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

MEG Can Map Short and Long-Term Changes in Brain Activity following Deep Brain Stimulation for Chronic Pain

Deep brain stimulation (DBS) has been shown to be clinically effective for some forms of treatment-resistant chronic pain, but the precise mechanisms of action are not well understood. Here, we present an analysis of magnetoencephalography (MEG) data from a patient with whole-body chronic pain, in order to investigate changes in neural activity induced by DBS for pain relief over both short- and long-term. This patient is one of the few cases treated using DBS of the anterior cingulate cortex (ACC). We demonstrate that a novel method, null-beamforming, can be used to localise accurately brain activity despite the artefacts caused by the presence of DBS electrodes and stimulus pulses. The accuracy of our source localisation was verified by correlating the predicted DBS electrode positions with their actual positions. Using this beamforming method, we examined changes in whole-brain activity comparing pain relief achieved with deep brain stimulation (DBS ON) and compared with pain experienced with no stimulation (DBS OFF). We found significant changes in activity in pain-related regions including the pre-supplementary motor area, brainstem (periaqueductal gray) and dissociable parts of caudal and rostral ACC. In particular, when the patient reported experiencing pain, there was increased activity in different regions of ACC compared to when he experienced pain relief. We were also able to demonstrate long-term functional brain changes as a result of continuous DBS over one year, leading to specific changes in the activity in dissociable regions of caudal and rostral ACC. These results broaden our understanding of the underlying mechanisms of DBS in the human brain

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

A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources

Abstract—Electroencephalography (EEG)-based brain computer interface (BCI) is the most studied non-invasive interface to build a direct communication pathway between the brain and an external device. However, correlated noises in EEG measurements still constitute a significant challenge. Alternatively, building BCIs based on filtered brain activity source signals instead of using their surface projections, obtained from the noisy EEG signals, is a promising and not well explored direction. In this context, finding the locations and waveforms of inner brain sources represents a crucial task for advancing source-based non-invasive BCI technologies. In this paper, we propose a novel Multi-core Beamformer Particle Filter (Multi-core BPF) to estimate the EEG brain source spatial locations and their corresponding waveforms. In contrast to conventional (single-core) Beamforming spatial filters, the developed Multi-core BPF considers explicitly temporal correlation among the estimated brain sources by suppressing activation from regions with interfering coherent sources. The hybrid Multi-core BPF brings together the advantages of both deterministic and Bayesian inverse problem algorithms in order to improve the estimation accuracy. It solves the brain activity localization problem without prior information about approximate areas of source locations. Moreover, the multi-core BPF reduces the dimensionality of the problem to half compared with the PF solution; thus alleviating the curse of dimensionality problem. The results, based on generated and real EEG data, show that the proposed framework recovers correctly the dominant sources of brain activity

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions

Neural Connectivity with Hidden Gaussian Graphical State-Model

The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

Choice of Magnetometers and Gradiometers after Signal Space Separation

Background: Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r2 = 0.3–0.8 before SSS and r2 > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r 2 > 0.8). Conclusions: After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments

Brain-informed speech separation (BISS) for enhancement of target speaker in multitalker speech perception

Hearing-impaired people often struggle to follow the speech stream of an individual talker in noisy environments. Recent studies show that the brain tracks attended speech and that the attended talker can be decoded from neural data on a single-trial level. This raises the possibility of “neuro-steered” hearing devices in which the brain-decoded intention of a hearing-impaired listener is used to enhance the voice of the attended speaker from a speech separation front-end. So far, methods that use this paradigm have focused on optimizing the brain decoding and the acoustic speech separation independently. In this work, we propose a novel framework called brain-informed speech separation (BISS)1 in which the information about the attended speech, as decoded from the subject’s brain, is directly used to perform speech separation in the front-end. We present a deep learning model that uses neural data to extract the clean audio signal that a listener is attending to from a multi-talker speech mixture. We show that the framework can be applied successfully to the decoded output from either invasive intracranial electroencephalography (iEEG) or non-invasive electroencephalography (EEG) recordings from hearing-impaired subjects. It also results in improved speech separation, even in scenes with background noise. The generalization capability of the system renders it a perfect candidate for neuro-steered hearing-assistive devices

Neural oscillatory signatures of auditory and audiovisual illusions

Questions of the relationship between human perception and brain activity can be approached from different perspectives: in the first, the brain is mainly regarded as a recipient and processor of sensory data. The corresponding research objective is to establish mappings of neural activity patterns and external stimuli. Alternatively, the brain can be regarded as a self-organized dynamical system, whose constantly changing state affects how incoming sensory signals are processed and perceived. The research reported in this thesis can chiefly be located in the second framework, and investigates the relationship between oscillatory brain activity and the perception of ambiguous stimuli. Oscillations are here considered as a mechanism for the formation of transient neural assemblies, which allows efficient information transfer. While the relevance of activity in distinct frequency bands for auditory and audiovisual perception is well established, different functional architectures of sensory integration can be derived from the literature. This dissertation therefore aims to further clarify the role of oscillatory activity in the integration of sensory signals towards unified perceptual objects, using illusion paradigms as tools of study. In study 1, we investigate the role of low frequency power modulations and phase alignment in auditory object formation. We provide evidence that auditory restoration is associated with a power reduction, while the registration of an additional object is reflected by an increase in phase locking. In study 2, we analyze oscillatory power as a predictor of auditory influence on visual perception in the sound-induced flash illusion. We find that increased beta-/ gamma-band power over occipitotemporal electrodes shortly before stimulus onset predicts the illusion, suggesting a facilitation of processing in polymodal circuits. In study 3, we address the question of whether visual influence on auditory perception in the ventriloquist illusion is reflected in primary sensory or higher-order areas. We establish an association between reduced theta-band power in mediofrontal areas and the occurrence of illusion, which indicates a top-down influence on sensory decision-making. These findings broaden our understanding of the functional relevance of neural oscillations by showing that different processing modes, which are reflected in specific spatiotemporal activity patterns, operate in different instances of sensory integration.Fragen nach dem Zusammenhang zwischen menschlicher Wahrnehmung und Hirnaktivität können aus verschiedenen Perspektiven adressiert werden: in der einen wird das Gehirn hauptsächlich als Empfänger und Verarbeiter von sensorischen Daten angesehen. Das entsprechende Forschungsziel wäre eine Zuordnung von neuronalen Aktivitätsmustern zu externen Reizen. Dieser Sichtweise gegenüber steht ein Ansatz, der das Gehirn als selbstorganisiertes dynamisches System begreift, dessen sich ständig verändernder Zustand die Verarbeitung und Wahrnehmung von sensorischen Signalen beeinflusst. Die Arbeiten, die in dieser Dissertation zusammengefasst sind, können vor allem in der zweitgenannten Forschungsrichtung verortet werden, und untersuchen den Zusammenhang zwischen oszillatorischer Hirnaktivität und der Wahrnehmung von mehrdeutigen Stimuli. Oszillationen werden hier als ein Mechanismus für die Formation von transienten neuronalen Zusammenschlüssen angesehen, der effizienten Informationstransfer ermöglicht. Obwohl die Relevanz von Aktivität in verschiedenen Frequenzbändern für auditorische und audiovisuelle Wahrnehmung gut belegt ist, können verschiedene funktionelle Architekturen der sensorischen Integration aus der Literatur abgeleitet werden. Das Ziel dieser Dissertation ist deshalb eine Präzisierung der Rolle oszillatorischer Aktivität bei der Integration von sensorischen Signalen zu einheitlichen Wahrnehmungsobjekten mittels der Nutzung von Illusionsparadigmen. In der ersten Studie untersuchen wir die Rolle von Leistung und Phasenanpassung in niedrigen Frequenzbändern bei der Formation von auditorischen Objekten. Wir zeigen, dass die Wiederherstellung von Tönen mit einer Reduktion der Leistung zusammenhängt, während die Registrierung eines zusätzlichen Objekts durch einen erhöhten Phasenangleich widergespiegelt wird. In der zweiten Studie analysieren wir oszillatorische Leistung als Prädiktor von auditorischem Einfluss auf visuelle Wahrnehmung in der sound-induced flash illusion. Wir stellen fest, dass erhöhte Beta-/Gamma-Band Leistung über occipitotemporalen Elektroden kurz vor der Reizdarbietung das Auftreten der Illusion vorhersagt, was auf eine Begünstigung der Verarbeitung in polymodalen Arealen hinweist. In der dritten Studie widmen wir uns der Frage, ob ein visueller Einfluss auf auditorische Wahrnehmung in der ventriloquist illusion sich in primären sensorischen oder übergeordneten Arealen widerspiegelt. Wir weisen einen Zusammenhang von reduzierter Theta-Band Leistung in mediofrontalen Arealen und dem Auftreten der Illusion nach, was einen top-down Einfluss auf sensorische Entscheidungsprozesse anzeigt. Diese Befunde erweitern unser Verständnis der funktionellen Bedeutung neuronaler Oszillationen, indem sie aufzeigen, dass verschiedene Verarbeitungsmodi, die sich in spezifischen räumlich-zeitlichen Aktivitätsmustern spiegeln, in verschiedenen Phänomenen von sensorischer Integration wirksam sind