David Moreno-Dominguez1, Alfred Anwander1, Thomas R. Knsche1
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Hierarchical clustering of probabilistic tractograms encodes the information of the connectivity structure at all granularity levels in a hierarchical tree or dendrogram. It might be the key to whole-brain connectivity based parcellation, where the correct number of clusters is unknown and depends on the desired granularity. The interpretation of the resulting dendrogram is not simple, due to outliers and the size of the dataset encoded, among other reasons. In this study a fast, fully hierarchical bottom-up algorithm is presented, and intelligent processing steps are introduced in order to ease the information extraction process, successfully enabling better performance of tree-partitioning algorithms.