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 δ4 band and low β1 band) demonstrated significant negative linear relationship (pFWE < 0.05) to the frequent stimulus and three patterns (two low δ2 and δ3 bands, and narrow θ1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ4 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 δ4 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 β1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ1 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 θ1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ4, β1, and θ1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.

• Publication type
• Journal Article MeSH

BACKGROUND: Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. NEW METHOD: We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms. RESULTS: The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. COMPARISON WITH EXISTING METHOD(S): Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. CONCLUSIONS: This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.

• MeSH
• Adult MeSH
• Electroencephalography methods   MeSH
• Functional Neuroimaging methods   MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Young Adult MeSH
• Nerve Net diagnostic imaging   physiology   MeSH
• Neurovascular Coupling physiology   MeSH
• Psycholinguistics MeSH
• Cerebrum diagnostic imaging   physiology   MeSH
• Visual Perception physiology   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

• MeSH
• Algorithms MeSH
• Principal Component Analysis MeSH
• Electroencephalography methods   statistics & numerical data   MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain Mapping methods   MeSH
• Young Adult MeSH
• Signal Processing, Computer-Assisted MeSH
• Psychomotor Performance physiology   MeSH
• Reproducibility of Results MeSH
• Decision Making physiology   MeSH
• Cluster Analysis MeSH
• Visual Perception physiology   MeSH
• Check Tag
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH