Combined EEG and MEG source analysis of epileptiform activity using calibrated realistic finite element head models

In dieser Arbeit wird eine neue Pipeline, welche die komplementären Informationen der Elektroenzephalographie (EEG) und Magnetoenzephalographie (MEG) berücksichtigen kann, vorgestellt und experimentell sowie methodisch analysiert. Um das Vorwärtsproblem zu lösen, wird ein hochrealistisches Finite-Elemente-Kopfmodell aus individuell gemessenen T1-gewichteten, T2-gewichteten und Diffusion-Tensor (DT)-MRIs generiert. Dafür werden die Kompartments Kopfhaut, spongioser Schädel, kompakter Schädel, Liquor Cerebrospinalis (CSF), graue Substanz und weiße Substanz segmentiert und ein individuelles Kopfmodell erstellt. Um eine sehr akkurate Quellenanalyse zu garantieren werden die individuelle Kopfform, die Anisotropie der weißen Substanz und die individuell kalibrierte Schädelleitfähigkeiten berücksichtigt. Die Anisotropie der weißen Substanz wird anhand der gemessenen DT-MRI Daten berechnet und in das segmentierte Kopfmodell integriert. Da sich die Leitfähigkeit des schwach-leitenden Schädels für verschiedene Probanden sehr stark unterscheidet und diese die Ergebnisse der EEG Quellenanalyse stark beeinflusst, wird ein Fokus auf die Untersuchung der Schädelleitfähigkeit gelegt. Um die individuelle Schädelleitfähigkeit möglichst genau zu bestimmen werden simultan gemessene somatosensorische Potentiale und Felder der Probanden verwendet und ein Verfahren zur Kalibrierung der Schädelleitfähigkeit durchgeführt. Wie in dieser Studie gezeigt, können individuell generierte Kopfmodelle dazu verwendet werden um, in einem nicht-invasivem Verfahren, interiktale Aktivität für Patienten, welche an medikamentenresistenter Epilepsie leiden, mit einer sehr hohen Genauigkeit zu detektieren. Außerdem werden diese akkuraten Kopfmodelle dazu verwendet um die unterschiedlichen Sensitivitäten von EEG, MEG und einer kombinierten EEG und MEG (EMEG) Quellenanalyse in Bezug auf verschiedene Gewebeleitfähigkeiten zu untersuchen. Wie in dieser Studie gezeigt wird liefert eine kombinierte EMEG Quellenanalyse zuverlässigere und robustere Ergebnisse für die Lokalisierung epileptischer Aktivität als eine einfache EEG oder MEG Quellenanalyse. Zuletzt werden die Auswirkungen einer Spikemittelung sowie die Effekte verschiedener Signal-Rausch-Verhältnisse (SNRs) anhand verschiedener Teilmittelungen untersucht. Wie in dieser Arbeit gezeigt wird sind realistische Kopfmodelle mit anisotroper weißer Substanz und kalibrierter Schädelleitfähigkeit nicht nur für die EEG Quellenanalyse, sondern auch für die MEG und EMEG Quellenanalyse vorteilhaft. Durch die Anwendung dieser akkuraten Kopfmodelle konnte gezeigt werden, dass EMEG Quellenanalyse sehr gute Quellenrekonstruktionen auch schon zu Beginn des epileptischen Spikes liefert, wo nur eine sehr geringe SNR vorhanden ist. Da zu diesem Zeitpunkt noch keine Ausbreitung der epileptischen Aktivität eingesetzt hat ist die Lokalisation von frühen Quellen von besonderer Bedeutung. Während die EMEG Quellenanalyse auch Ausbreitungseffekte für spätere Zeitpunkte genau darstellen kann, können einfache EEG oder MEG Quellenanalysen diese nicht oder nur teilweise darstellen. Die Validierung der Ausbreitung wird anhand eines invasiv gemessenen Stereo-EEG durchgeführt. Durch die durchgeführten Spikemittelungen und die SNR Analyse wird verdeutlicht, dass durch eine Teilmittelung wichtige und exakte Informationen über den Mittelpunkt sowie die Größe des epileptischen Gewebes gewonnen werden können, welche weder durch eine einfachen noch einer "Grand-average" Lokalisation des Spikes erreichbar sind. Eine weitere Anwendung einer genauen EMEG Quellenanalyse ist die Bestimmung einer "region of interest" anhand von standardisierten MRT Messungen. Diese kleinen Gebiete werden dann später mit einer optimalen und höher aufgelösten MRT-Sequenz gemessen. Dank dieses optimierte Verfahren können auch sehr kleine FCDs entdeckt werden, welche auf dem standardisierten gemessenen MRT-Sequenzen nicht erkennbar sind. Die Pipeline, welche in dieser Arbeit entwickelt wird, kann auch für gesunde Probanden angewendet werden. In einer ersten Studie wird eine Quellenanalyse der somatosensorischen und auditorisch-induzierten Reize durchgeführt. Die gewonnen Daten werden mit anderen Studien vergleichen und mögliche Gemeinsamkeiten diskutiert. Eine weitere Anwendung der realistischen Kopfmodelle ist die Untersuchung von Volumenleitungseffekten in nicht-invasiven Hirnstimulationsmethoden wie transkranielle Gleichstromstimulation und transkranielle Magnetstromstimulation.In this thesis, a new experimental and methodological analysis pipeline for combining the complementary information contained in electroencephalography (EEG) and magnetoencephalography (MEG) is introduced. The forward problem is solved using high resolution finite element head models that are constructed from individual T1 weighted, T2 weighted and diffusion tensor (DT-) MRIs. For this purpose, scalp, skull spongiosa, skull compacta, cerebrospinal fluid, white matter (WM) and gray matter (GM) are segmented and included into the head models. In order to obtain highly accurate source reconstructions, the realistic geometry, tissue conductivity anisotropy (i.e., WM tracts) and individually estimated conductivity values are taken into account. To achieve this goal, the brain anisotropy is modeled using the information obtained from DT-MRI. A main focus is placed on the skull conductivity due to its high inter-individual variance and different sensitivities of EEG and MEG source reconstructions to it. In order to estimate individual skull conductivity values that fit best to the constructed head models, simultaneously acquired somatosensory evoked potential and field data measured for the same individuals are analyzed. As shown in this work, the constructed head models could be used to non-invasively localize interictal spike activity in patients suffering from pharmaco-resistant focal epilepsy with higher reliability. In addition, by using these advanced head models, tissue sensitivities of EEG, MEG and combined EEG/MEG (EMEG) are compared by means of altering the distinguished tissue types and their conductivities. Finally, the effects of spike averaging and signal-to-noise-ratios (SNRs) on source analysis are evaluated by localizing subaverages. The results obtained in this thesis demonstrate the importance of using anisotropic and skull conductivity calibrated realistic finite element models not only for EEG but also for MEG and EMEG source analysis. By employing such advanced finite element models, it is possible to demonstrate that EMEG achieves accurate source reconstructions at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. It is also shown that EMEG is able to reveal the propagation pathway at later time points in agreement with invasive stereo-EEG, while EEG or MEG alone reconstruct only parts of it. Spike averaging and SNR analysis reveal that subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations could supply. Moreover, it is shown that accurate source reconstructions obtained with EMEG can be used to determine a region of interest, and new MRI sequences that acquire high resolution images in this restricted area can detect FCDs that were not detectable with other MRI sequences. The pipelines proposed in this work are also tested for source analysis of somatosensory and auditory evoked responses measured from healthy subjects and the results are compared with the literature. In addition, the finite element head models are also used to assess the volume conductor effects on simulations of non-invasive brain stimulation techniques such as transcranial direct current and transcranial magnetic stimulation

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Peer reviewe

Localization of deep brain activity with scalp and subdural EEG

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal- to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We ap- plied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were similar to 70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around similar to 2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. Significance Statement Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface

EpiGauss : caracterização espacio-temporal da actividade cerebral em epilepsia

Doutoramento em Engenharia ElectrotécnicaA epilepsia é uma patologia cerebral que afecta cerca de 0,5% da população mundial. Nas epilepsias focais, o principal objectivo clínico é a localização da zona epileptogénica (área responsável pelas crises), uma informação crucial para uma terapêutica adequada. Esta tese é centrada na caracterização da actividade cerebral electromagnética do cérebro epiléptico. As contribuições nesta área, entre a engenharia e neurologia clínica, são em duas direcções. Primeiro, mostramos que os conceitos associados às pontas podem ser imprecisos e não ter uma definição objectiva, tornando necessária uma reformulação de forma a definir uma referência fiável em estudos relacionados com a análise de pontas. Mostramos que as características das pontas em EEG são estatisticamente diferentes das pontas em MEG. Esta constatação leva a concluir que a falta de objectividade na definição de ponta na literatura pode induzir utilizações erradas de conceitos associados ao EEG na análise de MEG. Também verificamos que o uso de conjuntos de detecções de pontas efectuadas por especialistas (MESS) como referência pode fornecer resultados enganadores quando apenas baseado em critérios de consenso clínico, nomeadamente na avaliação da sensibilidade e especificidade de métodos computorizados de detecção de pontas Em segundo lugar, propomos o uso de métodos estatísticos para ultrapassar a falta de precisão e objectividade das definições relacionadas com pontas. Propomos um novo método de neuroimagem suportado na caracterização de geradores electromagnéticos – EpiGauss – baseado na análise individual dos geradores de eventos do EEG que explora as suas estruturas espacio-temporais através da análise de “clusters”. A aplicação de análise de “clusters” à análise geradores de eventos do EEG tem como objectivo usar um método não supervisionado, para encontrar estruturas espacio-temporais dps geradores relevantes. Este método, como processo não supervisionado, é orientado a utilizadores clínicos e apresenta os resultados sob forma de imagens médicas com interpretação similar a outras técnicas de imagiologia cerebral. Com o EpiGauss, o utilizador pode determinar a localização estatisticamente mais provável de geradores, a sua estabilidade espacial e possíveis propagações entre diferente áreas do cérebro. O método foi testado em dois estudos clínicos envolvendo doentes com epilepsia associada aos hamartomas hipotalâmicos e o outro com doentes com diagnóstico de epilepsia occipital. Em ambos os estudos, o EpiGauss foi capaz de identificar a zona epileptogénica clínica, de forma consistente com a história e avaliação clínica dos neurofisiologistas, fornecendo mais informação relativa à estabilidade dos geradores e possíveis percursos de propagação da actividade epileptogénica contribuindo para uma melhor caracterização clínica dos doentes. A conclusão principal desta tese é que o uso de técnicas não supervisionadas, como a análise de “clusters”, associadas as técnicas não-invasivas de EMSI, pode contribuir com um valor acrescido no processo de diagnóstico clínico ao fornecer uma caracterização objectiva e representação visual de padrões complexos espaciotemporais da actividade eléctrica epileptogénica.Epilepsy is a brain pathology that affects 0.5% of the world population. In focal epilepsies, the main clinical objective is the localization of the epileptogenic zone (brain area responsible for the epileptic seizures – EZ), a key information to decide an adequate therapeutic approach. This thesis is centred on electromagnetic activity characterization of the epileptic brain. Our contribution to this boundary area between engineering and clinical neurology is two-folded. First we show that spike related clinical concepts can be unprecise and some do not have objective definitions making necessary a reformulation in order to have a reliable reference in spike related studies. We show that EEG spike wave quantitative features are statistically different from their MEG counterparts. This finding leads to the conclusion that the lack of objective spike feature definitions in the literature can induce the wrong usage of EEG feature definition in MEG analysis. We also show that the use of multi-expert spike selections sets (MESS) as gold standard, although clinically useful, may be misleading whenever defined solely in terms of clinical agreement criteria, namely as references for automatic spike detection algorithms in sensitivity and specificity method analysis. Second, we propose the use of statistical methods to overcome some lack of precision and objectivity in spike related definitions. In this context, we propose a new ElectroMagnetic Source Imaging (EMSI) method – EpiGauss – based on cluster analysis that explores both spatial and temporal information contained in individual events sources analysis characterisation. This automatic cluster method for the analysis of spike related electric generators based in EEG is used to provide an unsupervised tool to find their relevant spatio-temporal structures. This method enables a simple unsupervised procedure aimed for clinical users and presents its results in an intuitive representation similar to other brain imaging techniques. With EpiGauss, the user is able to determine statistically probable source locations, their spatial stability and propagation patterns between different brain areas. The method was tested in two different clinical neurophysiology studies, one with a group of Hypothalamic Hamartomas and another with a group of Occipital Epilepsy patients. In both studies EpiGauss identified the clinical epileptogenic zone, consistent with the clinical background and evaluation of neurophysiologists, providing further information on stability of source locations and their probable propagation pathways that enlarges their clinical interpretation. This thesis main conclusion is that the use of unsupervised techniques, such as clustering, associated with EMSI non-invasive techniques, can bring an added value in clinical diagnosis process by providing objective and visual representation of complex epileptic brain spatio-temporal activity patterns

An Electroencephalographic Investigation of the Encoding of Sound Source Elevation in the Human Cortex

Sound localization is of great ecological importance because it provides spa- tial perception outside the visual field. However, unlike other sensory systems, the auditory system does not represent the location of a stimulus on the level of the sensory epithelium in the cochlea. Instead, the position of a sound source has to be computed based on different localization cues. Different cues are informative of a sound sources azimuth and elevation, which, when taken together, describe the sources location in a polar coordinate system. There is a body of knowledge regarding the acoustical cues and the neural circuits in the brainstem required to perceive sound source azimuth and elevation. However, our understanding of the encoding of sound source location on the level of the cortex is lacking especially what concerns elevation. Within the scope of this thesis, we established an experimental setup to study auditory spatial perception while recording the listeners brain activity using electroencephalography. We conducted two experiments on the encoding of sound source elevation in the human cortex. Both experiments results are compatible with the hypothesis that the cortex represents sound source elevation in a population rate code where the response amplitude decreases linearly with increasing elevation. Decoding of the recorded brain activity revealed that a distinct neural representation of differently elevated sound sources was predictive of behavioral performance. An exploratory analysis indicated an increase in the amplitude of oscillations in visual areas when the subject localized sounds during eccentric eye positions. More research in this direction could help shed light on the interactions between the visual and auditory systems regarding spatial perception. The experiments presented in this dissertation are, to our knowledge, the first studies that demonstrate the encoding of sound source elevation in the human cortex by using a direct measure of neural activity (i.e., electroencephalography).:Abstract . . . . . . . . . . . . . . . . . . . . . . 1 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . 7 1 Electroencephalography 13 1.1 Event Related Potentials and Oscillations . . . . . . . . . . . . 13 1.2 Comparison to other Methods . . . . . . . . . . . . . . . . . . . 14 1.3 EEG Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4.1 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.4.2 Referencing . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.4.3 Eye Blinks . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4.4 Epoch Rejection . . . . . . . . . . . . . . . . . . . . . . . 22 1.4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.5.1 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.5.2 Nonparametric Permutation Testing . . . . . . . . . . . 26 1.5.3 Source Separation . . . . . . . . . . . . . . . . . . . . . . 28 2 Sound Localization in the Brain . . . . . . . . . . . . . . . . . . . . 31 2.1 The Spatial Perception of Sound . . . . . . . . . . . . . . . . . . 32 2.1.1 Interaural Cues . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.2 Spectral Cues . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Brain Mechanisms for Sound Localization . . . . . . . . . . . . 37 2.2.1 Auditory Pathway . . . . . . . . . . . . . . . . . . . . . 38 2.2.2 Extracting Localization Cues . . . . . . . . . . . . . . . 40 2.2.3 Neural Representation of Auditory Space . . . . . . . . 42 2.2.4 The Dual Pathway Model . . . . . . . . . . . . . . . . . 45 2.2.5 A Dominant Hemisphere for Sound Localization? . . . 47 2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3 A Free Field Setup for Psychoacoustics 51 3.1 Design of the Experimental Setup . . . . . . . . . . . . . . . . . 51 3.1.1 Loudspeakers . . . . . . . . . . . . . . . . . . . . . . . . 54 3.1.2 Processors . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.1.3 Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.1.4 Coordinate Systems . . . . . . . . . . . . . . . . . . . . 56 3.2 Operating the Setup . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.2.1 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.2.2 Loudspeaker Equalization . . . . . . . . . . . . . . . . . 59 3.3 Head Pose Estimation . . . . . . . . . . . . . . . . . . . . . . . 61 3.3.1 Landmark Detection . . . . . . . . . . . . . . . . . . . . 62 3.3.2 Perspective-n-Point Problem . . . . . . . . . . . . . . . 62 3.3.3 Camera-to-World Conversion . . . . . . . . . . . . . . . 63 3.4 A Toolbox for Psychoacoustics . . . . . . . . . . . . . . . . . . 64 4 A Linear Population Rate Code for Elevation 67 4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.1.2 Experimental Protocol . . . . . . . . . . . . . . . . . . . 69 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2.1 Behavioral Performance . . . . . . . . . . . . . . . . . . 70 4.2.2 ERP Components . . . . . . . . . . . . . . . . . . . . . . 70 4.2.3 Elevation Encoding . . . . . . . . . . . . . . . . . . . . . 72 4.2.4 Effect of Eye-Position . . . . . . . . . . . . . . . . . . . . 74 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Decoding of Brain Responses Predicts Localization Accuracy . . . 81 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.1.2 Experimental Protocol . . . . . . . . . . . . . . . . . . . 82 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.2.1 Behavioral Performance . . . . . . . . . . . . . . . . . . 83 5.2.2 ERP Components . . . . . . . . . . . . . . . . . . . . . . 84 5.2.3 Decoding Brain Activity . . . . . . . . . . . . . . . . . . 86 5.2.4 Topography of Elevation Encoding . . . . . . . . . . . . 88 5.2.5 Elevation Tuning . . . . . . . . . . . . . . . . . . . . . . 89 5.2.6 Hemispheric Lateralization . . . . . . . . . . . . . . . . 91 5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 A Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 B Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Methods for noninvasive localization of focal epileptic activity with magnetoencephalography

Magnetoencephalography (MEG) is a noninvasive brain signal acquisition technique that provides excellent temporal resolution and a whole-head coverage allowing the spatial mapping of sources. These characteristics make MEG an appropriate technique to localize the epileptogenic zone (EZ) in the preoperative evaluation of refractory epilepsy. Presurgical evaluation with MEG can guide the placement of intracranial EEG (iEEG), the current gold standard in the clinical practice, and even supply sufficient information for a surgical intervention without invasive recordings, reducing invasiveness, discomfort, and cost of the presurgical epilepsy diagnosis. However, MEG signals have low signal-to-noise ratio compared with iEEG and can sometimes be affected by noise that masks or distorts the brain activity. This may prevent the detection of interictal epileptiform discharges (IEDs) and high-frequency oscillations (HFOs), two important biomarkers used in the preoperative evaluation of epilepsy. In this thesis, the reduction of two kinds of interference is aimed to improve the signal-to-noise ratio of MEG signals: metallic artifacts mask the activity of IEDs; and the high-frequency noise, that masks HFO activity. Considering the large number of MEG channels and the long duration of the recordings, reducing noise and marking events manually is a time-consuming task. The algorithms presented in this thesis provide automatic solutions aimed at the reduction of interferences and the detection of HFOs. Firstly, a novel automatic BSS-based algorithm to reduce metallic interference is presented and validated using simulated and real MEG signals. Three methods are tested: AMUSE, a second-order BSS technique; and INFOMAX and FastICA, based on high-order statistics. The automatic detection algorithm exploits the known characteristics of metallic-related interferences. Results indicate that AMUSE performes better when recovering brain activity and allows an effective removal of artifactual components.Secondly, the influence of metallic artifact filtering using the developed algorithm is evaluated in the source localization of IEDs in patients with refractory focal epilepsy. A comparison between the resulting positions of equivalent current dipoles (ECDs) produced by IEDs is performed: without removing metallic interference, rejecting only channels with large metallic artifacts, and after BSS-based reduction. The results show that a significant reduction on dispersion is achieved using the BSS-based reduction procedure, yielding feasible locations of ECDs in contrast to the other approaches. Finally, an algorithm for the automatic detection of epileptic ripples in MEG using beamformer-based virtual sensors is developed. The automatic detection of ripples is performed using a two-stage approach. In the first step, beamforming is applied to the whole head to determine a region of interest. In the second step, the automatic detection of ripples is performed using the time-frequency characteristics of these oscillations. The performance of the algorithm is evaluated using simultaneous intracranial EEG recordings as gold standard.The novel approaches developed in this thesis allow an improved noninvasive detection and localization of interictal epileptic biomarkers, which can help in the delimitation of the epileptogenic zone and guide the placement of intracranial electrodes, or even to determine these areas without additional invasive recordings. As a consequence of this improved detection, and given that interictal biomarkers are much more frequent and easy to record than ictal episodes, the presurgical evaluation process can be more comfortable for the patient and in a more economic way.La magnetoencefalografía (MEG) es una técnica no invasiva de adquisición de señales cerebrales que proporciona una excelente resolución temporal y una cobertura total de la cabeza, permitiendo el mapeo espacial de las fuentes cerebrales. Estas características hacen del MEG una técnica apropiada para localizar la zona epileptogénica (EZ) en la evaluación preoperatoria de la epilepsia refractaria. La evaluación prequirúrgica con MEG puede orientar la colocación del EEG intracraneal (iEEG), el actual modelo de referencia en la práctica clínica, e incluso suministrar información suficiente para una intervención quirúrgica sin registros invasivos; reduciendo la invasividad, la incomodidad y el costo del diagnóstico de la epilepsia prequirúrgica. Sin embargo, las señales MEG tienen baja relación señal ruido en comparación con el iEEG pudiendo imposibilitar la detección de descargas epileptiformes interictales (IEDs) y oscilaciones de alta frecuencia (HFOs), dos importantes biomarcadores utilizados en la evaluación preoperatoria de la epilepsia.En esta tesis, la reducción de dos tipos de interferencia está dirigida a mejorar la relación señal-ruido de la señal MEG: los artefactos metálicos que enmascaran la actividad de las IEDs; y el ruido de alta frecuencia, que enmascara la actividad de las HFOs. Debido al gran número de canales MEG y la larga duración de los registros, tanto reducir el ruido como seleccionar los biomarcadores manualmente es una tarea que consume mucho tiempo. Los algoritmos presentados en esta tesis aportan soluciones automáticas dirigidas a la reducción de interferencias y la detección de HFOs. En primer lugar, se presenta y valida un nuevo algoritmo automático basado en BSS para reducir interferencias metálicas mediante señales simuladas y reales. Se prueban tres métodos: AMUSE, una técnica BSS de segundo orden; y INFOMAX y FastICA, basados en estadísticos de orden superior. El algoritmo de detección automático utiliza las características conocidas de la señal producida por la interferencia metálica. Los resultados indican que AMUSE recupera mejor la actividad cerebral y permite una eliminación efectiva de componentes artefactuales.Posteriormente, se evalúa la influencia del filtrado de artefactos metálicos en la localización de IEDs en pacientes con epilepsia focal refractaria. Se realiza una comparación entre las posiciones resultantes de dipolos de corriente equivalentes (ECDs) producidos por IEDs: sin eliminar interferencias metálicas, rechazando solamente canales con elevados artefactos metálicos y, por último, después de una reducción utilizando el algoritmo BSS desarrollado. Los resultados muestran que se logra una reducción significativa en la dispersión utilizando el procedimiento de reducción basado en BSS, lo que produce ubicaciones factibles de los dipolos en contraste con los otros enfoques.En segundo lugar, se desarrolla un algoritmo para la detección automática ripples epilépticos en MEG utilizando sensores virtuales basados en la técnica de beamformer. La detección de ripples se realiza mediante un enfoque en dos etapas. Primero, se determina el área de interés usando beamformer. Posteriormente, se realiza la detección automática de ripples utilizando las características en tiempo-frecuencia. El rendimiento del algoritmo se evalúa utilizando registros iEEG simultáneos.Los nuevos enfoques desarrollados en esta tesis permiten una detección no invasiva mejor de los biomarcadores interictales, que pueden ayudar a delimitar la zona epileptogénica y guiar la colocación de electrodos intracraneales, o incluso determinar estas áreas sin este tipo de registros. Como consecuencia de esta mejora en la detección, y dado que los biomarcadores interictales son mucho más frecuentes y fáciles de registrar que los episodios ictales, la evaluación prequirúrgica puede ser más cómoda y menos costosa para el paciente.Postprint (published version

Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)

In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in non-invasive Electroencephalography. We consider a recently introduced hybrid reconstruction strategy combining a hierarchical Bayesian model to incorporate a priori information and the advanced randomized multiresolution scanning (RAMUS) source space decomposition approach to reduce modelling errors, respectively. In particular, we aim to generalize the previously extensively used conditionally Gaussian prior (CGP) formalism to achieve distributional reconstructions with higher focality. For this purpose, we introduce as a hierarchical prior, a general exponential distribution, which we refer to as conditionally exponential prior (CEP). The first-degree CEP corresponds to focality enforcing Laplace prior, but it also suffers from strong depth bias, when applied in numerical modelling, making the deep activity unrecoverable. We sample over multiple resolution levels via RAMUS to reduce this bias as it is known to depend on the resolution of the source space. Moreover, we introduce a procedure based on the physiological a priori knowledge of the brain activity to obtain the shape and scale parameters of the gamma hyperprior that steer the CEP. The posterior estimates are calculated using iterative statistical methods, expectation maximization and iterative alternating sequential algorithm, which we show to be algorithmically similar and to have a close resemblance to the iterative ℓ1 and ℓ2 reweighting methods. The performance of CEP is compared with the recent sampling-based dipole localization method Sequential semi-analytic Monte Carlo estimation (SESAME) in numerical experiments of simulated somatosensory evoked potentials related to the human median nerve stimulation. Our results obtained using synthetic sources suggest that a hybrid of the first-degree CEP and RAMUS can achieve an accuracy comparable to the second-degree case (CGP) while being more focal. Further, the proposed hybrid is shown to be robust to noise effects and compares well with the dipole reconstructions obtained with SESAME.publishedVersionPeer reviewe