Zhang Yipeng, Daida Atsuro, Liu Lawrence, Kuroda Naoto, Ding Yuanyi, Oana Shingo, Kanai Sotaro, Monsoor Tonmoy, Duan Chenda, Hussain Shaun A, Qiao Joe X, Salamon Noriko, Fallah Aria, Sim Myung Shin, Sankar Raman, Staba Richard J, Engel Jerome, Asano Eishi, Roychowdhury Vwani, Nariai Hiroki
Department of Electrical and Computer Engineering, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Epilepsia. 2025 Jul 12. doi: 10.1111/epi.18545.
Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial electroencephalographic (iEEG) recordings and whether this distinction could be captured by a deep generative model.
In a retrospective cohort of 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686 410 HFOs across 18 265 brain contacts. To learn morphological characteristics, each event was transformed into a time-frequency plot and input into a variational autoencoder. We characterized latent space clusters containing morphologically defined putative pathological HFOs (mpHFOs) using interpretability analysis, including latent space disentanglement and time-domain perturbation. We built a predictive model to forecast postoperative seizure outcomes at 12 months based on the resection status of brain regions exhibiting mpHFOs. This model was compared to current clinical standards that evaluate outcomes based on the extent of seizure onset zone (SOZ) removal.
mpHFOs showed strong associations with expert-defined spikes and were predominantly located within the SOZ. The interpretability analysis discovered novel pathological features, including high power in the gamma (30-80 Hz) and ripple (>80 Hz) bands centered on the event with spike-like activity. These characteristics were consistent across multiple variables, including institution, electrode type, patient demographics, and anatomical location. Predicting postoperative seizure outcomes using the resection ratio of mpHFOs outperformed unclassified HFOs (F1 = .72 vs. .68, p < .01) and matched current clinical standards using SOZ resection (F1 = .74, p = .76). Combining mpHFO data with demographic and SOZ resection status further improved prediction performance (F1 = .83, p < .01).
Our data-driven approach using the generative artificial intelligence model yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.
发作间期高频振荡（HFOs）是癫痫发作起源区（EZ）一种很有前景的神经生理学生物标志物。然而，区分病理性与生理性HFOs的客观标准仍不明确，这阻碍了其临床应用。我们研究了病理性和生理性HFOs的不同潜在机制是否体现在颅内脑电图（iEEG）记录的信号形态中，以及这种区别是否可以通过深度生成模型来捕捉。
在一个对185例接受iEEG监测的癫痫患者的回顾性队列中，我们分析了18265个脑电极触点上的686410次HFOs。为了了解形态学特征，每个事件都被转换为一个时频图，并输入到一个变分自编码器中。我们使用可解释性分析，包括潜在空间解缠和时域扰动，对包含形态学定义的假定病理性HFOs（mpHFOs）的潜在空间聚类进行了表征。我们建立了一个预测模型，根据显示mpHFOs的脑区切除情况预测术后12个月的癫痫发作结果。该模型与基于癫痫发作起始区（SOZ）切除范围评估结果的当前临床标准进行了比较。
mpHFOs与专家定义的棘波有很强的关联，并且主要位于SOZ内。可解释性分析发现了新的病理特征，包括以棘波样活动为中心的γ（30 - 80Hz）和涟漪（>80Hz）频段的高功率。这些特征在多个变量中都是一致的，包括机构、电极类型、患者人口统计学和解剖位置。使用mpHFOs的切除率预测术后癫痫发作结果优于未分类的HFOs（F1 = 0.72对0.68，p < 0.01），并且与使用SOZ切除的当前临床标准相当（F1 = 0.74，p = 0.76）。将mpHFO数据与人口统计学和SOZ切除状态相结合进一步提高了预测性能（F1 = 0.83，p < 0.01）。
我们使用生成式人工智能模型的数据驱动方法产生了一种新的、可解释的病理性HFOs定义，这有可能进一步提高HFOs在EZ划定中的临床应用。
