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• Clinical Article •
Resting-state brain functional network study of type 2 diabetes mellitus
FENG Mengmeng  DAI Hui  KE Jun  SU Yunyan  LI Yonggang  HU Chunhong 

Cite this article as: Feng MM, Dai H, Ke J, et al. Resting-state brain functional network study of type 2 diabetes mellitus. Chin J Magn Reson Imaging, 2020, 11(1): 1-5. DOI:10.12015/issn.1674-8034.2020.01.001.


[Abstract] Objective: To utilize resting-state fMRI and network analysis techniques to explore brain functional network changes in type 2 diabetes patients.Materials and Methods: Twenty-four patients with type 2 diabetes and twenty-six healthy volunteers were scanned with 3.0 T MRI. The resting-state fMRI data was collected. The brain area of each participant was divided into 90 regions by an anatomical automatic labeling (AAL) template. Region was defined as a node in a complex network. The time series for each node was extracted and Pearson correlation coefficients determined among the time series of the brain nodes. Undirected networks of patients and controls were established using the same thresholds. Network parameters such as global efficiency, clustering coefficients, node degree distributions, and module organization were calculated.Results: The global efficiency and average clustering coefficients of patients were less than those of the controls for different thresholds (P<0.05). There were a fewer number of nodes with a degree exceeding 8.5 in patient group than with the controls. The number of modules in the functional brain network of the patients was greater than that of the controls, the components of each module were altered in the patient group.Conclusions: Decreased global efficiency and clustering coefficients were found in diabetes patients. The number of modules and the components in each module were altered in diabetes patients, suggesting differentiation and reorganization of certain cortical functions related to the disease. Brain network analysis techniques provide a non-invasive way to evaluate the CNS changes of diabetes patients.
[Keywords] diabetes mellitus；resting-state functional magnetic resonance imaging；brain network analysis

FENG Mengmeng Radiology Department, the First Affiliated Hospital of Soochow University, Soochow 215006, China

DAI Hui* Radiology Department, the First Affiliated Hospital of Soochow University, Soochow 215006, China

KE Jun Radiology Department, the First Affiliated Hospital of Soochow University, Soochow 215006, China

SU Yunyan Radiology Department, the First Affiliated Hospital of Soochow University, Soochow 215006, China

LI Yonggang Radiology Department, the First Affiliated Hospital of Soochow University, Soochow 215006, China

HU Chunhong Radiology Department, the First Affiliated Hospital of Soochow University, Soochow 215006, China

*Correspondence to: Dai H, Email: huizi198208@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This paper is supported by the National Natural Science Foundation of China No. 81971573, 81201079 the Key Medical Talents Fund of Jiangsu Province No. QNRC2016709
Received  2019-05-27
Accepted  2019-11-21
DOI: 10.12015/issn.1674-8034.2020.01.001
Cite this article as: Feng MM, Dai H, Ke J, et al. Resting-state brain functional network study of type 2 diabetes mellitus. Chin J Magn Reson Imaging, 2020, 11(1): 1-5. DOI:10.12015/issn.1674-8034.2020.01.001.

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