Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study

Abstract

Background: It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking.

Objectives: We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy.

Methods: Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts.

Results: We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p_1-30Hz = 0.002, p_γ = 0.002, p_β = 0.002, p_α = 0.002, p_θ = 0.024, and p_δ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p_1-30Hz = 0.031, p_δ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (p_θ = 0.048) and decrease in WB node degree (p_α = 0.039) in PSEs versus PNSEs at the uncorrected level.

Conclusion: Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.

Keywords: brain metastasis; brain tumor; epilepsy; magnetoencephalography; network topology; resting-state.

Figure 1.

Summary of data collection and analysis. Patients with glial brain tumors and BMs were consecutively included and matched with healthy controls. Presence of structural epilepsy was recorded. MEG examination was performed several days preoperatively, whole brain connectivity was analyzed, and resultant analysis of network topological parameters (node degree, average shortest path length, local clustering coefficient) was performed with Fieldtrip, Brain Connectivity MATLAB toolboxes,42 and in-house built scripts. BMs, brain metastases; MEG, magnetoencephalography.

Figure 2.

T-test or Mann–Whitney U-test comparison (depending on normality distribution) between patients (red) and healthy controls (blue) revealed significantly lower node degree in patients at the corrected level (p_1–30Hz = 0.0015, p_γ = 0.0024, p_β = 0.0015, p_α = 0.0015, p_θ = 0.024, p_δ = 0.0015).

Figure 3.

Representative coherence plot thresholded at 75% of the maximum respective coherence of a patient and its matched control: on the left is patient 2, who was diagnosed of a metastasis in the right parietal lobe, on the right is its matched control depicted.