Abstract
Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.
Highlights
Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery
The first question we addressed is whether we can reconstruct ictal virtual intracranial electroencephalography (ViEEG) signals with distinct clinical characteristics to enable dynamical network models to identify brain areas that are involved and not involved in increased in silico seizure likelihood
Ictal waveforms from ViEEG can be visually identified solely from sources in anterior hippocampal and basal temporal structures, which is consistent with the intracranial electroencephalography (iEEG) seizure onset zone (SOZ)
Summary
Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. A major limitation of previous studies in this field is the dependence on invasive intracranial electrode recordings of cortical activity when applying dynamical network models to experimental data. This can lead to insufficient sampling of brain networks with the biased representation of the actual systems involved[19]. Presurgical evaluation is challenging when MRI shows no clear lesion, a large complex lesion, or multiple potential epileptogenic lesions[33,34] Such cases stand to benefit from noninvasive dynamical approaches to better characterise the brain networks involved in seizure dynamics. We attempt to define a virtual ictogenic zone (VIZ) non-invasively and assess our
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