Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

Abstract

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

Age-related differences in practice-dependent resting-state functional connectivity related to motor sequence learning

“…Notwithstanding the fact that a stronger involvement of the cortico-striatal network may have been undetected in our study, our results are consistent with [Tam as Kincses et al, 2008] who did not find correlated activity with the striatum in the early learning phase. Finally, although the ability of MEG to detect subcortical and cerebellar activity is a matter of debate, an increasing number of MEG studies have reported task-related activity modulation at the cerebellum [Bourguignon et al, 2013;Jerbi et al, 2007;Martin et al, 2006;Muthuraman et al, 2014;Stancak et al, 2011] and the caudate nucleus [Altamura et al, 2014], which supports the present rsFC findings.…”

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

“…Despite this, a direct comparison of the differences between these modalities has yet to be systematically performed. Moreover, the disadvantages of EEG and MEG may be compensated for by utilizing a combination of both modalities, as demonstrated in a number of studies [ 4 – 15 ]. To the best of our knowledge, no previous studies have shown whether the integration of EEG and MEG data can improve the analysis of spectral power, functional and effective connectivity, and RPR.…”

The paradox of Rapid Eye Movement sleep in the light of oscillatory activity and functional synchronization during phasic and tonic microstates

“…In the current study, the sources to be considered were defined by a regular 3D grid with a 5 mm resolution, thereby creating 3D voxels of 5 mm 3 . As beamforming techniques have been found to be ineffective for reliably reconstructing deep sources of EEG signals measured at the scalp (Muthuraman et al, 2014), only grid points located in the cortex were included in the source reconstruction analysis. Whether grid points were located in cortical areas or not was determined using the AAL atlas developed by (Tzourio-Mazoyer et al, 2002).…”