A Bayesian approach to network clustering is applied to probabilistic tractography data, obtained from diffusion-weighted MRI. This reveals that the brain's anatomy may be divided into areas that are densely connected, and into areas that form hub-clusters that connect the densely connected clusters.Provided data consists of probabilistic tractography streamline counts (obtained using FSL 5.0), for 20 healthy participants. More detail is provided in "Probabilistic clustering of the human connectome identifies communities and hubs", PLoS ONE.
Date Submitted: 2014-11-12
Data is provided in the form of a Matlab cell array as well as in the form of comma-separated values. The data is accompanied by a PDF in which the relevant elements of the associated publication are shown, which provide all required detail to work with and interpret this data.
