IEBGL：一种可解释性增强的脑图学习框架，结合大语言模型指导的拓扑与文献增强的语义
IEBGL:An Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and Literature-Augmented Semantics
对做医学影像图学习的人，这篇给出了用 LLM 推理重建脑连接拓扑、并用生物医学文献语义增强节点表示的范式，可作为把外部知识注入图结构、提升可解释性的可复用思路。
原文依据 LITR employs large language model (LLM) reasoning to refine brain connectivity and construct topological structure.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论静息态功能磁共振成像（rs-fMRI）为疾病诊断中的脑连接建模提供了丰富信息。然而，现有大多数脑图学习方法仅依赖影像数据，导致生物学可解释性有限，且难以有效整合外部医学知识。为应对这些挑战，我们提出一种可解释性增强的脑图学习（IEBGL）框架，将脑网络建模锚定于大规模医学知识之上。该框架引入两个互补模块：大语言模型指导的拓扑重建（LITR）与文献增强的语义聚合（LASA）。LITR 利用大语言模型（LLM）推理来精炼脑连接并构建拓扑结构。LASA 通过聚合生物医学文献中的语义信息来增强节点表示，从而保证模型的可解释性及其与临床疾病知识的相关性。最后，该框架采用图双向 Mamba 网络（GBMN）进行训练以完成疾病诊断。在 REST-meta-MDD 与 ABIDE 数据集上，结合 35,133 篇抑郁相关文献与 32,617 篇自闭症相关文献开展的大量实验表明，IEBGL 在分类性能上优于最优方法。进一步分析显示，LITR 模块揭示了具有生物学意义的脑连接改变，而 LASA 模块在这些脑区与疾病相关生物医学文献之间建立了可解释的关联。这些机制共同帮助 IEBGL 解释异常脑连接及其与疾病相关知识的联系。
英文原文摘要 1626 字符
Resting-state functional MRI (rs-fMRI) provides rich information for modeling brain connectivity in disease diagnosis. However, most existing brain graph learning methods rely solely on imaging data, leading to limited biological interpretability and poor integration of external medical knowledge. To address these challenges, we propose an Interpretability-Enhanced Brain Graph Learning (IEBGL) framework that anchors brain network modeling in large-scale medical knowledge. Our framework introduces two complementary modules: LLM-Instructed Topological Reconstruction (LITR) and Literature-Augmented Semantic Aggregation (LASA). LITR employs large language model (LLM) reasoning to refine brain connectivity and construct topological structure. LASA augments node representations by aggregating semantic information from biomedical literature, ensuring the model's interpretability and relevance to clinical disease knowledge. Finally, the framework is trained with the Graph Bi-directional Mamba Network (GBMN) for disease diagnosis. Extensive experiments on the REST-meta-MDD and ABIDE datasets, together with 35,133 depression-related and 32,617 autism-related publications, demonstrate that IEBGL outperforms state-of-the-art methods in classification performance. Further analyses show that the LITR module reveals biologically meaningful alterations in brain connectivity, while the LASA module establishes interpretable associations between these regions and disease-related biomedical literature. Together, these mechanisms help IEBGL explain abnormal brain connections and their links to disease-related knowledge.