Evaluating test–retest reliability and sex‐/age‐related effects on temporal clustering coefficient of dynamic functional brain networks

Abstract

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test–retest reliability and demographic‐related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22–37 years old), the present study investigated: (1) the test–retest reliability of temporal clustering coefficient across four repeated resting‐state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex‐ and age‐related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test–retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default‐mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST‐meta‐MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

Temporal Stability of the Dynamic Resting-State Functional Brain Network: a Narrative Review of Current Measures and Clinical Research Progress

“…For dFC studies, there have been also several studies to investigate the possible sex effects on the temporal stability of brain networks. Interestingly, although conflicting results exist [46], we notice many of these studies found that the females' brain networks seem to be more stable over time compared to the males' brain networks [14,43,47]. For instance, one research using a state-clustering algorithm suggested that the females' brain networks switch connectivity states less frequently than the males' and can thus be considered more stable over time [43]; in another study using the temporal clustering coefficient, it was found that the females' brain networks are more stable than the males' and such differences are significant at both the global and regional (particularly within the default-mode and subcortical regions) levels [14].…”

“…Interestingly, although conflicting results exist [46], we notice many of these studies found that the females' brain networks seem to be more stable over time compared to the males' brain networks [14,43,47]. For instance, one research using a state-clustering algorithm suggested that the females' brain networks switch connectivity states less frequently than the males' and can thus be considered more stable over time [43]; in another study using the temporal clustering coefficient, it was found that the females' brain networks are more stable than the males' and such differences are significant at both the global and regional (particularly within the default-mode and subcortical regions) levels [14]. These findings may not only highlight the necessity to control for sex effects in dFC studies, but also provide valuable insight into the sex differences in clinical characteristics of many mental problems and diseases (e.g., females are more likely to be affected by depression) [48][49][50].…”

“…(4) The temporal clustering coefficient [14,17,21,39,40]: this measure is also called "the temporal correlation coefficient"; it is derived from the definition of analogous metric (clustering coefficient) for conventional static networks. The temporal clustering coefficient estimates the tendency of a dynamic brain network to keep stable over time (its "temporal clustering") by quantifying the average topological overlap of the network connections between any two consecutive layers (i.e., neighboring time windows).…”

“…Therefore, it might be better to consider the possible influences of different levels of cognition in clinical studies on dFC, especially when performing direct comparisons between the groups of healthy controls and psychiatric populations. An alternative way to achieve this could be to include education level as a covariate in the analyzes, as done by many researchers [14,17,59,60].…”

“…2 of 17 emerged in recent years [10][11][12]. Several studies have shown that after excluding disturbing factors such as head movement, dFC can still accurately reflect the inherent individual differences between subjects [13,14]. Based on multiple repeated fMRI scans on the same subjects, it was also shown that multiple dFC measures have acceptable test-retest reliability [14,15], which demonstrates the feasibility and reliability of dFC measures.…”