Abstract
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty‐three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi‐scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.
Highlights
The human brain can be modelled as a network (Bassett & Sporns, 2017) and summarised as a graph
The test–retest time interval is shorter than the expected time over which maturation-induced structural changes can be measured with diffusion MRI
Two-way ANOVA revealed a main effect of network-averaged Agreement index of connector hubs across the four community detection algorithms
Summary
The human brain can be modelled as a network (Bassett & Sporns, 2017) and summarised as a graph. Modularity is a quintessential concept in network neuroscience, wherein neural units are densely connected to one another, forming clusters or modules (Meunier, Lambiotte, & Bullmore, 2010) This is an efficient architecture allowing a complex network to integrate information locally, while maintaining its adaptability to any external stimulus. Networks in nature often show hierarchical, modular organisation (Blondel, Guillaume, Lambiotte, & Lefebvre, 2008; Fortunato, 2010; Fortunato & Castellano, 2012; Lancichinetti & Fortunato, 2009; Lancichinetti, Fortunato, & Kertesz, 2009; Newman & Girvan, 2004 Newman, 2012; Meunier, Lambiotte, Fornito, Karen, & Bullmore, 2009) In the brain, such hierarchical modularity could support segregated neuronal interactions and their integration at the global level. Networks with such structure (Fortunato, 2010) are more complex than those with random structure (Sporns, Tononi, & Edelman, 2000), and have been well demonstrated in functional brain networks (Sporns, 2013; Sporns & Betzel, 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
