Reference Manual for GRETNA (v2.0) Page 1
A Graph Theoretical Network Analysis Toolbox
Reference Manual for GRETNA (v2.0)
June 2017
National Key Laboratory of Cognitive Neuroscience and Learning
Beijing Key Laboratory of Brain Imaging and Connectomics
IDG/McGovern Institute for Brain Research
Beijing Normal University, Beijing, China
Reference Manual for GRETNA (v2.0) Page 2
Table of Contents
1. Overview .............................................................................................................................................................. 4
2. License ................................................................................................................................................................. 5
3. Prerequisites ........................................................................................................................................................ 5
4. Installation ........................................................................................................................................................... 6
5. Network Construction.......................................................................................................................................... 8
5.1. R-fMRI Preprocessing ............................................................................................................................... 8
5.1.1. DICOM to NIfTI ........................................................................................................................... 10
5.1.2. Remove First Images .................................................................................................................. 11
5.1.3. Slice Timing ................................................................................................................................ 12
5.1.4. Realign........................................................................................................................................ 13
5.1.5. Normalize ................................................................................................................................... 14
5.1.6. Spatially Smooth ........................................................................................................................ 17
5.1.7. Regress Out Covariates .............................................................................................................. 18
5.1.8. Temporally Detrend ................................................................................................................... 19
5.1.9. Temporally Filter ........................................................................................................................ 20
5.1.10. Scrubbing ................................................................................................................................... 21
5.1.11. Results of R-fMRI Preprocessing ................................................................................................ 22
5.2. Functional Connectivity Matrix Construction ........................................................................................ 25
5.2.1. Static Correlation ....................................................................................................................... 26
5.2.2. Dynamical Correlation ............................................................................................................... 26
5.2.3. Results of Functional Connectivity Matrix Construction ............................................................ 27
6. Network Analysis ............................................................................................................................................... 28
6.1. Global Network Metrics ......................................................................................................................... 31
6.1.1. Small-World ............................................................................................................................... 32
6.1.2. Efficiency .................................................................................................................................... 32
6.1.3. Rich-Club .................................................................................................................................... 32
6.1.4. Assortativity ............................................................................................................................... 33
6.1.5. Synchronization .......................................................................................................................... 33
6.1.6. Hierarchy .................................................................................................................................... 33
6.2. Nodal and Modular Network Metrics .................................................................................................... 33
6.2.1. Clustering Coefficient ................................................................................................................. 34
6.2.2. Shortest Path Length .................................................................................................................. 34
6.2.3. Efficiency .................................................................................................................................... 34
6.2.4. Local Efficiency ........................................................................................................................... 34
6.2.5. Degree Centrality ....................................................................................................................... 34
6.2.6. Betweenness Centrality ............................................................................................................. 35
6.2.7. Community Index ....................................................................................................................... 35
6.2.8. Participant Coefficient ............................................................................................................... 36
6.2.9. Modular Interaction ................................................................................................................... 37
6.3. Results of Network Analysis ................................................................................................................... 37
Reference Manual for GRETNA (v2.0) Page 3
6.3.1. Global Network Metrics ............................................................................................................. 38
6.3.2. Nodal and Modular Network Metrics ........................................................................................ 41
7. Metric Comparison ............................................................................................................................................ 47
7.1. Network and Node ................................................................................................................................. 47
7.1.1. One-Sample t-Test ...................................................................................................................... 49
7.1.2. Two-Sample t-Test ...................................................................................................................... 50
7.1.3. Paired t-Test ............................................................................................................................... 51
7.1.4. One-Way ANCOVA ...................................................................................................................... 52
7.1.5. One-Way ANCOVA (Repeated Measures) .................................................................................. 53
7.1.6. Correlation Analysis ................................................................................................................... 54
7.2. Connection ............................................................................................................................................. 55
7.2.1. Averaged (Functional) ................................................................................................................ 56
7.2.2. Backbone (Structural) ................................................................................................................ 57
7.2.3. One-Sample t-Test ...................................................................................................................... 58
7.2.4. Two-Sample t-Test ...................................................................................................................... 59
7.3. Results of Metric Comparison ................................................................................................................ 60
7.3.1. Network and Node ..................................................................................................................... 60
7.3.2. Connection ................................................................................................................................. 62
8. Metric Plotting ................................................................................................................................................... 64
8.1. Bar .......................................................................................................................................................... 65
8.2. Dot ......................................................................................................................................................... 66
8.3. Violin ...................................................................................................................................................... 67
8.4. Shade ..................................................................................................................................................... 68
9. GANNM .............................................................................................................................................................. 69
Acknowledgements.................................................................................................................................................... 70
Reference ................................................................................................................................................................... 71
Reference Manual for GRETNA (v2.0) Page 4
1. Overview
The GRETNA toolbox has been designed for the graph-theoretical network analysis of fMRI data. It
is a suite of MATLAB functions and MATLAB-based interfaces for conventional fMRI
preprocessing and for the calculation and statistical analysis of the most frequently used network
metrics, such small-world parameters, efficiency, degree, betweenness, assortativity, hierarchy,
synchronization and modularity.
Thank you for using GRETNA (v2.0.0). When using this package in your publicized work, PLEASE
CITE:
Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). GRETNA: a graph theoretical
network analysis toolbox for imaging connectomics. Frontiers in human neuroscience, 9, 386.
Developed by Jinghui Wang and Xindi Wang
National Key Laboratory of Cognitive Neuroscience and Learning,
Beijing Normal University, China
Contact information:
Jinhui Wang: [email protected]
Xindi Wang: [email protected]
Yong He: [email protected]
Copyright © 2017 Dr. Yong He’s Lab, National Key Laboratory of Cognitive Neuroscience and
Learning, Beijing Normal University, Beijing, China.
Reference Manual for GRETNA (v2.0) Page 5
2. License
GRETNA is distributed under the terms of the GNU General Public License as published by the Free
Software Foundation (version 3). The details on ‘copyleft’ can be found at
http://www.gnu.org/copyleft/.
3. Prerequisites
Getting started to run GRETNA on your computer:
• MATLAB: A high-level numerical mathematics environment developed by MathWorks, Inc.
Natick, MA, USA. GRETNA requires MATLAB2010a or later version.
• SPM8/SPM12: SPM is freely available to the (neuro) imaging community andrepresents the
implementation of the theoretical concepts of Statistical Parametric Mapping in a complete
analysis package. Given that the names of certain functions in SPM8/SPM12 are the same as
those in GRETNA or MATLAB, we recommend that you add only the path of the home folder of
SPM8/SPM12 when you use GRETNA.
• MatlabBGL: MatlabBGL is a MATLAB package for working with graphs. It uses the Boost
Graph Library to efficiently implement graph algorithms. GRETNA has included this package in
its distribution. Thus, you do not need to download MatlabBGL again.
• PSOM: The pipeline system for GNU Octave and MATLAB (PSOM) is a lightweight library for
managing complex multi-stage data processing. A pipeline is a collection of jobs, i.e. MATLAB
or Octave codes, with a well identified set of options that use files for inputs and outputs.
GRETNA has included this package in its distribution. Thus, you do not need to download
PSOM again.
