High-Density Electroencephalography Detects Spatiotemporal Abnormalities in Brain Networks in Patients With Glioma-Related Epilepsy.

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.