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Protocols

GRETNA specifications

Information


Unique identifier OMICS_10619
Name GRETNA
Alternative name GRaph thEoreTical Network Analysis
Software type Toolkit/Suite
Interface Command line interface, Graphical user interface
Restrictions to use None
Input data A range of continuous threshold values.
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.2.1
Stability Stable
Maintained Yes

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Documentation


Maintainers


  • person_outline Yong He
  • person_outline Alan Evans

Publication for GRaph thEoreTical Network Analysis

GRETNA citations

 (81)
library_books

Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis

2018
PMCID: 5921324
PMID: 29719719
DOI: 10.1186/s40035-018-0115-y

[…] elation matrix was thresholded over a wide range of sparsity (6% -40%), and the properties of the resulting graphs at each threshold value were estimated. Subsequent indicators were calculated in the Gretna (http://www.nitrc.org/projects/gretna/) []. […]

library_books

Study of Resting State Functional Connectivity Networks Using EEG Electrodes Position As Seed

2018
Front Neurosci
PMCID: 5928390
PMID: 29740268
DOI: 10.3389/fnins.2018.00235

[…] f seeds that have correlation coefficient values greater than a given threshold. Figure shows the Pearson's correlation matrix of the 10-20 EEG electrode system seeds. That matrix was analyzed using GRETNA software (Wang et al., ) computing the small-world sigma (Watts and Strogatz, ) using different thresholds (from 0.05 to 0.5 in 0.05 intervals) obtaining small-world sigma greater than 1.0 in a […]

library_books

Aberrant brain functional connectome in patients with obstructive sleep apnea

2018
PMCID: 5912371
PMID: 29713176
DOI: 10.2147/NDT.S161085

[…] In this study, both the global and regional network measures of functional brain networks of patients with OSA and GSs were investigated using the graph theoretical network analysis toolbox (GRETNA) (http://www.nitrc.org/projects/gretna/). The brain functional networks were modeled based on an unweighted, undirected method. Sparsity (Sp) was def […]

library_books

Disrupted Module Efficiency of Structural and Functional Brain Connectomes in Clinically Isolated Syndrome and Multiple Sclerosis

2018
Front Hum Neurosci
PMCID: 5902485
PMID: 29692717
DOI: 10.3389/fnhum.2018.00138

[…] o select a single threshold, individual correlation matrices were thresholded over a consecutive sparsity range of 0.05 < S < 0.40 (interval = 0.025). All the above procedures were performed with the GRETNA () and DPARSF software (). […]

call_split

Brain Structural Covariance Network Topology in Remitted Posttraumatic Stress Disorder

2018
PMCID: 5885936
PMID: 29651256
DOI: 10.3389/fpsyt.2018.00090
call_split See protocol

[…] -derived p-values are ≤0.05 for at least two of four centrality measures. The statistical analyses were conducted using in-house Matlab scripts. The code for permutation testing was modified from the GRETNA toolbox (). […]

library_books

Emotion Regulation and Complex Brain Networks: Association Between Expressive Suppression and Efficiency in the Fronto Parietal Network and Default Mode Network

2018
Front Hum Neurosci
PMCID: 5890121
PMID: 29662443
DOI: 10.3389/fnhum.2018.00070

[…] Graph analyses were conducted using graph theoretical network analysis (GRETNA; Wang et al., ). The present study adopted the 10 predefined networks based on the parcellation of the brain with 264 cortical and subcortical 10-mm diameter […]

Citations

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GRETNA institution(s)
State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
GRETNA funding source(s)
Supported by the National Science Fund for Distinguished Young Scholars (Grant No. 81225012), National Key Basic Research Program of China (973 project, Grant No. 2014CB846102), the Natural Science Foundation (Grant Nos. 81030028, 31221003, 30870667 and 81401479), Beijing Funding for Training Talents (No 2012D009012000003), Beijing Natural Science Foundation (Grant No. Z111107067311036 and 7102090), Zhejiang Provincial Natural Science Foundation of China (No LZ13C090001) and Open Research Fund of Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments (No. PD11001005002013).

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