| Literature DB >> 33248254 |
Qiuhui Bi1, Wenxiao Wang1, Na Niu2, He Li3, Yezhou Wang1, Weijie Huang1, Kewei Chen4, Kai Xu1, Junying Zhang3, Yaojing Chen1, Dongfeng Wei3, Ruixue Cui5, Ni Shu6, Zhanjun Zhang1.
Abstract
Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.Entities:
Keywords: Brain network; Diffusion MRI; FDG-PET; Glucose metabolism; Graph theory; MRI; Normal aging
Year: 2020 PMID: 33248254 DOI: 10.1016/j.neuroimage.2020.117591
Source DB: PubMed Journal: Neuroimage ISSN: 1053-8119 Impact factor: 6.556