Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL _max ), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion’s sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL _max , a measure of phase (φ _max ) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α=0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL _max synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy.

中文翻译：

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测量癫痫发作时大脑动力学的重置：全局优化和空间同步技术的应用。

癫痫发作是人类大脑从混乱到有序的间歇性时空转换的表现。混沌的度量，即最大李雅普诺夫指数（STL _max )，根据对癫痫大脑关键部位的脑电图 (EEG) 的动态分析，在癫痫发作之前（之后）逐渐收敛（发散），这种现象被称为动态同步（不同步）。这种动态同步/去同步已经构成了长期（数十分钟）、在线、前瞻性癫痫发作预测系统的设计和开发的基础。此外，在癫痫发作点观察到的同步/去同步的时间常数变化的标准已被用于显示颞叶癫痫 (TLE) 患者癫痫大脑的重置，这种现象暗示可能的稳态作用癫痫发作本身以恢复正常的大脑活动。在本文中，我们引入了一个新标准来衡量这种重置，它利用观察到的同步/去同步水平的变化。我们根据观察到的同步/去同步的时间常数，将这个标准的重置灵敏度与旧标准进行比较。接下来，我们通过比较研究来测试复位现象在 EEG 动力学的使用测量方面的稳健性。STL _max，使用两个标准（即观察到的同步/去同步的电平和时间常数）的相位测量（φ _max）和能量测量（E）。这些测量值是从两名患有局灶性颞叶癫痫和总共 43 次癫痫发作的患者的硬膜下和深度电极的颅内脑电图 (iEEG) 记录中估计出来的。来自优化理论的技术，特别是二次二价规划，被应用于优化三个措施在检测发作前夹带中的性能。结果表明，使用两个重置标准中的任何一个，并且对于所有三个动态测量，癫痫发作时动态重置发生的概率显着更高（α =0.05) 而不是在每个患者的 EEG 记录天数中随机选择的非癫痫发作点重置。还表明，与使用其他同步措施相比，使用STL _最大同步/去同步的时间常数在癫痫发作时动态复位发生的概率更高，而使用同步/去同步标准水平在癫痫发作时动态复位使用任何三项同步措施。这些发现表明，癫痫复位在 EEG 动力学测量和所用复位标准方面的稳健性，以及它在进一步阐明癫痫发作以及开发癫痫新疗法方面可能发挥的关键作用。