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Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy
Published on: September 20, 2024
Detection of epileptogenic cortical malformations with surface-based MRI morphometry.
Thomas Thesen1, Brian T Quinn, Chad Carlson
1Comprehensive Epilepsy Center, Department of Neurology, New York University, New York, New York, United States of America. thomas.thesen@med.nyu.edu
Plos One
|February 18, 2011
Summary
Automated MRI morphometry detects subtle epilepsy-causing brain abnormalities with 92% sensitivity. This advanced technique aids in identifying patients with focal epilepsy who may benefit from surgery.
Area of Science:
- Neuroimaging
- Epileptology
- Medical Image Analysis
Background:
- Magnetic resonance imaging (MRI) is crucial for detecting structural abnormalities in epilepsy patients.
- Many focal abnormalities are missed during routine visual MRI inspection.
- Advanced automated methods are needed to improve detection of subtle or occult lesions.
Purpose of the Study:
- To develop and validate an automated, surface-based MRI morphometry method for detecting epileptogenic cortical malformations.
- To quantify morphometric features like cortical thickness and gray-white matter (GWC) contrast.
- To assess the method's sensitivity, specificity, and clinical utility in identifying epilepsy patients.
Main Methods:
- Utilized surface-based spherical averaging techniques for precise anatomical alignment across brains.
- Compared individual patients with known lesions to a large normal control group.
- Analyzed cortical thickness, GWC contrast, gyrification, sulcal depth, jacobian distance, and curvature.
- Systematically varied threshold and smoothing parameters to optimize detection.
- Validated findings against expert tracings, intracranial EEG, pathology, and surgical outcomes.
Main Results:
- The automated method achieved 92% sensitivity and 96% specificity in detecting cortical lesions.
- It successfully discriminated between patients and controls 94% of the time.
- Detected abnormalities correlated with seizure onset zones, histology, and surgical outcomes in relevant patients.
- The method showed limitations in accurately describing lesion extent.
Conclusions:
- Automated surface-based MRI morphometry, with optimized parameters, is a valuable tool for detecting subtle or occult cortical malformations in epilepsy.
- This technique can improve the identification of patients with intractable focal epilepsy who are candidates for surgical intervention.
- Further refinement is needed to improve lesion extent characterization.
