Abstract
Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.
Highlights
Electroencephalography (EEG) measures neural signals directly (Buzsáki, Anastassiou, & Koch, 2012) on a timescale of milliseconds, fast enough to be relevant for neural events
Our results suggest that incorporating information from the structural connectivity (SC) by means of graph filtering leads to a large-scale network structure in EEG-functional connectivity (FC) that is more similar to known canonical resting-state networks, making our approach a possible alternative to other methods that aim at correcting for volume conduction, especially for the study of large-scale functional networks
We test this assumption by fitting a stepwise general linear model (GLM) in order to quantify how well the following measures predict EEG-FCs computed as envelope correlations from three typical EEG frequency bands, averaged over all subjects: (1) SC in the form of search information Goñi et al (2014), referred to as SCSI, a measure that is derived from fiber counts and that is nonzero for all connections, yielding a connectivity matrix that is dense just like the FC matrix; the intuitive interpretation of search information is that it measures how “hidden” the shortest path between two regions of interest (ROIs) is
Summary
Electroencephalography (EEG) measures neural signals directly (Buzsáki, Anastassiou, & Koch, 2012) on a timescale of milliseconds, fast enough to be relevant for neural events. Concurring findings show that FC between regions of interest (ROIs)/ sources located in the gray matter is in part shaped by anatomical connections of the structural connectivity (SC; obtained from dMRI and fiber tracking), such that the strength of SC (fiber count, density) is predictive to some degree of the strength of FC (correlation, coherence, etc.; Abdelnour, Dayan, Devinsky, Thensen, & Raj, 2018; Atasoy, Donnelly, & Pearson, 2016; Cabral et al, 2014; Damoiseaux & Greicius, 2009; Deco et al, 2013; Glomb, Ponce-Alvarez, Gilson, Ritter, & Deco, 2017; Goñi et al, 2014; Hagmann et al, 2008; Honey et al, 2009; Meier et al, 2016; Tewarie et al, 2019, 2014; Vincent et al, 2007) This finding has been shown to extend to EEG data on the source level (i.e., signals recorded on the scalp projected into the gray matter) using analytical (Chu et al, 2015; Wirsich et al, 2017) and modeling approaches (Bhattacharya, Coyle, & Maguire, 2011; de Haan, Mott, van Straaten, Scheltens, & Stam, 2012; Finger et al, 2016; Pons, Cantero, Atienza, & Garcia-Ojalvo, 2010; Ponten, Daffertshofer, Hillebrand, & Stam, 2010; van Dellen et al, 2013). Approaches exist that orthogonalize the time series, removing common dependencies between sources (Brookes, Woolrich, & Barnes, 2012; Colclough, Brookes, Smith, & Woolrich, 2015; Hipp, Hawellek, Corbetta, Siegel, & Engel, 2012; Wens et al, 2015)
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