ArticleHuman brain mapping2017
Detecting large-scale networks in the human brain using high-density electroencephalography.
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled it.
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Who cites it
81 citing papers in PubMed, 1 synthesis or guideline pooled it, 179 citations in OpenAlex.
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- Resting-state EEG alpha-BOLD coupling spatially follows cortical cell-type and receptor gradients.bioRxiv : the preprint server for biology · 2026Article
- Modulatory effects of ketamine on EEG source-based resting state connectivity in treatment resistant depression.Translational psychiatry · 2026Article
- Human brain imaging with high-density electroencephalography: Techniques and applications.The Journal of physiology · 2026Review
- Prioritizing flexible working memory representations relies on the involvement of the parahippocampal-insula network during memory reactivation.Communications biology · 2025Article
- Differences in High-Frequency Connectivity Among Large-Scale Functional Networks Linked to Major Depressive Disorder and Treatment-Resistant Depression.Biological psychiatry global open science · 2025Article
- Exploring electroencephalographic chronic pain biomarkers: a mega-analysis.EBioMedicine · 2025Article
- Functional brain mapping using whole-head very high-density diffuse optical tomography.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Heart rate and EEG gamma band connectivity in the ventral attention network during emotional movie stimulation in women with high emotion dysregulation.Frontiers in neuroscience · 2025Article
- Spatial (mis)match between EEG and fMRI signal patterns revealed by spatio-spectral source-space EEG decomposition.Frontiers in neuroscience · 2025Article
- Relationship between regional relative theta power and amyloid deposition in mild cognitive impairment: an exploratory study.Frontiers in neuroscience · 2025Article
- Comparison between EEG and MEG of static and dynamic resting-state networks.Human brain mapping · 2024Article
- Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain.bioRxiv : the preprint server for biology · 2024Article
- Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Motor imagery ability scores are related to cortical activation during gait imagery.Scientific reports · 2024Article
- Data-driven MEG analysis to extract fMRI resting-state networks.Human brain mapping · 2024Article
- Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences.Behavioral and brain functions : BBF · 2023Article
- Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.Human brain mapping · 2023Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.
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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.