Using structural connectivity to augment community structure in EEG functional connectivity

Recently, EEG recording techniques (high-density EEG) as well as source analysis have improved markedly. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG signals suffer from volume conduction, introducing spurious functional connections (statistical dependencies) between nearby sources. Genuine functional connectivity (FC) reflecting functional relationships is impossible to disentangle from spurious FC, impairing the applicability of network neuroscience to EEG. Here, we use information from white matter structural connectivity (SC) to attenuate the impact of volume conduction on EEG-FC. We confirm that FC (power envelope correlations) is predicted by the SC beyond the impact of Euclidean distance. We then smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions is increased while FC between non-connected regions remains unchanged. We hypothesize that this results in an increase in genuine relative to spurious FC. We analyze changes in FC due to smoothing by assessing the resemblance between EEG- and fMRI-FC, and find that smoothing increases resemblance, both in terms of overall correlation and community structure. This suggests that information from the SC can indeed be used to attenuate the effect of volume conduction on EEG source signals.