{"ID":2882779,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09757","arxiv_id":"2508.09757","title":"NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg","abstract":"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.","short_abstract":"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 pr...","url_abs":"https://arxiv.org/abs/2508.09757","url_pdf":"https://arxiv.org/pdf/2508.09757v1","authors":"[\"Nashira Baena\",\"Mariana da Silva\",\"Irina Grigorescu\",\"Aakash Saboo\",\"Saga Masui\",\"Jaques-Donald Tournier\",\"Emma C. Robinson\"]","published":"2025-08-13T12:36:23Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.AI\"]","methods":"[]","has_code":false}