This MEGIN Masterclass is presented by guest speaker Christophe Grova PhD. "Accurate delineation of the epileptogenic zone (EZ) during presurgical workup of focal drug-resistant epilepsy patients can be challenging. Stereo-electroencephalography (SEEG) recordings, considered as the gold-standard for the localization of the EZ, might be the step towards mapping the seizure-onset zone (SOZ) and determining surgical candidacy. However, a successful investigation requires a strong pre-implantation hypothesis on the localization of the EZ, which can be derived from non-invasive investigations such as EEG or Magnetoencephalography (MEG) source imaging. The purpose of this talk is to introduce the Maximum Entropy on the Mean (MEM) source imaging framework, as a Bayesian approach to solve the ill-posed inverse problem of localizing the generators of EEG and MEG signals along the cortical surface. We will first review the time-domain version of MEM, which is sensitive to the spatial extent of the underlying generators, notably when localizing transient epileptic discharges. The localization accuracy of the MEM method and its ability to recover the spatial extent of the generators was quantitatively validated using SEEG (Abdallah et al Neurology 2022) or surgical cavity and postsurgical outcome as ground truth."
"In the second part of the talk, we will introduce the time-frequency wavelet-based extension of MEM (wavelet MEM) as a source image method of interest to localize transient oscillations, such as ictal oscillations localizing the seizure onset zone, transient high frequency oscillations and also resting state ongoing oscillations. The accuracy of wavelet-based MEM to recover oscillatory power spectra and functional connectivity patterns from resting state MEG data was validated using the MNI SEEG atlas of normal brain activity as ground truth (Afnan et al Neuroimage 2023). All MEM developments presented are available as a plugin in Brainstorm software: https://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst ".
Bio: Christophe Grova is Professor in the Department of Physics of Concordia University and a research member of Concordia School of Health (PERFORM center) since July 2014, while remaining adjunct Professor affiliated to Biomedical Engineering Dpt and Neurology and Neurosurgery Dpt at McGill Faculty of Medicine. He is also affiliated to the epilepsy group of the Montreal Neurological Institute (MNI), the McConnell Brain Imaging Center of the MNI and a member of Physnum team at Centre de Recherches Mathématiques. He received his Engineering and Master degrees in biomedical engineering at the University of Technology of Compiègne (France) in 1998, followed by a Ph.D. in SPECT/MRI registration at University of Rennes (France). From 2003 to 2008, his postdoctoral studies at the MNI were focussed on EEG source imaging of epileptic discharges and the correspondence with EEG/fMRI results, while acting as part time research associate for the set-up of the MEG centre of Université de Montreal (2006-2008). Dr Grova has been assistant Professor at McGill from July 2008 to July 2014. Since 2008, he is the director of the “Multimodal Functional Imaging Laboratory” (MultiFunkIm) which is now located on both McGill and Concordia campus. His areas of expertise are EEG/MEG source localization, multimodal data fusion involving EEG/MEG, fMRI and fNIRS, for application in epilepsy and sleep research. C. Grova is the scientific lead of the physiology platform at PERFORM centre promoting neuroimaging in realistic lifestyle environments using wearable technology (EEG, fNIRS). His team is also handling the development and validation of two software packages: MEM in Brainstorm for EEG/MEG source localization and NIRSTORM for fNIRS data analysis.
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