetal brain development and can serve as a promising generative engine for high-fidelity, longitudinal synthetic brain datasets to advance AI-driven developmental neuroscience and clinical translation.