Brain-MGF: Multimodal Graph Fusion Network for EEG-fMRI Brain Connectivity Analysis Under Psilocybin
arXiv (Cornell University) – November 23, 2025
Source: OpenAlex
Summary
Psilocybin profoundly reorganizes brain connectivity, a compelling finding in Psychedelics and Drug Studies. An Artificial intelligence framework, rooted in Computer science, employs an artificial neural network for graph fusion of functional magnetic resonance imaging data. This machine learning model, constructed to recognize patterns, achieved 74.0% accuracy distinguishing psilocybin's effects during meditation and 76.0% during rest. By adaptively encoding complex brain patterns at each brain node using a softmax mechanism, it offers interpretability into neural changes. Such insights could aid Forensic Toxicology and Drug Analysis, even illuminating profound subjective states.
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.