Brain-MGF: Multimodal Graph Fusion Network for EEG-fMRI Brain Connectivity Analysis Under Psilocybin
arXiv (Cornell University) November 23, 2025 Yap, Sin-Yee, Noman, Fuad, Loo, Junn Yong et al.
Psychedelics like psilocybin reorganize large-scale brain connectivity, but how these changes appear across EEG and fMRI networks has been unclear. A new multimodal graph fusion network, Brain-MGF, jointly analyzes EEG-fMRI connectivity by constructing graphs with partial-correlation edges and Pearson-profile node features, then learning subject-level embeddings via graph convolution. An adaptive softmax gate fuses modalities with sample-specific weights. Tested on the world's largest single-site psilocybin dataset, PsiConnect, the model distinguishes psilocybin from no-psilocybin conditions during meditation and rest. Fusion achieves 74.0% accuracy and 76.5% F1 score on meditation, and 76.0% accuracy with 85.8% ROC-AUC on rest, improving over unimodal and non-adaptive variants. UMAP visualizations show clearer class separation for fused embeddings, suggesting adaptive graph fusion effectively integrates complementary EEG-fMRI information for characterizing psilocybin-induced neural reorganization.