The ELUCID study is a research project that looks at older adults, specifically those aged 55 and older, who have developed seizures without a clear cause, known as late-onset unexplained epilepsy (LoUE).
This study looked at children diagnosed with Alexander disease (AD), a rare neurological condition, over the past ten years.
This study looked at how machine learning can help predict which patients with drug-resistant epilepsy might respond well to neuromodulation therapies, like vagus nerve stimulation (VNS).
Researchers studied how brain activity, measured through EEG, relates to cognitive difficulties caused by antiseizure medications in people with epilepsy.
This study looked at how well a new questionnaire-based tool can help classify seizure types in children with epilepsy.
Researchers studied how well machine learning (ML) models can predict seizures in patients with glioma-associated epilepsy (GAE), a common issue for people with gliomas, which are a type of brain tumor.
Researchers studied the prevalence of brain-related disorders in people with Noonan syndrome (NS), a genetic condition that can affect growth, heart health, and brain development.
Researchers studied brain networks in 91 patients who had surgery for epilepsy.
This study focused on a specific skin reaction caused by a medication called zonisamide (ZNS), which is used to treat epilepsy.