Transformer-Based Hierarchical Clustering for Brain Network Analysis

• URL: http://arxiv.org/abs/2305.04142v1
• Date: Sat, 6 May 2023 22:14:13 GMT
• Title: Transformer-Based Hierarchical Clustering for Brain Network Analysis
• Authors: Wei Dai, Hejie Cui, Xuan Kan, Ying Guo, Sanne van Rooij, Carl Yang
• Abstract summary: We propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification. 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.
• Score: 13.239896897835191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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