Year: 2025
Type: article
Abstract: ABSTRACT Functional brain networks have been used to diagnose brain disorders such as autism spectrum disorder (ASD) and attention‐deficit/hyperactivity disorder (ADHD). However, existing methods not only fail to fully consider various levels of interaction information between brain regions, but also limit the transmission of information among unconnected regions, resulting in the node information loss and bias. To address these issues, we propose a causality‐guided multi‐view diffusion (CG‐MVD) network, which can more comprehensively capture node information that is difficult to observe when aggregating direct neighbours alone. Specifically, our approach designs multi‐view brain graphs and multi‐hop causality graphs to represent multi‐level node interactions and guide the diffusion of interaction information. Building on this, a multi‐view diffusion graph attention module is put forward to learn node multi‐dimensional embedding features by broadening the interaction range and extending the receptive field. Additionally, we propose a bilinear adaptive fusion module to generate and fuse connectivity‐based features, addressing the challenge of high‐dimensional node‐level features and integrating richer feature information to enhance classification. Experimental results on the ADHD‐200 and ABIDE‐I datasets demonstrate the effectiveness of the CG‐MVD network, achieving average accuracies of 79.47% and 80.90%, respectively, and surpassing state‐of‐the‐art methods.
DOI: 10.1049/cit2.70046
Authors: Xubin Wu, Yan Niu, Xia Li, Jie Xiang, Yidi Li
Institutions: Taiyuan University of Technology, ETH Zurich
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,760
Awards: G481052531, G6691204964
OpenAlex ID: W4413315342