Multi-channel whole-head OPM-MEG: Helmet design and a comparison with a conventional system

Abstract

Magnetoencephalography (MEG) is a powerful technique for functional neuroimaging, offering a non-invasive window on brain electrophysiology. MEG systems have traditionally been based on cryogenic sensors which detect the small extracranial magnetic fields generated by synchronised current in neuronal assemblies, however, such systems have fundamental limitations. In recent years, non-cryogenic quantum-enabled sensors, called optically-pumped magnetometers (OPMs), in combination with novel techniques for accurate background magnetic field control, have promised to lift those restrictions offering an adaptable, motion-robust MEG system, with improved data quality, at reduced cost. However, OPM-MEG remains a nascent technology, and whilst viable systems exist, most employ small numbers of sensors sited above targeted brain regions. Here, building on previous work, we construct a wearable OPM-MEG system with ‘whole-head’ coverage based upon commercially available OPMs, and test its capabilities to measure alpha, beta and gamma oscillations. We design two methods for OPM mounting; a flexible (EEG-like) cap and rigid (additively-manufactured) helmet. Whilst both designs allow for high quality data to be collected, we argue that the rigid helmet offers a more robust option with significant advantages for reconstruction of field data into 3D images of changes in neuronal current. Using repeat measurements in two participants, we show signal detection for our device to be highly robust. Moreover, via application of source-space modelling, we show that, despite having 5 times fewer sensors, our system exhibits comparable performance to an established cryogenic MEG device. While significant challenges still remain, these developments provide further evidence that OPM-MEG is likely to facilitate a step change for functional neuroimaging.

Data-driven beamforming techniques to attenuate ballistocardiogram (BCG) artefacts in EEG-fMRI without detecting cardiac pulses in electrocardiography (ECG) recordings

“…Considering the effect of the data-driven beamforming approaches, the beamforming BCG corrected data revealed significantly greater motor beta ERD as compared to the beamforming+AAS BCG corrected data, whereas there was no significant difference in the visual alpha ERD. Additionally, the locations of visual alpha VEs in the visual cortex were consistent with the previous findings when the visual stimuli were used to trigger movement executions (Brookes et al, 2005;Scheeringa et al, 2011;Wilson et al, 2019), whereas the locations of motor beta VEs were found as expected in the right motor cortex contralateral to the left-hand button presses (Hill et al, 2020;Wilson et al, 2019). Based on these robust visual alpha ERD and motor beta ERD and VE locations, our findings revealed that our data-driven beamforming approaches did not only suppress the BCG artefacts, but also recovered meaningful task-based neural signals induced by the ANT task (Fan et al, 2007;Marshall et al, 2015).…”

“…Since the alpha and beta ERD exhibits power reduction when compared to the baseline, signal to noise ratios (SNRs) at the sensor and source levels were calculated as the absolute value of the difference. Then group mean SNR value was calculated by averaging SNR value across all trials first and then across subjects as suggested in Hill et al (2020), for each electrode (non-BCG corrected; AAS BCG corrected) and each VE location (beamforming+AAS BCG corrected; beamforming BCG corrected). The active and control baseline time windows were consistent throughout the data analysis.…”

Measuring functional connectivity with wearable MEG

“…"wearable" systems in which (if background fields are controlled appropriately) subjects can move their head freely during data acquisition (Boto et al, 2018). OPM arrays are beginning to be developed with up to 50 sensors surrounding the head (Hill et al, 2020) and there is a growing argument that these deviceswhich are also cheaper than conventional MEGwill ultimately supersede the current generation of MEG scanners. However, OPM-MEG remains a nascent technology and if the functional neuroimaging field is to gain confidence in it, OPMbased systems must be able to do everything a SQUID system can do.…”

Recent advances in the noninvasive detection of high-frequency oscillations in the human brain

“…OPMs is less affected by the head movement compared to conventional MEG, and can be worn more conveniently because of the sensors' positions on the scalp (Boto et al 2019). Although OPMs is not yet used clinically, there have been some recent studies using OPMs in several areas (Barry et al 2019;Borna et al 2020;Boto et al 2019;Elzenheimer et al 2020;Hill et al 2020;Iivanainen et al 2020;Lin et al 2019;Nardelli et al 2019). OPMs has not yet been used in HFO research, but this is a very promising future application of this technology.…”