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SurfGNN:一个强大的基于表面的预测模型,可用于空间和皮层特征的协同激活地图的解释性

Zhuoshuo Li¹, Jiong Zhang², Youbing Zeng¹

¹School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China.

Medical image analysis

|September 11, 2025

概括

表面图形神经网络 (SurfGNN) 通过有效处理复杂的皮质数据来改善新生儿大脑年龄的预测. 这种新的方法优于现有的方法,提供更准确的发育评估.

科学领域:

神经成像是一种神经成像.
计算神经科学是一种神经科学.
机器学习机器学习

背景情况:

当前的大脑表面模型往往无法捕捉皮层特征水平的区域变化.
图形神经网络 (GNN) 与皮层表面网格中常见的高密度图形结构进行斗争.

研究的目的:

提出一个可解释的预测模型,表面图神经网络 (SurfGNN),用于大脑表面分析.
解决在皮层数据中模拟区域异质性和高密度图形结构的挑战.

主要方法:

SurfGNN使用拓采样学习 (TSL) 和区域特定学习 (RSL) 来管理多个尺度上的皮质特征.
一种得分加权的融合 (SWF) 方法将节点表示合并为预测.
该模型被应用于预测新生儿大脑年龄,使用481名受试者的协调MR图像.

主要成果:

在月经后的几周内,SurfGNN实现了0.827 ± 0.056的平均绝对误差 (MAE),超过了最先进的方法至少9.0%.
该模型生成了特征级激活地图,确定了有助于预测的关键区域变异.
提出的方法有效地处理皮层表面的稀疏图形表示和区域异质性.

结论:

SurfGNN为基于大脑表面的预测任务提供了一种优越的方法,特别是在发育评估中.
通过激活地图来解释模型的可解释性有助于理解对年龄预测的形态学贡献.
这项工作通过解决复杂的图形结构和区域变异性来推进GNN在神经成像中的应用.

关键词:

大脑年龄预测预测皮层表面皮层表面在GNN中,GNN是最重要的.可以解释性解释性空间和皮层联合激活地图.

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