Original Research ARTICLE
Detecting large-scale brain networks using EEG: impact of electrode density, head modelling and source localization
- 1KU Leuven, Belgium
- 2ETH Zurich, Switzerland
- 3University of Oxford, United Kingdom
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.
Keywords: Electroencephalography, high-density montage, realistic head model, resting state network, functional connectivity, Neuronal communication, brain imaging
Received: 22 Oct 2017;
Accepted: 22 Jan 2018.
Edited by:Pedro A. Valdes-Sosa, Clinical Hospital of Chengdu Brain Science Institute, China
Reviewed by:Xu Lei, Southwest University, China
Stefania Della Penna, Università degli Studi G. d'Annunzio Chieti e Pescara, Italy
Copyright: © 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.
* Correspondence: Prof. Dante Mantini, MANTINI., KU Leuven, Leuven, Belgium, email@example.com