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Epilepsy and Seizures: Overview

Epilepsy and Seizures: Overview

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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Related Experiment Video

Updated: Feb 25, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Phenomenological network models: Lessons for epilepsy surgery.

Jurgen Hebbink^1,2, Hil Meijer², Geertjan Huiskamp¹

¹Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.

|July 27, 2017

Summary

This summary is machine-generated.

Epilepsy surgery may be more effective by targeting network drivers, not just pathological tissue. Computational models show removing critical network connections, rather than the seizure focus, can better reduce seizure frequency.

Keywords:

Computational model Epilepsy surgery Focal epilepsy Network disease

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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Area of Science:

Neuroscience
Computational Biology
Epilepsy Research

Background:

Epilepsy surgery traditionally focuses on resecting anatomically pathological cortical tissue.
Emerging research views epilepsy as a complex network disorder, necessitating new therapeutic strategies.

Purpose of the Study:

To investigate the influence of network connectivity on seizure dynamics using computational modeling.
To explore alternative surgical resection strategies beyond traditional lesionectomy.

Main Methods:

Development and simulation of a simple computational network model with four interconnected neuronal populations.
Modeling a hyperexcitable population to represent pathological cortex.
Simulating the effects of resecting different neuronal populations on seizure rate.

Main Results:

Resection of the primary hyperexcitable (pathological) population was often suboptimal for reducing seizure rate.
Removal of connected, non-pathological 'driver' populations proved more effective in mitigating seizures.
Network structure and interconnections significantly influence seizure occurrence.

Conclusions:

Epilepsy surgery outcomes may depend more on network topology than solely on the location of pathological tissue.
Targeting critical network nodes ('drivers') could offer a more effective surgical approach for epilepsy.
Computational models provide valuable insights into optimizing epilepsy surgery strategies.