用因果图神经网络分析阿尔茨海默病，区分真正影响疾病的脑区。
Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
- 引入反事实干预机制，通过因果调整识别关键脑区
- 在484名患者数据上表现媲美传统模型，且可解释性更强
- 适合关注疾病机制、需要可解释模型的研究者
深度图学习已推动从MRI中进行阿尔茨海默病（AD）分类，但多数模型仅具相关性，难以区分年龄、性别和APOE4基因型等混杂因素与疾病特异性特征。本文提出Causal-GCN，一种基于do-演算后门调整的干预式图卷积框架，用于识别对AD进展具有稳定因果影响的脑区。每位受试者的MRI被表示为结构连接组，节点代表皮层和皮层下区域，边编码解剖连接性。年龄、性别和APOE4基因型等混杂因素通过主成分分析汇总，并纳入因果调整集。训练后，通过改变特定区域的输入边和节点特征，模拟干预以估计其对疾病概率的平均因果效应。在来自ADNI队列的484名受试者上，Causal-GCN性能与基线GNN相当，同时提供可解释的因果效应排序，突出后部、扣带回和岛叶枢纽，与既定的AD神经病理学一致。
原文摘要 · Abstract (English)
Deep graph learning has advanced Alzheimer's (AD) disease classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease specific features. We present Causal-GCN, an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment to identify brain regions exerting stable causal influence on AD progression. Each subject's MRI is represented as a structural connectome where nodes denote cortical and subcortical regions and edges encode anatomical connectivity. Confounders such as age, sec, and APOE4 genotype are summarized via principal components and included in the causal adjustment set. After training, interventions on individual regions are simulated by serving their incoming edges and altering node features to estimate average causal effects on disease probability. Applied to 484 subjects from the ADNI cohort, Causal-GCN achieves performance comparable to baseline GNNs while providing interpretable causal effect rankings that highlight posterior, cingulate, and insular hubs consistent with established AD neuropathology.
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