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Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs

A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Sev...

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Autores principales: Hinne, Max, Ekman, Matthias, Janssen, Ronald J., Heskes, Tom, van Gerven, Marcel A. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311978/
https://www.ncbi.nlm.nih.gov/pubmed/25635390
http://dx.doi.org/10.1371/journal.pone.0117179
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author Hinne, Max
Ekman, Matthias
Janssen, Ronald J.
Heskes, Tom
van Gerven, Marcel A. J.
author_facet Hinne, Max
Ekman, Matthias
Janssen, Ronald J.
Heskes, Tom
van Gerven, Marcel A. J.
author_sort Hinne, Max
collection PubMed
description A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the ‘rich club’; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.
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spelling pubmed-43119782015-02-13 Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs Hinne, Max Ekman, Matthias Janssen, Ronald J. Heskes, Tom van Gerven, Marcel A. J. PLoS One Research Article A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the ‘rich club’; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures. Public Library of Science 2015-01-30 /pmc/articles/PMC4311978/ /pubmed/25635390 http://dx.doi.org/10.1371/journal.pone.0117179 Text en © 2015 Hinne et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Hinne, Max
Ekman, Matthias
Janssen, Ronald J.
Heskes, Tom
van Gerven, Marcel A. J.
Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs
title Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs
title_full Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs
title_fullStr Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs
title_full_unstemmed Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs
title_short Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs
title_sort probabilistic clustering of the human connectome identifies communities and hubs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4311978/
https://www.ncbi.nlm.nih.gov/pubmed/25635390
http://dx.doi.org/10.1371/journal.pone.0117179
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