Dimakopoulos Vasileios, Gotman Jean, Stacey William, von Ellenrieder Nicolás, Jacobs Julia, Papadelis Christos, Cimbalnik Jan, Worrell Gregory, Sperling Michael R, Zijlmans Maike, Imbach Lucas, Frauscher Birgit, Sarnthein Johannes
Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland.
Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada.
Brain Commun. 2022 Jun 9;4(3):fcac151. doi: 10.1093/braincomms/fcac151. eCollection 2022.
In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.
在耐药性局灶性癫痫中，从颅内脑电图（iEEG）记录到的发作间期高频振荡（HFOs）可为描绘致痫脑组织提供临床信息。据推测，包含HFO的iEEG电极触点可描绘致痫区；切除这些触点应能使术后不再发作。我们测试了我们对临床相关HFO的前瞻性定义是否与术后癫痫发作结果一致。该算法完全自动化，并且同等地应用于所有数据集。目的是评估所提出的检测器和分析方法的可靠性。我们使用一种与数据无关的临床相关HFO的自动化前瞻性定义，该定义已在来自两个独立癫痫中心的数据中得到验证。在本研究中，我们合并了来自九个独立癫痫中心的回顾性收集的数据集。分析对临床结果是盲态的。我们使用非快速眼动睡眠期间的iEEG记录，至少有12个5分钟的非快速眼动睡眠时段。我们自动检测80 - 250Hz的涟漪频段和250 - 500Hz的快速涟漪频段中的HFO。在这个完全自动化的算法中没有人工剔除事件。我们认为临床相关的HFO类型被定义为快速涟漪和涟漪同时出现。我们计算每位患者在几个夜间内和夜间之间的数据时段上HFO发生率的时间一致性。时间一致性<50%的患者被排除在进一步分析之外。我们确定所有高HFO发生率的电极触点是否都包含在切除范围内，以及在≥2年的随访中是否实现了无癫痫发作（国际抗癫痫联盟1级）。将先前验证的算法应用于来自几个独立癫痫中心的大型队列，可能会提高HFO分析的临床相关性和可推广性，这是在临床实践中使用HFO的关键下一步。
