BackgroundArgyrophilic grain disease (AGD) is a common tauopathy in the elderly, but its neuroimaging features remain less well characterized compared with Alzheimer's disease (AD). Notably, structural covariance network (SCN) analysis has not previously been applied to AGD.ObjectiveThis study aimed to investigate SCN alterations in pathologically confirmed AGD and AD and to characterize disease-specific patterns of network disruption.MethodsWe examined 12 AGD, 13 AD, and 18 healthy controls (HC). Individualized structural covariance matrices were constructed from regional gray matter volumes, and global and nodal graph-theoretical metrics were computed for each participant. Group differences were assessed using analysis of covariance adjusting for age and sex, and partial correlations were performed to examine associations between global metrics and Mini-Mental State Examination (MMSE) scores.ResultsGlobal SCN metrics showed a graded pattern, with strength, clustering coefficient, and efficiency lowest in AD, highest in HC, and intermediate in AGD. All global metrics except modularity were significantly correlated with MMSE. Nodal analyses revealed widespread reductions in closeness centrality in AD, with more limited decreases in AGD. Betweenness centrality showed an AD > AGD > HC pattern, whereas closeness centrality showed the opposite trend. Eigenvector centrality also suggested a graded trend (AD < AGD < HC), despite regional variability.ConclusionsSCN-derived metrics were consistent with disease-related volume patterns and revealed that AGD exhibits an intermediate network profile between AD and healthy aging. These findings suggest that SCN-based measures offer complementary insights into disease-related patterns of network disruption.
Sakurai et al. (Wed,) studied this question.