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Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

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Increasing the accuracy of electromagnetic inverses using functional area source correlation constraints.

Benoit R Cottereau¹, Justin M Ales, Anthony M Norcia

¹Department of Psychology, Stanford University, Stanford, California, USA. b.cottereau@stanford.edu

Human Brain Mapping

|September 23, 2011

Summary

This study introduces a new method using functional area constrained estimation (FACE) to improve electroencephalography (EEG) and magnetoencephalography (MEG) source reconstruction. FACE enhances accuracy by incorporating prior information from functional magnetic resonance imaging (fMRI) and retinotopic maps.

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Area of Science:

Neuroscience
Biophysics
Medical Imaging

Background:

Estimating cortical current distributions from EEG/MEG data is an inverse problem.
Prior information on neural responses can improve source reconstruction accuracy.

Purpose of the Study:

To develop a novel approach, Functional Area Constrained Estimator (FACE), to enhance EEG/MEG source reconstruction accuracy.
To utilize functional and retinotopic information to guide source localization.

Main Methods:

Developed FACE by deriving source correlation matrices from cortical segmentation based on visual field retinotopic maps or fMRI localizers.
Applied FACE to Monte Carlo simulations with diverse source configurations.
Validated FACE using a real EEG dataset for horizontal disparity processing.

Main Results:

Monte Carlo simulations demonstrated improved accuracy in estimating diverse source configurations using FACE.
Reconstructions from real EEG data showed enhanced localization of cortical areas involved in horizontal disparity processing.

Conclusions:

FACE provides a robust method to improve the accuracy of cortical current distribution estimation from EEG/MEG data.
The approach effectively integrates prior anatomical and functional information for better source localization in visual studies.