Keywords: Alzheimer's Disease, Susceptibility
Motivation: T1 mapping and QSM are effective in detecting Alzheimer’s disease, but their covariance networks have not been fully explored.
Goal(s): To determine whether network-based metrics can aid in early diagnosis and intervention for AD.
Approach: Construct covariance networks using QSM and T1 maps. Topological metrics—clustering coefficient, path length, and small-world index—were calculated to assess group differences. Correlation analysis examined the relationship between these metrics and cognitive function.
Results: There are significant differences among the three groups. The AD and MCI groups exhibited higher network parameters, with altered hub distributions. Network parameters are negatively correlated with cognition.
Impact: These findings provide new insights into AD pathology by revealing disrupted network organization, offering potential biomarkers for early diagnosis, intervention, and disease monitoring.
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