Pinto-Orellana Marco, Lopour Beth
Biomedical Engineering Department, University of California, Irvine, Irvine, CA, United States.
Front Netw Physiol. 2024 Sep 20;4:1441998. doi: 10.3389/fnetp.2024.1441998. eCollection 2024.
For patients with refractory epilepsy, the seizure onset zone (SOZ) plays an essential role in determining the specific regions of the brain that will be surgically resected. High-frequency oscillations (HFOs) and connectivity-based approaches have been identified among the potential biomarkers to localize the SOZ. However, there is no consensus on how connectivity between HFO events should be estimated, nor on its subject-specific short-term reliability. Therefore, we propose the channel-level connectivity dispersion (CLCD) as a metric to quantify the variability in synchronization between individual electrodes and to identify clusters of electrodes with abnormal synchronization, which we hypothesize to be associated with the SOZ. In addition, we developed a specialized filtering method that reduces oscillatory components caused by filtering broadband artifacts, such as sharp transients, spikes, or direct current shifts. Our connectivity estimates are therefore robust to the presence of these waveforms. To calculate our metric, we start by creating binary signals indicating the presence of high-frequency bursts in each channel, from which we calculate the pairwise connectivity between channels. Then, the CLCD is calculated by combining the connectivity matrices and measuring the variability in each electrode's combined connectivity values. We test our method using two independent open-access datasets comprising intracranial electroencephalography signals from 89 to 15 patients with refractory epilepsy, respectively. Recordings in these datasets were sampled at approximately 1000 Hz, and our proposed CLCDs were estimated in the ripple band (80-200 Hz). Across all patients in the first dataset, the average ROC-AUC was 0.73, and the average Cohen's d was 1.05, while in the second dataset, the average ROC-AUC was 0.78 and Cohen's d was 1.07. On average, SOZ channels had lower CLCD values than non-SOZ channels. Furthermore, based on the second dataset, which includes surgical outcomes (Engel I-IV), our analysis suggested that higher CLCD interquartile (as a measure of CLCD distribution spread) is associated with favorable outcomes (Engel I). This suggests that CLCD could significantly assist in identifying SOZ clusters and, therefore, provide an additional tool in surgical planning for epilepsy patients.
对于难治性癫痫患者，癫痫发作起始区（SOZ）在确定将接受手术切除的脑特定区域方面起着至关重要的作用。高频振荡（HFOs）和基于连接性的方法已被确定为定位SOZ的潜在生物标志物。然而，关于如何估计HFO事件之间的连接性，以及其个体特异性短期可靠性，目前尚无共识。因此，我们提出通道级连接性离散度（CLCD）作为一种度量，用于量化各个电极之间同步性的变异性，并识别同步异常的电极簇，我们假设这些电极簇与SOZ相关。此外，我们开发了一种专门的滤波方法，可减少由滤波宽带伪迹（如尖锐瞬变、尖峰或直流偏移）引起的振荡成分。因此，我们的连接性估计对这些波形的存在具有鲁棒性。为了计算我们的度量，我们首先创建表示每个通道中高频爆发存在的二进制信号，由此计算通道之间的成对连接性。然后，通过组合连接性矩阵并测量每个电极组合连接性值的变异性来计算CLCD。我们使用两个独立的开放获取数据集测试我们的方法，这两个数据集分别包含89例至15例难治性癫痫患者的颅内脑电图信号。这些数据集中的记录采样频率约为1000Hz，我们提出的CLCD在涟漪频段（80 - 200Hz）进行估计。在第一个数据集中的所有患者中，平均ROC - AUC为0.73，平均Cohen's d为1.05，而在第二个数据集中，平均ROC - AUC为0.78，Cohen's d为1.07。平均而言，SOZ通道的CLCD值低于非SOZ通道。此外，基于包含手术结果（Engel I - IV）的第二个数据集，我们的分析表明，较高的CLCD四分位数间距（作为CLCD分布离散度的度量）与良好的结果（Engel I）相关。这表明CLCD可以显著帮助识别SOZ簇，因此为癫痫患者的手术规划提供额外的工具。
