Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data

The integration of EEG and fMRI offers a powerful method for exploring the brain's spatial and temporal dynamics. However, existing approaches typically summarize both EEG and fMRI, often removing temporal information before combining the modalities. Our novel approach brings together group ICA of fMRI, group ICA of EEG, and joint ICA to propose a multimodal data fusion approach, named group joint ICA (gjICA), that links simultaneous EEG-fMRI data from multiple subjects. The proposed framework enables group-level mapping and provides single-subject estimates via back-reconstruction, facilitating a comprehensive picture of brain networks. The gjICA also introduces joint functional network connectivity (jFNC), which provides connectivity between fMRI networks as well as between EEG components, hence linking temporal and spatial information. When applied to concurrent resting EEG-fMRI data from 121 participants, we identified 63 multimodal brain components. The statistical analyses of these components further revealed that depression is characterized by widespread multimodal connectivity, with visual and higher cognition networks acting as hubs in these alterations. Further, joint histogram analysis revealed that depression is broadly associated with reduced EEG component expression with increased fMRI component expression in cerebellar, visual, subcortical, and sensorimotor networks, suggesting altered neurovascular coupling and modality-specific dysfunctions. Overall, the proposed gjICA approach provides a robust framework for fusing EEG and fMRI data while preserving the spatiotemporal information in both modalities, enabling the identification of a more comprehensive picture of multimodal neural relationships, enhancing our understanding of brain dynamics and providing new insights into complex brain disorders such as depression.