Epilepsy

Epilepsy is a multifaceted neurological disorder characterized by superfluous and recurrent seizure activity. Electroencephalogram (EEG) signals are indispensable tools for epilepsy diagnosis that reflect real-time insights of brain activity. Recently, epilepsy researchers have increasingly utilized Deep Learning (DL) architectures for early and timely diagnosis. This research focuses on resolving the challenges, such as data diversity, scarcity, limited labels, and privacy, by proposing potential contributions for epilepsy detection, prediction, and forecasting tasks without impacting the accuracy of the outcome. The proposed design of diversity-enhanced data augmentation initially averts data scarcity and inter-patient variability constraints for multiclass epilepsy detection. The potential features are extracted using a graph theory-based approach by analyzing the inherently dynamic characteristics of augmented EEG data. It utilizes a novel temporal weight fluctuation method to recognize the drastic temporal fluctuations and data patterns realized in EEG signals. Designing the Siamese neural network-based few-shot learning strategy offers a robust framework for multiclass epilepsy detection.

Epilepsy is a prevalent brain disorder that affects more than 1 in 26 people in the United States. The recurring increased neuronal excitability during seizures results in sleep disturbances and muscle convulsions that reduce the quality of life and increase the healthcare costs for these patients. An epilepsy diagnosis is made when patients have had two or more seizures. There are many types of seizures and an individual can have more than one type. Seizures are classified into two groups, 1) generalized seizures that affect both sides of the brain and 2) focal seizures that are located in just one area of the brain. The causes of epilepsy vary by the age of the person, some with no clear cause may have a genetic form of epilepsy. Due to the various causes and types of seizures, many treatments including invasive surgeries and antiepileptic drugs (AEDs) do not work for all epileptic/seizure patients and are merely used to ease symptoms. The physiological complexity of the disorder and limited knowledge on its specific molecular mechanisms may contribute to the lack of effective treatment. In recent years, there has been an estimated average cost in billions of dollars to bring new medicine to the market; due to the lack of novel antiseizure targets and mechanism-based therapies on seizure phenotypes. In response to this, we utilized the electroconvulsive seizure behavioral assay to characterize one generalized seizure phenotype, tonic-clonic/grand mal seizures.

Over 70 million people worldwide suffer from epilepsy, with 90% of those cases taking place in developing countries (Singh & Trevick, 2016). Epilepsy can be defined as at least two unprovoked seizures occurring more than 24 hours apart, one unprovoked seizure with at least 60% chance of another seizure occurring within the next 10 years, or a diagnosis of epilepsy syndrome (Fisher et al., 2005). Varying physiological, molecular, genetic, and environmental factors can contribute to epileptic episodes. Although antiepileptic drugs (AEDs) exist, the complexity and lack of understanding behind the molecular mechanisms of the syndrome leaves the few drugs available to be insufficient for many patients (Rho & White, 2018). Therefore, the discovery of genetic pathways involved in epilepsy is imperative for the innovation of antiepileptic drugs. This thesis explores a novel method to add to mutant C.elegans libraries and improve antiepileptic drug discovery in a cost-effective and efficient manner by uncovering candidate molecular pathways through the candidate genes involved with antiepileptic strains.

Epilepsy is a widely prevalent disease within the United States. It is estimated that about 1.2% of the total American population has active epilepsy, a condition of the brain that causes seizures. These seizures are marked by chemical alterations in neuronal firing that can cause abnormal behavior, sensations, muscle spasms, and loss of consciousness. Although the prevalence of seizures and epilepsy is high, effective treatments are limited and fail to provide effective treatment for nearly one-third of adult epileptic patients. Here, I conclude results of successful screening of novel compounds that can ameliorate seizures using an electroshock assay to examine seizure susceptibility and duration in C. elegans. The use of this assay provides an excellent platform for novel antiepileptic drug (AED) discovery efficiently.
Literature shows Resveratrol, a natural product from plants, provides neuroprotective effects in various model organisms and therefore, is an excellent candidate for a molecule that has never been related to seizure. However, it is easily metabolized, being a flat and planar molecule. Our research group has collaboratively identified a novel bicyclic bridge molecule derived from the scaffolding of two resveratrol molecules we named Resveramorph (RVM). We also used the candidate approach to test a number of Resveramorph analogs on this assay to find the analog with highest efficacy. The various molecules characterized with their efficacy for seizure-like behavior after an electroshock have helped elucidate the mechanism of action and the RVMs physical target to give us greater insight into this potential family of AEDs.

Seizures are a symptom of epilepsy, characterized by spontaneous firing due to an imbalance of excitatory and inhibitory features. While mammalian seizure models
receive the most attention, the simplicity and tractability of invertebrate model systems, specifically C. elegans and D. melanogaster, have many advantages in understanding
the molecular and cellular mechanisms of seizure behavior. This research explores C. elegans and D. melanogaster as electroconvulsive seizure models to investigate
methods to both modulate and better understand seizure susceptibility. A common underlying feature of seizures in mammals, worms, and flies involves regulating
excitation and inhibition. The C. elegans locomotor circuit is regulated via well characterized GABAergic and cholingeric motoneurons that innervate two rows of
dorsal and ventral body wall muscles. In this research, we developed an electroconvulsive seizure assay which utilizes the locomotor circuit as a behavioral read out of neuronal function. When inhibition is decreased in the circuit, for example by decreasing GABAergic input, we find a general increase in the time to recovery from a seizure. After establishing the contribution of excitation and inhibition to seizure recovery, we explored a ubiquitin ligase, associated with comorbidity of an X-linked Intellectual Disorder and epilepsy in humans, and established that the worm homolog, eel-1, contributes to seizure susceptibility similarly to the human gene. Next, we investigated a cGMP-dependent protein kinase (PKG) that functions in the nervous system of both worms and flies and determined that increasing PKG activity, decreases the time to recovery from an electroconvulsive seizure. These experiments suggest a potential novel role for a major protein, PKG, in seizure susceptibility and that the C. elegans and D. melanogaster electroconvulsive seizure assays can be used to investigate possible genes involved in seizure susceptibility and future therapeutic to treat epilepsy.