让脑图神经网络更贴近真实大脑结构，提升疾病检测准确率
Biologically Plausible Brain Graph Transformer
- 用节点重要性编码捕捉大脑全局通信中的结构关键点
- 引入功能模块感知注意力，保持大脑的分工与协同特性
- 在3个数据集上超越现有模型，适合脑疾病分析研究
当前最先进的脑图分析方法未能充分编码脑图的小世界架构（包含枢纽节点和功能模块），因而生物合理性不足，限制了其对脑结构与功能特性的准确表征，进而影响机器学习模型在脑疾病检测等任务中的表现。本文提出一种新型生物合理脑图变压器（BioBGT），显式编码脑图内在的小世界架构。具体而言，我们设计了一种基于网络纠缠的节点重要性编码技术，捕捉节点在脑图信息传播中的全局结构重要性，突出大脑结构的生物学特征；同时引入功能模块感知自注意力机制，保留脑图的功能分离与整合特性。在三个基准数据集上的实验结果表明，BioBGT优于现有先进模型，显著提升了各类脑图分析任务中的生物合理性表征能力。
原文摘要 · Abstract (English)
State-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and therefore lack biological plausibility to some extent. This limitation hinders their ability to accurately represent the brain's structural and functional properties, thereby restricting the effectiveness of machine learning models in tasks such as brain disorder detection. In this work, we propose a novel Biologically Plausible Brain Graph Transformer (BioBGT) that encodes the small-world architecture inherent in brain graphs. Specifically, we present a network entanglement-based node importance encoding technique that captures the structural importance of nodes in global information propagation during brain graph communication, highlighting the biological properties of the brain structure. Furthermore, we introduce a functional module-aware self-attention to preserve the functional segregation and integration characteristics of brain graphs in the learned representations. Experimental results on three benchmark datasets demonstrate that BioBGT outperforms state-of-the-art models, enhancing biologically plausible brain graph representations for various brain graph analytical tasks
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