用多尺度图注意力模型融合脑结构与功能，提升认知理解能力
Learning Structural-Functional Brain Representations through Multi-Scale Adaptive Graph Attention for Cognitive Insight
- 通过多尺度自适应图网络融合结构与功能脑连接信息
- 在ABCD数据集上显著优于现有基线方法
- 适合脑科学与神经计算交叉研究者参考
理解脑结构与功能的相互作用是解释智能的关键，但联合建模二者具有挑战性，因结构和功能连接组分别捕捉组织的不同方面。本文提出多尺度自适应图网络（MAGNet），一种类Transformer的图神经网络框架，可自适应学习结构-功能交互。MAGNet利用结构MRI中的源基形态测量提取区域间形态特征，并与静息态fMRI的功能网络连通性融合。一个混合图整合直接与间接路径，局部-全局注意力机制优化连通性重要性，联合损失函数端到端同时保证跨模态一致性与预测目标优化。在ABCD数据集上，MAGNet显著优于相关基线方法，验证了其在促进认知功能理解方面的有效多模态融合能力。
原文摘要 · Abstract (English)
Understanding how brain structure and function interact is key to explaining intelligence yet modeling them jointly is challenging as the structural and functional connectome capture complementary aspects of organization. We introduced Multi-scale Adaptive Graph Network (MAGNet), a Transformer-style graph neural network framework that adaptively learns structure-function interactions. MAGNet leverages source-based morphometry from structural MRI to extract inter-regional morphological features and fuses them with functional network connectivity from resting-state fMRI. A hybrid graph integrates direct and indirect pathways, while local-global attention refines connectivity importance and a joint loss simultaneously enforces cross-modal coherence and optimizes the prediction objective end-to-end. On the ABCD dataset, MAGNet outperformed relevant baselines, demonstrating effective multimodal integration for advancing our understanding of cognitive function.
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