NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866918124431867904 |
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| author | Baena, Nashira da Silva, Mariana Grigorescu, Irina Saboo, Aakash Masui, Saga Tournier, Jaques-Donald Robinson, Emma C. |
| author_facet | Baena, Nashira da Silva, Mariana Grigorescu, Irina Saboo, Aakash Masui, Saga Tournier, Jaques-Donald Robinson, Emma C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg Baena, Nashira da Silva, Mariana Grigorescu, Irina Saboo, Aakash Masui, Saga Tournier, Jaques-Donald Robinson, Emma C. Quantitative Methods Artificial Intelligence 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. |
| title | NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2508.09757 |