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Psychogenic non-epileptic seizures (PNES) are episodic events that bear a resemblance to epileptic seizures (ES) in their outward manifestations, yet they lack pathological electroencephalographic (EEG) activity during the ictal phase. In the Diagnostic and Statistical Manual 5th Edition (DSM-5), PNES is designated as "Functional Neurological Symptom Disorder with seizures". Individuals diagnosed with PNES commonly present with concurrent psychiatric disorders, notably depression, panic disorder, and chronic anxiety.

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Age-related semiology changes over time.

Epilepsy Behav

December 2024

Mayo Clinic, Department of Neurology, Divisions of Epilepsy and Child and Adolescent Neurology, 200 First Street SW, Rochester, MN 55905, United States.

Article Synopsis
  • Understanding seizure semiology across different ages helps identify the origin of seizures in patients with epilepsy.
  • In infants, recognizing seizures is difficult due to the immature brain, with common types including epileptic spasms and myoclonic seizures.
  • As children grow into preschool and school ages, the types of seizures become more complex and resemble those seen in adults, showing better-defined lateralizing behaviors during focal seizures.
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Introduction: Seizure disorders have often been found to be associated with corpus callosum injuries, but in most cases, they remain undiagnosed. Understanding the clinical, electrographic, and neuroradiological alternations can be crucial in delineating this entity.

Objective: This systematic review aims to analyze the effects of corpus callosum injuries on seizure semiology, providing insights into the neuroscientific and clinical implications of such injuries.

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Scalp video-electroencephalography (video-EEG) monitoring should be analyzed thoroughly to preoperatively evaluate stereoelectroencephalography (SEEG). Formulating the working hypotheses for the epileptogenic zone (EZ) considering "anatomo-electroclinical correlations" is the most crucial step, which determines the placement of SEEG electrodes. If these hypotheses are insufficient, precise EZ identification may not be achieved during SEEG recording.

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NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy.

Sci Rep

January 2024

Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy.

Article Synopsis
  • - Epilepsy surgery can benefit individuals with focal onset drug-resistant seizures, but accurate diagnosis of the epileptogenic zone (EZ) is crucial for effectiveness, often relying on experienced interpretation of seizure symptoms.
  • - This study aims to improve EZ localization by automatically analyzing seizure descriptions in video-EEG reports, utilizing Natural Language Processing (NLP) and Machine Learning (ML) techniques on a dataset of 536 seizure descriptions from 122 patients.
  • - The proposed method achieved over 70% accuracy in classifying the EZ's location within the brain, suggesting that improved recognition of the EZ through this approach could lead to better patient outcomes and quicker access to surgery.
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