Detecting large‐scale networks in the human brain using high‐density electroencephalography |
| |
| Authors: | Quanying Liu Seyedehrezvan Farahibozorg Camillo Porcaro Nicole Wenderoth Dante Mantini |
| |
| Affiliation: | 1. Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland;2. Laboratory of Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Belgium;3. Department of Experimental Psychology, Oxford University, United Kingdom;4. Cognition and Brain Sciences Unit, Medical Research Council, Cambridge, United Kingdom;5. LET'S‐ISTC, National Research Council, Rome, Italy;6. Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy |
| |
| 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. |
| |
| Keywords: | electroencephalography high‐density montage resting state network functional connectivity neuronal communication |
|
|
