GRETNA is a graph-theoretical network analysis toolbox designed for imaging connectomics, which allows for the comprehensive analysis of brain networks constructed from various imaging technologies such as EEG/MEG, structural MRI, and functional MRI. The toolbox is open-source, Matlab-based, and cross-platform, featuring a graphical user interface (GUI). Key features include: 1. **Topological Analysis**: GRETNA enables the analysis of global and local network properties, including clustering coefficient, characteristic path length, local efficiency, global efficiency, assortativity, modularity, hierarchy, synchronization, and more. 2. **Parallel Computing**: It supports parallel computing for efficient processing of large datasets. 3. **Flexibility**: Users can manipulate network nodes, connectivity processing, network types, and thresholding procedures. 4. **Statistical Comparisons**: GRETNA allows for statistical comparisons of network metrics and assessments of relationships between network properties and clinical or behavioral variables. 5. **Image Preprocessing**: It includes functionality for preprocessing resting-state functional MRI (R-fMRI) data. The toolbox was tested on a publicly available R-fMRI dataset of 54 healthy young adults, demonstrating that human brain functional networks exhibit small-world, assortative, hierarchical, and modular organizations with highly connected hubs. These findings are robust across different analytical strategies. GRETNA is freely available on the NITRC website and is expected to accelerate imaging connectomics research.GRETNA is a graph-theoretical network analysis toolbox designed for imaging connectomics, which allows for the comprehensive analysis of brain networks constructed from various imaging technologies such as EEG/MEG, structural MRI, and functional MRI. The toolbox is open-source, Matlab-based, and cross-platform, featuring a graphical user interface (GUI). Key features include: 1. **Topological Analysis**: GRETNA enables the analysis of global and local network properties, including clustering coefficient, characteristic path length, local efficiency, global efficiency, assortativity, modularity, hierarchy, synchronization, and more. 2. **Parallel Computing**: It supports parallel computing for efficient processing of large datasets. 3. **Flexibility**: Users can manipulate network nodes, connectivity processing, network types, and thresholding procedures. 4. **Statistical Comparisons**: GRETNA allows for statistical comparisons of network metrics and assessments of relationships between network properties and clinical or behavioral variables. 5. **Image Preprocessing**: It includes functionality for preprocessing resting-state functional MRI (R-fMRI) data. The toolbox was tested on a publicly available R-fMRI dataset of 54 healthy young adults, demonstrating that human brain functional networks exhibit small-world, assortative, hierarchical, and modular organizations with highly connected hubs. These findings are robust across different analytical strategies. GRETNA is freely available on the NITRC website and is expected to accelerate imaging connectomics research.