Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review

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

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 (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 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 (pθ = 0.048) and decrease in WB node degree (pα = 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.

Fiber Density and Structural Brain Connectome in Glioblastoma Are Correlated With Glioma Cell Infiltration

BACKGROUND

Glioblastoma (GBM) preferred to infiltrate into white matter (WM) beyond the recognizable tumor margin.

OBJECTIVE

To investigate whether fiber density (FD) and structural brain connectome can provide meaningful information about WM destruction and glioma cell infiltration.

METHODS

GBM cases were collected based on inclusion criteria, and baseline information and preoperative MRI results were obtained. GBM lesions were automatically segmented into necrosis, contrast-enhanced tumor, and edema areas. We obtained the FD map to compute the FD and lnFD values in each subarea and reconstructed the structural brain connectome to obtain the topological metrics in each subarea. We also divided the edema area into a nonenhanced tumor (NET) area and a normal WM area based on the contralesional lnFD value in the edema area, and computed the NET ratio.

RESULTS

Twenty-five GBM cases were included in this retrospective study. The FD/lnFD value and topological metrics (aCp, aLp, aEg, aEloc, and ar) were significantly correlated with GBM subareas, which represented the extent of WM destruction and glioma cell infiltration. The FD/lnFD values and topological parameters were correlated with the NET ratio. In particular, the lnFD value in the edema area was correlated with the NET ratio (coefficient, 0.92). Therefore, a larger lnFD value indicates more severe glioma infiltration in the edema area and suggests an extended resection for better clinical outcomes.

CONCLUSION

The FD and structural brain connectome in this study provide a new insight into glioma infiltration and a different consideration of their clinical application in neuro-oncology.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.