ArticleImaging neuroscience (Cambridge, Mass.)2025
Consistency of resting-state correlations between fMRI networks and EEG band power.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- How alexithymia shapes functional networks: Insights from a general population study.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- A multi-session simultaneous EEG-fMRI dataset with repeated experience sampling.bioRxiv : the preprint server for biology · 2026Article
- From surface to depth: Using deep learning to predict striatal fMRI reward signaling from EEG.Imaging neuroscience (Cambridge, Mass.)Article
- The link between steady-state EEG and rs-fMRI metrics in healthy young adults: The effect of macrovascular correction.Imaging neuroscience (Cambridge, Mass.)Article
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8 authors.
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Abstract
Several simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) studies have aimed to identify the relationship between EEG band power and fMRI resting-state networks (RSNs) to elucidate their neurobiological significance. Although common patterns have emerged, inconsistent results have also been reported. This study aims to explore the consistency of these correlations across subjects and to understand how factors such as the hemodynamic response delay and the use of different EEG data spaces (source/scalp) influence them. Using three distinct EEG-fMRI datasets, acquired independently on 1.5T, 3T, and 7T MRI scanners (comprising 42 subjects in total), we evaluate the generalizability of our findings across different acquisition conditions. We found consistent correlations between fMRI RSN and EEG band power time series across subjects in the three datasets studied, with systematic variations with RSN, EEG frequency band, and hemodynamic response function (HRF) delay, but not with EEG space. Several of these correlations were consistent across the three datasets, despite important differences in field strength and resting-state conditions. These included spatially widespread patterns observed across HRF delays from 2 to 10 s, such as positive delta correlations with the visual and somatomotor networks, negative delta correlations with the default mode network, positive theta correlations with the somatomotor network, negative alpha correlations with both the visual and dorsal attention networks, positive alpha correlations with the default mode network, and negative beta correlations with the somatomotor network. Our findings support consistent correlations across specific fMRI RSNs and EEG bands and highlight the importance of methodological considerations in interpreting them that may explain conflicting reports in the existing literature.
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