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Graph Neural Networks (GNNs) based on multimodal Magnetic Resonance Imaging (MRI) have garnered significant attention in medical imaging due to their ability to effectively characterize the non-Euclidean structure of brain networks. However， most existing studies overlook significant individual heterogeneity in patient symptoms and merely introduce demographic data via simple concatenation， thereby limiting classification performance. Furthermore， the heterogeneity of multi-site data often results in suboptimal model performance during cross-site external validation. To address these challenges， this study proposes a demographic-regulated multimodal graph attention network framework (DM-GAT). First， the model incorporates a demographic-aware dynamic graph structure learning (D-GSL) module， which utilizes demographic information to reconstruct the latent topology of brain networks， thereby mitigating noise and individual variability issues inherent in static functional connectivity. Second， a multi-view demographic interaction attention (M-DIA) mechanism is introduced. This mechanism employs demographic features as gating signals to dynamically regulate the attention weights of nodes and edges， enhancing the model's capability for individualized representation. Experimental results on the ABIDE and ADHD-200 cross-site datasets demonstrate that the proposed model achieves a classification accuracy of 74.51% on the ABIDE dataset (17 sites， 943 subjects)， outperforming the state-of-the-art MHNet model (73.27%). Ablation studies further validate the effectiveness of the D-GSL module (improving accuracy by approximately 4%) and the M-DIA module.
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Basic Information:
China Classification Code:TP183;R749.94
Citation Information:
[1]ZHANG Xiongtao,MA Yuan,WANG Feng ,et al.A population-regulated attention network for assisted diagnosis of autism spectrum disorder[J].Journal of Nantong University (Natural Science Edition)().
2026-06-26
2026-06-26
2026-06-26