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• Clinical Article •
Topological robustness and degree distribution characteristics of structural covariance networks in frontotemporal dementia based on network attack and degree distribution analyses
JIANG Mao  YANG Yue  FAN Lihua  TIAN Xin  CHEN Yuanyuan  QIANG Kun  WEI Wei  ZHOU Feng  ZHANG Zhongxu  ZHENG Yunsong 

Cite this article as: JIANG M, YANG Y, FAN L H, et al. Topological robustness and degree distribution characteristics of structural covariance networks in frontotemporal dementia based on network attack and degree distribution analyses[J]. Chin J Magn Reson Imaging, 2026, 17(10): 37-43, 93. DOI:10.12015/issn.1674-8034.2026.10.005.


[Abstract] Objective Using structural covariance networks (SCN) based on gray matter volume as the metric, and employing network attack analysis in conjunction with degree distribution analysis, we systematically investigated the topological robustness and degree distribution characteristics of brain SCN in patients with frontotemporal dementia (FTD).Materials and Methods All data analyzed were obtained from the Neuroimaging in Frontotemporal Dementia (NIFD) database, which included 92 patients with FTD and 89 age- and sex-matched healthy controls (HC). Clinical information and T1-weighted magnetic resonance images were collected for all participants. Image preprocessing was performed using the SPM8 software package, and a SCN was constructed based on the automated anatomical labeling (AAL) atlas. The topological robustness of the networks under targeted and random attack strategies was assessed using the graph analysis toolbox (GAT), and degree distribution fitting was performed.Results In terms of cognitive function assessment, the FTD and HC groups showed statistically significant differences in scores on the Mini-Mental State Examination (Z = -9.470, P < 0.001) and the Clinical Dementia Rating scale(Z = 12.541, P < 0.001), whereas no statistically significant differences were found in baseline variables of age (Z = 1.609, P = 0.108), sex distribution(χ2 = 0.533, P = 0.465), and total intracranial volume(Z = -0.297, P = 0.767). Under targeted attack, the difference in the area under the curve (AUC) between groups was not statistically significant (P = 0.148). Point-by-point analysis revealed that only at certain stages was the network relative size in the FTD group lower than that in the HC group (uncorrected P < 0.05; after false discovery rate correction, q > 0.05). Under the random attack scenario, the difference in AUC between groups similarly did not reach statistical significance (P = 0.306). Finally, degree distribution fitting based on the group-level aggregate network yielded the following parameters: for the HC group, a = 1.251 7, b = 11.560 0 (r2 = 0.991 1); for the FTD group, a = 1.198 0, b = 17.038 1 (r2 = 0.967 8), where a is the power-law exponent and b is the truncation value. The differences in parameters between the two groups are presented descriptively only, as no between-group statistical inference was performed.Conclusion In this study, we attempted to integrate network attack and degree distribution analyses for the investigation of SCN in FTD. At the group-level aggregated network level, our exploratory analysis revealed a trend of the degree distribution shifting toward a higher truncation degree, suggesting a potential enhancement of topological heterogeneity, whereas overall network robustness showed no significant alterations. these preliminary observations are mainly valuable in demonstrating a multi-dimensional analytical strategy, definitive conclusions warrant further validation in larger cohorts and at the individual network level.
[Keywords] frontotemporal dementia；gray matter volume；magnetic resonance imaging；morphological covariance；graph theory；structural covariance network；degree distribution；network robustness

JIANG Mao1   YANG Yue1   FAN Lihua2   TIAN Xin2   CHEN Yuanyuan2   QIANG Kun2   WEI Wei2   ZHOU Feng3   ZHANG Zhongxu2   ZHENG Yunsong1, 2*  

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

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

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

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

Conflicts of interest   None.

Received  2026-04-29
Accepted  2026-09-18
DOI: 10.12015/issn.1674-8034.2026.10.005
Cite this article as: JIANG M, YANG Y, FAN L H, et al. Topological robustness and degree distribution characteristics of structural covariance networks in frontotemporal dementia based on network attack and degree distribution analyses[J]. Chin J Magn Reson Imaging, 2026, 17(10): 37-43, 93. DOI:10.12015/issn.1674-8034.2026.10.005.

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