Transformer-Based Hierarchical Clustering for Brain Network Analysis
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.
Cite
@article{arxiv.2305.04142,
title = {Transformer-Based Hierarchical Clustering for Brain Network Analysis},
author = {Wei Dai and Hejie Cui and Xuan Kan and Ying Guo and Sanne van Rooij and Carl Yang},
journal= {arXiv preprint arXiv:2305.04142},
year = {2023}
}
Comments
Accepted to IEEE-ISBI 2023