Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

Abstract

Background: Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band) connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1), and six months later (t2). At these time points, magnetoencephalography (MEG) was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length.

Results: At the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology.

Conclusions: These results indicate that (pathologically) increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to seizures. Thus, functional connectivity and brain network architecture may prove to be important parameters of tumor-related epilepsy.

Figure 1

Scatterplots of correlations between functional connectivity, network characteristics, and total number of seizures at both t1 and t2. Note. Left column: scatterplots excluding outlying patients (patient 2 in table 1), right column: scatterplots including this patient. (A) correlation between theta band phase lag index (PLI) at t1 without outlying patient, (B) correlation between theta band PLI at t1 with outlying patient, (C) correlation between theta band path length and total number of seizures at t1 without outlying patient, (D) correlation between theta band path length and total number of seizures at t1 with outlying patient, (E) correlation between theta band edge weight correlation and total number of seizures at t1 without outlying patient, (F) correlation between theta band edge weight correlation and total number of seizures at t1 with outlying patient, and (G) correlation between theta band PLI and total number of seizures at t2.

Figure 2

Mean PLI at both time points. Note. PLI = phase lag index.

Figure 3

Mean network characteristics at both time points. Note. C_w/C_ws = weighted normalized clustering coefficient, L_w/L_ws = weighted normalized clustering coefficient, S = small-world index, W_r = edge weight correlation.