Group Joint Independent Component Analysis (Group jICA): a Novel Method to Jointly Decompose and Link Simultaneous EEG and fMRI

The integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provides a powerful approach to explore the brain’s spatial and temporal resolution. This study proposes a multimodal data fusion approach to link simultaneous EEG-fMRI data from multiple subjects using a two-step principal component analysis (PCA) followed by joint independent component analysis (jICA). The proposed method first transforms the two modalities into a unified feature space, preserving the concurrently collected temporal and spatial information, and then applies jICA, followed by back-reconstruction to estimate subject-specific joint source maps and timecourses. The analysis maps group data, enabling efficient group-level inferences on cross-modality covariation. When applied to concurrent resting EEG-fMRI datasets of 55 healthy subjects, we identified a total of 45 resting-state components, capturing prominent multimodal representations of default mode, and secondary somatomotor networks among others. The default mode network was active in both alpha and theta frequency bands, while somatomotor network was active in alpha. We observed that lower frequencies exhibit higher power compared to higher frequencies in their spectra. Further statistical group (old vs young) analysis shows that older adults show stronger functional connectivity in higher cognition, sensorimotor, and triple network regions, which may reflect age-related shifts in sensory integration.