Tato práce se zabývá automatickou detekcí vysokofrekvenčních oscilací jakožto moderního elektrofyziologického biomarkru epileptogenní tkáně v intrakraniálním EEG, jehož vizuální detekce je zdlouhavý proces, který je ovlivněn subjektivitou hodnotitele. Epilepsie je jedním z nejčastějších neurologických onemocnění postihující 1 % obyvatelstva. Přestože jsou přibližně dvě třetiny případů léčitelné farmakologicky, zbylá třetina pacientů je odkázána zejména na léčbu chirurgickým zákrokem, pro nějž je zapotřebí přesně lokalizovat ložisko patologické tkáně. Vysokofrekvenční oscilace jsou v posledním desetiletí studovány pro jejich potenciál lokalizace patologické tkáně. Součástí této práce je shrnutí dosavadního výzkumu vysokofrekvenčních oscilací a výčet detektorů používaných ve výzkumu. V rámci práce byly vyvinuty či vylepšeny tři detektory vysokofrekvenčních oscilací, na jejichž popis navazuje evaluace z hlediska shody s manuální detekcí, přesnosti výpočtu příznaků oscilací a schopnosti lokalizace patologické tkáně. V závěru práce jsou představeny vyvinuté metody vizualizace vysokofrekvenčních výskytu oscilací a stručně uvedeny dosažené vědecké výsledky.

Cette étude a été divisée en 2 parties principales. Dans la première partie, nous examinons la relation entre l'activité des sources neuronales et les HFOs observés sur les électrodes intracérébrales. La deuxième partie traite de l'étude des conditions d'observabilité des HFO sur les électrodes du cuir chevelu. Les simulations ont montré que le modèle de champ neuronal proposé est capable de générer des HFOs montrant une forte ressemblance avec les signaux réels dans les deux cas EEG (cuir chevelu) et SEEG (intracérébral). De plus, nous avons pu relier les mécanismes physiopathologiques (GABA dépolarisant, inhibition directe, activité désynchronisée des populations neuronales) aux différentes caractéristiques morphologiques et spectrales des HFOs intracérébrales. Une hypothèse unifiée pour la production des HFOs et des pointes intercritiques est également formulée. Enfin, nous avons réussi à établir les conditions nécessaires sur l'activité temporelle et l'organisation spatiale des sources neuronales pour observer des HFOs sur les électrodes intracérébrales.En ce qui concerne la deuxième partie, la baisse inexpliquée de fréquence dans les HFOs collectées sur les électrodes du cuir chevelu a été abordée. Nous avons constaté que les mécanismes «non oscillatoires» de la génération de HFOs sont à l'origine de la faible fréquence (<200Hz) des HFOs du cuir chevelu et que le rapport signal / bruit (SNR) influe fortement sur la fréquence des oscillations. De plus, nous avons étudié la topographie des HFOs sur les électrodes du cuir chevelu et analysé comment cette topographie est affectée par différents paramètres (étendue spatiale épileptique, SNR, géométrie 3D). Enfin, nous avons montré que les HFOs du cuir chevelu peuvent être utilisés efficacement pour identifier la zone épileptique lorsque le rapport signal sur bruit des signaux enregistrés est suffisamment élevé. Une perspective de ce travail est l'identification non-invasive de la zone épileptique sans la nécessité d'enregistrements intracérébraux pré-chirurgicaux.Pour les deux études (HFO observés sur les électrodes intracérébrales et du cuir chevelu), un logiciel original et convivial a été développé. Ce logiciel a fortement facilité la simulation des signaux dans l'environnement cerveau/électrode virtuel, signaux obtenus en résolvant le problème direct de l’EEG (projection de la contribution électrique des sources neuronales sur les capteurs). / This study was divided into 2 main parts. In the first part, we address the relationship between the activity of neuronal sources and the HFOs observed on intracerebral electrodes. The second part deals with the investigation of observability conditions of HFOs on scalp electrodes. Simulations showed that the proposed neural field model is capable of generating HFOs showing strong resemblance with real signals in both cases EEG (scalp) and SEEG (intracerebral). Moreover, we were able to relate the pathophysiological mechanisms (depolarizing GABA, feedforward inhibition, desynchronized activity of neuronal populations) to different morphological and spectral features of intracerebral HFOs. A unified hypothesis for generation of HFOs and interictal spikes is also formulated. Finally, we managed to establish the necessary conditions about the temporal activity and the spatial organization of neuronal sources and about for HFOs to be observed on intracerebral electrodes. Regarding the second part, the unexplained drop in frequency in the collected HFOs on scalp electrodes was addressed. We found that the “non-oscillatory” mechanisms of the HFO generation is behind the low frequency (<200Hz) in scalp HFOs and that signal to noise ratio (SNR) heavily impacts the frequency of the oscillations. Moreover, we studied the topography of HFOS on scalp electrodes and analyzed how this topography is affected by different parameters (epileptic spatial extent, SNR, 3D geometry). Finally we showed that scalp HFOs can be effectively used to identify the epileptic zone when the SNR of the recorded signals is sufficiently high. A perspective to this work is the non-invasive identification of epileptic zone without the need for presurgical intracerebral recordings. For the purpose of both studies (HFOs observed on intracerebral & scalp electrodes) an original and user-friendly software package was developed. This software strongly facilitated the simulation of signals in the virtual brain/electrode environment obtained by solving the (S)EEG forward problem (projection of the electric contribution of neuronal sources onto electrode contacts).

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23420号 / 医博第4765号 / 新制||医||1053(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 髙橋 良輔, 教授 林 康紀, 教授 福田 和彦 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23774号 / 医博第4820号 / 新制||医||1057(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 伊佐 正, 教授 高橋 淳, 教授 井上 治久 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

A eletroencefalografia (EEG) é uma das evidências tomadas na avaliação de indicação cirúrgica, em casos de pacientes portadores de epilepsia refratária a medicamentos, que pode auxiliar na localização da área responsável pela origem das crises epilépticas. Ao longo das últimas décadas, além das bandas de frequências já tradicionalmente avaliadas (até cerca de 40Hz), a EEG tem despertado o interesse de pesquisadores também para bandas de frequências mais altas. Passaram a ser encontradas evidências de que oscilações de alta frequência, conhecidas por HFO (High Frequency Oscillations), podem ser usadas como biomarcadores de epilepsia. Diversos estudos têm sido realizados em busca de uma melhor compreensão sobre HFO, a fim de viabilizar sua utilização em aplicações clínicas. Entretanto, características não lineares e de complexidade, que podem contribuir na análise de sinais com origem em sistemas biológicos, não têm sido investigadas neste tipo de sinais. Este estudo propôs a investigação de características extraídas de sinais de EEG com presença de HFO, de pacientes portadores de epilepsia refratária, através de métodos considerados como de análise não linear. Análise de Dinâmica Simbólica, Análise de Flutuações Destendenciadas (DFA), Entropia Multiescala (MSE) e Análise qSDiff foram aplicadas em segmentos de sinais de EEG intracraniano, amostrados a 5kHz, de pacientes portadores de epilepsia refratária, e também em alguns sinais simulados de características conhecidas para fins de comparação. Os resultados dos diferentes métodos investigados apontaram características semelhantes entre os segmentos de EEG analisados e séries simuladas de ruído browniano, sugerindo que os sinais de EEG em geral têm perfil bastante suavizado, são não estacionários e exibem correlações de longo alcance. Foram também levantadas evidências de que tanto HFO quanto os segmentos de EEG onde estão inseridas têm padrões mais regulares de variação e são menos complexas que segmentos de EEG sem HFO, sugerindo a degradação da complexidade fisiológica desta região cerebral, que poderia estar relacionada com mecanismos fisiopatológicos da epilepsia. Todos os métodos investigados sugeriram que as características e propriedades não lineares, relacionadas a complexidade inerente dos sinais de EEG, podem ser úteis na análise de HFO, principalmente pelas evidências de que estas características se alteram nas HFO, quando comparadas ao restante do sinal onde elas se encontram e também a outros sinais sem sua presença. / Electroencephalography (EEG) is one of the evidences taken in the evaluation of surgical indication, in cases of patients with drug refractory epilepsy, which may help in locating the area responsible for the origin of epileptic seizures. Over the last few decades, in addition to the frequency bands that have traditionally been evaluated (up to about 40Hz), the EEG has attracted researchers also to higher frequency bands. Evidence has been found that high frequency oscillations (HFO), can be used as biomarkers of epilepsy. Many studies have been carried out in search of a better understanding about HFO, in order to make it feasible to use in clinical applications. However, nonlinear and complex features, which may contribute to the analysis of signals originating from biological systems, have not been investigated in this type of signals. This study proposed the investigation of features extracted from EEG signals with HFO of patients with refractory epilepsy, using nonlinear analysis methods. Symbolic Dynamics Analysis, Detrended Fluctuation Analysis (DFA), Multiscale Entropy (MSE) and qSDiff Analysis were applied to segments of intracranial EEG signals, sampled at 5kHz, from patients with refractory epilepsy, as well as some features-known simulated signals for comparison purposes. Results of the different investigated methods pointed out similar features between the analyzed EEG segments and the simulated series of Brownian noise, suggesting that EEG signals, in general, have a very smoothed profile, are nonstationary and exhibit long- range correlations. Evidence has also been raised that both HFO and the EEG segments where they are inserted have more regular patterns of variation and are less complex than EEG segments without HFO, suggesting the degradation of the physiological complexity of this brain region, which could be related to pathophysiological mechanisms of epilepsy. All the investigated methods suggested that nonlinear features and properties, related to the inherent complexity of EEG signals, may be useful in HFO analysis, mainly because of the evidence that these features change in HFOs when compared to the rest of the signal where they are and other signals without their presence.

Recent experimental studies point to the notion that the brain is a complex dynamical system whose behaviors relating to brain functions and dysfunctions can be described by the physics of network phenomena. The brain consists of anatomical axonal connections among neurons and neuronal populations in various spatial scales. Neuronal interactions and synchrony of neuronal oscillations are central to normal brain functions. Breakdowns in interactions and modifications in synchronization behaviors are usual hallmarks of brain dysfunctions. Here, in this dissertation for PhD degree in physics, we report discoveries of brain oscillatory network activity from two separate studies. These studies investigated the large-scale brain activity during tactile perceptual decision-making and epileptic seizures. In the perceptual decision-making study, using scalp electroencephalography (EEG) recordings of brain potentials, we investigated how oscillatory activity functionally organizes different neocortical regions as a network during a tactile discrimination task. While undergoing EEG recordings, blindfolded healthy participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. Based on the current dipole modeling in the brain, we found that the source-level peak activity appeared in the left primary somatosensory cortex (SI), right lateral occipital complex (LOC), right posterior intraparietal sulcus (pIPS) and finally left dorsolateral prefrontal cortex (dlPFC) at 45, 130, 160 and 175 ms respectively. Spectral interdependency analysis showed that fine tactile discrimination is mediated by distinct but overlapping ~15 Hz beta and ~80 Hz gamma band large-scale oscillatory networks. The beta-network that included all four nodes was dominantly feedforward, similar to the propagation of peak cortical activity, implying its role in accumulating and maintaining relevant sensory information and mapping to action. The gamma-network activity, occurring in a recurrent loop linked SI, pIPS and dlPFC, likely carrying out attentional selection of task-relevant sensory signals. Behavioral measure of task performance was correlated with the network activity in both bands. In the study of epileptic seizures, we investigated high-frequency (> 50 Hz) oscillatory network activity from intracranial EEG (IEEG) recordings of patients who were the candidates for epilepsy surgery. The traditional approach of identifying brain regions for epilepsy surgery usually referred as seizure onset zones (SOZs) has not always produced clarity on SOZs. Here, we investigated directed network activity in the frequency domain and found that the high frequency (>80 Hz) network activities occur before the onset of any visible ictal activity, andcausal relationships involve the recording electrodes where clinically identifiable seizures later develop. These findings suggest that high-frequency network activities and their causal relationships can assist in precise delineation of SOZs for surgical resection.

A eletroencefalografia (EEG) é uma das evidências tomadas na avaliação de indicação cirúrgica, em casos de pacientes portadores de epilepsia refratária a medicamentos, que pode auxiliar na localização da área responsável pela origem das crises epilépticas. Ao longo das últimas décadas, além das bandas de frequências já tradicionalmente avaliadas (até cerca de 40Hz), a EEG tem despertado o interesse de pesquisadores também para bandas de frequências mais altas. Passaram a ser encontradas evidências de que oscilações de alta frequência, conhecidas por HFO (High Frequency Oscillations), podem ser usadas como biomarcadores de epilepsia. Diversos estudos têm sido realizados em busca de uma melhor compreensão sobre HFO, a fim de viabilizar sua utilização em aplicações clínicas. Entretanto, características não lineares e de complexidade, que podem contribuir na análise de sinais com origem em sistemas biológicos, não têm sido investigadas neste tipo de sinais. Este estudo propôs a investigação de características extraídas de sinais de EEG com presença de HFO, de pacientes portadores de epilepsia refratária, através de métodos considerados como de análise não linear. Análise de Dinâmica Simbólica, Análise de Flutuações Destendenciadas (DFA), Entropia Multiescala (MSE) e Análise qSDiff foram aplicadas em segmentos de sinais de EEG intracraniano, amostrados a 5kHz, de pacientes portadores de epilepsia refratária, e também em alguns sinais simulados de características conhecidas para fins de comparação. Os resultados dos diferentes métodos investigados apontaram características semelhantes entre os segmentos de EEG analisados e séries simuladas de ruído browniano, sugerindo que os sinais de EEG em geral têm perfil bastante suavizado, são não estacionários e exibem correlações de longo alcance. Foram também levantadas evidências de que tanto HFO quanto os segmentos de EEG onde estão inseridas têm padrões mais regulares de variação e são menos complexas que segmentos de EEG sem HFO, sugerindo a degradação da complexidade fisiológica desta região cerebral, que poderia estar relacionada com mecanismos fisiopatológicos da epilepsia. Todos os métodos investigados sugeriram que as características e propriedades não lineares, relacionadas a complexidade inerente dos sinais de EEG, podem ser úteis na análise de HFO, principalmente pelas evidências de que estas características se alteram nas HFO, quando comparadas ao restante do sinal onde elas se encontram e também a outros sinais sem sua presença. / Electroencephalography (EEG) is one of the evidences taken in the evaluation of surgical indication, in cases of patients with drug refractory epilepsy, which may help in locating the area responsible for the origin of epileptic seizures. Over the last few decades, in addition to the frequency bands that have traditionally been evaluated (up to about 40Hz), the EEG has attracted researchers also to higher frequency bands. Evidence has been found that high frequency oscillations (HFO), can be used as biomarkers of epilepsy. Many studies have been carried out in search of a better understanding about HFO, in order to make it feasible to use in clinical applications. However, nonlinear and complex features, which may contribute to the analysis of signals originating from biological systems, have not been investigated in this type of signals. This study proposed the investigation of features extracted from EEG signals with HFO of patients with refractory epilepsy, using nonlinear analysis methods. Symbolic Dynamics Analysis, Detrended Fluctuation Analysis (DFA), Multiscale Entropy (MSE) and qSDiff Analysis were applied to segments of intracranial EEG signals, sampled at 5kHz, from patients with refractory epilepsy, as well as some features-known simulated signals for comparison purposes. Results of the different investigated methods pointed out similar features between the analyzed EEG segments and the simulated series of Brownian noise, suggesting that EEG signals, in general, have a very smoothed profile, are nonstationary and exhibit long- range correlations. Evidence has also been raised that both HFO and the EEG segments where they are inserted have more regular patterns of variation and are less complex than EEG segments without HFO, suggesting the degradation of the physiological complexity of this brain region, which could be related to pathophysiological mechanisms of epilepsy. All the investigated methods suggested that nonlinear features and properties, related to the inherent complexity of EEG signals, may be useful in HFO analysis, mainly because of the evidence that these features change in HFOs when compared to the rest of the signal where they are and other signals without their presence.

This Master’s thesis deals with investigation of high-frequency oscillations in intracranial electroencephalography in patients with pharmacoresistant epilepsy. It describes individual types of oscillations with respect to their frequency definition, examines their physiological differences and occurrence. In addition to conventional high-frequency oscillations (up to about 600 Hz), it also focuses on oscillations with a frequency component above 1kHz. According to recent studies, these oscillations could have higherspecificity for the determination of pathological tissue in the epileptic brain. The data for this work was obtained by manual labeling and categorization of approximately 1500 sections of the stereoencephalographic record signals of patients undergoing surgical removal of the epileptic foci and subsequently monitored for success in the operation. Differences between individual groups of oscillations and resected or unresected tissues are investigated in this work by methods using calculations of entropy signals or cross frequency coupling. The most significant results were achieved for the classification group (FR + vFR) vs. uFR, methods frequency-amplitude coupling and sample entropy 1. When categorizing according to information about channel resection, the Shannon entropy is the most successful classification parameter.

Touchant plus de 50 millions de personnes dans le monde, l’épilepsie est un problème majeur de santé publique. Un tiers des patients souffrent d’épilepsie pharmaco-résistante. Une chirurgie visant à enlever la région cérébrale à l’origine des crises – la zone épileptogène – est considérée comme l’option de référence pour rendre libre de crises ces patients. Le taux d’échec chirurgical non négligeable a poussé la recherche d’autres marqueurs. Un marqueur potentiel est les oscillations à haute fréquence (HFOs). Une HFO est une brève oscillation entre 80-500 Hz qui dure au moins 4 périodes enregistrée en EEG intracérébrale. Par leur caractère très bref, le marquage visuel de ces petites oscillations est fastidieux et chronophage.. Il semble impératif de trouver un moyen de détecter automatiquement ces oscillations pour étudier les HFOs sur des cohortes de patients. Aucun détecteur automatique existant ne fait cependant l’unanimité. Durant cette thèse, nous avons développé un nouveau moyen de visualiser les HFOs grâce à une normalisation originale de la transformée en ondelettes pour ensuite mieux les détecter automatiquement. Puis, nous avons mise en place une stratégie pour caractériser et valider des détecteurs. Enfin, nous avons appliqué le nouveau détecteur à une cohorte de patients pour déterminer la fiabilité des HFOs et des pointes épileptiques - le marqueur standard - dans la prédiction de la zone épileptogène. La conclusion de cette thèse est que les HFOs ne sont pas meilleurs que les pointes épileptiques pour prédire la zone épileptogène mais que combiner ces deux marqueurs permettait d’obtenir un marqueur plus robuste. / Epilepsy is a major health problem as it affects 50 million people worldwide. One third of the patients are resistant to medication. Surgical removal of the brain areas generating the seizure – the epileptogenic zone – is considered as the standard option for these patients to be seizure free. The non-negligible rate of surgical failure has led to seek other electrophysiological criteria. One putative marker is the high-frequency oscillations (HFOs).An HFO is a brief oscillation between 80-500 Hz lasting at least 4 periods recorded in intracerebral EEG. Due to their short-lasting nature, visually marking of these small oscillations is tedious and time-consuming. Automatically detecting these oscillations seems an imperative stage to study HFOs on cohorts of patients. There is however no general agreement on existing detectors.In this thesis, we developed a new way of representing HFOs thanks to a novel normalisation of the wavelet transform and to use this representation as a base for detecting HFOs automatically. We secondly designed a strategy to properly characterise and validate automated detectors. Finally, we characterised, in a cohort of patients, the reliability of HFOs and epileptic spikes - the standard marker - as predictors of the epileptogenic zone using the validated detector. The conclusion of this thesis is that HFOs are not better than epileptic spikes in predicting the epileptogenic zone but combining the two leads to a more robust biomarker.

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