ArticleScientific reports2018
A comparison between scalp- and source-reconstructed EEG networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers of stroke recovery using EEG-based resting-state functional connectivity: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- The effects of transcranial random noise stimulation on excitation/inhibition balance in ADHD.NeuroImage. Clinical · 2026Trial
- Predicting upper extremity motor improvement following therapy using EEG-based connectivity in chronic stroke.NeuroRehabilitation · 2022Trial
- Visual information modulates brain network characteristics during static balance following ACL reconstruction - A graph theoretical analysis.Scientific reports · 2026Article
- Physiological Measures of Reduced Listening Effort in Active Noise Canceling: Evidence From a Low-β Functional Network in an Event-Related Potential Study.Clinical and experimental otorhinolaryngology · 2026Article
- Perspective-taking in video advertising: insights from EEG connectivity and channel-wise spectral analysis.Frontiers in behavioral neuroscience · 2026Article
- Associations between attenuated auditory p300 event-related potential and cognitive basic symptoms in young people at clinical high risk for psychosis.Psychiatry and clinical neurosciences · 2025Article
- Functional Importance Backbones of the Brain at Rest, Wakefulness, and Sleep.Brain sciences · 2025Article
- Altered intrinsic thalamic network based on electroencephalography source-level analysis in poststroke epilepsy.Medicine · 2025Observational
- Transient cortical beta-frequency oscillations associated with contextual novelty in high density mouse EEG.Scientific reports · 2025Article
- Phase-synchronized brain connectivity during emotion regulation: attachment as a moderator.Social cognitive and affective neuroscience · 2025Article
- Brain Segregation and Integration Relate to Word-Finding Abilities in Older and Younger Adults.Neurobiology of language (Cambridge, Mass.) · 2025Article
- Neural mechanisms of adaptive behavior: Dissociating local cortical modulations and interregional communication patterns.iScience · 2024Article
- Functional Connectivity Alterations in Patients with Post-stroke Epilepsy Based on Source-level EEG and Graph Theory.Brain topography · 2024Article
- Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states.Scientific reports · 2024Article
- An interpretable model based on graph learning for diagnosis of Parkinson's disease with voice-related EEG.NPJ digital medicine · 2024Article
- Dynamic multilayer networks reveal mind wandering.Frontiers in neuroscience · 2024Article
- Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses.Network neuroscience (Cambridge, Mass.) · 2024Article
- Enhancing precision in human neuroscience.eLife · 2023Review
- Dynamic segregation and integration of brain functional networks associated with emotional arousal.iScience · 2023Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
EEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use different assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to find an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in different experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the effects of volume conduction/signal leakage. These findings suggest that these effects alter the estimated EEG network organization, limiting the interpretation of results of scalp analysis. Finally, this study also suggests that the use of metrics that address the problem of zero lag correlations may give more reliable estimates of the underlying network topology.
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