Comparison of imaging modalities and source-localization algorithms in locating the induced activity during deep brain stimulation of the STN.

One of the most commonly used therapy to treat patients with Parkinson's disease (PD) is deep brain stimulation (DBS) of the subthalamic nucleus (STN). Identifying the most optimal target area for the placement of the DBS electrodes have become one of the intensive research area. In this study, the first aim is to investigate the capabilities of different source-analysis techniques in detecting deep sources located at the sub-cortical level and validating it using the a-priori information about the location of the source, that is, the STN. Secondly, we aim at an investigation of whether EEG or MEG is best suited in mapping the DBS-induced brain activity. To do this, simultaneous EEG and MEG measurement were used to record the DBS-induced electromagnetic potentials and fields. The boundary-element method (BEM) have been used to solve the forward problem. The position of the DBS electrodes was then estimated using the dipole (moving, rotating, and fixed MUSIC), and current-density-reconstruction (CDR) (minimum-norm and sLORETA) approaches. The source-localization results from the dipole approaches demonstrated that the fixed MUSIC algorithm best localizes deep focal sources, whereas the moving dipole detects not only the region of interest but also neighboring regions that are affected by stimulating the STN. The results from the CDR approaches validated the capability of sLORETA in detecting the STN compared to minimum-norm. Moreover, the source-localization results using the EEG modality outperformed that of the MEG by locating the DBS-induced activity in the STN.

Comparison of EEG and MEG in source localization of induced human gamma-band oscillations during visual stimulus.

High frequency gamma oscillations are indications of information processing in cortical neuronal networks. Recently, non-invasive detection of these oscillations have become one of the main research areas in magnetoencephalography (MEG) and electroencephalography (EEG) studies. The aim of this study, which is a continuation of our previous MEG study, is to compare the capability of the two modalities (EEG and MEG) in localizing the source of the induced gamma activity due to a visual stimulus, using a spatial filtering technique known as dynamic imaging of coherent sources (DICS). To do this, the brain activity was recorded using simultaneous MEG and EEG measurement and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head modeling technique, such as, the three-shell concentric spheres and an overlapping sphere (local sphere) have been used as a forward model to calculate the external electromagnetic potentials and fields recorded by the EEG and MEG, respectively. Our results from the time-frequency analysis, at the sensor level, revealed that the parieto-occipital electrodes and sensors from both modalities showed a clear and sustained gamma-band activity throughout the post-stimulus duration and that both modalities showed similar strongest gamma-band peaks. It was difficult to interpret the spatial pattern of the gamma-band oscillatory response on the scalp, at the sensor level, for both modalities. However, the source analysis result revealed that MEG3 sensor type, which measure the derivative along the longitude, showed the source more focally and close to the visual cortex (cuneus) as compared to that of the EEG.

Impact of head modeling and sensor types in localizing human gamma-band oscillations.

An effective mechanism in neuronal communication is oscillatory neuronal synchronization. The neuronal gamma-band (30-100 Hz) synchronization is associated with attention which is induced by a certain visual stimuli. Numerous studies have shown that the gamma-band activity is observed in the visual cortex. However, impact of different head modeling techniques and sensor types to localize gamma-band activity have not yet been reported. To do this, the brain activity was recorded using 306 magnetoencephalography (MEG) sensors, consisting of 102 magnetometers and 102 pairs of planar gradiometers (one measuring the derivative of the magnetic field along the latitude and the other along the longitude), and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head models with a single-shell and overlapping spheres (local sphere) have been used as a forward model for calculating the external magnetic fields generated from the gamma-band activity. For each sensor type, the subject-specific frequency range of the gamma-band activity was obtained from the spectral analysis. The identified frequency range of interest with the highest gamma-band activity is then localized using a spatial-filtering technique known as dynamic imaging of coherent sources (DICS). The source analysis for all the subjects revealed that the gradiometer sensors which measure the derivative along the longitude, showed sources close to the visual cortex (cuneus) as compared to the other gradiometer sensors which measure the derivative along the latitude. However, using the magnetometer sensors, it was not possible to localize the sources in the region of interest. When comparing the two head models, the local-sphere model helps in localizing the source more focally as compared to the single-shell head model.

Coherent source and connectivity analysis on simultaneously measured EEG and MEG data during isometric contraction.

The most well-known non-invasive electric and magnetic field measurement modalities are the electroencephalography (EEG) and magnetoencephalography (MEG). The first aim of the study was to implement the recently developed realistic head model which uses an integrative approach for both the modalities. The second aim of this study was to find the network of coherent sources and the modes of interactions within this network during isometric contraction (ISC) at (15-30 Hz) in healthy subjects. The third aim was to test the effective connectivity revealed by both the modalities analyzing them separately and combined. The Welch periodogram method was used to estimate the coherence spectrum between the EEG and the electromyography (EMG) signals followed by the realistic head modelling and source analysis method dynamic imaging of coherent sources (DICS) to find the network of coherent sources at the individual peak frequency within the beta band in healthy subjects. The last step was to identify the effective connectivity between the identified sources using the renormalized partial directed coherence method. The cortical and sub-cortical network comprised of the primary sensory motor cortex (PSMC), secondary motor area (SMA), and the cerebellum (C). The cortical and sub-cortical network responsible for the isometric contraction was similar in both the modalities when analysing them separately and combined. The SNR was not significantly different between the two modalities separately and combined. However, the coherence values were significantly higher in the combined modality in comparison to each of the modality separately. The effective connectivity analysis revealed plausible additional connections in the combined modality analysis.

Dipole source analysis for readiness potential and field using simultaneously measured EEG and MEG signals.

Various source localization techniques have indicated the generators of each identifiable component of movement-related cortical potentials, since the discovery of the surface negative potential prior to self-paced movement by Kornhuber and Decke. Readiness potentials and fields preceding self-paced finger movements were recorded simultaneously using multichannel electroencephalography (EEG) and magnetoencephalography (MEG) from five healthy subjects. The cortical areas involved in this paradigm are the supplementary motor area (SMA) (bilateral), pre-SMA (bilateral), and contralateral motor area of the moving finger. This hypothesis is tested in this paper using the dipole source analysis independently for only EEG, only MEG, and both combined. To localize the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for spherical head models consisting of a set of connected volumes, typically representing the scalp, skull, and brain. In the source reconstruction it is to be expected that EEG predominantly localizes radially oriented sources while MEG localizes tangential sources at the desired region of the cortex. The effect of MEG on EEG is also observed when analyzing both combined data. When comparing the two head models, the spherical and the realistic head models showed similar results. The significant points for this study are comparing the source analysis between the two modalities (EEG and MEG) so as to assure that EEG is sensitive to mostly radially orientated sources while MEG is only sensitive to only tangential sources, and comparing the spherical and individual head models.

Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals.

The sources of somatosensory evoked potentials (SEPs) and fields (SEFs), which is a standard paradigm, is investigated using multichannel EEG and MEG simultaneous recordings. The hypothesis that SEP & SEF sources are generated in the posterior bank of the central sulcus is tested, and analyses are compared based on EEG only, MEG only, bandpass filtered MEG, and both combined. To locate the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for averaged head models consisting of a set of connected volumes, typically representing the skull, scalp, and brain. The location of each dipole is then estimated using fixed MUSIC and current-density-reconstruction (CDR) algorithms. For both analyses, the results demonstrate that the band-pass filtered MEG can localize the sources accurately at the desired region as compared to only EEG and unfiltered MEG. For CDR analysis, it looks like MEG affects EEG during the combined analyses. The MUSIC algorithm gives better results than CDR, and when comparing the two head models, the averaged and the realistic head models showed the same result.