The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks

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.

Test-retest reliability of modular-relevant analysis in brain functional network

“…Regarding regional-level module-derived network metrics, we found both within-modular degree and participation coefficient from brain functional networks were less reliable than those from structural networks. Comparing to moderate-to-good reliability from rs-fMRI in present work, a recent reliability assessment in structural brain network from diffusion tensor imaging (DTI) using the similar approach reported good TRT reliability in within-modular degree and excellent reliability in participation coefficient on AAL atlas ( Dimitriadis et al, 2021 ). This finding proves again that structural connectivity (SC) from DTI is more robust than FC from rs-fMRI as previous studies announced good reliability in SC of averaged ICC = 0.62 ( Buchanan et al, 2014 ) versus poor reliability in FC of averaged ICC = 0.29 ( Noble et al, 2019 ).…”

“…Lastly , using rs-fMRI, we examined the reliability of modular-relevant analysis in functional brain networks. Previous studies have performed similar analyses of structural brain networks using DTI data ( Dimitriadis et al, 2021 ). A systematic reliability evaluation using multimodal data from the same population is warranted to gain a deeper understanding of the structural and functional architecture of the human brain.…”

The impact of genetic risk for Alzheimer’s disease on the structural brain networks of young adults

“…Compared to the FA-weighted networks, using NS-weighted networks resulted in more statistically significant relationships between the PRS and structural network metrics, such as the graph theoretical metrics we employed and the connectivity strength between the rich-club, and feeder connections. Even though both the NS and the FA are routinely used to assign significance to the edges of structural networks, it has been argued (Huang and Ding, 2016) and proven experimentally (Messaritaki et al, 2021) that the NS is more relevant from a functional perspective to the network organization of the human brain compared to the FA. This may be contributing to the increased sensitivity of the NS in the differences observed in our study.…”

“…We generated two connectivity matrices for each participant, one in which the edges are weighted by the number of streamlines (NS) and one in which they are weighted by the mean fractional anisotropy (FA) of the diffusion tensor along the streamlines of the tracts. Both these metrics have been shown to result in measures of connectivity that exhibit heritability ( Arnatkeviciute et al, 2021 ), repeatability ( Yuan et al, 2018 ; Messaritaki et al, 2019 ; Roine et al, 2019 ; Dimitriadis et al, 2021 ), and functional relevance ( Honey et al, 2009 ; Goni et al, 2014 ; Messaritaki et al, 2021 ). To reduce the possible number of false connections, structural connections reconstructed with 5 or fewer streamlines were discarded from the analysis.…”

The impact of genetic risk for Alzheimer’s disease on the structural brain networks of young adults

“…We generated two connectivity matrices for each participant, one in which the edges are weighted by the number of streamlines (NS) and one in which they are weighted by the mean fractional anisotropy (FA) of the diffusion tensor along the streamlines of the tracts. Both these metrics have been shown to result in measures of connectivity that exhibit heritability (Arnatkeviciute et al, 2020), repeatability (Yuan et al, 2018; Roine et al, 2019; Messaritaki et al, 2019; Dimitriadis et al, 2021) and functional relevance (Honey et al, 2009; Goni et al, 2014; Messaritaki et al, 2021). To reduce the possible number of false connections, structural connections reconstructed with 5 or fewer streamlines were discarded from the analysis.…”