EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network.
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.
Department of Neurology, Christian-Albrechts-University, Kiel, Germany.
Muthuraman
M
Muthuraman
Institute of Medical Psychology and Medical Sociology, Christian-Albrechts-University, Kiel, Germany; Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-University Frankfurt am Main, Frankfurt, Germany.
Vera
V
Moliadze
Digital signal processing and system theory, Christian-Albrechts-University, Kiel, Germany.
Kidist Gebremariam
KG
Mideksa
Digital signal processing and system theory, Christian-Albrechts-University, Kiel, Germany.
Abdul Rauf
AR
Anwar
Department of Neuropediatrics, Christian-Albrechts-University, Kiel, Germany.
Ulrich
U
Stephani
Department of Neurology, Christian-Albrechts-University, Kiel, Germany.
Günther
G
Deuschl
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-University Frankfurt am Main, Frankfurt, Germany.
Christine M
CM
Freitag
Institute of Medical Psychology and Medical Sociology, Christian-Albrechts-University, Kiel, Germany; Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-University Frankfurt am Main, Frankfurt, Germany.
Michael
M
Siniatchkin
D000328:Adult; D001931:Brain Mapping; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D009434:Neural Pathways / Q000502:physiology; D055815:Young Adult