Abstract #3767

Brain Structural Connectome Accurately Classifies Alzheimer’s Disease Related Dementia

Jiook Cha¹, Yun Wang¹, Allen Liu^2,3, 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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.