Detecting large-scale networks in the human brain using high-density electroencephalography

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.

Directed functional connections underlying spontaneous brain activity

“…Power in the delta, theta, alpha, beta and gamma frequency bands measured with M/EEG has been shown to be correlated with BOLD signals or with the RSNs depicted by fMRI (Brookes, Hale, et al, ; de Pasquale et al, ; Laufs et al, ; Mantini et al, ). In addition, the networks derived from MEG (Brookes, Woolrich, et al, ; Maldjian et al, ), low‐density EEG data (19 electrodes) (Chen et al, ), or high‐density EEG (Britz et al, ; Liu et al, ) were similar to RSNs derived from fMRI.…”

“…EEG and magnetoencephalography (MEG) could be key to gain important additional insights into whole brain resting‐state directed functional connectivity, because they provide a more direct measure of neuronal activity than fMRI, and have a much higher temporal resolution (Lopes da Silva, ). RSNs have individual complex electrophysiological signatures (Brookes, Hale, et al, ; de Pasquale et al, ; Laufs et al, ; Mantini, Perrucci, Del Gratta, Romani, & Corbetta, ) and networks obtained from MEG and EEG recordings were shown to be similar to the fMRI RSNs (Britz, Van De Ville, & Michel, ; Brookes, Woolrich, et al, ; Chen, Ros, & Gruzelier, ; Liu, Farahibozorg, Porcaro, Wenderoth, & Mantini, ; Maldjian, Davenport, & Whitlow, ). These studies, however, looked at spatial correlations with EEG and not at the temporal properties of these networks.…”

Perspectives and Challenges in Robotic Neurorehabilitation

“…Thus, combining these two scales might increase the chance of understanding the recovery process. Achieving both these resolutions is nowadays possible thanks to the combination of high density electroencephalography (hdEEG) recordings and source imaging techniques that recently allowed reliable reconstruction of the brain's resting-state networks [199] and even measurement of electrophysiological sub-cortical activity [200]. Furthermore, hdEEG (or EEG) is attractive because it is inexpensive and portable compared to other non-invasive neuroimaging techniques, such as magnetoencephalography (MEG) or functional and structural magnetic resonance imaging (MRI).…”

Resting-state directed brain connectivity patterns in adolescents from source-reconstructed EEG signals based on information flow rate

“…fMRI studies were the first to show that subsets of these regions tend to act in concert, giving rise to functionally relevant “resting-state” brain networks ( Raichle et al, 2001 ; Greicius et al, 2009 ) that provide a basis for information processing and coordinated activity. More recently, Yuan et al (2016) and Liu et al (2017) have found that functional resting-state networks can also be extracted from source-space EEG data, and Hillebrand et al (2016) have done the same using MEG data. The most commonly reported resting-state functional networks observed in children ( Muetzel et al, 2016 ), adolescents ( Borich et al, 2015 ) and adults ( Yuan et al, 2016 ; Liu et al, 2017 ) (and references therein) include the visual, the fronto-parietal, the sensory motor and the default mode network (DMN).…”

“…More recently, Yuan et al (2016) and Liu et al (2017) have found that functional resting-state networks can also be extracted from source-space EEG data, and Hillebrand et al (2016) have done the same using MEG data. The most commonly reported resting-state functional networks observed in children ( Muetzel et al, 2016 ), adolescents ( Borich et al, 2015 ) and adults ( Yuan et al, 2016 ; Liu et al, 2017 ) (and references therein) include the visual, the fronto-parietal, the sensory motor and the default mode network (DMN). These studies also highlight that the above resting-state functional networks are not independent, and that there is a high degree of interconnections between them.…”