Fine-scale individualized gyral folding-based cortical similarity networks reveal distinct organizational patterns in Alzheimer’s disease and Lewy body dementia

Alzheimer’s disease (AD) and Lewy body dementia (LBD) are common neurodegenerative dementias with overlapping clinical presentations, making differential diagnosis challenging. While structural magnetic resonance imaging (MRI) has revealed characteristic regional atrophy patterns, regional morphometric measures alone may not fully capture distributed cortical alterations. Morphometric similarity networks (MSNs) offer a systems-level framework to characterize coordinated structural organization, but existing approaches typically rely on atlas-based parcellations that may obscure individual-specific cortical folding geometry. Here, we propose a fine-scale, folding-informed cortical similarity network framework based on automatically detected three-hinge gyral (3HG) landmarks. Using a thickness-constrained arealization strategy in native surface space, we define individualized cortical regions and construct subject-specific MSNs without cross-subject registration. We then investigate how network topology relates to landmark-defined node count and how these properties differ between AD and LBD. We find that several graph theoretical metrics, particularly global efficiency and characteristic path length, exhibit clear associations with the number of detected landmarks, indicating that topology in individualized networks is partly shaped by node availability. When accounting for landmark count, several apparent group differences in global topology are attenuated, whereas multiple heterogeneity-related metrics remain significant, indicating that node-count scaling substantially influences the interpretation of individualized network topology. Nevertheless, multivariate topological patterns remain informative for AD/LBD classification after residualizing for node count, and landmark count itself provides modest diagnostic information. These findings highlight node-count scaling as a key methodological consideration in individualized structural networks and suggest that folding-based MSNs capture disease-related variation in cortical network organization between AD and LBD.