Development and Evaluation of Data Processing Techniques in Magnetoencephalography

With MEG, the tiny magnetic fields produced by neuronal currents within the brain can be measured completely non-invasively. But the signals are very small (~100 fT) and often obscured by spontaneous brain activity and external noise. So, a recurrent issue in MEG data analysis is the identification and elimination of this unwanted interference within the recordings. Various strategies exist to meet this purpose. In this thesis, two of these strategies are scrutinized in detail. The first is the commonly used procedure of averaging over trials which is a successfully applied data reduction method in many neurocognitive studies. However, the brain does not always respond identically to repeated stimuli, so averaging can eliminate valuable information. Alternative approaches aiming at single trial analysis are difficult to realize and many of them focus on temporal patterns. Here, a compromise involving random subaveraging of trials and repeated source localization is presented. A simulation study with numerous examples demonstrates the applicability of the new method. As a result, inferences about the generators of single trials can be drawn which allows deeper insight into neuronal processes of the human brain. The second technique examined in this thesis is a preprocessing tool termed Signal Space Separation (SSS). It is widely used for preprocessing of MEG data, including noise reduction by suppression of external interference, as well as movement correction. Here, the mathematical principles of the SSS series expansion and the rules for its application are investigated. The most important mathematical precondition is a source-free sensor space. Using three data sets, the influence of a violation of this convergence criterion on source localization accuracy is demonstrated. The analysis reveals that the SSS method works reliably, even when the convergence criterion is not fully obeyed. This leads to utilizing the SSS method for the transformation of MEG data to virtual sensors on the scalp surface. Having MEG data directly on the individual scalp surface would alleviate sensor space analysis across subjects and comparability with EEG. A comparison study of the transformation results obtained with SSS and those produced by inverse and subsequent forward computation is performed. It shows strong dependence on the relative position of sources and sensors. In addition, the latter approach yields superior results for the intended purpose of data transformation

MEG Source Localization via Deep Learning

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization

Localising epileptiform activity and eloquent cortex using magnetoencephalography

In patients with drug resistant epilepsy, the surgical resection of epileptogenic cortex allows the possibility for seizure freedom, provided that epileptogenic and eloquent brain tissue can be accurately identified prior to surgery. This is often achieved using various techniques including neuroimaging, electroencephalographic (EEG), neuropsychological and invasive measurements. Over the last 20 years, magnetoencephalography (MEG) has emerged as a non-invasive tool that can provide important clinical information to patients with suspected neocortical epilepsy being considered for surgery. The standard clinical MEG analyses to localise abnormalities are not always successful and therefore the development and evaluation of alternative methods are warranted. There is also a continuous need to develop MEG techniques to delineate eloquent cortex. Based on this rationale, this thesis is concerned with the presurgical evaluation of drug resistant epilepsy patients using MEG and consists of two themes: the first theme focuses on the refinement of techniques to functionally map the brain and the second focuses on evaluating alternative techniques to localise epileptiform activity. The first theme involved the development of an alternative beamformer pipeline to analyse Elekta Neuromag data and was subsequently applied to data acquired using a pre-existing and a novel language task. The findings of the second theme demonstrated how beamformer based measures can objectively localise epileptiform abnormalities. A novel measure, rank vector entropy, was introduced to facilitate the detection of multiple types of abnormal signals (e.g. spikes, slow waves, low amplitude transients). This thesis demonstrates the clinical capacity of MEG and its role in the presurgical evaluation of drug resistant epilepsy patients

On-scalp MEG using high-Tc SQUIDs: Measuring brain activity with superconducting magnetometers

This thesis describes work done towards realizing on-scalp magnetoencephalography (MEG) based on high critical temperature (high-Tc) superconducting quantum interference device (SQUID) sensors. MEG is a non-invasive neuroimaging modality that records the magnetic fields produced by neural currents with good spatial and high temporal resolution. However, state-of-the-art MEG is limited by the use of liquid helium-cooled sensors (T ~ 4 K). The amount of thermal insulation between the sensors and the subject\u27s head that is required to achieve the extreme temperature difference (~300 K), typically realized in the form of superinsulation foil and ~2 centimeters of vacuum, limits measurable signals. Replacing the sensors with high-Tc SQUIDs can mitigate this problem. High-Tc SQUIDs operate at much higher temperatures (90 K) allowing significant reduction of the stand-off distance (to ~1 mm). They can furthermore be cooled with liquid nitrogen (77 K), a cheaper, more sustainable alternative to the liquid helium used for cooling in conventional MEG systems.The work described in this thesis can be divided into three main areas: (I) simulation work for practical implementations of on-scalp systems, (II) development of a 7-channel high-Tc SQUID-based on-scalp MEG system, and (III) on-scalp MEG recordings.In the first part, spatial information density (SID), a metric to evaluate the performance of simulated MEG sensor arrays, is introduced and - along with total information capacity - used to compare the performance of various simulated full-head on-scalp MEG sensor arrays.\ua0Simulations demonstrate the potential of on-scalp MEG, with all on-scalp systems exhibiting higher information capacity than the state-of-the-art. SID further reveals more homogeneous sampling of the brain with flexible systems. A method for localizing magnetometers in on-scalp MEG systems is introduced and tested in simulations. The method uses small, magnetic dipole-like coils to determine the location and orientation of individual sensors, enabling straightforward co-registration in flexible on-scalp MEG systems. The effects of different uncertainties and errors on the accuracy of the method were quantified.In the second part, design, construction, and performance of a 7-channel on-scalp MEG system is described. The system houses seven densely-packed (2 mm edge-to-edge), head-aligned high-Tc SQUID magnetometers (9.2 mm x 8.6 mm) inside a single, liquid nitrogen-cooled cryostat. With a single filling, the system can be utilized for MEG recordings for >16 h with low noise levels (~0-130 fT). Using synchronized clocks and a direct injection feedback scheme, the system achieves low sensor crosstalk (<0.6%).\ua0In the third part, on-scalp MEG recordings with the 7-channel system as well as its predecessor, a single-channel system, are presented. The recordings are divided into proof-of-principle and benchmarking experiments. The former consist of well-studied, simple paradigms such as auditory evoked activity and visual alpha. Expected signal components were clearly seen in the on-scalp recordings. The benchmarking studies were done to compare and contrast on-scalp with state-of-the-art MEG. To this end, a number of experimental stimulus paradigms were recorded on human subjects with the high-Tc SQUID-based on-scalp systems as well as a state-of-the-art, commercial full-head MEG system. Results include the expected signal gains that are associated with recording on-scalp as well as new details of the neurophysiological signals. Using the previously described on-scalp MEG co-registration method enabled source localization with high agreement to the full-head recording (the distance between dipoles localized with the two systems was 4.2 mm)

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

A Real-time Non-contact Localization Method for Faulty Electric Energy Storage Components using Highly Sensitive Magnetometers

With the wide application of electric energy storage component arrays, such as battery arrays, capacitor arrays, inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot meet the needs of non-contact and real-time diagnosis for faulty components inside these massive arrays. To solve this problem, this paper proposes a new method based on the beamforming spatial filtering algorithm to precisely locate the faulty components within the arrays in real-time. The method uses highly sensitive magnetometers to collect the magnetic signals from energy storage component arrays, without damaging or even contacting any component. The experimental results demonstrate the potential of the proposed method in securing energy storage component arrays. Within an imaging area of 80 mm $\times$ 80 mm, the one faulty component out of nine total components can be localized with an accuracy of 0.72 mm for capacitor arrays and 1.60 mm for battery arrays

On the potential of a new generation of magnetometers for MEG: a beamformer simulation study

Magnetoencephalography (MEG) is a sophisticated tool which yields rich information on the spatial, spectral and temporal signatures of human brain function. Despite unique potential, MEG is limited by a low signal-to-noise ratio (SNR) which is caused by both the inherently small magnetic fields generated by the brain, and the scalp-to-sensor distance. The latter is limited in current systems due to a requirement for pickup coils to be cryogenically cooled. Recent work suggests that optically-pumped magnetometers (OPMs) might be a viable alternative to superconducting detectors for MEG measurement. They have the advantage that sensors can be brought to within ~4 mm of the scalp, thus offering increased sensitivity. Here, using simulations, we quantify the advantages of hypothetical OPM systems in terms of sensitivity, reconstruction accuracy and spatial resolution. Our results show that a multi-channel whole-head OPM system offers (on average) a fivefold improvement in sensitivity for an adult brain, as well as clear improvements in reconstruction accuracy and spatial resolution. However, we also show that such improvements depend critically on accurate forward models; indeed, the reconstruction accuracy of our simulated OPM system only outperformed that of a simulated superconducting system in cases where forward field error was less than 5%. Overall, our results imply that the realisation of a viable whole-head multi-channel OPM system could generate a step change in the utility of MEG as a means to assess brain electrophysiological activity in health and disease. However in practice, this will require both improved hardware and modelling algorithms

REITERATIVE MINIMUM MEAN SQUARE ERROR ESTIMATOR FOR DIRECTION OF ARRIVAL ESTIMATION AND BIOMEDICAL FUNCTIONAL BRAIN IMAGING

Two novel approaches are developed for direction-of-arrival (DOA) estimation and functional brain imaging estimation, which are denoted as ReIterative Super-Resolution (RISR) and Source AFFine Image REconstruction (SAFFIRE), respectively. Both recursive approaches are based on a minimum mean-square error (MMSE) framework. The RISR estimator recursively determines an optimal filter bank by updating an estimate of the spatial power distribution at each successive stage. Unlike previous non-parametric covariance-based approaches, which require numerous time snapshots of data, RISR is a parametric approach thus enabling operation on as few as one time snapshot, thereby yielding very high temporal resolution and robustness to the deleterious effects of temporal correlation. RISR has been found to resolve distinct spatial sources several times better than that afforded by the nominal array resolution even under conditions of temporally correlated sources and spatially colored noise. The SAFFIRE algorithm localizes the underlying neural activity in the brain based on the response of a patient under sensory stimuli, such as an auditory tone. The estimator processes electroencephalography (EEG) or magnetoencephalography (MEG) data simulated for sensors outside the patient's head in a recursive manner converging closer to the true solution at each consecutive stage. The algorithm requires a minimal number of time samples to localize active neural sources, thereby enabling the observation of the neural activity as it progresses over time. SAFFIRE has been applied to simulated MEG data and has shown to achieve unprecedented spatial and temporal resolution. The estimation approach has also demonstrated the capability to precisely isolate the primary and secondary auditory cortex responses, a challenging problem in the brain MEG imaging community

Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain

Magnetoencephalography (MEG) is a noninvasive technique for investigating neuronal activity in the living human brain. The time resolution of the method is better than 1 ms and the spatial discrimination is, under favorable circumstances, 2-3 mm for sources in the cerebral cortex. In MEG studies, the weak 10 fT-1 pT magnetic fields produced by electric currents flowing in neurons are measured with multichannel SQUID (superconducting quantum interference device) gradiometers. The sites in the cerebral cortex that are activated by a stimulus can be found from the detected magnetic-field distribution, provided that appropriate assumptions about the source render the solution of the inverse problem unique. Many interesting properties of the working human brain can be studied, including spontaneous activity and signal processing following external stimuli. For clinical purposes, determination of the locations of epileptic foci is of interest. The authors begin with a general introduction and a short discussion of the neural basis of MEG. The mathematical theory of the method is then explained in detail, followed by a thorough description of MEG instrumentation, data analysis, and practical construction of multi-SQUID devices. Finally, several MEG experiments performed in the authors' laboratory are described, covering studies of evoked responses and of spontaneous activity in both healthy and diseased brains. Many MEG studies by other groups are discussed briefly as well.Peer reviewe

Temporal Connectivity Patterns of the Corticolimbic Learning and Rewards System

The human learning and rewards system is comprised of a number of cortical and subcortical neural regions, including the orbitofrontal cortex, striatum, and anterior cingulate. While modern neural imaging methods such as functional magnetic resonance imaging (fMRI) and functional positron emission tomography (PET) can successfully detect the activity of these regions, they cannot discern temporal activation patterns, due to the slow onset of the blood oxygen level dependent (BOLD) effect. Magnetoencephalographic imaging (MEG) is able to capture these temporal patterns but traditionally has been unable to detect activity originating from the deeper regions of the brain due to signal attenuation and high noise levels. The recently published exSSS method has shown significant promise extracting deep signals from MEG data. To elicit appropriate subcortical activity we utilized a previously published gambling task. This paradigm has been shown to differentially activate a number of subcortical regions within the rewards system, including the orbitofrontal cortex (OFC), striatum, and anterior cingulate cortex (ACC), based on reward-related feedback. MEG analysis using source localization methods in conjunction with source signal reconstruction techniques yielded neural activation time courses for each of the regions of interest. Granger causality was used to identify the temporal relationships between each of these regions, and a possible functional connectivity map is presented. The behavioral paradigm was replicated using functional magnetic resonance imaging. fMRI activity patterns were similar to those previously reported in the literature using this paradigm. Additionally, the fMRI activation patterns were similar to those obtained via MEG source reconstruction of the exSSS-processed data. Our results support the literature finding that the rewards network is differentially activated based on feedback. Additionally, these results demonstrate the efficacy of the exSSS signal processing method for extracting deep activity, and suggest a possible use for MEG in the imaging of deep activity using other behavioral paradigms