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Electroencephalographic Resting-State Networks: Source Localization of Microstates

1Functional Brain Mapping Lab, University of Geneva, Geneva, Switzerland.
2Center for Biomedical Imaging (CIBM) , Geneva, Switzerland.
3CSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.
Publication Date:
Brain Connect
Volume Number:
Issue Number:
Brain Connect. 2017 Dec;7(10):671-82.
PubMed ID:
EEG resting-state source localization, EEG source imaging, resting-state networks
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Custo A., Van De Ville D., Wells III W.M., Tomescu M.I., Brunet D., Michel C.M. Electroencephalographic Resting-State Networks: Source Localization of Microstates. Brain Connect. 2017 Dec;7(10):671-82. PMID: 28938855. PMCID: PMC5736178.
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Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (Nā€‰=ā€‰164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a "router," crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.