SurfGNN: A robust surface-based prediction model with interpretability for coactivation maps of spatial and cortical features

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhuoshuo Li , Jiong Zhang , Youbing Zeng , Jiaying Lin , Dan Zhang , Jianjia Zhang , Duan Xu , Hosung Kim , Bingguang Liu , Mengting Liu
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引用次数: 0

Abstract

Current brain surface-based prediction models often overlook the variability of regional attributes at the cortical feature level. While graph neural networks (GNNs) excel at capturing regional differences, they encounter challenges when dealing with complex, high-density graph structures. In this work, we consider the cortical surface mesh as a sparse graph and propose an interpretable prediction model—Surface Graph Neural Network (SurfGNN). SurfGNN employs topology-sampling learning (TSL) and region-specific learning (RSL) structures to manage individual cortical features at both lower and higher scales of the surface mesh, effectively tackling the challenges posed by the overly abundant mesh nodes and addressing the issue of heterogeneity in cortical regions. Building on this, a novel score-weighted fusion (SWF) method is implemented to merge nodal representations associated with each cortical feature for prediction. We apply our model to a neonatal brain age prediction task using a dataset of harmonized MR images from 481 subjects (503 scans). SurfGNN outperforms all existing state-of-the-art methods, demonstrating an improvement of at least 9.0% and achieving a mean absolute error (MAE) of 0.827 ± 0.056 in postmenstrual weeks. Furthermore, it generates feature-level activation maps, indicating its capability to identify robust regional variations in different morphometric contributions for prediction. The codes will be available at https://github.com/ZhuoshL/SurfGNN.
SurfGNN:一个强大的基于表面的预测模型,具有空间和皮质特征的协同激活图的可解释性。
目前基于脑表面的预测模型往往忽略了皮层特征水平上区域属性的可变性。虽然图神经网络(gnn)擅长捕捉区域差异,但它们在处理复杂、高密度的图结构时遇到了挑战。在这项工作中,我们将皮质表面网格视为一个稀疏图,并提出了一个可解释的预测模型-表面图神经网络(SurfGNN)。SurfGNN采用拓扑采样学习(TSL)和区域特定学习(RSL)结构,在表面网格的低尺度和高尺度上管理单个皮质特征,有效解决了网格节点过多带来的挑战,并解决了皮质区域的异质性问题。在此基础上,实现了一种新的评分加权融合(SWF)方法来合并与每个皮质特征相关的节点表示进行预测。我们将我们的模型应用于新生儿脑年龄预测任务,使用来自481名受试者(503次扫描)的协调磁共振图像数据集。SurfGNN优于所有现有的最先进的方法,至少改善了9.0%,月经后周的平均绝对误差(MAE)为0.827±0.056。此外,它还生成特征级激活图,表明它有能力识别不同形态测量值对预测的鲁棒区域变化。这些代码可在https://github.com/ZhuoshL/SurfGNN上获得。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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