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Park, Eun-Hyoung

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Eun-Hyoung

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Park, Eun-Hyoung

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Author's Publications: search.filters.isAuthorOfPublication.1a9e2b1a-e250-4e24-aa2d-8f300d79be87

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    Granger Causality Analysis of Interictal iEEG Predicts Seizure Focus and Ultimate Resection
    (Oxford University Press, 2018) Park, Eun-Hyoung; Madsen, Joseph
    Abstract BACKGROUND A critical conceptual step in epilepsy surgery is to locate the causal region of seizures. In practice, the causal region may be inferred from the set of electrodes showing early ictal activity. There would be advantages in deriving information about causal regions from interictal data as well. We applied Granger's statistical approach to baseline interictal data to calculate causal interactions. We hypothesized that maps of the Granger causality network (or GC maps) from interictal data might inform about the seizure network, and set out to see if “causality” in the Granger sense correlated with surgical targets. OBJECTIVE To determine whether interictal baseline data could produce GC maps, and whether the regions of high GC would statistically resemble the topography of the ictally active electrode (IAE) set and resection. METHODS Twenty-minute interictal baselines obtained from 25 consecutive patients were analyzed. The “GC maps” were quantitatively compared to conventionally constructed surgical plans, by using rank order and Cartesian distance statistics. RESULTS In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were lower than predicted by chance (P < .05). The aggregate probability of such a match by chance alone is very small (P < 10−20) suggesting that interictal GC maps correlated with ictal networks. The distance of the highest GC electrode to the IAE set and to the resection averaged 4 and 6 mm (Wilcoxon P < .001). CONCLUSION GC analysis has the potential to help localize ictal networks from interictal data.