Exploring brain network oscillations during seizures in drug-naïve patients with juvenile absence epilepsy - Tan, Linjie; Tang, Haoling; Luo, Hua; Chen, Xiu; Zheng, Zhong; Ruan, Jianghai; Zhang, Dechou (Frontiers in Neurology, 2024)
Objective We aimed to investigate the brain network activity during seizures in patients with untreated juvenile absence epilepsy. Methods Thirty-six juvenile absence epilepsy (JAE) patients with a current high frequency of seizures (more than five seizures during a 2 h EEG examination) were included. Each participant underwent a 2 h video EEG examination. Five 10 s EEG epochs for inter-ictal, pre-ictal, and post-ictal, and five 5 s EEG epochs for ictal states were extracted. Five 10 s resting-state EEG epochs for each participant from a sex- and age-matched healthy control (HC) were enrolled. The topological parameters of the brain networks were calculated using a graph theory analysis. Results Compared with the resting state of the HC group, the global efficiency, local efficiency, and clustering coefficients of the JAE group decreased in the inter-ictal state. In addition, the ictal state showed significantly increased global and local efficiency and clustering coefficients (p < 0.05) and a decreased small-world index and the shortest path length (p < 0.05) in the theta and alpha bands, compared to the remaining states within the JAE group. Moreover, subgroup analysis revealed that those JAE patients with typical 3 Hz discharges had upgraded global efficiency, local efficiency, and clustering coefficients in both delta and beta1 bands, compared to those JAE patients with non-3 Hz discharges during seizures. Conclusion The present study supported the idea that the changes in the EEG brain networks in JAE patients are characterized by decreased global and local efficiency and clustering coefficient in the alpha band. Moreover, the onset of seizures is accompanied by excessively enhanced network efficiency. JAE patients with different ictal discharge patterns may have different functional network oscillations.