BREAKDOWN OF CAUSALITY AND CORTICAL DOWNSTATE WITHIN THE SLEEPING BRAIN

Theoretically, consciousness depends on the brain\u2019s ability to engage in complex activity patterns that are, at once, distributed among interacting cortical areas (integrated) and differentiated in space and time (information-rich). In a recent series of experiments the electroencephalographic response to a direct cortical stimulation in humans was recorded during wakefulness and non-rapid eyes movement sleep (NREM) by means of a combination of transcranial magnetic stimulation (TMS) and high-density electroencephalogram (hd-EEG). TMS/hd-EEG measurements showed that, while during wakefulness the brain is able to sustain long-range specific patterns of activation, during NREM sleep, when consciousness fades, this ability is lost: the thalamocortical system, despite being active and reactive, either breaks down in causally independent modules (producing a local slow wave), or it bursts into an explosive and non-specific response (producing a global EEG slow wave). We hypothesize that, like spontaneous sleep slow waves, the slow waves triggered by TMS during deep sleep are due to bistability between periods of hyperpolarized down-state in cortical neurons, and periods of activation (up-state). In this condition, the inescapable occurrence of a silent, down-state after an initial activation could impair the ability of thalamocortical circuits to sustain long-range, differentiated patterns of activation, a theoretical requisite for consciousness. According to animal experiments the extracellular signature of the downstate is a transient suppression of high frequency (20Hz) oscillations \u2013 that is followed by a loss of both PLF and PLV, in spite of restored levels of neuronal activity. These results point to bistability as the underlying critical mechanism that prevents the emergence of complex interactions in human thalamocortical networks when consciousness is lost during NREM sleep. This finding is particularly relevant because a similar mechanism may play a role in other conditions where loss of consciousness is paralleled by the appearance of spontaneous (or TMS evoked) slow waves such as some kind of anesthesia and in brain injured subjects

Imaging functional and structural networks in the human epileptic brain

Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and subcortical grey matter as well as their connections via white matter fibres. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. A better knowledge of physiological and pathological brain networks in patients with epilepsy is critical for a better understanding the patterns of seizure generation, propagation and termination as well as the alteration of physiological brain networks by a chronic neurological disorder. Moreover, the identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been remodelled by the disease. This work describes the combination of neuroimaging methods to study the functional epileptic networks in the brain, structural connectivity changes of the motor networks in patients with localisation-related or generalised epilepsy and finally structural connectivity of the epileptic network. The combination between EEG source imaging and simultaneous EEG-fMRI recordings allowed to distinguish between regions of onset and propagation of interictal epileptic activity and to better map the epileptic network using the continuous activity of the epileptic source. These results are complemented by the first recordings of simultaneous intracranial EEG and fMRI in human. This whole-brain imaging technique revealed regional as well as distant haemodynamic changes related to very focal epileptic activity. The combination of fMRI and DTI tractography showed subtle changes in the structural connectivity of patients with Juvenile Myoclonic Epilepsy, a form of idiopathic generalised epilepsy. Finally, a combination of intracranial EEG and tractography was used to explore the structural connectivity of epileptic networks. Clinical relevance, methodological issues and future perspectives are discussed

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

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

Imaging brain networks in focal epilepsy: a prospective study of the clinical application of simultaneous EEG-fMRI in pre-surgical evaluation

Epilepsy is a common disorder with significant associated morbidity and mortality. Despite advances in treatment, there remain a minority of people with pharmacoresistant focal epilepsy for whom surgery may be beneficial. It has been suggested that not enough people are offered surgical treatment, partly owing to the fact that current non-invasive techniques do not always adequately identify the seizure onset zone so that invasive EEG is required. EEG-fMRI is an imaging technique, developed in the 1990s (Ives, Warach et al. 1993) which identifies regions of interictal epileptiform discharge associated haemodynamic changes, that are concordant with the seizure onset zone in some patients (Salek-Haddadi, Diehl et al. 2006). To date there has been no large scale prospective comparison with icEEG and postoperative outcome. This thesis presents a series of experiments, carried out in a cohort of patients scanned using EEG-fMRI as part of a multi-centre programme, designed to investigate the relationship between EEG-fMRI and intracranial EEG and to assess its potential role in pre-surgical evaluation of patients with focal epilepsy. The results suggested that positive, localised IED-related BOLD signal changes were sensitive for the seizure onset zone, as determined on icEEG, both in patients neocortical epilepsies, but were not predictive of outcome. Widespread regions of positive IEDrelated BOLD signal change were associated with widespread or multifocal abnormalities on icEEG and poor outcome. Patterns of haemodynamic change, identified using both data driven and EEG derived modeling approaches, correspond to regions of seizure onset on icEEG, but improvements for modeling seizures are required. A study of a single seizure in a patient who underwent simultaneous icEEGfMRI, showed similar findings.. An exploratory investigation of fMRI-DCM in EEG-fMRI, suggested it can provide information about seizure propagation and this opens new avenues for the non-invasive study of the epileptic network and interactions with function

Data-driven neural mass modelling

The brain is a complex organ whose activity spans multiple scales, both spatial and temporal. The computational unit of the brain is thought to be the neurone. At the microscopic level, neurones communicate via action potentials. These may be observed experimentally by means of precise techniques that work with a small number of these cells and their interactions, and that can be modelled mathematically in a variety of ways. Other techniques consider the averaged activity of large groups of neurones in the mesoscale, or cortical columns; theoretical models of these signals also abound. The problem of relating the microscopic scale to the mesoscopic is not trivial. Analytical derivations of mesoscopic models are based on assumptions that are not always justified. Also, traditionally there has been a separation between the clinically oriented analysts that process neural signals for medical purposes and the theoretical modelling community. This Thesis aims to lay bridges both between the microscopic and mesoscopic scales of brain activity, and between the experimental and theoretical angles of its study. This is achieved via the unscented Kalman filter (UKF), which allows us to combine knowledge from different sources (microscopic/mesoscopic and experimental/theoretical). The outcome is a better understanding of the system than each of the sources of information could provide separately. The Thesis is organised as follows. Chapter 1 is a brief reflection on the current methodology in Science and its underlying motivations. This is followed by chapters 2 to 4, which introduce and contextualise the concepts discussed in the remainder of the work. Chapter 5 tackles the interrelationship of the microscopic and mesoscopic scales. Although efforts have been made to derive mesoscopic equations from models of microscopic networks, they are based on assumptions that may not always hold. We use the UKF to assimilate the output of microscopic networks into a mesoscopic model and study a variety of dynamical situations. Our results show that using the Kalman filter compensates for the loss of information that is common in analytical derivations. Chapters 6 and 7 address the combination of experimental data with neural mass models. More specifically, we extend Jansen and Rit's model of a cortical column with a model of the head, which allows us to use electroencephalography (EEG) data. With this, we estimate the state of the system and a relevant parameter of choice. In chapter 6 we use in silico data to test the UKF under a variety of dynamical conditions, comparing simulated intracranial data with simulated EEG. Extracranial estimation is always superior in speed and quality to intracortical estimation, even though intracortical electrodes are closer to the source of activity than extracranial electrodes. We suggest that this is due to the more complete picture of the cortex that is visible with the set of extracranial electrodes. Chapter 7 feeds experimental EEG data of an epileptic patient into Jansen and Rit's model; the goal is to estimate a parameter that governs the dynamical behaviour of the system, again with the UKF. The estimation of the state closely follows the experimental data, while the parameter shows sensitivity to the changes in brain regimes, especially seizures. These results show promise for using data assimilation to address some shortcomings of brain modelling techniques. On the one hand, the mutual influence of neural structures at the microscopic and the mesoscopic scales may become better characterised, by means of filtering approaches that bypass analytical limitations. On the other hand, fusing experimental EEG data with mathematical models of the brain may enable us to determine the underlying dynamics of observed physiological signals, and at the same time to improve our models with patient-specific information. The potential of these enhanced algorithms spans a wide range of brain-related applications.El cervell humà és un òrgan de gran complexitat l’activitat del qual es desenvolupa en múltiples escales, tant espacials com temporals. Es creu que la unitat computacional del cervell és la neurona, una cèl·lula altament especialitzada que té com a funció rebre, processar i transmetre informació. A nivell microscòpic, les neurones es comuniquen les unes amb les altres per potencials d’acció. Aquests es poden observar experimentalment “in vivo” per mitjà de tècniques de gran precisió que només poden tenir en compte un nombre relativament reduït de cèl·lules i interaccions, i que es poden modelar matemàticament de diverses maneres. Altres tècniques tracten amb grans grups de neurones a escala mesoscòpica, o columnes corticals, i detecten l’activitat mitjana de la població neuronal; en aquest cas també abunden els models teòrics que intenten reproduir aquests senyals. Malgrat que està ben establert que hi ha una intercomunicació entre les escales microscòpica i mesoscòpica, relacionar una escala amb una altra no és gens trivial. Les derivacions analítiques de models mesoscòpics a partir de xarxes microscòpiques es basen en suposicions que no sempre es poden justificar. A part, tradicionalment hi ha hagut una frontera de separació entre els analistes clínics que processen senyals neuronals amb fins mèdics (i que sovint usen tècniques molt invasives i/o costoses), i la comunitat teòrica que modelitza aquests senyals, per a qui el repte més gran és caracteritzar els paràmetres que governen els models perquè aquests s’acostin el més possible a la realitat. Aquesta Tesi té com a objectiu, per una banda, fer un pas més a caracteritzar la relació entre les escales microscòpica i mesoscòpica d’activitat cerebral, i, per l’altra, establir ponts entre els punts de vista experimental i teòric del seu estudi. Ho aconseguim amb un algoritme d’assimilació de dades, el filtre de Kalman desodorat (UKF, de les sigles en anglès), que ens permet combinar informació de diverses procedències (microscòpica/mesoscòpica o experimental/teòrica). El resultat és una comprensió més àmplia del sistema estudiat que la que haurien permès les fonts d’informació per separat. La Tesi està organitzada de la següent manera. El capítol 1 comença amb una breu reflexió sobre la metodologia científica actual i les seves motivacions subjacents (segons l’autora). El segueixen els capítols del 2 al 4, que introdueixen i posen en context els conceptes que s’exposen a la resta del treball. El capítol 5 aborda el problema de la relació entre l’escala microscòpica i la mesoscòpica. Tot i que existeixen diverses derivacions d’equacions mesoscòpiques partint de models de xarxes neuronals, sovint es basen en suposicions fràgils que no es compleixen en situacions més complicades. Aquí utilitzem l’UKF per assimilar la sortida de xarxes microscòpiques en un model mesoscòpic simple i estudiar diverses situacions dinàmiques. Els resultats mostren que la manera que el filtre de Kalman gestiona les incerteses del model compensa les pèrdues d’informació pròpies de les derivacions analítiques de models mesoscòpics. Els capítols 6 i 7 tracten la combinació de dades experimentals del cervell amb models de masses neurals que descriuen la dinàmica de grups de neurones. Concretament, estenem el model de Jansen i Rit d’una columna cortical amb un model del cap, el qual ens permet fer servir dades extracranials no invasives. Amb això estimem l’estat del sistema i un paràmetre d’interès de possible rellevància en l’estudi clínic d’afeccions com l’epilèpsia. En el capítol 6 fem servir dades “in silico” per provar l’UKF en diversos escenaris dinàmics: conjunts de paràmetres que causen comportaments diferents en les columnes corticals, diferents nivells de soroll de mesura i dues modalitats de transmissió d’informació; tot això comparant dades intracranials simulades amb simulacions d’electroencefalogrames (EEG). En totes les situacions estudiades, l’estimació extracranial és sempre superior, en velocitat i precisió, a l’estimació intracortical, encara que els elèctrodes intracorticals són molt més propers a la font de l’activitat que els elèctrodes de la superfície cranial. Suggerim que això pot ser causat per la visió més completa del còrtex que es pot obtenir amb el conjunt d’elèctrodes extracranials. Aquesta idea ve reforçada pels resultats observats amb elèctrodes extracranials individuals treballant de manera independent, que apunten a la sensibilitat espacial de les mesures. En el capítol 7 alimentem el model de Jansen i Rit amb dades experimentals de l’EEG d’un pacient epilèptic; l’objectiu és estimar un paràmetre significatiu que governa l’evolució dinàmica del sistema, de nou amb l’UKF. L’estimació de l’estat és precisa i el paràmetre es veu afectat pels canvis de règim, especialment (però no exclusivament) per les convulsions. Aquests resultats són prometedors a l’hora d’utilitzar l’assimilació de dades per superar les diverses carències de les tècniques de modelització cerebral. Per una banda, la influència mútua entre estructures a escala microscòpica i a escala mesoscòpica es pot caracteritzar millor, gràcies a tècniques de filtrat que permeten esquivar les habituals limitacions analítiques. Això dóna com a resultat una millor comprensió de l’estructura i funció cerebrals. Per una altra banda, fusionar dades experimentals d’EEG amb els models matemàtics del cervell existents ens pot permetre determinar les dinàmiques subjacents dels senyals fisiològics que tenim disponibles, a la vegada que millorem els nostres models amb informació individual de cada pacient. Aquests algoritmes augmentats tenen potencial per a un ampli espectre d’aplicacions en el camp de les neurociències, des d’interfícies cervell/ordinador fins a tota mena d’usos en medicina personalitzada com el diagnòstic precoç de malalties neurodegeneratives, la predicció de crisis convulsives o la monitorització de la rehabilitació postisquèmica o posttraumàtica, entre molts altres.Postprint (published version

Spatio-temporal evolution of interictal epileptic activity : a study with unaveraged multichannel MEG data in association with MRIs.

This thesis addresses issues relating to MEG modelling, analysis and interpretation of results. A source model employing current density distributions, namely Magnetic Field Tomography (MET), is used to obtain the MEG results. The first issue of concern refers to the registration of MEG data with structural MR images in an attempt to improve the localisation capability of MEG/MET. Simulations testing some spatial and tem poral aspects of the reconstruction capability of MET are also provided. A novel way of conducting MET studies in depth is suggested and implemented: the iterative use of a source space designed to cover deep situated structures on either side of the brain. The main bulk of this thesis is concerned with research into interictal epileptic activity as recorded by means of multichannel MEG system s and analysed using MET. The major aim is to investigate whether or not MET analysis of unaveraged MEG data (single epochs) is feasible in cases of pathophysiological signals and more specifically interictal signals from patients with epilepsy of a complex partial type. The investigation is undertaken against the "traditional" view of the impropriety and absurdity of using single epoch records in the MEG analysis due to noise dominance; we provide evidence that analysis of single, unaveraged epileptic spikes is actually feasible: we demonstrate spatio-temporal coherence in the MET results of the various single interictal events and show that activity extracted from the "averaged event" is made up of activity contributions which occur intermittently and at variable latencies. Our statements are drawn from the study of both superficial and deep activity

Neurophysiological correlates of preparation for action measured by electroencephalography

The optimal performance of an action depends to a great extend on the ability of a person to prepare in advance the appropriate kinetic and kinematic parameters at a specific point in time in order to meet the demands of a given situation and to foresee its consequences to the surrounding environment. In the research presented in this thesis, I employed high-density electroencephalography in order to study the neural processes underlying preparation for action. A typical way for studying preparation for action in neuroscience is to divide it in temporal preparation (when to respond) and event preparation (what response to make). In Chapter 2, we identified electrophysiological signs of implicit temporal preparation in a task where such preparation was not essential for the performance of the task. Electrophysiological traces of implicit timing were found in lateral premotor, parietal as well as occipital cortices. In Chapter 3, explicit temporal preparation was assessed by comparing anticipatory and reactive responses to periodically or randomly applied external loads, respectively. Higher (pre)motor preparatory activity was recorded in the former case, which resulted in lower post-load motor cortex activation and consequently to lower long-latency reflex amplitude. Event preparation was the theme of Chapter 4, where we introduced a new method for studying (at the source level) the generator mechanisms of lateralized potentials related to response selection, through the interaction with steady-state somatosensory responses. Finally, in Chapter 5 we provided evidence for the existence of concurrent and mutually inhibiting representations of multiple movement options in premotor and primary motor areas.EThOS - Electronic Theses Online ServiceGBUnited Kingdo