NeuraConnect Lab

Understanding the networked brain through its injury

Biophysical modeling of anatomically realistic prenatal cortical folding development.


Journal article


Jixin Hou, Zhengwang Wu, Kun Jiang, Taotao Wu, Lu Zhang, Dajiang Zhu, Wei Gao, M. Razavi, Tianming Liu, Ellen Kuhl, Gang Li, Xianqiao Wang
Nature Communications, 2026

Semantic Scholar DOI PubMed
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APA   Click to copy
Hou, J., Wu, Z., Jiang, K., Wu, T., Zhang, L., Zhu, D., … Wang, X. (2026). Biophysical modeling of anatomically realistic prenatal cortical folding development. Nature Communications.


Chicago/Turabian   Click to copy
Hou, Jixin, Zhengwang Wu, Kun Jiang, Taotao Wu, Lu Zhang, Dajiang Zhu, Wei Gao, et al. “Biophysical Modeling of Anatomically Realistic Prenatal Cortical Folding Development.” Nature Communications (2026).


MLA   Click to copy
Hou, Jixin, et al. “Biophysical Modeling of Anatomically Realistic Prenatal Cortical Folding Development.” Nature Communications, 2026.


BibTeX   Click to copy

@article{jixin2026a,
  title = {Biophysical modeling of anatomically realistic prenatal cortical folding development.},
  year = {2026},
  journal = {Nature Communications},
  author = {Hou, Jixin and Wu, Zhengwang and Jiang, Kun and Wu, Taotao and Zhang, Lu and Zhu, Dajiang and Gao, Wei and Razavi, M. and Liu, Tianming and Kuhl, Ellen and Li, Gang and Wang, Xianqiao}
}

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 whole-brain developmental framework that integrates region-specific, data-driven growth laws with anatomically realistic cortical geometry to enable 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 heterogeneous growth yields folding patterns that match key anatomical 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.