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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...

Seizures: Classification

Seizures: Classification

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: Nov 4, 2025

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

Behavioral phenotypes of temporal lobe epilepsy.

Bruce P Hermann¹, Aaron F Struck^1,2, Kevin Dabbs¹

¹Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.

|May 25, 2021

Summary

Psychopathology in temporal lobe epilepsy (TLE) presents in distinct phenotypes, not a uniform condition. Machine learning identified three patient groups with varying symptom severity and associated factors.

Keywords:

behavior phenotypes psychopathology temporal lobe epilepsy

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Last Updated: Nov 4, 2025

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Published on: June 29, 2022

Preparation and Implantation of Electrodes for Electrically Kindling VGAT-Cre Mice to Generate a Model for Temporal Lobe Epilepsy

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Behavioral And Physiological Analysis In A Zebrafish Model Of Epilepsy

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Published on: October 19, 2021

Area of Science:

Neurology
Psychiatry
Machine Learning

Background:

Temporal lobe epilepsy (TLE) is frequently associated with psychopathology.
Understanding the heterogeneity of these symptoms is crucial for effective patient management.

Purpose of the Study:

To identify distinct phenotypes of self-reported psychopathological symptoms in TLE patients.
To explore the correlates of these identified phenotypes.

Main Methods:

Unsupervised machine learning (cluster analysis) was applied to Symptom Checklist 90-Revised (SCL-90-R) data from 96 TLE patients and 82 controls.
Identified clusters were compared to controls across demographic, clinical, neuropsychological, psychiatric, and neuroimaging variables.

Main Results:

TLE patients as a group showed higher SCL-90-R scores than controls.
Cluster analysis revealed three TLE phenotypes: unimpaired (42%), mild-to-moderate (35%), and marked symptomatology (23%).
Phenotypes correlated with education, seizure characteristics, cognitive functions, psychiatric disorders, and quality of life, with minimal neuroimaging links.

Conclusions:

Psychopathology in TLE is characterized by discrete phenotypes with associated sociodemographic, cognitive, and clinical factors.
Machine learning aids in developing a taxonomy for epilepsy comorbidities, similar to cognitive research in TLE.