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.

翻译：理解大脑结构与功能的交互是解释智能的关键，但联合建模两者极具挑战，因为结构连接组和功能连接组捕捉了组织结构的互补特征。我们提出多尺度自适应图网络（MAGNet），这是一种基于Transformer架构的图神经网络框架，能自适应学习结构-功能交互。MAGNet利用结构MRI的源基形态测量提取区域间形态特征，并将其与静息态fMRI的功能网络连接性相融合。混合图整合了直接与间接通路，局部-全局注意力机制优化连接重要性，联合损失函数同步实现跨模态一致性并端到端优化预测目标。在ABCD数据集上，MAGNet显著优于相关基线模型，展示了多模态融合在推进认知功能理解方面的有效性。