Assessing HD-EEG functional connectivity states using a human brain computational model

Abstract

Objective. Electro/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. Approach. We 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 Results. Using 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. Significance. We 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.

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

“…Since the task is more complex and brain networks often change states rapidly, we analyzed dynamic networks using a sliding window approach for the memory task. Overall, our results show that both PCA and DMD approaches successfully extracted dominant connectivity configurations and their respective time dynamics on the simulated and the real MEG data when compared with the results of previous works that used the same dataset [15,42]. Simulated data results that were reported in the supplementary material showed how network configurations shifted across time and were comparative with the ground truth.…”

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

“…The COALIA model ( Bensaid et al., 2019 ), for example, has been used to mimick network activity in epilepsy for the purpose of validating FC estimates ( Allouch et al., 2022 ). Further studies used the same model family to study the effect of parameters such as electrode density on FC estimates ( Allouch, Kabbara, Duprez, Khalil, Modolo, Hassan, 2023 , Tabbal, Kabbara, Yochum, Khalil, Hassan, Benquet, 2022 ). Similarly, Jirsa and Müller (2013) have used TVB to evaluate metrics of cross-frequency coupling.…”