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Brain parcellation with Bayesian connectomics

Cite as:

Hinne, MSc M (Radboud University Nijmegen) (): Brain parcellation with Bayesian connectomics. DANS. https://doi.org/10.17026/dans-zza-j97d

2014-11-11 Hinne, MSc M (Radboud University Nijmegen) 10.17026/dans-zza-j97d

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.

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