Age-Associated Differences of Modules and Hubs in Brain Functional Networks

Abstract

Healthy aging is usually accompanied by changes in the functional modular organization of the human brain, which may result in the decline of cognition and underlying brain dysfunction. However, the relationship between age-related brain functional modular structure differences and cognition remain debatable. In this study, we investigated the age-associated differences of modules and hubs from young, middle and old age groups, using resting-state fMRI data from a large cross-sectional adulthood sample. We first divided the subjects into three age groups and constructed an individual-level network for each subject. Subsequently, a module-guided group-level network construction method was applied to form a weighted network for each group from which functional modules were detected. The intra- and inter-modular connectivities were observed negatively correlated with age. According to the detected modules, we found the number of connector hubs in the young group was more than middle-age and old group, while the quantity of provincial hubs in middle-age group was discovered more than other two groups. Further ROI-wise analysis shows that different hubs have distinct age-associated trajectories of intra- and inter-modular connections, which suggests the different types of topological role transitions in functional networks across age groups. Our results indicated an inverse association between functional segregation/integration with age, which demonstrated age-associated differences in communication effeciency. This study provides a new perspective and useful information to better understand the normal aging of brain networks.

Early Path Dominance as a Principle for Neurodevelopment

“…In addition, no differences are found in α for individuals diagnosed with diabetes compared to healthy individuals (Figure S11) or for individuals diagnosed with bipolar disorder or depression compared to healthy individuals (Figure S12). While previous studies based on fMRI found modularity to be sensitive to age and disease, α is a coarse modularity proxy that, like the total number of modules across the brain, has been found to be fairly constant with respect to age and disease [33,34]. This result is consistent with our hypothesis that percolation is a necessary condition for brain function, rather than a parameter that tunes performance.…”

Hallucinations in the general population across the adult lifespan: prevalence and psychopathologic significance

“…For one, brain maturation processes from middle age to old age may play a role in the reduced prevalence of hallucinations in older adults. 23,24 For example, there is structural re-organisation of brain networks from middle adulthood to old age 25 that potentially results in weakened functional segregation and integration, 26 which may reduce the prevalence of hallucinations in older adults. 27 From a psychosocial perspective, research suggests that older adulthood is associated with the development of a number of skills that may reduce risk of hallucinations or their psychopathologic effects.…”

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

“…For each category, we selected a variety of widely used module detection methods to generate different network partition results to evaluate the influence of module detection algorithm on reliability. To ensure the quality of network partitions, we only kept top 10% strongest positive connections to ensure the sparsity of FC networks ( Wen et al, 2019 ; Zhang et al, 2021 ). After obtaining individual- and group-level modular structures, we projected them on brain surface for further comparison between different methods and different brain atlases.…”