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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...
Epilepsy ll: Types01:22

Epilepsy ll: Types

Recurrent seizures, stemming from abnormal electrical activity in the brain, are the defining characteristic of epilepsy, a chronic neurological condition. Because seizure features vary greatly, epilepsy is classified using two systems: by seizure type and by epilepsy syndromes. These classifications enable clinicians to describe seizure patterns and select suitable treatment strategies.I. Classification by Seizure Type1. Focal EpilepsyFocal epilepsy begins in one hemisphere of the brain.

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Related Experiment Video

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Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue
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Published on: January 19, 2019

Assimilating seizure dynamics.

Ghanim Ullah1, Steven J Schiff

  • 1Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, USA. ghanim@psu.edu

Plos Computational Biology
|May 14, 2010
PubMed
Summary
This summary is machine-generated.

Researchers can now estimate neuronal network dynamics using just one measurement. This data assimilation approach reconstructs brain activity, including microenvironment and cell type dynamics, even during seizures.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Control Theory Applications

Background:

  • Observing complex dynamical systems like neuronal networks is challenging due to limitations in measuring numerous variables.
  • Current methods can only capture a small fraction of the physical variables and parameters in neuronal networks.
  • Understanding neuronal network states requires comprehensive measurement of collective variable values.

Purpose of the Study:

  • To develop a model-based framework for estimating neuronal network dynamics from limited measurements.
  • To demonstrate the feasibility of reconstructing neural and microenvironment dynamics using single-variable assimilation.
  • To apply data assimilation for understanding brain dynamics during pathological conditions like seizures.

Main Methods:

  • Constructed biophysical models of neuronal membrane, synaptic, and microenvironment dynamics.
  • Integrated these models into a predictor-controller framework from modern control theory.
  • Employed data assimilation to fuse noisy membrane potential measurements with computational models.

Main Results:

  • Successfully estimated the dynamics of small neuronal networks using as few as a single measured variable.
  • Reconstructed the dynamics of hippocampal neuron networks, their extracellular microenvironment, and neuronal type activities during seizures.
  • Validated the reconstruction of cellular dynamical interactions against actual measurements.

Conclusions:

  • Data assimilation offers a powerful approach to overcome measurement limitations in observing brain dynamics.
  • This method allows for accounting for unmeasured system components, linking metabolic processes to cellular excitability.
  • The framework significantly enhances the ability to observe and understand complex brain dynamics.