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A magnetocardiograph that enables the clear observation of heart magnetic field mappings without magnetically shielded rooms at room temperatures has been successfully manufactured. Compared to widespread electrocardiographs, magnetocardiographs commonly have a higher spatial resolution, which is expected to lead to early diagnoses of ischemic heart disease and high diagnostic accuracy of ventricular arrhythmia, which involves the risk of sudden death. However, as the conventional superconducting quantum interference device (SQUID) magnetocardiographs require large magnetically shielded rooms and huge running costs to cool the SQUID sensors, magnetocardiography is still unfamiliar technology. Here, in order to achieve the heart field detectivity of 1.0 pT without magnetically shielded rooms and enough magnetocardiography accuracy, we aimed to improve the detectivity of tunneling magnetoresistance (TMR) sensors and to decrease the environmental and sensor noises with a mathematical algorithm. The magnetic detectivity of the TMR sensors was confirmed to be 14.1 pTrms on average in the frequency band between 0.2 and 100 Hz in uncooled states, thanks to the original multilayer structure and the innovative pattern of free layers. By constructing a sensor array using 288 TMR sensors and applying the mathematical magnetic shield technology of signal space separation (SSS), we confirmed that SSS reduces the environmental magnetic noise by −73 dB, which overtakes the general triple magnetically shielded rooms. Moreover, applying digital processing that combined the signal average of heart magnetic fields for one minute and the projection operation, we succeeded in reducing the sensor noise by about −23 dB. The heart magnetic field resolution measured on a subject in a laboratory in an office building was 0.99 pTrms and obtained magnetocardiograms and current arrow maps as clear as the SQUID magnetocardiograph does in the QRS and ST segments. Upon utilizing its superior spatial resolution, this magnetocardiograph has the potential to be an important tool for the early diagnosis of ischemic heart disease and the risk management of sudden death triggered by ventricular arrhythmia.

Background: Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r2 = 0.3–0.8 before SSS and r2 > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r2 > 0.8). Conclusions: After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments.

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising.

Deep brain stimulation (DBS) is an established and effective treatment for several movement disorders and is being developed to treat a host of neuropsychiatric disorders including epilepsy, chronic pain, obsessive compulsive disorder, and depression. However, the neural mechanisms through which DBS produces therapeutic benefits, and in some cases unwanted side effects, in these disorders are only partially understood. Non-invasive neuroimaging techniques that can assess the neural effects of active stimulation are important for advancing our understanding of the neural basis of DBS therapy. Magnetoencephalography (MEG) is a safe, passive imaging modality with relatively high spatiotemporal resolution, which makes it a potentially powerful method for examining the cortical network effects of DBS. However, the degree to which magnetic artifacts produced by stimulation and the associated hardware can be suppressed from MEG data, and the comparability between signals measured during DBS-on and DBS-off conditions, have not been fully quantified. The present study used machine learning methods in conjunction with a visual perception task, which should be relatively unaffected by DBS, to quantify how well neural data can be salvaged from artifact contamination introduced by DBS and how comparable DBS-on and DBS-off data are after artifact removal. Machine learning also allowed us to determine whether the spatiotemporal pattern of neural activity recorded during stimulation are comparable to those recorded when stimulation is off. The spatiotemporal patterns of visually evoked neural fields could be accurately classified in all 8 patients with DBS implants during both DBS-on and DBS-off conditions and performed comparably across those two conditions. Further, the classification accuracy for classifiers trained on the spatiotemporal patterns evoked during DBS-on trials and applied to DBS-off trials, and vice versa, were similar to that of the classifiers trained and tested on either trial type, demonstrating the comparability of these patterns across conditions. Together, these results demonstrate the ability of MEG preprocessing techniques, like temporal signal space separation, to salvage neural data from recordings contaminated with DBS artifacts and validate MEG as a powerful tool to study the cortical consequences of DBS.

Unlike EEG sensors, which are attached to the head, MEG sensors are located outside the head surface on a fixed external device. Subject head movements during acquisition thus distort the magnetic field distributions measured by the sensors. Previous studies have looked at the effect of head movements, but no study has comprehensively looked at the effect of head movements across age groups, particularly in infants. Using MEG recordings from subjects ranging in age from 3 months through adults, here we first quantify the variability in head position as a function of age group. We then combine these measured head movements with brain activity simulations to determine how head movements bias source localization from sensor magnetic fields measured during movement. We find that large amounts of head movement, especially common in infant age groups, can result in large localization errors. We then show that proper application of head movement compensation techniques can restore localization accuracy to pre-movement levels. We also find that proper noise covariance estimation (e.g., during the baseline period) is important to minimize localization bias following head movement compensation. Our findings suggest that head position measurement during acquisition and compensation during analysis is recommended for researchers working with subject populations or age groups that could have substantial head movements. This is especially important in infant MEG studies.

MEG and EEG data contain additive correlated noise generated by environmental and physiological sources. To suppress this type of spatially coloured noise, source estimation is often performed with spatial whitening based on a measured or estimated noise covariance matrix. However, artifacts that span relatively small noise subspaces, such as cardiac, ocular, and muscle artifacts, are often explicitly removed by a variety of denoising methods (e.g., signal space projection) before source imaging. Here, we introduce a new approach, the spectral signal space projection (S 3P) algorithm, in which time–frequency (TF)-specific spatial projectors are designed and applied to the noisy TF-transformed data, and whitened source estimation is performed in the TF domain. The approach can be used to derive spectral variants of all linear time domain whitened source estimation algorithms. The denoised sensor and source time series are obtained by the corresponding inverse TF-transform. The method is evaluated and compared with existing subspace projection and signal separation techniques using experimental data. Altogether, S 3P provides an expanded framework for MEG/EEG data denoising and whitened source imaging in both the time and frequency/scale domains. ► MEG and EEG data are corrupted by frequency-specific (FS) spatially coloured noise. ► S 3P suppresses this noise with FS spatial projections and whitened source imaging. ► S 3P performs better than other methods because the noise spatial patterns are FS. ► S 3P provides a new expanded framework for imaging denoised brain oscillations.

The mapping of the sensorimotor cortex gives information about the cortical motor and sensory functions. Typical mapping methods are navigated transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG). The differences between these mapping methods are, however, not fully known. TMS center of gravities (CoGs), MEG somatosensory evoked fields (SEFs), corticomuscular coherence (CMC), and corticokinematic coherence (CKC) were mapped in ten healthy adults. TMS mapping was performed for first dorsal interosseous (FDI) and extensor carpi radialis (ECR) muscles. SEFs were induced by tactile stimulation of the index finger. CMC and CKC were determined as the coherence between MEG signals and the electromyography or accelerometer signals, respectively, during voluntary muscle activity. CMC was mapped during the activation of FDI and ECR muscles separately, whereas CKC was measured during the waving of the index finger at a rate of 3–4 Hz. The maximum CMC was found at beta frequency range, whereas maximum CKC was found at the movement frequency. The mean Euclidean distances between different localizations were within 20 mm. The smallest distance was found between TMS FDI and TMS ECR CoGs and longest between CMC FDI and CMC ECR sites. TMS-inferred localizations (CoGs) were less variable across participants than MEG-inferred localizations (CMC, CKC). On average, SEF locations were 8 mm lateral to the TMS CoGs (p < 0.01). No differences between hemispheres were found. Based on the results, TMS appears to be more viable than MEG in locating motor cortical areas.

A patient with intractable epilepsy, previous right frontal resection, and active vagus nerve stimulation (VNS) developed new onset quasi-continuous twitching around the left eye. Electroencephalography showed no correlate to the orbicularis oculi twitches apart from myographic potentials at the left supraorbital and anterior frontal electrodes. Magnetoencephalography was performed using spatiotemporal signal space separation to suppress magnetic artifacts associated with the VNS apparatus. Magnetoencephalographic source imaging performed on the data back-averaged from the left supraorbital myographic potentials revealed an intrasulcal cortical generator situated in the posterior wall of the right precentral gyrus representing the eye area of the motor homunculus. Localisation par magnétoencéphalographie de la zone oculaire qui correspond à l’homoncule moteur. Un patient atteint d’épilepsie réfractaire chez qui on avait pratiqué une résection frontale du côté droit du cerveau et qu’on avait soumis à une stimulation neuro-vagale (SNV) a fini par développer des contractions musculaires quasi-continues autour de l’œil gauche. Hormis des potentiels myographiques détectés par des électrodes situées dans les régions supraorbitale gauche et frontale antérieure, un électroencéphalogramme (EEG) n’a révélé aucun phénomène corrélatif en lien avec les contractions du muscle orbiculaire de l’œil. Un examen de magnétoencéphalographie (MEG) a été ensuite réalisé au moyen d’une rupture de l’espace entre les signaux spatio-temporels afin d’étouffer les artefacts magnétiques associés à l’appareil de SNV. L’origine de l’imagerie de l’examen de MEG réalisé à partir des données moyennes correspondant aux potentiels myographiques de la région gauche supraorbitale a révélé un générateur cortical situé dans la paroi postérieure du gyrus précentral, lequel représente en fin de compte la zone oculaire correspondant à l’homoncule moteur.

Source localization of interictal spikes in patients with medically refractory epilepsy is the most common clinical application of magnetoencephalography ( MEG ). In recent decades, many patients with intractable epilepsy have been treated with various forms of neurostimulation, including thalamic deep brain stimulation ( DBS ). Patients with suboptimal seizure control after DBS might in some cases benefit from further investigations for resective epilepsy surgery, including MEG source imaging ( MSI ). We sought to determine the feasibility and accuracy of MSI in the setting of active thalamic DBS . Simultaneous EEG / MEG was obtained in a patient using an Elekta 306‐channel MEG system, with high‐frequency (100 Hz) DBS of the thalamic anterior nuclei cycling between on and off states. Magnetic artifacts associated with the DBS apparatus were successfully suppressed using the spatiotemporal signal space separation (t SSS ) method. Electrical stimulation artifact was removed by standard digital low‐pass filtering. Dipole source modeling results for spike foci in frontal and posterior temporal regions were comparable between stimulation on and stimulation off states, and the source solutions corresponded well to the localization of spikes documented by intracranial EEG . MSI is thus feasible and source solutions can be accurate when performed in patients with active thalamic DBS for epilepsy.

Signal space separation (SSS) method is an advanced signal-processing approach that can be used to recover bio-magnetic signal and remove external disturbance in empirical magnetoencephalography (MEG) measurements. SSS is based on the solution of the quasi-static approximation of Maxwell equations (i.e., Laplace’s equation) which can be expressed as linear combinations of spherical harmonic functions. In applying SSS, MEG measurements can be split into two parts: brain signals and external interferences. In this paper, after a brief review of the basics of SSS, we evaluate SSS systematically via computer simulation and real MEG data. In the simulations of this paper, two types of interference sources with magnetic and electric current dipoles are used. The interference suppression effects and the quality of the reconstruction of the interested signal are investigated. Also, the degree of spherical harmonic functions and its relationship with signal reconstruction and interference suppression are studied thoroughly. Finally, we provide objective assessments of the advantages and limitations of the SSS approach, and its practical value in MEG measurements.