Bayesian multi–dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings

Rossi, Filippo Canali De; Luria, Gianvittorio; Sommariva, Sara; Sorrentino, Alberto

doi:10.1007/978-981-10-5122-7_211

Cited by 2 publications

(1 citation statement)

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“…Evidence for the sensitivity of EEG to tangential and MEG to radial sources are found in many studies (Cuffin, 1983) (Haueisen, Funke, Güllmar, & Eichardt, 2012). However, there are results showing the opposite case and that opens the discussion of the sensitivity of EEG and MEG to different source orientation (Hunold, Funke, Eichardt, Stenroos, & Haueisen, 2016) (Rossi, Luria, Sommariva, & Sorrentino, 2017). The sensitivity of EEG and MEG to deep and superficial sources has been discussed in many studies claiming MEG is not able to give high SNRs for deep sources while EEG is successful in that (Hunold, Funke, Eichardt, Stenroos, & Haueisen, 2016).…”

Section: Introductionmentioning

confidence: 95%

Are Sources of EEG and MEG rhythmic activity the same? An analysis based on BC-VARETA

Yuan

Riaz

Razzaq

et al. 2019

Preprint

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In the resting state (closed or open eyes) the electroencephalogram (EEG) and the magnetoencephalogram (MEG) exibit rhythmic brain activity is typically the 10 Hz alpha rhythm. It has a topographic frequency spectral distribution that is, quite similar for both modalities--something not surprising since both EEG and MEG are generated by the same basic oscillations in thalamocortical circuitry. However, different physical aspects underpin the two types of signals. Does this difference lead to a different distribution of reconstructed sources for EEG and MEG rhythms? This question is important for the transferal of results from one modality to the other but has surprisingly received scant attention till now. We address this issue by comparing eyes open EEG source spectra recorded from 77 subjects from the Cuban Human Brain Mapping project with the MEG of 63 subjects from the Human Connectome Project. Source spectra for each voxel and frequency were obtained via a novel sparsecovariance inverse method (BC-VARETA) based on individualized BEM head models with subject-specific regularization parameters (noise to signal ratio). We circumvent the zero inflated statistical issue arising from sparse estimation by employing a novel dimensionality reduction technique known as Zero-inflated Factor Analysis (ZIFA). Both minimum energy and Hotelling's T-2 tests showed that ZIFA scores for MEG and EEG sources were significantly different at all frequency bands. These results exclude a simple identification of MEG and EEG sources of resting-state EEG rhythms. Further study is required to determine the relative contribution of instrumental, physical or physiological mechanisms to these differences.

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