journal article·2026-07-08Open access

Assessing Strengths and Limitations of Magnetoencephalography Source Imaging With Intracerebral EEG

Jawata Afnan, Maria Fratello, Francesca Bonini, Samuel Medina Villalon, Zhengchen Cai, Jean‐Marc Lina, Jean‐Michel Badier, Fabrice Bartolomei, Jean Gotman, Christian G. Bénar, Christophe Grova

Abstract

ABSTRACT Due to the ill‐posed nature of source imaging, EEG/MEG source localization remains challenging, particularly for localizing deep brain activity and resting‐state signals with low signal‐to‐noise ratios. Functional connectivity estimated by EEG/MEG is further affected by source‐leakage, a spatial blurring effect that complicates interpretation. Validation is therefore essential before applying EEG/MEG source imaging in clinical contexts. Using simultaneous MEG and stereotactic EEG (SEEG) recordings from patients with focal epilepsy, we present a comprehensive validation of MEG source imaging of epileptic discharges, resting‐state oscillations, and connectivity. Virtual SEEG was computed from MEG source maps to enable direct quantitative comparison with in situ SEEG. Using the Maximum Entropy on the Mean method, MEG localized the generators of epileptic spikes with a median error of 15 ± 12 mm, with deep generators showing larger errors. MEG‐derived resting‐state power showed significant spatial correlations with SEEG, with frequency‐specific correspondence, stronger in the alpha and beta bands than in the theta band. MEG functional connectomes estimated using leakage‐corrected amplitude envelope correlation aligned relatively well with SEEG, whereas connectomes from the leakage‐corrected phase‐based metric were inaccurate. These findings delineate conditions under which MEG source imaging reliably captures epileptic and resting‐state activity, even with low signal‐to‐noise ratios, and conditions when it is unreliable.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Functional Brain Connectivity Studies · EEG and Brain-Computer Interfaces · Advanced MRI Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Multimodal Functional Imaging Lab Biomedical Engineering Department McGill University Montréal Québec Canada

Integrated Program in Neuroscience McGill University Montréal Québec Canada

Montreal Neurological Institute Department of Neurology and Neurosurgery McGill University Montréal Québec Canada

Aix Marseille University INSERM INS Inst Neurosci Syst Marseille France

APHM Timone Hospital Epileptology and Cerebral Rhythmology Marseille France

Physnum Team Centre De Recherches Mathématiques Montréal Québec Canada

Electrical Engineering Department École De Technologie Supérieure Montréal Québec Canada

Multimodal Functional Imaging Lab Department of Physics and Concordia School of Health Concordia University Montréal Québec Canada

Funding

Canadian Institutes of Health Research

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