Abstract
The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.
Recommended Citation
P. Chavoshnejad and L. Chen and X. Yu and J. Hou and N. Filla and D. Zhu and T. Liu and G. Li and M. J. Razavi and X. Wang, "An Integrated Finite Element Method And Machine Learning Algorithm For Brain Morphology Prediction," Cerebral Cortex, vol. 33, no. 15, pp. 9354 - 9366, Oxford University Press, Aug 2023.
The definitive version is available at https://doi.org/10.1093/cercor/bhad208
Department(s)
Computer Science
Publication Status
Free Access
Keywords and Phrases
brain development; computational modeling; cortical folding; machine learning; surrogate model
International Standard Serial Number (ISSN)
1460-2199; 1047-3211
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Oxford University Press, All rights reserved.
Publication Date
01 Aug 2023
PubMed ID
37288479
Comments
National Science Foundation, Grant CMMI-2123061