Structural Brain Network Changes across the Adult Lifespan

A number of magnetic resonance imaging (MRI) studies have shown age-related alterations in brain structural networks in different age groups. However, the specific age-associated changes in brain structural networks across the adult lifespan is underexplored. In the current study, we performed a mul...

Full description

Bibliographic Details
Main Authors: Ke Liu, Shixiu Yao, Kewei Chen, Jiacai Zhang, Li Yao, Ke Li, Zhen Jin, Xiaojuan Guo
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-08-01
Series:Frontiers in Aging Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/article/10.3389/fnagi.2017.00275/full
Description
Summary:A number of magnetic resonance imaging (MRI) studies have shown age-related alterations in brain structural networks in different age groups. However, the specific age-associated changes in brain structural networks across the adult lifespan is underexplored. In the current study, we performed a multivariate independent component analysis (ICA) to identify structural brain networks based on covariant gray matter volume and then investigated the age-related trajectories of structural networks over the adult lifespan in 536 healthy subjects aged 20–86 years. Twenty independent components (ICs) were extracted in the ICA, and statistical analyses between age and ICA weights revealed 16 age-related ICs across the adult lifespan. Most of the trajectories of ICA weights demonstrated significant linear decline tendencies, and the corresponding structural networks primarily included the anterior and posterior dorsal attention networks, the ventral and posterior default mode networks, the auditory network, five cerebellum networks and the hippocampus-related network with the most significant decreased tendency among all ICs (p of age = 1.11E-77). Only the temporal lobe-related network showed a significant quadratic tendency with age (p of age2 = 5.66E-06). Our findings not only provide insight into the patterns of the age-related changes of structural networks but also provide a foundation for understanding abnormal aging.
ISSN:1663-4365