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Abstract #3767

Brain Structural Connectome Accurately Classifies Alzheimer’s Disease Related Dementia

Jiook Cha1, Yun Wang1, Allen Liu2,3, Jonathan Posner1, Jong-Hun Kim4, Shinjae Yoo3, and Hyoung-Seop Kim4

1Department of Psychiatry, Columbia University, New York, NY, United States, 2Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, United States, 3Brookhaven National Laboratory, Upton, NY, United States, 4National Health Insurance Service Ilsan Hospital, Ilsan, Republic of Korea

There is an urgent, unmet need for biomarkers of risk for Alzheimer’s disease related dementia (ADRD) suitable for routine examination in naïve patients. Studies suggest MRI-derived brain connectome may contain salient information about brain health status. However, it has yet to be tested in sufficiently large samples whether connectome can be used to predict reliably diagnosis of ADRD. Here we performed high-throughput computational analysis using structure and diffusion MRI in a clinical cohort (N=211) to estimate morphometry and connectome. Our results show potential utility of data-driven machine learning models using large-scale MRI-derived brain phenotypes in classifying ADRD, particularly structural connectome.

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