Abstract
Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks’ dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.
Highlights
A large-scale functional brain network is defined as a subset of interconnected, possibly distant, brain regions that interact with each other in order to perform a plethora of tasks of different levels of complexity (Bressler and Menon, 2010)
resting-state networks (RSNs) are identified under the assumption that their functional connectivity is static; current literature suggests that brain networks in general continuously reorganize in response to both internal and external stimuli at multiple time-scales, resulting in temporal fluctuations of their FC – the so-called dynamic functional connectivity (Hutchison et al, 2013; Calhoun et al, 2014; Preti et al, 2017)
The selected independent components (ICs) and the RSN templates show a substantial overlap for all RSNs and imaging techniques, slightly higher when considering the RSNs identified on the Functional magnetic resonance imaging (fMRI) data, as expected and quantified by the Dice coefficient
Summary
A large-scale functional brain network is defined as a subset of interconnected, possibly distant, brain regions that interact with each other in order to perform a plethora of tasks of different levels of complexity (Bressler and Menon, 2010). The identification of such networks led to pivotal findings regarding brain function in healthy humans (van den Heuvel and Hulshoff Pol, 2010), and by discriminating changes in some properties of those networks due to disease, a better understanding of their pathophysiology was possible (Du et al, 2018). Under the assumption that brain function dynamics can be described by a limited number of states (Preti et al, 2017), a significant number of studies have dedicated to the identification of such brain states from the dFC, by applying pattern recognition techniques, clustering (Allen et al, 2014), principal component analysis (Leonardi et al, 2013), and dictionary learning (Leonardi et al, 2014; Abreu et al, 2019)
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