用Transformer引导扩散过程，提升多模态脑图谱的阿尔茨海默病早期诊断能力
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
- 通过Transformer指导节点扩散过程，融合局部与远距离图结构信息
- 在多模态数据上实现更高的前临床阿尔茨海默病分类准确率
- 能有效识别与疾病早期相关的关键脑区，助力早筛
脑部区域间的关系图谱为神经退行性疾病诊断与预后提供重要依据。尽管已有多种图神经网络用于捕捉关系信息，但其解释性仍受限：卷积方法难以聚合远距离邻域信息，注意力机制则在保留关键节点特征方面表现不足，导致难以从多模态异构特征中识别疾病特异性变化。为此，我们提出一种集成框架，利用下游Transformer在每个节点处引导扩散过程，分别通过扩散核和多头注意力聚合图的短程与长程属性。实验表明，该模型在多模态数据上显著提升了前临床阿尔茨海默病（AD）分类性能；同时可精准识别与疾病早期密切相关的关键脑区（ROIs），具备早期诊断与预测的重大潜力。
原文摘要 · Abstract (English)
The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between regions of interest (ROIs). Despite recent emergence of various Graph Neural Networks (GNNs) to effectively capture the relational information, there remain inherent limitations in interpreting the brain networks. Specifically, convolutional approaches ineffectively aggregate information from distant neighborhoods, while attention-based methods exhibit deficiencies in capturing node-centric information, particularly in retaining critical characteristics from pivotal nodes. These shortcomings reveal challenges for identifying disease-specific variation from diverse features from different modalities. In this regard, we propose an integrated framework guiding diffusion process at each node by a downstream transformer where both short- and long-range properties of graphs are aggregated via diffusion-kernel and multi-head attention respectively. We demonstrate the superiority of our model by improving performance of pre-clinical Alzheimer's disease (AD) classification with various modalities. Also, our model adeptly identifies key ROIs that are closely associated with the preclinical stages of AD, marking a significant potential for early diagnosis and prevision of the disease.
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