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Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

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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.
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Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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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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Optimal control based seizure abatement using patient derived connectivity.

Peter N Taylor1, Jijju Thomas2, Nishant Sinha3

  • 1Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University Newcastle upon Tyne, UK.

Frontiers in Neuroscience
|June 20, 2015
PubMed
Summary

This study demonstrates a computational model using a pseudospectral method to effectively stop simulated epileptic seizures. Accounting for individual brain connectivity is key for developing optimal, localized brain stimulation treatments.

Keywords:
bistabilityconnectomeepilepsy modelnumerical methodsoptimal controlspike-wavestimulation

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Area of Science:

  • Neurology
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Epilepsy is a neurological disorder characterized by recurrent seizures due to abnormal brain electrical activity, often recorded as spike-wave oscillations on electroencephalograms (EEG).
  • Current methods for terminating seizures via active brain stimulation lack generalizable optimal protocols.
  • Understanding and modeling seizure dynamics is crucial for developing effective therapeutic interventions.

Purpose of the Study:

  • To evaluate the effectiveness of a pseudospectral method for simulated seizure abatement using a computational model.
  • To investigate the impact of patient-specific brain connectivity on seizure termination strategies.
  • To explore optimal control approaches for brain stimulation in epilepsy treatment.

Main Methods:

  • Development of a computational model simulating epileptic spike-wave dynamics.
  • Application of a pseudospectral method to find time-varying stimuli for seizure abatement.
  • Incorporation of heterogeneous brain connectivity data derived from MRI of an epilepsy patient.

Main Results:

  • The pseudospectral method successfully generated stimuli that abated simulated seizures.
  • The model demonstrated effectiveness even with patient-specific, heterogeneous brain connectivity.
  • Stimulus strength varied across different brain regions, highlighting the need for spatially localized solutions.

Conclusions:

  • The pseudospectral method, framed as an optimal control problem, presents a promising approach for in vivo brain stimulation techniques to treat epilepsy.
  • Accounting for heterogeneous cortical connectivity is essential for successful experimental brain stimulation protocols.
  • Spatially localized stimulation solutions may be preferable for optimizing seizure abatement in individual patients.