Nejvíce citovaný článek - PubMed ID 28875402
Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA
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.
- Klíčová slova
- EEG-fMRI integration, EEG-informed fMRI, electrical source imaging, independent component analysis, resting-state networks, spatio-spectral decomposition,
- Publikační typ
- časopisecké články MeSH
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ 4 band and low β 1 band) demonstrated significant negative linear relationship (p FWE < 0.05) to the frequent stimulus and three patterns (two low δ 2 and δ 3 bands, and narrow θ 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ 4, β 1, and θ 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.
