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Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease
Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer’s disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clust...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072788/ https://www.ncbi.nlm.nih.gov/pubmed/30072774 http://dx.doi.org/10.1038/s41598-018-29927-0 |
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author | Mårtensson, Gustav Pereira, Joana B. Mecocci, Patrizia Vellas, Bruno Tsolaki, Magda Kłoszewska, Iwona Soininen, Hilkka Lovestone, Simon Simmons, Andrew Volpe, Giovanni Westman, Eric |
author_facet | Mårtensson, Gustav Pereira, Joana B. Mecocci, Patrizia Vellas, Bruno Tsolaki, Magda Kłoszewska, Iwona Soininen, Hilkka Lovestone, Simon Simmons, Andrew Volpe, Giovanni Westman, Eric |
author_sort | Mårtensson, Gustav |
collection | PubMed |
description | Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer’s disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious. |
format | Online Article Text |
id | pubmed-6072788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-60727882018-08-07 Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease Mårtensson, Gustav Pereira, Joana B. Mecocci, Patrizia Vellas, Bruno Tsolaki, Magda Kłoszewska, Iwona Soininen, Hilkka Lovestone, Simon Simmons, Andrew Volpe, Giovanni Westman, Eric Sci Rep Article Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer’s disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious. Nature Publishing Group UK 2018-08-02 /pmc/articles/PMC6072788/ /pubmed/30072774 http://dx.doi.org/10.1038/s41598-018-29927-0 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Mårtensson, Gustav Pereira, Joana B. Mecocci, Patrizia Vellas, Bruno Tsolaki, Magda Kłoszewska, Iwona Soininen, Hilkka Lovestone, Simon Simmons, Andrew Volpe, Giovanni Westman, Eric Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease |
title | Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease |
title_full | Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease |
title_fullStr | Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease |
title_full_unstemmed | Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease |
title_short | Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease |
title_sort | stability of graph theoretical measures in structural brain networks in alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072788/ https://www.ncbi.nlm.nih.gov/pubmed/30072774 http://dx.doi.org/10.1038/s41598-018-29927-0 |
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