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