ArticlePloS one2015
EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
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- Stationary correlation pattern in highly non-stationary MEG recordings of healthy subjects and its relation to former EEG studies.PloS one · 2024Article
- Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning.Frontiers in neuroscience · 2024Article
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- Separating vascular and neuronal effects of age on fMRI BOLD signals.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Review
- Local syntactic violations evoke fast mismatch-related neural activity detected by optical neuroimaging.Experimental brain research · 2020Article
- Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders.Scientific reports · 2019Article
- A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD).Frontiers in computational neuroscience · 2018Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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