Open AccessAge‐Related Changes in Resting‐State Networks of A Large Sample Size of Healthy Elderly
Author(s) -
Huang ChunChao,
Hsieh WenJin,
Lee PeiLin,
Peng LiNing,
Liu LiKuo,
Lee WeiJu,
Huang JonKway,
Chen LiangKung,
Lin ChingPo
Publication year - 2015
Publication title -
cns neuroscience and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 69
eISSN - 1755-5949
pISSN - 1755-5930
DOI - 10.1111/cns.12396
Subject(s) - default mode network , resting state fmri , functional magnetic resonance imaging , subnetwork , neuroscience , functional connectivity , healthy aging , population , correlation , cognition , psychology , audiology , medicine , computer science , gerontology , computer security , environmental health , geometry , mathematics
Summary Aims Population aging is burdening the society globally, and the evaluation of functional networks is the key toward understanding cognitive changes in normal aging. However, the effect of age on default mode subnetworks has not been documented well, and age‐related changes in many resting‐state networks remain debatable. The purpose of this study was to propose more precise results for these issues using a large sample size. Methods We used group‐level meta‐ ICA analysis and dual regression approach for identifying resting‐state networks from functional magnetic resonance imaging data of 430 healthy elderly participants. Partial correlation was used to observe age‐related correlations within and between resting‐state networks. Results In the default mode network, only the ventral subnetwork negatively correlated with age. Age‐related decrease in functional connectivity was also noted in the auditory, right frontoparietal, sensorimotor, and visual medial networks. Further, some age‐related increases and decreases were observed for between‐network correlations. Conclusion The results of this study suggest that only the ventral default mode subnetwork had age‐related decline in functional connectivity and several reverse patterns of resting‐state networks for network development. Understanding age‐related network changes may provide solutions for the impact of population aging and diagnosis of neurodegenerative diseases.