ArticleFluids and barriers of the CNS2025
Individual-level cortical morphological network analysis in idiopathic normal pressure hydrocephalus: diagnostic and prognostic insights.
Article in Fluids and barriers of the CNS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Real-Time Functional Lesion Simulation in Connectomics: Rapid Sensitivity Analysis of Neural Pathways via Traversal Tree-Correlated Cuts.Interdisciplinary sciences, computational life sciences · 2026Article
- Precision neurodiversity: personalized brain network architecture as a window into cognitive variability.Frontiers in human neuroscience · 2025Review
- Investigation of cerebral cortical morphological similarity and network topological abnormalities in hepatic encephalopathy utilizing a morphometric inverse divergence network framework.Frontiers in neurologyArticle
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Abstract
backgroundIdiopathic normal-pressure hydrocephalus (iNPH) is a neurodegenerative disorder characterized by treatable cognitive impairment, remains poorly understood in terms of its underlying pathological mechanisms. Cortical morphological similarity network, which quantify synchronized morphological changes across brain regions, offer novel insights into inter-individual neuroanatomical variability. This study investigates individual-level cortical morphological network patterns in iNPH, explores their diagnostic utility and prognostic value for postoperative outcomes.
methodsWe enrolled 56 confirmed iNPH patients, 50 Alzheimer's disease (AD) patients, and 60 healthy controls (HC). Cortical morphological similarity networks were constructed using a morphometric inverse divergence network (MIND) framework, integrating five key cortical features: cortical thickness, mean curvature, sulcal depth, surface area, and cortical volume. Graph theory analysis was employed to quantify global and nodal network properties. Partial correlations with MMSE scores assessed network-cognition relationships. A LASSO-regularized support vector machine (SVM) classifier differentiated iNPH, AD, and HC groups using regional MIND similarity (MINDs) features. Finally, preoperative MRI-derived MINDs were integrated into a LASSO-regularized support vector regression (SVR) model to predict postoperative cognitive and gait improvements following shunt surgery.
resultsBoth iNPH and AD exhibited disrupted MIND network topology versus HC, including lower clustering coefficient, global efficiency, and local efficiency (all p < 0.05). Distinct spatial patterns emerged: iNPH showed localized lower values in cingulate subregions (degree centrality, node efficiency, MINDs), whereas AD demonstrated widespread alterations in fusiform, insular, and temporoparietal cortices. MMSE-associated MINDs in iNPH localized to frontostriatal circuits, contrasting with diffuse associations in AD. The multimodal classifier combining ventricular enlargement, regional brain volume, and MINDs achieved 87.00% accuracy (macro-AUC = 0.96) in three-group discrimination. Moreover, preoperative MINDs effectively predicted postoperative improvements in cognition and gait, with correlation coefficients of 0.941 and 0.889, respectively, between predicted and actual scores.
conclusionsThe MIND-based morphological similarity network reveals coordinated cortical morphological alterations in iNPH and highlights its heterogeneity compared to AD. These findings offer potential biomarkers to differentiate iNPH from AD. Furthermore, the predictive efficacy of MIND-based features for postoperative outcomes underscores their utility as non-invasive preoperative tools for evaluating shunt surgery effectiveness.
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