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Published March 2, 2018 | Published + Supplemental Material
Journal Article Open

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization


Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.

Additional Information

© 2018 Liu, Ganzetti, Wenderoth and Mantini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 22 October 2017; Accepted: 22 January 2018; Published: 02 March 2018. Ethics Statement: EEG and fMRI data collection was approved by the Ethics Commission of ETH Zürich and Chieti University, respectively. All participants signed a written informed consent. Author Contributions: DM and NW designed the research. QL and MG produced the results. QL and DM wrote the manuscript, which was read and approved by the other co-authors. This work was supported by the Swiss National Science Foundation (Grant 320030_146531), the KU Leuven Special Research Fund (Grant C16/15/070), the Research Foundation Flanders (FWO) (Grants G0F76.16N, G0936.16N and EOS.30446199), the Chinese Scholarship Council (scholarship 201306180008 to QL), and the Marie Skłodowska-Curie program of the FWO and the European Commission (fellowship 665501 to MG). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgments: The authors are grateful to the reviewers of the manuscript for their comments and insightful suggestions. They would also like to thank BioRxiv for hosting a preliminary version of the manuscript (https://www.biorxiv.org/content/early/2016/09/23/077107).

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Supplemental Material - 4020616.zip

Published - fninf-12-00004.pdf


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August 19, 2023
August 19, 2023