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Dendrogram processing for whole-brain connectivity-based hierarchical parcellation

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Moreno-Dominguez,  David
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Knösche,  Thomas R.
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Moreno-Dominguez, D., Anwander, A., & Knösche, T. R. (2012). Dendrogram processing for whole-brain connectivity-based hierarchical parcellation. Talk presented at 20th Annual Meeting of the International Society for Magnetic Resonance in Medicine. Melbourne, Australia. 2012-05-05 - 2012-05-11.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-9DB0-4
Abstract
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, intelligent processing steps are implemented in order to ease the information extraction process (successfully enabling better performance of tree-partitioning algorithms) and a method for whole-brain connectivity similarity structure comparison is introduced.