Fabbri Lorenzo, Matarrese Margherita A G, Jahromi Saeed, Perry Michael Scott, Tamilia Eleonora, Madsen Joseph R, Stone Scellig S D, Pearl Phillip L, Papadelis Christos
Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA.
Department of Bioengineering, The University of Texas at Arlington, Arlington, TX 76010, USA.
Brain Commun. 2025 Feb 8;7(1):fcaf056. doi: 10.1093/braincomms/fcaf056. eCollection 2025.
Spikes are the most established interictal epilepsy biomarkers. Yet, they suffer from low specificity since they are partially concordant with the epileptogenic zone and are often found in non-epileptogenic areas. High-frequency oscillations, classified as ripples and fast ripples, are considered more specific biomarkers compared with spikes. Ripples occur more often than fast ripples but are believed to be less specific, since they are more frequently generated by physiological mechanisms. Here, we examine the temporal relationship between spikes, ripples and fast ripples, and assess the ability of these biomarkers (and their combinations) to delineate the epileptogenic zone and predict outcome. We hypothesize that spikes on ripples (temporal co-occurrence of spikes and ripples) can identify the epileptogenic zone and predict outcome better than spikes or ripples. We analysed intracranial EEG data from 40 children with drug-resistant epilepsy. Spikes, ripples and fast ripples were classified based on their temporal occurrence. Their rates were compared with resection by performing a receiver operating characteristic analysis. The resection ratio, quantifying the extent of each biomarker's removal, was computed, and correlated with patients' outcome. Spikes on ripples were seen in all patients; fast ripples were seen in 43% of patients. In good outcome patients, fast ripple and spike on ripple rates were higher inside resection ( = 0.027; = 0.003, respectively). Fast ripples and spikes on ripples resection ratio predicted outcome ( < 0.05). For fast ripples, outcome was predicted in 82% of patients; this proportion was higher than the one for spikes (48%, = 0.015) and ripples (40%, = 0.003), and spikes on ripples (53%, = 0.034). Fast ripples were the most accurate (82%) to predict outcome; spikes on ripples were the most precise (positive predictive value = 90%). Spike rate and spikes on ripples performance to predict the epileptogenic zone were correlated ( = 0.36, = 0.035). For patients with frequent spikes, spikes on ripples accuracy to predict outcome reached 70%. Fast ripples are the best biomarker, but they can be seen in only half of patients with drug-resistant epilepsy. Spikes on ripples are a good alternative with more universal applicability since they can be seen in all patients while their resection predicts good outcome; their performance is improved in patients with frequent spikes. Overall, in the absence of fast ripples, spike on ripple areas should be targeted during surgery.
棘波是最成熟的发作间期癫痫生物标志物。然而,它们的特异性较低,因为它们与致痫区部分一致,且常在非致痫区域出现。高频振荡,分为涟漪波和快涟漪波,与棘波相比被认为是更具特异性的生物标志物。涟漪波比快涟漪波出现得更频繁,但被认为特异性较低,因为它们更常由生理机制产生。在此,我们研究棘波、涟漪波和快涟漪波之间的时间关系,并评估这些生物标志物(及其组合)描绘致痫区和预测预后的能力。我们假设棘波与涟漪波同时出现(棘波和涟漪波在时间上同时发生)比棘波或涟漪波能更好地识别致痫区并预测预后。我们分析了40例耐药性癫痫儿童的颅内脑电图数据。根据棘波、涟漪波和快涟漪波的时间出现情况进行分类。通过进行受试者工作特征分析,将它们的发生率与切除情况进行比较。计算切除率,以量化每个生物标志物的切除范围,并将其与患者的预后相关联。所有患者均出现棘波与涟漪波同时出现的情况;43%的患者出现快涟漪波。在预后良好的患者中,切除区域内快涟漪波和棘波与涟漪波同时出现的发生率更高(分别为P = 0.027;P = 0.003)。快涟漪波和棘波与涟漪波的切除率可预测预后(P < 0.05)。对于快涟漪波,82%的患者可预测预后;这一比例高于棘波(48%,P = 0.015)、涟漪波(40%,P = 0.003)以及棘波与涟漪波同时出现的情况(53%,P = 0.034)。快涟漪波预测预后的准确性最高(82%);棘波与涟漪波同时出现的情况预测精度最高(阳性预测值 = 90%)。棘波发生率与棘波与涟漪波同时出现的情况预测致痫区的性能相关(r = 0.36,P = 0.035)。对于棘波频繁出现的患者,棘波与涟漪波同时出现的情况预测预后的准确性达到70%。快涟漪波是最佳生物标志物,但仅在一半的耐药性癫痫患者中可见。棘波与涟漪波同时出现的情况是一种很好的替代方法且具有更广泛的适用性,因为在所有患者中均可出现且其切除可预测良好预后;在棘波频繁出现的患者中其性能有所提高。总体而言,在没有快涟漪波的情况下,手术时应靶向棘波与涟漪波同时出现的区域。