The cortical and sub-cortical network of sensory evoked response in healthy subjects

The aim of this study was to find the cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects during electrical stimulation of right median nerve at wrist. The multitaper method was used to estimate the power and coherence spectrum followed by the source analysis method dynamic imaging of coherent sources (DICS) to find the highest coherent source for the basic frequency 3Hz and the complete cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects. The highest coherent source for the basic frequency was in the posterior parietal cortex for all the subjects. The cortical and sub-cortical network comprised of the primary sensory motor cortex (SI), secondary sensory motor cortex (SII), frontal cortex and medial pulvinar nucleus in the thalamus. The cortical and sub-cortical network responsible for the sensory evoked coherence was found successfully with a 64-channel EEG system. The sensory evoked coherence is involved with a thalamo-cortical network in healthy subjects

Imaging coherent sources of tremor related EEG activity in patients with Parkinson's disease

The cortical sources of both the basic and first 'harmonic' frequency of Parkinsonian tremor are addressed in this paper. The power and coherence was estimated using the multitaper method for EEG and EMG data from 6 Parkinsonian patients with a classical rest tremor. The Dynamic Imaging of Coherent Sources (DICS) was used to find the coherent sources in the brain. Before hand this method was validated for the application to the EEG by showing in 3 normal subjects that rhythmic stimuli (1-5Hz) to the median nerve leads to almost identical coherent sources for the basic and first harmonic frequency in the contralateral sensorimotor cortex which is the biologically plausible result. In all the Parkinson patients the corticomuscular coherence was also present in the basic and the first harmonic frequency of the tremor. However, the source for the basic frequency was close to the frontal midline and the first harmonic frequency was in the region of premotor and sensory motor cortex on the contralateral side for all the patients. Thus the generation of these two oscillations involves different cortical areas and possibly follows different pathways to the periphery

Relation between post-movement-beta-synchronisation and corticomuscular coherence [Abstract]

Objective: To analyse post-movement-beta-synchronisation in the EEG and EEG-EMG coherence simultaneously. Background: The mechanisms and function of EEG synchronistion in the beta-band after the end of a short movement is not clear. The corticomusucular coupling during isometric muscle contractions occurs in the same beta-band. It is unclear however, if these two features of cortical motor physiology are related. Methods: 64-channel EEG was measured simultaneously with surface EMG of the right FDI-muscle in 11 healthy volunteers. Subjects kept a constant medium strength contraction of the FDI-muscle during the entire experiment. Superimposed on this they performed repetitive self-paced brisk short contractions. Time-frequency analysis including coherence over time was performed with respect to the onset of the brisk movements and averaged for 40 contrcations in each subject. Results: Post-movement-beta synchronisation (PMBS) was found in the contralateral electrodes C1, C3 and C5 with a maximum 1-2.5sec. after the brisk movements in the frequency range between 16 and 27 Hz for all the subjects. In 9 of the subjects there was coherence between the EEG recorded from these electrodes and the FDI in the same frequency range as the PMBS and with the maximum occuring at the same time. The other two subjects did not show any corticomuscular coherence. Conclusions: Post-movement-beta-synchronisation coincides with corticomuscular coherence in the same frequency band. Thus PMBS is not merely a cortical phenomen but seems to involve the whole corticomuscular system, possibly reflecting recalibration after brisk movements

Locating the STN-DBS electrodes and resolving their subsequent networks using coherent source analysis on EEG

The deep brain stimulation (DBS) of the subthalamic nucleus (STN) is the most effective surgical therapy for Parkinson's disease (PD). The first aim of the study was to locate the STN-DBS electrode by applying source analysis on EEG. Secondly, to identify tremor related areas which are associated with the STN. The Dynamic imaging of coherent sources (DICS) was used to find the coherent sources in the brain. The capability of the source analysis to detect deep sources like STN in the brain using EEG data was tested with two model dipole simulations. The simulations were concentrated on two aspects, the angle of the dipole orientation and the disturbance of the cortical areas on locating subcortical regions. In all the DBS treated Parkinsonian tremor patients the power spectrum showed a clear peak at the stimulated frequency and followed by there harmonics. The DBS stimulated frequency constituted a network of primary sensory motor cortex, supplementary motor area, prefrontal cortex, diencephalon, cerebellum and brainstem. Thus the STN was located in the region of the diencephalon. The resolved network may give better understanding to the pathophysiology of the effected tremor network in PD patients with STN-DBS

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

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

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

A multimodal approach to locate the STN electrodes in Parkinson disease patients [Poster]

Background / Purpose The first aim of this study was to localize the electrodes in the sub-thalamic nucleus in Parkinson's disease (PD) patients using the stimulation artifact during deep brain stimulation. The source analysis method used here is the dynamic imaging of coherent sources (DICS) ( Gross et al. 2001 ) with a realistic boundary element method forward model ( Fuchs et al. 2002 ). The second aim was to find out which of the modalities, electroencephalography (EEG), magnetoencephalography (MEG) or the combined approach is precise in estimating the localization of the electrodes. Main conclusion The source analysis method was able to localize the subthalamic nucleus in all the patients using the artifact induced by the deep brain stimulation. The combined approach could be very efficient in localizing the electrodes in the subthalamic nucleus compared to the individual modalities. The localization of the electrodes is an excellent approach for the validation of the applied source analysis method and to test which is the best modality in such applications

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