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Molecular Imaging and Biology

, Volume 20, Issue 5, pp 789–797 | Cite as

Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats

  • Hongkai Wang
  • Ziyu Tan
  • Qiang Zheng
  • Jing Yu
Research Article
  • 146 Downloads

Abstract

Purpose

Recent researches have demonstrated the value of using 2-deoxy-2-[18F]fluoro-d-glucose ([18F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom.

Procedures

For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups.

Results

We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism.

Conclusion

Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [18F]FDG-PET images and facilitates future study on human subjects.

Key words

PET Hypothyroidism Brain networks Graph theory Small-world 

Notes

Funding Information

Our study was funded by the Nature Science Foundation of Liaoning Province of China (No. 201602243), the general program of the National Natural Science Fund of China (No. 61571076), the youth program of the National Natural Science Fund of China (No. 81401475), the general program of Liaoning Science & Technology Project (No. 2015020040), the cultivating program of the Major National Natural Science Fund of China (No. 91546123), the National Key Research and Development Program (Nos. 2016YFC0103101, 2016YFC0103102, 2016YFC0106402, 2016YFC0106403), the Science and Technology Star Project Fund of Dalian City (No. 2016RQ019), the Fundamental Research Funds for the Central Universities (Nos. DUT14RC(3)066 and DUT16RC(3)099), and the Xinghai Scholar Cultivating Funding of Dalian University of Technology (No. DUT15LN02).

Compliance with Ethical Standards

Ethical Approval

All applicable international, national, and/or institutional guidelines for the care and use of animals in this study were followed, and no human experiment is involved in this study.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© World Molecular Imaging Society 2018
corrected publication July/2018

Authors and Affiliations

  1. 1.Nuclear Medicine Division, Second Affiliated HospitalDalian Medical UniversityDalianChina
  2. 2.Department of Biomedical EngineeringDalian University of TechnologyDalianChina

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