用自聚类注意力机制提升脑功能网络建模，更好捕捉子网络结构。
Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity
- 提出自聚类图变压器，按子网络分组更新节点，避免全图统一更新
- 在7957人数据集上，预测认知总分和性别分类均优于传统方法
- 适合研究脑功能连接与认知、性别差异的神经科学研究者
静息态功能磁共振成像（rs-fMRI）能无任务地揭示大脑功能组织，是研究脑功能与认知关系的重要工具。本文提出一种新型图注意力机制——自聚类图变压器（SCGT），用于解决图变压器中节点更新均一化的问题。通过将静态功能连接（FC）相关性特征作为输入，SCGT可对大脑子网络结构进行聚类特异性节点更新，从而有效捕捉脑区间的子网络模式，并支持子簇的可解释学习。我们在包含7,957名参与者的青少年大脑认知发育（ABCD）数据集上验证该方法，用于预测总体认知得分与性别分类。结果表明，SCGT在两项任务中均优于原始图变压器及其他最新模型，展现出建模脑功能连接与解析潜在子网络结构的潜力。
原文摘要 · Abstract (English)
Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes, as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self-Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes, unlike uniform node updates in vanilla graph transformers, further allowing us to learn and interpret the subclusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures.
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