Title
Relationship between topological efficiency in white matter structural networks with cerebral oxygen metabolism in young adults.
Abstract
The relationship between the topological characteristics of the white matter (WM) network have been shown to be related to neural development, intelligence, and various diseases; however, few studies have been conducted to explore the relationship between topological characteristics of the WM network and cerebral metabolism. In a recent study we investigated the relationship between WM network topological and metabolic metrics of the cerebral parenchyma in healthy volunteers using the newly developed T2-relaxation-under-spin-tagging (TRUST) magnetic resonance imaging technique and graph theory approaches. Ninety-six healthy adults (25.5 ± 1.8 years of age) were recruited as volunteers in the current study. The cerebral metabolic rate of oxygen (CMRO2), oxygen extraction fraction, and the global topological metrics of the WM network (global efficiency [Eglob], local efficiency, and small-worldliness) were assessed. A stepwise multiple linear regression model was estimated. CMRO2 was entered as the dependent variable. The topological and demographic parameters (age, gender, FIQ, SBP, gray matter volume, and WM volume) were entered as independent variables in the model. The final performing models were comprised of predictors of Eglob, FIQ, and age (adjusted R2 values were 0.489 [L-AAL] and 0.424 [H-1024]). Our study initially revealed a relationship between Eglob and cerebral oxygen metabolism in healthy young adults.
Year
DOI
Venue
2019
10.1016/j.neuroimage.2019.06.013
NeuroImage
Keywords
Field
DocType
Cerebral metabolic rate of oxygen,TRUST MRI,White matter network,Diffusion tensor imaging,Topological metrics,Global efficiency
Topology,White matter,Psychology,Young adult,Oxygen extraction,Multiple linear regression model,Magnetic resonance imaging
Journal
Volume
ISSN
Citations 
199
1053-8119
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Bing Yu100.34
Mingzhu Huang200.34
Xu Zhang300.34
Miao Peng400.34
Yang Hou510.70
Qiyong Guo6225.31