EEG Source Connectivity Analysis: From Dense Array Recordings to Brain Networks

Abstract

The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. Using High-Resolution (hr) EEG recordings in healthy volunteers, we evaluated these factors on evoked responses during picture recognition and naming task. The main reason for selection this task is that a solid literature background is available about involved brain networks (ground truth). From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.

Upregulation of cortico-cerebellar functional connectivity after motor learning

“…As the dependent variable we took the imaginary part of the complex-valued coherence in order to minimize the effect of volume conduction on the estimation of true functional connectivity (Nolte et al, 2004;Mehrkanoon et al, 2014a). Using only the imaginary part reduces the effect of source leakage on functional connectivity, as volume conduction (and hence source leakage) results in spurious correlations with a zero time (and phase) lag (see also Nolte et al (2004);Hassan et al (2014)). …”

“…In addition, the distance between voxels located in the cerebellum and the closest EEG electrodes is between 1−7 cm, which is similar to the distance between voxels in the occipital cortex and EEG sensors and not as deep as many other subcortical structures. Future work could employ higher-density EEG (128-256 channel caps) together with a realistic head model derived from individual MRIs in order to more accurately identify the cerebellar contributions to cortico-cerebellar functional connectivity in motor learning (Hassan et al, 2014). Indeed, several studies have revealed oscillatory MEG activity originating from the cerebellum in the theta, alpha and beta bands (Gross et al, 2002;Pollok et al, 2009;Jerbi et al, 2007;Fujioka et al, 2012;Dalal et al, 2013).…”

“…Given that both the spatial and spectral pattern of increased connectivity observed in this study is consistent with existing literature, these findings confirm the ability to measure cerebellar connectivity using source-reconstructed EEG. To improve electric source imaging analyses, it is hence suggested to use high-density EEG (128-256 channel caps) with realistic head models derived from individual MRIs (Hassan et al, 2014). We examined the PSDs of occipital EEG signals and voxels' signals in the cerebellum to clarify the specificity of the results against the potential effects of muscu-lar artifacts on the observed changes in functional connectivity (see supplementary material).…”

Brain network modules of meaningful and meaningless objects

“…Functional connectivity matrices were calculated using method called 'EEG source connectivity' [13,14]. It includes two main steps: i) solving the EEG inverse problem to reconstruct regional time series and ii) measuring the statistical couplings, functional connectivity, between these reconstructed regional time series.…”

Origin of Hyperbolicity in Brain-to-Brain Coordination Networks

“…Potentials of these experimental and theoretical approaches still remain elusive. Some recent studies [13][14][15] imply that EEG imaging supplemented by proper nonlinear analysis can detect changes in the emotional states of the brain that play an important role in traversing from the social brain functions to social networking [16].…”