GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

TLDR

Connectomics can be constructed from various imaging modalities and characterized by graph theory, yet comprehensive toolboxes for network construction, analysis, and statistics remain scarce. We developed GRETNA, a graph theoretical network analysis toolbox, to address this gap. GRETNA is an open‑source, cross‑platform MATLAB package with a GUI that enables global and local topological analyses, parallel computing, flexible manipulation of network construction steps, statistical comparisons of network metrics, and preprocessing and construction for resting‑state fMRI data. Applying GRETNA to a publicly available resting‑state fMRI dataset of 54 healthy adults revealed that human brain functional networks exhibit efficient small‑world, assortative, hierarchical, and modular organization with highly connected hubs, and these results were robust across analytical strategies; the toolbox is freely available on NITRC and is expected to accelerate imaging connectomics.

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.