EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network

Abstract

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful.

Neuronal networks in epileptic encephalopathies with CSWS

“…All assessed EEG studies were recorded during physiologic sleep for the period of the planned admission to the epilepsy center. Based on our previous studies we consider the number of electrodes to be sufficient for the correct detection of the sources …”

“…Based on our previous studies we consider the number of electrodes to be sufficient for the correct detection of the sources. [14][15][16][17] EEG recordings during physiologic sleep, at two different time points, were retrospectively taken for the DICS and RPDC analyses. EEG recordings from the time of the first admission to the epilepsy center and showing the maximum expression of the CSWS pattern (EEG1) were selected for the analyses.…”

Resting-state directed brain connectivity patterns in adolescents from source-reconstructed EEG signals based on information flow rate

“…Measures of effective connectivity, such as Granger causality ( Kamiński et al, 2001 ; Hesse et al, 2003 ; Bressler and Seth, 2011 ; Seth et al, 2015 ) and transfer entropy ( Schreiber, 2000 ; Liu and Aviyente, 2012 ; Salvador et al, 2010 ; Shovon et al, 2014 ; Hillebrand et al, 2016 ), have been applied to EEG data to identify patterns of information flow in the functional brain networks during cognitive activity. Recently, Muthuraman et al (2015) applied renormalized partial directed coherence , a measure based on the principle of Granger causality, to the combination of EEG and magnetoencephalography (MEG) signals to identify the direction of information flow between two signals and ultimately characterize the functional and effective connectivity in resting-state brain connectivity patterns. Thus, effective connectivity measures offer insights into the dynamics of the neuronal clusters that underpin cognitive function.…”

“…To our knowledge, there are only three studies that have investigated the source space information flow pathways in adults (age: over 20 years) during eyes closed resting state, and considered the relationship between these pathways and the underlying functional networks: (1) Michels et al (2013) study EEG data using the partial directed coherence (PDC) measure, which is based on Granger causality, to quantify effective connectivity; (2) Muthuraman et al (2015) analyze both EEG and MEG data, also by means of the partial directed coherence (PDC) measure; and (3) Hillebrand et al (2016) study MEG recordings using directed phase transfer entropy (dPTE) to assess effective connectivity. All three studies find that the dominant pattern in adults is a posterior to anterior flow, originating in the regions associated with the primary visual cortex and the posterior DMN, and flowing to the frontal regions.…”

“…All three studies find that the dominant pattern in adults is a posterior to anterior flow, originating in the regions associated with the primary visual cortex and the posterior DMN, and flowing to the frontal regions. Michels et al (2013) and Muthuraman et al (2015) observe only one-way connectivity between the brain regions. However, Hillebrand et al (2016) find that the dominant patterns are complemented by weaker anterior to posterior connections which make the flow bidirectional at finer connection strength resolution.…”