Understanding individual cortical development is essential for identifying deviations linked to neurodevelopmental disorders. However, current normative modelling frameworks struggle to capture fine-scale anatomical details due to their reliance on modelling data within a population-average reference space. Here, we present a novel framework for learning individual growth trajectories from biomechanically constrained, longitudinal, diffeomorphic image registration, implemented via a hierarchical network architecture. Trained on neonatal MRI data from the Developing Human Connectome Project, the method improves the biological plausibility of warps, generating growth trajectories that better follow population-level trends while generating smoother warps, with fewer negative Jacobians, relative to state-of-the-art baselines. The resulting subject-specific deformations provide interpretable, biologically grounded mappings of development. This framework opens new possibilities for predictive modeling of brain maturation and early identification of malformations of cortical development.
@article{arxiv.2508.09757,
title = {NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg},
author = {Nashira Baena and Mariana da Silva and Irina Grigorescu and Aakash Saboo and Saga Masui and Jaques-Donald Tournier and Emma C. Robinson},
journal= {arXiv preprint arXiv:2508.09757},
year = {2025}
}