Abstracts

Rationale: There is currently a debate whether focal epilepsies can be regarded as a solely local or as a network phenomenon. Graph theoretical approaches may help to resolve this controversy. We here address the question whether the clinically identified focus can be regarded as an important node in large scale functional brain networks. Importance is assessed via different centrality metrics (degree-, closeness-, and betweenness-centrality). Methods: We analyzed multi-day, multi-channel EEG data recorded intercranially from up to now 13 epilepsy patients (8 with unifocal and 5 with multifocal epilepsies; all patients are seizure-free post-operatively). EEGs were recorded from on average 55 recording sites for a period of on average 118 hours per patient. Using a moving-window approach (duration of each window: 20.48 s corresponding to 4096 data points; no overlap) we estimated the strength of interactions (via mean phase coherence) between all pairs of sampled brain regions. We then derived weighted functional brain networks by applying different rules to transform the interaction matrices to adjacency matrices, which allowed us to estimate the centralities in a time-resolved manner. We here concentrate on the inter-ictal state of the patients only and dicarded peri-ictal data (1 h pre- and post-ictally). Results: The temporal average of the degree-centrality indexed the epileptic focus as a node with highest importance in the epileptic brain networks from 9/15 patients. In contrast, with both the betweenness- and closeness-centralities (averaged over time) we observed recording sites in the vicinity of the reference electrodes as important nodes in 14/15 patients. Findings were consistent for the networks derived by applying different transformation rules. Conclusions: The different centrality metrics differentially indexed the clinically defined epileptic focus as an important node in epileptic brain networks during the inter-ictal state. Further work is necessary to clarify the impact of recording montages on network construction and centrality estimation. This work was supported by the Deutsche Forschungsgemeinschaft (Grant No: LE 660/4-2)