Functional MRI observation of the aging selective degradation mode of large-scale brain functional networks
10.3760/cma.j.issn.0254-9026.2016.04.002
- VernacularTitle:功能磁共振观察老年人大尺度脑功能网络选择性的退化模式
- Author:
Jingtao WU
;
Wenxin CHEN
;
Hongying ZHANG
;
Tongtong TIAN
;
Haishan YANG
- Publication Type:Journal Article
- Keywords:
Aging;
Brain;
Neve net;
Magnetic resonance imaging
- From:
Chinese Journal of Geriatrics
2016;35(4):347-351
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the degradation characteristics of the large-scale brain functional networks during aging by functional magnetic resonance imaging measurement and explore its intrinsic mechanism.Methods 40 healthy subjects including 20 elderly persons [mean aged(72.4 ±4.6)years] and 18 young persons [mean aged(23.9± 1.8) years] were enrolled in this study.All subjects underwent functional MRI scanning at blood oxygenation level-dependent contrast resting state.Four canonical resting-state networks,including the default mode network (DMN),dorsal attention network (DAN),executive control network (ECN),salience network,and visual network,were extracted by the seed zone and double regression methods.The functional connectivities in these canonical networks were compared between the young and elderly persons.Results Compared with young persons,the elderly showed the distinct and disruptive alterations in the large-scale aging-related resting brain networks.The impairment of ECN was the most serious,followed by the impairment of DAN.The salience networks and DMN showed relatively limited functional connectivity disruption.The networks associated to higher-order brain functions were impaired,while the visual network,which served as a network related to low-order brain functions,had no significant change.Conclusions The aged brain in healthy subjects is characterized by organized change in networks,and the selective impairments of large-scale brain networks were more significant in the networks associated to higher-order brain functions as compared with the networks related to low-order brain functions.
