用扩散模型提升脑图谱预训练，更好保留连接模式。
Diffusion-Guided Pretraining for Brain Graph Foundation Models
- 用扩散过程指导图结构的掩蔽与丢弃，保持脑连接语义。
- 通过扩散实现全局结构感知的读出与节点重建，提升表征能力。
- 在超2.5万受试者数据上验证，适用于多种精神疾病研究。
随着脑信号基础模型兴起，基于图的预训练成为从连接组数据学习可迁移表征的有前景范式。然而，现有对比学习和掩码自编码方法通常采用随机丢弃或掩蔽进行增强，这不适用于脑图谱与超图，因会破坏有意义的连接模式。此外，常用的图级读出与重建方案难以捕捉全局结构信息，限制了表征的鲁棒性。本文提出统一的扩散基预训练框架，解决上述问题：首先，扩散过程引导结构感知的丢弃与掩蔽策略，在保持脑图语义的同时维持有效的预训练多样性；其次，扩散支持拓扑感知的图级读出与节点级全局重建，使图嵌入与被掩蔽节点能聚合来自全局相关区域的信息。在包含超过25,000名受试者、60,000次扫描、多种精神障碍及脑图谱的多神经影像数据集上，实验表明该方法具有一致性能提升。
原文摘要 · Abstract (English)
With the growing interest in foundation models for brain signals, graph-based pretraining has emerged as a promising paradigm for learning transferable representations from connectome data. However, existing contrastive and masked autoencoder methods typically rely on naive random dropping or masking for augmentation, which is ill-suited for brain graphs and hypergraphs as it disrupts semantically meaningful connectivity patterns. Moreover, commonly used graph-level readout and reconstruction schemes fail to capture global structural information, limiting the robustness of learned representations. In this work, we propose a unified diffusion-based pretraining framework that addresses both limitations. First, diffusion is designed to guide structure-aware dropping and masking strategies, preserving brain graph semantics while maintaining effective pretraining diversity. Second, diffusion enables topology-aware graph-level readout and node-level global reconstruction by allowing graph embeddings and masked nodes to aggregate information from globally related regions. Extensive experiments across multiple neuroimaging datasets with over 25,000 subjects and 60,000 scans involving various mental disorders and brain atlases demonstrate consistent performance improvements.
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