Abstracts

Rationale: We hypothesized that the modulation index (MI), a summary measure of the strength of phase-amplitude coupling between high-frequency activity_{>150 Hz} and the phase of slow wave_{3-4 Hz}, would serve as a useful interictal biomarker for epilepsy presurgical evaluation. Methods: We investigated 123 patients who underwent focal cortical resection following extraoperative electrocorticography recording and had at least one-year of postoperative follow-up. We examined whether consideration of MI would improve the prediction of postoperative seizure outcome. MI was measured at each intracranial electrode site during interictal slow-wave sleep. We compared the accuracy of prediction of patients achieving ILAE Class-1 outcome between the full multivariate logistic regression model incorporating MI in addition to conventional clinical, seizure-onset zone (SOZ), and neuroimaging variables and the reduced logistic regression model incorporating all variables other than MI. Results: Ninety patients had Class-1 outcome at the time of most recent follow-up (mean follow-up: 5.7 years). The full model had a noteworthy outcome predictive ability, as reflected by regression model fit R² of 0.409 and area under the curve (AUC) of receiver operating characteristic plot of 0.838. ‘Incomplete resection of SOZ (p<0.001)’, ‘larger number of AEDs at the time of surgery (p=0.007)’, and ‘larger MI in non-resected tissues relative to that in resected tissue (p=0.020)’ were independently associated with a reduced probability of Class-1 outcome. The reduced model had a lower predictive ability as reflected by R² of 0.266 and AUC of 0.767. Anatomical variability in MI existed among non-epileptic electrode sites, defined as those unaffected by MRI lesion, SOZ, or interictal spike discharges. With MI adjusted for anatomical variability, the full model yielded the outcome predictive ability of R² of 0.422, AUC of 0.844, and sensitivity/specificity of 0.86/0.76. Conclusions: MI during interictal recording may provide useful information for prediction of postoperative seizure outcome. Funding: NIH grants NS047550, NS064033