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

Jinhui Wang,Xuhong Liao,Alan Evans,Yong He,Xindi Wang,Mingrui Xia

doi:10.3389/fnhum.2015.00386

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.1

Highlights

The human brain operates as an interconnected network that responds to various inputs from different sensory systems in real time
After applying the GRaph thEoreTical Network Analysis (GRETNA) to a publicly released resting-state functional MRI (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
Given the fact that: (i) the R-fMRI data were mainly used to illustrate the usage of GRETNA; (ii) the analyzed network properties have been frequently studied under both healthy and pathological conditions (For relevant reviews, see Bullmore and Sporns, 2009; He and Evans, 2010; Stam, 2014); and (iii) our findings were largely comparable with previous studies and were qualitatively independent of the brain parcellation schemes used in the current study; we only took Power-264 as an example to present our findings since this parcellation provided the highest spatial resolution among the 6 atlases used

Summary

Introduction

The human brain operates as an interconnected network that responds to various inputs from different sensory systems in real time. Recent advances in the human connectomics have shown that human brain networks can be non-invasively obtained from a variety of neurophysiological and neuroimaging techniques, such as electroencephalography/magnetoencephalography (EEG/MEG), functional near infrared spectroscopy (fNIRS), structural MRI, diffusion MRI and functional MRI. Functional brain networks can be derived by estimating interregional statistical dependences in the BOLD signal from functional MRI (Biswal et al, 1995; Salvador et al, 2005), regional cerebral blood flow from arterial spin labeling (Liang et al, 2014), oxygenated/deoxygenated hemoglobin concentrations from functional near-infrared spectroscopy (fNIRS; Niu et al, 2012) or electrophysiological signals from EEG/MEG (Stam, 2004; Stam et al, 2007). Nodes typically represent structurally, functionally or randomly defined regions of interest (ROIs), and edges represent inter-nodal structural or functional connectivity that can be derived from the above-mentioned data modalities

Methods

Results

Conclusion