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

AbstractGraph theory is a branch of mathematics that allows for the characterization of complex networks, and has rapidly grown in popularity in network neuroscience in recent years. Researchers have begun to use graph theory to describe the brain networks of individuals with brain tumors to shed light on disrupted networks.  This systematic review summarizes the current literature on graph theoretical analysis of magnetic resonance imaging data in the brain tumor population with particular attention paid to treatment effects and other clinical factors. Included papers were published through June 24th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory ORgraph analysis) AND (brain tumor ORbrain tumour ORbrain neoplasm) AND (MRI OREEG ORMEG).  Studies were eligible for inclusion if they: evaluated participants with a primary brain tumor, used graph theoretical analyses on structural or functional MRI data, MEG, or EEG, were in English, and were an empirical research study. Seventeen papers met criteria for inclusion. Results suggest al terations in network properties are often found in people with brain tumors, although the directions of differences are inconsistent and few studies reported effect sizes. The most consistent finding suggests increased network segregation. Changes are most prominent with more intense treatment, in h ub regions, and with factors such as faster tumor growth. The use of graph theory to st...
Source: Neuropsychology Review - Category: Neuroscience Source Type: research