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Selective Vulnerability Related to Aging in Large-Scale Resting Brain Networks
Authors:Hong-Ying Zhang  Wen-Xin Chen  Yun Jiao  Yao Xu  Xiang-Rong Zhang  Jing-Tao Wu
Affiliation:1. Department of Radiology, Subei People''s Hospital of Jiangsu Province, Yangzhou University, Yangzhou, China.; 2. Jiangsu Key Laboratory of Molecular and Functional Imaging, Southeast University, Nanjing, China.; 3. Department of Neurology, Subei People''s Hospital of Jiangsu Province, Yangzhou University, Yangzhou, China.; 4. Department of Psychiatry, Zhongda Hospital, Southeast University, Nanjing, China.; National Scientific and Technical Research Council (CONICET), Argentina,
Abstract:Normal aging is associated with cognitive decline. Evidence indicates that large-scale brain networks are affected by aging; however, it has not been established whether aging has equivalent effects on specific large-scale networks. In the present study, 40 healthy subjects including 22 older (aged 60–80 years) and 18 younger (aged 22–33 years) adults underwent resting-state functional MRI scanning. Four canonical resting-state networks, including the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN) and salience network, were extracted, and the functional connectivities in these canonical networks were compared between the younger and older groups. We found distinct, disruptive alterations present in the large-scale aging-related resting brain networks: the ECN was affected the most, followed by the DAN. However, the DMN and salience networks showed limited functional connectivity disruption. The visual network served as a control and was similarly preserved in both groups. Our findings suggest that the aged brain is characterized by selective vulnerability in large-scale brain networks. These results could help improve our understanding of the mechanism of degeneration in the aging brain. Additional work is warranted to determine whether selective alterations in the intrinsic networks are related to impairments in behavioral performance.
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