Objective:

To explore cerebral cortical morphological similarity and topological abnormalities in hepatic encephalopathy (HE) by integrating the Morphometric Inverse Divergence (MIND) network with graph theory.

Approach:

• Participants: 31 HE patients, 30 cirrhotic non-HE (NHE) patients, and 30 healthy controls (HC) underwent 3.0T MRI scanning.
• Image Processing: FreeSurfer was utilized for preprocessing images, extracting five cortical morphological features based on the Schaefer-400 atlas.
• Network Construction: MIND networks were constructed using symmetric Kullback–Leibler divergence, and graph theory was applied to analyze topological properties.
• Statistical Analysis: Intergroup differences were analyzed using a general linear model (GLM).

Key Findings:

• HE patients showed significantly elevated mean MIND values across multiple functional subnetworks compared to HCs, including the visual (VIS; t = 3.629, p = 0.004), default mode (DMN; t = 3.115, p = 0.009), limbic (LMB; t = 2.969, p = 0.009), frontoparietal (FPN; t = 2.917, p = 0.009), and ventral attention (VAN; t = 2.212, p = 0.043).
• Graph theoretical analysis indicated increased global efficiency (Eglob; t = 2.681, p = 0.0100) and local efficiency (Eloc; t = 2.683, p = 0.010).
• NHE patients exhibited mild DMN connectivity enhancement but no significant differences in MIND and nodal metrics (all p > 0.05).

Interpretation:

HE is associated with abnormal whole-brain cortical morphological covariance networks characterized by enhanced connectivity and efficiency.

Limitations:

• The study's sample size may limit the generalizability of the findings.
• Potential confounding factors related to patient characteristics and imaging protocols were not fully addressed.

Conclusion:

MIND network indices may serve as potential imaging biomarkers for HE diagnosis and monitoring, addressing limitations of traditional structural covariance network research.