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Abstract Details

Network Analysis of Focal Seizure Dynamics Suggests a Possible Mechanism of Seizure Termination
Epilepsy/Clinical Neurophysiology (EEG)
S7 - Epilepsy and Clinical Neurophysiology (EEG) 1 (5:06 PM-5:18 PM)
009

Network analysis is a powerful tool to understand the epileptic brain, but the hypersynchronized nature of seizures makes traditional network estimation methods difficult to apply. We use model-based network estimation to overcome this limitation and investigate the network correlates of seizure dynamics and termination.

To use network analysis of intracranial EEG recordings to understand seizure dynamics.

Intracranial EEG data from 20 epilepsy patients from the iEEG.org database were analyzed. Each clinical seizure was divided into five periods: initiation, mid-seizure, termination, one minute and two minutes post-seizure.  Each period was further divided into consecutive 5 second epochs. In each epoch, a multiple input, single output (MISO) state space model was estimated for each channel output with all other channels as inputs. Model parameters were used to infer a directed network graph of all channels for each time window. iEEG contacts (nodes) were assigned to 3 clinically-determined regions: seizure onset zone (SOZ), within 2 contacts of SOZ (PeriSOZ or PSZ) and other non-SOZ (NSZ). The resulting networks were analyzed across seizures and patients using degree centrality, an index of the proportion of directed connections through each node.

Degree centrality in all 3 ictal periods was significantly higher than interictal in all regions. Surprisingly, by the mid-seizure period, SOZ degree fell significantly below both PSZ and NSZ groups, but rose again during termination, with distant channel degree falling significantly in this time period. Degree centrality fell below interictal values post-seizure, at both one- and two-minute epochs.

Mid-seizure reduction in degree centrality in SOZ suggests exhaustion of the connectivity-driving mechanism, while the subsequent increase at termination may be related to increased surround inhibition. Analysis of MISO estimated network structure provides quantitative computational evidence using human seizure data suggesting a mechanism of seizure termination involving a combination of SOZ exhaustion and surround inhibition.

Authors/Disclosures
L Greenfield (UConn Health Center)
PRESENTER
Dr. Greenfield has received publishing royalties from a publication relating to health care.
Stefan Sumsky (UConn Health) Dr. Sumsky has nothing to disclose.