Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories

doi:10.1007/978-3-032-05472-2_30

2026-01-01

PRE

Zhenshan Xie

Levente Baljer

Maria Vasconcelos-Cardoso

Emma C. Robinson *

DOI：10.1007/978-3-032-05472-2_30

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早产扰乱了皮层神经发育的典型轨迹，增加了认知和行为困难的患病风险。然而，其结局差异巨大，给早期预测带来了显著挑战。为解决这一问题，个体化模拟通过建模特定个体的神经发育轨迹，提供了有前景的解决方案，能够识别与正常模式偏离的细微变化，这些变化可能作为风险生物标志物。尽管生成模型在模拟神经发育方面显示出潜力，但先前的方法往往难以保留个体特异的皮层褶皱模式或再现区域特异的形态学变化。在本文中，我们提出了一种新颖的图扩散网络，支持可控的皮层成熟模拟。使用来自发育中人类连接组计划（dHCP）的皮层表面数据，我们证明该模型在保持个体特异皮层形态的同时，能够充分模拟皮层成熟，足以欺骗一个独立训练的年龄回归网络，实现0.85±0.62的预测准确率。

Keyword:

Generative Models

Diffusion Models

Cortical Surfaces

期刊

DEEP GENERATIVE MODELS, DGM4MICCAI 2025

IF:

论文数:

被引数:

机构

university of london

学者数:

21.5W

论文数: 19.7W

被引数: 305

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