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• Clinical Article •
Graph theory analysis of Alzheimer's disease patients based on gray matter structural covariance network
FAN Lihua  WEI Wei  CHEN Yuanyuan  TIAN Xin  ZHOU Feng  YU Qunwei  ZHENG Yunsong 

Cite this article as: FAN L H, WEI W, CHEN Y Y, et al. Graph theory analysis of Alzheimer's disease patients based on gray matter structural covariance network[J]. Chin J Magn Reson Imaging, 2025, 16(6): 27-33. DOI:10.12015/issn.1674-8034.2025.06.004.


[Abstract] Objective Alzheimer's disease (AD) can alter brain structure, but there is limited research on the topological properties of structural covariance network (SCN) based on gray matter. Therefore, this study used structural magnetic resonance imaging and graph theory analysis to evaluate changes in SCN in AD patients.Materials and Methods This study screened 32 AD patients and 29 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, followed by T1 high-resolution imaging. The structural images were preprocessed using the SPM8 software package, and the gray matter SCN was constructed using the Graph Analysis Toolbox (GAT). Global and local network metrics were calculated and compared using graph theory analysis.Results Compared to the HC group, AD patients showed a decrease in global network metrics, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, small-world properties (Lambda, Sigma, Gamma), edge betweenness, node betweenness, and transitivity. Modularity and global efficiency increased, but the differences were not statistically significant according to permutation tests (P > 0.05). Additionally, at the minimum density, the node degree in the AD group decreased in regions such as the right calcarine fissure, right fusiform gyrus, and right middle temporal gyrus. Node betweenness decreased in the right cerebellum and right supramarginal gyrus. Node betweenness increased in the right calcarine fissure, left orbital inferior frontal gyrus, left medial superior frontal gyrus, and right olfactory cortex. Cp decreased in the right temporal pole of the middle temporal gyrus and increased in the cerebellar vermis. The differences between the two groups were statistically significant (P < 0.05), but after false discovery rate (FDR) correction, the differences were not significant (P > 0.05). The area under the curve (AUC) results of standardized node metrics showed that node degree increased in the left cerebellum and left medial superior frontal gyrus in the AD group. Node betweenness increased in the left cerebellum, left orbital middle frontal gyrus, and left medial superior frontal gyrus, while it decreased in the right cerebellum. Cp increased in the right cerebellum and left orbital middle frontal gyrus, and decreased in the right temporal pole of the middle temporal gyrus and left thalamus. Local efficiency was higher in the right cerebellum and lower in the right temporal pole of the superior temporal gyrus in the AD group compared to the HC group, with statistically significant differences (P < 0.05). The analysis of target-based and random network attacks showed no significant differences in the remaining network metrics (largest component) between the two groups after node attacks (P > 0.05). The AUC results of target-based and random network attacks also showed no significant differences in the remaining network metrics between the two groups (P > 0.05).Conclusions The global and node metrics of SCN in the AD group showed changes, but the remaining network metrics did not significantly change after target-based and random network attacks. These changes in metrics may be related to cognitive impairment in AD patients.
[Keywords] Alzheimer's disease；structural covariance network；graph theory analysis；magnetic resonance imaging

FAN Lihua1   WEI Wei1   CHEN Yuanyuan1   TIAN Xin1   ZHOU Feng2   YU Qunwei3   ZHENG Yunsong1, 3*  

1 Department of Medical Imaging, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang 712000, China

2 Department of Scientific Research, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang 712000, China

3 School of Medical Technology, Shaanxi University of Traditional Chinese Medicine, Xianyang 712046, China

Corresponding author: ZHENG Y S, E-mail: 576753017@qq.com

Conflicts of interest   None.

Received  2025-02-25
Accepted  2025-05-19
DOI: 10.12015/issn.1674-8034.2025.06.004
Cite this article as: FAN L H, WEI W, CHEN Y Y, et al. Graph theory analysis of Alzheimer's disease patients based on gray matter structural covariance network[J]. Chin J Magn Reson Imaging, 2025, 16(6): 27-33. DOI:10.12015/issn.1674-8034.2025.06.004.

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