Modeling Functional Brain Networks with Multi-Head Attention-based Region-Enhancement for ADHD Classification

Increasing attention has been paid to attention-deficit hyperactivity disorder (ADHD)-assisted diagnosis using functional brain networks (FBNs) since FBNs-based ADHD diagnosis can not only extract the functional connectivities from FBNs as potential biomarkers for brain disease classification, but also identify the focal regions of disease. Therefore, modeling FBNs has become a key topic for ADHD diagnosis via resting state functional magnetic resonance imaging (rfMRI). However, the dominant models either ignore the strong regional correlation between adjacent time series or fail to capture the long-distance dependency (LDD) in imaging series. To address the issues, we propose a multi-head attention-based region-enhancement model (MAREM) for ADHD classification. Firstly, a multi-head attention mechanism with region-enhancement is designed to represent the FBNs, where region-enhancement module are designed to process strong regional correlation between adjacent time series. Secondly, multi-head attention is used to map the region information of each time point into different subspaces for establishing global dependencies in imaging series. Thirdly, the proposed model is applied to the ADHD-200 dataset for classification. The results show the proposed model's out-performance of the state-of-the-art in both classification accuracy and generalization ability. Furthermore, we identify several brain networks that have been considered to be associated with ADHD in clinical studies.