On electromagnetic head digitization in MEG and EEG.

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.

Cortical beta burst dynamics are altered in Parkinson's disease but normalized by deep brain stimulation.

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.

Sensitivity of a 29-Channel MEG Source Montage.

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.

Extended Signal-Space Separation Method for Improved Interference Suppression in MEG.

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.

Comparison of beamformer implementations for MEG source localization.

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 (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) 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 the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 â€‹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 for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.

Automatic detection and visualisation of MEG ripple oscillations in epilepsy.

High frequency oscillations (HFOs, 80-500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80-250 Hz) 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 15 min 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 5 min 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.

Virtual MEG Helmet: Computer Simulation of an Approach to Neuromagnetic Field Sampling.

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.

Improving MEG performance with additional tangential sensors.

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.

Validation of head movement correction and spatiotemporal signal space separation in magnetoencephalography.

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 5-7mm 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.

Signal space separation beamformer.

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.

Magnetocardiographic assessment of healed myocardial infarction.

BACKGROUND: We evaluated the capability of multichannel magnetocardiography (MCG) to detect healed myocardial infarction (MI). METHODS: Multichannel MCG over frontal chest was recorded at rest in 21 patients with healed MI, detected by cine- and contrast-enhanced magnetic resonance imaging, and in 26 healthy controls. Of the 21 MI patients, 11 had non-Q wave and 10 Q wave MIs. QRS, ST-segment, T wave and ST-T wave integrals, ST-segment and T wave amplitudes, and QRS and ST-T wave magnetic field map orientations were measured. RESULTS: The MCG repolarization indexes, such as ST segment and ST-T wave integrals, separated the MI group from the controls (ST-T wave integral -1.4 +/- 5.3 vs 1.5 +/- 4.7 pTs, P = 0.034). The abnormalities were more distinct in the Q wave-MI than in the non-Q wave MI subgroup. In the latter, however, a trend similar to the Q wave MI group was found. The relation of QRS area to ST segment and T wave integral improved the detection of healed MIs compared to the ST-T wave indexes alone (QRS-ST-T discordance 14 +/- 10 vs 5.0 +/- 7.1 pTs, P = 0.003). When comparing the MI group to the controls, the orientation of the magnetic field maps differed in the ST-T wave maps (163 +/- 119 degrees vs 58 +/- 17 degrees, P < 0.001) but not in the QRS maps (111 +/- 95 degrees vs 106 +/-93 degrees, P = 0.646). CONCLUSIONS: The MCG repolarization variables can detect healed MI. These ST-T wave abnormalities are more pronounced in patients with Q wave MI than in patients with non-Q wave MIs. Relating the signals of depolarization and repolarization phases improves the detection of healed MI. Repolarization abnormalities are common in healed MI and thus should not always be interpreted as present ongoing ischemia.

Spatial repolarization abnormalities in old myocardial infarction.

Conventional electrocardiogram criteria for myocardial infarction (MI) rely on QRS features, but ST-T segment is also affected. We recorded body surface potential mapping in 24 patients with prior MI and in 24 controls. T-wave maximum amplitude and QRS and ST-T integrals were automatically determined. Old MI was verified by magnetic resonance imaging. ST-T integral and T-wave maximum amplitude outperformed QRS integral in detecting MI, with area under receiver operating characteristic curve of 94%, 95%, and 83%, respectively. ST-T integral performed better in non-Q-wave than Q-wave MI, with area under receiver operating characteristic curve of 97% and 92%, respectively. QRS integral correlated negatively with ST-T integral in patients with MI (r = -0.58, P < .001) and positively in controls (r = 0.45, P < .001). In conclusion, ST-T integral proved equal to QRS integral in old MI detection. Inclusion of ventricular repolarization phase and development of electrocardiographic analysis over larger chest area may improve the QRS-based diagnosis of old myocardial infarction.

Representation of bioelectric current sources using Whitney elements in the finite element method.

Bioelectric current sources of magneto- and electroencephalograms (MEG, EEG) are usually modelled with discrete delta-function type current dipoles, despite the fact that the currents in the brain are naturally continuous throughout the neuronal tissue. In this study, we represent bioelectric current sources in terms of Whitney-type elements in the finite element method (FEM) using a tetrahedral mesh. The aim is to study how well the Whitney elements can reproduce the potential and magnetic field patterns generated by a point current dipole in a homogeneous conducting sphere. The electric potential is solved for a unit sphere model with isotropic conductivity and magnetic fields are calculated for points located on a cap outside the sphere. The computed potential and magnetic field are compared with analytical solutions for a current dipole. Relative difference measures between the FEM and analytical solutions are less than 1%, suggesting that Whitney elements as bioelectric current sources are able to produce the same potential and magnetic field patterns as the point dipole sources.

Vortex shaped current sources in a physical torso phantom.

Recent studies reported differential information in human magnetocardiogram and in electrocardiogram. Vortex currents have been discussed as a possible source of this divergence. With the help of physical phantom experiments, we quantified the influence of active vortex currents on the strength of electric and magnetic signals, and we tested the ability of standard source localization algorithms to reconstruct vortex currents. The active vortex currents were modeled by a set of twelve single current dipoles arranged in a circle and mounted inside a phantom that resembles a human torso. Magnetic and electric data were recorded simultaneously while the dipoles were switched on stepwise one after the other. The magnetic signal strength increased continuously for an increasing number of dipoles switched on. The electric signal strength increased up to a semicircle and decreased thereafter. Source reconstruction with unconstrained focal source models performed well for a single dipole only (less than 3-mm localization error). Minimum norm source reconstruction yielded reasonable results only for a few of the dipole configurations. In conclusion active vortex currents might explain, at least in part, the difference between magnetically and electrically acquired data, but improved source models are required for their reconstruction.

Short-term memory functions of the human fetus recorded with magnetoencephalography.

Studies in fetuses and in prematurely born infants show that auditory discriminative skills are present prior to birth. The magnetic fields generated by the fetal brain activity pass the maternal tissues and, despite their weakness, can be detected externally using MEG. Recent studies on the auditory evoked magnetic responses show that the fetal brain responds to sound onset. In contrast, higher-level auditory skills, such as those involving discriminative and memory functions, were not so far studied in fetuses with MEG. Here we show that fetal responses related to discriminating sounds can be recorded, implicating that the auditory change-detection system is functional. These results open new views to developmental neuroscience by enabling one to determine the sensory capabilities as well as the extent and accuracy of the short-term memory system of the fetus, and, further, to follow the development of these crucial processes.

Activation dynamics in anisotropic cardiac tissue via decoupling.

Bidomain theory for cardiac tissue assumes two interpenetrating anisotropic media--intracellular (i) and extracellular (e)--connected everywhere via a cell membrane; four local parameters sigma(i,e)(l,t) specify conductivities in the longitudinal (l) and transverse (t) directions with respect to cardiac muscle fibers. The full bidomain model for the propagation of electrical activation consists of coupled elliptic-parabolic partial differential equations for the transmembrane potential upsilon(m) and extracellular potential phi(e), together with quasistatic equations for the flow of current in the extracardiac regions. In this work we develop a preliminary assessment of the consequences of neglecting the effect of the passive extracardiac tissue and intracardiac blood masses on wave propagation in isolated whole heart models and describe a decoupling procedure, which requires no assumptions on the anisotropic conductivities and which yields a single reaction-diffusion equation for simulating the propagation of activation. This reduction to a decoupled model is justified in terms of the dimensionless parameter epsilon = (sigma(i)(l)sigma(e)(t) - sigma(i)(t)sigma(e)(l))/(sigma(i)(l) + sigma(e)(l))(sigma(i)(t) + sigma(e)(t)). Numerical simulations are generated which compare propagation in a sheet H of cardiac tissue using the full bidomain model, an isolated bidomain model, and the decoupled model. Preliminary results suggest that the decoupled model may be adequate for studying general properties of cardiac dynamics in isolated whole heart models.

Temporal analysis of the depolarization wave of healed myocardial infarction in body surface potential mapping.

BACKGROUND: We studied the ability of different time segments of the depolarization wave recorded with body surface potential mapping (BSPM) to detect and localize myocardial infarction (MI). METHODS: BSPM was recorded in 24 patients with remote MI and in 24 healthy controls. Cine and contrast-enhanced magnetic resonance imaging (MRI) was used as a reference method. Patients were grouped according to anatomical location of their MI. The QRS complex was divided into six temporally equal segments, for which time integrals were calculated. RESULTS: The time segments of the QRS complex showed different MI detection capability depending on MI location. For anterior infarction the second segment of the QRS complex was the best in MI detection and the optimal area was on the right inferior quadrant of the thorax (time integral average -1.5 +/- 1.8 mVms patients, 1.0 +/- 1.6 mVms controls, P = 0.002). For lateral infarction the first segment of the QRS complex performed best and the optimal area for MI detection was the left fourth intercostal area (time integral average 1.8 +/- 1.0 mVms patients, 0.7 +/- 0.5 mVms controls, P = 0.024). For inferior and posterior MI the mid-phases of the QRS complex were the best and the optimal area was the mid-inferior area of the thorax (time integral average -6.2 +/- 8.3 mVms patients, 3.3 +/- 4.3 mVms controls, P = 0.002; -9.1 +/- 6.1 mVms patients, 0.6 +/- 7.1 mVms controls, P = 0.001, respectively). CONCLUSIONS: Time segment analysis of the depolarization wave offers potential for improving the detection and localization of healed MI.

Baseline reconstruction for localization of rapid ventricular tachycardia from body surface potential maps.

Determination of an accurate electrocardiographic (ECG) baseline is generally needed for localization of ventricular arrhythmias with body surface potential mapping (BSPM). We suggest a novel signal processing method for ECG baseline reconstruction during monomorphic ventricular tachycardias (VT). The method is based on an assumption that VT consists of similar ventricular extrasystolic beats with overlapping depolarization and repolarization. The sequential reconstruction algorithm utilizes information of small variations in the heart rate and yields a non-overlapping QRST-signal, provided that the measurement set-up has a high enough temporal resolution to avoid distortions due to sampling differences and misalignment of individual beats. The reconstructed QRST-signal is utilized to subtract overlapping T-waves from the QRS complexes during VT. The use of the method is demonstrated with clinically measured BSPM data.

Spatiotemporal characterization of paced cardiac activation with body surface potential mapping and self-organizing maps.

In this study self-organizing maps (SOM) were utilized for spatiotemporal analysis and classification of body surface potential mapping (BSPM) data. Altogether 86 cardiac depolarization (QRS) sequences paced by a catheter in 18 patients were included. Spatial BSPM distributions at every 5 ms over the QRS complex were first presented to an untrained SOM. The learning process of the SOM units organized the maps in such a way that similar BSPMs are represented in particular areas of the SOM network. Thereafter, time trajectories and distance maps were created on the trained SOM from sequential maps in a selected paced QRS. The trajectories and distance maps can be applied as such for the localization of abnormal ventricular activation, as well as quantitative input for statistical classification. The results indicate that the method has potential for locating endocardial sites of abnormal ventricular activation, despite the patient material being too limited to provide a reliable statistical evaluation of the source localization accuracy.

A 3-D model-based registration approach for the PET, MR and MCG cardiac data fusion.

In this paper, a new approach is presented for the assessment of a 3-D anatomical and functional model of the heart including structural information from magnetic resonance imaging (MRI) and functional information from positron emission tomography (PET) and magnetocardiography (MCG). The method uses model-based co-registration of MR and PET images and marker-based registration for MRI and MCG. Model-based segmentation of MR anatomical images results in an individualized 3-D biventricular model of the heart including functional parameters from PET and MCG in an easily interpretable 3-D form.