Antiseizure Medications in Poststroke Seizures: A Systematic Review and Network Meta-Analysis.

Background And Objectives: The most effective antiseizure medications (ASMs) for poststroke seizures (PSSs) remain unclear. We aimed to determine outcomes associated with ASMs in people with PSS.

Methods: We systematically searched electronic databases for studies on patients with PSS on ASMs.

D2HGDH Deficiency Regulates Seizures through GSH/Prdx6/ROS-Mediated Excitatory Synaptic Activity.

Current antiepileptic drugs are ineffective in one-third of patients with epilepsy; however, identification of genes involved in epilepsy can enable a precision medicine approach. Here, it is demonstrated that downregulating D-2-hydroxyglutarate dehydrogenase (D2HGDH) enhances susceptibility to epilepsy. Furthermore, its potential involvement in the seizure network through synaptic function modulation is investigated.

Factors associated with placebo response rate in randomized controlled trials of antiseizure medications for focal epilepsy.

Objective: Randomized controlled trials (RCTs) are necessary to evaluate the efficacy of novel treatments for epilepsy. However, there have been concerning increases in the placebo responder rate over time. To understand these trends, we evaluated features associated with increased placebo responder rate.

Neuropsychological Outcomes After Stereo-EEG Radiofrequency Thermocoagulation.

Background And Objectives: Stereo-EEG-guided radiofrequency thermocoagulation (RFTHC) has been proposed as relatively safe from a cognitive perspective; however, there is a lack of evidence based on neuropsychological assessments supporting this. This study is the first prospective evaluation of neuropsychological outcomes associated with stereo-EEG-guided RFTHC in patients with focal drug-resistant epilepsy.

Methods: This cohort study involved prospective recruitment of consecutive patients undergoing stereo-EEG from 2 Australian centers.

Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy.

Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research and clinical care. Already there have been resultant cutting-edge applications in both the clinical and research arenas of epileptology. Because there is a need to disseminate knowledge about these approaches, how to use them, their advantages, and their potential limitations, the goal of the 2023 Merritt-Putnam Symposium and of this synopsis review of that symposium has been to present the background and state of the art and then to draw conclusions on current and future applications of these approaches through the following: (1) Initially provide an explanation of the fundamental principles of artificial intelligence, machine learning, and deep learning.