On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG

Abstract

AbstractMagnetoencephalography (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 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 (15-30 ms). This corresponded to electroencephalographic (EEG) response components N16 and P23, and was an unexpected observation as these components are usually not observed in conventional MEG. They indicate that on-scalp MEG opens up for a richer registration of the cortical signal, allowing for sensitivity to what are potentially sources in the thalamo-cortical radiation and to quasi-radial sources.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 believe this may be due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We discuss how sensor density and coverage as well as between-phalange source field dissimilarities may influence our hypothesis testing, which we believe to be useful for future benchmarking measurements.