Abstract
Magnetoencephalography (MEG) has a unique capacity to resolve the spatio-temporal development of brain activity from non-invasive measurements. Conventional MEG, however, relies on sensors that sample from a distance (20–40 mm) to the head due to thermal insulation requirements (the MEG sensors function at 4 K in a helmet). A gain in signal strength and spatial resolution may be achieved if sensors are moved closer to the head. Here, we report a study comparing measurements from a seven-channel on-scalp SQUID MEG system to those from a conventional (in-helmet) SQUID MEG system.We compared the spatio-temporal resolution between on-scalp and conventional MEG by comparing the discrimination accuracy for neural activity patterns resulting from stimulating five different phalanges of the right hand. Because of proximity and sensor density differences between on-scalp and conventional MEG, we hypothesized that on-scalp MEG would allow for a more high-resolved assessment of these activity patterns, and therefore also a better classification performance in discriminating between neural activations from the different phalanges.We observed that on-scalp MEG provided better classification performance during an early post-stimulus period (10–20 ms). This corresponded to the electroencephalographic (EEG) component P16/N16 and was an unexpected observation as this component is usually not observed in conventional MEG. This finding shows that on-scalp MEG enables a richer registration of the cortical signal, indicating a sensitivity to what are potentially sources in the thalamo-cortical radiation.We had originally expected that on-scalp MEG would provide better classification accuracy based on activity in proximity to the P60m component compared to conventional MEG. This component indeed allowed for the best classification performance for both MEG systems (60–75%, chance 50%). However, we did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We suggest that this absence of differences is due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We show how the current sensor coverage may have limited our chances to register the necessary between-phalange source field dissimilarities for fair hypothesis testing, an approach we otherwise believe to be useful for future benchmarking measurements.
Highlights
On-scalp magnetoencephalography (MEG) holds great promise for improving MEG in terms of better spatial resolution
We did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We suggest that this absence of differences is due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors
While low-Tc SQUIDs demonstrate noise levels of ~3 fT/√Hz, the very best high-Tc sensors show noise levels of ~10 fT/√Hz (Faley et al, 2006) and often near 50 fT/√Hz (Pfeiffer et al, 2020b). These differences in noise levels mean that a trade-off might be expected between on-scalp and in-helmet sensors, with on-scalp sensors promising the highest gain in signal-to-noise ratio (SNR) for superficial sources (Iivanainen et al, 2017; Riaz et al, 2017)
Summary
On-scalp magnetoencephalography (MEG) holds great promise for improving MEG in terms of better spatial resolution. The present on-scalp SQUID sensor technology, though, has significantly higher noise levels than the state-of-the-art MEG sensors, which are operated at 4 K due to their low critical temperature (low-Tc). While low-Tc SQUIDs (hereafter called in-helmet sensors) demonstrate noise levels of ~3 fT/√Hz, the very best high-Tc sensors show noise levels of ~10 fT/√Hz (Faley et al, 2006) and often near 50 fT/√Hz (Pfeiffer et al, 2020b). These differences in noise levels mean that a trade-off might be expected between on-scalp and in-helmet sensors, with on-scalp sensors promising the highest gain in signal-to-noise ratio (SNR) for superficial sources (Iivanainen et al, 2017; Riaz et al, 2017)
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