Resection of individually identified high‐rate high‐frequency oscillations region is associated with favorable outcome in neocortical epilepsy

Abstract

SUMMARYObjectives: High-frequency oscillations (HFOs) represent a novel electrophysiologic marker of endogenous epileptogenicity. Clinically, this propensity can be utilized to more accurately delineate the resection margin before epilepsy surgery. Currently, prospective application of HFOs is limited because of a lack of an exact quantitative measure to reliably identify HFO-generating areas necessary to include in the resection. Here, we evaluated the potential of a patient-individualized approach of identifying high-rate HFO regions to plan the neocortical resection. Methods: Fifteen patients with neocortical seizure-onset zones (SOZs) underwent intracranial electroencephalographic monitoring. To identify interictal HFOs, we applied an automated, hypersensitive HFO-detection algorithm followed by post hoc processing steps to reject false detections. The spatial relationship between HFO distribution and the SOZ was evaluated. To address high interpatient variability in HFO properties, we evaluated the high-rate HFO region, an unbiased statistical parameter, in each patient. The relationship between resection of the high-rate HFO region and postoperative outcome was examined. Results: Grouped data demonstrated that the rate of ripple (60-200 Hz) and fast ripple (200-500 Hz) was increased in the SOZ (both p < 0.01). Intrapatient analysis of the HFO distribution localized the SOZ in 11 patients. High-rate HFO regions were determined in all patients by an individually adjusted threshold. Resection of high-rate HFO regions was significantly associated with a seizure-free outcome (p < 0.01). The extent/ratio of SOZ or spiking region resection did not differ between seizure-free and seizure-persistent groups. Significance: Intrapatient analysis of high-rate HFOs provides more detailed description of HFO-generating areas and can mark the areas of clinically significant epileptogenicity-a crucial component of the neocortical epileptic network that should be removed to achieve a good outcome. Validating and adopting an unbiased quantitative HFO parameter has the potential to propel wider and prospective utilization of HFOs in the surgical treatment of neocortical epilepsy and to improve its outcome.

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

“…In Urrestarazu's study based on seven patients with mTLE or neocortical epilepsy, the median rate of ripples and fast ripples was 14/min (range, 0.4-41) and 5/min (range, 0.3-33) respectively in each type of patient [17]. Cho et al [27]. reported much lower mean rates of ripples (*1.1/ min) and fast ripples (*0.25/min) in the SOZs in patients with neocortical epilepsy.…”

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

“…Another option to reduce the FDR and detection of artifacts is to apply a post-processing step to eliminate falsely detected events and leave only "true" HFOs. This can be done either automatically, using an artifact rejection algorithm (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016) or data classification via clustering (Blanco et al 2010;Malinowska et al 2015), or manually with supervision by experts.…”

“…There are several means to reduce the FDR, e.g. applying post-processing steps (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016) or using human validation (Staba et al 2002;Gardner et al 2007;Crépon et al 2010). Here we propose another approach, in which α is optimized based on FDR instead of FPR.…”

“…More recently, several algorithms for automated artifact rejection have been developed (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016). These algorithms are independent from the initial detection, which is accomplished through either human visual analysis (Amiri et al 2016) or an existing detector (Burnos et al 2014;Cho et al 2014;Gliske et al 2016).…”

“…These algorithms are independent from the initial detection, which is accomplished through either human visual analysis (Amiri et al 2016) or an existing detector (Burnos et al 2014;Cho et al 2014;Gliske et al 2016). These methods reduce the number of false detections, but again increase the complexity and number of parameters, making implementation more challenging.…”