Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease

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.

White Matter Network Alterations in Alzheimer’s Disease Patients

“…Recent studies show that consistent results on graph metrics measured using cortical features and weighted network metrics can be achieved with fewer number of subjects in AD and control groups [38]. Thus, our study is more concerned about the use of graph theoretical analysis technique that generates stable results.…”

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review

“…Additionally, lower clustering coefficient values in SCD indicated a more randomly organized GMN suggesting a rapid decline in overall cognitive performance especially in the language (Verfaillie et al 2018b). Decreased values of path length and clustering coefficient have also been related with a subsequent future impairment in language, which is usually deteriorated in AD, as longitudinal studies have shown (Brier et al 2014;Tijms et al 2014;Mårtensson et al 2018).…”

Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis

“…Here, we observed reduced local and global efficiency and degree centrality. A decreased local (Zhou and Lui, 2013) and global (Martensson et al, 2018) efficiency is a common characteristic of patients with AD. In addition, Aβ mouse model of amyloidosis (5XFAD mice) also showed a decreased network efficiency (Kesler et al, 2018).…”