Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression
Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression
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DOI：
10.1093/braincomms/fcaa182
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发表时间：
2020-11-02
影响因子：
4.8
通讯作者：
Bardakjian BL
中科院分区：
文献类型：
--
作者：
Grigorovsky V;Jacobs D;Breton VL;Tufa U;Lucasius C;del Campo JM;Chinvarun Y;Carlen PL;Wennberg R;Bardakjian BL
癫痫发作后的广泛性脑电抑制是癫痫发作结束时电活动的抑制状态。这种状态的延长与癫痫猝死的风险增加有关，使得在癫痫后抑制期间表征潜在的电节律活动是改进癫痫治疗的重要步骤。脑电中的相位-幅度耦合反映了大脑网络中的认知编码，其中一些编码突出了癫痫的活动；因此，我们假设在癫痫患者意外死亡的患者风险的背景下，在癫痫后抑制状态中存在明显的相位-幅度耦合特征，可以提供对这种状态的更好的估计。我们使用了11名患者(6名男性，5名女性，年龄21-41 岁)的脑电数据，包括25次癫痫发作，以确定发作期和发作期脑电抑制状态下的频率动力学。交叉频率耦合分析表明，与发作前相比，癫痫发作过程中，增量(0.5~4 Hz)与伽马(30+Hz)之间的耦合的相位频率逐渐降低，然后在癫痫发作后状态下，0.5~1.5 Hz信号的相位与30~50 Hz信号的幅度之间的耦合增强。这一标记物在患者中是一致的。然后，利用这些发作后特有的特征，无监督状态分类器--隐马尔可夫模型--能够可靠地对癫痫发作的四种不同状态进行分类，包括发作后抑制状态。此外，对癫痫发作后抑制状态的连接组分析显示，与癫痫发作前基线相比，在癫痫发作后抑制状态期间，网络内的信息流增加，这表明网络沟通增强。当同样的工具被应用于意外死亡的癫痫患者的脑电时，发作期耦合动力学消失，发作后相位-幅度耦合始终保持恒定。总体而言，我们的发现表明，存在活跃的发作后网络，通过耦合动力学的定义，可以用于客观地分类发作后抑制状态；此外，在癫痫猝死的案例研究中，尽管存在惊厥发作，但该网络并没有表现出发作性发作样的相位-幅度耦合特征，而是表现出类似发作后的活动。癫痫后抑制状态是与基线活动相比网络活动升高的时期，这可以为癫痫的病理提供关键的见解。相位-幅度耦合分析表明，后向广义脑电抑制状态增加了增量-伽马耦合。这些耦合特征与无监督隐马尔可夫模型一起使用，可靠地区分了癫痫发作中的四个亚状态。癫痫突发意外死亡的病例研究显示，偶联活动类似于癫痫发作后的状态。
Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5–4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5–1.5 Hz signal and amplitude of 30–50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier—a hidden Markov model—was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology. Phase-amplitude coupling analysis shows that a state of postictal generalized EEG suppression has increased delta-gamma coupling. These coupling features, used with an unsupervised hidden Markov model, reliably differentiated four substates in seizure episodes. A sudden unexpected death in epilepsy case study showed coupling activity similar to a postictal state.
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θ-γ神经代码。
影响因子：
16.2
作者：
Lisman JE;Jensen O
通讯作者：
Jensen O
影响因子：
5.6
作者：
Nariai, Hiroki;Matsuzaki, Naoyuki;Asano, Eishi
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Asano, Eishi
影响因子：
3.4
作者：
DeGiorgio CM;Markovic D;Mazumder R;Moseley BD
通讯作者：
Moseley BD
影响因子：
4
作者：
Colic, Sinisa;Wither, Robert G.;Bardakjian, Berj L.
通讯作者：
Bardakjian, Berj L.
影响因子：
4.6
作者：
Li, Chunsheng;Jacobs, Daniel;Bardakjian, Berj L.
通讯作者：
Bardakjian, Berj L.