UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS

Quality of life for the more than 15 million people with drug-resistant epilepsy is tied to how precisely the brain areas responsible for generating their seizures can be localized. High-frequency (100-500 Hz) field-potential oscillations (HFOs) are emerging as a candidate biomarker for epileptogenic networks, but quantitative HFO studies are hampered by selection bias arising out of the need to reduce large volumes of data in the absence of capable automated processing methods. In this thesis, I introduce and evaluate an algorithm for the automatic detection and classification of HFOs that can be deployed without human intervention across long, continuous data records from large numbers of patients. I then use the algorithm in analyzing unique macro- and microelectrode intracranial electroencephalographic recordings from human neocortical epilepsy patients and controls. A central finding is that one class of HFOs discovered by the algorithm (median bandpassed spectral centroid ~140 Hz) is more prevalent in the seizure onset zone than outside. The outcomes of this work add to our understanding of epileptogenic networks and are suitable for near-term translation into improved surgical and device-based treatments

Loss of neuronal network resilience precedes seizures and determines the ictogenic nature of interictal synaptic perturbations

The mechanisms of seizure emergence, and the role of brief interictal epileptiform discharges (IEDs) in seizure generation are two of the most important unresolved issues in modern epilepsy research. Our study shows that the transition to seizure is not a sudden phenomenon,but a slow process characterized by the progressive loss of neuronal network resilience. From a dynamical perspective, the slow transition is governed by the principles of critical slowing, a robust natural phenomenon observable in systems characterized by transitions between dynamical regimes. In epilepsy, this process is modulated by the synchronous synaptic input from IEDs. IEDs are external perturbations that produce phasic changes in the slow transition process and exert opposing effects on the dynamics of a seizure-generating network, causing either anti-seizure or pro-seizure effects. We show that the multifaceted nature of IEDs is defined by the dynamical state of the network at the moment of the discharge occurrence

Doctor of Philosophy

dissertationPerinatal hypoxic-ischemic (PHI) encephalopathy afflicts roughly 1-2 in every 1000 live births, predisposing affected infants to a higher probability of developing epilepsy, cerebral palsy, and other neurological disorders. In many forms of acquired epilepsy, including PHI, there is a seizure-free period of time between the injury and the onset of the first spontaneous recurrent seizure (SRS) termed the latent period. In animal models of PHI, we aim to better understand the mechanisms that lead to an epileptic network that occur during this latent period. Due to limitations in performing electrophysiological experiments in immature animals, this time period remains under-studied in the pediatric population. We start our study at the cellular level using immunohistochemistry and whole-cell patch clamp methods before moving to the whole brain level with magnetic resonance imaging and the electroencephalogram (EEG) to examine anatomical and physiological changes that precede the development of epilepsy. We find that immediately after injury, early cell loss results in a reduction in the amount of excitatory and inhibitory synaptic input to pyramidal cells within the peri-infarct region. However, this reduction is short term, as there is a rapid recovery in the synaptic inputs 2 weeks later without any identifiable increase in the number of cells. As the brain continues to develop, the cellular loss that occurs early on leads to atrophy, and sometimes complete loss of the cortex, hippocampus, and thalamus. Even with major cell loss, power spectral analysis of the EEG identified no obvious reduction or increase in the power of any of the various cortical rhythms (delta, theta, alpha, beta, and gamma). However, EEG analysis did reveal the earliest known time point at which seizures occur in this animal model, as well as a previously undescribed short-duration convulsive seizure. Our findings suggest that the mechanisms responsible for the development of SRSs begin immediately after injury and result in a variable and progressive latent period

The negative BOLD response as a marker of the seizure onset zone

Epilepsy is a neurological disease affecting 70 million people worldwide. For most individuals, these seizures can be controlled using medications, however nearly 1 in 3 people may need surgery to achieve seizure freedom. For this surgery to be successful, the brain region generating the seizures, which contains the critical seizure onset zone (SOZ), must be accurately identified and removed. Unfortunately, the surgical success rate is low likely due to imprecise determination of the SOZ. As a novel approach to SOZ identification, the collection of intracranial electroencephalography and functional magnetic resonance imaging (iEEG-fMRI) has been proposed as a novel method of identifying the SOZ. However, iEEG-fMRI faces the methodological challenge of artifact introduced from MR scanning which completely obscures the physiological EEG signal. Therefore, the first step towards bringing iEEG-fMRI into the clinical realm is to improve methods for extracting the physiological EEG signal from the iEEG-fMRI data. To this end, the first study in this thesis validated a set of methods aimed at removing fMRI artifact from iEEG, culminating in the creation of the first automatic iEEG pre-processing pipeline. The next step towards clinical utility for iEEG-fMRI is improving our interpretation of iEEG-fMRI results. Traditionally, only positive IED related fMRI activation maps were considered in relation to SOZ localization, and the negative response was ignored. It has been suggested that both positive and negative activation maps should be considered, and the maximal cluster of these two maps, regardless of polarity, should be used to localize the SOZ. In the second study, the concept was tested using iEEG-fMRI and it was found that the use of the maximal negative cluster had limited utility for SOZ localization. The results of this thesis provide a new method for preparing EEG data from iEEG-fMRI experiments and it shows that the bulk of maximal negative fMRI clusters have limited reliability for clinical applications

Mining Biomarkers Of Epilepsy From Large-Scale Intracranial Electroencephalography

Epilepsy is a chronic neurological disorder characterized by seizures. Affecting over 50 million people worldwide, the quality of life of a patient with uncontrolled epilepsy is degraded by medical, social, cognitive, and psychological dysfunction. Fortunately, two-thirds of these patients can achieve adequate seizure control through medications. Unfortunately, one-third cannot. Improving treatment for this patient population depends upon improving our understanding of the underlying epileptic network. Clinical therapies modulate this network to some degree of success, including surgery to remove the seizure onset zone or neuromodulation to alter the brain\u27s dynamics. High resolution intracranial EEG (iEEG) is often employed to study the dynamics of cortical networks, from interictal patterns to more complex quantitative features. These interictal patterns include epileptiform biomarkers whose detection and mapping, along with seizures and neuroimaging, form the mainstay of data for clinical decision making around drug therapy, surgery, and devices. They are also increasingly important to assess the effects of epileptic physiology on brain functions like behavior and cognition, which are not well characterized. In this work, we investigate the significance and trends of epileptiform biomarkers in animal and human models of epilepsy. We develop reliable methods to quantify interictal patterns, applying state of the art techniques from machine learning, signal processing, and EEG analysis. We then validate these tools in three major applications: 1. We study the effect of interictal spikes on human cognition, 2. We assess trends of interictal epileptiform bursts and their relationship to seizures in prolonged recordings from canines and rats, and 3. We assess the stability of long-term iEEG spanning several years. These findings have two main impacts: (1) they inform the interpretation of interictal iEEG patterns and elucidate the timescale of post-implantation changes. These findings have important implications for research and clinical care, particularly implantable devices and evaluating patients for epilepsy surgery. (2) They provide an analytical framework to enable others to mine large-scale iEEG datasets. In this way we hope to make a lasting contribution to accelerate collaborative research not only in epilepsy, but also in the study of animal and human electrophysiology in acute and chronic conditions

Characterization of the neocortical network excitability with multielectrode arrays: alterations in migraine and epilepsy mouse models.

missin

Dynamics of biologically informed neural mass models of the brain

This book contributes to the development and analysis of computational models that help brain function to be understood. The mean activity of a brain area is mathematically modeled in such a way as to strike a balance between tractability and biological plausibility. Neural mass models (NMM) are used to describe switching between qualitatively different regimes (such as those due to pharmacological interventions, epilepsy, sleep, or context-induced state changes), and to explain resonance phenomena in a photic driving experiment. The description of varying states in an ordered sequence gives a principle scheme for the modeling of complex phenomena on multiple time scales. The NMM is matched to the photic driving experiment routinely applied in the diagnosis of such diseases as epilepsy, migraine, schizophrenia and depression. The model reproduces the clinically relevant entrainment effect and predictions are made for improving the experimental setting.Die vorliegende Arbeit stellt einen Beitrag zur Entwicklung und Analyse von Computermodellen zum Verständnis von Hirnfunktionen dar. Es wird die mittlere Aktivität eines Hirnareals analytisch einfach und dabei biologisch plausibel modelliert. Auf Grundlage eines Neuronalen Massenmodells (NMM) werden die Wechsel zwischen Oszillationsregimen (z.B. durch pharmakologisch, epilepsie-, schlaf- oder kontextbedingte Zustandsänderungen) als geordnete Folge beschrieben und Resonanzphänomene in einem Photic-Driving-Experiment erklärt. Dieses NMM kann sehr komplexe Dynamiken (z.B. Chaos) innerhalb biologisch plausibler Parameterbereiche hervorbringen. Um das Verhalten abzuschätzen, wird das NMM als Funktion konstanter Eingangsgrößen und charakteristischer Zeitenkonstanten vollständig auf Bifurkationen untersucht und klassifiziert. Dies ermöglicht die Beschreibung wechselnder Regime als geordnete Folge durch spezifische Eingangstrajektorien. Es wird ein Prinzip vorgestellt, um komplexe Phänomene durch Prozesse verschiedener Zeitskalen darzustellen. Da aufgrund rhythmischer Stimuli und der intrinsischen Rhythmen von Neuronenverbänden die Eingangsgrößen häufig periodisch sind, wird das Verhalten des NMM als Funktion der Intensität und Frequenz einer periodischen Stimulation mittels der zugehörigen Lyapunov-Spektren und der Zeitreihen charakterisiert. Auf der Basis der größten Lyapunov-Exponenten wird das NMM mit dem Photic-Driving-Experiment überein gebracht. Dieses Experiment findet routinemäßige Anwendung in der Diagnostik verschiedener Erkrankungen wie Epilepsie, Migräne, Schizophrenie und Depression. Durch die Anwendung des vorgestellten NMM wird der für die Diagnostik entscheidende Mitnahmeeffekt reproduziert und es werden Vorhersagen für eine Verbesserung der Indikation getroffen

Epileptic Seizures and the EEG

A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction

Epileptic Seizures and the EEG

A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction