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Towards a New Era of MEG Imaging: New Technologies and Clinical Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 20245

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Prof. Dr. Etienne Labyt

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Guest Editor

CEA LETI, MINATEC Campus, University Grenoble Alpes, F-38000 Grenoble, France
Interests: magnetoencephalography (MEG); electroencephalography (EEG); magnetocardiography (MCG); optically pumped magnetometers; signal processing; epilepsy; MEG clinical applications

Special Issue Information

Dear Colleagues,

Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording the tiny magnetic fields produced by electrical currents occurring naturally in the brain. MEG requires ultrasensitive sensors. Since the first brain magnetic field recording performed by D. Cohen in 1968, SQUIDs (superconducting quantum interference devices) are currently used. These sensors require cooling with cryogenic fluid (liquid helium) for reaching their operating mode, which is the basis of the major limitations of SQUID-based MEG. Faced with these limitations, new sensor technologies are currently being actively developed and assessed in comparison to the gold standard sensors used for recording brain activity: SQUID MEG as well as electroencephalography, that records the electrical counterpart of brain activity. These new sensors, which can be placed directly on the subject's scalp, pave the way for smaller magnetic shields, or even body-sized shieldings or active compensation approaches for unshielded biomagnetic signal recordings. This also makes it possible to envisage new uses and clinical applications for MEG. This Special Issue aims at covering the latest developments in new sensor technologies applied to biomagnetic recordings, but also the developments in new magnetic shielding approaches, passive as well as active, and current studies aiming at evaluating, in clinical conditions or by using data simulation, MEG based on these new sensors. Both review articles and original research papers are solicited.

Prof. Dr. Etienne Labyt
Guest Editor

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Keywords

optically pumped magnetometers (OPM)
high temperature SQUID (HTc SQUID)
giant magneto resistive (GMR) sensors
magnetoencephalography
magnetic shielding
biomagnetism

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Published Papers (6 papers)

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Research

11 pages, 3695 KiB

Open AccessCommunication

An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts

by Niall Holmes, Richard Bowtell, Matthew J Brookes and Samu Taulu

Sensors 2023, 23(14), 6537; https://doi.org/10.3390/s23146537 - 20 Jul 2023

Cited by 6 | Viewed by 1844

Abstract

The signal space separation (SSS) method is routinely employed in the analysis of multichannel magnetic field recordings (such as magnetoencephalography (MEG) data). In the SSS method, signal vectors are posed as a multipole expansion of the magnetic field, allowing contributions from sources internal and external to a sensor array to be separated via computation of the pseudo-inverse of a matrix of the basis vectors. Although powerful, the standard implementation of the SSS method on MEG systems based on optically pumped magnetometers (OPMs) is unstable due to the approximate parity of the required number of dimensions of the SSS basis and the number of channels in the data. Here we exploit the hierarchical nature of the multipole expansion to perform a stable, iterative implementation of the SSS method. We describe the method and investigate its performance via a simulation study on a 192-channel OPM-MEG helmet. We assess performance for different levels of truncation of the SSS basis and a varying number of iterations. Results show that the iterative method provides stable performance, with a clear separation of internal and external sources. Full article

(This article belongs to the Special Issue Towards a New Era of MEG Imaging: New Technologies and Clinical Applications)

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15 pages, 4170 KiB

Open AccessArticle

Naturalistic Hyperscanning with Wearable Magnetoencephalography

by Niall Holmes, Molly Rea, Ryan M. Hill, Elena Boto, James Leggett, Lucy J. Edwards, Natalie Rhodes, Vishal Shah, James Osborne, T. Mark Fromhold, Paul Glover, P. Read Montague, Matthew J. Brookes and Richard Bowtell

Sensors 2023, 23(12), 5454; https://doi.org/10.3390/s23125454 - 9 Jun 2023

Cited by 11 | Viewed by 3146

Abstract

The evolution of human cognitive function is reliant on complex social interactions which form the behavioural foundation of who we are. These social capacities are subject to dramatic change in disease and injury; yet their supporting neural substrates remain poorly understood. Hyperscanning employs functional neuroimaging to simultaneously assess brain activity in two individuals and offers the best means to understand the neural basis of social interaction. However, present technologies are limited, either by poor performance (low spatial/temporal precision) or an unnatural scanning environment (claustrophobic scanners, with interactions via video). Here, we describe hyperscanning using wearable magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs). We demonstrate our approach by simultaneously measuring brain activity in two subjects undertaking two separate tasks—an interactive touching task and a ball game. Despite large and unpredictable subject motion, sensorimotor brain activity was delineated clearly, and the correlation of the envelope of neuronal oscillations between the two subjects was demonstrated. Our results show that unlike existing modalities, OPM-MEG combines high-fidelity data acquisition and a naturalistic setting and thus presents significant potential to investigate neural correlates of social interaction. Full article

(This article belongs to the Special Issue Towards a New Era of MEG Imaging: New Technologies and Clinical Applications)

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16 pages, 2051 KiB

Open AccessArticle

Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer

by Rach Dawson, Carolyn O’Dwyer, Edward Irwin, Marcin S. Mrozowski, Dominic Hunter, Stuart Ingleby, Erling Riis and Paul F. Griffin

Sensors 2023, 23(8), 4007; https://doi.org/10.3390/s23084007 - 15 Apr 2023

Cited by 5 | Viewed by 2357

Abstract

Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (

T / \sqrt{Hz}

), is optimised through direct measurement of the noise floor, and indirectly through measurement of the on-resonance demodulated gradient (mV/nT) of the zero-field resonance. Both methods provide a viable strategy for the optimisation of sensitivity through effective control of the OPM’s operational parameters. Ultimately, this machine learning approach increased the optimal sensitivity from 500

fT / \sqrt{Hz}

< 109 fT / \sqrt{Hz}

. The flexibility and efficiency of the ML approaches can be utilised to benchmark SERF OPM sensor hardware improvements, such as cell geometry, alkali species and sensor topologies. Full article

(This article belongs to the Special Issue Towards a New Era of MEG Imaging: New Technologies and Clinical Applications)

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16 pages, 5177 KiB

Open AccessArticle

Simulation Study of Different OPM-MEG Measurement Components

by Urban Marhl, Tilmann Sander and Vojko Jazbinšek

Sensors 2022, 22(9), 3184; https://doi.org/10.3390/s22093184 - 21 Apr 2022

Cited by 10 | Viewed by 3324

Abstract

Magnetoencephalography (MEG) is a neuroimaging technique that measures the magnetic fields of the brain outside of the head. In the past, the most suitable magnetometer for MEG was the superconducting quantum interference device (SQUID), but in recent years, a new type has also been used, the optically pumped magnetometer (OPM). OPMs can be configured to measure multiple directions of magnetic field simultaneously. This work explored whether combining multiple directions of the magnetic field lowers the source localization error of brain sources under various conditions of noise. We simulated dipolar-like sources for multiple configurations of both SQUID- and OPM-MEG systems. To test the performance of a given layout, we calculated the average signal-to-noise ratio and the root mean square of the simulated magnetic field; furthermore, we evaluated the performance of the dipole fit. The results showed that the field direction normal to the scalp yields a higher signal-to-noise ratio and that ambient noise has a much lower impact on its localization error; therefore, this is the optimal choice for source localization when only one direction of magnetic field can be measured. For a low number of OPMs, combining multiple field directions greatly improves the source localization results. Lastly, we showed that MEG sensors that can be placed closer to the brain are more suitable for localizing deeper sources. Full article

(This article belongs to the Special Issue Towards a New Era of MEG Imaging: New Technologies and Clinical Applications)

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18 pages, 5908 KiB

Open AccessArticle

Performance Analysis of Optically Pumped ⁴He Magnetometers vs. Conventional SQUIDs: From Adult to Infant Head Models

by Saeed Zahran, Mahdi Mahmoudzadeh, Fabrice Wallois, Nacim Betrouni, Philippe Derambure, Matthieu Le Prado, Agustin Palacios-Laloy and Etienne Labyt

Sensors 2022, 22(8), 3093; https://doi.org/10.3390/s22083093 - 18 Apr 2022

Cited by 13 | Viewed by 3525

Abstract

Optically pumped magnetometers (OPMs) are new, room-temperature alternatives to superconducting quantum interference devices (SQUIDs) for measuring the brain’s magnetic fields. The most used OPM in MagnetoEncephaloGraphy (MEG) are based on alkali atoms operating in the spin-exchange relaxation-free (SERF) regime. These sensors do not require cooling but have to be heated. Another kind of OPM, based on the parametric resonance of ⁴He atoms are operated at room temperature, suppressing the heat dissipation issue. They also have an advantageous bandwidth and dynamic range more suitable for MEG recordings. We quantitatively assessed the improvement (relative to a SQUID magnetometers array) in recording the magnetic field with a wearable ⁴He OPM-MEG system through data simulations. The OPM array and magnetoencephalography forward models were based on anatomical MRI data from an adult, a nine-year-old child, and 10 infants aged between one month and two years. Our simulations showed that a ⁴He OPMs array offers markedly better spatial specificity than a SQUID magnetometers array in various key performance areas (e.g., signal power, information content, and spatial resolution). Our results are also discussed regarding previous simulation results obtained for alkali OPM. Full article

(This article belongs to the Special Issue Towards a New Era of MEG Imaging: New Technologies and Clinical Applications)

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17 pages, 4145 KiB

Open AccessArticle

Calibration and Localization of Optically Pumped Magnetometers Using Electromagnetic Coils

by Joonas Iivanainen, Amir Borna, Rasmus Zetter, Tony R. Carter, Julia M. Stephen, Jim McKay, Lauri Parkkonen, Samu Taulu and Peter D. D. Schwindt

Sensors 2022, 22(8), 3059; https://doi.org/10.3390/s22083059 - 15 Apr 2022

Cited by 9 | Viewed by 3793

Abstract

In this paper, we propose a method to estimate the position, orientation, and gain of a magnetic field sensor using a set of (large) electromagnetic coils. We apply the method for calibrating an array of optically pumped magnetometers (OPMs) for magnetoencephalography (MEG). We first measure the magnetic fields of the coils at multiple known positions using a well-calibrated triaxial magnetometer, and model these discreetly sampled fields using vector spherical harmonics (VSH) functions. We then localize and calibrate an OPM by minimizing the sum of squared errors between the model signals and the OPM responses to the coil fields. We show that by using homogeneous and first-order gradient fields, the OPM sensor parameters (gain, position, and orientation) can be obtained from a set of linear equations with pseudo-inverses of two matrices. The currents that should be applied to the coils for approximating these low-order field components can be determined based on the VSH models. Computationally simple initial estimates of the OPM sensor parameters follow. As a first test of the method, we placed a fluxgate magnetometer at multiple positions and estimated the RMS position, orientation, and gain errors of the method to be 1.0 mm, 0.2°, and 0.8%, respectively. Lastly, we calibrated a 48-channel OPM array. The accuracy of the OPM calibration was tested by using the OPM array to localize magnetic dipoles in a phantom, which resulted in an average dipole position error of 3.3 mm. The results demonstrate the feasibility of using electromagnetic coils to calibrate and localize OPMs for MEG. Full article

(This article belongs to the Special Issue Towards a New Era of MEG Imaging: New Technologies and Clinical Applications)

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