Seizures

The communication in the nervous system is a pharmacological balance between excitatory and inhibitory signals, and seizure behavior is one of the most common manifestations of when an imbalance occurs. Environmental toxins can cause significant disruption of excitation-inhibition balance, but while some toxins, like nerve agents, have known targets and require novel antidotes, some have unknown neurobiological mechanisms and require exploration. Of particular concern, there is little knowledge on how herbicides may affect neurological signaling. Glyphosate, the world’s most popular herbicide, was found to be in 80% of people’s urine, and since it is so prevalent, it is critical to understand its impact on both excitatory and inhibitory signaling. We used an electroshock assay developed for C. elegans to uncover evidence that glyphosate, and the commercial formula Roundup, disrupted the excitation-inhibition balance by blocking GABA-A receptors. This presented a novel hypothesis of an inhibitory neurobiological target for glyphosate. As glutamate is the major excitatory neurotransmitter in the human central nervous system, an electrophysiology assay using Drosophila was used and found that Roundup, but not glyphosate, reduced synaptic viability. This result directs attention to the undisclosed adjuvant component which may have a significant effect on synaptic transmission, though the exact mechanism requires further investigation.

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.

Deep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and forecasting performances of individual classifiers by fusing results of participating deep learning models. The performance of such an ensemble model, however, depends heavily on the following two design features. Firstly, the diversity of the participating (or base) deep learning models is crucial. If all base deep learning models produce similar classification results, then combining these results will not provide much improvement. Thus, diversity is considered to be a key design feature of any successful MMDL system. Secondly, the selection of a fusion function, namely, a suitable function to integrate the results of all the base models, is important. In short, building an effective MMDL system is a complex and challenging process which requires deep knowledge of the problem context and a well-defined prediction process. The proposed MMDL method utilizes a bank of Convolutional Neural Networks (CNNs) and Stacked AutoEncoders (SAEs). To reduce the design complexity, a randomized generation process is applied to assign values to hyperparameters of base models. To speed up the training process, new feature extraction procedures which captures time-spatial characteristics of input signals are also explored. The effectiveness of the MMDL method is validated in this dissertation study with three real-world case studies. In the first case study, the MMDL model is applied to classify call types of groupers, an important fishery resource in the Caribbean that produces sounds associated with reproductive behaviors during yearly spawning aggregations. In the second case study, the MMDL model is applied to detect upcalls of North Atlantic Right Whales (NARWs), a type of endangered whales. NARWs use upcalls to communicate among themselves. In the third case study, the MMDL model is modified to predict seizure episodes. In all these cases, the proposed MMDL model outperforms existing state-of-the-art methods.

Diseases such as epilepsy, pain, and neurodegenerative disorders are associated with changes in neuronal dysfunction due to an imbalance of excitation and inhibition. This work details a novel electroconvulsive seizure assay for C. elegans using the well characterized cholinergic and GABAergic excitation and inhibition of the body wall muscles and the resulting locomotion patterns to better understand neuronal excitability. The time to recover normal locomotion from an electroconvulsive seizure could be modulated by increasing and decreasing inhibition. GABAergic deficits and a chemical proconvulsant resulted in an increased recovery time while anti-epileptic drugs decreased seizure duration. Successful modulation of excitation and inhibition in the new assay led to the investigation of a cGMP-dependent protein kinase (PKG) which modulates potassium (K+) channels, affecting neuronal excitability, and determined that increasing PKG activity decreases the time to recovery from an electroconvulsive seizure. The new assay was used as a forward genetic screening tool using C. elegans and several potential genes that affect seizure susceptibility were found to take longer to recover from a seizure. A naturally occurring polymorphism for PKG in D. melanogaster confirmed that both genetic and pharmacological manipulation of PKG influences seizure duration. PKG has been implicated in stress tolerance, which can be affected by changes in neuronal excitability associated with aging, so stress tolerance and locomotor behavior in senescent flies was investigated. For the first time, PKG has been implicated in aging phenotypes with high levels of PKG resulting in reduced locomotion and lifespan in senescent flies. The results suggest a potential new role for PKG in seizure susceptibility and aging.

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.