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Related Concept Videos

Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.1K
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
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Seizures: Classification01:13

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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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Related Experiment Video

Updated: Jan 7, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
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Linking New Onset Epilepsy Risk-of-Relapse to EEG Connectivity and Microstates.

Eric Ménétré1, Patrick Stancu, Sara Jekic

  • 1EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Switzerland.

Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society
|January 2, 2026
PubMed
Summary
This summary is machine-generated.

Electroencephalography (EEG) brain connectivity and microstates can predict new-onset epilepsy treatment response. Early EEG analysis may identify patients at risk of relapse, guiding timely therapeutic adjustments.

Keywords:
BiomarkersEEG connectivityEpilepsyFirst seizureMicrostatesTreatment response

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Area of Science:

  • Neuroscience
  • Epileptology
  • Biomarker Discovery

Background:

  • Antiseizure medication is the primary treatment for new-onset epilepsy, achieving seizure control in 70-80% of patients.
  • Early identification of effective treatment is crucial to prevent seizure relapses.
  • Biomarkers are needed to predict relapse risk and avoid treatment delays.

Purpose of the Study:

  • To investigate electroencephalography (EEG)-based brain connectivity and microstates in new-onset epilepsy patients.
  • To differentiate between patients who responded to treatment (SZ-FREE) and those who relapsed (SZ-REL).
  • To identify potential EEG markers linked to treatment response and relapse risk.

Main Methods:

  • Analyzed EEG data from 62 new-onset epilepsy patients before treatment initiation.
  • Computed EEG connectivity across delta, theta, alpha, beta, and gamma frequencies.
  • Assessed microstate characteristics and performed mixed-model analyses to compare SZ-FREE and SZ-REL groups.

Main Results:

  • A significant interaction between frequency bands and patient groups was observed (P < 0.001).
  • Patients with relapse (SZ-REL) showed decreased delta and theta, and increased alpha band connectivity compared to seizure-free patients (SZ-FREE).
  • Microstate C global explained variance and Microstate A frequency were higher in the SZ-REL group.

Conclusions:

  • Early EEG connectivity and microstates within 24 hours of the initial seizure contain valuable information on drug responsiveness in new-onset epilepsy.
  • EEG-derived brain connectivity and microstates show potential as predictive biomarkers for epilepsy treatment response.