# Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories

arXiv: https://arxiv.org/abs/2508.03706 · HTML: https://mlsanity.com/paper/2508.03706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 10 of 10)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [MeshDiffusion: Score-based Generative 3D Mesh Modeling](https://mlsanity.com/paper/2303.08133/) (2023)
- [Riemannian Diffusion Models](https://mlsanity.com/paper/2208.07949/) (2022)
- [Spherical U-Net on Cortical Surfaces: Methods and Applications](https://mlsanity.com/paper/1904.00906/) (2019)
- [Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia](https://mlsanity.com/paper/1912.01526/) (2019)
- [Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis](https://mlsanity.com/paper/2203.16414/) (2022)
- [ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans](https://mlsanity.com/paper/2103.02561/) (2021)
- [Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging](https://mlsanity.com/paper/1811.09897/) (2018)

## Abstract (arXiv)

Preterm birth disrupts the typical trajectory of cortical neurodevelopment, increasing the risk of cognitive and behavioral difficulties. However, outcomes vary widely, posing a significant challenge for early prediction. To address this, individualized simulation offers a promising solution by modeling subject-specific neurodevelopmental trajectories, enabling the identification of subtle deviations from normative patterns that might act as biomarkers of risk. While generative models have shown potential for simulating neurodevelopment, prior approaches often struggle to preserve subject-specific cortical folding patterns or to reproduce region-specific morphological variations. In this paper, we present a novel graph-diffusion network that supports controllable simulation of cortical maturation. Using cortical surface data from the developing Human Connectome Project (dHCP), we demonstrate that the model maintains subject-specific cortical morphology while modeling cortical maturation sufficiently well to fool an independently trained age regression network, achieving a prediction accuracy of 0.85 ± 0.62.