Brain parcellation based on information theory

Bonmati Coll, E., Bardera, A. and Boada, I. 2017. Brain parcellation based on information theory. Computer Methods and Programs in Biomedicine. 151, pp. 203-212. https://doi.org/10.1016/j.cmpb.2017.07.012

TitleBrain parcellation based on information theory
TypeJournal article
AuthorsBonmati Coll, E., Bardera, A. and Boada, I.
Abstract

Background and objective
In computational neuroimaging, brain parcellation methods subdivide the brain into individual regions that can be used to build a network to study its structure and function. Using anatomical or functional connectivity, hierarchical clustering methods aim to offer a meaningful parcellation of the brain at each level of granularity. However, some of these methods have been only applied to small regions and strongly depend on the similarity measure used to merge regions. The aim of this work is to present a robust whole-brain hierarchical parcellation that preserves the global structure of the network.

Methods
Brain regions are modeled as a random walk on the connectome. From this model, a Markov process is derived, where the different nodes represent brain regions and in which the structure can be quantified. Functional or anatomical brain regions are clustered by using an agglomerative information bottleneck method that minimizes the overall loss of information of the structure by using mutual information as a similarity measure.

Results
The method is tested with synthetic models, structural and functional human connectomes and is compared with the classic k-means. Results show that the parcellated networks preserve the main properties and are consistent across subjects.

Conclusion
This work provides a new framework to study the human connectome using functional or anatomical connectivity at different levels.

JournalComputer Methods and Programs in Biomedicine
Journal citation151, pp. 203-212
ISSN0169-2607
Year2017
PublisherElsevier
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cmpb.2017.07.012
Publication dates
Published in printNov 2017
Published online31 Aug 2017

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