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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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Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Neuroimaging in epilepsy.

Meneka Kaur Sidhu^1,2, John S Duncan^1,2, Josemir W Sander^1,2,3

¹Department of Clinical and Experimental Epilepsy, National Institute for Health Research University College London Hospitals Biomedical Research Centre, Institute of Neurology, University College London, London.

Current Opinion in Neurology

|May 22, 2018

Summary

This summary is machine-generated.

Advanced neuroimaging techniques enhance epilepsy diagnosis by pinpointing seizure onset zones. Integrating structural and functional imaging with machine learning offers personalized epilepsy treatment strategies.

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Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

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Published on: July 24, 2010

Area of Science:

Neurology
Radiology
Medical Imaging

Background:

Epilepsy neuroimaging is crucial for identifying seizure origins, predicting surgical outcomes, and understanding epileptogenesis.
Personalized epilepsy treatments aim to integrate imaging and genetic biomarkers.

Purpose of the Study:

To review advancements in neuroimaging for epilepsy diagnosis and treatment.
To highlight the role of multimodal imaging and machine learning in epilepsy research.

Main Methods:

Ultra-high-field imaging and advanced postprocessing techniques (volumetry, T2 relaxometry, morphometry).
Statistical analysis of Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT).
Functional Magnetic Resonance Imaging (fMRI) for language and memory lateralization.

Main Results:

Ultra-high-field imaging improves detection of lesions like focal cortical dysplasia and hippocampal sclerosis.
Statistical analysis of PET/SPECT is superior to qualitative analysis for identifying abnormalities in MRI-negative epilepsy.
fMRI aids in localizing/lateralizing language and predicting surgical outcomes in temporal lobe epilepsy.

Conclusions:

Multimodal and machine learning models combining structural, functional, and post-processing methods enhance seizure onset zone identification.
These integrated approaches improve understanding of epilepsy mechanisms and inform personalized treatment strategies.