Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories

TL;DR

This paper introduces a controllable graph-diffusion generative model that simulates neurodevelopmental trajectories on cortical surfaces, preserving individual morphology and accurately predicting age, aiding early detection of neurodevelopmental deviations.

Contribution

The paper presents a novel graph-diffusion network for controllable cortical maturation simulation that maintains subject-specific features and improves age prediction accuracy.

Findings

Model achieves 0.85 ± 0.62 accuracy in age prediction.

Preserves individual cortical folding patterns during simulation.

Demonstrates potential for early neurodevelopmental risk biomarkers.

Abstract

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 $0.85 \pm 0.62$.