Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes.
10.11817/j.issn.1672-7347.2022.220085
- Author:
Lingling DENG
1
;
Huasheng LIU
2
;
Wen LIU
2
;
Yunjie LIAO
2
;
Qi LIANG
3
;
Wei WANG
2
Author Information
1. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China. 307027521@qq.com.
2. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
3. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China. csuliangqi10@163.com.
- Publication Type:Journal Article
- Keywords:
graph theoretical analysis;
gray matter volume;
prediabetes;
structural covariance network;
topological measures
- MeSH:
Humans;
Gray Matter/diagnostic imaging*;
Prediabetic State;
Magnetic Resonance Imaging;
Cerebral Cortex;
Brain
- From:
Journal of Central South University(Medical Sciences)
2022;47(10):1375-1384
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes.
METHODS:A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters.
RESULTS:The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001).
CONCLUSIONS:The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.
