Fan Denggui, Qi Lixue, Yang Zecheng, Luan Guoming, Wang Qingyun
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China.
Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China.
Front Neurosci. 2023 Jan 19;17:1126875. doi: 10.3389/fnins.2023.1126875. eCollection 2023.
The dynamic reconfiguration of network oscillations is connected with cognitive processes. Changes in how neural networks and signaling pathways work are crucial to how epilepsy and related conditions develop. Specifically, there is evidence that prolonged or recurrent seizures may induce or exacerbate cognitive impairment. However, it still needs to be determined how the seizure brain configures its functional structure to shape the battle of strong local oscillations vs. slow global oscillations in the network to impair cognitive function.
In this paper, we aim to deduce the network mechanisms underlying seizure-induced cognitive impairment by comparing the evolution of strong local oscillations with slow global oscillations and their link to the resting state of healthy controls. Here, we construct a dynamically efficient network of pathological seizures by calculating the synchrony and directionality of information flow between nine patients' SEEG signals. Then, using a pattern-based method, we found hierarchical modules in the brain's functional network and measured the functional balance between the network's local strong and slow global oscillations.
According to the findings, a tremendous rise in strong local oscillations during seizures and an increase in slow global oscillations after seizures corresponded to the initiation and recovery of cognitive impairment. Specifically, during the interictal period, local strong and slow global oscillations are in metastable balance, which is the same as a normal cognitive process and can be switched easily. During the pre-ictal period, the two show a bimodal pattern of separate peaks that cannot be easily switched, and some flexibility is lost. During the seizure period, a single-peak pattern with negative peaks is showcased, and the network eventually transitions to a very intense strong local oscillation state. These results shed light on the mechanism behind network oscillations in epilepsy-induced cognitive impairment. On the other hand, the differential (similarity) of oscillatory reorganization between the local (non) epileptogenic network and the global network may be an emergency protective mechanism of the brain, preventing the spread of pathological information flow to more healthy brain regions.
网络振荡的动态重构与认知过程相关。神经网络和信号通路工作方式的变化对于癫痫及相关病症的发展至关重要。具体而言，有证据表明长时间或反复发作的癫痫可能诱发或加剧认知障碍。然而，癫痫发作的大脑如何配置其功能结构，以塑造网络中强烈的局部振荡与缓慢的全局振荡之间的对抗从而损害认知功能，仍有待确定。
在本文中，我们旨在通过比较强烈的局部振荡与缓慢的全局振荡的演变及其与健康对照静息状态的联系，推断癫痫发作诱发认知障碍的网络机制。在此，我们通过计算九名患者的立体定向脑电图（SEEG）信号之间信息流的同步性和方向性，构建了一个动态高效的病理性癫痫发作网络。然后，使用基于模式的方法，我们在大脑功能网络中发现了分层模块，并测量了网络局部强烈振荡与缓慢全局振荡之间的功能平衡。
根据研究结果，癫痫发作期间强烈局部振荡的大幅上升以及发作后缓慢全局振荡的增加与认知障碍的起始和恢复相对应。具体而言，在发作间期，局部强烈振荡与缓慢全局振荡处于亚稳态平衡，这与正常认知过程相同且易于切换。在发作前期，两者呈现出无法轻易切换的双峰分离峰值模式，并且失去了一些灵活性。在癫痫发作期，展示出具有负峰值的单峰模式，并且网络最终转变为非常强烈的局部振荡状态。这些结果揭示了癫痫诱发认知障碍中网络振荡背后的机制。另一方面，局部（非）致痫网络与全局网络之间振荡重组的差异（相似性）可能是大脑的一种应急保护机制，可防止病理信息流扩散到更多健康脑区。
