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Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI
Representing brain morphology as a network has the advantage that the regional morphology of ‘isolated’ structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in ind...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633111/ https://www.ncbi.nlm.nih.gov/pubmed/26536598 http://dx.doi.org/10.1371/journal.pone.0141840 |
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author | Kong, Xiang-zhen Liu, Zhaoguo Huang, Lijie Wang, Xu Yang, Zetian Zhou, Guangfu Zhen, Zonglei Liu, Jia |
author_facet | Kong, Xiang-zhen Liu, Zhaoguo Huang, Lijie Wang, Xu Yang, Zetian Zhou, Guangfu Zhen, Zonglei Liu, Jia |
author_sort | Kong, Xiang-zhen |
collection | PubMed |
description | Representing brain morphology as a network has the advantage that the regional morphology of ‘isolated’ structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics. |
format | Online Article Text |
id | pubmed-4633111 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46331112015-11-13 Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI Kong, Xiang-zhen Liu, Zhaoguo Huang, Lijie Wang, Xu Yang, Zetian Zhou, Guangfu Zhen, Zonglei Liu, Jia PLoS One Research Article Representing brain morphology as a network has the advantage that the regional morphology of ‘isolated’ structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics. Public Library of Science 2015-11-04 /pmc/articles/PMC4633111/ /pubmed/26536598 http://dx.doi.org/10.1371/journal.pone.0141840 Text en © 2015 Kong 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 Kong, Xiang-zhen Liu, Zhaoguo Huang, Lijie Wang, Xu Yang, Zetian Zhou, Guangfu Zhen, Zonglei Liu, Jia Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI |
title | Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI |
title_full | Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI |
title_fullStr | Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI |
title_full_unstemmed | Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI |
title_short | Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI |
title_sort | mapping individual brain networks using statistical similarity in regional morphology from mri |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633111/ https://www.ncbi.nlm.nih.gov/pubmed/26536598 http://dx.doi.org/10.1371/journal.pone.0141840 |
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