Realistic three-layer head phantom for optically pumped magnetometer-based magnetoencephalography

The advent of optically pumped magnetometer-based magnetoencephalography (OPM-MEG) has introduced new tools for neuroscience and clinical research. As it is still under development, the achievable performance of OPM-MEG remains to be tested, particularly in terms of source localization accuracy, which can be influenced by various factors, including software and hardware aspects. A feasible approach to comprehensively test the performance of the OPM-MEG system is to utilize a phantom that simulates the actual electrophysiological properties of the head while ensuring the precise locations of dipole sources. However, conventional water or dry phantoms can only simulate a single-sphere head model. In this work, a more realistic three-layer phantom was designed and fabricated. The proposed phantom included the scalp, skull, and cortex tissues of the head, as well as the simulated dipole sources. The scalp and cortex tissues were simulated using an electrolyte solution, while the dipole source was constructed from a coaxial cable. All main structures in the phantom were produced using 3D printing techniques, making the phantom easy to manufacture. The fabricated phantom was tested on a 36-channel OPM-MEG system, and the results showed that the dipole source inside the phantom could generate a magnetic field distribution on the scalp that was close to its theoretical values. The average source localization accuracy of 5.51 mm verified the effectiveness of the designed phantom and the performance of our OPM-MEG system. This work provides an effective test platform for OPM-MEG.

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.

OMMR: Co-registration toolbox of OPM-MEG and MRI

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPM-MEG) has shown better flexibility in sensor configuration compared with the conventional superconducting quantum interference devices-based MEG system while being better suited for all-age groups. However, this flexibility presents challenges for the co-registration of MEG and magnetic resonance imaging (MRI), hindering adoption. This study presents a toolbox called OMMR, developed in Matlab, that facilitates the co-registration step for researchers and clinicians. OMMR integrates the co-registration methods of using the electromagnetic digitization system and two types of optical scanners (the structural-light and laser scanner). As the first open-source co-registration toolbox specifically for OPM-MEG, the toolbox aims to standardize the co-registration process and set the ground for future applications of OPM-MEG.

Co-registration Comparison of On-Scalp Magnetoencephalography and Magnetic Resonance Imaging

Magnetoencephalography (MEG) can non-invasively measure the electromagnetic activity of the brain. A new type of MEG, on-scalp MEG, has attracted the attention of researchers recently. Compared to the conventional SQUID-MEG, on-scalp MEG constructed with optically pumped magnetometers is wearable and has a high signal-to-noise ratio. While the co-registration between MEG and magnetic resonance imaging (MRI) significantly influences the source localization accuracy, co-registration error requires assessment, and quantification. Recent studies have evaluated the co-registration error of on-scalp MEG mainly based on the surface fit error or the repeatability error of different measurements, which do not reflect the true co-registration error. In this study, a three-dimensional-printed reference phantom was constructed to provide the ground truth of MEG sensor locations and orientations relative to MRI. The co-registration performances of commonly used three devices—electromagnetic digitization system, structured-light scanner, and laser scanner—were compared and quantified by the indices of final co-registration errors in the reference phantom and human experiments. Furthermore, the influence of the co-registration error on the performance of source localization was analyzed via simulations. The laser scanner had the best co-registration accuracy (rotation error of 0.23° and translation error of 0.76 mm based on the phantom experiment), whereas the structured-light scanner had the best cost performance. The results of this study provide recommendations and precautions for researchers regarding selecting and using an appropriate device for the co-registration of on-scalp MEG and MRI.

Automatic coregistration of MRI and on-scalp MEG

BACKGROUND

Recent progress in optically pumped magnetometers (OPMs) and high-temperature superconducting quantum interference devices (SQUIDs) has facilitated the development of an on-scalp magnetoencephalography (MEG) system that offers high signal intensity and flexibility at a lower cost. While the on-scalp sensor array has high flexibility, it brings new challenges to accurate sensor-to-brain coregistration, which is essential for MEG source localization.

NEW METHOD

A novel automatic filtering algorithm based on plane segmentation was proposed to locate on-scalp MEG sensors in 3D images reconstructed from optical scanning. Global image registration was employed for the automatic alignment of anatomical images and sensor positions.

RESULTS

Seventy-one sensor dummies on the scalp were located and registered to brain anatomical images. The deviations of the sensor location and orientation from the averaged result of 10 measurements were less than 1 mm and 0.6°, respectively. The entire process could be completed in less than 4 min.

COMPARISON WITH EXISTING METHODS

Compared with existing methods that involve various manual procedures, such as moving digitizers to fiducials and repeatedly pulling out sensors, our proposed coregistration method is more efficient and accurate.

CONCLUSION

An automatic method for the coregistration of anatomical structure and on-scalp sensors that will have a large impact on the practical use of on-scalp MEG is developed.

Esthetic treatment planning with digital animation of the smile dynamics: A technique to create a 4-dimensional virtual patient

A method is presented for obtaining a virtual 4-dimensional patient that replicates the intended esthetic treatment. The process involves facial and intraoral scanning to acquire records and software manipulation to enable a virtual waxing of the smile. Once the digital design is complete, patient information can be merged to generate an animated video of the projected rehabilitation, displaying movement and smile dynamics. This strategy provides a noninvasive and reliable diagnostic tool for predicting clinical outcomes.

Dependence of stimulus-induced rotary saturation on the direction of target oscillating magnetic fields: A phantom and simulation study

Several noninvasive techniques for the direct measurement of the neuronal activity using magnetic resonance imaging (MRI) have recently been reported. As a promising candidate, we focus on a spin-lock MRI sequence (i.e., stimulus-induced rotary saturation (SIRS)) directly measuring a tiny oscillating magnetic field. Previous phantom studies on SIRS have applied the target oscillating magnetic field parallel to the direction of the static magnetic field B₀. However, in practice, the neuromagnetic fields are not always aligned in the same direction as in such a condition. This study investigates the MR signal changes during SIRS when the target magnetic field direction is not the same as that of the B₀ field through both phantom experiments and Bloch simulations. The experimental results indicate that only the target magnetic field component along the B₀ field affects the signal change, indicating that SIRS has partial sensitivity, even if the target magnetic fields are tilted from the B₀ field. Furthermore, the simulation results show good agreements with the experimental results. These results clarify the sensitivity direction of SIRS-based fMRI and lead to the possibility that the direction of the generated neuromagnetic fields can be estimated, such that we can separate directional information from the other information contained in neuromagnetic fields (e.g., phase information).

A comparison of automated and manual co-registration for magnetoencephalography

Magnetoencephalography (MEG) is a neuroimaging technique that accurately captures the rapid (sub-millisecond) activity of neuronal populations. Interpretation of functional data from MEG relies upon registration to the participant’s anatomical MRI. The key remaining step is to transform the participant’s MRI into the MEG head coordinate space. Although both automated and manual approaches to co-registration are available, the relative accuracy of two approaches has not been systematically evaluated. The goal of the present study was to compare the accuracy of manual and automated co-registration. Resting MEG and T1-weighted MRI data were collected from 90 participants. Automated and manual co-registration were performed on the same subjects, and the inter-method reliability of the two methods assessed using the intra-class correlation. Median co-registration error for both methods was within acceptable limits. Inter-method reliability was in the “good” range for co-registration error, and the “good” to “excellent” range for translation and rotation. These results suggest that the output of the automated co-registration procedure is comparable to that achieved using manual co-registration.

On-scalp MEG sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration

Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individual's head. In conventional MEG, the positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subject's head shape and size. The positions and orientations of on-scalp sensors must therefore be measured at every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around. In this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subject's head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-T_c SQUIDs). The method allows individually localizing the sensor positions, orientations, and responsivities with high accuracy using only a short averaging time (≤ 2 ​mm, < 3° and < 3%, respectively, with 1-s averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the accuracy of position and orientation (< 1 ​mm and < 1°, respectively, with 1-s coil recordings). We demonstrate source localization of on-scalp recorded somatosensory evoked activity based on co-registration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 4.2 ​mm) with those based on a commercial, whole-head MEG system.

SPOT3D: Spatial positioning toolbox for head markers using 3D scans

Recent studies have highlighted the importance of an accurate individual head model for reliably using high-density electroencephalography (hdEEG) as a brain imaging technique. Correct identification of sensor positions is fundamental for accurately estimating neural activity from hdEEG recordings. We previously introduced a method of automated localization and labelling of hdEEG sensors using an infrared colour-enhanced 3D scanner. Here, we describe an extension of this method, the spatial positioning toolbox for head markers using 3D scans (SPOT3D), which integrates a graphical user interface (GUI). This enables the correction of imprecisions in EEG sensor positioning and the inclusion of additional head markers. The toolbox was validated using 3D scan data collected in four participants wearing a 256-channel hdEEG cap. We quantified the misalignment between the 3D scan and the head shape, and errors in EEG sensor locations. We assessed these parameters after using the automated approach and after manually adjusting its results by means of the GUI. The GUI overcomes the main limitations of the automated method, yielding enhanced precision and reliability of head marker positioning.

Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG-MRI

With a hybrid magnetoencephalography (MEG)-MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. In the ULF MRI, the profiles are independent of the sample and therefore well-defined. In the most basic form, the spatial information of the profiles, captured in parallel ULF-MR acquisitions, is used to find the exact coordinate transformation required. We assessed our calibration method by simulations assuming a helmet-shaped pickup-coil-array geometry. Using a carefully constructed objective function and sufficient approximations, even with low-SNR images, sub-voxel and sub-millimeter calibration accuracy were achieved. After the calibration, distortion-free MRI and high spatial accuracy for MEG source localization can be achieved. For an accurate sensor-array geometry, the co-registration and associated errors are eliminated, and the positional error can be reduced to a negligible level.

Optical Co-registration of MRI and On-scalp MEG

To estimate the neural generators of magnetoencephalographic (MEG) signals, MEG data have to be co-registered with an anatomical image, typically an MR image. Optically-pumped magnetometers (OPMs) enable the construction of on-scalp MEG systems providing higher sensitivity and spatial resolution than conventional SQUID-based MEG systems. We present a co-registration method that can be applied to on-scalp MEG systems, regardless of the number of sensors. We apply a structured-light scanner to create a surface mesh of the subject’s head and the sensor array, which we fit to the MR image. We quantified the reproducibility of the mesh and localised current dipoles with a phantom. Additionally, we measured somatosensory evoked fields (SEFs) to median nerve stimulation and compared the dipole positions between on-scalp and SQUID-based systems. The scanner reproduced the head surface with <1 mm error. Phantom dipoles were localised with 2.1 mm mean error. SEF dipoles corresponding to the P35m response for OPMs were well localised to the somatosensory cortex, while SQUID dipoles for two subjects were erroneously localised to the motor cortex. The developed co-registration method is inexpensive, fast and can easily be applied to on-scalp MEG. It is more convenient than traditional co-registration methods while also being more accurate.

Accessing knowledge of the 'here and now': a new technique for capturing electromagnetic markers of orientation processing

OBJECTIVE

The ability to orient with respect to the current context (e.g. current time or location) is crucial for daily functioning, and is used to measure overall cognitive health across many frontline clinical assessments. However, these tests are often hampered by their reliance on verbal probes (e.g. 'What city are we in?') in evaluating orientation. Objective, physiology-based measures of orientation processing are needed, but no such measures are currently in existence. We report the initial development of potential brainwave-based markers of orientation processing as characterized using electroencephlography (EEG) and magnetoencephalography (MEG).

APPROACH

An auditory stimulus sequence embedded with words corresponding to orientation-relevant (i.e. related to the 'here and now') and orientation-irrelevant (i.e. unrelated to the current context) conditions was used to elicit orientation processing responses. EEG/MEG data, in concert with clinical assessments, were collected from 29 healthy adults. Analysis at sensor and source levels identified and characterized neural signals related to orientation processing.

MAIN RESULTS

Orientation-irrelevant stimuli elicited increased negative amplitude in EEG-derived event-related potential (ERP) waveforms during the 390-570 ms window (p  <  0.05), with cortical activations across the left frontal, temporal, and parietal regions. These effects are consistent with the well-known N400 response to semantic incongruence. In contrast, ERP responses to orientation-relevant stimuli exhibited increased positive amplitude during the same interval (p  <  0.05), with activations across the bilateral temporal and parietal regions. Importantly, these differential responses were robust at the individual level, with machine-learning classification showing high accuracy (89%), sensitivity (0.88) and specificity (0.90).

SIGNIFICANCE

This is the first demonstration of a neurotechnology platform that elicits, captures, and evaluates electrophysiological markers of orientation processing. We demonstrate neural responses to orientation stimuli that are validated across EEG and MEG modalities and robust at the individual level. The extraction of physiology-based markers through this technique may enable improved objective brain functional evaluation in clinical applications.

Requirements for Coregistration Accuracy in On-Scalp MEG

Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual's head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be [Formula: see text] and [Formula: see text], respectively.

Variability and bias between magnetoencephalography systems in non-invasive localization of the primary somatosensory cortex

OBJECTIVES

Magnetoencephalography (MEG) provides functional neuroimaging data for pre-surgical planning in patients with epilepsy or brain tumour. For mapping the primary somatosensory cortex (S1), MEG data are acquired while a patient undergoes median nerve stimulation (MNS) to localize components of the somatosensory evoked field (SEF). In clinical settings, only one MEG imaging session is usually possible due to limited resources. As such, it is important to have an a priori estimate of the expected variability in localization. Variability in S1 localization between mapping sessions using the same MEG system has been previously measured as 8 mm. There are different types of MEG systems available with varied hardware and software, and it is not known how using a different MEG system will impact on S1 localization.

PATIENTS AND METHODS

In our study, healthy participants underwent the MNS procedure with two different MEG systems (Vector View and CTF). We compared the location, amplitude and latency of SEF components between data from each system to quantify variability and bias between MEG systems.

RESULTS

We found 8-11 mm variability in S1 localization between the two MEG systems, and no evidence for a systematic bias in location, amplitude or latency between the two systems.

CONCLUSION

These findings suggest that S1 localization is not biased by the type of MEG system used, and that differences between the two systems are not a major contributor to variability in localization.

Localizing on-scalp MEG sensors using an array of magnetic dipole coils

Accurate estimation of the neural activity underlying magnetoencephalography (MEG) signals requires co-registration i.e., determination of the position and orientation of the sensors with respect to the head. In modern MEG systems, an array of hundreds of low-Tc SQUID sensors is used to localize a set of small, magnetic dipole-like (head-position indicator, HPI) coils that are attached to the subject's head. With accurate prior knowledge of the positions and orientations of the sensors with respect to one another, the HPI coils can be localized with high precision, and thereby the positions of the sensors in relation to the head. With advances in magnetic field sensing technologies, e.g., high-Tc SQUIDs and optically pumped magnetometers (OPM), that require less extreme operating temperatures than low-Tc SQUID sensors, on-scalp MEG is on the horizon. To utilize the full potential of on-scalp MEG, flexible sensor arrays are preferable. Conventional co-registration is impractical for such systems as the relative positions and orientations of the sensors to each other are subject-specific and hence not known a priori. Herein, we present a method for co-registration of on-scalp MEG sensors. We propose to invert the conventional co-registration approach and localize the sensors relative to an array of HPI coils on the subject's head. We show that given accurate prior knowledge of the positions of the HPI coils with respect to one another, the sensors can be localized with high precision. We simulated our method with realistic parameters and layouts for sensor and coil arrays. Results indicate co-registration is possible with sub-millimeter accuracy, but the performance strongly depends upon a number of factors. Accurate calibration of the coils and precise determination of the positions and orientations of the coils with respect to one another are crucial. Finally, we propose methods to tackle practical challenges to further improve the method.

Head movement compensation in real-time magnetoencephalographic recordings

Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we provide preliminary validation of a novel analysis technique, real-time source estimation (rtSE), that measures head movement and generates corrected current source time course estimates in real-time. rtSE was applied while recording a calibrated phantom to determine phantom position localization accuracy and source amplitude estimation accuracy under stationary and moving conditions. Results were compared to off-line analysis methods to assess validity of the rtSE technique. The rtSE method allowed for accurate estimation of current source activity at the source-level in real-time, and accounted for movement of the source due to changes in phantom position. The rtSE technique requires modifications and specialized analysis of the following MEG work flow steps.•Data acquisition•Head position estimation•Source localization•Real-time source estimation This work explains the technical details and validates each of these steps.

Faster and improved 3-D head digitization in MEG using Kinect

Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices—Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows—for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20–30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.

Validity of the 3D VECTRA photogrammetric surface imaging system for cranio-maxillofacial anthropometric measurements

PURPOSE

The use of three-dimensional (3D) photography for anthropometric measurements is of increasing interest, especially in the cranio-maxillofacial field. Before standard implementation, accurate determination of the precision and accuracy of each system is mandatory.

METHODS

A mannequin head was labelled with 52 landmarks, and 28 three-dimensional images were taken using a commercially available five-pod 3D photosystem (3D VECTRA; Canfield, Fairfield, NJ) in different head positions. Distances between the landmarks were measured manually using a conventional calliper and compared with the digitally calculated distances acquired from labelling by two independent observers. The experimental set-up accounted for clinical circumstances by varying the positioning (vertical, horizontal, sagittal) of the phantom.

RESULTS

In the entire calliper measurement data set (n = 410), a significant difference (p = 0.02) between the directly measured and corresponding virtually calculated distances was found. The mean aberration between both modalities covering all data was 7.96 mm. No differences (p = 0.94) between the two groups were found using a cut-off of 10 % (leaving n = 369 distances) due to considerable errors in direct measurements and the necessary manual data translation. The mean diversity of both measurement modalities after cut-off was 1.33 mm (maximum, 6.70 mm). Inter-observer analysis of all 1,326 distances showed no difference (p = 0.99; maximal difference, 0.58 mm) in the digital measurements.

CONCLUSION

The precision and accuracy of this five-pod 3D photosystem suggests its suitability for clinical applications, particularly anthropometric studies. Three-hundred-and-sixty degree surface-contour mapping of the craniofacial region within milliseconds is particularly useful in paediatric patients. Proper patient positioning is essential for high-quality imaging.