1 Integrated Program in Neuroscience, McGill University, Montréal, Québec H3A 1A1, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada. Electronic address: jawata.afnan@mail.mcgill.ca.
2 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada.
3 Centre De Recherches Mathématiques, Montréal, Québec H3C 3J7, Canada; Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec H3C 1K3, Canada; Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada.
4 Epilepsy program, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5C1, Canada.
5 Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy.
6 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada.
7 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.
8 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada.
9 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.
10 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada. Electronic address: jean.gotman@mcgill.ca.
11 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada; Centre De Recherches Mathématiques, Montréal, Québec H3C 3J7, Canada; Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada. Electronic address: christophe.grova@concordia.ca.

Background: Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation.

Method: We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas.

Research: mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands.

Results: The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed.

Conclusion: This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.