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dc.contributor.authorMartensson, Gustav
dc.contributor.authorPereira, Joana B
dc.contributor.authorMecocci, Patrizia
dc.contributor.authorVellas, Bruno
dc.contributor.authorTsolaki, Magda
dc.contributor.authorKloszewska, Iwona
dc.contributor.authorSoininen, Hilkka
dc.contributor.authorLovestone, Simon
dc.contributor.authorSimmons, Andrew
dc.contributor.authorVolpe, Giovanni
dc.contributor.authorWestman, Eric
dc.date.accessioned2018-08-24T11:05:59Z
dc.date.available2018-08-24T11:05:59Z
dc.date.issued2018
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/6850
dc.description.abstractGraph 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.
dc.language.isoenglanti
dc.publisherSpringer Nature
dc.relation.ispartofseriesScientific reports
dc.relation.urihttp://dx.doi.org/10.1038/s41598-018-29927-0
dc.rightsCC BY http://creativecommons.org/licenses/by/4.0/
dc.titleStability of graph theoretical measures in structural brain networks in Alzheimer's disease
dc.description.versionpublished version
dc.contributor.departmentSchool of Medicine / Clinical Medicine
uef.solecris.id56598604en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© Authors
dc.relation.doi10.1038/s41598-018-29927-0
dc.description.reviewstatuspeerReviewed
dc.relation.articlenumber11592
dc.relation.volume8
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessOpen access -julkaisukanavassa ilmestynyt julkaisu


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