Journal
FRONTIERS IN HUMAN NEUROSCIENCE
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnhum.2015.00386
Keywords
network; graph theory; connectome; resting fMRI; small-world; huh
Categories
Funding
- National Science Fund [81225012]
- National Key Basic Research Program of China (973 project) [2014CB846102]
- Natural Science Foundation [81030028, 31221003, 30870667, 81401479]
- Beijing Funding for Training Talents [2012D009012000003]
- Beijing Natural Science Foundation [Z111107067311036, 7102090]
- Zhejiang Provincial Natural Science Foundation of China [LZ13C090001]
- Open Research Fund of Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments [PD11001005002013]
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available