用大脑图谱和人群数据提升脑疾病诊断准确率
A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders
- 融合脑区语义与人群特征构建动态图模型
- 在多个数据集上准确率超越现有方法
- 适合脑科学与临床辅助诊断研究者
基于功能连接的脑疾病诊断图方法通常依赖预设脑图谱，却忽视图谱中蕴含的丰富信息及站点和表型差异带来的干扰。为此，我们提出两阶段脑-人群图学习（B2P-GL）框架，整合脑区语义相似性与基于表型的人群图建模。第一阶段通过GPT-4获取脑图谱知识，利用自适应节点重分配图注意力网络增强图表示并优化脑图；第二阶段将表型数据融入人群图构建与特征融合，降低混杂效应，提升诊断性能。在ABIDE I、ADHD-200和Rest-meta-MDD数据集上的实验表明，B2P-GL在预测准确率上优于当前最优方法，同时增强可解释性。整体框架为脑疾病诊断提供了可靠且个性化的解决方案，推动临床应用进展。
原文摘要 · Abstract (English)
Recent developed graph-based methods for diagnosing brain disorders using functional connectivity highly rely on predefined brain atlases, but overlook the rich information embedded within atlases and the confounding effects of site and phenotype variability. To address these challenges, we propose a two-stage Brain-to-Population Graph Learning (B2P-GL) framework that integrates the semantic similarity of brain regions and condition-based population graph modeling. In the first stage, termed brain representation learning, we leverage brain atlas knowledge from GPT-4 to enrich the graph representation and refine the brain graph through an adaptive node reassignment graph attention network. In the second stage, termed population disorder diagnosis, phenotypic data is incorporated into population graph construction and feature fusion to mitigate confounding effects and enhance diagnosis performance. Experiments on the ABIDE I, ADHD-200, and Rest-meta-MDD datasets show that B2P-GL outperforms state-of-the-art methods in prediction accuracy while enhancing interpretability. Overall, our proposed framework offers a reliable and personalized approach to brain disorder diagnosis, advancing clinical applicability.
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