The Impact of Graph Construction Scheme and Community Detection Algorithm on the Repeatability of Community and Hub Identification in Structural Brain Networks

Abstract

The human brain is a complex network of volumes of tissue (nodes) that are interconnected by white matter tracts (edges). It can be represented as a graph to allow us to use graph theory to gain insight into normal human development and brain disorders. Most graph theoretical metrics measure either whole-network (global) or node-specific (local) properties of the network. However, a critical question in network neuroscience is how nodes cluster together to form communities, each of which possibly plays a specific functional role. Community partition analysis allows us to investigate the mesoscale organization of the brain. Various algorithms have been proposed in the literature, that allow the identification of such communities, with each algorithm resulting in different communities for the network. Those communities also depend on the method used to weigh the edges of the graphs representing the brain networks. In this work, we use the test-retest data from the Human Connectome Project to compare 32 such community detection algorithms, each paired with 7 graph construction schemes, and assess the reproducibility of the resulting community partitions.The reproducibility of community partition depended heavily on both the graph construction scheme and the community detection algorithm. Hard community detection algorithms, via which each node is assigned to only one community, outperformed soft ones, via which each node can be a part of more than one community. The best reproducibility was observed for the graph construction scheme that combines 9 white matter tract metrics paired with the greedy stability optimization algorithm, with either discrete or continuous Markovian chain. This graph-construction scheme / community detection algorithm pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs were better reproduced than provincial hubs.

Dementia Risk Factors Modify Hubs but Leave Other Connectivity Measures Unchanged in Asymptomatic Individuals: A Graph Theoretical Analysis

“…In addition, they were shown to have very good discrimination capability in a binary classification problem (Dimitriadis et al, 2017b ), and to exhibit good scan/rescan reliability (Messaritaki et al, 2019a , b ). A recent study demonstrated that community partitions and provincial hubs are highly reproducible in a test/retest study when structural brain networks were constructed with the integrated approach (Dimitriadis et al, 2020 ). For those reasons, we created integrated weighted brain networks instead of pursuing a single-metric structural connectivity matrix.…”

“…The above-mentioned metrics are chosen because they could reflect the signal transport and integration abilities of the structural connectome. However, it is not clear yet to what extent they achieve that (Messaritaki et al, 2020). Additionally, the strength of the structural connectivity between brain areas depends on the metric used to weight the network edges.…”

“…Our findings would benefit from replication in a larger sample due to the fragmentation of the initial sample into subgroups with the different risk profiles. It would also be beneficial for structural network analyses to include measures which are believed to play a more important role in the functional performance of the brain, such as myelination of the white matter tracts (Messaritaki et al, 2020) and axonal diameter. We finally note that the thresholding of structural connectivity matrices derived from tractography is still issue of debate.…”

Dementia risk factors modify hubs but leave other connectivity measures unchanged in asymptomatic individuals: a graph theoretical analysis

“…In addition, they were shown to have very good discrimination capability in a binary classification problem (Dimitriadis, Drakesmith, et al, 2017), and to exhibit good scan-rescan reliability (Messaritaki et al, 2019b, 2019a). A recent study demonstrated that community partitions and provincial hubs are highly reproducible in a test-retest study when structural brain networks were constructed with the integrated approach (Dimitriadis et al, 2020). For those reasons we created integrated weighted brain networks instead of pursuing a single-metric structural connectivity matrix.…”