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• Clinical Article •
Research on MHE brain network characteristics based on graph attack and degree distribution
JIANG Mao  YANG Yue  MA Xiaotong  LI Wenbo  CHEN Yuanyuan  FAN Lihua  ZHOU Feng  ZHENG Yunsong 

DOI:10.12015/issn.1674-8034.2026.05.009.


[Abstract] Objective To construct structural covariance networks (SCN) based on gray matter volume and investigate the differences in network robustness and the distribution properties of hub nodes in patients with minimal hepatic encephalopathy (MHE).Materials and Methods This study collected clinical and T1 high-resolution imaging data from 20 patients with MHE and 25 education-matched healthy control (HC) at the Affiliated Hospital of Shaanxi University of Chinese Medicine between July 2024 and July 2025. Using VBM8 within the SPM8 toolbox to preprocess images, gray matter morphological measures were extracted for both the HC group and the MHE group using the CAT12 toolbox. Based on the automated anatomical labeling (AAL) atlas, SCNs were constructed using gray matter volume. The Graph Analysis Toolbox (GAT) was then employed to analyze network resilience against targeted and random attacks, assessing network robustness. The distribution characteristics of hub nodes were examined through degree distribution analysis.Results Statistically significant differences were observed in age, NCT-A, NCT-B, LTT, SDT, and DST (P < 0.05), whereas no significant differences were found in height, gender, years of education, or total intracranial volume (TIV) (P > 0.05). In the SCNs constructed from gray matter volume, when using betweenness centrality as the target for targeted attacks and the relative size of the largest connected component as the robustness metric, no statistically significant difference was found between the HC and MHE groups (P > 0.05). However, under random attacks, the HC group demonstrated lower robustness compared to the MHE group (P < 0.05). Degree distribution analysis revealed that compared to HCs, MHE patients exhibited a decreased cut-off parameter b (17.26 vs. 32.80) and an increased power-law exponent a (1.20 vs. 1.11) in their brain networks, with goodness-of-fit R² values all exceeding 0.94.Conclusions Graph attack analysis suggests that MHE brains undergo network reorganization, forming a complex and selective adaptive pattern. The degree distribution analysis, showing a significant decrease in the b value and an increase in the a value, indicates a topological simplification of the brain network in MHE patients. This represents a shift from an efficient, hierarchical system towards a simplified, homogenized architecture, which may underlie the neural basis of their cognitive impairment.
[Keywords] minimal hepatic encephalopathy；structural covariance networks；magnetic resonance imaging；graph theory；gray matter；network robustness

JIANG Mao1   YANG Yue1   MA Xiaotong2   LI Wenbo1   CHEN Yuanyuan2   FAN Lihua2   ZHOU Feng3   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  2025-12-03
Accepted  2026-04-18
DOI: 10.12015/issn.1674-8034.2026.05.009
DOI:10.12015/issn.1674-8034.2026.05.009.

[1]
NAGEL M, WEIDNER V, SCHULZ S, et al. Continued alcohol consumption and hepatic encephalopathy determine quality of life and psychosocial burden of caregivers in patients with liver cirrhosis[J/OL]. Health Qual Life Outcomes, 2022, 20(1): 23 [2025-12-03]. https://doi.org/10.1186/s12955-022-01923-z. DOI: 10.1186/s12955-022-01923-z.
[2]
NARDELLI S, GIOIA S, FACCIOLI J, et al. Hepatic encephalopathy-recent advances in treatment and diagnosis[J]. Expert Rev Gastroenterol Hepatol, 2023, 17(3): 225-235. DOI: 10.1080/17474124.2023.2183386.
[3]
MONTAGNESE S, RAUTOU P E, ROMERO-GÓMEZ M, et al. EASL Clinical Practice Guidelines on the management of hepatic encephalopathy[J]. J Hepatol, 2022, 77(3): 807-824. DOI: 10.1016/j.jhep.2022.06.001.
[4]
AMPUERO J, SIMÓN M, MONTOLIÚ C, et al. Minimal hepatic encephalopathy and critical flicker frequency are associated with survival of patients with cirrhosis[J]. Gastroenterology, 2015, 149(6): 1483-1489. DOI: 10.1053/j.gastro.2015.07.067.
[5]
GAIRING S J, MANGINI C, ZARANTONELLO L, et al. Minimal hepatic encephalopathy is associated with a higher risk of overt hepatic encephalopathy and poorer survival[J]. J Intern Med, 2024, 295(3): 331-345. DOI: 10.1111/joim.13747.
[6]
BAJAJ J S, DUARTE-ROJO A, XIE J J, et al. Minimal hepatic encephalopathy and mild cognitive impairment worsen quality of life in elderly patients with cirrhosis[J/OL]. Cli Gastroenterol Hepatol, 2020, 18(13): 3008-3016.e2 [2025-12-03]. https://doi.org/10.1016/j.cgh.2020.03.033. DOI: 10.1016/j.cgh.2020.03.033.
[7]
GOU L B, ZHANG W, GUO D J, et al. Aberrant brain structural network and altered topological organization in minimal hepatic encephalopathy[J]. Diagn Interv Radiol, 2020, 26(3): 255-261. DOI: 10.5152/dir.2019.19216.
[8]
TAMAI Y, IWASA M, YOSHIDA Y, et al. Development of a new index to distinguish hepatic encephalopathy through automated quantification of globus pallidal signal intensity using MRI[J/OL]. Diagnostics (Basel), 2022, 12(7): 1584 [2025-12-03]. https://doi.org/10.3390/diagnostics12071584. DOI: 10.3390/diagnostics12071584.
[9]
PING L, SUN S, ZHOU C, et al. Altered topology of individual brain structural covariance networks in major depressive disorder[J]. Psychol Med, 2023, 53(14): 6921-6932. DOI: 10.1017/S003329172300168X.
[10]
SUN X, WANDELT S. Robustness of air transportation as complex networks: Systematic review of 15 years of research and outlook into the future[J/OL]. Sustainability, 2021, 13(11): 6446 [2025-12-03]. https://doi.org/10.3390/su13116446. DOI: 10.3390/su13116446.
[11]
DIAO M, KONG H, ZHAO J. Impacts of transportation network companies on urban mobility[J]. Nature Sustainability, 2021, 4(6): 494-500. DOI: 10.1038/s41893-020-00678-z.
[12]
WANG E, JIA Y, YA Y, et al. Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease[J/OL]. Front Aging Neurosci, 2021, 12: 575672 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/33519416/. DOI: 10.3389/fnagi.
[13]
PALANIYAPPAN L, HODGSON O, BALAIN V, et al. Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study[J]. Psycholl Med, 2019, 49(3): 412-420. DOI: 10.1017/S0033291718001010.
[14]
FAN L H, WEI W, CHEN Y Y, et al. Graph theoretical 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.
[15]
YANG S, WU Y, SUN L, et al. Abnormal topological organization of structural covariance networks in patients with temporal lobe epilepsy comorbid sleep disorder[J/OL]. Brain Sci, 2023, 13(10): 1493 [2025-12-03]. https://doi.org/10.3390/brainsci13101493. DOI: 10.3390/brainsci13101493.
[16]
XU X Y, DING H G, LI W G, et al. Guidelines on the diagnosis and treatment of hepatic encephalopathy in cirrhosis (2018, Beijing)[J]. Chinese Journal of Gastrointestinal Endoscopy (Electronic Edition), 2018, 5(3): 97-113. DOI: 10.3877/cma.j.issn.2095-7157.2018.03.001.
[17]
TZOURIO-MAZOYER N, LANDEAU B, PAPATHANASSIOU D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J]. Neuroimage, 2002, 15(1): 273-289. DOI: 10.1006/nimg.2001.0978.
[18]
HABECK C, ARGIRIS G, STERN Y. Brain resilience to targeted attack of resting BOLD networks as a measure of cognitive reserve[J/OL]. Res Sq, 2024 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/39678345/. DOI: 10.21203/rs.3.rs-5356022/v1.
[19]
MENARDI A, REINEBERG A E, VALLESI A, et al. Heritability of brain resilience to perturbation in humans[J/OL]. Neuroimage, 2021, 235: 118013 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/33794357/. DOI: 10.1016/j.neuroimage.2021.118013.
[20]
CARTLEDGE C L, NELSON M L. Connectivity damage to a graph by the removal of an edge or a vertex[J/OL]. arXiv preprint arXiv: 1103.3075,2011 [2025-12-03]. https://arxiv.org/abs/1103.3075. DOI: 10.48550/arXiv.1103.3075.
[21]
FICARA A, CELESTI A, VILLARI M, et al. Disrupting road networks: a case study on the vulnerabilities of the city of Messina[J/OL]. Applied Network Science, 2025, 10(1): 51 [2025-12-03]. https://doi.org/10.1007/s41109-025-00739-2. DOI: 10.1007/s41109-025-00739-2.
[22]
ZHANG Y, NG S T. Identification and quantification of node criticality through EWM-TOPSIS: a study of Hong Kong's MTR system[J]. Urban Rail Transit, 2021, 7(3): 226-239. DOI: 10.1007/s40864-021-00155-6.
[23]
CHEN H J, SHI H B, JIANG L F, et al. Disrupted topological organization of brain structural network associated with prior overt hepatic encephalopathy in cirrhotic patients[J]. Eur Radiol, 2018, 28(1): 85-95. DOI: 10.1007/s00330-017-4887-8.
[24]
BULLMORE E, SPORNS O. The economy of brain network organization[J]. Nat rev neurosci, 2012, 13(5): 336-349. DOI: 10.1038/nrn3214.
[25]
GARCÍA-GARCÍA R, CRUZ-GÓMEZ Á J, URIOS A, et al. Learning and memory impairments in patients with minimal hepatic encephalopathy are associated with structural and functional connectivity alterations in hippocampus[J/OL]. Sci Rep, 2018, 8(1): 9664 [2025-12-03]. https://doi.org/10.1038/s41598-018-27978-x. DOI: 10.1038/s41598-018-27978-x.
[26]
CAI L M, SHI J Y, DONG Q Y, et al. Aberrant stability of brain functional architecture in cirrhotic patients with minimal hepatic encephalopathy[J]. Brain Imaging and Behav, 2022, 16(5): 2258-2267. DOI: 10.1007/s11682-022-00696-9.
[27]
ZOU T X, SHE L, ZHAN C, et al. Altered topological properties of gray matter structural covariance networks in minimal hepatic encephalopathy[J/OL]. Front Neuroanat, 2018, 12: 101 [2025-12-03]. https://doi.org/https://pubmed.ncbi.nlm.nih.gov/30555305/. DOI: 10.3389/fnana.2018.00101.
[28]
CHEN H J, ZHANG X H, SHI J Y, et al. Thalamic structural connectivity abnormalities in minimal hepatic encephalopathy[J/OL]. Front Neuroanat, 2021, 15: 592772 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/33716679/. DOI: 10.3389/fnana.2021.592772.
[29]
LU C Q, JIAO Y, MENG X P, et al. Structural change of thalamus in cirrhotic patients with or without minimal hepatic encephalopathy and the relationship between thalamus volume and clinical indexes related to cirrhosis[J]. Neuroimage Clin, 2018, 20: 800-807. DOI: 10.1016/j.nicl.2018.09.015.
[30]
LI J L, JIANG H, ZHANG X D, et al. Microstructural brain abnormalities correlate with neurocognitive dysfunction in minimal hepatic encephalopathy: a diffusion kurtosis imaging study[J]. Neuroradiology, 2019, 61(6): 685-694. DOI: 10.1007/s00234-019-02201-4.
[31]
CHEN Q F, ZOU T X, YANG Z T, et al. Identification of patients with and without minimal hepatic encephalopathy based on gray matter volumetry using a support vector machine learning algorithm[J/OL]. Sci Rep, 2020, 10(1): 2490 [2025-12-03]. https://doi.org/10.1038/s41598-020-59433-1. DOI: 10.1038/s41598-020-59433-1.
[32]
SHA G, LIU Y, CAO Y, et al. Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy[J/OL]. Magn Reson Lett, 2025, 5(2): 200156 [2025-12-03]. https://doi.org/10.1016/j.mrl.2024.200156. DOI: 10.1016/j.mrl.2024.200156.
[33]
ZHU L, ZHANG W, CHEN L, et al. Brain gray matter alterations in hepatic encephalopathy: A voxel-based meta-analysis of whole-brain studies[J/OL]. Front Hum Neurosci, 2022, 16: 838666 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/35517986/. DOI: 10.3389/fnhum.2022.838666.
[34]
LIN S, GUO Z, CHEN S, et al. Progressive brain structural impairment assessed via network and causal analysis in patients with hepatitis B virus-related cirrhosis[J/OL]. Front Neuro, 2022, 13: 849571 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/35599731/. DOI: 10.3389/fneur.2022.849571.
[35]
SUN Q, FAN W, LIU Y, et al. Characterization of brain microstructural abnormalities in cirrhotic patients without overt hepatic encephalopathy using diffusion kurtosis imaging[J]. Brain Imaging Behav, 2020, 14(2): 627-638. DOI: 10.1007/s11682-019-00141-4.
[36]
KUANG Y, WU X, LAI H, et al. Abnormal corpus callosum induced by overt hepatic encephalopathy impairs interhemispheric functional coordination in cirrhosis patients[J/OL]. Ann Transl Med, 2021, 9(20): 1579 [2025-12-03]. https://doi.org/10.21037/atm-21-5109. DOI: 10.21037/atm-21-5109.
[37]
KHALILIAN M, TOBA M N, ROUSSEL M, et al. Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study[J/OL]. Aging Brain, 2024, 5: 100105 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/38273866/. DOI: 10.1016/j.nbas.2023.100105.
[38]
STANFORD W, MUCHA P J, DAYAN E. Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks[J/OL]. Commun Biol, 2024, 7(1): 701 [2025-12-03]. https://doi.org/10.1038/s42003-024-06392-2. DOI: 10.1038/s42003-024-06392-2.
[39]
TANG R, ELMAN J A, FRANZ C E, et al. Longitudinal association of executive function and structural network controllability in the aging brain[J]. GeroScience, 2023, 45(2): 837-849. DOI: 10.1007/s11357-022-00676-3.
[40]
NA X, CUI J, SU C, et al. The synergistic effects of structural evolution and attack strategies on network matching robustness[J/OL]. Entropy (Basel). 2025, 27(8): 847 [2025-12-03]. https://doi.org/10.3390/e27080847. DOI: 10.3390/e27080847.
[41]
CAI L M, ZHUANG S P, CAI Z W, et al. Structural and functional abnormalities of thalamic subregions in minimal hepatic encephalopathy[J/OL]. Brain Res Bull, 2025: 111654 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/41285323/. DOI: 10.1016/j.brainresbull.2025.111654.
[42]
JU C, YANG L, DAI Z, et al. Associations among neuropsychological performance, brain structural alterations, and glymphatic function in patients with minimal hepatic encephalopathy[J/OL]. Brain Res Bull, 2025: 111416 [2025-12-03]. https://pubmed.ncbi.nlm.nih.gov/40480479/. DOI: 10.1016/j.brainresbull.2025.111416.
[43]
HUANG H W, ZENG J Y, TANG Y, et al. Cortical microstructural changes in minimal hepatic encephalopathy: a gray matter-based spatial statistics study[J]. Quant Imaging in Med Surg, 2025, 15(7): 6068-6086. DOI: 10.21037/qims-24-1903.
[44]
WANG J, AI J, LU M, et al. A survey of neural network robustness assessment in image recognition[J/OL]. arXiv preprint arXiv: 2404.08285, 2024 [2025-12-03]. https://arxiv.org/abs/2404.08285. DOI: 10.48550/arXiv.2404.08285.

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