View clinical trials related to Epilepsy.
Filter by:A randomized, double-blind, placebo-controlled, crossover study to assess the safety, tolerability, and pharmacokinetics of single doses of AUT00201 at 100 mg or matching placebo in patients with myoclonus epilepsy and ataxia due to potassium channel mutation (MEAK).
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.
This is an exploratory case control study with the aim to compare the Onchocerca volvulus virome between persons with onchocerciasis-associated epilepsy (OAE) and persons with onchocerciasis but without epilepsy. The main question we want to address is: Is there a virus contained in the O. volulus worm that may have a pathogenic role in causing OAE. In Maridi County, South Sudan, 20 persons with OAE with onchocerciasis nodules, and 20 age- and village-matched controls without OAE will be enrolled in a nodulectomy study.The adult O. volvulus worms will be extracted from the nodules and a viral metagenomic study of the worms. The O. volvulus virome of persons with and without OAE will be compared.
The purpose of this study is to describe the effectiveness of the adjunctive ASM treatment on the clinical response, safety profile and quality of life of patients affected by focal onset seizures in a real-world setting.
The unpredictable nature of epileptic seizures places people with epilepsy under permanent psychological stress, which contributes significantly to a restriction in their quality of life. The possibility of predicting the arrival of epileptic seizures would allow, in addition to taking a preventive treatment if the risk of seizure is close, to prevent traumas and accidents linked to possible falls during seizures, to authorize driving for certain people with epilepsy and to reduce the costs of medical care. To date and to our knowledge, no seizure detection device has been commercialized. There are commercialized devices based on biometric sensors other than EEG, but these are strictly dedicated to the detection of seizures and do not allow the anticipation of seizures. Regarding prediction, current research seems to have difficulties in developing convincing algorithms. The only system used successfully in real time would require a device implantable in the brain, but this would raise problems of acceptability. In addition, 20% of people with drug-resistant epilepsy have psychogenic non-epileptic seizures (PNES). These are sometimes difficult to differentiate from epileptic seizures by people with epilepsy and their caregivers, and their management differs from that of epileptic seizures. The distinction between these 2 types of events should also be taken into account by these prediction/detection tools. From the field of biomedical detection dogs, there is currently a converging body of evidence supporting that people with epilepsy emit specific odors associated with seizure events. Trained dogs have been shown to be able to discriminate body odors sampled during or just after an epileptic seizure from those sampled from the same subjects in various contexts outside of a seizure. It was also shown that a seizure can also be predicted by the volatile organic compounds (VOCs) released by the patient (human volatilome); the olfactory signature being already detectable up to 3h before a seizure. Another study used trained dogs to confirm that they are able to detect a seizure by smell and that this olfactory difference is already detectable before a seizure. The human volatilome VOCs lead is particularly promising, notably for its non-invasiveness and for the pre-ictal precocity that prediction allows. But at the moment, the studies are too studies are too preliminary, with sample sizes too small to conclude on the inter-individual generalization of the odor, taking into account the type of seizure involved and the influence of other variables (e.g., gender, age, medications). Moreover, in order to develop a reliable and transportable electronic detection tool, the identification of the VOCs involved is necessary, since the choice of sensors (e.g., to constitute an electronic nose) depends on it. The objective of this study is to overcome these shortcomings, by aiming at the identification of the informative odor(s) associated with epileptic events during the pre-ictal, ictal and post-ictal periods, taking into account the type of seizures (focal seizures, secondary generalized focal seizures, primary generalized seizures - motor and non-motor) and the inter-individual differences.
A randomized trial to compare patient-completion success of REDCap and Electronic Health Record (EHR)-based anxiety & depression instrument delivery methods. Study hypothesis is that the screening completion proportion will vary across the 4 modalities tested.
The goal of this observational study is to learn about epilepsy after a stroke (post-stroke epilepsy). The main questions it aims to answer are: - What make some patients develop epilepsy after a stroke? - Does sleep have an impact on the development of post-stroke epilepsy? Participants will undergo: - Electroencephalography (EEG) - Magnetic resonance imaging (MRI) - Polysomnography (only patients) Blood tests will also be taken. The patient group will be compared to the healthy controls. Researchers will also look into medical records of stroke patients hospitalized at St. Olavs hospital and collect relevant information.
CAN-RWE is an observational study that is following 500 children who have authorizations for medical cannabis for two years from across Canada.
The BLESS Study contributes to filling this information gap by collecting data from the Italian clinical practice and the Compassionate Use Program, to better characterize the clinical profile of cenobamate describing its effectiveness, safety and tolerability in adult patients diagnosed with uncontrolled focal epilepsy despite the use of at least two antiepileptic medicinal products.
The purpose of this research is to better understand how emotion processing unfolds in the brain using stereoelectroencephalography (sEEG) and direct brain stimulation. This study will use standard behavioral emotion processing tasks combined with neural recording and direct brain stimulation to assess different aspects of emotion processing. Stimulation pulses during pre and post-test periods will assess the effects of stimulation before and after conditioning, the results of which will be combined with results from the activity of each electrode during the emotion tasks to inform us of the nature of emotion processing in the brain and allow us to devise brain modulation protocols in the future.