Experimental treatment options in absence epilepsy

Contains fulltext : 182124.pdf (preprint version ) (Open Access)Background: The benign character of absence epilepsy compared to other genetic generalized epilepsy syndromes has often hampered the search for new treatment options. Absence epilepsy is most often treated with ethosuximide or valproic acid. However, both drugs are not always well tolerated or fail, and seizure freedom for a larger proportion of patients remains to be achieved. The availability of genuine animal models of epilepsy does allow to search for new treatment options not only for absence epilepsy perse but also for other genetic - previously called idiopathic - forms of epilepsy. The recent discovery of a highly excitable cortical zone in these models is considered as a new therapeutic target area. Methods: Here, we provide an overview regarding the search for new therapeutical options as has been investigated in the genetic rodent models (mainly WAG/Rij and GAERS) including drugs and whether antiepileptogenesis can be achieved, various types of electrical and optogenetical invasive stimulations, different types of non-invasive stimulation and finally whether absence seizures can be predicted and prevented. Results: Many factors determine either the cortical and or thalamic excitability or the interaction between cortex and thalamus and offer new possibilities for new anti-absence drugs, among others metabotropic glutamatergic positive and negative allosteric modulators. The inhibition of epileptogenesis by various drugs with its widespread consequences seems feasible, although its mechanisms remain obscure and seems different from the anti-absence action. Surgical intervention on the cortical zone initiating seizures, either with radiosurgery using synchrotron-generated microbeams, or ablation techniques might reduce spike-and-wave discharges in the rodent models. High frequency electrical subcortical or cortical stimulation might be a good way to abort ongoing spike-and-wave discharges. In addition, possibilities for prevention with real-time EEG analyses in combination with electrical stimulation could also be a way to fully control these seizures. Conclusion: Although it is obvious that some of these treatment possibilities will not be used for absence epilepsy and/or need to be further developed, all can be considered as proof of principle and provide clear directives for further developments

The spatiotemporal dynamics of human focal seizures

Spontaneous human focal seizures can present with a plethora of behavioral manifestations that vary according to the affected cortical regions; however, several key features have been consistently observed. During my doctoral studies, I applied both theoretical and experimental methods to study mechanisms underpinning these consistently seen dynamics. I first analyzed human intracranial EEG recordings, describing statistical methods for measuring their electrophysiological signatures. I next proposed several neurophysiological hypotheses that could explain seizure dynamics and verified them in rodent seizure models. Finally, a computational model was developed, successfully explaining how the complex spatiotemporal evolution of focal seizures emerges from simple neurophysiological principles. In Chapter 1, the long-standing behavioral manifestations and the most up-to-date electrophysiology findings are reviewed. This section details the inspiration for the studies reported in the subsequent chapters. In Chapter 2, I describe several statistical methods for estimating traveling wave velocities. I show most ictal discharges can be described as traveling waves whose velocities contain rich information about the stages of seizure evolution. I compare performance of various statistical methods and propose a robust approach to boost the quality of each method’s estimation results. In Chapter 3, I show how inhibition modulates seizure propagation patterns. Surround inhibition spatially restrains focal seizures and masks excitatory projections of ictal activities. When compromised, two patterns of seizure propagation emerge according to the position of inhibition defects relative to the ictal focus. I show that two distant ictal foci can communicate via physiological connectivity without any chronic rewiring processes – confirming the existence of long-range propagation pathways that could lead to epileptic network formation. In Chapter 4, I show that thalamic inputs might be necessary for interictal epileptiform discharges (IEDs). The relative positions between IEDs and ictal foci indicate that surround inhibition, shown in the previous chapter, can be exhausted by repetitive exposure to ictal projections. In Chapter 5, I propose a neural network model that can explain both long-standing behavioral observations of seizures and account for the most up-to-date electrophysiological recordings of spontaneous human focal seizures. The model relies on few assumptions, all of which are proved or supported in earlier chapters of this thesis. The model explains phasic evolution of seizure dynamics – how the commonly observed patterns arise from simple neurophysiological principles, as well as seizure onset subtypes, traveling wave directions and speeds. The model also predicts how spontaneous seizures might arise from synaptic plasticity. The chapter ends with a discussion of the model’s implications and future work. The thesis is organized in a way that each chapter can be read independently, with Chapter 5 summarizing the central theory spanning the whole study. Each chapter is also tightly linked to a clinically relevant question. In sum, the dissertation’s goal is to provide an in-principle understanding of focal seizure dynamics. With rapid advancement of clinical and experimental tools, I believe this work provides a roadmap for future therapies for epilepsy patients

Seizure prediction : ready for a new era

Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin

Influence of deep structures on the EEG and their invasive and non-invasive assessment

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, Departamento de Fisiología, leída el 22-11-2019El EEG es la prueba diagnóstica de mayor utilidad en el diagnóstico de la epilepsia. Consiste esencialmente en la representación gráfica de los potenciales postsinápticos generados en las neuronas piramidales de la corteza. Los campos eléctricos registrados en la superficie tienen principalmente dos mecanismos de origen: conducción de volumen desde regiones adyacentes y propagación interneuronal sináptica. Las neuronal piramidales se agrupan formando microcircuitos locales siendo estos circuitos los responsables de la generación delos ritmos registrados en el EEG. Uno de los principales retos de la electroencefalografía consiste en descifrar la relación entre la actividad registrada y la actividad subyacente en las redes neuronales. Para encontrar la fuente de dichas actividades, es necesario tener en cuenta complejos mecanismos tanto no lineales como lineales, así como el efecto de la conducción de volumen y la influencia de la morfología y las propiedades eléctricas del cerebro y el cráneo. Además, las regiones cerebrales se encuentran profusamente interconectadas a menudo produciendo una modulación recíproca que añade un mayor grado complejidad...The EEG is the most valuable diagnostic test in epilepsy. In essence, it mainly consists in agraphical representation of the summated postsynaptic potentials generated in the pyramidal neurons from the cortex. The electrical fields can be generated on the scalp by two mechanisms: volume conduction from nearby regions and synaptic inter‐neuronal propagation. Pyramidal cells align conforming local microcircuit configurations which activation lead to the generation of EEG rhythms. One of the main challenges of EEG is to decipher the relation between the recorded EEG activity and the activity in the neuronal networks. To find the source of EEG activity, complex non‐linear and linear mechanisms as well as volume conduction effect and influence of the shape and electrical properties of the brain and skull need to be taken in consideration. In addition, brain regions are profusely interconnected and functionally connected regions often produce mutual modulation that adds additional complexity...Depto. de FisiologíaFac. de MedicinaTRUEunpu

Imaging physiological brain activity and epilepsy with Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) allows reconstructing conductivity changes into images. EIT detects fast impedance changes occurring over milliseconds, due to ion channel opening, and slow impedance changes, appearing in seconds, due to cell swelling/increased blood flow. The purpose of this work was to examine the feasibility of using EIT for imaging a gyrencephalic brain with implanted depth electrodes during seizures. Chapter 1 summarises the principles of EIT. In Chapter 2, it is investigated whether recent technical improvements could enable EIT to image slow impedance changes upon visual stimulation non-invasively. This was unsuccessful so the remaining studies were undertaken on intracranial recordings. Chapter 3 presents a computer modelling study using data from patients, for whom the detection of simulated seizure-onset perturbations for both, fast and slow impedance changes, were improved with EIT compared to stereotactic electroencephalography (SEEG) detection or EEG inverse-source modelling. Chapter 4 describes the development of a portable EIT system that could be used on patients. The system does not require averaging and post-hoc signal processing to remove switching artefacts, which was the case previously. Chapter 5 describes the use of the optimised method in chemically-induced focal epilepsy in anaesthetised pigs implanted with depth electrodes. This shows for the first time EIT was capable of producing reproducible images of the onset and spread of seizure-related slow impedance changes in real-time. Chapter 6 presents a study on imaging ictal/interictal-related fast impedance changes. The feasibility of reconstructing ictal-related impedance changes is demonstrated for one pig and interictal-related impedance changes were recorded for the first time in humans. Chapter 7 summarises all work and future directions. Overall, this work suggests EIT in combination with SEEG has a potential to improve the diagnostic yield in epilepsy and demonstrates EIT can be performed safely and ethically creating a foundation for further clinical trials

Diagnostic value of combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) in epilepsy.

Purpose: The main aim of the project is to estimate the value of combined TMS-EEG responses and EEG to increase the sensitivity and/or specificity of the routine EEG in the diagnosis of newly onset epilepsies. Methods: The project is a combined cross-sectional and longitudinal study involving 60 patients recruited from the First Seizure Clinic at Guy's and St Thomas Hospital NHS Foundation Trust who have had their first presumed epileptic seizure. All the participants had a sleep-deprived EEG (baseline EEG) followed by a combined TMS and EEG study (TMS-EEG). The EEG responses to TMS were visually analysed, looking for two different types of TMS-evoked responses or late responses: The delayed responses were assessed in the unprocessed EEG and the repetitive responses (RRs) after averaging the EEG signals synchronized with the TMS pulse. The late responses were compared between epileptic and non-epileptic patients, looking for responses associated with epilepsy. In patients where the baseline EEG was normal, the additional diagnostic value provided by TMS-EEG was estimated by their ability to predict the final diagnosis based on the clinical history and other tests. A quantitative analysis was performed to compare the power ratio in different frequency bands between epilepsy and no epilepsy cohorts and to select epilepsy-associated variables to generate a machine learning-based classification model for epilepsy prediction. Results: In patients with normal baseline EEG, abnormal TMS-EEG evoked responses (late responses) had no statistically significant association with the presence of epilepsy (Fisher’s exact test, p=0.063), but the late responses correctly classified as epilepsy the 36% of patients with a false-negative baseline EEG. The combined presence of late responses and interictal epileptiform discharges (IEDs) in TMS-EEG records has a higher sensitivity (74%) but lower specificity (85%) than baseline EEG alone. The grand average power-ratio differences between epilepsy and no-epilepsy cohorts were not statistically significant. The epilepsy-associated variables selected for machine learning-based classification were predominantly in the alpha-theta and gamma frequency ranges when TMS activation was present and, in the beta-gamma range with Sham. The TMS support vector machine (SVM)-classifier’s disease prediction over an independent cohort had a sensitivity of 83%. Conclusions: The TMS-EEG significantly increased the sensitivity of the baseline EEG and correctly classified as epilepsy approximately one-third of the patients with a false negative baseline EEG and a final clinical diagnosis of epilepsy. TMS stimulation modified the spectral and topographic properties of the epilepsy-associated variables used for disease detection with machine learning linear regression algorithms. The performance of the TMS SVM-classifier in the training cohort has a high sensitivity, high specificity and low misclassification rate. The TMS SVM-classifier performed better than the Sham as an epilepsy disease prediction model in an independent TMS-EEG cohort. The TMS SVM-classifier has a promising value for disease prediction in TMS-EEG datasets