Small-worldness and gender differences of large scale brain metabolic covariance networks in young adults: a FDG PET study of 400 subjects.
BACKGROUND
Many studies have demonstrated the small-worldness of the human brain, and have revealed a sexual dimorphism in brain network properties. However, little is known about the gender effects on the topological organization of the brain metabolic covariance networks.


OBJECTIVE
To investigate the small-worldness and the gender differences in the topological architectures of human brain metabolic networks.


METHODS
FDG-PET data of 400 healthy right-handed subjects (200 women and 200 age-matched men) were involved in the present study. Metabolic networks of each gender were constructed by calculating the covariance of regional cerebral glucose metabolism (rCMglc) across subjects on the basis of AAL parcellation. Gender differences of network and nodal properties were investigated by using the graph theoretical approaches. Moreover, the gender-related difference of rCMglc in each brain region was tested for investigating the relationships between the hub regions and the brain regions showing significant gender-related differences in rCMglc.


RESULTS
We found prominent small-world properties in the domain of metabolic networks in each gender. No significant gender difference in the global characteristics was found. Gender differences of nodal characteristic were observed in a few brain regions. We also found bilateral and lateralized distributions of network hubs in the females and males. Furthermore, we first reported that some hubs of a gender located in the brain regions showing weaker rCMglc in this gender than the other gender.


CONCLUSIONS
The present study demonstrated that small-worldness was existed in metabolic networks, and revealed gender differences of organizational patterns in metabolic network. These results maybe provided insights into the understanding of the metabolic substrates underlying individual differences in cognition and behaviors.
Department of Nuclear Medicine, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, PR China.
Yuxiao
Y
Hu
Department of Medical Imaging, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, PR China.
Qiang
Q
Xu
Department of Medical Imaging, The Second Affiliated Hospital, Soochow University, Suzhou, PR China.
Junkang
J
Shen
Department of Pharmacology, Soochow University, Suzhou, PR China.
Kai
K
Li
Department of Nuclear Medicine, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, PR China.
Hong
H
Zhu
Department of Medical Imaging, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, PR China.
Zhiqiang
Z
Zhang
Department of Medical Imaging, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, PR China cjr.luguangming@vip.163.com zhangzq2001@126.com.
Guangming
G
Lu
D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging* / Q000378:metabolism*; D002681:China; D003198:Computer Simulation; D004292:Dominance, Cerebral / Q000502:physiology*; D005260:Female; D019788:Fluorodeoxyglucose F18 / Q000493:pharmacokinetics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000378:metabolism*; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012737:Sex Factors; D014018:Tissue Distribution; D055815:Young Adult
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
CNS; PET; adults; brain; neural networks