Central benzodiazepine receptors in hippocampal sclerosis and idiopathic generalised epilepsies and opiod receptors in reading epilepsy

Background: Epilepsy is the most common serious disease of the brain. In order to better understand the processes and neuronal circuits involved in the pathophysiology of the epilepsies and to provide structural / functional correlations, positron emission tomography (PET) needs to be evaluated in the light of high quality MRI. Aims: To determine the extent and amount of central benzodiazepine/GABAA receptor (cBZR) abnormalities in mesial temporal lobe epilepsy (mTLE) due to hippocampal sclerosis (HS); to quantify cBZR in idiopathic generalised epilepsy (IGE) and the effect of treatment with sodium valproate (VPA); to investigate dynamic changes of opioid receptors in reading epilepsy (RE) at the time of reading-induced seizures. Methods: 11C-flumazenil (FMZ)-PET scans of 37 controls, 25 candidates for temporal lobe resections with HS, and 10 patients with IGE before and after taking VPA, were analysed with statistical parametric mapping (SPM) and a partial-volume-effect (PVE) corrected regions-of-interest approach to quantify FMZ binding to cBZR. Paired 11C-diprenorphine (DPN)-PET scans of 6 control subjects and 5 patients with RE were analysed with SPM to detect significant localised reductions of DPN binding during reading-induced seizures implying a focal release of endogenous opioids. Results: Using SPM, reductions of cBZR were restricted to the sclerotic hippocampus in unilateral mTLE. Using PVE correction loss of cBZR in HS was shown to be over and above that due to neurone loss and hippocampal atrophy. In-vivo 1IC-FMZ-PET correlated well with ex-vivo 3H-FMZ autoradiography in HS. Subtle reductions of cBZR are seen contralaterally in unilateral mTLE in 30%. cBZR in IGE are increased in the cortex and thalamus, and FMZ binding is not affected by VPA. Endogenous opioids are released locally in the left temporo-parietal cortex at the time of reading-induced seizures. Conclusions: The identification of functional abnormalities of major inhibitory neurotransmitter systems, over and above structural abnormalities, has profound implications for the presurgical investigation of patients, in whom MRI does not reveal a relevant underlying lesion. Elucidation of the neurochemical and functional abnormalities underlying seizures assists the design of new anti-epileptic drugs and helps to identify neurochemical abnormalities underlying specific epilepsy syndromes

데이터 마이닝에 기반한 신피질 뇌전증에서 예후와 관련된 두개강 내 뇌파

학위논문 (박사)-- 서울대학교 대학원 의과대학 의학과, 2017. 8. 정천기.Introduction: We introduce a new data-mining method to select interictal pathologic activities based on the outcome of resective epilepsy surgery defined as the presence/absence of seizures in neocortical epilepsy (NE). Methods: We analyzed electrocorticographies from 39 patients with medically intractable NE. We separately analyzed 37 frequency-bins from 0.9 to 600 Hz to select the bands related to the seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. The two different interictal electrocorticography datasets containing epileptiform activities were selected. In the first training dataset, the automatic detector was optimized to best differentiate the seizure-free group from the not-seizure-free-group based on the ranks of resection percentages of the activities detected using a genetic algorithm. We optimized in a patient group with 20 patients and validated optimized threshold in a different patient group with 19 patients to evaluate stability of results in a different patient group. Significant reproducibility was determined from expected numbers of significant results from the binomial distribution. The differences in the resection percentage of the detected activities between the seizure outcome groups (Dif-R) in the validation dataset were measured. Results: There were 16 seizure-free (41%) of 39 patients. The mean follow-up duration was 21 ± 11 months (13 to 44 months). In the validation dataset from the different 19 patient group, delta in 2.0 – 2.3 Hz were significantly reproducible. Low-frequency activities (LFAs) between 4.9 – 43 Hz including theta, alpha, beta and low-gamma were significantly reproducible. High-gamma in 62 and 75 Hz and high-frequency activities (HFAs) in 108 and 322 Hz were reproducibly related to seizure outcome. Dif-Rs in the different patient group was about mean 10 – 20 % in reproducible frequency-bins. In LFAs, the resection of detected activities were positively related with better seizure outcome as intended. However, high-gamma activities are paradoxically negatively related with seizure outcome. Conclusion: Using the presented method, in a different interictal segment validation, we achieved Dif-Rs that were higher than the best manual and automatic HFA detections described in the literature using only training dataset (17 to 27 %). In a different patient group validation, our results, 10 – 20 % Dif-Rs were comparable to literature analyzing only training dataset. A new method selecting pathologic activities based on seizure outcome can be potentially useful finding pathologic activities to be resected.1 Introduction 1 2 Methods 11 3 Results 15 4 Discussion 17 5 Conclusion 21 References 22 Abstract (Korean) 30Docto

Refractory Epilepsy: Natural History and Pathogenesis

Despite antiepileptic drug (AED) treatment, up to one third of patients continue to have seizures. Refractory epilepsy is a poorly understood subject, both in terms of its development and pathogenesis. Outcome studies have focused on terminal remission, but little is known about the natural history of epilepsy in terms of its progression to eventual remission or persistent refractoriness. Such an understanding is essential to the formulation of a rational management approach. Natural history of treated epilepsy Long-term outcome of newly diagnosed patients was investigated longitudinally for up to 15 years. Response to the first drug and successive substitution monotherapy or polytherapy was analysed. Outcome among patients with different neuropathologies was compared in a separate study. Among 470 newly diagnosed patients, 64% became seizure-free for at least a year. Patients with high numbers of pre-treatment seizures were less likely to become seizure- free. Epilepsy was controlled by the first AED in 47% of patients. Patients with symptomatic or cryptogenic epilepsy were less likely to become seizure-free on the first AED, partly because they were more likely to develop intolerable side-effects compared to those with idiopathic epilepsy. The majority of such withdrawals occurred at low doses of the three most commonly prescribed AEDs, carbamazepine (CBZ), sodium valproate (VPA) and lamotrigine (LTG). Over 90% of seizure-free patients also required only a moderate daily dose (up to 800mg CBZ, ISOOmg VPA, 300mg LTG). The probability of attaining seizure-freedom declined progressively with successive AED regimens. While 47% became seizure-free on the first drug, only 10% did so on the second drug and 1% on the third monotherapy. Three percent became seizure-free on a combination of two AEDs. The subsequent seizure-free rate was 41% among those failing the first drug due to intolerable side effects, but only 11% among those in whom the first AED was well tolerated but did not control the seizures completely. Among patients with inadequate seizure control on the first well tolerated AED, those who received substituted monotherapy and those who received add-on treatment had similar seizure-free rates and incidence of intolerable side effects. More patients became seizure- free on combinations involving an AED that blocked sodium channels and one with multiple mechanisms of action than on other combinations. Combination treatment was more effective when prescribed immediately after the first drug failed due to inadequate seizure control than w'hen it was delayed until a substitution also proved unsuccessful. In a separate study, compared with other pathologies identified on magnetic resonance imaging, mesial temporal sclerosis (MTS) was associated with the worst prognosis, although 42% did become seizure-free on AED treatment. Pathogenesis of refractory epilepsy Two candidate biological mechanisms in the pathogenesis of refractory epilepsy were studied. Glutamic acid decarboxylase (GAD) autoantibody titres were compared between patients with controlled and uncontrolled epilepsy. GAD catalyses the conversion of glutamate to y-aminobutyric acid, the major inhibitory neurotransmitter. Autoantibodies against GAD are prevalent in insulin dependent diabetes mellitus, and have been documented in anecdotal cases of refractory epilepsy. The drug transporter P-glycoprotein (P-gp) was investigated in a series of laboratory-based pilot experiments. Encoded by the multidrug resistance gene family (MDRl in man and mdrla and lb in rodents), P-gp actively extrudes a wide range of xenobiotics out of cells. Its over-expression is thought to underlie the resistance of some cancers to multiple chemotherapeutics. P-gp is physiologically expressed at high level in the cerebral capillary endothelium where it contributes to the integrity of the blood-brain barrier. Overexpression of P-gp in brain tissues resected from patients with refractory epilepsy has been reported in surgical case series. Mdrla(-/-) mice devoid of cerebral P-gp were used to determine whether AEDs were substrates of the drug transporter. The pharmacokinetic profiles of four established and four new AEDs in mdrla(-/-) mice and wild-type mice were compared. The technique of quantitative reverse transcriptase-polymerase chain reaction was developed and validated to determine tissue concentration of mdrl mRNA as an indicator of gene expression. Expression was determined in different regions of the normal rat brain, and in brains of genetically epilepsy-prone rats (GEPRs) subject to a single audiogenic seizure. To explore the effect of tissue damage, a laser beam was impinged upon the cerebral cortex of rats. Mdrl expression was measured in tissues surrounding the focal necrosis. Human brain tissues resected during epilepsy surgery were also analysed for MDRl gene expression. There was no difference in GAD autoantibody titres between patients with controlled and uncontrolled epilepsy. (Abstract shortened by ProQuest.)

Dynamics of brain states and cortical excitability in paroxysmal neurological conditions

Epilepsy and migraine are neurological conditions that are characterised by periods of disruption of normal neuronal functioning. Aside from this paroxysmal feature, both conditions share genetic mutations and altered cortical excitability. People with epilepsy appear to be diagnosed with migraine more often than people without epilepsy and, likewise, people with migraine seem to be diagnosed with epilepsy more often than people without migraine. Changes in cortical excitability may help explain the pathophysiological link between both conditions, and could be a biomarker to monitor disease activity. In this thesis, the association between migraine and epilepsy and their relation to cortical excitability is further explored. A meta-analysis of previous population based studies provides epidemiological evidence for the co-occurrence of migraine and epilepsy. The combination of computer modelling with human electroencephalographic recordings offers insight into multi-stability of brain states in epilepsy. Results described in this thesis show that Transcranial Magnetic Stimulation can be used to measure cortical excitability, but that its use as a biomarker of disease activity in epilepsy is limited due to large interindividual variability. By combining Transcranial Magnetic Stimulation with electroencephalography, two novel variables that may contribute to cortical excitability are investigated: phase clustering, which possibly reflecting functional neuronal connectivity, and the non-linear residual of a stimulus-response curve, which may reflect brain state multi-stability. The results presented in this thesis suggest that the higher propensity to global synchronisation is not shared between epilepsy and migraine. These new variables have potential value to differentiate people with epilepsy, but not people with migraine, from normal controls

Prognosis of Patients with Epilepsy

BookWritten primarily for the physician who is responsible for the treatment of epileptic patients and the clinical investigator interested in the basic mechanisms underlying this disorder. The book also addresses itself to psychologists interested in the study of mind-brain relationships. Factors related to employability of the patient are discussed as well as characteristics of patients who have to be institutionalized

Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings

L’épilepsie est une des maladies neurologiques les plus fréquentes, touchant près d’un pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients épileptiques répondent adéquatement aux traitements pharmacologiques, il reste qu’un tiers des patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie d’épilepsie puisse être une autre option thérapeutique envisageable, le recours à la chirurgie de résection demeure très faible en partie pour des raisons diverses (taux de réussite modeste, peur des complications, perceptions négatives). D’autres avenues de traitement sont donc souhaitables. Une piste actuellement explorée par des groupes de chercheurs est de tenter de prédire les crises à partir d’enregistrements de l’activité cérébrale des patients. La capacité de prédire la survenue de crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des mesures de précaution pour éviter les désagréments reliés aux crises voire même instaurer un traitement pour les faire avorter. Au cours des dernières années, d’importants efforts ont été déployés pour développer des algorithmes de prédiction de crises et d’en améliorer les performances. Toutefois, le manque d’enregistrements électroencéphalographiques intracrâniens (iEEG) de longue durée de qualité, la quantité limitée de crises, ainsi que la courte durée des périodes interictales constituaient des obstacles majeurs à une évaluation adéquate de la performance des algorithmes de prédiction de crises. Récemment, la disponibilité en ligne d’enregistrements iEEG continus avec échantillonnage bilatéral (des deux hémisphères) acquis chez des chiens atteints d’épilepsie focale à l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a partiellement facilité cette tâche. Cependant, une des limitations associées à l’utilisation de ces données durant la conception d’un algorithme de prédiction de crises était l’absence d’information concernant la zone exacte de début des crises (information non fournie par les gestionnaires de cette base de données en ligne). Le premier objectif de cette thèse était la mise en oeuvre d’un algorithme précis de prédiction de crises basé sur des enregistrements iEEG canins de longue durée. Les principales contributions à cet égard incluent une localisation quantitative de la zone d’apparition des crises (basée sur la fonction de transfert dirigé –DTF), l’utilisation d’une nouvelle fonction de coût via l’algorithme génétique proposé, ainsi qu’une évaluation quasi-prospective des performances de prédiction (données de test d’un total de 893 jours). Les résultats ont montré une amélioration des performances de prédiction par rapport aux études antérieures, atteignant une sensibilité moyenne de 84.82 % et un temps en avertissement de 10 %. La DTF, utilisée précédemment comme mesure de connectivité pour déterminer le réseau épileptique (objectif 1), a été préalablement validée pour quantifier les relations causales entre les canaux lorsque les exigences de quasi-stationnarité sont satisfaites. Ceci est possible dans le cas des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face aux exigences de non-stationnarité, la fonction de transfert adaptatif pondérée par le spectre (Spectrum weighted adaptive directed transfer function - swADTF) a été introduit en tant qu’une version variant dans le temps de la DTF. Le second objectif de cette thèse était de valider la possibilité d’identifier les endroits émetteurs (ou sources) et récepteurs d’activité épileptiques en appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients admis pour évaluation pré-chirurgicale au CHUM. Les générateurs d’activité épileptique étaient dans le volume réséqué pour les patients ayant des bons résultats post-chirurgicaux alors que différents foyers ont été identifiés chez les patients ayant eu de mauvais résultats postchirurgicaux. Ces résultats démontrent la possibilité d’une identification précise des sources et récepteurs d’activités épileptiques au moyen de la swADTF ouvrant la porte à la possibilité d’une meilleure sélection d’électrodes de manière quantitative dans un contexte de développement d’algorithme de prédiction de crises chez l’humain. Dans le but d’explorer de nouvelles avenues pour la prédiction de crises épileptiques, un nouveau précurseur a aussi été étudié combinant l’analyse des spectres d’ordre supérieur et les réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences statistiquement significatives (p<0.05) entre l’état préictal et l’état interictal en utilisant chacune des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche, l’entropie bispectrale normalisée, l’entropie carré normalisée, et la moyenne ont atteint des précisions respectives de 78.11 %, 72.64% et 73.26%. Les résultats de cette thèse confirment la faisabilité de prédiction de crises à partir d’enregistrements d’électroencéphalographie intracrâniens. Cependant, des efforts supplémentaires en termes de sélection d’électrodes, d’extraction de caractéristiques, d’utilisation des techniques d’apprentissage profond et d’implémentation Hardware, sont nécessaires avant l’intégration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT Epilepsy is a chronic condition characterized by recurrent “unpredictable” seizures. While the first line of treatment consists of long-term drug therapy about one-third of patients are said to be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to persisting negative attitudes towards resective surgery, fear of complications and only moderate success rates. An important direction of research is to investigate the possibility of predicting seizures which, if achieved, can lead to novel interventional avenues. The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of ictal events, and the short duration of interictal periods have been important obstacles for an adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable NeuroVista ambulatory monitoring device have been made available on line for the benefit of researchers. Still, an important limitation of these recordings for seizure-prediction studies was that the seizure onset zone was not disclosed/available. The first objective of this thesis was to develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG recordings. Main contributions include a quantitative, directed transfer function (DTF)-based, localization of the seizure onset zone (electrode selection), a new fitness function for the proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed performance improvement compared to previous studies, achieving an average sensitivity of 84.82% and a time in warning of 10 %. The DTF has been previously validated for quantifying causal relations when quasistationarity requirements are met. Although such requirements can be fulfilled in the case of canine recordings due to the relatively low number of channels (objective 1), the identification of stationary segments would be more challenging in the case of high density iEEG recordings. To cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function (swADTF) was recently introduced as a time-varying version of the DTF. The second objective of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for patients with good post-surgical outcomes, whereas different or additional seizure foci were identified in patients with poor post-surgical outcomes. Results confirmed the possibility of accurate identification of seizure origin and propagation by means of swADTF paving the way for its use in seizure prediction algorithms by allowing a more tailored electrode selection. Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new precursor of seizure activity by combining higher order spectral analysis and artificial neural networks (objective 3). Results showed statistically significant differences (p<0.05) between preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and 73.26%, respectively. Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings; the transition into the ictal state is not random and consists of a “build-up”, leading to seizures. However, additional efforts in terms of electrode selection, feature extraction, hardware and deep learning implementation, are required before the translation of current approaches into commercial devices