NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg

TL;DR

This paper introduces NEUBORN, a novel framework that models individual brain development trajectories using biomechanically constrained image registration, improving biological plausibility and interpretability over existing methods.

Contribution

The authors develop a hierarchical network architecture for longitudinal image registration that captures fine-scale cortical development with enhanced biological realism.

Findings

Improved biological plausibility of growth trajectories.

Smoother warps with fewer negative Jacobians.

Better alignment with population-level developmental trends.

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.