Abstract

Graph 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 24^th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory OR graph analysis) AND (brain tumor OR brain tumour OR brain neoplasm) AND (MRI OR EEG OR MEG). 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 alterations 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 hub regions, and with factors such as faster tumor growth. The use of graph theory to study brain tumor patients is in its infancy, though some conclusions can be drawn. Future studies should focus on treatment factors, changes over time, and correlations with functional outcomes to better identify those in need of early intervention.

中文翻译：

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脑肿瘤患者脑网络特征的图论分析：系统评价

摘要

图论是数学的一个分支，它允许对复杂网络进行表征，并且近年来在网络神经科学中迅速普及。研究人员已经开始使用图论来描述脑肿瘤患者的大脑网络，以揭示被破坏的网络。本系统综述总结了当前关于脑肿瘤人群磁共振成像数据图论分析的文献，特别关注治疗效果和其他临床因素。收录的论文发表于 2020 年 6 月 24^{日。使用搜索词（}图论或图分析）和（脑肿瘤）在 Pubmed、PsycInfo 和 Web of Science 上进行搜索或脑肿瘤或脑肿瘤）和（MRI或EEG或MEG）。如果研究符合以下条件，则符合纳入条件：评估患有原发性脑肿瘤的参与者，对结构或功能 MRI 数据、MEG 或 EEG 使用图论分析，使用英语，并且是一项实证研究。十七篇论文符合纳入标准。结果表明，脑肿瘤患者经常发现网络属性的改变，尽管差异的方向不一致，很少有研究报告影响大小。最一致的发现表明网络隔离增加。变化最突出的是更密集的治疗、中心区域以及肿瘤生长更快等因素。使用图论研究脑肿瘤患者尚处于起步阶段，尽管可以得出一些结论。未来的研究应侧重于治疗因素、随时间的变化、