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 clustering, path length, global efciency and transitivity in a cohort of AD (N=293) and control subjects (N=293). More specifcally, we studied the efect 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 diferences between groups of AD and control subjects. Our results showed that specifc group composition heavily infuenced 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 signifcant diferences, 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, signifcant diferences were only found in the global efciency and transitivity measures when using cortical thickness measures to defne edges. The fndings were consistent across the two atlases, but no diferences were found when using cortical volumes. Our fndings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be referred 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 fndings are spurious.