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发作持续时间与发作间期颅内脑电图频段功率的多个时间尺度相关。

Seizure duration is associated with multiple timescales in interictal iEEG band power.

作者信息

Panagiotopoulou Mariella, Schroeder Gabrielle M, Blickwedel Jess, Chowdhury Fahmida A, Diehl Beate, de Tisi Jane, Duncan John S, Cronie Alison, Falconer Jennifer, Faulder Ryan, Leach Veronica, Livingstone Shona, Thomas Rhys H, Taylor Peter N, Wang Yujiang

出版信息

ArXiv. 2025 Apr 24:arXiv:2504.17888v1.

Abstract

Background Seizure severity can change from one seizure to the next within individual people with epilepsy. It is unclear if and how seizure severity is modulated over longer timescales. Characterising seizure severity variability over time could lead to tailored treatments. In this study, we test if continuously-recorded interictal intracranial EEG (iEEG) features encapsulate signatures of such modulations. Methods We analysed 20 subjects with iEEG recordings of at least one day. We identified cycles on timescales of hours to days embedded in long-term iEEG band power and associated them with seizure severity, which we approximated using seizure duration. In order to quantify these associations, we created linear-circular statistical models of seizure duration that incorporated different band power cycles within each subject. Findings In most subjects, seizure duration was weakly to moderately correlated with individual band power cycles. Combinations of multiple band power cycles significantly explained most of the variability in seizure duration. Specifically, we found 70% of the models had a higher than 60% adjusted $R^2$ across all subjects. From these models, around 80% were deemed to be above chance-level (p-value < 0.05) based on permutation tests. Models included cycles of ultradian, circadian and slower timescales in a subject-specific manner. Interpretation These results suggest that seizure severity, as measured by seizure duration, may be modulated over timescales of minutes to days by subject-specific cycles in interictal iEEG signal properties. These cycles likely serve as markers of seizure modulating processes. Future work can investigate biological drivers of these detected fluctuations and may inform novel treatment strategies that minimise seizure severity.

摘要

背景

癫痫患者个体的发作严重程度在不同发作之间可能会发生变化。目前尚不清楚发作严重程度在更长的时间尺度上是否以及如何受到调节。表征发作严重程度随时间的变化可能会带来个性化治疗。在本研究中,我们测试连续记录的发作间期颅内脑电图(iEEG)特征是否包含此类调节的特征。

方法

我们分析了20名有至少一天iEEG记录的受试者。我们在长期iEEG频段功率中识别出数小时至数天时间尺度上的周期,并将它们与发作严重程度相关联,我们用发作持续时间来近似发作严重程度。为了量化这些关联,我们创建了发作持续时间的线性-循环统计模型,该模型纳入了每个受试者不同的频段功率周期。

结果

在大多数受试者中,发作持续时间与个体频段功率周期呈弱至中度相关。多个频段功率周期的组合显著解释了发作持续时间的大部分变异性。具体而言,我们发现所有受试者中70%的模型调整后的$R^2$高于60%。根据置换检验,这些模型中约80%被认为高于机遇水平(p值<0.05)。模型以受试者特异性的方式包括了超日节律、昼夜节律和更慢时间尺度的周期。

解读

这些结果表明,以发作持续时间衡量的发作严重程度可能在数分钟至数天的时间尺度上受到发作间期iEEG信号特性中受试者特异性周期的调节。这些周期可能是发作调节过程的标志物。未来的工作可以研究这些检测到的波动的生物学驱动因素,并可能为最小化发作严重程度的新治疗策略提供信息。

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