Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain system, differences in neuronal connection strengths parcellate the brain into various functional modules (network communities), which are critical for brain analysis. However, identifying such communities within the brain has been a nontrivial issue due to the complexity of neuronal interactions. In this work, we propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification. Extensive experimental results on real-world brain network datasets show that with the help of hierarchical clustering, the model achieves increased accuracy and reduced runtime complexity while providing plausible insight into the functional organization of brain regions. The implementation is available at https://github.com/DDVD233/THC.

翻译：脑网络（如基于MRI构建的图模型）已广泛应用于脑功能的病理预测与分析。在复杂脑系统中，神经元连接强度的差异将大脑划分为不同功能模块（网络社群），这对脑分析至关重要。然而，由于神经元相互作用的复杂性，识别脑内此类社群一直是棘手问题。本文提出一种新型可解释的Transformer模型，用于联合层次聚类识别与脑网络分类。在真实脑网络数据集上的广泛实验表明，借助层次聚类，该模型在提升准确率的同时降低运行时间复杂度，并为脑区功能组织提供可靠的洞见。实现代码已开源至https://github.com/DDVD233/THC。