title: GRETNA: a graph theoretical network analysis toolbox for 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.
authors.affiliation: 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.
authors.forename: Jinhui
authors.initials: J
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China.
authors.forename: Xindi
authors.initials: X
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China.
authors.forename: Mingrui
authors.initials: M
authors.lastname: Xia
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China.
authors.forename: Xuhong
authors.initials: X
authors.lastname: Liao
authors.affiliation: McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC, Canada.
authors.forename: Alan
authors.initials: A
authors.lastname: Evans
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC, Canada.
authors.forename: Yong
authors.initials: Y
authors.lastname: He
keywords: connectome; graph theory; hub; network; resting fMRI; small-world