ArticleBrain topography2013
Electrophysiological correlation patterns of resting state networks in single subjects: a combined EEG-fMRI study.
Article in Brain topography, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A geometry aware framework enhances noninvasive mapping of whole human brain dynamics.Nature biomedical engineering · 2026Article
- Establishing the link between post-concussive symptoms and brain network dysfunction: A systematic scoping review of neuroimaging evidence.NeuroImage. Clinical · 2026Article
- Consistency of resting-state correlations between fMRI networks and EEG band power.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Investigating mu and alpha oscillations as indicators of intra-individual success and inter-individual ability in motor imagery performance.Frontiers in psychology · 2025Article
- Channel selection from source localization: A review of four EEG-based brain-computer interfaces paradigms.Behavior research methods · 2023Review
- Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.Frontiers in neuroscience · 2023Article
- Right-lateralized intrinsic brain dynamics predict monitoring abilities.Cognitive, affective & behavioral neuroscience · 2020Article
- Inter- and Intra-individual Variability in Brain Oscillations During Sports Motor Imagery.Frontiers in human neuroscience · 2020Article
- Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review.Frontiers in computational neuroscience · 2019Review
- Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes.Neural computation · 2018Article
- fMRI BOLD Correlates of EEG Independent Components: Spatial Correspondence With the Default Mode Network.Frontiers in human neuroscience · 2018Article
- Infraslow Electroencephalographic and Dynamic Resting State Network Activity.Brain connectivity · 2017Article
- Network dynamics predict improvement in working memory performance following donepezil administration in healthy young adults.NeuroImage · 2014Article
- EEG correlates of self-referential processing.Frontiers in human neuroscience · 2013Article
- Article
- The Quest for EEG Power Band Correlation with ICA Derived fMRI Resting State Networks.Frontiers in human neuroscience · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With combined EEG-fMRI a powerful combination of methods was developed in the last decade that seems promising for answering fundamental neuroscientific questions by measuring functional processes of the human brain simultaneously with two complementary modalities. Recently, resting state networks (RSNs), representing brain regions of coherent BOLD fluctuations, raised major interest in the neuroscience community. Since RSNs are reliably found across subjects and reflect task related networks, changes in their characteristics might give insight to neuronal changes or damage, promising a broad range of scientific and clinical applications. The question of how RSNs are linked to electrophysiological signal characteristics becomes relevant in this context. In this combined EEG-fMRI study we investigated the relationship of RSNs and their correlated electrophysiological signals [electrophysiological correlation patterns (ECPs)] using a long (34 min) resting state scan per subject. This allowed us to study ECPs on group as well as on single subject level, and to examine the temporal stability of ECPs within each subject. We found that the correlation patterns obtained on group level show a large inter-subject variability. During the long scan the ECPs within a subject show temporal fluctuations, which we interpret as a result of the complex temporal dynamic of the RSNs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.