PT  - JOURNAL ARTICLE
AU  - Quanying Liu
AU  - Seyedehrezvan Farahibozorg
AU  - Camillo Porcaro
AU  - Nicole Wenderoth
AU  - Dante Mantini
TI  - Detecting large-scale networks in the human brain using high-density electroencephalography
AID  - 10.1101/077107
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 077107
4099  - http://biorxiv.org/content/early/2016/09/23/077107.short
4100  - http://biorxiv.org/content/early/2016/09/23/077107.full
AB  - High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can permit investigating fast dynamics of cortical 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 showing 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, with a spatial accuracy comparable to fMRI networks. This result was achieved by setting up a tailored analysis pipeline including state-of-the-art tools for data preprocessing, realistic head model generation, source localization and functional connectivity analysis. Specifically, we obtained the highest similarity between hdEEG and fMRI networks by means of realistic 12-layer head models and eLORETA source localization, together with spatial ICA for functional connectivity analysis. Spatial ICA overcomes the spatial leakage problem by identifying patterns of coherent power fluctuations that are spatially independent over time. Our analyses showed that the number of electrodes in particular, but also the accuracy of the head model and the source localization method used have impact on network reconstruction. We believe that our methodological work can contribute to rise of hdEEG as a powerful tool for brain research.