量化癫痫发作终止模式揭示了癫痫发作结束的有限途径。
Quantifying seizure termination patterns reveals limited pathways to seizure end.
机构信息
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Department of Cognitive Science, University of California, San Diego, CA, USA; Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
出版信息
Neurobiol Dis. 2022 Apr;165:105645. doi: 10.1016/j.nbd.2022.105645. Epub 2022 Jan 29.
OBJECTIVE
Despite their possible importance in the design of novel neuromodulatory approaches and in understanding status epilepticus, the dynamics and mechanisms of seizure termination are not well studied. We examined intracranial recordings from patients with epilepsy to differentiate seizure termination patterns and investigated whether these patterns are indicative of different underlying mechanisms.
METHODS
Seizures were classified into one of two termination patterns: (a) those that end simultaneously across the brain (synchronous), and (b) those whose termination is piecemeal across the cortex (asynchronous). Both types ended with either a burst suppression pattern, or continuous seizure activity. These patterns were quantified and compared using burst suppression ratio, absolute energy, and network connectivity.
RESULTS
Seizures with electrographic generalization showed burst suppression patterns in 90% of cases, compared with only 60% of seizures which remained focal. Interestingly, we found similar absolute energy and burst suppression ratios in seizures with synchronous and asynchronous termination, while seizures with continuous seizure activity were found to be different from seizures with burst suppression, showing lower energy during seizure and lower burst suppression ratio at the start and end of seizure. Finally, network density was observed to increase with seizure progression, with significantly lower densities in seizures with continuous seizure activity compared to seizures with burst suppression.
SIGNIFICANCE
Based on this spatiotemporal classification scheme, we suggest that there are a limited number of seizure termination patterns and dynamics. If this bears out, it would imply that the number of mechanisms underlying seizure termination is also constrained. Seizures with different termination patterns exhibit different dynamics even before their start. This may provide useful clues about how seizures may be managed, which in turn may lead to more targeted modes of therapy for seizure control.
目的
尽管在设计新的神经调节方法和理解癫痫持续状态方面具有重要意义，但癫痫发作终止的动力学和机制仍未得到很好的研究。我们检查了癫痫患者的颅内记录，以区分癫痫发作终止模式，并研究这些模式是否表明不同的潜在机制。
方法
将癫痫发作分为两种终止模式之一：（a）大脑同时终止（同步），和（b）皮质逐渐终止（异步）。这两种类型的终止都伴随着爆发抑制模式或持续的癫痫活动。使用爆发抑制比、绝对能量和网络连通性对这些模式进行量化和比较。
结果
具有电描记图泛化的癫痫发作中，90%的病例出现爆发抑制模式，而仅有 60%的持续局灶性癫痫发作出现爆发抑制模式。有趣的是，我们发现同步和异步终止的癫痫发作具有相似的绝对能量和爆发抑制比，而持续癫痫活动的癫痫发作与爆发抑制的癫痫发作不同，表现为癫痫发作期间能量较低，癫痫发作开始和结束时爆发抑制比较低。最后，观察到网络密度随着癫痫发作的进展而增加，与爆发抑制相比，持续癫痫活动的癫痫发作中网络密度显著降低。
意义
基于这种时空分类方案，我们建议存在有限数量的癫痫发作终止模式和动力学。如果这是正确的，那么这意味着癫痫发作终止的机制数量也受到限制。具有不同终止模式的癫痫发作甚至在发作开始之前就表现出不同的动力学。这可能为如何管理癫痫发作提供有用的线索，进而可能导致针对癫痫发作控制的更有针对性的治疗模式。
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