Spatial (mis)match between EEG and fMRI signal patterns revealed by spatio-spectral source-space EEG decomposition.

This study aimed to directly compare electroencephalography (EEG) whole-brain patterns of neural dynamics with concurrently measured fMRI BOLD data. To achieve this, we aim to derive EEG patterns based on a spatio-spectral decomposition of band-limited EEG power in the source-reconstructed space. In a large dataset of 72 subjects undergoing resting-state hdEEG-fMRI, we demonstrated that the proposed approach is reliable in terms of both the extracted patterns as well as their spatial BOLD signatures. The five most robust EEG spatio-spectral patterns not only include the well-known occipital alpha power dynamics, ensuring consistency with established findings, but also reveal additional patterns, uncovering new insights into brain activity. We report and interpret the most reproducible source-space EEG-fMRI patterns, along with the corresponding EEG electrode-space patterns, which are better known from the literature. The EEG spatio-spectral patterns show weak, yet statistically significant spatial similarity to their functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signatures, particularly in the patterns that exhibit stronger temporal synchronization with BOLD. However, we did not observe a statistically significant relationship between the EEG spatio-spectral patterns and the classical fMRI BOLD resting-state networks (as identified through independent component analysis), tested as the similarity between their temporal synchronization and spatial overlap. This provides evidence that both EEG (frequency-specific) power and the BOLD signal capture reproducible spatio-temporal patterns of neural dynamics. Instead of being mutually redundant, these only partially overlap, providing largely complementary information regarding the underlying low-frequency dynamics.