Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset

Seizures likely result from aberrant network activity and synchronization. Changes in brain network connectivity may underlie seizure onset. We used a novel method of rapid network model estimation from intracranial electroencephalography (iEEG) data to characterize pre-ictal changes in network structure prior to seizure onset. We analyzed iEEG data from 20 patients from the iEEG.org database. Using 10 s epochs sliding by 1 s intervals, a multiple input, single output (MISO) state space model was estimated for each output channel and time point with all other channels as inputs, generating sequential directed network graphs of channel connectivity. These networks were assessed using degree and betweenness centrality. Both degree and betweenness increased at seizure onset zone (SOZ) channels 37.0 +/- 2.8 s before seizure onset. Degree rose in all channels 8.2 +/- 2.2 s prior to seizure onset, with increasing connections between the SOZ and surrounding channels. Interictal networks showed low and stable connectivity. A novel MISO model-based network estimation method identified changes in brain network structure just prior to seizure onset. Increased connectivity was initially isolated within the SOZ and spread to non-SOZ channels before electrographic seizure onset. Such models could help confirm localization of SOZ regions.

Automated summary

Seizures are likely caused by abnormal network activity and synchronization. Changes in brain network connectivity may be the basis of seizure onset. A novel method of estimating network models from intracranial electroencephalography (iEEG) data was used to study pre-ictal changes in network structure before seizure onset. The results showed an increase in network connectivity prior to seizure onset, starting in the seizure onset zone and spreading to other channels.