Abstracts

Rationale: Stereoelectroencephalography (sEEG) is increasingly utilized for invasive localization of ictal onsets in patients with refractory focal epilepsy. Recently we have shown that Granger causality (GC) analysis has the potential to reveal seizure networks from interictal baseline data obtained from subdural grids. In this study, we produced “causal density” maps from interictal sEEG data and tested whether the regions of high GC would statistically resemble the topography of the seizure onset zone and resection. Methods: Following sEEG implantation, the first 10 minutes of data after all channels were connected was analyzed (n=20). Causality maps were quantitatively compared to conventionally-constructed surgical plans and resections, by using non-parametric, rank-order statistics. Results: In 17 of 20 cases, the interictal GC rankings of the electrodes mapped to the seizure onset zone had higher causality than predicted by chance (range: p<10^-5 to 0.01). In 15 of 18 cases who had resection or ablation, causality in the resection zone was significantly increased (range: p<10^-5 to 0.03). The aggregate probability of such a match is very small (p<10^-32), suggesting that the networks highlighted in interictal GC maps correlate with surgically-relevant seizure networks. Conclusions: GC analysis applied to sEEG data has the potential to localize ictal networks from interictal data. Since interictal recordings can be obtained in the operating room as the electrodes are being placed, it is possible that live GC analysis of sEEG data could eventually aid surgical decision making, such as modification of the sEEG stereotactic plan to better sample highly causal regions and define the epileptogenic zone.  Funding: This work is funded in part by NIH (1R01NS069696 and U01EB023820-01) and in part by Wyss Institute for network data storage and management.