用可控制的生成模型模拟早产儿脑发育轨迹，识别风险标志物。
Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories
- 基于图扩散网络，实现对个体脑皮层形态的可控演化建模。
- 生成结果能骗过年龄预测模型，准确率达0.85±0.62。
- 适合研究早产儿神经发育异常与脑结构变化的临床医生和算法开发者。
早产会扰乱皮层神经发育的正常轨迹，增加认知与行为障碍风险。但个体结局差异大，早期预测困难。个体化模拟通过建模受试者特异的神经发育轨迹，有助于发现偏离正常模式的细微异常，可能成为风险生物标志物。尽管生成模型在神经发育模拟中展现潜力，但以往方法常难以保持个体特异的皮层折叠模式或区域特异性形态变化。本文提出一种新型图扩散网络，支持皮层成熟过程的可控模拟。基于发育人类连接组计划（dHCP）的皮层表面数据，实验表明该模型在保持个体特异皮层形态的同时，能充分模拟皮层成熟过程，使独立训练的年龄回归网络误判，预测准确率达 $0.85 \pm 0.62$。
原文摘要 · Abstract (English)
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 $0.85 \pm 0.62$.
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