Transformer-Based Hierarchical Clustering for Brain Network Analysis
Wei Dai Hejie Cui Xuan Kan Ying Guo Sanne van Rooij Carl Yang
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.