Abstract
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.
Highlights
Ives et al and Huang-Hellinger et al optimized initial simultaneous EEG-fMRI data acquisition [1, 2] and Allen et al and Goldman et al implemented first algorithms suppressing gradient MR artifacts induced in the simultaneous EEG recordings [3, 4]
Our results on visual oddball task data represent the systematic objective comparison of spectral (i.e., absolute spectral model (ASM), relative spectral model (RSM)) and spatiospectral (i.e., absolute spatiospectral model (ASSM), relative spatiospectral model (RSSM)) EEG-fMRI data fusion methods with the variable hemodynamic response function (HRF) permitting the variable delay between the immediate EEG following the BOLD signal changes
The automatically quantified effect of the variable HRF in the EEG-fMRI data fusion was remarkable. Both ASM and RSM results gained from the same dataset with fixed canonical HRF were far from reaching pFWE < 0.05 in EEG-fMRI statistical parametric maps [18, 43]
Summary
Ives et al and Huang-Hellinger et al optimized initial simultaneous EEG-fMRI data acquisition [1, 2] and Allen et al and Goldman et al implemented first algorithms suppressing gradient MR artifacts induced in the simultaneous EEG recordings [3, 4]. Over various existing EEG-fMRI data fusion techniques, the ability to blindly and automatically visualize and quantify robust task-related functional networks and their EEG-fMRI associations (e.g., via variable HRF) is lacking. We have focused on the simple GLM fusion approach with variable HRF aggregating automatically induced EEG spectra [19, 20] and tested whether we can identify fusion settings that blindly visualizes task-related networks. This automated method may offer high reproducibility with tremendous potential in the clinical research or even clinical practice applications to quantitatively measure cognitive dysfunction.
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