Assessing HD-EEG functional connectivity states using ahuman brain computational model

Abstract

ObjectiveElectro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool for tracking fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of pipeline steps is challenging due to the lack of realistic controlled data. Here, our aim is two-folded: 1) provide a quantitative assessment of the advantages and limitations of the analyzed techniques and 2) introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity. ApproachWe used a human brain computational model containing both physiologically based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging, to generate high-density EEG recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas to emulate a virtual picture-naming task. We identified fast time-varying network states and quantified the performance of the key steps involved in the pipeline: 1) inverse models to reconstruct cortical-level sources, 2) functional connectivity measures to compute statistical interdependency between regional signals, and 3) dimensionality reduction methods to derive dominant brain network states (BNS). Main ResultsUsing a systematic evaluation of the different decomposition techniques, results show significant variability among tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, and the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states. SignificanceWe suggest using such brain models to go further in the evaluation of the different steps and parameters involved in the EEG/MEG source-space network analysis. This can reduce the empirical selection of inverse model, connectivity measure, and dimensionality reduction method as some of the methods can have a considerable impact on the results and interpretation.