Brain-MGF: Multimodal Graph Fusion Network for EEG-fMRI Brain Connectivity Analysis Under Psilocybin

TL;DR

This paper introduces Brain-MGF, a novel multimodal graph fusion network that effectively combines EEG and fMRI data to analyze brain connectivity changes induced by psilocybin, achieving high accuracy in distinguishing conditions.

Contribution

Brain-MGF is the first adaptive graph fusion method for joint EEG-fMRI analysis under psilocybin, demonstrating improved classification and interpretability over unimodal approaches.

Findings

Fusion improves classification accuracy over unimodal methods.

Adaptive fusion captures context-dependent modality contributions.

Clearer class separation in fused embeddings visualizations.

Abstract

Psychedelics, such as psilocybin, reorganise large-scale brain connectivity, yet how these changes are reflected across electrophysiological (electroencephalogram, EEG) and haemodynamic (functional magnetic resonance imaging, fMRI) networks remains unclear. We present Brain-MGF, a multimodal graph fusion network for joint EEG-fMRI connectivity analysis. For each modality, we construct graphs with partial-correlation edges and Pearson-profile node features, and learn subject-level embeddings via graph convolution. An adaptive softmax gate then fuses modalities with sample-specific weights to capture context-dependent contributions. Using the world's largest single-site psilocybin dataset, PsiConnect, Brain-MGF distinguishes psilocybin from no-psilocybin conditions in meditation and rest. Fusion improves over unimodal and non-adaptive variants, achieving 74.0% accuracy and 76.5% F1 score on meditation, and 76.0% accuracy with 85.8% ROC-AUC on rest. UMAP visualisations reveal clearer class separation for fused embeddings. These results indicate that adaptive graph fusion effectively integrates complementary EEG-fMRI information, providing an interpretable framework for characterising psilocybin-induced alterations in large-scale neural organisation.