Movement correction and clinical implementation of wearable magnetoencephalography (MEG)

Magnetoencephalography (MEG) is the non-invasive measurement of magnetic fields due to neuronal current flow. The magnitude of the magnetic fields (10 fT to 1000 fT) is millions of times smaller than the Earth’s static field. Consequently, highly sensitive magnetic sensors are required for MEG. Until recently, MEG systems have been based on sensors requiring cryogenic cooling. Hardware limitations from this cooling have made MEG systems large, immobile and expensive. In recent years, Optically Pumped Magnetometers (OPMs) have become viable sensors with which to measure neuromagnetic fields. These can be placed directly on the scalp. This wearability means that the participant is no longer required to remain still and the cost of the system, both financial and in terms of space, is generally lower. The freedom of movement opens up new neuroscientific and clinical applications. However, this new system is not without limitations. Movement in particular leads to artefacts unlike those previously seen in MEG; the OPM properties (gain, sensitive axis orientation, phase) are dependent on the ambient magnetic field at the sensor, which changes with position. In this thesis, we look at the impact of movement on OPM based MEG (OP-MEG) and how it can be reduced. In Chapter 2, we look into the cause of movement artefacts in OP-MEG, by mapping the spatial variation in the background magnetic field in our OP-MEG system. We show that the field varies both spatially and temporally, and that by modelling it we can reduce the interference in an OP-MEG recording. In Chapters 3 and 4, we correct for this changing field in real-time, first in simulation and then empirically. Based on the simulation results, we updated our empirical method to remove reliance on recording the position of the participant and to minimise time delays in providing the correction. Finally, in Chapters 5 and 6, we record interictal (between seizure) and ictal (seizure) OP-MEG in patients with epilepsy, while considering the impact movement has on the recordings and interictal event detection

Multimodal Integration: fMRI, MRI, EEG, MEG

This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specically, we look at correlative analysis, decomposition techniques, equivalent dipole tting, distributed sources modeling, beamforming, and Bayesian methods. Due to difculties in assessing ground truth of a combined signal in any realistic experiment difculty further confounded by lack of accurate biophysical models of BOLD signal we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difculties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research