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GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. ​

文献信息 ​

DOI10.3389/fnhum.2015.00386
PMID26175682
期刊Frontiers in human neuroscience
影响因子2.7
JCR 分区Q2
发表年份2015
被引次数722
关键词连接组，图论，枢纽，网络，静息态功能性磁共振成像
文献类型Journal Article
ISSN1662-5161
页码386
期号9()
作者Jinhui Wang, Xindi Wang, Mingrui Xia, Xuhong Liao, Alan Evans, Yong He

一句话小结 ​

本研究开发了一个名为GRETNA的开源图论网络分析工具箱，用于成像连接组学的构建与分析，具备多种灵活功能和并行计算能力。在对健康年轻成人的功能性磁共振成像数据进行分析后，发现人脑功能网络呈现出高效的“小世界”特性等结构，这为连接组学研究提供了简便而高效的工具，有助于推动该领域的发展。

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连接组 · 图论 · 枢纽 · 网络 · 静息态功能性磁共振成像

摘要 ​

最近的研究表明，大脑的结构和功能网络（即连接组学）可以通过各种成像技术（例如，脑电图/磁共振脑电图；结构、扩散和功能性磁共振成像）构建，并进一步通过图论进行表征。鉴于网络构建、分析和统计的巨大复杂性，缺乏集成这些功能的工具箱。在此，我们开发了用于成像连接组学的图论网络分析工具箱（GRETNA）。GRETNA包含以下几个关键特性：（i）一个开源的基于Matlab的跨平台（Windows和UNIX操作系统）软件包，配有图形用户界面（GUI）；（ii）允许对全球和局部网络特性的拓扑分析，具备并行计算能力，独立于成像模式和物种；（iii）在网络构建和分析的几个关键步骤中提供灵活的操作，包括网络节点定义、网络连通性处理、网络类型选择和阈值处理方法的选择；（iv）允许对全球、节点和连接网络指标进行统计比较，并评估这些网络指标与临床或行为变量之间的关系；（v）包含基于静息态功能性磁共振成像（R-fMRI）数据的图像预处理和网络构建功能。在将GRETNA应用于54名健康年轻成人的公开发布的R-fMRI数据集后，我们展示了人脑功能网络表现出高效的“小世界”、同类连接、层次化和模块化组织，并且具有高度连接的枢纽，这些发现对不同的分析策略具有稳健性。通过这些努力，我们预计GRETNA将以简单、快速和灵活的方式加速成像连接组学的发展。GRETNA可以在NITRC网站上免费下载。

英文摘要 ​

Recent studies have suggested that the brain's structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.

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主要研究问题 ​

  1. GRETNA工具箱在不同成像技术中的应用效果如何？
  2. GRETNA如何处理不同物种的网络分析需求？
  3. 在使用GRETNA进行网络构建时，选择阈值的方法对结果有什么影响？
  4. GRETNA在临床研究中如何评估网络指标与行为变量之间的关系？
  5. 使用GRETNA进行小世界网络分析时，有哪些常见的挑战和解决方案？

核心洞察 ​

研究背景和目的 ​

近年来，脑的结构和功能网络（即连接组学）在神经科学研究中引起了广泛关注。尽管有多种成像技术（如EEG、MEG、结构MRI、扩散MRI和功能MRI）可以用于构建和分析这些网络，但目前缺乏一个全面、灵活且高效的工具箱来支持复杂的网络构建、分析和统计。因此，本文旨在开发一个名为GRETNA（图论网络分析工具箱）的开源软件包，以促进成像连接组学的研究。

主要方法/材料/实验设计 ​

GRETNA是一个基于Matlab的跨平台工具箱，具备图形用户界面（GUI），支持多种功能模块，具体功能包括：

  • 网络构建：包括R-fMRI数据预处理（去除前几帧、切片时序校正、空间归一化等）和功能连接矩阵的构建。
  • 网络分析：计算网络的全局和节点指标，如聚类系数、特征路径长度、局部效率等。
  • 网络比较：执行统计推断，比较不同组之间的网络特征。

以下是GRETNA的工作流程图（Mermaid代码）：

Mermaid diagram

关键结果和发现 ​

在对54名健康年轻成年人进行的R-fMRI数据分析中，研究发现：

  • 人脑功能网络呈现出高效的小世界、同类连接、层次和模块化结构，具有高度连接的枢纽。
  • 这些结果在不同的分析策略下均表现出稳健性。

主要结论/意义/创新性 ​

GRETNA工具箱的开发为连接组学研究提供了一个完整的、灵活的分析平台。其显著特点包括支持并行计算、全面的网络构建和分析模块，能够快速处理大规模数据集。GRETNA不仅可以提高脑网络研究的效率，还能促进研究人员在不同分析策略下的结果比较和验证。

研究局限性和未来方向 ​

尽管GRETNA在脑网络分析中展现了良好的性能，但仍存在一些局限性：

  • 当前版本仅支持无向网络，未来将增加对有向网络的支持。
  • 统计推断主要基于参数方法，未来可考虑引入非参数方法。
  • 需要进一步扩展对丰富俱乐部结构和网络模式等特征的支持。

未来的研究可以整合独立成分分析和动态网络分析，以更深入地探讨脑网络的功能和拓扑特征。

参考文献 ​

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  3. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. - Urs Braun;Michael M Plichta;Christine Esslinger;Carina Sauer;Leila Haddad;Oliver Grimm;Daniela Mier;Sebastian Mohnke;Andreas Heinz;Susanne Erk;Henrik Walter;Nina Seiferth;Peter Kirsch;Andreas Meyer-Lindenberg - NeuroImage (2012)
  4. Apolipoprotein E ε4 modulates functional brain connectome in Alzheimer's disease. - Jinhui Wang;Xiao Wang;Yi He;Xin Yu;Huali Wang;Yong He - Human brain mapping (2015)
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  6. Synchronization in small-world systems. - Mauricio Barahona;Louis M Pecora - Physical review letters (2002)
  7. Neurophysiological architecture of functional magnetic resonance images of human brain. - Raymond Salvador;John Suckling;Martin R Coleman;John D Pickard;David Menon;Ed Bullmore - Cerebral cortex (New York, N.Y. : 1991) (2005)
  8. Functional connectivity patterns of human magnetoencephalographic recordings: a 'small-world' network? - C J Stam - Neuroscience letters (2004)
  9. Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder. - Qingjiu Cao;Ni Shu;Li An;Peng Wang;Li Sun;Ming-Rui Xia;Jin-Hui Wang;Gao-Lang Gong;Yu-Feng Zang;Yu-Feng Wang;Yong He - The Journal of neuroscience : the official journal of the Society for Neuroscience (2013)
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引用本文的文献 ​

  1. Aberrant Brain Network Efficiency in Parkinson's Disease Patients with Tremor: A Multi-Modality Study. - Delong Zhang;Jinhui Wang;Xian Liu;Jun Chen;Bo Liu - Frontiers in aging neuroscience (2015)
  2. A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults. - Qixiang Lin;Zhengjia Dai;Mingrui Xia;Zaizhu Han;Ruiwang Huang;Gaolang Gong;Chao Liu;Yanchao Bi;Yong He - Scientific data (2015)
  3. Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI. - Xiang-zhen Kong;Zhaoguo Liu;Lijie Huang;Xu Wang;Zetian Yang;Guangfu Zhou;Zonglei Zhen;Jia Liu - PloS one (2015)
  4. Alterations of Functional and Structural Networks in Schizophrenia Patients with Auditory Verbal Hallucinations. - Jiajia Zhu;Chunli Wang;Feng Liu;Wen Qin;Jie Li;Chuanjun Zhuo - Frontiers in human neuroscience (2016)
  5. Single-subject morphological brain networks: connectivity mapping, topological characterization and test-retest reliability. - Hao Wang;Xiaoqing Jin;Ye Zhang;Jinhui Wang - Brain and behavior (2016)
  6. Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain. - Mingrui Xia;Qixiang Lin;Yanchao Bi;Yong He - Frontiers in human neuroscience (2016)
  7. Representing object categories by connections: Evidence from a mutivariate connectivity pattern classification approach. - Xiaosha Wang;Yuxing Fang;Zaixu Cui;Yangwen Xu;Yong He;Qihao Guo;Yanchao Bi - Human brain mapping (2016)
  8. Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients. - Xiangzhe Qiu;Yanjun Zhang;Hongbo Feng;Donglang Jiang - Frontiers in neuroscience (2016)
  9. Altered functional network architecture in orbitofronto-striato-thalamic circuit of unmedicated patients with obsessive-compulsive disorder. - Wi Hoon Jung;Murat Yücel;Je-Yeon Yun;Youngwoo B Yoon;Kang Ik K Cho;Linden Parkes;Sung Nyun Kim;Jun Soo Kwon - Human brain mapping (2017)
  10. Spatial Disassociation of Disrupted Functional Connectivity for the Default Mode Network in Patients with End-Stage Renal Disease. - Xiaofen Ma;Junzhang Tian;Zhanhong Wu;Xiaopeng Zong;Jianwei Dong;Wenfeng Zhan;Yikai Xu;Zibo Li;Guihua Jiang - PloS one (2016)

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