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Abstract #4373

Abnormal topological organization of T1 and quantitative susceptibility mapping covariance networks in Alzheimer’s disease

Maochang Huang1, Yihao Guo2, and Feng Chen2
1Hainan university, Haikou, China, 2Hainan General Hospital, Haikou, China

Synopsis

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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