Computational protocol: Altered Topological Properties of Brain Networks in Social Anxiety Disorder: A Resting-state Functional MRI Study

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Protocol publication

[…] We analysed the global metrics in this study using the following parameters: (1) The “small-world” parameter clustering coefficients included the shortest path length, normalized characteristic path length (λ), normalized clustering coefficient (γ), and small-worldness (σ). The shortest path length is defined as the shortest mean distance from a particular vertex to all other vertices. Thus, a smaller path length represents greater integration. The clustering coefficient is defined as the fraction of a vertex’s neighbours that are neighbours themselves, while a larger clustering coefficient represents greater segregation. The path length and clustering coefficient were normalized by the related mean metrics of the 100 random networks. These random networks had the same number of nodes, edges, and degree distributions as the real brain networks. (2) Network efficiency included the local efficiency of the whole network (Eloc), the global efficiency of the network (Eglob), the nodal global efficiency of the node (nodalEglob), and the nodal local efficiency of the node (nodalEloc). (3) Nodal centrality (the degree number of nodes (nodalDeg)) was the final parameter. These definitions and descriptions of the metrics are listed in and are provided in a reference .All of the network metrics were calculated using the GRaph thEoreTical Network Analysis (GRETNA) toolbox (https://www.nitrc.org/projects/gretna/), and this method of network construction and calculation has been used in previous studies of brain networks. The brain networks were visualized using the BrainNet Viewer. […]

Pipeline specifications

Software tools GRETNA, BrainNet Viewer
Application Connectivity analysis
Organisms Homo sapiens