Abstract
AIMS: The current study aimed to investigate brain network abnormalities in glioma-related epilepsy (gre) patients through high-density electroencephalography (eeg) data analysis. METHODS: The study included 35 patients with newly diagnosed frontal gliomas. All participants underwent 128-channel resting-state EEG recordings before surgery. Afterward, graph theory and microstate analyses were performed, and the resulting metrics were compared between patients with GRE and those without GRE. RESULTS: The network topology analysis demonstrated that the GRE group had a higher clustering coefficient, global efficiency, and local efficiency; a lower characteristic path length; and a higher small-worldness coefficient than the non-GRE group (adjusted p < 0.05 for all). Additionally, the microstate analysis indicated that the GRE group had lower occurrence and global explained variance of microstate E and higher global explained variance of microstate D (adjusted p < 0.05 for all). Moreover, the occurrence of microstate D was significantly negatively correlated with the maximum tumor diameter in the non-GRE group (r = -0.542, p = 0.009). CONCLUSION: The current study revealed specific brain network abnormalities in GRE patients based on graph theory and microstate analyses of resting-state high-density EEG data. These findings can enhance our comprehension of the mechanisms behind GRE and offer potential biomarkers for improving individualized management of glioma patients.