TY - JOUR
T1 - Age-Associated Differences of Modules and Hubs in Brain Functional Networks
AU - Zhang, Yinghui
AU - Wang, Yin
AU - Chen, Nan
AU - Guo, Man
AU - Wang, Xiuzhen
AU - Chen, Guangcai
AU - Li, Yongchao
AU - Yang, Lin
AU - Li, Shan
AU - Yao, Zhijun
AU - Hu, Bin
N1 - Publisher Copyright:
© Copyright © 2021 Zhang, Wang, Chen, Guo, Wang, Chen, Li, Yang, Li, Yao and Hu.
PY - 2021/1/18
Y1 - 2021/1/18
N2 - 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.
AB - 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.
KW - age
KW - brain functional network
KW - hub
KW - module
KW - resting-state functional magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85100527497&partnerID=8YFLogxK
U2 - 10.3389/fnagi.2020.607445
DO - 10.3389/fnagi.2020.607445
M3 - Article
AN - SCOPUS:85100527497
SN - 1663-4365
VL - 12
JO - Frontiers in Aging Neuroscience
JF - Frontiers in Aging Neuroscience
M1 - 607445
ER -