Abstract

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.

Details

Title
Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease
Author
Mårtensson, Gustav 1   VIAFID ORCID Logo  ; Pereira, Joana B 1 ; Mecocci, Patrizia 2 ; Vellas, Bruno 3 ; Tsolaki, Magda 4 ; Kłoszewska, Iwona 5 ; Soininen, Hilkka 6 ; Lovestone, Simon 7 ; Simmons, Andrew 8 ; Volpe, Giovanni 9 ; Westman, Eric 10   VIAFID ORCID Logo 

 Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden 
 Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy 
 INSERM U 558, University of Toulouse, Toulouse, France 
 3rd Department of Neurology, Memory and Dementia Unit, Aristotle University of Thessaloniki, Thessaloniki, Greece 
 Medical University of Lodz, Lodz, Poland 
 Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland; Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland 
 Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK 
 NIHR Biomedical Research Centre for Mental Health, London, UK; NIHR Biomedical Research Unit for Dementia, London, UK; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK 
 Department of Physics, University of Gothenburg, Gothenburg, Sweden 
10  Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK 
Pages
1-15
Publication year
2018
Publication date
Aug 2018
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2082079642
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.