Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability.
Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability.
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DOI：
10.1371/journal.pcbi.1010508
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发表时间：
2023-10
影响因子：
4.3
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中科院分区：
文献类型：
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癫痫是一种严重的神经系统疾病，其特征是有反复、自发性癫痫发作的倾向。传统上，癫痫发作被认为是随机发生的。然而，最近的研究发现了癫痫发作和癫痫关键特征（所谓的发作间期癫痫样活动）的潜在节律，其时间尺度从几小时、几天到几个月不等。了解决定癫痫样放电节律模式的生理机制仍然是一个悬而未决的问题。许多癫痫患者都知道癫痫发作的诱因，其中最常见的因素包括压力、睡眠不足和疲劳。为了量化这些生理因素的影响，我们分析了 107 名特发性全身性癫痫患者的 24 小时脑电图记录。我们发现两个亚组具有不同的癫痫样放电分布：一个在睡眠期间发生率最高，另一个在白天发生率最高。我们使用数学模型来询问这些数据，该模型描述了大规模大脑网络中背景活动和癫痫样活动之间的转变。该模型被扩展为包含一个依赖于时间的强迫项，其中网络内节点的兴奋性可以通过其他因素来调节。我们使用独立收集的人类皮质醇（主要的压力反应激素，以昼夜节律和超昼夜分泌模式为特征）数据和来自健康人类参与者的睡眠阶段脑电图来校准这个强迫项。我们发现，皮质醇的动态或睡眠阶段的转变，或两者的结合，可以解释大多数观察到的癫痫样放电的分布。我们的研究结果为癫痫样放电节律的潜在生理驱动因素的存在提供了概念证据。这些发现应该激发未来的研究，利用动物模型或癫痫患者精心设计的实验来探索这些机制。全球有 6500 万人患有癫痫症。其中许多人报告说，有特定的触发因素使他们更有可能癫痫发作（癫痫的主要症状）。在这里，我们使用数学模型来了解全天观察到的癫痫样活动的可能触发因素和节律之间的关系。该数学模型描述了相连的大脑区域的活动，以及这些区域的兴奋性如何响应不同的刺激而变化。根据从特发性全身性癫痫患者收集的数据，我们将睡眠阶段之间的转变和应激激素皮质醇浓度的变化确定为影响癫痫样活动发生可能性的候选因素。通过将这些因素纳入模型，我们证明它们可以解释大部分日常变化。更广泛地说，我们的方法提供了一个框架，可以更好地理解哪些因素驱动癫痫样活动的发生，并提供了建议可以验证模型预测的实验的潜力。
Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy—so-called interictal epileptiform activity—with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy. 65 million people have epilepsy worldwide. Many of these people report specific triggers that make their seizures (the primary symptom of epilepsy) more likely. Here, we use a mathematical model to understand the relationship between possible triggers and rhythms in epileptiform activity observed across the day. The mathematical model describes the activity of connected brain regions, and how the excitability of these regions can change in response to different stimuli. Based on data collected from people with idiopathic generalized epilepsy, we identify transitions between sleep stages and variation in concentration of the stress-hormone cortisol as candidate factors that influence how likely it is for epileptiform activity to occur. By including those factors into the model, we show they can explain most of the daily variability. More broadly, our approach provides a framework for better understanding what factors drive the occurrence of epileptiform activity and offers the potential to suggest experiments that can validate model predictions.
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