Predicting Brain Morphogenesis via Physics-Transfer Learning

Brain morphology emerges from the interplay of genetic programming and mechanical forces. However, its fractal-like folding patterns, regional anisotropy, and uneven curvature distributions pose longstanding challenges for quantitative analysis in medical and developmental studies. These difficulties are exacerbated by the limited availability of labeled data for existing statistical learning approaches. Here, we introduce a theory-grounded physics-transfer (PT) learning framework that enables generalization analysis and prediction in complex physical systems by leveraging the continuity of underlying physical laws across different levels of complexity. Specifically, mechanistic insights into nonlinear elasticity of simple, analytically tractable geometries are embedded directly into neural networks and then transferred to brain models through a sequence of physics-anchored domains, providing, for the first time, a formulation that integrates explicit physics constraints into learning theory. In this sense, PT advances classical statistical learning and domain adaptation theories by enabling generalization analysis in regimes where purely statistical assumptions are insufficient. Building on PT, we derive a generalization bound that explains the model’s strong performance in feature characterization and morphogenesis prediction for brain. The results reveal that localized deformation modes, rather than global geometry, dominantly shape cortical folding dynamics. Beyond predictive accuracy, the framework also yields reduced-dimensional evolutionary representations that distill the essential physics of the highly folded cerebral cortex. Validation against medical imaging data and expert clinical assessment demonstrates the promise of physics-aware digital-twin technologies for understanding, diagnosing, and ultimately intervening in the morphological complexity of the developing and diseased brain.