Tracking dynamics of functional brain networks using dense EEG

Abstract Cognition is formed from networks between functionally specific but distributed brain regions. A very challenging issue in cognition is how to precisely track brain networks at very short temporal scales (often very short 1 s ). So far, very few studies have addressed this problem as it requires high temporal and spatial resolution simultaneously. Due to its excellent temporal resolution, electroencephalography (EEG) is a key neuroimaging technique to access real-time information flow among large scale neuronal networks. Here, we propose a new method based on EEG source connectivity to map large-scale networks at high temporal (in the order of ms) and spatial (∼1000 regions of interest) resolution. We show clear evidence of the ability of EEG source connectivity to track brain networks with high time/space resolutions during picture naming task. Our results reveal that the cognitive process can be decomposed into a sequence of transiently-stable and partially-overlapping networks. Our qualitative and quantitative observations show that the identified brain networks are in accordance with results reported in the literature regarding involved brain areas during the analyzed task.