Detecting large-scale networks in the human brain using high-density electroencephalography.
Author: Liu Q1,2,3, Farahibozorg S3,4, Porcaro C2,5,6, Wenderoth N1,2, Mantini D1,2,3
Affiliation:
1Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland.
2Laboratory of Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Belgium.
3Department of Experimental Psychology, Oxford University, United Kingdom.
4Cognition and Brain Sciences Unit, Medical Research Council, Cambridge, United Kingdom.
5LET'S-ISTC, National Research Council, Rome, Italy.
6Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
Conference/Journal: Hum Brain Mapp.
Date published: 2017 Jun 20
Other:
Special Notes: doi: 10.1002/hbm.23688. [Epub ahead of print] , Word Count: 269
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, 2017. © 2017 Wiley Periodicals, Inc.
© 2017 Wiley Periodicals, Inc.
KEYWORDS: electroencephalography; functional connectivity; high-density montage; neuronal communication; resting state network
PMID: 28631281 DOI: 10.1002/hbm.23688
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