RT Journal Article
SR Electronic
T1 Evaluating test-retest reliability and sex/age-related effects on temporal clustering coefficient of dynamic functional brain networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.21.465376
DO 10.1101/2021.10.21.465376
A1 Yicheng Long
A1 Chaogan Yan
A1 Zhipeng Wu
A1 Xiaojun Huang
A1 Hengyi Cao
A1 Zhening Liu
A1 Lena Palaniyappan
YR 2021
UL http://biorxiv.org/content/early/2021/10/23/2021.10.21.465376.abstract
AB The multilayer dynamic network model has been proposed as an effective method to understand how the brain functions dynamically. Specially, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of topological stability of dynamic brain networks and shows potential in predicting altered brain functions in both normal and pathological conditions. However, test–retest reliability and demographic-related effects on this measure remain to be evaluated. Using a publicly available dataset from the Human Connectome Project consisting of 337 young healthy adults (157 males/180 females; 22 to 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, in particular within the default-mode and subcortical regions; (3) temporal clustering coefficient of the subcortical subnetwork was negatively correlated with age in young adults. Our findings suggest that temporal clustering coefficient is a reliable and reproducible approach for the identification of individual differences in brain function, and provide evidence for sex and age effects on human brain dynamic connectome.Competing Interest StatementThe authors have declared no competing interest.