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- W3004630946 abstract "Abstract Brain functional networks are essential for understanding functional connectome. Computing the temporal dependencies between the regions of brain activities of functional magnetic resonance imaging (fMRI) gives us the functional connectivity between the regions. The pairwise connectivities in matrix form correspond to the functional network (fNet), also referred to as a functional connectivity network (FCN). We start with analyzing a correlation matrix, which is an adjacency matrix of the FCN. In this work, we perform a case study of comparison of different analytical approaches in finding node-communities of the brain network. We use five different methods of community detection, out of which two methods are implemented on the network after filtering out the edges with weight below a predetermined threshold. We additionally compute and observe the following characteristics of the outcomes: (i) modularity of the communities, (ii) symmetrical node-partition between the left and right hemispheres of the brain, i.e., hemispheric symmetry , and (iii) hierarchical modular organization . Our contribution is in identifying an appropriate test-bed for comparison of outcomes of approaches using different semantics, such as network science, information theory, multivariate analysis, and data mining." @default.
- W3004630946 created "2020-02-14" @default.
- W3004630946 creator A5046135392 @default.
- W3004630946 creator A5051540251 @default.
- W3004630946 date "2020-02-07" @default.
- W3004630946 modified "2023-09-26" @default.
- W3004630946 title "Comparing Community Detection Methods in Brain Functional Connectivity Networks" @default.
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- W3004630946 doi "https://doi.org/10.1101/2020.02.06.935783" @default.
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