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Abstract #0226

Identifying Brain Regions Vulnerable and Resistant to Aging Using AI-Derived Cerebral Blood Volume from T1-Weighted MRI

Zongyu Li1, Yiren Zhang1, Aaron Cao2, Scott A. Small3,4, and Jia Guo1,3,4
1Biomedical Engineering, Columbia University, New York, NY, United States, 2University of California, Santa Barbara, Santa Barbara, CA, United States, 3Psychiatry, Columbia University, New York, NY, United States, 4Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Image Reconstruction, Aging, CBV, Medical Image Synthesis, 3D Mamba

Motivation: Aging impacts brain regions differently, leading to cognitive decline and vascular changes. Identifying the most susceptible or resilient regions is crucial for understanding neurodegeneration and developing interventions.

Goal(s): To identify aging-sensitive and resilient brain regions by developing a pipeline generating AI-derived CBV (AICBV) maps from standard T1-weighted MRI.

Approach: We used DeepContrast AI model to generate AICBV maps from T1-weighted MRI scans of 2,851 subjects. FreeSurfer provided segmentation for regional CBV analysis.

Results: Dentate gyrus and inferior frontal gyrus subregions are highly vulnerable to aging; the entorhinal cortex shows resilience. Our findings align with prior studies, extending them to a larger cohort.

Impact: This non-invasive AICBV mapping technique offers an efficient alternative to traditional CBV fMRI scans, enabling large-scale and longitudinal studies of brain aging. It allows early detection of high-risk regions and guides targeted interventions to preserve cognitive health and resilience.

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Keywords