AUTHOR=Cao Miao , Vogrin Simon J. , Peterson Andre D. H. , Woods William , Cook Mark J. , Plummer Chris 

TITLE=Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.837893

DOI=10.3389/fneur.2022.837893

ISSN=1664-2295

ABSTRACT=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.