Altered Network Topology in Patients with Primary Brain Tumors After Fractionated Radiotherapy

Abstract

Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, although it can cause adverse effects. Recent data suggest that brain RT is associated with dose-dependent cortical atrophy, which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces were parcellated into 68 regions and correlation matrices were created for patients pre- and post-RT. Significant changes in graph network properties were tested using nonparametric permutation tests. Linear regressions were conducted to measure the association between dose and changes in nodal network connectivity. Increases in transitivity, modularity, and global efficiency (n = 54, p < 0.0001) were all observed in patients post-RT. Decreases in local efficiency (n = 54, p = 0.007) and clustering coefficient (n = 54, p = 0.005) were seen in regions receiving higher RT doses, including the inferior parietal lobule and rostral anterior cingulate. These findings demonstrate alterations in global and local network topology following RT, characterized by increased segregation of brain regions critical to cognition. These pathological network changes may contribute to the late delayed cognitive impairments observed in many patients following brain RT.

Keywords: brain tumor; graph theory; magnetic resonance imaging; radiotherapy; structural network connectivity.

FIG. 1.

Cortical regions from the Desikan–Killiany atlas using FreeSurfer. Color images available online at www.liebertpub.com/brain

FIG. 2.

Graph theory measures are depicted in a rendering of a simple graph with 13 nodes and 21 edges. (A) The network forms two modules (a and b) interconnected by a single hub node. (B) The distance between two nodes (A and B) is the length of the shortest path. Nodes A and B connect passing three links (edges). The inverse of the average of the distances among all node pairs is the graph's global efficiency. (C) The clustering coefficient is depicted for a central node and its six neighbors. These neighbor nodes provide 8 of 14 possible edges and the clustering coefficient of 0.57. (D) Node degree presents the number of edges attached to the given node, depicted for a node with large degree (left) and a node with low degree (right). Color images available online at www.liebertpub.com/brain

FIG. 3.

Pre- to post-RT changes in global network measures. Plots show the differences in transitivity (A), modularity (B), and global efficiency (C) across network densities. Shaded areas represent the upper and lower bounds of each measure across densities. Color images available online at www.liebertpub.com/brain

FIG. 4.

Scatterplots depict the association between pre- to post-RT changes in local efficiency (A) and clustering coefficient (B) as a function of dose in each region. Solid red line shows the regression line, dotted red lines identify the 95% confidence interval, and the dotted blue line defines the mean percentage change. IPL, PC, and RAC were among the regions with the highest decreases (top 5th percentile) in local efficiency and clustering coefficient. IPL, inferior parietal lobule; PC, postcentral gyrus; RAC, rostral anterior cingulate. Color images available online at www.liebertpub.com/brain

FIG. 5.

Network hubs depicted on the cortical surface. Red nodes are those that were identified in the pre-RT analysis that remained as hubs in post-RT analysis. Blue nodes are those that were no longer identified as hubs in the post-RT analysis. IP, inferior parietal; IT, inferior temporal; LO, lateral occipital; SP, superior parietal; LOF, lateral orbito-frontal; PCUN, precuneus. Color images available online at www.liebertpub.com/brain