NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg

• URL: http://arxiv.org/abs/2508.09757v1
• Date: Wed, 13 Aug 2025 12:36:23 GMT
• Title: NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg
• Authors: Nashira Baena, Mariana da Silva, Irina Grigorescu, Aakash Saboo, Saga Masui, Jaques-Donald Tournier, Emma C. Robinson,
• Abstract summary: We present a novel framework for learning individual growth trajectories from biomechanically constrained, longitudinal, diffeomorphic image registration.<n>The method improves the biological plausibility of warps, generating growth trajectories that better follow population-level trends.<n>This framework opens new possibilities for predictive modeling of brain maturation and early identification of malformations of cortical development.
• Score: 1.62955730276521
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• SurfAge-Net: A Hierarchical Surface-Based Network for Interpretable Fine-Grained Brain Age Prediction [9.571325956619743]
  We propose a novel surface-based brain age prediction network (SurfAge-Net) to capture region-specific developmental patterns.<n>SurfAge-Net establishes a new modeling paradigm by incorporating the connectomic principles of cortical organization.<n>It provides spatially precise and biologically interpretable maps of cortical maturation, effectively identifying heterogeneous delays and regional-specific abnormalities.
  arXiv  Detail & Related papers  (2026-01-28T07:01:57Z)
• Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting [51.906871559732245]
  Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns.<n>Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and individualized 'digital-twin' forecasting.<n>We propose CNODE, a novel framework for continuous, individualized PD progression forecasting.
  arXiv  Detail & Related papers  (2025-11-06T20:16:33Z)
• Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective [89.99666725996975]
  The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from data and their chronological age.<n>This tutorial article provides an overview of BAGP and introduces a principled framework for this application based on recent advancements in graph signal processing (GSP)<n>VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions.
  arXiv  Detail & Related papers  (2025-10-14T17:44:45Z)
• A Stage-Aware Mixture of Experts Framework for Neurodegenerative Disease Progression Modelling [6.577465893404979]
  Long-term progression of neurodegenerative diseases is commonly conceptualized as a graph diffusion process.<n>We propose a novel stage-aware Mixture Experts framework that explicitly models how different contributing mechanisms dominate at different disease stages.<n>The resulting IGND-MoE model dynamically integrates these components across temporal states, offering a principled way to understand how stage-specific pathological mechanisms contribute to progression.
  arXiv  Detail & Related papers  (2025-08-09T16:09:00Z)
• Neural Autoregressive Modeling of Brain Aging [13.402122520389366]
  High-dimensionality of data, subtle changes of structure across ages, and subject-specific patterns constitute challenges in the synthesis of the aging brain.<n>We propose NeuroAR, a novel brain aging simulation model based on generative autoregressive transformers.<n>NeuroAR significantly outperforms key models, including LDM, demonstrating its ability to model subject-specific brain aging trajectories with high fidelity.
  arXiv  Detail & Related papers  (2025-07-29T20:22:23Z)
• Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories [2.630319261167542]
  We present a novel graph-diffusion network that supports controllable simulation of cortical maturation.<n>We demonstrate that the model maintains subject-specific cortical morphology while modeling cortical maturation sufficiently well to fool an independently trained age regression network.
  arXiv  Detail & Related papers  (2025-07-21T09:16:24Z)
• NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models [68.89389652724378]
  NOBLE is a neural operator framework that learns a mapping from a continuous frequency-modulated embedding of interpretable neuron features to the somatic voltage response induced by current injection.<n>It predicts distributions of neural dynamics accounting for the intrinsic experimental variability.<n>NOBLE is the first scaled-up deep learning framework validated on real experimental data.
  arXiv  Detail & Related papers  (2025-06-05T01:01:18Z)
• Causal Disentanglement for Robust Long-tail Medical Image Generation [80.15257897500578]
  We propose a novel medical image generation framework, which generates independent pathological and structural features.<n>We leverage a diffusion model guided by pathological findings to model pathological features, enabling the generation of diverse counterfactual images.
  arXiv  Detail & Related papers  (2025-04-20T01:54:18Z)
• Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks [1.2115617129203957]
  Quantifying divergence from neurotypical connectivity patterns offers a promising pathway to inform diagnosis and therapeutic interventions. We present the Brain Representation via Individualized Deep Generative Embedding (BRIDGE) framework, which integrates normative modeling with a bio-inspired deep generative model. BRIDGE provides a global neurodivergence score based on the difference between connectivity-based brain age and chronological age, along with region-wise neurodivergence maps that highlight localized connectivity differences.
  arXiv  Detail & Related papers  (2024-10-14T20:21:11Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
  Brain decoding aims to reconstruct stimuli from acquired brain signals. Currently, brain decoding is confined to a per-subject-per-model paradigm. We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
  arXiv  Detail & Related papers  (2024-04-11T15:46:42Z)
• Cortical Surface Diffusion Generative Models [4.050721206558478]
  Cortical surface analysis has gained increased prominence, given its potential implications for neurological and developmental disorders. Traditional vision diffusion models, while effective in generating natural images, present limitations in capturing intricate development patterns in neuroimaging due to limited datasets. In this work, we proposed a novel diffusion model for the generation of cortical surface metrics, using modified surface vision transformers as the principal architecture.
  arXiv  Detail & Related papers  (2024-02-07T11:12:09Z)
• Epigenetic evolution of deep convolutional models [81.21462458089142]
  We build upon a previously proposed neuroevolution framework to evolve deep convolutional models. We propose a convolutional layer layout which allows kernels of different shapes and sizes to coexist within the same layer. The proposed layout enables the size and shape of individual kernels within a convolutional layer to be evolved with a corresponding new mutation operator.
  arXiv  Detail & Related papers  (2021-04-12T12:45:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.