Epilepsy Paid Clinical Trials in Texas

The purpose of this study is to determine whether BHV-7000 is effective in the treatment of refractory focal epilepsy. Read Less

The purpose of this study is to determine whether BHV-7000 is effective in the treatment of idiopathic generalized epilepsy with generalized tonic-clonic seizures and includes an additional open-label extension (OLE) phase. Read Less

Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this cognitive flexibility is human's ability to respond correctly to novel contextual situations on the first attempt, without prior rehearsal. The investigators refer to this ability as 'ad hoc self-programming': 'ad hoc' because these new behavioral repertoires are cobbled together on the fly, based on immediate demand, and then discarded when no longer necessary; 'self-programming' because the brain has to configure itself appropriately based on task demands and some combination of prior experience and/or instruction. The overall goal of our research effort is to understand the neurophysiological and computational basis for ad hoc self-programmed behavior. The previous U01 project (NS 108923) focused on how these programs of action are initially created. The results thus far have revealed tantalizing notions of how the brain represents these programs and navigates through the programs. In this proposal, therefore, the investigators focus on the question of how these mental programs are executed. Based on the preliminary findings and critical conceptual work, the investigators propose that the medial temporal lobe (MTL) and ventral prefrontal cortex (vPFC) creates representations of the critical elements of these mental programs, including concepts such as 'rules' and 'locations', to allow for effective navigation through the algorithm. These data suggest the existence of an 'algorithmic state space' represented in medial temporal and prefrontal regions. This proposal aims to understand the neurophysiological underpinnings of this algorithmic state space in humans. By studying humans, the investigators will profit from our species' powerful capacity for generalization to understand how such state spaces are constructed. The investigators therefore leverage the unique opportunities available in human neuroscience research to record from single cells and population-level signals, as well as to use intracranial stimulation for causal testing, to address this challenging problem. In Aim 1 the investigators study the basic representations of algorithmic state space using a novel behavioral task that requires the immediate formation of unique plans of action. Aim 2 directly compares representations of algorithmic state space to that of physical space by juxtaposing balanced versions of spatial and algorithmic tasks in a virtual reality (VR) environment. Finally, in Aim 3, the investigators test hypotheses regarding interactions between vPFC and MTL using intracranial stimulation. Read Less

The purpose of this study is to determine whether BHV-7000 is effective in the treatment of refractory focal epilepsy. Read Less

This clinical trial is designed to test whether a single stereotactic intracerebral administration of inhibitory nerve cells into subjects with drug-resistant mesial temporal lobe epilepsy is safe (frequency of adverse events) and effective (seizure frequency). Read Less

The purpose of the study is to evaluate the efficacy of lacosamide (LCM) versus an Active Comparator chosen based on standard of care (StOC) in severe and nonsevere seizure burden (defined as total minutes of electroencephalographic neonatal seizures (ENS) per hour) in neonates with seizures that are not adequately controlled with previous anti-epileptic drug (AED) treatment. Read Less

Depression is one of the most common disorders of mental health, affecting 7-8% of the population and causing tremendous disability to afflicted individuals and economic burden to society. In order to optimize existing treatments and develop improved ones, the investigators need a deeper understanding of the mechanistic basis of this complex disorder. Previous work in this area has made important progress but has two main limitations. (1) Most studies have used non-invasive and therefore imprecise measures of brain activity. (2) Black box modeling used to link neural activity to behavior remain difficult to interpret, and although sometimes successful in describing activity within certain contexts, may not generalize to new situations, provide mechanistic insight, or efficiently guide therapeutic interventions. To overcome these challenges, the investigators combine precise intracranial neural recordings in humans with a suite of new eXplainable Artificial Intelligence (XAI) approaches. The investigators have assembled a team of experimentalists and computational experts with combined experience sufficient for this task. Our unique dataset comprises two groups of subjects: the Epilepsy Cohort consists of patients with refractory epilepsy undergoing intracranial seizure monitoring, and the Depression Cohort consists of subjects in an NIH/BRAIN-funded research trial of deep brain stimulation for treatment-resistant depression (TRD). As a whole, this dataset provides precise, spatiotemporally resolved human intracranial recording and stimulation data across a wide dynamic range of depression severity. Our Aims apply a progressive approach to modeling and manipulating brain-behavior relationships. Aim 1 seeks to identify features of neural activity associated with mood states. Beginning with current state-of-the-art AI models and then uses a "ladder" approach to bridge to models of increasing expressiveness while imposing mechanistically explainable structure. Whereas Aim 1 focuses on self-reported mood level as the behavioral index of interest, Aim 2 uses an alternative approach of focusing on measurable neurobiological features inspired by the Research Domain Criteria (RDoC). These features, such as reward sensitivity, loss aversion, executive attention, etc. are extracted from behavioral task performance using a novel "inverse rational control" XAI approach. Relating these measures to neural activity patterns provides additional mechanistic and normative understanding of the neurobiology of depression. Aim 3 uses recurrent neural networks to model the consequences of richly varied patterns of multi-site intracranial stimulation on neural activity. Then employing an innovative "inception loop" XAI approach to derive stimulation strategies for open- and closed-loop control that can drive the neural system towards a desired, healthier state. If successful, this project would enhance our understanding of the pathophysiology of depression and improve neuromodulatory treatment strategies. This can also be applied to a host of other neurological and psychiatric disorders, taking an important step towards XAI-guided precision neuroscience. Read Less

This trial is intended to study the safety and effectiveness of an new anti-epileptic drug (AED) on Primary Generalized Tonic-Clonic (PGTC) Seizures. Eligible Subjects, adults and adolescents, will continue to take their usual AEDs and receive either cenobamate or placebo. Subjects will have a 50% chance or receiving cenobamate or placebo (sugar pill). Subjects will initially receive 12.5 mg of cenobamate or placebo (study drug) and increase the dose every two weeks until they reach a target dose of 200 mg. Subjects will take study drug at approximately the same time in the morning (once a day) with or without food. If tolerability issues arise, dosing can be changed to evening. Also, once a subject reaches 200 mg, the dose can be decreased one time to 150 mg, if necessary. The treatment period is 22 weeks and there is a 3 week follow up period, which includes a one week decrease in study drug to 100 mg prior to stopping. Adolescents will follow the same every two week regimen and receive cenobamate as an oral suspension based on weight. Subjects who complete may be eligible for an extension study and will not have to complete the follow up period. Subjects will track their seizure types and frequency in a diary throughout the study. Read Less

In this study, participants will receive unilateral Deep Brain Stimulation (DBS) for treatment of epilepsy, with network-based stimulation targets specifically defined using a stereo-electro-encephalographic evaluation and chronic recordings using the Medtronic Percept™ primary cell (PC) Neurostimulator DBS System with BrainSense™ Technology. The hypothesis is that, compared to no stimulation or to standard duty cycle high frequency stimulation, epilepsy neuromodulation using low frequency stimulation and informed by network architecture in patients with epilepsy that arises in a hippocampus that also subserves memory - epilepsy in a precious hippocampus (EPH) - will result in a significant decrease in seizure frequency and severity, paralleled by a decrease in EEG spike counts and improved memory function. Read Less

The Epilepsy Learning Health System (ELHS) is a quality improvement and research network to improve outcomes for people with epilepsy. The ELHS is designed as a model of value-based chronic care for epilepsy as envisioned by the National Academies of Medicine Committee in their landmark reports "The Learning Health System" and "Epilepsy Across the Spectrum: Promoting Health and Understanding". The ELHS network is a collaboration among clinicians, patients and researchers that promotes the use of data for multiple purposes including one-on-one clinical care, population management, quality improvement and research. The ELHS Registry includes data on children and adults with epilepsy collected during the process of standard epilepsy care. These data are used to create population health reports and to track changes in outcomes over time. ELHS teams use quality improvement methods, such as Plan-Do-Study-Act (PDSA) cycles, to continuously learn how to improve care. Read Less

Primary objective: To evaluate the safety and tolerability of cenobamate in pediatric subjects 2-17 years of age with partial-onset (focal) seizures Read Less

By carrying a careful, large-scale and ambitious prospective study of a cohort of participants with generalized epilepsy, the study team hopes to clarify the likelihood of response and remission in this type of epilepsy, and try to explore the underlying biological drivers of treatment response, including novel realms of exploration such as impact of the microbiome, and genetics. The identification of biomarkers that predict the likelihood of disease response would allow epilepsy patients to make more informed decisions about the factors affecting their quality of life, including plans for driving, relationships, pregnancy, schooling, work, and play. In addition to its impact on clinical care, the data and specimens collected in HEP3, including sequential electrophysiology, biochemical profiles and neuroimaging and banked DNA for future genomics studies, have the potential to provide new insights into the biological basis of IGE, thereby advancing the discovery of effective treatments and cures. By enrolling both newly diagnosed subjects (prognosis unknown) as well as subjects with established IGE who are already determined to be treatment resistant or treatment responsive, the study team can immediately test potential biomarkers in a confirmation cohort, which will accelerate identification of predictive biomarkers. Read Less