PERFORM Publications

Title:	MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy.
Authors:	Chowdhury RA , Zerouali Y , Hedrich T , Heers M , Kobayashi E , Lina JM , Grova C
Link:	https://www.ncbi.nlm.nih.gov/pubmed/26016950?dopt=Abstract
Category:	Brain Topogr
PMID:	26016950
Dept Affiliation:	PERFORM 1 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Duff Medical Building, 3775, rue University, Room 316, Montreal, QC, H3A 2B4, Canada. rasheda.chowdhury@mail.mcgill.ca. 2 Ecole de Technologie Supérieure, Montreal, Canada. 3 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Duff Medical Building, 3775, rue University, Room 316, Montreal, QC, H3A 2B4, Canada. 4 Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada. 5 Epilepsy Center, University Hospital Freiburg, Freiburg, Germany. 6 Centre de Recherches Mathématiques, Université de Montréal, Montreal, Canada. 7 Physics Department and PERFORM Centre, Concordia University, Montreal, Canada.

Description:

MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy.

Brain Topogr. 2015 Nov;28(6):785-812

Authors: Chowdhury RA, Zerouali Y, Hedrich T, Heers M, Kobayashi E, Lina JM, Grova C

Abstract

The purpose of this study is to develop and quantitatively assess whether fusion of EEG and MEG (MEEG) data within the maximum entropy on the mean (MEM) framework increases the spatial accuracy of source localization, by yielding better recovery of the spatial extent and propagation pathway of the underlying generators of inter-ictal epileptic discharges (IEDs). The key element in this study is the integration of the complementary information from EEG and MEG data within the MEM framework. MEEG was compared with EEG and MEG when localizing single transient IEDs. The fusion approach was evaluated using realistic simulation models involving one or two spatially extended sources mimicking propagation patterns of IEDs. We also assessed the impact of the number of EEG electrodes required for an efficient EEG-MEG fusion. MEM was compared with minimum norm estimate, dynamic statistical parametric mapping, and standardized low-resolution electromagnetic tomography. The fusion approach was finally assessed on real epileptic data recorded from two patients showing IEDs simultaneously in EEG and MEG. Overall the localization of MEEG data using MEM provided better recovery of the source spatial extent, more sensitivity to the source depth and more accurate detection of the onset and propagation of IEDs than EEG or MEG alone. MEM was more accurate than the other methods. MEEG proved more robust than EEG and MEG for single IED localization in low signal-to-noise ratio conditions. We also showed that only few EEG electrodes are required to bring additional relevant information to MEG during MEM fusion.

PMID: 26016950 [PubMed - indexed for MEDLINE]

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