用大模型提示词增强脑图神经网络，提升阿尔茨海默病早期识别能力
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
- 融合脑区、个体和疾病三层次提示词，引入非影像知识增强模型
- 在两个fMRI数据集上优于现有方法，准确率显著提升
- 结果可解释性强，适合临床辅助诊断与神经科学研究
神经系统疾病（如阿尔茨海默病）的早期诊断极具挑战性，尤其当症状与健康对照组极为相似时。现有脑网络分析方法多依赖仅基于影像数据的图神经网络，忽视了非影像因素，限制了预测性能与可解释性。本文提出BrainPrompt框架，通过将大语言模型（LLM）与知识驱动提示词结合，增强图神经网络对复杂非影像信息及外部知识的捕捉能力。该框架包含三类提示词：脑区（ROI）级提示词编码各脑区身份与功能；个体级提示词整合人口学信息；疾病级提示词反映疾病进展时间特征。借助多层级提示词，BrainPrompt有效融合来自LLM的知识增强多模态信息，提升神经系统疾病阶段预测能力，并提供更具解释性的结果。在两个来自神经疾病患者的静息态功能磁共振成像（fMRI）数据集上评估，其表现优于当前最优方法。生物标志物研究进一步验证了框架提取的信息与神经科学领域知识高度一致。代码已开源。
原文摘要 · Abstract (English)
Neurological conditions, such as Alzheimer's Disease, are challenging to diagnose, particularly in the early stages where symptoms closely resemble healthy controls. Existing brain network analysis methods primarily focus on graph-based models that rely solely on imaging data, which may overlook important non-imaging factors and limit the model's predictive power and interpretability. In this paper, we present BrainPrompt, an innovative framework that enhances Graph Neural Networks (GNNs) by integrating Large Language Models (LLMs) with knowledge-driven prompts, enabling more effective capture of complex, non-imaging information and external knowledge for neurological disease identification. BrainPrompt integrates three types of knowledge-driven prompts: (1) ROI-level prompts to encode the identity and function of each brain region, (2) subject-level prompts that incorporate demographic information, and (3) disease-level prompts to capture the temporal progression of disease. By leveraging these multi-level prompts, BrainPrompt effectively harnesses knowledge-enhanced multi-modal information from LLMs, enhancing the model's capability to predict neurological disease stages and meanwhile offers more interpretable results. We evaluate BrainPrompt on two resting-state functional Magnetic Resonance Imaging (fMRI) datasets from neurological disorders, showing its superiority over state-of-the-art methods. Additionally, a biomarker study demonstrates the framework's ability to extract valuable and interpretable information aligned with domain knowledge in neuroscience. The code is available at https://github.com/AngusMonroe/BrainPrompt
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