Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction

Shennan A. Weiss*, Itzhak Fried, Chengyuan Wu, Ashwini Sharan, Daniel Rubinstein, Jerome Engel, Michael R. Sperling, Richard J. Staba

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Fast ripples (FR) are a biomarker of epileptogenic brain, but when larger portions of FR generating regions are resected seizure freedom is not always achieved. To evaluate and improve the diagnostic accuracy of FR resection for predicting seizure freedom we compared the FR resection ratio (RR) with FR network graph theoretical measures. In 23 patients FR were semi-automatically detected and quantified in stereo EEG recordings during sleep. MRI normalization and co-registration localized contacts and relation to resection margins. The number of FR, and graph theoretical measures, which were spatial (i.e., FR rate-distance radius) or temporal correlational (i.e., FR mutual information), were compared with the resection margins and with seizure outcome We found that the FR RR did not correlate with seizure-outcome (p > 0.05). In contrast, the FR rate-distance radius resected difference and the FR MI mean characteristic path length RR did correlate with seizure-outcome (p < 0.05). Retesting of positive FR RR patients using either FR rate-distance radius resected difference or the FR MI mean characteristic path length RR reduced seizure-free misclassifications from 44 to 22% and 17%, respectively. These results indicate that graph theoretical measures of FR networks can improve the diagnostic accuracy of the resection of FR events for predicting seizure freedom.

Original languageEnglish
Article number367
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - Dec 2023
Externally publishedYes

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