癫痫的前额高连接状态:颅内脑电图的证据,与癫痫发作区和网络模型的相互作用。

IF 3.8
Nicolas Medina, Manel Vila-Vidal, Ana Tost, Mariam Khawaja, Mar Carreño, Pedro Roldán, Jordi Rumià, María Centeno, Estefanía Conde, Antonio Donaire, Adrià Tauste Campo
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引用次数: 0

摘要

全球约有5000万人患有癫痫,可靠的癫痫发作前生物标志物可以显著改善耐药患者的神经调节治疗。最近使用立体脑电图(sEEG)的研究揭示了癫痫发作前网络动力学的短暂变化。特别是,我们之前的工作表明,这些变化是由反复出现的、持续时间短(0.6秒)的高连接网络配置驱动的,即高连接状态(HCS)。在这里,我们的目标是在多中心患者队列中复制并进一步表征HCS作为生物标志物,评估其在记录方式和蒙太奇中的稳健性,探索其与可解释的生理变量的关系,并检查其与癫痫发作区(SOZ)动态的网络水平关联。我们分析了12例患者的长期颅内脑电图(iEEG)记录,并进行了sEEG和皮质电图(ECoG)。在两例具有广泛临床信息的患者中,我们检查了HCS和SOZ动力学之间的相互作用。我们还建立了一个低维随机网络模型来研究HCS出现的机制原理。此外,我们比较了HCS动态与γ波段活动和心率,并测试了不同蒙太奇配置下的稳健性。在大多数患者中,HCS的概率在癫痫发作前数小时增加。在两个深度表征的患者中,这种增加与SOZ内网络中心性的增加特别相关。网络模型显示,HCS概率的变化主要源于拓扑重构,而不是平均连通性的变化,这强调了癫痫发生区域和非癫痫发生区域之间动态相互作用的重要性。这些结果支持HCS概率作为早期癫痫发作预测的有希望的生物标志物,并为癫痫发作前大脑网络动力学提供机制见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preictal high-connectivity states in epilepsy: evidence of intracranial EEG, interplay with the seizure onset zone and network modeling.

Objective.Epilepsy affects around 50 million people worldwide, and reliable pre-seizure biomarkers could significantly improve neuromodulation therapies for drug-resistant patients. Recent research using stereo-electroencephalography (sEEG) has revealed transient changes in network dynamics preceding seizures. In particular, our previous work showed that these alterations are driven by recurrent, short-lasting (0.6 s) high-connectivity network configurations-termed high-connectivity states (HCSs). Here, we aim to replicate and further characterize HCS as a biomarker in a multicentric patient cohort, assess its robustness across recording modalities and montages, explore its relationship with interpretable physiological variables, and examine its network-level association with seizure-onset zone (SOZ) dynamics.Approach.We analyzed long-term intracranial EEG recordings from 12 patients with sEEG and electrocorticography. In two patients with extensive clinical information, we examined the interplay between HCS and SOZ dynamics. We also developed a low-dimensional stochastic network model to investigate mechanistic rationales of HCS emergence. Additionally, we compared HCS dynamics with gamma-band activity and heart rate, and tested robustness across different montage configurations.Main Results.In most patients, HCS probability reliably increased hours before seizure onset. In the two deeply characterized patients, this increase was specifically linked to an increased network centrality within the SOZ. The network model revealed that changes in HCS probability stem primarily from topological reconfigurations rather than changes in mean connectivity, underscoring the importance of dynamic interactions between epileptogenic and non-epileptogenic regions.Significance.These results support HCS probability as a promising biomarker for early seizure prediction and offer mechanistic insights into pre-seizure brain network dynamics.

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