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

Jinhui Wang¹, Xindi Wang², Mingrui Xia²

¹State 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.

Frontiers in Human Neuroscience

|July 16, 2015

Summary

Researchers developed GRETNA, a toolbox for brain network analysis using imaging connectomics. This tool simplifies the complex process of constructing and analyzing brain networks, revealing efficient small-world and modular organizations in the human brain.

Keywords:

connectome graph theory hub network resting fMRI small-world

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Area of Science:

Neuroscience
Computational Biology
Medical Imaging

Background:

Brain connectomics involves constructing and analyzing structural and functional brain networks.
Existing toolboxes lack comprehensive functions for network construction, analysis, and statistics.
Graph theory is increasingly used to characterize complex brain networks.

Purpose of the Study:

To develop an open-source toolbox, GRaph thEoreTical Network Analysis (GRETNA), for imaging connectomics.
To provide a user-friendly, flexible, and efficient platform for brain network analysis.
To facilitate the study of brain network properties and their relationship with clinical/behavioral data.

Main Methods:

Development of GRETNA, a Matlab-based, cross-platform toolbox with a GUI.
Implementation of topological analyses for global and local network properties with parallel computing.
Inclusion of flexible network construction options (node definition, connectivity, thresholding).
Integration of statistical comparisons for network metrics and their correlations with variables.
Incorporation of preprocessing and network construction for resting-state fMRI (R-fMRI).

Main Results:

Application of GRETNA to R-fMRI data from 54 healthy adults.
Demonstration of efficient small-world, assortative, hierarchical, and modular organization in human brain functional networks.
Identification of highly connected hubs within brain networks.
Validation of findings across different analytical strategies, indicating robustness.

Conclusions:

GRETNA accelerates imaging connectomics research through an easy, quick, and flexible approach.
The toolbox supports robust analysis of brain network topology and properties.
GRETNA is freely available, promoting wider adoption in neuroscience research.