Student’s abstracts 2023-Inferring

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 uncertain and requires validation. We aimed to validate the ability of MSI to estimate the background resting state power spectra and connectivity profiles of 45 healthy participants by comparing MSI findings to in-situ measurements obtained from the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/), at the population level.
We applied wavelet-based Maximum Entropy on the Mean (wMEM) as a MSI technique dedicated to localization oscillations. 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 ViEEG with actual iEEG signals from the same region in terms of spectral characteristics and connectivity profiles in the canonical frequency bands. For connectivity analysis, we calculated four widely used metrics- Amplitude Envelope Correlation (AEC), orthogonalized AEC (OAEC), Phase Locking Value (PLV) and corrected imaginary part of PLV (ciPLV) and compared the cross-modal spatial correlation between MEG and iEEG connectomes.
Spectral analysis: MEG spectra were more accurately estimated in the lateral regions compared to the deeper medial regions. 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. Moreover, 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. Connectivity analysis: Preliminary results showed that the cross-modal correlation between MEG and iEEG connectomes was band-specific and the highest correlations were found for the beta band when connectivity measures considered zero-lag connectivity such as AEC and PLV. In contrast, the connectivity metrics that remove the zero-lag connectivity (such as OAEC and ciPLV) significantly decreased the cross-modal correlation. Compared to iEEG, MEG showed higher zero-lag connectivity, a well-known disadvantage of non-invasive source imaging resulting from source-leakage.
This study identifies brain regions, frequency bands, and connectivity metrics 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.

Astrocytes, a major type of glia, possess assorted structural and functional properties making them inseparable from their neighbouring neurons. However, most published computational models of whole-brain activity, if not all, remain focused on neurons while ignoring astrocytes. We herewith introduce a biophysical model built upon neural network mass and compartmental modelling techniques, where large-scale astrocytic and neuronal networks couple their activity through glutamatergic and GABAergic transmission systems. We formulate a network scheme where neural dynamics are constrained by a two-layered structural network interconnecting either astrocytic or neuronal populations, and we ask how astrocytic networks contribute to whole-brain activity and emerging functional connectivity patterns. By developing a biologically plausible simulation approach based on bifurcation and multilayer network theories, we demonstrate that astrocytic and neuronal networks engage in a dialogue over fast and slow fluctuations or over phase-based and amplitude-based network connectivity. Our study is a step forward for more thoroughly investigating the role of glia alongside neurons in health or disease conditions.

Background
Simultaneous electroencephalography–functional MRI (EEG-fMRI) is a unique and noninvasive method for epilepsy presurgical evaluation. When selecting voxels by null-hypothesis tests, the conventional analysis may overestimate fMRI response amplitudes related to interictal epileptic discharges (IEDs), especially when IEDs are rare.

Methods
Eighty-two epilepsy patients who underwent EEG-fMRI scans and surgeries at the MNI were included in this study. Surgical outcomes were classified with a follow-up of at least one year. IEDs were annotated by experienced neurophysiologists on processed EEGs in which scanner artifacts were removed. Anatomical and functional MRI preprocessing were performed using fMRIPrep. We proposed a hierarchical model involving local and global hemodynamic responses to IEDs using the Schaefer2018 atlas and Freesurfer subcortical parcellation as the prior. Hence, the fMRI response in each voxel was estimated not only by its own data, but also adjusted according to the local homogeneity of responses within each region of interest (ROI). ROI-level responses were further regularized to take into account the global homogeneity using the resting state networks to which they belong. The IED-related fMRI response was represented by the relative percentage change of the blood oxygen level dependent (BOLD) signal. A voxel-wise model was compared to ours from three perspectives: 1) the “type M(agnitude) error” of the estimated effect, as assessed by the ratio of BOLD percentage changes between two models; 2) the ability to differentiate BOLD percentage changes induced by brief (e.g., spike) and long-lasting (e.g., burst) IEDs; and 3) clinical utility in presurgical evaluation, as determined by the concordance between the cluster exhibiting the highest fMRI response and the sur