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.

Figure 1. For a representative single subject, the created realistic head model is shown.

The layers are represented separately the brain (A), the skull (B), the scalp (C) followed by all the layers (D) with the interpolated electrodes and sensors on the scalp with transparent sections. (E) shows the location of the electrodes and sensors with respect to the subject’s head.

Figure 2. EEG - The grand average statistical map of network of sources for the finger tapping (FT) task by taking EMG as the reference signal for the whole recording data length.

MEG - The network of sources for MEG separately. MEG+EEG - The coherent network of sources for the combined approach (MEG+EEG). The network of sources was primary sensory motor cortex (PSMC), premotor cortex (PMC), supplementary motor area (SMA) and posterior parietal cortex (PPC) for the modality EEG. Additionally, two sources at the thalamus (TH), and cerebellum (CER) were identified only for the modalities MEG and for the combined approach (MEG+EEG).

Figure 3. The grand average sources for the higher SNR time segment analysis for EEG, MEG and for the combined approach (MEG+EEG).

In this analysis all the modalities showed a similar network of sources.

Figure 4. This figure illustrates the information flow between the coherent sources in the brain for the FT task using EEG, Meg and MEG+EEG.

The dashed line indicates significant bi-directional interaction and the bold lines with the arrow heads indicate the corresponding significant uni-directional interaction between the sources. The two dotted lines indicate the two additional interactions found between the sources for only the combined approach (MEG+EEG).