Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach - Cao, Miao; Vogrin, Simon J.; Peterson, Andre D. H.; Woods, William; Cook, Mark J.; Plummer, Chris (Frontiers in Neurology, 2022)
3 There is an urgent need for more informative quantitative techniques that non-invasively and 4 objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography 5 (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) 6 before surgical resection. However, there are major limitations of iEEG, such as the limited spatial 7 sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG 8 data. Recent advances in network analysis and dynamical network modelling provide a novel 9 aspect towards a more objective assessment of the EZ. The advantage of such approaches is 10 that they are data-driven and require less or no human input. Multiple studies have demonstrated 11 success using these approaches when applied to iEEG data in characterising the EZ and 12 predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these 13 studies – limited spatial sampling and the implicit assumption that iEEG electrodes, whether 14 strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, 15 it is of interest to clinicians and scientists to see whether the same analysis and modelling 16 techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based 17 techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing 18 the limitation of spatial sampling, while safely and objectively characterising the EZ. This review 19 aims to summarise current state of the art non-invasive methods that inform epilepsy surgery 20 using network analysis and dynamical network models. We also present perspectives on future 21 directions and clinical applications of these promising approaches.