Related Experiment Video
Updated: Jun 3, 2025
09:57
How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
Published on: January 2, 2012
27.8K
Role of data-driven regional growth model in shaping brain folding patterns
Jixin Hou1, Zhengwang Wu2, Xianyan Chen3
1School of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA. xqwang@uga.edu.
Soft Matter
|January 10, 2025
Summary
Regional brain growth variations significantly impact cortical folding patterns. Computational models incorporating this heterogeneity accurately simulate brain development, aiding in understanding neurodevelopmental disorders.
Area of Science:
- Neuroscience
- Computational Biology
- Developmental Biology
Background:
- Brain surface morphology is key to function and dysfunction.
- Computational modeling aids understanding of early brain folding.
- Regional variations in brain growth's role in cortical development is unclear.
Purpose of the Study:
- To explore how regional cortical growth affects brain folding patterns using computational simulation.
- To develop and validate computational models of early brain folding.
Main Methods:
- Developed regional cortical growth models using machine learning-assisted symbolic regression.
- Utilized longitudinal MRI data from 735 pediatric subjects (29 postmenstrual weeks to 2 years).
- Simulated cortical development with anatomically realistic models and quantified folding patterns.
Main Results:
- Regional growth models produced folding patterns more closely matching actual brain structures than uniform models.
- Growth magnitude was the dominant factor in shaping folding patterns.
- Multi-region models captured folding intricacies better than single-region models.
Conclusions:
- Incorporating regional growth heterogeneity is essential for accurate brain folding simulations.
- These findings can improve diagnosis and treatment of cortical malformations and neurodevelopmental disorders.
- Highlights the importance of personalized computational models in neuroscience.
