Open AccessA hierarchical method for whole‐brain connectivity‐based parcellation
Author(s) -
MorenoDominguez David,
Anwander Alfred,
Knösche Thomas R.
Publication year - 2014
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22528
Subject(s) - connectome , computer science , artificial intelligence , hierarchical clustering , pattern recognition (psychology) , tractography , representation (politics) , diffusion mri , cluster analysis , similarity (geometry) , tree (set theory) , voxel , connectomics , dendrogram , brain atlas , tree structure , neuroscience , functional connectivity , psychology , mathematics , magnetic resonance imaging , data structure , law , image (mathematics) , mathematical analysis , political science , radiology , programming language , medicine , politics , population , sociology , demography , genetic diversity
In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps. Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function‐anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole‐brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them. Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole‐brain parcellation. Hum Brain Mapp 35:5000–5025, 2014 . © 2014 Wiley Periodicals, Inc .