Medina Nicolas, Vila-Vidal Manel, Tost Ana, Khawaja Mariam, Carreño Mar, Roldán Pedro, Rumià Jordi, Centeno María, Conde Estefanía, Donaire Antonio, Campo Adrià Tauste
Computational Biology and Complex Systems Group, Department of Physics, Universitat Politècnica de Catalunya, Barcelona, Spain.
Institute for Research and Innovation in Health, Barcelona, Spain.
J Neural Eng. 2025 Aug 4;22(4). doi: 10.1088/1741-2552/adf097.
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.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.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.These results support HCS probability as a promising biomarker for early seizure prediction and offer mechanistic insights into pre-seizure brain network dynamics.
癫痫影响着全球约5000万人，可靠的发作前生物标志物可显著改善对耐药患者的神经调节疗法。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瞬态变化。特别是，我们之前的工作表明，这些改变是由反复出现的、持续时间短（0.6秒）的高连接性网络配置驱动的，称为高连接性状态（HCSs）。在这里，我们旨在在一个多中心患者队列中复制并进一步将HCS表征为一种生物标志物，评估其在不同记录方式和导联中的稳健性，探索其与可解释的生理变量的关系，并检查其与癫痫发作起始区（SOZ）动力学的网络水平关联。我们分析了12例接受sEEG和皮质脑电图检查患者的长期颅内脑电图记录。在两名有广泛临床信息的患者中，我们研究了HCS与SOZ动力学之间的相互作用。我们还开发了一个低维随机网络模型来研究HCS出现的机制原理。此外，我们将HCS动力学与γ波段活动和心率进行了比较，并测试了不同导联配置下的稳健性。在大多数患者中，癫痫发作前数小时HCS概率可靠增加。在两名深入研究的患者中，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，而非平均连接性的变化，这突出了致痫区和非致痫区之间动态相互作用的重要性。这些结果支持将HCS概率作为早期癫痫发作预测的一个有前景的生物标志物，并为发作前脑网络动力学提供了机制性见解。
