Structural head MRIs are a crucial ingredient in MEG/EEG source imaging; they are used to define a realistically shaped volume conductor model, constrain the source space, and visualize the source estimates. However, individual MRIs are not always available, or they may be of insufficient quality for segmentation, leading to the use of a generic template MRI, matched MRI, or the application of a spherical conductor model. Such approaches deviate the model geometry from the true head structure and limit the accuracy of the forward solution. Here, we implemented an easy-to-use tool, pseudo-MRI engine, which utilizes the head-shape digitization acquired during a MEG/EEG measurement for warping an MRI template to fit the subject's head. To this end, the algorithm first removes outlier digitization points, densifies the point cloud by interpolation if needed, and finally warps the template MRI and its segmented surfaces to the individual head shape using the thin-plate-spline method. To validate the approach, we compared the geometry of segmented head surfaces, cortical surfaces, and canonical brain regions in the real and pseudo-MRIs of 25 subjects. We also tested the MEG source reconstruction accuracy with pseudo-MRIs against that obtained with the real MRIs from individual subjects with simulated and real MEG data. We found that the pseudo-MRI enables comparable source localization accuracy to the one obtained with the subject's real MRI. The study indicates that pseudo-MRI can replace the need for individual MRI scans in MEG/EEG source imaging for applications that do not require subcentimeter spatial accuracy.

In MEG and EEG studies, the accuracy of the head digitization impacts the co-registration between functional and structural data. The co-registration is one of the major factors that affect the spatial accuracy in MEG/EEG source imaging. Precisely digitized head-surface (scalp) points do not only improve the co-registration but can also deform a template MRI. Such an individualized-template MRI can be used for conductivity modeling in MEG/EEG source imaging if the individual's structural MRI is unavailable. Electromagnetic tracking (EMT) systems (particularly Fastrak, Polhemus Inc., Colchester, VT, USA) have been the most common solution for digitization in MEG and EEG. However, they may occasionally suffer from ambient electromagnetic interference which makes it challenging to achieve (sub-)millimeter digitization accuracy. The current study-(i) evaluated the performance of the Fastrak EMT system under different conditions in MEG/EEG digitization, and (ii) explores the usability of two alternative EMT systems (Aurora, NDI, Waterloo, ON, Canada; Fastrak with a short-range transmitter) for digitization. Tracking fluctuation, digitization accuracy, and robustness of the systems were evaluated in several test cases using test frames and human head models. The performance of the two alternative systems was compared against the Fastrak system. The results showed that the Fastrak system is accurate and robust for MEG/EEG digitization if the recommended operating conditions are met. The Fastrak with the short-range transmitter shows comparatively higher digitization error if digitization is not carried out very close to the transmitter. The study also evinces that the Aurora system can be used for MEG/EEG digitization within a constrained range; however, some modifications would be required to make the system a practical and easy-to-use digitizer. Its real-time error estimation feature can potentially improve digitization accuracy.

Exaggerated subthalamic beta oscillatory activity and increased beta range cortico-subthalamic synchrony have crystallized as the electrophysiological hallmarks of Parkinson's disease. Beta oscillatory activity is not tonic but occurs in 'bursts' of transient amplitude increases. In Parkinson's disease, the characteristics of these bursts are altered especially in the basal ganglia. However, beta oscillatory dynamics at the cortical level and how they compare with healthy brain activity is less well studied. We used magnetoencephalography (MEG) to study sensorimotor cortical beta bursting and its modulation by subthalamic deep brain stimulation in Parkinson's disease patients and age-matched healthy controls. We show that the changes in beta bursting amplitude and duration typical of Parkinson's disease can also be observed in the sensorimotor cortex, and that they are modulated by chronic subthalamic deep brain stimulation, which, in turn, is reflected in improved motor function at the behavioural level. In addition to the changes in individual beta bursts, their timing relative to each other was altered in patients compared to controls: bursts were more clustered in untreated Parkinson's disease, occurring in 'bursts of bursts', and re-burst probability was higher for longer compared to shorter bursts. During active deep brain stimulation, the beta bursting in patients resembled healthy controls' data. In summary, both individual bursts' characteristics and burst patterning are affected in Parkinson's disease, and subthalamic deep brain stimulation normalizes some of these changes to resemble healthy controls' beta bursting activity, suggesting a non-invasive biomarker for patient and treatment follow-up.

In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with the aim of facilitating the review of MEG data containing epileptiform discharges. Test data were obtained by superposing simulated signals from 100-nAm dipolar sources to a resting state MEG recording from a healthy subject. Simulated sources were placed systematically to different cortical locations for defining the optimal regularization for the source montage reconstruction and for assessing the detectability of the source activity from the 29-channel MEG source montage. The signal-to-noise ratio (SNR), computed for each source from the sensor-level and source-montage signals, was used as the evaluation parameter. Without regularization, the SNR from the simulated sources was larger in the sensor-level signals than in the source montage reconstructions. Setting the regularization to 2% increased the source montage SNR to the same level as the sensor-level SNR, improving the detectability of the simulated events from the source montage reconstruction. Sources producing a SNR of at least 15 dB were visually detectable from the source-montage signals. Such sources are located closer than about 75 mm from the MEG sensors, in practice covering all areas in the grey matter. The 29-channel source montage creates more focal signals compared to the sensor space and can significantly shorten the detection time of epileptiform MEG discharges for focus localization.

Abstract In magnetoencephalographic (MEG) and electroencephalographic (EEG) studies, the accuracy of the head digitization impacts the co-registration between MEG/EEG and structural magnetic resonance imaging (MRI) data. The co-registration accuracy in turn affects the spatial accuracy in neural source imaging. Precisely digitized head-surface (scalp) points do not only improve the co-registration but can also be used to deform a template MRI to create an individualized-template MRI that can be used for conductivity modeling in MEG/EEG source imaging if the individual’s structural MRI is unavailable. Although optical scanners have been introduced for digitization, especially for dense EEG electrode sets, electromagnetic tracking (EMT) systems (particularly Fastrak®, Polhemus Inc., Colchester, VT, USA) have been the most often applied solution for digitization in MEG and EEG acquisition. EMT systems may occasionally suffer from ambient electromagnetic interference which makes it challenging to achieve (sub-)millimeter digitization accuracy. The current study i) evaluated the performance of the Fastrak® EMT system under different conditions in MEG/EEG digitization, and ii) explores the usability of two alternative EMT systems (Aurora®, NDI, Waterloo, ON, Canada; Fastrak® with a short-range transmitter) for digitization. Tracking fluctuation, digitization accuracy, and robustness of the systems were evaluated in several test cases using test frames and human head models. The digitization performance of the two alternative systems was compared against the Fastrak® system. The results showed that the Fastrak® system is accurate and robust for MEG/EEG digitization if the recommended operating conditions are met. The Fastrak® with the short-range transmitter shows comparatively higher digitization error if digitization is not carried out very close to the transmitter. The study also evinces that the Aurora® system can be used for MEG/EEG digitization. Its real-time error estimation feature can potentially improve digitization accuracy, albeit some modifications would be required to make the system a practical and easy-to-use digitizer.

Objective: Magnetoencephalography (MEG) signals typically reflect a mixture of neuromagnetic fields, subject-related artifacts, external interference and sensor noise. Even inside a magnetically shielded room, external interference can be significantly stronger than brain signals. Methods such as signal-space projection (SSP) and signal-space separation (SSS) have been developed to suppress this residual interference, but their performance might not be sufficient in cases of strong interference or when the sources of interference change over time. Methods: Here we suggest a new method, extended signal-space separation (eSSS), which combines a physical model of the magnetic fields (as in SSS) with a statistical description of the interference (as in SSP). We demonstrate the performance of this method via simulations and experimental MEG data. Results: The eSSS method clearly outperforms SSS and SSP in interference suppression regardless of the extent of a priori information available on the interference sources. We also show that the method does not cause location or amplitude bias in dipole modeling. Conclusion: Our eSSS method provides better data quality than SSP or SSS and can be readily combined with other SSS-based methods, such as spatiotemporal SSS or head movement compensation. Thus, eSSS extends and complements the interference suppression techniques currently available for MEG. Significance: Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression is especially important in environments with large interference fields.

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes. When applied carefully to MEG data with a typical SNR (3–15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs. We also found that the SNR improvement offered by SSS led to more accurate localization.

This data set is a part of the study 'Comparison of beamformers implementations for MEG source localization'. The dataset includes 64 phantom datasets, 2 human datasets, and 50 simulated datasets. The data also include segmented MRI files from FreeSurfer for MEG phantom (Megin Oy, Helsinki, Finland) and a human subject MRI. It also includes the used versions of the four beamforming packages (MNE-Python, FieldTrip, SPM12(DAiSS), and Brainstorm) and codes used for the analysis.

High frequency oscillations (HFOs, 80–500Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

Head movements during an MEG recording are commonly considered an obstacle. In this computer simulation study, we introduce an approach, the virtual MEG helmet (VMH), which employs the head movements for data quality improvement. With a VMH, a denser MEG helmet is constructed by adding new sensors corresponding to different head positions. Based on the Shannon's theory of communication, we calculated the total information as a figure of merit for comparing the actual 306-sensor Elekta Neuromag helmet to several types of the VMH. As source models, we used simulated randomly distributed source current (RDSC), simulated auditory and somatosensory evoked fields. Using the RDSC model with the simulation of 360 recorded events, the total information (bits/sample) was 989 for the most informative single head position and up to 1272 for the VMH (addition of 28.6%). Using simulated AEFs, the additional contribution of a VMH was 12.6% and using simulated SEF only 1.1%. For the distributed and bilateral sources, a VMH can provide a more informative sampling of the neuromagnetic field during the same recording time than measuring the MEG from one head position. VMH can, in some situations, improve source localization of the neuromagnetic fields related to the normal and pathological brain activity. This should be investigated further employing real MEG recordings.

Recently, the signal space separation (SSS) method, based on the multipole expansion of the magnetic field, has become increasingly important in magnetoencephalography (MEG). Theoretical arguments and simulations suggest that increasing the asymmetry of the MEG sensor array from the traditional, rather symmetric geometry can significantly improve the performance of the method. To test this concept, we first simulated addition of tangentially oriented standard sensor elements to the existing 306-channel Elekta Neuromag sensor array, and evaluated and optimized the performance of the new sensor configuration. Based on the simulation results, we then constructed a prototype device with 18 additional tangential triple-sensor elements and a total of 360 channels. The experimental results from the prototype are largely in agreement with the simulations. In application of the spatial SSS method, the 360-channel device shows an approximately 100% increase in software shielding capability, while residual reconstruction noise of evoked responses is decreased by 20%. Further, the new device eliminates the need for regularization while applying the SSS method. In conclusion, we have demonstrated in practice the benefit of reducing the symmetry of the MEG array, without the need for a complete redesign.

Objective: Our aim was to assess the effectiveness and reliability of spatiotemporal signal space separation (tSSS) and movement correction (MC) in magnetoencephalography (MEG) recordings disturbed by head movements and magnetized material on the head.Methods: We recorded MEG from 20 healthy adults in stationary (reference) head position and during controlled head movements. Nearby magnetic interference sources were simulated by attaching magnetized particles on the subject's head. Auditory and somatosensory stimuli were presented. MC, tSSS and averaging were performed to obtain auditory (AEF) and somatosensory (SEF) evoked fields. Neuronal sources were modeled as equivalent current dipoles. MC was also validated by reconstructing signals generated by current dipoles in a phantom.Results: After MC, the AEF and SEF responses recorded during intermittent head movements were similar in amplitude to the reference recordings and differed by 5-7 mm in source location. The tSSS method removed artifacts due to the attached magnetized particles but did not affect the reference data.Conclusions: The methods are able to reliably recover MEG responses contaminated by movements and magnetic artifacts on the head.Significance: The combination of tSSS and MC methods is especially useful in clinical measurements, where movements and magnetic disturbances are commonly present. (C) 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

This study was performed to assess the effectiveness of the spatiotemporal Signal Space Separation (tSSS) and head movement compensation (MC). Continuous MEG data were acquired from 20 adult and 10 child subjects in four conditions: (1) A reference with a static head position, (2) the subject changed the head position twice, (3) the subject moved the head constantly, and (4) two magnetized pieces were attached to the scalp and the subject moved the head. The recordings were repeated with two whole-head MEG systems.Auditory and tactile stimuli were applied. The tSSS and MC were performed on the recordings, and evoked fields were selectively averaged. Neuronal activity was modeled with equivalent current dipoles (ECDs) whose locations and strengths were fitted to the auditory and somatosensory evoked fields (AEF and SEF). ECD parameters were compared to those obtained from the reference responses.The average differences of the dipole localizations from the reference recordings with the two systems were below 10 mm. In condition 1, the responses were unaffected by tSSS. In conditions 2-4, the head position could be tracked reliably, and tSSS suppressed the interference caused by magnetized impurities. In condition 2, the average dipole localization difference from the reference location was below 10 mm both for SEF and AEF. Dipole waveforms and amplitudes were similar to the reference results. In conditions 3 and 4, continuous and regular movements influenced the AEFs and triggered bursting rhythmic activity from the motor cortex and muscles moving the head. Consequently, the average AEF dipole localization differences were between 10 and IS mm and the amplitudes were about 40% smaller than the references. The SEE; results were, however, similar to the reference results.In summary, tSSS and MC effectively suppressed disturbances caused by head movements and magnetized impurities.

We have combined Signal Space Separation and beamformers (SSS beamformer). The SSS beamformer was tested by simulation in the presence of simulated brain noise. The SSS beamformer performs at least as well as the conventional beamformer, provided that the expansion order is sufficiently high. For beamformer outputs which depend on power or power difference normalized by the projected noise, the spatial resolution of the SSS beamformer is significantly better than that of the conventional beamformers if the sources are deeper, and about the same as that of the conventional beamformer when the sources are superficial. For beamformer outputs which depend on the ratio of powers, the spatial resolutions of the SSS and conventional beamfomers are the same. The sensor noise covariance matrix in the SSS basis is non-diagonal. The SSS beamformers with diagonalized noise covariance matrix exhibit better spatial resolution than that with non-diagonal noise covariance matrix. The SSS beamformers are computationally more efficient than the conventional beamformers.

We studied the degree of repeatability of auditory and somatosensory evoked fields (AEF and SEF). A single subject was studied repeatedly with a 306-channel Elekta Neuromag® system over a period of six years. In 21 recordings the subject kept his head as immobile as possible. Averaged AEF and SEF data were analyzed with equivalent current dipoles (ECDs). The mean 3D variations of the dipole localizations were 5.6 mm for SEF, 6.7 mm for ipsilateral AEF, and 5.9 mm for contralateral AEF.

The assessment of myocardial viability is a major diagnostic challenge in patients with coronary artery disease (CAD) after myocardial infarction. Novel threedimensional current density (CD) imaging algorithms use high-resolution magnetic field mapping to determine the electrical activity of myocardial segments at rest. We, for the first time, compared CD activity obtained with several algorithms to 18-F-fluoro-deoxyglucose positron emission tomography (FDG-PET) in evaluation of myocardial viability. Magnetic field maps were obtained in nine adult patients (pt) with CAD and a history of infarction. The criterion for non-viable myocardium was an FDG-PET uptake with less than 45% of the maximum in the respective segments. CD imaging was applied to the left ventricle by using six different methods to solve the inverse problem. Mean CD activity was calculated for a close meshed grid of 90 locations of the left ventricle. A cardiologist compared bull's eye plots of CD and FDG-PET activity by eye. Spearman's correlation coefficients and specificity at a given level of sensitivity (70%) were calculated. Bull's eye plots revealed a significant correlation of CD/PET in 5 pt and no correlation in 3 pt. One pt had a negative correlation. The six different CD reconstruction methods performed similar. While CD reconstruction has the principal potential to image viable myocardium, we found that the reconstructed CD magnitude was low in scar segments but also reduced in some segments with preserved metabolic activity under resting conditions. New vector measurement techniques, the use of additional stress testing and advances in mathematical methodology are expected to improve CD imaging in future.