Epileptic Tissue Localization through Skewness-Based Functional Connectivity in the High-Frequency Band of Intracranial EEG

Abstract

Functional connectivity analysis of intracranial electroencephalography (iEEG) plays an important role in understanding the mechanism of epilepsy and seizure dynamics. However, existing connectivity analysis is only suitable for low-frequency bands below 80 Hz. High-frequency oscillations (HFOs) and high-frequency activity (HFA) in the high-frequency band (80–500 Hz) are thought to be specific biomarkers in epileptic tissue localization. However, the transience in duration and variability of occurrence time and amplitudes of these events pose a challenge for conducting effective connectivity analysis. To deal with this problem, we proposed skewness-based functional connectivity (SFC) in the high-frequency band and explored its utility in epileptic tissue localization and surgical outcome evaluation. SFC comprises three main steps. The first step is the quantitative measurement of amplitude distribution asymmetry between HFOs/HFA and baseline activity. The second step is functional network construction on the basis of rank correlation of asymmetry across time. The third step is connectivity strength extraction from the functional network. Experiments were conducted in two separate datasets which consist of iEEG recordings from 59 patients with drug-resistant epilepsy. Significant difference (p<0.001) in connectivity strength was found between epileptic and non-epileptic tissue. Results were quantified via the receiver operating characteristic curve and the area under the curve (AUC). Compared with low-frequency bands, SFC demonstrated superior performance. With respect to pooled and individual epileptic tissue localization for seizure-free patients, AUCs were 0.66 (95% confidence interval (CI): 0.63–0.69) and (0.63 95% CI 0.56–0.71), respectively. For surgical outcome classification, the AUC was 0.75 (95% CI 0.59–0.85). Therefore, SFC can act as a promising assessment tool in characterizing the epileptic network and potentially provide better treatment options for patients with drug-resistant epilepsy.