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GRETNA: a graph theoretical network analysis toolbox for imaging connectomics.
Literature Information
| DOI | 10.3389/fnhum.2015.00386 |
|---|---|
| PMID | 26175682 |
| Journal | Frontiers in human neuroscience |
| Impact Factor | 2.7 |
| JCR Quartile | Q2 |
| Publication Year | 2015 |
| Times Cited | 722 |
| Keywords | connectome, graph theory, hub, network, resting fMRI |
| Literature Type | Journal Article |
| ISSN | 1662-5161 |
| Pages | 386 |
| Issue | 9() |
| Authors | Jinhui Wang, Xindi Wang, Mingrui Xia, Xuhong Liao, Alan Evans, Yong He |
TL;DR
The study introduces the GRaph thEoreTical Network Analysis (GRETNA) toolbox, an open-source Matlab-based platform designed for imaging connectomics that facilitates the construction and analysis of brain network properties using various imaging modalities. By applying GRETNA to resting-state functional MRI data from 54 healthy young adults, the researchers demonstrated the efficient organization of human brain functional networks, highlighting its potential to enhance the accessibility and flexibility of connectomics research.
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connectome · graph theory · hub · network · resting fMRI
Abstract
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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Primary Questions Addressed
- How does GRETNA compare to other existing toolboxes for connectomics in terms of features and usability?
- What specific imaging technologies can be integrated with GRETNA for effective network analysis?
- Can GRETNA be applied to datasets from different species, and if so, how does it handle interspecies variations in connectomics?
- What are the implications of the findings from the R-fMRI dataset analyzed with GRETNA for understanding brain disorders?
- How does GRETNA facilitate the statistical comparison of network metrics with clinical or behavioral variables, and what are some examples of such applications?
Key Findings
Research Background and Objectives
The study presents the development of the GRETNA (Graph theoretical Network Analysis) toolbox, designed for comprehensive analysis of brain connectomics using graph theory. With the increasing complexity of brain network studies, there is a need for a robust and flexible toolbox that integrates various functionalities, including preprocessing, network construction, and topological analysis.
Main Methods/Materials/Experimental Design
GRETNA is an open-source, Matlab-based toolbox that operates on both Windows and UNIX platforms. It features a graphical user interface (GUI) and supports parallel computing, enhancing its efficiency for large datasets. The toolbox includes modules for:
Network Construction:
- Preprocessing of resting-state functional MRI (R-fMRI) data (e.g., volume removal, slice timing correction, spatial normalization).
- Calculation of functional connectivity matrices.
- Flexibility in defining network nodes and connectivity types.
Network Analysis:
- Calculation of global and nodal metrics (e.g., clustering coefficient, efficiency).
- Options for binary or weighted networks and various thresholding procedures.
Network Comparison:
- Statistical comparisons of network metrics between groups.
- Assessment of relationships between network properties and clinical or behavioral variables.
The following flowchart outlines the process of using GRETNA:
Key Results and Findings
- Network Construction: GRETNA successfully processed R-fMRI data from 54 healthy young adults, resulting in functional connectivity matrices that exhibit small-world, assortative, hierarchical, and modular organizations.
- Topological Metrics: The analysis revealed significant differences between the constructed brain networks and random networks, characterized by higher clustering coefficients and lower global efficiency.
- Hubs Identification: Key hubs were identified in regions such as the posterior cingulate gyrus and medial prefrontal cortex, consistent across different analytical strategies.
Main Conclusions/Significance/Innovation
GRETNA provides a comprehensive and flexible platform for the analysis of brain connectomics, facilitating the study of brain networks through an integrated approach. Its ability to handle complex datasets with parallel computing makes it a valuable tool for researchers in neuroscience, promoting advancements in understanding brain connectivity and function.
Research Limitations and Future Directions
- Limitations: The current version of GRETNA only supports undirected networks, and some topological attributes are not included (e.g., rich-club architecture).
- Future Directions: Planned enhancements include the integration of independent component analysis, improved methods for directed networks, and the addition of nonparametric statistical testing options.
Summary Table of GRETNA Features
| Feature | GRETNA | Other Toolboxes |
|---|---|---|
| R-fMRI Preprocessing | Yes | Limited |
| Network Construction | Yes | Limited |
| Parallel Computing | Yes | No |
| GUI Interface | Yes | Varies |
| Statistical Analysis | Yes | Varies |
| Flexibility in Network Types | High | Limited |
This structured summary highlights the innovative aspects of GRETNA, emphasizing its potential to significantly impact the field of brain connectomics through enhanced analytical capabilities.
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Literatures Citing This Work
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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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