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Related Concept Videos

Seizures: Classification01:13

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:
Seizures l: Introduction01:20

Seizures l: Introduction

Understanding seizures and epilepsy relies on key definitions that help in recognizing, classifying, and managing these disorders. These definitions provide a framework for recognizing, classifying, and managing seizure disorders.DefinitionsA seizure is a sudden, abnormal burst of electrical activity in the brain that can cause changes in awareness, movement, sensation, or behavior, depending on the area involved. Epilepsy is a chronic condition characterized by recurrent, unprovoked seizures,...
Seizures ll: Types01:19

Seizures ll: Types

Seizures are sudden bursts of abnormal electrical discharge in the brain that interfere with normal function. They are commonly divided into three groups: focal seizures, generalized seizures, and other types that do not fit neatly into either category.Focal SeizuresFocal seizures begin in a single brain region. When awareness is preserved, they are called focal aware seizures and may cause sensations such as tingling, unusual smells, or flashing lights. When awareness is impaired, they are...
Epilepsy and Seizures: Overview01:24

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: May 25, 2026

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

Modeling cortical source dynamics and interactions during seizure.

Tim Mullen1, Zeynep Akalin Acar, Gregory Worrell

  • 1Department of Cognitive Science and Swartz Center for Computational Neuroscience, Institute for Neural Computation, UCSD, USA. tim@sccn.ucsd.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study maps brain activity during seizures using advanced EEG analysis. It reveals dynamic changes in connectivity, aiding epilepsy diagnosis and treatment strategies.

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Last Updated: May 25, 2026

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
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Published on: June 6, 2015

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Published on: September 20, 2024

Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue
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Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue

Published on: January 19, 2019

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Epilepsy diagnosis and treatment require understanding seizure initiation and propagation.
  • Intracranial electroencephalography (iEEG) provides detailed brain activity data.

Purpose of the Study:

  • To analyze neuronal dynamics before and during epileptic seizures.
  • To visualize the spatial distribution of causal sources and sinks of ictal activity.

Main Methods:

  • Adaptive multivariate autoregressive (VAR) models applied to independent component analysis (ICA) sources of iEEG/ECoG data.
  • Multivariate Granger-causal and graph-theoretic metrics combined with Sparse Bayesian Learning for source localization.
  • Visualization of spatiotemporal dynamics and connectivity on the cortical surface.

Main Results:

  • Identified seizure stage-dependent shifts in inter-component spatiotemporal dynamics and connectivity.
  • Visualized distinct patterns of causal sources and sinks of ictal activity.
  • Revealed changes in connectivity patterns linked to clinically-identified epileptic foci.

Conclusions:

  • Advanced EEG analysis can map seizure dynamics and connectivity.
  • This approach offers insights into epilepsy pathophysiology.
  • Findings support improved diagnosis and surgical intervention planning for epilepsy.