A hypergraph transformer method for brain disease diagnosis - Han, Xiangmin; Feng, Jingxi; Xu, Heming; Du, Shaoyi; Li, Junchang (Frontiers in Medicine, 2024)
Objective: To address the high-order correlation modeling and fusion challenges between functional and structural brain networks.This paper proposes a hypergraph transformer method for modeling high-order correlations between functional and structural brain networks. By utilizing hypergraphs, we can effectively capture the high-order correlations within brain networks. The Transformer model provides robust feature extraction and integration capabilities that are capable of handling complex multimodal brain imaging.The proposed method is evaluated on the ABIDE and ADNI datasets. It outperforms all the comparison methods, including traditional and graph-based methods, in diagnosing different types of brain diseases. The experimental results demonstrate its potential and application prospects in clinical practice.The proposed method provides new tools and insights for brain disease diagnosis, improving accuracy and aiding in understanding complex brain network relationships, thus laying a foundation for future brain science research.