Qiang Zekai, Norris Jamie, Cooray Gerald, Rosch Richard, Miller Kai, Hermes Dora, Chari Aswin, Tisdall Martin
Developmental Neuroscience, Great Ormond Street Institute of Child Health, University College London, London, UK.
Wellcome Centre for Human Neuroimaging, University College London, London, UK.
Epilepsia. 2025 Sep;66(9):3087-3104. doi: 10.1111/epi.18467. Epub 2025 May 23.
Cortico-cortical evoked potentials (CCEPs) are an active electrophysiological technique used during intracranial electroencephalography to evaluate the effective connectivity and influence of therapeutic stimulation between distinct cortical regions and pinpoint epileptogenic zones (EZs) in patients with epilepsy. Various methodologies have been implemented to analyze CCEPs and characterize the epileptogenic networks for EZ localization. Despite its promise, their interpretation remains challenging due to the large volumes of spatially and temporally complex data generated. Early studies focused largely on qualitative descriptors and predefined, semi-quantitative features such as waveform morphology and peak latencies. However, these methods are limited by the significant heterogeneity in CCEP waveform conformations across patients and cortical regions. The specific technique used for extraction of features, such as the spectral band power and root mean squared values, remains open to empirical refinement, as does choice of appropriate latency windows, with no consensus reached regarding the optimal approach. Graph theoretical metrics such as degree centrality, betweenness centrality, and clustering coefficients can provide a rich representation of epileptogenic network connectivity. However, these metrics are often abstract and difficult to interpret in a clinical setting or to the non-expert, and their neuroscientific substrates remains poorly understood. The lack of standardization in stimulation protocol and data-processing pipelines has further contributed to inconsistency in reported findings. Emerging machine learning approaches have been increasingly applied to CCEP data, offering a more data-driven and potentially generalizable way to identify electrophysiological biomarkers of the epileptogenic effective connectivity. In this article, we discuss qualitative, quantitative, and spectral features; network-analytical metrics; and more recently, data driven methodologies aimed at improving the interpretability and clinical utility of CCEP data.
皮质-皮质诱发电位（CCEPs）是一种在颅内脑电图检查过程中使用的主动电生理技术，用于评估不同皮质区域之间治疗性刺激的有效连接性和影响，并确定癫痫患者的致痫区（EZs）。已经实施了各种方法来分析CCEPs并表征用于EZ定位的致痫网络。尽管其前景广阔，但由于产生的大量时空复杂数据，对其进行解释仍然具有挑战性。早期研究主要集中在定性描述符和预定义的半定量特征，如波形形态和峰值潜伏期。然而，这些方法受到患者和皮质区域CCEP波形构象显著异质性的限制。用于提取特征的具体技术，如谱带功率和均方根值，以及合适潜伏期窗口的选择，仍有待经验性优化，对于最佳方法尚未达成共识。诸如度中心性、介数中心性和聚类系数等图论指标可以提供致痫网络连接性的丰富表示。然而，这些指标通常比较抽象，在临床环境中或对于非专业人员来说难以解释，并且它们的神经科学基础仍然知之甚少。刺激方案和数据处理流程缺乏标准化进一步导致了报告结果的不一致。新兴的机器学习方法越来越多地应用于CCEP数据，提供了一种更数据驱动且可能具有通用性的方法来识别致痫有效连接性的电生理生物标志物。在本文中，我们讨论了定性、定量和频谱特征；网络分析指标；以及最近旨在提高CCEP数据的可解释性和临床实用性的数据驱动方法。
