Comparative Evolution Between Different Source Localizations of Epileptic MEG Oscillations Using Different Connectivity Metrics

Electromagnetic sources of biomarkers in magnetoencephalography (MEG) enables recognition of excessive discharges generators in epilepsy. Neurologists rely on MEG biomarker source localization for diagnostic purposes (presurgical epilepsy inquiry). Several ways are proposed to overcome the forward and inverse source localization problems. The goal of this study is to evaluate four distributed inverse problems: minimum norm estimation MNE, standardized lowresolution brain electromagnetic tomography sLORETA, wavelet maximum entropy on the mean wMEM, and dynamic statistical parametric maps dSPM used to define network connectivity of oscillatory epileptic events. We employed Jmail et al.'s 2016 pre-processing chain to assess epileptic oscillations among MEG sources in pharmaco-resistant patients. Then, we explored granger causality and spatial granger causality metrics among extended active sources for each inverse approach. Granger causality determines causal influence of one time series on another, providing insights into directional flow of information between brain regions. Spatial Granger causality extends this concept to incorporate spatial information, evaluating how brain activity in one region can predict future activity in another region, accounting of brain spatial structure. As a result, for granger causality, wMEM method identifies strong connections between active sources, suggesting robust interaction and influence among these regions. In contrast, dSPM method reveals weaker connections, indicating a less pronounced directional influence between active sources. For spatial granger causality wMEM again demonstrates strong connections between active sources, highlighting its sensitivity and effectiveness in detecting robust functional interactions. On the other hand, MNE and sLORETA methods exhibit weaker connections, suggesting that these techniques may be less effective in capturing directional influences between active sources. These findings underscore the importance of using multiple localization techniques to to accurately determine interictal MEG oscillation locations and epileptic zones.