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

LOCAL SYNAPTIC NETWORK INTERACTIONS IN THE DENTATE GYRUS OF A CORTICAL CONTUSION MODEL OF POSTTRAUMATIC EPILEPSY

Posttraumatic epilepsy is a common consequence of brain trauma. However, little is known about how long-term changes in local excitatory and inhibitory synaptic networks contribute to epilepsy after closed-head brain injury. This study adapted a widely used model of experimental brain injury as a mouse model of posttraumatic epilepsy. Behavioral seizure activity and alterations in synaptic circuitry in the dentate gyrus were examined in mice after experimental cortical contusion brain injury. Spontaneous behavioral seizures were observed in 20% of mice after moderate injury and 36-40% of mice weeks after severe injury. In the dentate gyrus, most mice displayed regionally localized mossy fiber reorganization ipsilateral to the injury that was absent in control mice or sections contralateral to the injury. Extracellular field and whole-cell patch clamp recordings were performed in acute brain slice preparations of the dentate gyrus. Dentate granule cells displayed spontaneous and evoked activity that was consistent with network synchronization and the formation of recurrent excitatory network only in slices that had posttraumatic mossy fiber sprouting. The excitability of surviving hilar GABAergic interneurons, which provide important feedback inhibition to granule cells, was examined at similar time points. Cell-attached and whole-cell voltage-clamp recordings revealed increased spontaneous and glutamate photostimulation-evoked excitatory input to hilar GABA neurons ipsilateral to the injury, versus control and contralateral slices. Despite increased excitatory synaptic input to interneurons, whole-cell voltage-clamp recordings revealed a reduction in inhibitory synaptic input to granule cells. These findings suggest that there are alterations in excitatory and inhibitory circuits in mice with posttraumatic mossy fiber sprouting and seizures after cortical contusion head injury

Metric Classification of Traumatic Brain Injury Epileptiform Activity from Electroencephalography Data

Prediction algorithm of Epilepsy Seizures and Sleep Spindles in electroencephalography (EEG) data is studied in this article. EEG data was measured in rats with Post-Traumatic Epilepsy (PTE) before and after Traumatic Brain Injury (TBI). Experts manually partitioned records into two classes: one, which refers to epileptic activity - Epilepsy Seizures, and second class, which refers to normal behavior of rats - Sleep Spindles (SS). Proposed algorithm was trained and tested on the collected data, which contained EEG features, previously extracted by detection algorithm. Feature importance was evaluated, and logistic regression model was built. Cross validation results were 79% Area Under Curve (AUC) for the best model.Authors would like to express gratitude to Ilya Komoltsev and Ivan Kershner for providing the dataset. The research was supported by the Russian Scientific Foundation, project No. 16-11-10258

Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients

Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms of the disorder, and the development of antiepileptogenic interventions could potentially prevent or cure epilepsy in many of them. However, the discovery of potential antiepileptogenic treatments and clinical validation would require a means to identify populations of patients at very high risk for epilepsy after a potential epileptogenic insult, to know when to treat and to document prevention or cure. A fundamental challenge in discovering biomarkers of epileptogenesis is that this process is likely multifactorial and crosses multiple modalities. Investigators must have access to a large number of high quality, well-curated data points and study subjects for biomarker signals to be detectable above the noise inherent in complex phenomena, such as epileptogenesis, traumatic brain injury (TBI), and conditions of data collection. Additionally, data generating and collecting sites are spread worldwide among different laboratories, clinical sites, heterogeneous data types, formats, and across multi-center preclinical trials. Before the data can even be analyzed, these data must be standardized. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-center project with the overarching goal that epileptogenesis after TBI can be prevented with specific treatments. The identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. We have been analyzing human data collected from UCLA and rat data collected from the University of Eastern Finland, both centers collecting data for EpiBioS4Rx, to identify biomarkers of epileptogenesis. Big data techniques and rigorous analysis are brought to longitudinal data collected from humans and an animal model of TBI, epilepsy, and their interaction. The prolonged continuous data streams of intracranial, cortical surface, and scalp EEG from humans and an animal model of epilepsy span months. By applying our innovative mathematical tools via supervised and unsupervised learning methods, we are able to subject a robust dataset to recently pioneered data analysis tools and visualize multivariable interactions with novel graphical methods

Recording, analysis, and interpretation of spreading depolarizations in neurointensive care: Review and recommendations of the COSBID research group.

Spreading depolarizations (SD) are waves of abrupt, near-complete breakdown of neuronal transmembrane ion gradients, are the largest possible pathophysiologic disruption of viable cerebral gray matter, and are a crucial mechanism of lesion development. Spreading depolarizations are increasingly recorded during multimodal neuromonitoring in neurocritical care as a causal biomarker providing a diagnostic summary measure of metabolic failure and excitotoxic injury. Focal ischemia causes spreading depolarization within minutes. Further spreading depolarizations arise for hours to days due to energy supply-demand mismatch in viable tissue. Spreading depolarizations exacerbate neuronal injury through prolonged ionic breakdown and spreading depolarization-related hypoperfusion (spreading ischemia). Local duration of the depolarization indicates local tissue energy status and risk of injury. Regional electrocorticographic monitoring affords even remote detection of injury because spreading depolarizations propagate widely from ischemic or metabolically stressed zones; characteristic patterns, including temporal clusters of spreading depolarizations and persistent depression of spontaneous cortical activity, can be recognized and quantified. Here, we describe the experimental basis for interpreting these patterns and illustrate their translation to human disease. We further provide consensus recommendations for electrocorticographic methods to record, classify, and score spreading depolarizations and associated spreading depressions. These methods offer distinct advantages over other neuromonitoring modalities and allow for future refinement through less invasive and more automated approaches

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well